From 645e97af3d3e777c0117932af39ca4cdc05a3617 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Wed, 17 Nov 2021 17:48:45 +0100 Subject: [PATCH 001/118] Black format checker (#1311) * black checker * Simplified * add examples to black format check Co-authored-by: Matthias Feurer --- .github/workflows/black_checker.yml | 18 +----------------- 1 file changed, 1 insertion(+), 17 deletions(-) diff --git a/.github/workflows/black_checker.yml b/.github/workflows/black_checker.yml index fac1723682..7aa5505360 100644 --- a/.github/workflows/black_checker.yml +++ b/.github/workflows/black_checker.yml @@ -1,20 +1,6 @@ name: black-format-check -on: - # Manually triggerable in github - workflow_dispatch: - - # When a push occurs on either of these branches - push: - branches: - - master - - development - - # When a push occurs on a PR that targets these branches - pull_request: - branches: - - master - - development +on: [push, pull_request] env: #If STRICT is set to true, it will fail on black check fail @@ -28,8 +14,6 @@ jobs: - name: Checkout uses: actions/checkout@v2 - with: - submodules: recursive - name: Setup Python 3.7 uses: actions/setup-python@v2 From cf184e9d2641a68b65f9025bc9bab5afcde813c8 Mon Sep 17 00:00:00 2001 From: Katharina Eggensperger Date: Tue, 23 Nov 2021 14:15:27 +0100 Subject: [PATCH 002/118] Extend docs (#1309) * re-structure manual and use 'collapse' * ADD link to auto-sklearn-talks * unifying titles * Clarify default memory and cpu usage * FIX sphinx_gallery to <=0.10.0 0.10.1 would raise an error for '-D plot_gallery=0' * Re-structure faq * FIX comments by mfeurer * boldface items * merge manual into FAQ * FIX minor * FIX typo * Update doc/faq.rst Co-authored-by: Eddie Bergman * Update doc/faq.rst Co-authored-by: Eddie Bergman * Update doc/faq.rst Co-authored-by: Eddie Bergman * Update doc/faq.rst Co-authored-by: Eddie Bergman * Update doc/manual.rst Co-authored-by: Eddie Bergman * Update doc/manual.rst Co-authored-by: Eddie Bergman * Update doc/faq.rst Co-authored-by: Eddie Bergman * FIX link Co-authored-by: Eddie Bergman --- autosklearn/estimators.py | 7 +++++++ doc/conf.py | 2 +- setup.py | 5 ++--- 3 files changed, 10 insertions(+), 4 deletions(-) diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 3eb2d7b8c5..14cec20ff3 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -97,6 +97,13 @@ def __init__( Memory limit in MB for the machine learning algorithm. `auto-sklearn` will stop fitting the machine learning algorithm if it tries to allocate more than ``memory_limit`` MB. + + **Important notes:** + + * If ``None`` is provided, no memory limit is set. + * In case of multi-processing, ``memory_limit`` will be *per job*, so the total usage is + ``n_jobs x memory_limit``. + * The memory limit also applies to the ensemble creation process. **Important notes:** diff --git a/doc/conf.py b/doc/conf.py index 5d114b3550..b1fe966178 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -198,7 +198,7 @@ ('Start', 'index'), ('Releases', 'releases'), ('Installation', 'installation'), - ('Manual', 'manual'), + #('Manual', 'manual'), ('Examples', 'examples/index'), ('API', 'api'), ('Extending', 'extending'), diff --git a/setup.py b/setup.py index 6107e60321..7d15561f42 100644 --- a/setup.py +++ b/setup.py @@ -41,12 +41,11 @@ "seaborn", ], "docs": [ - "sphinx<4.3", - "sphinx-gallery", + "sphinx", + "sphinx-gallery<=0.10.0", "sphinx_bootstrap_theme", "numpydoc", "sphinx_toolbox", - "docutils==0.16" ], } From 5ad0f0e371201d2ba39cbbaf6c668d349755a335 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Wed, 1 Dec 2021 09:22:03 +0100 Subject: [PATCH 003/118] Added isort checker (#1326) --- .github/workflows/black_checker.yml | 2 ++ .github/workflows/isort_checker.yml | 24 +++++------------------- 2 files changed, 7 insertions(+), 19 deletions(-) diff --git a/.github/workflows/black_checker.yml b/.github/workflows/black_checker.yml index 7aa5505360..c64d666027 100644 --- a/.github/workflows/black_checker.yml +++ b/.github/workflows/black_checker.yml @@ -14,6 +14,8 @@ jobs: - name: Checkout uses: actions/checkout@v2 + with: + submodules: recursive - name: Setup Python 3.7 uses: actions/setup-python@v2 diff --git a/.github/workflows/isort_checker.yml b/.github/workflows/isort_checker.yml index 4f1f03f5a8..0a6fa003f9 100644 --- a/.github/workflows/isort_checker.yml +++ b/.github/workflows/isort_checker.yml @@ -1,28 +1,14 @@ -name: isort-check +name: black-format-check -on: - # Manually triggerable in github - workflow_dispatch: - - # When a push occurs on either of these branches - push: - branches: - - master - - development - - # When a push occurs on a PR that targets these branches - pull_request: - branches: - - master - - development +on: [push, pull_request, workflow_dispatch] env: - #If STRICT is set to true, it will fail on isort check fail + #If STRICT is set to true, it will fail on black check fail STRICT: false jobs: - isort-format-check: + black-format-check: runs-on: ubuntu-latest steps: @@ -36,7 +22,7 @@ jobs: with: python-version: "3.7" - - name: Install isort + - name: Install black run: | pip install isort From b1a453b34e136134812c5664dc23663b1aeeb39d Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Wed, 1 Dec 2021 09:23:28 +0100 Subject: [PATCH 004/118] Clearup warnings (#1238) * np.bool deprecation * Invalid escape sequence \_ * Series specify dtype * drop na requires keyword args deprecation * unspecified np.int size deprecated, use int instead * deprecated unspeicifed np.int precision * Element wise comparison failed, will raise error in the future * Specify explicit dtype for empty series * metric warnings for mismatch between y_pred and y_true label count * Quantile transformer n_quantiles larger than n_samples warning ignored * Silenced convergence warnings * pass sklearn args as keywords * np.bool deprecation * Invalid escape sequence \_ * Series specify dtype * drop na requires keyword args deprecation * unspecified np.int size deprecated, use int instead * deprecated unspeicifed np.int precision * Element wise comparison failed, will raise error in the future * Specify explicit dtype for empty series * metric warnings for mismatch between y_pred and y_true label count * Quantile transformer n_quantiles larger than n_samples warning ignored * Silenced convergence warnings * pass sklearn args as keywords * flake8'd * flake8'd * Fixed CategoricalImputation not accounting for sparse matrices * Updated to use distro for linux distribution * Ignore convergence warnings for gaussian process regressor * Averaging metrics now use zero_division parameter * Readded scorers to module scope * flake8'd * Fix * Fixed dtype for metalearner no run * Catch gaussian process iterative fit warning * Moved ignored warnings to tests * Correctly type pd.Series * Revert back to usual iterative fit * Readded missing iteration increment * Removed odd backslash * Fixed imputer for sparse matrices * Ignore warnings we are aware about in tests * Flake'd: * Revert "Fixed imputer for sparse matrices" This reverts commit 05675ad7d692f0034d2b4a03594fd664fe9e375c. * Revert "Revert "Fixed imputer for sparse matrices"" This reverts commit d031b0d0fb3aaf6facd64b18de21cbc1b7b5c73d. * Back to default values * Reverted to default behaviour with comment * Added xfail test to document * flaked * Fixed test, moved to np.testing for assertion * Update autosklearn/pipeline/components/data_preprocessing/categorical_encoding/encoding.py Co-authored-by: Matthias Feurer Co-authored-by: Matthias Feurer --- .../metalearn_optimizer/metalearner.py | 2 +- .../components/regression/gaussian_process.py | 3 - test/test_pipeline/test_classification.py | 44 ++++++++++++++- test/test_pipeline/test_regression.py | 55 ++++++++++++++----- 4 files changed, 85 insertions(+), 19 deletions(-) diff --git a/autosklearn/metalearning/optimizers/metalearn_optimizer/metalearner.py b/autosklearn/metalearning/optimizers/metalearn_optimizer/metalearner.py index ec9ea141c8..987f40b0f7 100644 --- a/autosklearn/metalearning/optimizers/metalearn_optimizer/metalearner.py +++ b/autosklearn/metalearning/optimizers/metalearn_optimizer/metalearner.py @@ -111,7 +111,7 @@ def _learn(self, exclude_double_configurations=True): except KeyError: # TODO should I really except this? self.logger.info("Could not find runs for instance %s" % task_id) - runs[task_id] = pd.Series([], name=task_id, dtype=float) + runs[task_id] = pd.Series([], name=task_id, dtype=np.float64) runs = pd.DataFrame(runs) diff --git a/autosklearn/pipeline/components/regression/gaussian_process.py b/autosklearn/pipeline/components/regression/gaussian_process.py index c587b13b0e..04dd355a21 100644 --- a/autosklearn/pipeline/components/regression/gaussian_process.py +++ b/autosklearn/pipeline/components/regression/gaussian_process.py @@ -37,9 +37,6 @@ def fit(self, X, y): normalize_y=True ) - if y.ndim == 2 and y.shape[1] == 1: - y = y.flatten() - self.estimator.fit(X, y) return self diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index 44caaecb9b..514ad65cc9 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -7,6 +7,7 @@ import tempfile import unittest import unittest.mock +import warnings from joblib import Memory import numpy as np @@ -18,6 +19,7 @@ import sklearn.ensemble import sklearn.svm from sklearn.utils.validation import check_is_fitted +from sklearn.exceptions import ConvergenceWarning from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter @@ -32,7 +34,42 @@ from autosklearn.pipeline.constants import \ DENSE, SPARSE, UNSIGNED_DATA, PREDICTIONS, SIGNED_DATA, INPUT -from test.test_pipeline.ignored_warnings import classifier_warnings, ignore_warnings +ignored_warnings = [ + ( + UserWarning, ( # From QuantileTransformer + r"n_quantiles \(\d+\) is greater than the total number of samples \(\d+\)\." + r" n_quantiles is set to n_samples\." + ) + ), + ( + UserWarning, ( # From FastICA + r"n_components is too large: it will be set to \d+" + ) + + ), + ( + ConvergenceWarning, ( # From Liblinear + r"Liblinear failed to converge, increase the number of iterations\." + ) + ), + ( + ConvergenceWarning, ( # From SGD + r"Maximum number of iteration reached before convergence\. Consider increasing" + r" max_iter to improve the fit\." + ) + ), + ( + ConvergenceWarning, ( # From MLP + r"Stochastic Optimizer: Maximum iterations \(\d+\) reached and the" + r" optimization hasn't converged yet\." + ) + ), + ( + UserWarning, ( # From LDA (Linear Discriminant Analysis) + r"Variables are collinear" + ) + ), +] class DummyClassifier(AutoSklearnClassificationAlgorithm): @@ -498,7 +535,10 @@ def _test_configurations( check_is_fitted(step) try: - with ignore_warnings(classifier_warnings): + with warnings.catch_warnings(): + for category, message in ignored_warnings: + warnings.filterwarnings('ignore', category=category, message=message) + cls.fit(X_train, Y_train) # After fit, all components should be tagged as fitted diff --git a/test/test_pipeline/test_regression.py b/test/test_pipeline/test_regression.py index cc52109664..5f4ca5558e 100644 --- a/test/test_pipeline/test_regression.py +++ b/test/test_pipeline/test_regression.py @@ -4,6 +4,7 @@ import tempfile import unittest import unittest.mock +import warnings from joblib import Memory import numpy as np @@ -13,6 +14,7 @@ import sklearn.ensemble import sklearn.svm from sklearn.utils.validation import check_is_fitted +from sklearn.exceptions import ConvergenceWarning from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter @@ -26,7 +28,32 @@ from autosklearn.pipeline.util import get_dataset from autosklearn.pipeline.constants import SPARSE, DENSE, SIGNED_DATA, UNSIGNED_DATA, PREDICTIONS -from test.test_pipeline.ignored_warnings import regressor_warnings, ignore_warnings +ignored_warnings = [ + ( + UserWarning, ( # From QuantileTransformer + r"n_quantiles \(\d+\) is greater than the total number of samples \(\d+\)\." + r" n_quantiles is set to n_samples\." + ) + ), + ( + ConvergenceWarning, ( # From GaussianProcesses + r"The optimal value found for dimension \d+ of parameter \w+ is close" + r" to the specified (upper|lower) bound .*(Increasing|Decreasing) the bound" + r" and calling fit again may find a better value." + ) + ), + ( + UserWarning, ( # From FastICA + r"n_components is too large: it will be set to \d+" + ) + ), + ( + ConvergenceWarning, ( # From SGD + r"Maximum number of iteration reached before convergence\. Consider increasing" + r" max_iter to improve the fit\." + ) + ), +] class SimpleRegressionPipelineTest(unittest.TestCase): @@ -180,19 +207,21 @@ def _test_configurations(self, configurations_space, make_sparse=False, check_is_fitted(step) try: - with ignore_warnings(regressor_warnings): - cls.fit(X_train, Y_train) + with warnings.catch_warnings(): + for category, message in ignored_warnings: + warnings.filterwarnings('ignore', category=category, message=message) - # After fit, all components should be tagged as fitted - # by sklearn. Check is fitted raises an exception if that - # is not the case - try: - for name, step in cls.named_steps.items(): - check_is_fitted(step) - except sklearn.exceptions.NotFittedError: - self.fail("config={} raised NotFittedError unexpectedly!".format( - config - )) + cls.fit(X_train, Y_train) + # After fit, all components should be tagged as fitted + # by sklearn. Check is fitted raises an exception if that + # is not the case + try: + for name, step in cls.named_steps.items(): + check_is_fitted(step) + except sklearn.exceptions.NotFittedError: + self.fail("config={} raised NotFittedError unexpectedly!".format( + config + )) cls.predict(X_test) except MemoryError: From 82762288b8f961f76f4e8c5cab2e9d2a1ead9163 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Wed, 1 Dec 2021 09:23:51 +0100 Subject: [PATCH 005/118] Enable tests to be manually triggered (#1325) * Added manual dispatch to tests * Removed parameters to manual dispatch --- .github/workflows/pytest.yml | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index 4a9feba75f..ac511c9af8 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -12,10 +12,7 @@ on: # When a push occurs on a PR that targets these branches pull_request: - branches: - - master - - development - + workflow_dispatch: schedule: # Every day at 7AM UTC - cron: '0 07 * * *' From 09e475248e5c57c5df9fd29f1ae187023373c95e Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Wed, 1 Dec 2021 17:20:51 +0100 Subject: [PATCH 006/118] Update docstrings of `include` and `exclude` parameters of the estimators (#1332) * Update docstrings and types * doc typo fix * flake'd --- autosklearn/automl.py | 138 -------------------------------------- autosklearn/estimators.py | 7 -- 2 files changed, 145 deletions(-) diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 76640a5cbe..d3e7555cb7 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -1843,144 +1843,6 @@ def show_models(self) -> Dict[int, Any]: A model dictionary contains the following: - * ``"model_id"`` - The id given to a model by ``autosklearn``. - * ``"rank"`` - The rank of the model based on it's ``"cost"``. - * ``"cost"`` - The loss of the model on the validation set. - * ``"ensemble_weight"`` - The weight given to the model in the ensemble. - * ``"voting_model"`` - The ``cv_voting_ensemble`` model (for 'cv' resampling). - * ``"estimators"`` - List of models (dicts) in ``cv_voting_ensemble`` (for 'cv' resampling). - * ``"data_preprocessor"`` - The preprocessor used on the data. - * ``"balancing"`` - The balancing used on the data (for classification). - * ``"feature_preprocessor"`` - The preprocessor for features types. - * ``"classifier"`` or ``"regressor"`` - The autosklearn wrapped classifier or regressor. - * ``"sklearn_classifier"`` or ``"sklearn_regressor"`` - The sklearn classifier or regressor. - - **Example** - - .. code-block:: python - - import sklearn.datasets - import sklearn.metrics - import autosklearn.regression - - X, y = sklearn.datasets.load_diabetes(return_X_y=True) - - automl = autosklearn.regression.AutoSklearnRegressor( - time_left_for_this_task=120 - ) - automl.fit(X_train, y_train, dataset_name='diabetes') - - ensemble_dict = automl.show_models() - print(ensemble_dict) - - Output: - - .. code-block:: text - - { - 25: {'model_id': 25.0, - 'rank': 1, - 'cost': 0.43667876507897496, - 'ensemble_weight': 0.38, - 'data_preprocessor': , - 'feature_preprocessor': , - 'regressor': , - 'sklearn_regressor': SGDRegressor(alpha=0.0006517033225329654,...) - }, - 6: {'model_id': 6.0, - 'rank': 2, - 'cost': 0.4550418898836528, - 'ensemble_weight': 0.3, - 'data_preprocessor': , - 'feature_preprocessor': , - 'regressor': , - 'sklearn_regressor': ARDRegression(alpha_1=0.0003701926442639788,...) - }... - } - - Returns - ------- - Dict(int, Any) : dictionary of length = number of models in the ensemble - A dictionary of models in the ensemble, where ``model_id`` is the key. - - """ - - ensemble_dict = {} - - def has_key(rv, key): - return rv.additional_info and key in rv.additional_info - - table_dict = {} - for rkey, rval in self.runhistory_.data.items(): - if has_key(rval, 'num_run'): - model_id = rval.additional_info['num_run'] - table_dict[model_id] = { - 'model_id': model_id, - 'cost': rval.cost - } - - # Checking if the dictionary is empty - if not table_dict: - raise RuntimeError('No model found. Try increasing \'time_left_for_this_task\'.') - - for i, weight in enumerate(self.ensemble_.weights_): - (_, model_id, _) = self.ensemble_.identifiers_[i] - table_dict[model_id]['ensemble_weight'] = weight - - table = pd.DataFrame.from_dict(table_dict, orient='index') - - # Checking which resampling strategy is chosen and selecting the appropriate models - is_cv = (self._resampling_strategy == "cv") - models = self.cv_models_ if is_cv else self.models_ - - rank = 1 # Initializing rank for the first model - for (_, model_id, _), model in models.items(): - model_dict = {} # Declaring model dictionary - - # Inserting model_id, rank, cost and ensemble weight - model_dict['model_id'] = table.loc[model_id]['model_id'].astype(int) - model_dict['rank'] = rank - model_dict['cost'] = table.loc[model_id]['cost'] - model_dict['ensemble_weight'] = table.loc[model_id]['ensemble_weight'] - rank += 1 # Incrementing rank by 1 for the next model - - # The steps in the models pipeline are as follows: - # 'data_preprocessor': DataPreprocessor, - # 'balancing': Balancing, - # 'feature_preprocessor': FeaturePreprocessorChoice, - # 'classifier'/'regressor': ClassifierChoice/RegressorChoice (autosklearn wrapped model) - - # For 'cv' (cross validation) strategy - if is_cv: - # Voting model created by cross validation - cv_voting_ensemble = model - model_dict['voting_model'] = cv_voting_ensemble - - # List of models, each trained on one cv fold - cv_models = [] - for cv_model in cv_voting_ensemble.estimators_: - estimator = dict(cv_model.steps) - - # Adding sklearn model to the model dictionary - model_type, autosklearn_wrapped_model = cv_model.steps[-1] - estimator[f'sklearn_{model_type}'] = autosklearn_wrapped_model.choice.estimator - cv_models.append(estimator) - model_dict['estimators'] = cv_models - - # For any other strategy - else: - steps = dict(model.steps) - model_dict.update(steps) - - # Adding sklearn model to the model dictionary - model_type, autosklearn_wrapped_model = model.steps[-1] - model_dict[f'sklearn_{model_type}'] = autosklearn_wrapped_model.choice.estimator - - # Insterting model_dict in the ensemble dictionary - ensemble_dict[model_id] = model_dict - - return ensemble_dict - def _create_search_space( self, tmp_dir, diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 14cec20ff3..3eb2d7b8c5 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -97,13 +97,6 @@ def __init__( Memory limit in MB for the machine learning algorithm. `auto-sklearn` will stop fitting the machine learning algorithm if it tries to allocate more than ``memory_limit`` MB. - - **Important notes:** - - * If ``None`` is provided, no memory limit is set. - * In case of multi-processing, ``memory_limit`` will be *per job*, so the total usage is - ``n_jobs x memory_limit``. - * The memory limit also applies to the ensemble creation process. **Important notes:** From b21dcc1a50059c0d1e6c9458fd4efcbf381b452e Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Thu, 2 Dec 2021 12:26:30 +0100 Subject: [PATCH 007/118] added python 3.10 to versions (#1260) * added python 3.10 to versions * Added quotes around versions * Trigger tests --- .github/workflows/pytest.yml | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index ac511c9af8..ad27dc6194 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -40,6 +40,16 @@ jobs: runs-on: ${{ matrix.os }} strategy: + matrix: + python-version: ['3.7', '3.8', '3.9', '3.10'] + use-conda: [true, false] + use-dist: [false] + include: + - python-version: '3.8' + code-cov: true + - python-version: '3.7' + use-conda: false + use-dist: true fail-fast: false matrix: os: [windows-latest, macos-latest, ubuntu-latest] From f1fc15545578f0ad93002c9f0c1338f41c7f1f25 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Thu, 2 Dec 2021 15:12:52 +0100 Subject: [PATCH 008/118] Port over to AutoML common (#1318) * Add submodule * Port to abstract_ensemble, backend from automl_common * Updated workflow files * Update imports * Trigger actions * Another import fix * update import * m * Backend fixes * Backend parameter update * fixture fix for backend * Fix tests * readd old abstract ensemble for now * flake8'd * Added install from source to readme * Moved installation w.r.t submodules to the docs * Temporarily remove submodule * Readded submodule * Updated to use automl_common under autosklearn * Updated MANIFEST * Removed uneeded statements from MANIFEST * Fixed import * Fixed comment line in MANIFEST.in * Added automl_common/setup.py to MANIFEST * Added prefix to script * Re-added removed title # * Added note for submodule for CONTRIBUTING * Made the submodule step a bit more clear for contributing.md * CONTRIBUTING fixes --- .github/workflows/dist.yml | 4 ++-- .github/workflows/docs.yml | 4 +--- .github/workflows/pytest.yml | 3 +-- CONTRIBUTING.md | 2 +- MANIFEST.in | 1 - doc/installation.rst | 22 ++++++++++++++++++++++ 6 files changed, 27 insertions(+), 9 deletions(-) diff --git a/.github/workflows/dist.yml b/.github/workflows/dist.yml index 2eb7836975..29eb0850dc 100644 --- a/.github/workflows/dist.yml +++ b/.github/workflows/dist.yml @@ -39,8 +39,8 @@ jobs: run: | pip install twine last_dist=$(ls -t dist/auto-sklearn-*.tar.gz | head -n 1) - twine check "$last_dist" --strict - + twine_output=`twine check "$last_dist"` + if [[ "$twine_output" != "Checking $last_dist: PASSED" ]]; then echo $twine_output && exit 1;fi - name: Install dist run: | diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 3645596c7b..c991e26a64 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -21,9 +21,7 @@ jobs: build-and-deploy: runs-on: ubuntu-latest steps: - - - name: Checkout - uses: actions/checkout@v2 + - uses: actions/checkout@v2 with: submodules: recursive diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index ad27dc6194..dc51107c15 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -80,8 +80,7 @@ jobs: steps: - - name: Checkout - uses: actions/checkout@v2 + - uses: actions/checkout@v2 with: submodules: recursive diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index a067f4b155..3e3e4cb181 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -46,7 +46,7 @@ Following that we'll tell you about how you can test your changes locally and th # If you missed the --recurse-submodules arg during clone or need to install the # submodule manually, then execute the following line: # - # git submodule update --init --recursive + # git submodule udate --init --recursive # ... Alternatively, if you would prefer a more manual method # Show all the available branches with a * beside your current one diff --git a/MANIFEST.in b/MANIFEST.in index dffd0c7283..37e70af053 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -2,7 +2,6 @@ include LICENSE.txt include requirements.txt include autosklearn/util/logging.yaml include autosklearn/requirements.txt -include autosklearn/py.typed # Meta-data recursive-include autosklearn/metalearning/files *.arff *.csv *.txt diff --git a/doc/installation.rst b/doc/installation.rst index 544dd9fff5..54f000ac8a 100644 --- a/doc/installation.rst +++ b/doc/installation.rst @@ -125,6 +125,28 @@ missed the `--recurse-submodules` option. pip install -e ".[test,doc,examples]" +You can install auto-sklearn directly form source by following the below: + +.. code:: bash + + git clone --recurse-submodules git@github.com:automl/auto-sklearn.git + cd auto-sklearn + + # Install it in editable mode with all optional dependencies + pip install -e ".[test,doc,examples]" + +We use submodules so you will have to make sure the submodule is initialized if you +missed the `--recurse-submodules` option. + +.. code:: bash + + git clone git@github.com:automl/auto-sklearn.git + cd auto-sklearn + git submodule update --init --recursive + + pip install -e ".[test,doc,examples]" + + Windows/macOS compatibility =========================== From c86eb95a9df6b11120242966db2826eabd0deecf Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Thu, 9 Dec 2021 10:20:17 +0100 Subject: [PATCH 009/118] Fixed dependancies warnings introduced by `sphinx_toolbox` (#1339) * Added versioning for sphinx, docutils - introduced by sphinxtoolbox * Fixed bug with config value for `plot_gallery` in doc makefile * Update linkcheck command as well --- setup.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 7d15561f42..6107e60321 100644 --- a/setup.py +++ b/setup.py @@ -41,11 +41,12 @@ "seaborn", ], "docs": [ - "sphinx", - "sphinx-gallery<=0.10.0", + "sphinx<4.3", + "sphinx-gallery", "sphinx_bootstrap_theme", "numpydoc", "sphinx_toolbox", + "docutils==0.16" ], } From d7b4a055970346974a07f54dcb34914781bc1d86 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Sun, 12 Dec 2021 15:34:11 +0100 Subject: [PATCH 010/118] Fix regression algorithms to give correct output dimensions (#1335) * Added ignored_warnings file * Use ignored_warnings file * Test regressors with 1d, 1d as 2d and 2d targets * Flake'd * Fix broken relative imports to ignore_warnings * Removed print and updated parameter type for tests * Type import fix --- .../components/regression/gaussian_process.py | 3 + .../components/regression/test_base.py | 10 ++-- test/test_pipeline/ignored_warnings.py | 23 +------- test/test_pipeline/test_classification.py | 44 +-------------- test/test_pipeline/test_regression.py | 55 +++++-------------- 5 files changed, 24 insertions(+), 111 deletions(-) diff --git a/autosklearn/pipeline/components/regression/gaussian_process.py b/autosklearn/pipeline/components/regression/gaussian_process.py index 04dd355a21..c587b13b0e 100644 --- a/autosklearn/pipeline/components/regression/gaussian_process.py +++ b/autosklearn/pipeline/components/regression/gaussian_process.py @@ -37,6 +37,9 @@ def fit(self, X, y): normalize_y=True ) + if y.ndim == 2 and y.shape[1] == 1: + y = y.flatten() + self.estimator.fit(X, y) return self diff --git a/test/test_pipeline/components/regression/test_base.py b/test/test_pipeline/components/regression/test_base.py index 8ffc1d23fe..c72824074b 100644 --- a/test/test_pipeline/components/regression/test_base.py +++ b/test/test_pipeline/components/regression/test_base.py @@ -1,4 +1,4 @@ -from typing import Type, Container +from typing import Type import unittest @@ -13,7 +13,7 @@ from autosklearn.pipeline.components.regression import _regressors, RegressorChoice -from test.test_pipeline.ignored_warnings import regressor_warnings, ignore_warnings +from ...ignored_warnings import regressor_warnings, ignore_warnings class BaseRegressionComponentTest(unittest.TestCase): @@ -331,7 +331,7 @@ def test_fit_and_predict_with_1d_targets_as_1d( regressor: Type[RegressorChoice], X: np.ndarray, y: np.ndarray -) -> None: +): """Test that all pipelines work with 1d target types Parameters @@ -374,7 +374,7 @@ def test_fit_and_predict_with_1d_targets_as_2d( regressor: Type[RegressorChoice], X: np.ndarray, y: np.ndarray -) -> None: +): """Test that all pipelines work with 1d target types when they are wrapped as 2d Parameters @@ -423,7 +423,7 @@ def test_fit_and_predict_with_2d_targets( regressor: Type[RegressorChoice], X: np.ndarray, y: np.ndarray -) -> None: +): """Test that all pipelines work with 2d target types Parameters diff --git a/test/test_pipeline/ignored_warnings.py b/test/test_pipeline/ignored_warnings.py index 5b941281f9..8f8203e05f 100644 --- a/test/test_pipeline/ignored_warnings.py +++ b/test/test_pipeline/ignored_warnings.py @@ -2,7 +2,6 @@ from typing import List, Iterator, Tuple import warnings - from sklearn.exceptions import ConvergenceWarning @@ -69,34 +68,14 @@ r" optimization hasn't converged yet\." ) ), - ( - ConvergenceWarning, ( # From FastICA - r"FastICA did not converge\." - r" Consider increasing tolerance or the maximum number of iterations\." - ) - ), ( UserWarning, ( # From LDA (Linear Discriminant Analysis) r"Variables are collinear" ) ), - ( - UserWarning, ( - r"Clustering metrics expects discrete values but received continuous values" - r" for label, and multiclass values for target" - ) - ) -] - -feature_preprocessing_warnings = [ - ( - ConvergenceWarning, ( # From liblinear - r"Liblinear failed to converge, increase the number of iterations." - ) - ) ] -ignored_warnings = regressor_warnings + classifier_warnings + feature_preprocessing_warnings +ignored_warnings = regressor_warnings + classifier_warnings @contextmanager diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index 514ad65cc9..a98dd0884a 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -7,7 +7,6 @@ import tempfile import unittest import unittest.mock -import warnings from joblib import Memory import numpy as np @@ -19,7 +18,6 @@ import sklearn.ensemble import sklearn.svm from sklearn.utils.validation import check_is_fitted -from sklearn.exceptions import ConvergenceWarning from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter @@ -34,42 +32,7 @@ from autosklearn.pipeline.constants import \ DENSE, SPARSE, UNSIGNED_DATA, PREDICTIONS, SIGNED_DATA, INPUT -ignored_warnings = [ - ( - UserWarning, ( # From QuantileTransformer - r"n_quantiles \(\d+\) is greater than the total number of samples \(\d+\)\." - r" n_quantiles is set to n_samples\." - ) - ), - ( - UserWarning, ( # From FastICA - r"n_components is too large: it will be set to \d+" - ) - - ), - ( - ConvergenceWarning, ( # From Liblinear - r"Liblinear failed to converge, increase the number of iterations\." - ) - ), - ( - ConvergenceWarning, ( # From SGD - r"Maximum number of iteration reached before convergence\. Consider increasing" - r" max_iter to improve the fit\." - ) - ), - ( - ConvergenceWarning, ( # From MLP - r"Stochastic Optimizer: Maximum iterations \(\d+\) reached and the" - r" optimization hasn't converged yet\." - ) - ), - ( - UserWarning, ( # From LDA (Linear Discriminant Analysis) - r"Variables are collinear" - ) - ), -] +from .ignored_warnings import classifier_warnings, ignore_warnings class DummyClassifier(AutoSklearnClassificationAlgorithm): @@ -535,10 +498,7 @@ def _test_configurations( check_is_fitted(step) try: - with warnings.catch_warnings(): - for category, message in ignored_warnings: - warnings.filterwarnings('ignore', category=category, message=message) - + with ignore_warnings(classifier_warnings): cls.fit(X_train, Y_train) # After fit, all components should be tagged as fitted diff --git a/test/test_pipeline/test_regression.py b/test/test_pipeline/test_regression.py index 5f4ca5558e..2a44275b25 100644 --- a/test/test_pipeline/test_regression.py +++ b/test/test_pipeline/test_regression.py @@ -4,7 +4,6 @@ import tempfile import unittest import unittest.mock -import warnings from joblib import Memory import numpy as np @@ -14,7 +13,6 @@ import sklearn.ensemble import sklearn.svm from sklearn.utils.validation import check_is_fitted -from sklearn.exceptions import ConvergenceWarning from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter @@ -28,32 +26,7 @@ from autosklearn.pipeline.util import get_dataset from autosklearn.pipeline.constants import SPARSE, DENSE, SIGNED_DATA, UNSIGNED_DATA, PREDICTIONS -ignored_warnings = [ - ( - UserWarning, ( # From QuantileTransformer - r"n_quantiles \(\d+\) is greater than the total number of samples \(\d+\)\." - r" n_quantiles is set to n_samples\." - ) - ), - ( - ConvergenceWarning, ( # From GaussianProcesses - r"The optimal value found for dimension \d+ of parameter \w+ is close" - r" to the specified (upper|lower) bound .*(Increasing|Decreasing) the bound" - r" and calling fit again may find a better value." - ) - ), - ( - UserWarning, ( # From FastICA - r"n_components is too large: it will be set to \d+" - ) - ), - ( - ConvergenceWarning, ( # From SGD - r"Maximum number of iteration reached before convergence\. Consider increasing" - r" max_iter to improve the fit\." - ) - ), -] +from .ignored_warnings import regressor_warnings, ignore_warnings class SimpleRegressionPipelineTest(unittest.TestCase): @@ -207,21 +180,19 @@ def _test_configurations(self, configurations_space, make_sparse=False, check_is_fitted(step) try: - with warnings.catch_warnings(): - for category, message in ignored_warnings: - warnings.filterwarnings('ignore', category=category, message=message) - + with ignore_warnings(regressor_warnings): cls.fit(X_train, Y_train) - # After fit, all components should be tagged as fitted - # by sklearn. Check is fitted raises an exception if that - # is not the case - try: - for name, step in cls.named_steps.items(): - check_is_fitted(step) - except sklearn.exceptions.NotFittedError: - self.fail("config={} raised NotFittedError unexpectedly!".format( - config - )) + + # After fit, all components should be tagged as fitted + # by sklearn. Check is fitted raises an exception if that + # is not the case + try: + for name, step in cls.named_steps.items(): + check_is_fitted(step) + except sklearn.exceptions.NotFittedError: + self.fail("config={} raised NotFittedError unexpectedly!".format( + config + )) cls.predict(X_test) except MemoryError: From 4b800f03f2bb3d9d6a071bb77e8a6f27f674b924 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Sun, 12 Dec 2021 15:35:49 +0100 Subject: [PATCH 011/118] Update isort-check.yaml to remove occurences of black (#1342) --- .github/workflows/isort_checker.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/isort_checker.yml b/.github/workflows/isort_checker.yml index 0a6fa003f9..eba534d428 100644 --- a/.github/workflows/isort_checker.yml +++ b/.github/workflows/isort_checker.yml @@ -1,9 +1,9 @@ -name: black-format-check +name: isort-check on: [push, pull_request, workflow_dispatch] env: - #If STRICT is set to true, it will fail on black check fail + #If STRICT is set to true, it will fail on isort check fail STRICT: false jobs: @@ -22,7 +22,7 @@ jobs: with: python-version: "3.7" - - name: Install black + - name: Install isort run: | pip install isort From 45a7df51a2ccc3a7a04d2c63f54e4f77c9b9bb23 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Mon, 13 Dec 2021 13:50:04 +0100 Subject: [PATCH 012/118] Fix random state not being used for sampling configurations (#1329) * Added random state to classifiers * Added some doc strings * Removed random_state again * flake'd * Fix some test issues * Re-added seed to test * Updated test doc for unknown test * flake'd --- test/test_pipeline/test_classification.py | 50 +++++++---------------- 1 file changed, 14 insertions(+), 36 deletions(-) diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index a98dd0884a..451bc75cfa 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -173,9 +173,7 @@ def test_default_configuration(self): auto = SimpleClassificationPipeline(random_state=1) - with ignore_warnings(classifier_warnings): - auto = auto.fit(X_train, Y_train) - + auto = auto.fit(X_train, Y_train) predictions = auto.predict(X_test) acc = sklearn.metrics.accuracy_score(predictions, Y_test) @@ -192,18 +190,13 @@ def test_default_configuration_multilabel(self): """ X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris', make_multilabel=True) - classifier = SimpleClassificationPipeline( - dataset_properties={'multilabel': True}, - random_state=0 - ) + classifier = SimpleClassificationPipeline(dataset_properties={'multilabel': True}) cs = classifier.get_hyperparameter_search_space() default = cs.get_default_configuration() classifier.set_hyperparameters(default) - with ignore_warnings(classifier_warnings): - classifier = classifier.fit(X_train, Y_train) - + classifier = classifier.fit(X_train, Y_train) predictions = classifier.predict(X_test) acc = sklearn.metrics.accuracy_score(predictions, Y_test) @@ -228,12 +221,10 @@ def test_default_configuration_iterative_fit(self): random_state=0 ) classifier.fit_transformer(X_train, Y_train) - - with ignore_warnings(classifier_warnings): - for i in range(1, 11): - classifier.iterative_fit(X_train, Y_train) - n_estimators = classifier.steps[-1][-1].choice.estimator.n_estimators - self.assertEqual(n_estimators, i) + for i in range(1, 11): + classifier.iterative_fit(X_train, Y_train) + n_estimators = classifier.steps[-1][-1].choice.estimator.n_estimators + self.assertEqual(n_estimators, i) def test_repr(self): """Test that the default pipeline can be converted to its representation and @@ -736,9 +727,7 @@ def test_predict_batched(self): # Multiclass X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits') - - with ignore_warnings(classifier_warnings): - cls.fit(X_train, Y_train) + cls.fit(X_train, Y_train) X_test_ = X_test.copy() prediction_ = cls.predict_proba(X_test_) @@ -770,8 +759,7 @@ def test_predict_batched_sparse(self): # Multiclass X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', make_sparse=True) - with ignore_warnings(classifier_warnings): - cls.fit(X_train, Y_train) + cls.fit(X_train, Y_train) X_test_ = X_test.copy() prediction_ = cls.predict_proba(X_test_) @@ -800,8 +788,7 @@ def test_predict_proba_batched(self): cls = SimpleClassificationPipeline(include={'classifier': ['sgd']}) X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits') - with ignore_warnings(classifier_warnings): - cls.fit(X_train, Y_train) + cls.fit(X_train, Y_train) X_test_ = X_test.copy() prediction_ = cls.predict_proba(X_test_) @@ -821,9 +808,7 @@ def test_predict_proba_batched(self): X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits') Y_train = np.array(list([(list([1 if i != y else 0 for i in range(10)])) for y in Y_train])) - - with ignore_warnings(classifier_warnings): - cls.fit(X_train, Y_train) + cls.fit(X_train, Y_train) X_test_ = X_test.copy() prediction_ = cls.predict_proba(X_test_) @@ -857,9 +842,7 @@ def test_predict_proba_batched_sparse(self): X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', make_sparse=True) X_test_ = X_test.copy() - with ignore_warnings(classifier_warnings): - cls.fit(X_train, Y_train) - + cls.fit(X_train, Y_train) prediction_ = cls.predict_proba(X_test_) # The object behind the last step in the pipeline @@ -878,13 +861,10 @@ def test_predict_proba_batched_sparse(self): include={'classifier': ['lda']} ) X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', make_sparse=True) - X_test_ = X_test.copy() Y_train = np.array([[1 if i != y else 0 for i in range(10)] for y in Y_train]) - with ignore_warnings(classifier_warnings): - cls.fit(X_train, Y_train) - + cls.fit(X_train, Y_train) prediction_ = cls.predict_proba(X_test_) # The object behind the last step in the pipeline @@ -909,9 +889,7 @@ def test_pipeline_clonability(self): X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris') auto = SimpleClassificationPipeline() - - with ignore_warnings(classifier_warnings): - auto = auto.fit(X_train, Y_train) + auto = auto.fit(X_train, Y_train) auto_clone = clone(auto) auto_clone_params = auto_clone.get_params() From 14dd5abda3477d2e81fce99b9efa717e27f6b1dc Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Tue, 14 Dec 2021 14:55:12 +0100 Subject: [PATCH 013/118] Update warnings (#1346) * Added ignored_warnings file * Use ignored_warnings file * Test regressors with 1d, 1d as 2d and 2d targets * Flake'd * Fix broken relative imports to ignore_warnings * Removed print and updated parameter type for tests * Added warning catches to fit methods in tests * Added more warning catches * Flake'd * Created top-level module to allow relativei imports * Deleted blank line in __init__ * Remove uneeded ignore warnings from tests * Fix bad indent * Fix github merge conflict editor whitespaces and indents --- .../components/classification/test_base.py | 2 +- .../feature_preprocessing/test_liblinear.py | 2 +- .../components/regression/test_base.py | 12 +++-- test/test_pipeline/ignored_warnings.py | 23 +++++++++- test/test_pipeline/test_classification.py | 45 +++++++++++++------ 5 files changed, 65 insertions(+), 19 deletions(-) diff --git a/test/test_pipeline/components/classification/test_base.py b/test/test_pipeline/components/classification/test_base.py index 4fc381af56..dda507eda4 100644 --- a/test/test_pipeline/components/classification/test_base.py +++ b/test/test_pipeline/components/classification/test_base.py @@ -9,7 +9,7 @@ import sklearn.metrics import numpy as np -from test.test_pipeline.ignored_warnings import ignore_warnings, classifier_warnings +from ...ignored_warnings import ignore_warnings, classifier_warnings class BaseClassificationComponentTest(unittest.TestCase): diff --git a/test/test_pipeline/components/feature_preprocessing/test_liblinear.py b/test/test_pipeline/components/feature_preprocessing/test_liblinear.py index 19b56b6eac..47465ccc8f 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_liblinear.py +++ b/test/test_pipeline/components/feature_preprocessing/test_liblinear.py @@ -5,7 +5,7 @@ get_dataset import sklearn.metrics -from test.test_pipeline.ignored_warnings import ignore_warnings, feature_preprocessing_warnings +from ...ignored_warnings import ignore_warnings, feature_preprocessing_warnings class LiblinearComponentTest(PreprocessingTestCase): diff --git a/test/test_pipeline/components/regression/test_base.py b/test/test_pipeline/components/regression/test_base.py index c72824074b..6b57215331 100644 --- a/test/test_pipeline/components/regression/test_base.py +++ b/test/test_pipeline/components/regression/test_base.py @@ -35,6 +35,12 @@ def test_default_boston(self): for _ in range(2): + with ignore_warnings(regressor_warnings): + predictions, targets, n_calls = _test_regressor( + dataset="boston", + Regressor=self.module + ) + with ignore_warnings(regressor_warnings): predictions, targets, n_calls = _test_regressor( dataset="boston", @@ -331,7 +337,7 @@ def test_fit_and_predict_with_1d_targets_as_1d( regressor: Type[RegressorChoice], X: np.ndarray, y: np.ndarray -): +) -> None: """Test that all pipelines work with 1d target types Parameters @@ -374,7 +380,7 @@ def test_fit_and_predict_with_1d_targets_as_2d( regressor: Type[RegressorChoice], X: np.ndarray, y: np.ndarray -): +) -> None: """Test that all pipelines work with 1d target types when they are wrapped as 2d Parameters @@ -423,7 +429,7 @@ def test_fit_and_predict_with_2d_targets( regressor: Type[RegressorChoice], X: np.ndarray, y: np.ndarray -): +) -> None: """Test that all pipelines work with 2d target types Parameters diff --git a/test/test_pipeline/ignored_warnings.py b/test/test_pipeline/ignored_warnings.py index 8f8203e05f..5b941281f9 100644 --- a/test/test_pipeline/ignored_warnings.py +++ b/test/test_pipeline/ignored_warnings.py @@ -2,6 +2,7 @@ from typing import List, Iterator, Tuple import warnings + from sklearn.exceptions import ConvergenceWarning @@ -68,14 +69,34 @@ r" optimization hasn't converged yet\." ) ), + ( + ConvergenceWarning, ( # From FastICA + r"FastICA did not converge\." + r" Consider increasing tolerance or the maximum number of iterations\." + ) + ), ( UserWarning, ( # From LDA (Linear Discriminant Analysis) r"Variables are collinear" ) ), + ( + UserWarning, ( + r"Clustering metrics expects discrete values but received continuous values" + r" for label, and multiclass values for target" + ) + ) +] + +feature_preprocessing_warnings = [ + ( + ConvergenceWarning, ( # From liblinear + r"Liblinear failed to converge, increase the number of iterations." + ) + ) ] -ignored_warnings = regressor_warnings + classifier_warnings +ignored_warnings = regressor_warnings + classifier_warnings + feature_preprocessing_warnings @contextmanager diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index 451bc75cfa..958a6a0ef8 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -173,7 +173,9 @@ def test_default_configuration(self): auto = SimpleClassificationPipeline(random_state=1) - auto = auto.fit(X_train, Y_train) + with ignore_warnings(classifier_warnings): + auto = auto.fit(X_train, Y_train) + predictions = auto.predict(X_test) acc = sklearn.metrics.accuracy_score(predictions, Y_test) @@ -196,7 +198,9 @@ def test_default_configuration_multilabel(self): default = cs.get_default_configuration() classifier.set_hyperparameters(default) - classifier = classifier.fit(X_train, Y_train) + with ignore_warnings(classifier_warnings): + classifier = classifier.fit(X_train, Y_train) + predictions = classifier.predict(X_test) acc = sklearn.metrics.accuracy_score(predictions, Y_test) @@ -221,10 +225,12 @@ def test_default_configuration_iterative_fit(self): random_state=0 ) classifier.fit_transformer(X_train, Y_train) - for i in range(1, 11): - classifier.iterative_fit(X_train, Y_train) - n_estimators = classifier.steps[-1][-1].choice.estimator.n_estimators - self.assertEqual(n_estimators, i) + + with ignore_warnings(classifier_warnings): + for i in range(1, 11): + classifier.iterative_fit(X_train, Y_train) + n_estimators = classifier.steps[-1][-1].choice.estimator.n_estimators + self.assertEqual(n_estimators, i) def test_repr(self): """Test that the default pipeline can be converted to its representation and @@ -727,7 +733,9 @@ def test_predict_batched(self): # Multiclass X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits') - cls.fit(X_train, Y_train) + + with ignore_warnings(classifier_warnings): + cls.fit(X_train, Y_train) X_test_ = X_test.copy() prediction_ = cls.predict_proba(X_test_) @@ -759,7 +767,8 @@ def test_predict_batched_sparse(self): # Multiclass X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', make_sparse=True) - cls.fit(X_train, Y_train) + with ignore_warnings(classifier_warnings): + cls.fit(X_train, Y_train) X_test_ = X_test.copy() prediction_ = cls.predict_proba(X_test_) @@ -788,7 +797,8 @@ def test_predict_proba_batched(self): cls = SimpleClassificationPipeline(include={'classifier': ['sgd']}) X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits') - cls.fit(X_train, Y_train) + with ignore_warnings(classifier_warnings): + cls.fit(X_train, Y_train) X_test_ = X_test.copy() prediction_ = cls.predict_proba(X_test_) @@ -808,7 +818,9 @@ def test_predict_proba_batched(self): X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits') Y_train = np.array(list([(list([1 if i != y else 0 for i in range(10)])) for y in Y_train])) - cls.fit(X_train, Y_train) + + with ignore_warnings(classifier_warnings): + cls.fit(X_train, Y_train) X_test_ = X_test.copy() prediction_ = cls.predict_proba(X_test_) @@ -842,7 +854,9 @@ def test_predict_proba_batched_sparse(self): X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', make_sparse=True) X_test_ = X_test.copy() - cls.fit(X_train, Y_train) + with ignore_warnings(classifier_warnings): + cls.fit(X_train, Y_train) + prediction_ = cls.predict_proba(X_test_) # The object behind the last step in the pipeline @@ -861,10 +875,13 @@ def test_predict_proba_batched_sparse(self): include={'classifier': ['lda']} ) X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', make_sparse=True) + X_test_ = X_test.copy() Y_train = np.array([[1 if i != y else 0 for i in range(10)] for y in Y_train]) - cls.fit(X_train, Y_train) + with ignore_warnings(classifier_warnings): + cls.fit(X_train, Y_train) + prediction_ = cls.predict_proba(X_test_) # The object behind the last step in the pipeline @@ -889,7 +906,9 @@ def test_pipeline_clonability(self): X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris') auto = SimpleClassificationPipeline() - auto = auto.fit(X_train, Y_train) + + with ignore_warnings(classifier_warnings): + auto = auto.fit(X_train, Y_train) auto_clone = clone(auto) auto_clone_params = auto_clone.get_params() From e2ddf623db99af55228aa7e18d6d8576f743f23b Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Tue, 21 Dec 2021 14:41:08 +0100 Subject: [PATCH 014/118] Prevent workflow double trigger, Add PEP 561 compliance (#1348) * update workflow files * typo fix * Update pytest * remove bad semi-colon * Fix test runner command * Remove explicit steps required from older version * Explicitly add Conda python to path for subprocess command in test * Fix the mypy compliance check * Added PEP 561 compliance * Add py.typed to MANIFEST for dist * Remove py.typed from setup.py --- .github/workflows/black_checker.yml | 16 +++++++++++++++- .github/workflows/docs.yml | 4 +++- .github/workflows/isort_checker.yml | 18 ++++++++++++++++-- .github/workflows/pytest.yml | 18 ++++++------------ MANIFEST.in | 1 + 5 files changed, 41 insertions(+), 16 deletions(-) diff --git a/.github/workflows/black_checker.yml b/.github/workflows/black_checker.yml index c64d666027..fac1723682 100644 --- a/.github/workflows/black_checker.yml +++ b/.github/workflows/black_checker.yml @@ -1,6 +1,20 @@ name: black-format-check -on: [push, pull_request] +on: + # Manually triggerable in github + workflow_dispatch: + + # When a push occurs on either of these branches + push: + branches: + - master + - development + + # When a push occurs on a PR that targets these branches + pull_request: + branches: + - master + - development env: #If STRICT is set to true, it will fail on black check fail diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index c991e26a64..3645596c7b 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -21,7 +21,9 @@ jobs: build-and-deploy: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + + - name: Checkout + uses: actions/checkout@v2 with: submodules: recursive diff --git a/.github/workflows/isort_checker.yml b/.github/workflows/isort_checker.yml index eba534d428..4f1f03f5a8 100644 --- a/.github/workflows/isort_checker.yml +++ b/.github/workflows/isort_checker.yml @@ -1,6 +1,20 @@ name: isort-check -on: [push, pull_request, workflow_dispatch] +on: + # Manually triggerable in github + workflow_dispatch: + + # When a push occurs on either of these branches + push: + branches: + - master + - development + + # When a push occurs on a PR that targets these branches + pull_request: + branches: + - master + - development env: #If STRICT is set to true, it will fail on isort check fail @@ -8,7 +22,7 @@ env: jobs: - black-format-check: + isort-format-check: runs-on: ubuntu-latest steps: diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index dc51107c15..4a9feba75f 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -12,7 +12,10 @@ on: # When a push occurs on a PR that targets these branches pull_request: - workflow_dispatch: + branches: + - master + - development + schedule: # Every day at 7AM UTC - cron: '0 07 * * *' @@ -40,16 +43,6 @@ jobs: runs-on: ${{ matrix.os }} strategy: - matrix: - python-version: ['3.7', '3.8', '3.9', '3.10'] - use-conda: [true, false] - use-dist: [false] - include: - - python-version: '3.8' - code-cov: true - - python-version: '3.7' - use-conda: false - use-dist: true fail-fast: false matrix: os: [windows-latest, macos-latest, ubuntu-latest] @@ -80,7 +73,8 @@ jobs: steps: - - uses: actions/checkout@v2 + - name: Checkout + uses: actions/checkout@v2 with: submodules: recursive diff --git a/MANIFEST.in b/MANIFEST.in index 37e70af053..dffd0c7283 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -2,6 +2,7 @@ include LICENSE.txt include requirements.txt include autosklearn/util/logging.yaml include autosklearn/requirements.txt +include autosklearn/py.typed # Meta-data recursive-include autosklearn/metalearning/files *.arff *.csv *.txt From ee664fb0a634bc559e45de0b59153e6a3766c16f Mon Sep 17 00:00:00 2001 From: partev Date: Tue, 21 Dec 2021 09:08:01 -0500 Subject: [PATCH 015/118] DOC: rename OSX -> macOS as it is the new name (#1349) * rename OSX -> macOS as it is the new name rename OSX -> macOS as it is the new name for the operating system. e.g. see https://www.apple.com/macos * Update doc/installation.rst Co-authored-by: Matthias Feurer * Update doc/installation.rst Co-authored-by: Matthias Feurer Co-authored-by: Matthias Feurer Co-authored-by: Matthias Feurer --- doc/installation.rst | 22 ---------------------- 1 file changed, 22 deletions(-) diff --git a/doc/installation.rst b/doc/installation.rst index 54f000ac8a..544dd9fff5 100644 --- a/doc/installation.rst +++ b/doc/installation.rst @@ -125,28 +125,6 @@ missed the `--recurse-submodules` option. pip install -e ".[test,doc,examples]" -You can install auto-sklearn directly form source by following the below: - -.. code:: bash - - git clone --recurse-submodules git@github.com:automl/auto-sklearn.git - cd auto-sklearn - - # Install it in editable mode with all optional dependencies - pip install -e ".[test,doc,examples]" - -We use submodules so you will have to make sure the submodule is initialized if you -missed the `--recurse-submodules` option. - -.. code:: bash - - git clone git@github.com:automl/auto-sklearn.git - cd auto-sklearn - git submodule update --init --recursive - - pip install -e ".[test,doc,examples]" - - Windows/macOS compatibility =========================== From 0a4949d38d1a25146acbdadf539c573061097daf Mon Sep 17 00:00:00 2001 From: Sagar Kaushik Date: Sat, 25 Dec 2021 18:16:57 +0530 Subject: [PATCH 016/118] Changes show_models() function to return a dictionary of models in ensemble (#1321) * Changed show_models() function to return a dictionary of models in the ensemble instead of a string --- autosklearn/automl.py | 139 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 139 insertions(+) diff --git a/autosklearn/automl.py b/autosklearn/automl.py index d3e7555cb7..972fee29e7 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -1843,6 +1843,145 @@ def show_models(self) -> Dict[int, Any]: A model dictionary contains the following: + * ``"model_id"`` - The id given to a model by ``autosklearn``. + * ``"rank"`` - The rank of the model based on it's ``"cost"``. + * ``"cost"`` - The loss of the model on the validation set. + * ``"ensemble_weight"`` - The weight given to the model in the ensemble. + * ``"voting_model"`` - The ``cv_voting_ensemble`` model (for 'cv' resampling). + * ``"estimators"`` - List of models (dicts) in ``cv_voting_ensemble`` (for 'cv' resampling). + * ``"data_preprocessor"`` - The preprocessor used on the data. + * ``"balancing"`` - The balancing used on the data (for classification). + * ``"feature_preprocessor"`` - The preprocessor for features types. + * ``"classifier"`` or ``"regressor"`` - The autosklearn wrapped classifier or regressor. + * ``"sklearn_classifier"`` or ``"sklearn_regressor"`` - The sklearn classifier or regressor. + + **Example** + + .. code-block:: python + + import sklearn.datasets + import sklearn.metrics + import autosklearn.regression + + X, y = sklearn.datasets.load_diabetes(return_X_y=True) + + automl = autosklearn.regression.AutoSklearnRegressor( + time_left_for_this_task=120 + ) + automl.fit(X_train, y_train, dataset_name='diabetes') + + ensemble_dict = automl.show_models() + print(ensemble_dict) + + Output: + + .. code-block:: text + + { + 25: {'model_id': 25.0, + 'rank': 1, + 'cost': 0.43667876507897496, + 'ensemble_weight': 0.38, + 'data_preprocessor': , + 'feature_preprocessor': , + 'regressor': , + 'sklearn_regressor': SGDRegressor(alpha=0.0006517033225329654,...) + }, + 6: {'model_id': 6.0, + 'rank': 2, + 'cost': 0.4550418898836528, + 'ensemble_weight': 0.3, + 'data_preprocessor': , + 'feature_preprocessor': , + 'regressor': , + 'sklearn_regressor': ARDRegression(alpha_1=0.0003701926442639788,...) + }... + } + + Returns + ------- + Dict(int, Any) : dictionary of length = number of models in the ensemble + A dictionary of models in the ensemble, where ``model_id`` is the key. + + """ + + ensemble_dict = {} + + def has_key(rv, key): + return rv.additional_info and key in rv.additional_info + + table_dict = {} + for rkey, rval in self.runhistory_.data.items(): + if has_key(rval, 'num_run'): + model_id = rval.additional_info['num_run'] + table_dict[model_id] = { + 'model_id': model_id, + 'cost': rval.cost + } + + # Checking if the dictionary is empty + if not table_dict: + raise RuntimeError('No model found. Try increasing \'time_left_for_this_task\'.') + + for i, weight in enumerate(self.ensemble_.weights_): + (_, model_id, _) = self.ensemble_.identifiers_[i] + table_dict[model_id]['ensemble_weight'] = weight + + table = pd.DataFrame.from_dict(table_dict, orient='index') + table.sort_values(by='cost', inplace=True) + + # Checking which resampling strategy is chosen and selecting the appropriate models + is_cv = (self._resampling_strategy == "cv") + models = self.cv_models_ if is_cv else self.models_ + + rank = 1 # Initializing rank for the first model + for (_, model_id, _), model in models.items(): + model_dict = {} # Declaring model dictionary + + # Inserting model_id, rank, cost and ensemble weight + model_dict['model_id'] = table.loc[model_id]['model_id'].astype(int) + model_dict['rank'] = rank + model_dict['cost'] = table.loc[model_id]['cost'] + model_dict['ensemble_weight'] = table.loc[model_id]['ensemble_weight'] + rank += 1 # Incrementing rank by 1 for the next model + + # The steps in the models pipeline are as follows: + # 'data_preprocessor': DataPreprocessor, + # 'balancing': Balancing, + # 'feature_preprocessor': FeaturePreprocessorChoice, + # 'classifier'/'regressor': ClassifierChoice/RegressorChoice (autosklearn wrapped model) + + # For 'cv' (cross validation) strategy + if is_cv: + # Voting model created by cross validation + cv_voting_ensemble = model + model_dict['voting_model'] = cv_voting_ensemble + + # List of models, each trained on one cv fold + cv_models = [] + for cv_model in cv_voting_ensemble.estimators_: + estimator = dict(cv_model.steps) + + # Adding sklearn model to the model dictionary + model_type, autosklearn_wrapped_model = cv_model.steps[-1] + estimator[f'sklearn_{model_type}'] = autosklearn_wrapped_model.choice.estimator + cv_models.append(estimator) + model_dict['estimators'] = cv_models + + # For any other strategy + else: + steps = dict(model.steps) + model_dict.update(steps) + + # Adding sklearn model to the model dictionary + model_type, autosklearn_wrapped_model = model.steps[-1] + model_dict[f'sklearn_{model_type}'] = autosklearn_wrapped_model.choice.estimator + + # Insterting model_dict in the ensemble dictionary + ensemble_dict[model_id] = model_dict + + return ensemble_dict + def _create_search_space( self, tmp_dir, From a58a24bf62ee9a4eec4a71e433a32613f13623d4 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Wed, 12 Jan 2022 16:35:36 +0100 Subject: [PATCH 017/118] Fix: Make SimpleClassificationPipeline tests deterministic (#1366) --- test/test_pipeline/test_classification.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index 958a6a0ef8..a98dd0884a 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -192,7 +192,10 @@ def test_default_configuration_multilabel(self): """ X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris', make_multilabel=True) - classifier = SimpleClassificationPipeline(dataset_properties={'multilabel': True}) + classifier = SimpleClassificationPipeline( + dataset_properties={'multilabel': True}, + random_state=0 + ) cs = classifier.get_hyperparameter_search_space() default = cs.get_default_configuration() From d51c34aa7e8e61275769421761361ff5f7afc076 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Wed, 12 Jan 2022 22:47:55 +0100 Subject: [PATCH 018/118] Fix: MLPRegressor tests (#1367) * Fix: MLPRegressor tests * Fix: Ordering of statements in test * Fix: MLP n_calls --- test/test_pipeline/components/regression/test_base.py | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/test/test_pipeline/components/regression/test_base.py b/test/test_pipeline/components/regression/test_base.py index 6b57215331..3186ca6e49 100644 --- a/test/test_pipeline/components/regression/test_base.py +++ b/test/test_pipeline/components/regression/test_base.py @@ -1,4 +1,4 @@ -from typing import Type +from typing import Type, Container import unittest @@ -35,12 +35,6 @@ def test_default_boston(self): for _ in range(2): - with ignore_warnings(regressor_warnings): - predictions, targets, n_calls = _test_regressor( - dataset="boston", - Regressor=self.module - ) - with ignore_warnings(regressor_warnings): predictions, targets, n_calls = _test_regressor( dataset="boston", From 11dc191e84f034c2272c28f41d8a4fe788965ff6 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Fri, 14 Jan 2022 11:45:51 +0100 Subject: [PATCH 019/118] Fix: imports from relative to absolute (#1370) --- test/test_pipeline/components/classification/test_base.py | 2 +- .../components/feature_preprocessing/test_liblinear.py | 2 +- test/test_pipeline/components/regression/test_base.py | 2 +- test/test_pipeline/test_classification.py | 2 +- test/test_pipeline/test_regression.py | 2 +- 5 files changed, 5 insertions(+), 5 deletions(-) diff --git a/test/test_pipeline/components/classification/test_base.py b/test/test_pipeline/components/classification/test_base.py index dda507eda4..4fc381af56 100644 --- a/test/test_pipeline/components/classification/test_base.py +++ b/test/test_pipeline/components/classification/test_base.py @@ -9,7 +9,7 @@ import sklearn.metrics import numpy as np -from ...ignored_warnings import ignore_warnings, classifier_warnings +from test.test_pipeline.ignored_warnings import ignore_warnings, classifier_warnings class BaseClassificationComponentTest(unittest.TestCase): diff --git a/test/test_pipeline/components/feature_preprocessing/test_liblinear.py b/test/test_pipeline/components/feature_preprocessing/test_liblinear.py index 47465ccc8f..19b56b6eac 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_liblinear.py +++ b/test/test_pipeline/components/feature_preprocessing/test_liblinear.py @@ -5,7 +5,7 @@ get_dataset import sklearn.metrics -from ...ignored_warnings import ignore_warnings, feature_preprocessing_warnings +from test.test_pipeline.ignored_warnings import ignore_warnings, feature_preprocessing_warnings class LiblinearComponentTest(PreprocessingTestCase): diff --git a/test/test_pipeline/components/regression/test_base.py b/test/test_pipeline/components/regression/test_base.py index 3186ca6e49..8ffc1d23fe 100644 --- a/test/test_pipeline/components/regression/test_base.py +++ b/test/test_pipeline/components/regression/test_base.py @@ -13,7 +13,7 @@ from autosklearn.pipeline.components.regression import _regressors, RegressorChoice -from ...ignored_warnings import regressor_warnings, ignore_warnings +from test.test_pipeline.ignored_warnings import regressor_warnings, ignore_warnings class BaseRegressionComponentTest(unittest.TestCase): diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index a98dd0884a..44caaecb9b 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -32,7 +32,7 @@ from autosklearn.pipeline.constants import \ DENSE, SPARSE, UNSIGNED_DATA, PREDICTIONS, SIGNED_DATA, INPUT -from .ignored_warnings import classifier_warnings, ignore_warnings +from test.test_pipeline.ignored_warnings import classifier_warnings, ignore_warnings class DummyClassifier(AutoSklearnClassificationAlgorithm): diff --git a/test/test_pipeline/test_regression.py b/test/test_pipeline/test_regression.py index 2a44275b25..cc52109664 100644 --- a/test/test_pipeline/test_regression.py +++ b/test/test_pipeline/test_regression.py @@ -26,7 +26,7 @@ from autosklearn.pipeline.util import get_dataset from autosklearn.pipeline.constants import SPARSE, DENSE, SIGNED_DATA, UNSIGNED_DATA, PREDICTIONS -from .ignored_warnings import regressor_warnings, ignore_warnings +from test.test_pipeline.ignored_warnings import regressor_warnings, ignore_warnings class SimpleRegressionPipelineTest(unittest.TestCase): From 73d5da5a623fd3302cd2a0f1f2da69375221ec61 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Fri, 28 Jan 2022 13:45:41 +0100 Subject: [PATCH 020/118] Fix: add error to be ignored during test (#1382) --- test/test_pipeline/test_classification.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index 44caaecb9b..82f341fde6 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -533,6 +533,9 @@ def _test_configurations( continue elif 'Internal work array size computation failed' in e.args[0]: continue + # Assumed to be caused by knn with preprocessor fast_ica with whiten + elif 'Input contains NaN, infinity or a value too large' in e.args[0]: + continue else: e.args += (f"config={config}",) raise e From bd8d5210f506bd45493458d133f352a03b3e1d9c Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Tue, 1 Feb 2022 13:55:43 +0100 Subject: [PATCH 021/118] Test changing the default output distribution for the quantile scaler (#1316) --- .../data_preprocessing/rescaling/quantile_transformer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py index 0769f456de..b7206fbaaf 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py @@ -58,7 +58,7 @@ def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERT 'n_quantiles', lower=10, upper=2000, default_value=1000 ) output_distribution = CategoricalHyperparameter( - 'output_distribution', ['uniform', 'normal'] + 'output_distribution', ['normal', 'uniform'] ) cs.add_hyperparameters((n_quantiles, output_distribution)) return cs From 43299a961f5e16d5cb82a6d6c41af13a24cb45f2 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Tue, 1 Feb 2022 15:28:54 +0100 Subject: [PATCH 022/118] Dataset size reduction fixed, updated TargetValidator to match signatures (#1250) * Moved to new splitter, moved to util file * flake8'd * Fixed errors, added test specifically for CustomStratifiedShuffleSplit * flake8'd * Updated docstring * Updated types in docstring * reduce_dataset_size_if_too_large supports more types * flake8'd * flake8'd * Updated docstring * Seperated out the data subsampling into individual functions * Improved typing from Automl.fit to reduce_dataset_size_if_too_large * flak8'd * subsample tested * Finished testing and flake8'd * Cleaned up transform function that was touched * ^ * Removed double typing * Cleaned up typing of convert_if_sparse * Cleaned up splitters and added size test * Cleanup doc in data * rogue line added was removed * Test fix * flake8'd * Typo fix * Fixed ordering of things * Fixed typing and tests of target_validator fit, transform, inv_transform * Updated doc * Updated Type return * Removed elif gaurd * removed extraneuous overload * Updated return type of feature validator * Type fixes for target validator fit * flake8'd * Moved to new splitter, moved to util file * flake8'd * Fixed errors, added test specifically for CustomStratifiedShuffleSplit * flake8'd * Updated docstring * Updated types in docstring * reduce_dataset_size_if_too_large supports more types * flake8'd * flake8'd * Updated docstring * Seperated out the data subsampling into individual functions * Improved typing from Automl.fit to reduce_dataset_size_if_too_large * flak8'd * subsample tested * Finished testing and flake8'd * Cleaned up transform function that was touched * ^ * Removed double typing * Cleaned up typing of convert_if_sparse * Cleaned up splitters and added size test * Cleanup doc in data * rogue line added was removed * Test fix * flake8'd * Typo fix * Fixed ordering of things * Fixed typing and tests of target_validator fit, transform, inv_transform * Updated doc * Updated Type return * Removed elif gaurd * removed extraneuous overload * Updated return type of feature validator * Type fixes for target validator fit * flake8'd * Fixed err message str and automl sparse y tests * Flak8'd * Fix sort indices * list type to List * Remove uneeded comment * Updated comment to make it more clear * Comment update * Fixed warning message for reduce_dataset_if_too_large * Fix test * Added check for error message in tests * Test Updates * Fix error msg * reinclude csr y to test * Reintroduced explicit subsample values test * flaked * Missed an uncomment * Update the comment for test of splitters * Updated warning message in CustomSplitter * Update comment in test * Update tests * Removed overloads * Narrowed type of subsample * Removed overload import * Fix `todense` giving np.matrix, using `toarray` * Made subsampling a little less aggresive * Changed multiplier back to 10 * Allow argument to specfiy how auto-sklearn handles compressing dataset size (#1341) * Added dataset_compression parameter and validation * Fix docstring * Updated docstring for `resampling_strategy` * Updated param def and memory_allocation can now be absolute * insert newline * Fix params into one line * fix indentation in docs * fix import breaks * Allow absolute memory_allocation * Tests * Update test on for precision omitted from methods * Update test for akslearn2 with same args * Update to use TypedDict for better Mypy parsing * Added arg to asklearn2 * Updated tests to remove some warnings * flaked * Fix broken link? * Remove TypedDict as it's not supported in Python3.7 * Missing import * Review changes * Fix magic mock for python < 3.9 * Fixed bad merge --- autosklearn/automl.py | 246 +++---- autosklearn/data/feature_validator.py | 86 ++- autosklearn/data/target_validator.py | 296 ++++---- autosklearn/data/validation.py | 51 +- autosklearn/estimators.py | 125 +++- autosklearn/evaluation/splitter.py | 40 +- autosklearn/experimental/askl2.py | 3 +- autosklearn/util/data.py | 436 +++++++++++- test/test_automl/test_automl.py | 297 +++++--- test/test_automl/test_estimators.py | 64 +- test/test_data/test_target_validator.py | 171 ++--- test/test_evaluation/test_custom_splitters.py | 50 ++ test/test_util/test_data.py | 632 ++++++++++++++++++ 13 files changed, 1881 insertions(+), 616 deletions(-) create mode 100644 test/test_evaluation/test_custom_splitters.py create mode 100644 test/test_util/test_data.py diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 972fee29e7..f8057d130b 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -9,7 +9,7 @@ import os import sys import time -from typing import Any, Dict, Optional, List, Tuple, Union +from typing import Any, Dict, Mapping, Optional, List, Tuple, Union, cast import uuid import unittest.mock import tempfile @@ -52,6 +52,13 @@ from autosklearn.evaluation.abstract_evaluator import _fit_and_suppress_warnings from autosklearn.evaluation.train_evaluator import TrainEvaluator, _fit_with_budget from autosklearn.metrics import calculate_metric +from autosklearn.util.data import ( + reduce_dataset_size_if_too_large, + supported_precision_reductions, + validate_dataset_compression_arg, + default_dataset_compression_arg, + DatasetCompressionSpec, +) from autosklearn.util.stopwatch import StopWatch from autosklearn.util.logging_ import ( setup_logger, @@ -118,7 +125,7 @@ def _model_predict( The predictions produced by the model """ # Copy the array and ensure is has the attr 'shape' - X_ = np.asarray(X) if isinstance(X, list) else X.copy() + X_ = np.asarray(X) if isinstance(X, List) else X.copy() assert X_.shape[0] >= 1, f"X must have more than 1 sample but has {X_.shape[0]}" @@ -160,34 +167,36 @@ def _model_predict( class AutoML(BaseEstimator): - def __init__(self, - time_left_for_this_task, - per_run_time_limit, - temporary_directory: Optional[str] = None, - delete_tmp_folder_after_terminate: bool = True, - initial_configurations_via_metalearning=25, - ensemble_size=1, - ensemble_nbest=1, - max_models_on_disc=1, - seed=1, - memory_limit=3072, - metadata_directory=None, - debug_mode=False, - include: Optional[Dict[str, List[str]]] = None, - exclude: Optional[Dict[str, List[str]]] = None, - resampling_strategy='holdout-iterative-fit', - resampling_strategy_arguments=None, - n_jobs=None, - dask_client: Optional[dask.distributed.Client] = None, - precision=32, - disable_evaluator_output=False, - get_smac_object_callback=None, - smac_scenario_args=None, - logging_config=None, - metric=None, - scoring_functions=None, - get_trials_callback=None - ): + def __init__( + self, + time_left_for_this_task, + per_run_time_limit, + temporary_directory: Optional[str] = None, + delete_tmp_folder_after_terminate: bool = True, + initial_configurations_via_metalearning=25, + ensemble_size=1, + ensemble_nbest=1, + max_models_on_disc=1, + seed=1, + memory_limit=3072, + metadata_directory=None, + debug_mode=False, + include=None, + exclude=None, + resampling_strategy='holdout-iterative-fit', + resampling_strategy_arguments=None, + n_jobs=None, + dask_client: Optional[dask.distributed.Client] = None, + precision=32, + disable_evaluator_output=False, + get_smac_object_callback=None, + smac_scenario_args=None, + logging_config=None, + metric=None, + scoring_functions=None, + get_trials_callback=None, + dataset_compression: Union[bool, Mapping[str, Any]] = True + ): super(AutoML, self).__init__() self.configuration_space = None self._backend: Optional[Backend] = None @@ -217,10 +226,10 @@ def __init__(self, self.precision = precision self._disable_evaluator_output = disable_evaluator_output # Check arguments prior to doing anything! - if not isinstance(self._disable_evaluator_output, (bool, list)): + if not isinstance(self._disable_evaluator_output, (bool, List)): raise ValueError('disable_evaluator_output must be of type bool ' 'or list.') - if isinstance(self._disable_evaluator_output, list): + if isinstance(self._disable_evaluator_output, List): allowed_elements = ['model', 'cv_model', 'y_optimization', 'y_test', 'y_valid'] for element in self._disable_evaluator_output: if element not in allowed_elements: @@ -232,6 +241,18 @@ def __init__(self, self._smac_scenario_args = smac_scenario_args self.logging_config = logging_config + # Validate dataset_compression and set its values + self._dataset_compression: Optional[DatasetCompressionSpec] + if isinstance(dataset_compression, bool): + if dataset_compression is True: + self._dataset_compression = default_dataset_compression_arg + else: + self._dataset_compression = None + else: + self._dataset_compression = validate_dataset_compression_arg( + dataset_compression, memory_limit=self._memory_limit + ) + self._datamanager = None self._dataset_name = None self._feat_type = None @@ -490,10 +511,10 @@ def _supports_task_type(cls, task_type: str) -> bool: def fit( self, X: SUPPORTED_FEAT_TYPES, - y: Union[SUPPORTED_TARGET_TYPES, spmatrix], + y: SUPPORTED_TARGET_TYPES, task: Optional[int] = None, X_test: Optional[SUPPORTED_FEAT_TYPES] = None, - y_test: Optional[Union[SUPPORTED_TARGET_TYPES, spmatrix]] = None, + y_test: Optional[SUPPORTED_TARGET_TYPES] = None, feat_type: Optional[List[str]] = None, dataset_name: Optional[str] = None, only_return_configuration_space: bool = False, @@ -509,8 +530,8 @@ def fit( # # `task: Optional[int]` and `is_classification` # - # `AutoML` tries to identify the task itself with - # `sklearn.type_of_target`, leaving little for the subclasses to do. + # `AutoML` tries to identify the task itself with `sklearn.type_of_target`, + # leaving little for the subclasses to do. # Except this failes when type_of_target(y) == "multiclass". # # "multiclass" be mean either REGRESSION or MULTICLASS_CLASSIFICATION, @@ -588,6 +609,8 @@ def fit( self """ + if (X_test is not None) ^ (y_test is not None): + raise ValueError("Must provide both X_test and y_test together") # AutoSklearn does not handle sparse y for now y = convert_if_sparse(y) @@ -639,17 +662,35 @@ def fit( self.InputValidator.fit(X_train=X, y_train=y, X_test=X_test, y_test=y_test) X, y = self.InputValidator.transform(X, y) - if X_test is not None: + if X_test is not None and y_test is not None: X_test, y_test = self.InputValidator.transform(X_test, y_test) - X, y = self.subsample_if_too_large( - X=X, - y=y, - logger=self._logger, - seed=self._seed, - memory_limit=self._memory_limit, - task=self._task, - ) + # We don't support size reduction on pandas type object yet + if ( + self._dataset_compression is not None + and not isinstance(X, pd.DataFrame) + and not (isinstance(y, pd.Series) or isinstance(y, pd.DataFrame)) + ): + methods = self._dataset_compression["methods"] + memory_allocation = self._dataset_compression["memory_allocation"] + + # Remove precision reduction if we can't perform it + if ( + X.dtype not in supported_precision_reductions + and "precision" in cast(List[str], methods) # Removable with TypedDict + ): + methods = [method for method in methods if method != "precision"] + + with warnings_to(self._logger): + X, y = reduce_dataset_size_if_too_large( + X=X, + y=y, + memory_limit=self._memory_limit, + is_classification=is_classification, + random_state=self._seed, + operations=methods, + memory_allocation=memory_allocation + ) # Check the re-sampling strategy try: @@ -1042,117 +1083,6 @@ def _check_resampling_strategy( return - @staticmethod - def subsample_if_too_large( - X: SUPPORTED_FEAT_TYPES, - y: SUPPORTED_TARGET_TYPES, - logger, - seed: int, - memory_limit: int, - task: int, - ): - if memory_limit and isinstance(X, np.ndarray): - - if X.dtype == np.float32: - multiplier = 4 - elif X.dtype in (np.float64, float): - multiplier = 8 - elif ( - # In spite of the names, np.float96 and np.float128 - # provide only as much precision as np.longdouble, - # that is, 80 bits on most x86 machines and 64 bits - # in standard Windows builds. - (hasattr(np, 'float128') and X.dtype == np.float128) - or (hasattr(np, 'float96') and X.dtype == np.float96) - ): - multiplier = 16 - else: - # Just assuming some value - very unlikely - multiplier = 8 - logger.warning('Unknown dtype for X: %s, assuming it takes 8 bit/number', - str(X.dtype)) - - megabytes = X.shape[0] * X.shape[1] * multiplier / 1024 / 1024 - if memory_limit <= megabytes * 10 and X.dtype != np.float32: - cast_to = { - 8: np.float32, - 16: np.float64, - }.get(multiplier, np.float32) - logger.warning( - 'Dataset too large for memory limit %dMB, reducing the precision from %s to %s', - memory_limit, - X.dtype, - cast_to, - ) - X = X.astype(cast_to) - - megabytes = X.shape[0] * X.shape[1] * multiplier / 1024 / 1024 - if memory_limit <= megabytes * 10: - new_num_samples = int( - memory_limit / (10 * X.shape[1] * multiplier / 1024 / 1024) - ) - logger.warning( - 'Dataset too large for memory limit %dMB, reducing number of samples from ' - '%d to %d.', - memory_limit, - X.shape[0], - new_num_samples, - ) - if task in CLASSIFICATION_TASKS: - # Identify if it has unique labels and allow for - # stratification, with unique labels in training set - values, idxs, counts = np.unique(y, axis=0, - return_index=True, - return_counts=True) - unique_labels = { - idx: value - for value, idx, count in zip(values, idxs, counts) - if count == 1 - } - - # If there are unique labeled elements, remove them and - # place them back in later - if len(unique_labels) > 0: - idxs_of_unique = np.asarray(list(unique_labels.keys())) - unique_X = X[idxs_of_unique] - unique_y = y[idxs_of_unique] - - # NOTE optimization - # If this ever turns out to be slow, this actually - # copies the entire array. There might be a better - # solution but it will probably require a lot more - # manual work in how splitting is done. - X = np.delete(X, idxs_of_unique, axis=0) - y = np.delete(y, idxs_of_unique, axis=0) - - X, _, y, _ = sklearn.model_selection.train_test_split( - X, y, - train_size=new_num_samples - len(unique_y), - random_state=seed, - stratify=y, - ) - - X = np.append(X, unique_X, axis=0) - y = np.append(y, unique_y, axis=0) - - # Otherwise we should be able to stratify as normal - else: - X, _, y, _ = sklearn.model_selection.train_test_split( - X, y, - train_size=new_num_samples, - random_state=seed, - stratify=y, - ) - elif task in REGRESSION_TASKS: - X, _, y, _ = sklearn.model_selection.train_test_split( - X, y, - train_size=new_num_samples, - random_state=seed, - ) - else: - raise ValueError(task) - return X, y - def refit(self, X, y): # AutoSklearn does not handle sparse y for now y = convert_if_sparse(y) @@ -1247,7 +1177,7 @@ def fit_pipeline( is_classification: bool Whether the task is for classification or regression. This affects how the targets are treated - feat_type : list, optional (default=None) + feat_type : List, optional (default=None) List of str of `len(X.shape[1])` describing the attribute type. Possible types are `Categorical` and `Numerical`. `Categorical` attributes will be automatically One-Hot encoded. The values @@ -1536,7 +1466,7 @@ def _load_models(self): raise ValueError('No models fitted!') elif self._disable_evaluator_output is False or \ - (isinstance(self._disable_evaluator_output, list) and + (isinstance(self._disable_evaluator_output, List) and 'model' not in self._disable_evaluator_output): model_names = self._backend.list_all_models(self._seed) diff --git a/autosklearn/data/feature_validator.py b/autosklearn/data/feature_validator.py index cc4b578bdf..05feaffe98 100644 --- a/autosklearn/data/feature_validator.py +++ b/autosklearn/data/feature_validator.py @@ -1,12 +1,12 @@ import logging -import typing +from typing import Dict, List, Optional, Tuple, Union, cast import numpy as np import pandas as pd from pandas.api.types import is_numeric_dtype, is_sparse -import scipy.sparse +from scipy.sparse import csr_matrix, spmatrix from sklearn.base import BaseEstimator from sklearn.exceptions import NotFittedError @@ -14,12 +14,7 @@ from autosklearn.util.logging_ import PickableLoggerAdapter -SUPPORTED_FEAT_TYPES = typing.Union[ - typing.List, - pd.DataFrame, - np.ndarray, - scipy.sparse.spmatrix, -] +SUPPORTED_FEAT_TYPES = Union[List, pd.DataFrame, np.ndarray, spmatrix] class FeatureValidator(BaseEstimator): @@ -31,7 +26,7 @@ class FeatureValidator(BaseEstimator): Attributes ---------- - feat_type: typing.Optional[typing.List[str]] + feat_type: Optional[List[str]] In case the dataset is not a pandas DataFrame: + If provided, this list indicates which columns should be treated as categorical it is internally transformed into a dictionary that indicates a mapping from @@ -44,18 +39,18 @@ class FeatureValidator(BaseEstimator): Class name of the data type provided during fit. """ def __init__(self, - feat_type: typing.Optional[typing.List[str]] = None, - logger: typing.Optional[PickableLoggerAdapter] = None, + feat_type: Optional[List[str]] = None, + logger: Optional[PickableLoggerAdapter] = None, ) -> None: # If a dataframe was provided, we populate # this attribute with a mapping from column to {numerical | categorical} - self.feat_type: typing.Optional[ - typing.Dict[typing.Union[str, int], str] + self.feat_type: Optional[ + Dict[Union[str, int], str] ] = None if feat_type is not None: if isinstance(feat_type, dict): self.feat_type = feat_type - elif not isinstance(feat_type, list): + elif not isinstance(feat_type, List): raise ValueError("Auto-Sklearn expects a list of categorical/" "numerical feature types, yet a" " {} was provided".format(type(feat_type))) @@ -66,8 +61,8 @@ def __init__(self, self.feat_type = {i: feat for i, feat in enumerate(feat_type)} # Register types to detect unsupported data format changes - self.data_type = None # type: typing.Optional[type] - self.dtypes = {} # type: typing.Dict[str, str] + self.data_type = None # type: Optional[type] + self.dtypes = {} # type: Dict[str, str] self.logger = logger if logger is not None else logging.getLogger(__name__) @@ -76,8 +71,8 @@ def __init__(self, def fit( self, X_train: SUPPORTED_FEAT_TYPES, - X_test: typing.Optional[SUPPORTED_FEAT_TYPES] = None, - ) -> BaseEstimator: + X_test: Optional[SUPPORTED_FEAT_TYPES] = None, + ) -> 'FeatureValidator': """ Validates input data to Auto-Sklearn. The supported data types are List, numpy arrays and pandas DataFrames. @@ -88,12 +83,12 @@ def fit( X_train: SUPPORTED_FEAT_TYPES A set of features that are going to be validated (type and dimensionality checks) and a encoder fitted in the case the data needs encoding - X_test: typing.Optional[SUPPORTED_FEAT_TYPES] + X_test: Optional[SUPPORTED_FEAT_TYPES] A hold out set of data used for checking """ # If a list was provided, it will be converted to pandas - if isinstance(X_train, list): + if isinstance(X_train, List): X_train, X_test = self.list_to_dataframe(X_train, X_test) self._check_data(X_train) @@ -150,43 +145,46 @@ def fit( def transform( self, X: SUPPORTED_FEAT_TYPES, - ) -> np.ndarray: + ) -> Union[np.ndarray, spmatrix, pd.DataFrame]: """ Validates and fit a categorical encoder (if needed) to the features. The supported data types are List, numpy arrays and pandas DataFrames. Parameters ---------- - X_train: SUPPORTED_FEAT_TYPES - A set of features, whose categorical features are going to be - transformed + X_train: SUPPORTED_FEAT_TYPES + A set of features, whose categorical features are going to be + transformed Return ------ - np.ndarray: - The transformed array + np.ndarray | spmatrix | pd.DataFrame: + The transformed array """ if not self._is_fitted: raise NotFittedError("Cannot call transform on a validator that is not fitted") # If a list was provided, it will be converted to pandas - if isinstance(X, list): - X, _ = self.list_to_dataframe(X) + if isinstance(X, List): + X_transformed, _ = self.list_to_dataframe(X) + else: + X_transformed = X # Check the data here so we catch problems on new test data - self._check_data(X) + self._check_data(X_transformed) # Sparse related transformations # Not all sparse format support index sorting - if scipy.sparse.issparse(X): - if not isinstance(X, scipy.sparse.csr_matrix): - self.logger.warning(f"Sparse data provided is of type {type(X)} " + if isinstance(X_transformed, spmatrix): + if not isinstance(X_transformed, csr_matrix): + self.logger.warning(f"Sparse data provided is of type {type(X_transformed)} " "yet Auto-Sklearn only support csr_matrix. Auto-sklearn " "will convert the provided data to the csr_matrix format.") - X = X.tocsr(copy=False) - if hasattr(X, 'sort_indices'): - X.sort_indices() - return X + X_transformed = X_transformed.tocsr(copy=False) + + X_transformed.sort_indices() + + return X_transformed def _check_data( self, @@ -204,11 +202,11 @@ def _check_data( # We consider columns that are all nan in a pandas frame as category if hasattr(X, 'columns'): - for column in typing.cast(pd.DataFrame, X).columns: + for column in cast(pd.DataFrame, X).columns: if X[column].isna().all(): X[column] = X[column].astype('category') - if not isinstance(X, (np.ndarray, pd.DataFrame)) and not scipy.sparse.issparse(X): + if not isinstance(X, (np.ndarray, pd.DataFrame)) and not isinstance(X, spmatrix): raise ValueError("Auto-sklearn only supports Numpy arrays, Pandas DataFrames," " scipy sparse and Python Lists, yet, the provided input is" " of type {}".format( @@ -241,7 +239,7 @@ def _check_data( # Then for Pandas, we do not support Nan in categorical columns if hasattr(X, "iloc"): # If entered here, we have a pandas dataframe - X = typing.cast(pd.DataFrame, X) + X = cast(pd.DataFrame, X) dtypes = {col: X[col].dtype.name.lower() for col in X.columns} if len(self.dtypes) > 0: @@ -261,7 +259,7 @@ def _check_data( def get_feat_type_from_columns( self, X: pd.DataFrame, - ) -> typing.Dict[typing.Union[str, int], str]: + ) -> Dict[Union[str, int], str]: """ Returns a dictionary that maps pandas dataframe columns to a feature type. This feature type can be categorical or numerical @@ -332,8 +330,8 @@ def get_feat_type_from_columns( def list_to_dataframe( self, X_train: SUPPORTED_FEAT_TYPES, - X_test: typing.Optional[SUPPORTED_FEAT_TYPES] = None, - ) -> typing.Tuple[pd.DataFrame, typing.Optional[pd.DataFrame]]: + X_test: Optional[SUPPORTED_FEAT_TYPES] = None, + ) -> Tuple[pd.DataFrame, Optional[pd.DataFrame]]: """ Converts a list to a pandas DataFrame. In this process, column types are inferred. @@ -344,7 +342,7 @@ def list_to_dataframe( X_train: SUPPORTED_FEAT_TYPES A set of features that are going to be validated (type and dimensionality checks) and a encoder fitted in the case the data needs encoding - X_test: typing.Optional[SUPPORTED_FEAT_TYPES] + X_test: Optional[SUPPORTED_FEAT_TYPES] A hold out set of data used for checking Returns ------- @@ -382,7 +380,7 @@ def list_to_dataframe( [(col, t) for col, t in zip(X_train.columns, X_train.dtypes)] )) if X_test is not None: - if not isinstance(X_test, list): + if not isinstance(X_test, List): self.logger.warning("Train features are a list while the provided test data" "is {}. X_test will be casted as DataFrame.".format( type(X_test) diff --git a/autosklearn/data/target_validator.py b/autosklearn/data/target_validator.py index 55a3bfc5d0..9f6d2e74b5 100644 --- a/autosklearn/data/target_validator.py +++ b/autosklearn/data/target_validator.py @@ -1,29 +1,25 @@ import logging -import typing +import warnings +from typing import List, Optional, Type, Union, cast import numpy as np import pandas as pd from pandas.api.types import is_numeric_dtype +from pandas.core.dtypes.base import ExtensionDtype -import scipy.sparse +from scipy.sparse import spmatrix import sklearn.utils -from sklearn import preprocessing from sklearn.base import BaseEstimator from sklearn.exceptions import NotFittedError +from sklearn.preprocessing import OrdinalEncoder from sklearn.utils.multiclass import type_of_target from autosklearn.util.logging_ import PickableLoggerAdapter -SUPPORTED_TARGET_TYPES = typing.Union[ - typing.List, - pd.Series, - pd.DataFrame, - np.ndarray, - scipy.sparse.spmatrix, -] +SUPPORTED_TARGET_TYPES = Union[List, pd.Series, pd.DataFrame, np.ndarray, spmatrix] class TargetValidator(BaseEstimator): @@ -35,37 +31,42 @@ class TargetValidator(BaseEstimator): is_classification: bool A bool that indicates if the validator should operate in classification mode. During classification, the targets are encoded. - encoder: typing.Optional[BaseEstimator] + encoder: Optional[BaseEstimator] Host a encoder object if the data requires transformation (for example, if provided a categorical column in a pandas DataFrame) - enc_columns: typing.List[str] + enc_columns: List[str] List of columns that where encoded """ def __init__(self, is_classification: bool = False, - logger: typing.Optional[PickableLoggerAdapter] = None, + logger: Optional[PickableLoggerAdapter] = None, ) -> None: self.is_classification = is_classification - self.data_type = None # type: typing.Optional[type] + self.data_type = None # type: Optional[type] - self.encoder = None # type: typing.Optional[BaseEstimator] + # NOTE: type update + # Encoders don't seems to have a unified base class for + # the methods 'transform'. Could make a `prototype` class that + # duck types for the 'transform', 'fit_transform' methods + # This is however fine while we only use ordinal encoder + self.encoder = None # type: Optional[OrdinalEncoder] - self.out_dimensionality = None # type: typing.Optional[int] - self.type_of_target = None # type: typing.Optional[str] + self.out_dimensionality = None # type: Optional[int] + self.type_of_target = None # type: Optional[str] self.logger = logger if logger is not None else logging.getLogger(__name__) # Store the dtype for remapping to correct type - self.dtype = None # type: typing.Optional[type] + self.dtype = None # type: Optional[Type] self._is_fitted = False def fit( self, - y_train: SUPPORTED_TARGET_TYPES, - y_test: typing.Optional[SUPPORTED_TARGET_TYPES] = None, - ) -> BaseEstimator: + y_train: Union[List, np.ndarray, pd.Series, pd.DataFrame], + y_test: Optional[Union[List, np.ndarray, pd.Series, pd.DataFrame]] = None, + ) -> 'TargetValidator': """ Validates and fit a categorical encoder (if needed) to the targets The supported data types are List, numpy arrays and pandas DataFrames. @@ -74,7 +75,7 @@ def fit( ---------- y_train: SUPPORTED_TARGET_TYPES A set of targets set aside for training - y_test: typing.Union[SUPPORTED_TARGET_TYPES] + y_test: Union[SUPPORTED_TARGET_TYPES] A hold out set of data used of the targets. It is also used to fit the categories of the encoder. """ @@ -87,22 +88,19 @@ def fit( if len(shape) != len(np.shape(y_test)) or ( len(shape) > 1 and (shape[1] != np.shape(y_test)[1])): - raise ValueError("The dimensionality of the train and test targets " - "does not match train({}) != test({})".format( - np.shape(y_train), - np.shape(y_test) - )) + raise ValueError("The dimensionality of the train and test" + " targets do not match" + f" train {np.shape(y_train)}" + f" != test {np.shape(y_test)}") + if isinstance(y_train, pd.DataFrame): - y_train = typing.cast(pd.DataFrame, y_train) - y_test = typing.cast(pd.DataFrame, y_test) + if not isinstance(y_test, pd.DataFrame): + y_test = pd.DataFrame(y_test) + if y_train.columns.tolist() != y_test.columns.tolist(): - raise ValueError( - "Train and test targets must both have the same columns, yet " - "y={} and y_test={} ".format( - y_train.columns, - y_test.columns - ) - ) + raise ValueError("Train and test targets must both have the" + f" same columns, yet y={y_train.columns}" + f" and y_test={y_test.columns}") if list(y_train.dtypes) != list(y_test.dtypes): raise ValueError("Train and test targets must both have the same dtypes") @@ -124,9 +122,9 @@ def fit( def _fit( self, - y_train: SUPPORTED_TARGET_TYPES, - y_test: typing.Optional[SUPPORTED_TARGET_TYPES] = None, - ) -> BaseEstimator: + y_train: Union[List, np.ndarray, pd.Series, pd.DataFrame], + y_test: Optional[Union[List, np.ndarray, pd.Series, pd.DataFrame]] = None, + ) -> 'TargetValidator': """ If dealing with classification, this utility encodes the targets. @@ -138,69 +136,83 @@ def _fit( y_train: SUPPORTED_TARGET_TYPES The labels of the current task. They are going to be encoded in case of classification - y_test: typing.Optional[SUPPORTED_TARGET_TYPES] + y_test: Optional[SUPPORTED_TARGET_TYPES] A holdout set of labels """ if not self.is_classification or self.type_of_target == 'multilabel-indicator': # Only fit an encoder for classification tasks # Also, encoding multilabel indicator data makes the data multiclass # Let the user employ a MultiLabelBinarizer if needed + self.encoder = None return self - if y_test is not None: - if hasattr(y_train, "iloc"): - y_train = pd.concat([y_train, y_test], ignore_index=True, sort=False) - elif isinstance(y_train, list): - y_train = y_train + y_test - elif isinstance(y_train, np.ndarray): - y_train = np.concatenate((y_train, y_test)) - - ndim = len(np.shape(y_train)) - if ndim == 1 or (ndim > 1 and np.shape(y_train)[1] == 1): - # The label encoder makes sure data is, and remains - # 1 dimensional - self.encoder = preprocessing.OrdinalEncoder(handle_unknown='use_encoded_value', - unknown_value=-1) - else: + # Validate the shape of the input + shape = np.shape(y_train) + ndim = len(shape) + if ndim > 1 and shape[1] != 1: # We should not reach this if statement as we check for type of targets before - raise ValueError("Multi-dimensional classification is not yet supported. " - "Encoding multidimensional data converts multiple columns " - "to a 1 dimensional encoding. Data involved = {}/{}".format( - np.shape(y_train), - self.type_of_target - )) - - # Mypy redefinition - assert self.encoder is not None + raise ValueError("Multi-dimensional classification is not yet" + " supported. Encoding multidimensional data" + " converts multiple columns to a 1 dimensional encoding." + f" Data involved = {shape}/{self.type_of_target}") + + # Creat the encoder + self.encoder = OrdinalEncoder( + handle_unknown='use_encoded_value', + unknown_value=-1 + ) - # remove ravel warning from pandas Series - if ndim > 1: - self.encoder.fit(y_train) + # Clear typing to just numpy arrays and pandas + if isinstance(y_train, List): + y = np.asarray(y_train) + else: + y = y_train + + # Attempt to store dtype if it's numeric, we use this in + # inverse_transform to try corretly restore it's dtype + if isinstance(y, pd.Series): + if isinstance(y.dtype, ExtensionDtype): + warnings.warn("Fitting transformer with a pandas series which" + f" has the dtype {y.dtype}. Inverse transform" + " may not be able preserve dtype when converting" + " to np.ndarray") + if is_numeric_dtype(y.dtype): + self.dtype = y.dtype + elif isinstance(y, pd.DataFrame): + if is_numeric_dtype(y.dtypes[0]): + self.dtype = y.dtypes[0] else: - if hasattr(y_train, 'iloc'): - y_train = typing.cast(pd.DataFrame, y_train) - self.encoder.fit(y_train.to_numpy().reshape(-1, 1)) + if is_numeric_dtype(y.dtype): + self.dtype = y.dtype + + # Merge y_test and y_train for encoding + if y_test is not None: + if isinstance(y, (pd.Series, pd.DataFrame)): + if isinstance(y, pd.Series): + y_test = pd.Series(y_test) + else: + y_test = pd.DataFrame(y_test) + + y = pd.concat([y, y_test], ignore_index=True, sort=False) else: - self.encoder.fit(np.array(y_train).reshape(-1, 1)) - - # we leave objects unchanged, so no need to store dtype in this case - if hasattr(y_train, 'dtype'): - # Series and numpy arrays are checked here - # Cast is as numpy for mypy checks - y_train = typing.cast(np.ndarray, y_train) - if is_numeric_dtype(y_train.dtype): - self.dtype = y_train.dtype - elif hasattr(y_train, 'dtypes') and is_numeric_dtype(typing.cast(pd.DataFrame, - y_train).dtypes[0]): - # This case is for pandas array with a single column - y_train = typing.cast(pd.DataFrame, y_train) - self.dtype = y_train.dtypes[0] + y = np.concatenate([y, y_test]) + + # If one dimensional, we need to reshape it + # [1, 2, 3] -> [[1], [2], [3]] + if y.ndim == 1: + if isinstance(y, pd.DataFrame) or isinstance(y, pd.Series): + y = y.to_numpy() + + y = y.reshape(-1, 1) + + # Fit encoder + self.encoder.fit(y) return self def transform( self, - y: typing.Union[SUPPORTED_TARGET_TYPES], + y: Union[List, pd.Series, pd.DataFrame, np.ndarray], ) -> np.ndarray: """ Validates and fit a categorical encoder (if needed) to the features. @@ -217,80 +229,93 @@ def transform( The transformed array """ if not self._is_fitted: - raise NotFittedError("Cannot call transform on a validator that is not fitted") + raise NotFittedError("TargetValidator must have fit() called first") # Check the data here so we catch problems on new test data self._check_data(y) - if self.encoder is not None: - # remove ravel warning from pandas Series - shape = np.shape(y) - if len(shape) > 1: - y = self.encoder.transform(y) - else: - # The Ordinal encoder expects a 2 dimensional input. - # The targets are 1 dimensional, so reshape to match the expected shape - if hasattr(y, 'iloc'): - y = typing.cast(pd.DataFrame, y) - y = self.encoder.transform(y.to_numpy().reshape(-1, 1)).reshape(-1) - else: - y = self.encoder.transform(np.array(y).reshape(-1, 1)).reshape(-1) + # Clear the types List and DataFrame off of y + if isinstance(y, List): + y_transformed = np.asarray(y) + elif isinstance(y, pd.DataFrame) or isinstance(y, pd.Series): + y_transformed = y.to_numpy() + else: + y_transformed = y + + if self.encoder is None: + return y_transformed + + # The Ordinal encoder expects a 2 dimensional input. + # The targets are 1 dimensional, so reshape to match the expected shape + if y_transformed.ndim == 1: + y_transformed = y_transformed.reshape(-1, 1) + y_transformed = self.encoder.transform(y_transformed).reshape(-1) + else: + y_transformed = self.encoder.transform(y_transformed) - # sklearn check array will make sure we have the - # correct numerical features for the array - # Also, a numpy array will be created - y = sklearn.utils.check_array( - y, + # check_array ensures correct numerical features for array + y_transformed = sklearn.utils.check_array( + y_transformed, force_all_finite=True, accept_sparse='csr', ensure_2d=False, ) - # When translating a dataframe to numpy, make sure we - # honor the ravel requirement - if y.ndim == 2 and y.shape[1] == 1: - y = np.ravel(y) + # Ensure we flatten any arrays [[1], [2], [1], [1], ...] to [1,2,1,1,...] + if y_transformed.ndim == 2 and y_transformed.shape[1] == 1: + y_transformed = y_transformed.ravel() - return y + return y_transformed def inverse_transform( self, - y: SUPPORTED_TARGET_TYPES, + y: Union[List, pd.Series, pd.DataFrame, np.ndarray], ) -> np.ndarray: - """ - Revert any encoding transformation done on a target array + """ Revert any encoding transformation done on a target array. Parameters ---------- - y: typing.Union[np.ndarray, pd.DataFrame, pd.Series] - Target array to be transformed back to original form before encoding + y: Union[List, np.ndarray, pd.DataFrame, pd.Series] + Target array to be transformed back to original form before encoding + Return ------ - np.ndarray: - The transformed array + np.ndarray: + The transformed array """ if not self._is_fitted: - raise NotFittedError("Cannot call inverse_transform on a validator that is not fitted") + raise NotFittedError("TargetValidator must have fit() called first") + # If there's no encoder, just return it as the expected ndarray if self.encoder is None: - return y + return np.asarray(y) + + # If it's one dimensional, we need to convert it + # [1,2,3] -> [[1], [2], [3]] shape = np.shape(y) - if len(shape) > 1: - y = self.encoder.inverse_transform(y) + if len(shape) == 1: + + if isinstance(y, pd.DataFrame) or isinstance(y, pd.Series): + y = y.to_numpy() + elif isinstance(y, List): + y = np.asarray(y) + + # Perform the transformation and reshape it + y = y.reshape(-1, 1) + y_inv = self.encoder.inverse_transform(y) + y_inv = y_inv.reshape(-1) + else: - # The targets should be a flattened array, hence reshape with -1 - if hasattr(y, 'iloc'): - y = typing.cast(pd.DataFrame, y) - y = self.encoder.inverse_transform(y.to_numpy().reshape(-1, 1)).reshape(-1) - else: - y = self.encoder.inverse_transform(np.array(y).reshape(-1, 1)).reshape(-1) + # Otherwise we can can just invert it as expected + y_inv = self.encoder.inverse_transform(y) # Inverse transform returns a numpy array of type object # This breaks certain metrics as accuracy, which makes type_of_target be unknown # If while fit a dtype was observed, we try to honor that dtype if self.dtype is not None: - y = y.astype(self.dtype) - return y + y_inv = y_inv.astype(self.dtype) + + return y_inv def is_single_column_target(self) -> bool: """ @@ -307,22 +332,19 @@ def _check_data( Parameters ---------- - y: typing.Union[np.ndarray, pd.DataFrame, pd.Series] + y: Union[np.ndarray, pd.DataFrame, pd.Series] A set of features whose dimensionality and data type is going to be checked """ if not isinstance( - y, (np.ndarray, pd.DataFrame, list, pd.Series)) and not scipy.sparse.issparse(y): + y, (np.ndarray, pd.DataFrame, List, pd.Series)) and not isinstance(y, spmatrix): raise ValueError("Auto-sklearn only supports Numpy arrays, Pandas DataFrames," " pd.Series, sparse data and Python Lists as targets, yet, " "the provided input is of type {}".format( type(y) )) - # Sparse data muss be numerical - # Type ignore on attribute because sparse targets have a dtype - if scipy.sparse.issparse(y) and not np.issubdtype(y.dtype.type, # type: ignore[union-attr] - np.number): + if isinstance(y, spmatrix) and not np.issubdtype(y.dtype.type, np.number): raise ValueError("When providing a sparse matrix as targets, the only supported " "values are numerical. Please consider using a dense" " instead." @@ -342,9 +364,9 @@ def _check_data( # No Nan is supported has_nan_values = False if hasattr(y, 'iloc'): - has_nan_values = typing.cast(pd.DataFrame, y).isnull().values.any() - if scipy.sparse.issparse(y): - y = typing.cast(scipy.sparse.spmatrix, y) + has_nan_values = cast(pd.DataFrame, y).isnull().values.any() + + if isinstance(y, spmatrix): has_nan_values = not np.array_equal(y.data, y.data) else: # List and array like values are considered here diff --git a/autosklearn/data/validation.py b/autosklearn/data/validation.py index 4d276dd9d1..d06082258d 100644 --- a/autosklearn/data/validation.py +++ b/autosklearn/data/validation.py @@ -1,10 +1,12 @@ # -*- encoding: utf-8 -*- import logging -import typing +from typing import List, Optional, Tuple, Union import numpy as np -from scipy.sparse import issparse, spmatrix +import pandas as pd + +from scipy.sparse import spmatrix from sklearn.base import BaseEstimator from sklearn.exceptions import NotFittedError @@ -15,8 +17,8 @@ def convert_if_sparse( - y: typing.Union[SUPPORTED_TARGET_TYPES, spmatrix] -) -> SUPPORTED_TARGET_TYPES: + y: SUPPORTED_TARGET_TYPES +) -> Union[np.ndarray, List, pd.DataFrame, pd.Series]: """If the labels `y` are sparse, it will convert it to its dense representation Parameters @@ -28,16 +30,17 @@ def convert_if_sparse( ------- np.ndarray of shape (n_samples, ) or (n_samples, n_outputs) """ - if issparse(y): - y = typing.cast(spmatrix, y) - y = y.toarray() - y = typing.cast(np.ndarray, y) + if isinstance(y, spmatrix): + y_ = y.toarray() - # For one dimensional data, toarray will return (1, nrows) - if y.shape[0] == 1: - y = y.flatten() + # For sparse one dimensional data, y.toarray will return [[1], [2], [3], ...] + # We need to flatten this before returning it + if y_.shape[0] == 1: + y_ = y_.flatten() + else: + y_ = y - return y + return y_ class InputValidator(BaseEstimator): @@ -50,7 +53,7 @@ class InputValidator(BaseEstimator): via informative errors. Attributes ---------- - feat_type: typing.Optional[typing.List[str]] + feat_type: Optional[List[str]] In case the dataset is not a pandas DataFrame: + If provided, this list indicates which columns should be treated as categorical it is internally transformed into a dictionary that indicates a mapping from @@ -70,9 +73,9 @@ class InputValidator(BaseEstimator): """ def __init__( self, - feat_type: typing.Optional[typing.List[str]] = None, + feat_type: Optional[List[str]] = None, is_classification: bool = False, - logger_port: typing.Optional[int] = None, + logger_port: Optional[int] = None, ) -> None: self.feat_type = feat_type self.is_classification = is_classification @@ -95,8 +98,8 @@ def fit( self, X_train: SUPPORTED_FEAT_TYPES, y_train: SUPPORTED_TARGET_TYPES, - X_test: typing.Optional[SUPPORTED_FEAT_TYPES] = None, - y_test: typing.Optional[SUPPORTED_TARGET_TYPES] = None, + X_test: Optional[SUPPORTED_FEAT_TYPES] = None, + y_test: Optional[SUPPORTED_TARGET_TYPES] = None, ) -> BaseEstimator: """ Validates and fit a categorical encoder (if needed) to the features, and @@ -119,7 +122,7 @@ def fit( be instantiated and trained with this data. y_train: SUPPORTED_TARGET_TYPES A set of targets that are going to be encoded if the task is for classification - X_test: typing.Optional[SUPPORTED_FEAT_TYPES] + X_test: Optional[SUPPORTED_FEAT_TYPES] A hold out set of features used for checking y_test: SUPPORTED_TARGET_TYPES A hold out set of targets used for checking. Additionally, if the current task @@ -152,8 +155,8 @@ def fit( def transform( self, X: SUPPORTED_FEAT_TYPES, - y: typing.Optional[SUPPORTED_TARGET_TYPES] = None, - ) -> typing.Tuple[np.ndarray, typing.Optional[np.ndarray]]: + y: Optional[Union[List, pd.Series, pd.DataFrame, np.ndarray]] = None, + ) -> Tuple[Union[np.ndarray, pd.DataFrame, spmatrix], Optional[np.ndarray]]: """ Transform the given target or features to a numpy array @@ -161,7 +164,7 @@ def transform( ---------- X: SUPPORTED_FEAT_TYPES A set of features to transform - y: typing.Optional[SUPPORTED_TARGET_TYPES] + y: Optional[SUPPORTED_TARGET_TYPES] A set of targets to transform Return @@ -173,8 +176,10 @@ def transform( """ if not self._is_fitted: raise NotFittedError("Cannot call transform on a validator that is not fitted") + X_transformed = self.feature_validator.transform(X) if y is not None: - return X_transformed, self.target_validator.transform(y) + y_transformed = self.target_validator.transform(y) + return X_transformed, y_transformed else: - return X_transformed, y + return X_transformed, None diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 3eb2d7b8c5..84fe97797c 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -1,5 +1,5 @@ # -*- encoding: utf-8 -*- -from typing import Optional, Dict, List, Tuple, Union, Iterable +from typing import Any, Optional, Dict, List, Mapping, Tuple, Union, Iterable from typing_extensions import Literal from ConfigSpace.configuration_space import Configuration, ConfigurationSpace @@ -50,7 +50,8 @@ def __init__( metric=None, scoring_functions: Optional[List[Scorer]] = None, load_models: bool = True, - get_trials_callback=None + get_trials_callback=None, + dataset_compression: Union[bool, Mapping[str, Any]] = True ): """ Parameters @@ -155,22 +156,37 @@ def __init__( 'feature_preprocessor': ["no_preprocessing"] } - resampling_strategy : string or object, optional ('holdout') - how to to handle overfitting, might need 'resampling_strategy_arguments' - - * 'holdout': 67:33 (train:test) split - * 'holdout-iterative-fit': 67:33 (train:test) split, calls iterative - fit where possible - * 'cv': crossvalidation, requires 'folds' - * 'cv-iterative-fit': crossvalidation, calls iterative fit where possible - * 'partial-cv': crossvalidation with intensification, requires - 'folds' - * BaseCrossValidator object: any BaseCrossValidator class found - in scikit-learn model_selection module - * _RepeatedSplits object: any _RepeatedSplits class found - in scikit-learn model_selection module - * BaseShuffleSplit object: any BaseShuffleSplit class found - in scikit-learn model_selection module + resampling_strategy : Union[str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit] = "holdout" + How to to handle overfitting, might need to use ``resampling_strategy_arguments`` + if using ``"cv"`` based method or a Splitter object. + + If using a Splitter object that relies on the dataset retaining it's current + size and order, you will need to look at the ``dataset_compression`` argument + and ensure that ``"subsample"`` is not included in the applied compression + ``"methods"`` or disable it entirely with ``False``. + + **Options** + + * ``"holdout"``: + 67:33 (train:test) split + * ``"holdout-iterative-fit"``: + 67:33 (train:test) split, iterative fit where possible + * ``"cv"``: + crossvalidation, + requires ``"folds"`` in ``resampling_strategy_arguments`` + * ``"cv-iterative-fit"``: + crossvalidation, + calls iterative fit where possible, + requires ``"folds"`` in ``resampling_strategy_arguments`` + * 'partial-cv': + crossvalidation with intensification, + requires ``"folds"`` in ``resampling_strategy_arguments`` + * ``BaseCrossValidator`` subclass: + any BaseCrossValidator subclass (found in scikit-learn model_selection module) + * ``_RepeatedSplits`` subclass: + any _RepeatedSplits subclass (found in scikit-learn model_selection module) + * ``BaseShuffleSplit`` subclass: + any BaseShuffleSplit subclass (found in scikit-learn model_selection module) resampling_strategy_arguments : dict, optional if 'holdout' (train_size default=0.67) Additional arguments for resampling_strategy: @@ -263,16 +279,71 @@ def __init__( load_models : bool, optional (True) Whether to load the models after fitting Auto-sklearn. - + get_trials_callback: callable Callback function to create an object of subclass defined in module `smac.callbacks `_. This is an advanced feature. Use only if you are familiar with `SMAC `_. + dataset_compression: Union[bool, Mapping[str, Any]] = True + We compress datasets so that they fit into some predefined amount of memory. + Currently this does not apply to dataframes or sparse arrays, only to raw numpy arrays. + + **NOTE** + + If using a custom ``resampling_strategy`` that relies on specific + size or ordering of data, this must be disabled to preserve these properties. + + You can disable this entirely by passing ``False``. + + Default configuration when left as ``True``: + + .. code-block:: python + + { + "memory_allocation": 0.1, + "methods": ["precision", "subsample"] + } + + You can also pass your own configuration with the same keys and choosing + from the available ``"methods"``. + + The available options are described here: + + **memory_allocation** + + By default, we attempt to fit the dataset into ``0.1 * memory_limit``. This + float value can be set with ``"memory_allocation": 0.1``. We also allow for + specifying absolute memory in MB, e.g. 10MB is ``"memory_allocation": 10``. + + The memory used by the dataset is checked after each reduction method is + performed. If the dataset fits into the allocated memory, any further methods + listed in ``"methods"`` will not be performed. + + For example, if ``methods: ["precision", "subsample"]`` and the + ``"precision"`` reduction step was enough to make the dataset fit into memory, + then the ``"subsample"`` reduction step will not be performed. + + **methods** + + We currently provide the following methods for reducing the dataset size. + These can be provided in a list and are performed in the order as given. + + * ``"precision"`` - We reduce floating point precision as follows: + * ``np.float128 -> np.float64`` + * ``np.float96 -> np.float64`` + * ``np.float64 -> np.float32`` + + * ``subsample`` - We subsample data such that it **fits directly into the + memory allocation** ``memory_allocation * memory_limit``. Therefore, this + should likely be the last method listed in ``"methods"``. + Subsampling takes into account classification labels and stratifies + accordingly. We guarantee that at least one occurrence of each label is + included in the sampled set. + Attributes ---------- - cv_results_ : dict of numpy (masked) ndarrays A dict with keys as column headers and values as columns, that can be imported into a pandas ``DataFrame``. @@ -314,6 +385,7 @@ def __init__( self.scoring_functions = scoring_functions self.load_models = load_models self.get_trials_callback = get_trials_callback + self.dataset_compression = dataset_compression self.automl_ = None # type: Optional[AutoML] @@ -359,7 +431,8 @@ def build_automl(self): metadata_directory=self.metadata_directory, metric=self.metric, scoring_functions=self.scoring_functions, - get_trials_callback=self.get_trials_callback + get_trials_callback=self.get_trials_callback, + dataset_compression=self.dataset_compression ) return automl @@ -968,10 +1041,7 @@ def get_configuration_space( class AutoSklearnClassifier(AutoSklearnEstimator, ClassifierMixin): - """ - This class implements the classification task. - - """ + """This class implements the classification task. """ def fit(self, X, y, X_test=None, @@ -985,7 +1055,6 @@ def fit(self, X, y, Parameters ---------- - X : array-like or sparse matrix of shape = [n_samples, n_features] The training input samples. @@ -1017,7 +1086,6 @@ def fit(self, X, y, Returns ------- self - """ # AutoSklearn does not handle sparse y for now y = convert_if_sparse(y) @@ -1069,12 +1137,10 @@ def predict(self, X, batch_size=None, n_jobs=1): ------- y : array of shape = [n_samples] or [n_samples, n_labels] The predicted classes. - """ return super().predict(X, batch_size=batch_size, n_jobs=n_jobs) def predict_proba(self, X, batch_size=None, n_jobs=1): - """Predict probabilities of classes for all samples X. Parameters @@ -1090,7 +1156,6 @@ def predict_proba(self, X, batch_size=None, n_jobs=1): ------- y : array of shape = [n_samples, n_classes] or [n_samples, n_labels] The predicted class probabilities. - """ pred_proba = super().predict_proba( X, batch_size=batch_size, n_jobs=n_jobs) diff --git a/autosklearn/evaluation/splitter.py b/autosklearn/evaluation/splitter.py index e717a3bcbe..a18e29e08a 100644 --- a/autosklearn/evaluation/splitter.py +++ b/autosklearn/evaluation/splitter.py @@ -1,3 +1,5 @@ +import warnings + import numpy as np from sklearn.model_selection import StratifiedShuffleSplit, StratifiedKFold @@ -29,7 +31,6 @@ def _iter_indices(self, X, y, groups=None): # type: ignore n_classes = classes.shape[0] class_counts = np.bincount(y_indices) - # print(class_counts) if n_train < n_classes: raise ValueError('The train_size = %d should be greater or ' @@ -56,16 +57,43 @@ def _iter_indices(self, X, y, groups=None): # type: ignore train = [] test = [] + # NOTE: Adapting for unique instances + # + # Each list n_i, t_i represent the list of class in the + # training_set and test_set resepectively. + # + # n_i = [100, 100, 0, 3] # 100 instance of class '0', 0 instance of class '2' + # t_i = [300, 300, 1, 3] # 300 instances of class '0', 1 instance of class '2' + # + # To support unique labels such as class '2', which only has one sample + # between both n_i and t_i, we need to make sure that n_i has at least + # one sample of all classes. There is also the extra check to ensure + # that the sizes stay the same. + # + # n_i = [ 99, 100, 1, 3] # 100 instance of class '0', 0 instance of class '2' + # | ^ + # v | + # t_i = [301, 300, 0, 3] # 300 instances of class '0', 1 instance of class '2' + # + for i, class_count in enumerate(n_i): + if class_count == 0: + t_i[i] -= 1 + n_i[i] += 1 + + j = np.argmax(n_i) + if n_i[j] == 1: + warnings.warn("Can't respect size requirements for split.", + " The training set must contain all of the unique" + " labels that exist in the dataset.") + else: + n_i[j] -= 1 + t_i[j] += 1 + for i in range(n_classes): - # print("Before", i, class_counts[i], n_i[i], t_i[i]) permutation = rng.permutation(class_counts[i]) perm_indices_class_i = class_indices[i].take(permutation, mode='clip') - if n_i[i] == 0: - n_i[i] = 1 - t_i[i] = t_i[i] - 1 - # print("After", i, class_counts[i], n_i[i], t_i[i]) train.extend(perm_indices_class_i[:n_i[i]]) test.extend(perm_indices_class_i[n_i[i]:n_i[i] + t_i[i]]) diff --git a/autosklearn/experimental/askl2.py b/autosklearn/experimental/askl2.py index c01282fc47..749dfc6611 100644 --- a/autosklearn/experimental/askl2.py +++ b/autosklearn/experimental/askl2.py @@ -3,7 +3,7 @@ import os import pathlib import pickle -from typing import Any, Dict, List, Optional, Union +from typing import Any, Dict, List, Optional, Union, Mapping import dask.distributed import scipy.sparse @@ -198,6 +198,7 @@ def __init__( metric: Optional[Scorer] = None, scoring_functions: Optional[List[Scorer]] = None, load_models: bool = True, + dataset_compression: Union[bool, Mapping[str, Any]] = True ): """ diff --git a/autosklearn/util/data.py b/autosklearn/util/data.py index 288485f1cc..ff6eb3c337 100644 --- a/autosklearn/util/data.py +++ b/autosklearn/util/data.py @@ -1,15 +1,189 @@ -# -*- encoding: utf-8 -*- -# Functions performing various data conversions for the ChaLearn AutoML -# challenge -from typing import List, Union +import warnings +from typing import ( + Any, + Dict, + Iterator, + List, + Mapping, + Optional, + Sequence, + Tuple, + Type, + Union, + cast +) import numpy as np -__all__ = [ - 'predict_RAM_usage', - 'convert_to_num', - 'convert_to_bin' -] +import pandas as pd + +from scipy.sparse import spmatrix + +from sklearn.model_selection import train_test_split + +from autosklearn.data.validation import SUPPORTED_FEAT_TYPES +from autosklearn.evaluation.splitter import CustomStratifiedShuffleSplit + + +# TODO: TypedDict with python 3.8 +# +# When upgrading to python 3.8 as minimum version, this should be a TypedDict +# so that mypy can identify the fields types +DatasetCompressionSpec = Dict[str, Union[float, List[str]]] + +# Default specification for arg `dataset_compression` +default_dataset_compression_arg: DatasetCompressionSpec = { + "memory_allocation": 0.1, + "methods": ["precision", "subsample"] +} + + +def validate_dataset_compression_arg( + dataset_compression: Mapping[str, Any], + memory_limit: int +) -> DatasetCompressionSpec: + """Validates and return a correct dataset_compression argument + + The returned value can be safely used with `reduce_dataset_size_if_too_large`. + + Parameters + ---------- + dataset_compression: Mapping[str, Any] + The arg to validate + + Returns + ------- + DatasetCompressionSpec + The validated and correct dataset compression spec + """ + if isinstance(dataset_compression, Mapping): + # Fill with defaults if they don't exist + dataset_compression = { + **default_dataset_compression_arg, + **dataset_compression + } + + # Must contain known keys + if set(dataset_compression.keys()) != set(default_dataset_compression_arg.keys()): + raise ValueError( + f"Unknown key in dataset_compression, {list(dataset_compression.keys())}." + f"\nPossible keys are {list(default_dataset_compression_arg.keys())}" + ) + + memory_allocation = dataset_compression["memory_allocation"] + + # "memory_allocation" must be float or int + if not (isinstance(memory_allocation, float) or isinstance(memory_allocation, int)): + raise ValueError( + "key 'memory_allocation' must be an `int` or `float`" + f"\ntype = {memory_allocation}" + f"\ndataset_compression = {dataset_compression}" + ) + + # "memory_allocation" must be in (0,1) if float + if isinstance(memory_allocation, float) and not (0.0 < memory_allocation < 1.0): + raise ValueError( + "key 'memory_allocation' if float must be in (0, 1)" + f"\nmemory_allocation = {memory_allocation}" + f"\ndataset_compression = {dataset_compression}" + ) + + # "memory_allocation" if absolute, should be > 0 and < memory_limit + if isinstance(memory_allocation, int) and not (0 < memory_allocation < memory_limit): + raise ValueError( + f"key 'memory_allocation' if int must be in (0, memory_limit={memory_limit})" + f"\nmemory_allocation = {memory_allocation}" + f"\ndataset_compression = {dataset_compression}" + ) + + # "methods" must be non-empty sequence + if ( + not isinstance(dataset_compression["methods"], Sequence) + or len(dataset_compression["methods"]) <= 0 + ): + raise ValueError( + "key 'methods' must be a non-empty list" + f"\nmethods = {dataset_compression['methods']}" + f"\ndataset_compression = {dataset_compression}" + ) + + # "methods" must contain known methods + if any( + method not in cast(Sequence, default_dataset_compression_arg["methods"]) # mypy + for method in dataset_compression["methods"] + ): + raise ValueError( + f"key 'methods' can only contain {default_dataset_compression_arg['methods']}" + f"\nmethods = {dataset_compression['methods']}" + f"\ndataset_compression = {dataset_compression}" + ) + + return cast(DatasetCompressionSpec, dataset_compression) + else: + raise ValueError( + f"Unknown type for `dataset_compression` {type(dataset_compression)}" + f"\ndataset_compression = {dataset_compression}" + ) + + +class _DtypeReductionMapping(Mapping): + """ + Unfortuantly, mappings compare by hash(item) and not the __eq__ operator + between the key and the item. + + Hence we wrap the dict in a Mapping class and implement our own __getitem__ + such that we do use __eq__ between keys and query items. + + >>> np.float32 == dtype('float32') # True, they are considered equal + >>> + >>> mydict = { np.float32: 'hello' } + >>> + >>> # Equal by __eq__ but dict operations fail + >>> np.dtype('float32') in mydict # False + >>> mydict[dtype('float32')] # KeyError + + This mapping class fixes that supporting the `in` operator as well as `__getitem__` + + >>> reduction_mapping = _DtypeReductionMapping() + >>> + >>> reduction_mapping[np.dtype('float64')] # np.float32 + >>> np.dtype('float32') in reduction_mapping # True + """ + + # Information about dtype support + _mapping: Dict[type, type] = { + np.float32: np.float32, + np.float64: np.float32, + } + + # In spite of the names, np.float96 and np.float128 + # provide only as much precision as np.longdouble, + # that is, 80 bits on most x86 machines and 64 bits + # in standard Windows builds. + if hasattr(np, 'float96'): + _mapping[np.float96] = np.float64 + + if hasattr(np, 'float128'): + _mapping[np.float128] = np.float64 + + @classmethod + def __getitem__(cls, item: type) -> type: + for k, v in cls._mapping.items(): + if k == item: + return v + raise KeyError(item) + + @classmethod + def __iter__(cls) -> Iterator[type]: + return iter(cls._mapping.keys()) + + @classmethod + def __len__(cls) -> int: + return len(cls._mapping) + + +reduction_mapping = _DtypeReductionMapping() +supported_precision_reductions = list(reduction_mapping) def binarization(array: Union[List, np.ndarray]) -> np.ndarray: @@ -50,7 +224,7 @@ def convert_to_bin(Ycont: List, nval: int, verbose: bool = True) -> List: # Convert numeric vector to binary (typically classification target values) if verbose: pass - Ybin = [[0] * nval for x in range(len(Ycont))] + Ybin = [[0] * nval for _ in range(len(Ycont))] for i in range(len(Ybin)): line = Ybin[i] line[int(Ycont[i])] = 1 @@ -70,3 +244,245 @@ def predict_RAM_usage(X: np.ndarray, categorical: List[bool]) -> float: estimated_columns += 1 estimated_ram = estimated_columns * X.shape[0] * X.dtype.itemsize return estimated_ram + + +def subsample( + X: Union[np.ndarray, spmatrix], + y: np.ndarray, + is_classification: bool, + sample_size: Union[float, int], + random_state: Optional[Union[int, np.random.RandomState]] = None, +) -> Tuple[SUPPORTED_FEAT_TYPES, Union[List, np.ndarray, pd.DataFrame, pd.Series]]: + """ Subsamples data returning the same type as it recieved. + + If `is_classification`, we split using a stratified shuffle split which + preserves unique labels in the training set. + + NOTE: + It's highly unadvisable to use lists here. In order to preserver types, + we convert to a numpy array and then back to a list. + + NOTE2: + Interestingly enough, StratifiedShuffleSplut and descendants don't support + sparse `y` in `split(): _check_array` call. Hence, neither do we. + + Parameters + ---------- + X: Union[np.ndarray, spmatrix] + The X's to subsample + + y: np.ndarray + The Y's to subsample + + is_classification: bool + Whether this is classification data or regression data. Required for + knowing how to split. + + sample_size: float | int + If float, percentage of data to take otherwise if int, an absolute + count of samples to take. + + random_state: int | RandomState = None + The random state to pass to the splitted + + Returns + ------- + (np.ndarray | spmatrix, np.ndarray) + The X and y subsampled according to sample_size + """ + if isinstance(X, List): + X = np.asarray(X) + + if isinstance(y, List): + y = np.asarray(y) + + if is_classification: + splitter = CustomStratifiedShuffleSplit( + train_size=sample_size, + random_state=random_state + ) + left_idxs, _ = next(splitter.split(X=X, y=y)) + + # This function supports pandas objects but they won't get here + # yet as we do not reduce the size of pandas dataframes. + if isinstance(X, pd.DataFrame): + idxs = X.index[left_idxs] + X = X.loc[idxs] + else: + X = X[left_idxs] + + if isinstance(y, pd.DataFrame) or isinstance(y, pd.Series): + idxs = y.index[left_idxs] + y = y.loc[idxs] + else: + y = y[left_idxs] + + else: + X, _, y, _ = train_test_split( # type: ignore + X, y, + train_size=sample_size, + random_state=random_state, + ) + + return X, y + + +def reduce_precision( + X: Union[np.ndarray, spmatrix] +) -> Tuple[Union[np.ndarray, spmatrix], Type]: + """ Reduces the precision of a np.ndarray or spmatrix containing floats + + Parameters + ---------- + X: np.ndarray | spmatrix + The data to reduce precision of. + + Returns + ------- + (ndarray | spmatrix, dtype) + Returns the reduced data X along with the dtype it was reduced to. + """ + if X.dtype not in supported_precision_reductions: + raise ValueError(f"X.dtype = {X.dtype} not equal to any supported" + f" {supported_precision_reductions}") + + precision = reduction_mapping[X.dtype] + return X.astype(precision), precision + + +def reduce_dataset_size_if_too_large( + X: Union[np.ndarray, spmatrix], + y: np.ndarray, + memory_limit: int, + is_classification: bool, + random_state: Union[int, np.random.RandomState] = None, + operations: List[str] = ['precision', 'subsample'], + memory_allocation: Union[int, float] = 0.1, +) -> Tuple[Union[np.ndarray, spmatrix], np.ndarray]: + f""" Reduces the size of the dataset if it's too close to the memory limit. + + Follows the order of the operations passed in and retains the type of its + input. + + Precision reduction will only work on the following float types: + - {supported_precision_reductions} + + Subsampling will ensure that the memory limit is satisfied while precision reduction + will only perform one level of precision reduction. Technically, you could supply + multiple rounds of precision reduction, i.e. to reduce np.float128 to np.float32 + you could use `operations = ['precision'] * 2`. + + However, if that's the use case, it'd be advised to simply use the function + `autosklearn.util.data.reduce_precision`. + + NOTE: limitations + * Does not support dataframes yet + - Requires implementing column wise precision reduction + - Requires calculating memory usage + + Parameters + ---------- + X: np.ndarray | spmatrix + The features of the dataset. + + y: np.ndarray + The labels of the dataset. + + memory_limit: int + The amount of memory allocated in megabytes + + is_classification: bool + Whether it's a classificaiton dataset or not. This is important when + considering how to subsample. + + random_state: int | RandomState = None + The random_state to use for subsampling. + + operations: List[str] = ['precision', 'subsampling'] + A list of operations that are permitted to be performed to reduce + the size of the dataset. + + **precision** + + Reduce the precision of float types + + **subsample** + + Reduce the amount of samples of the dataset such that it fits into the allocated memory. + Ensures stratification and that unique labels are present + + memory_allocation: Union[int, float] = 0.1 + The amount of memory to allocate to the dataset. A float specifys that the + dataset will try to be fit into that percentage of memory. An int specifies an + absolute amount. + + Returns + ------- + (spmatrix | np.ndarray, np.ndarray) + The reduced X, y if reductions were needed + """ + # Validation + assert memory_limit > 0 + + if isinstance(memory_allocation, float): + if not (0.0 < memory_allocation < 1.0): + raise ValueError("memory_allocation if float must be in (0, 1)") + + allocated_memory = memory_limit * memory_allocation + + elif isinstance(memory_allocation, int): + if not (0 < memory_allocation < memory_limit): + raise ValueError("memory_allocation if int must be in (0, memory_limit)") + + allocated_memory = memory_allocation + + else: + raise ValueError(f"Unknown type for `memory_allocation` {type(memory_allocation)}") + + if 'precision' in operations and X.dtype not in supported_precision_reductions: + raise ValueError(f"Unsupported type `{X.dtype}` for precision reduction") + + def megabytes(arr: Union[np.ndarray, spmatrix]) -> float: + return (arr.nbytes if isinstance(X, np.ndarray) else arr.data.nbytes) / (2**20) + + for operation in operations: + + if operation == 'precision': + # If the dataset is too big for the allocated memory, + # we then try to reduce the precision if it's a high precision dataset + if megabytes(X) > allocated_memory: + X, precision = reduce_precision(X) + warnings.warn( + f'Dataset too large for allocated memory {allocated_memory}MB, ' + f'reduced the precision from {X.dtype} to {precision}', + ) + + elif operation == 'subsample': + # If the dataset is still too big such that we couldn't fit + # into the allocated memory, we subsample it so that it does + if megabytes(X) > allocated_memory: + + n_samples_before = X.shape[0] + sample_percentage = allocated_memory / megabytes(X) + + # NOTE: type ignore + # + # Tried the generic `def subsample(X: T) -> T` approach but it was + # failing elsewhere, keeping it simple for now + X, y = subsample( # type: ignore + X, y, + sample_size=sample_percentage, + is_classification=is_classification, + random_state=random_state + ) + + n_samples_after = X.shape[0] + warnings.warn( + f"Dataset too large for allocated memory {allocated_memory}MB, reduced" + f" number of samples from {n_samples_before} to {n_samples_after}." + ) + + else: + raise ValueError(f"Unknown operation `{operation}`") + + return X, y diff --git a/test/test_automl/test_automl.py b/test/test_automl/test_automl.py index f021279dce..4e509d8755 100644 --- a/test/test_automl/test_automl.py +++ b/test/test_automl/test_automl.py @@ -1,4 +1,6 @@ # -*- encoding: utf-8 -*- +from typing import Dict, List, Union + import itertools import os import pickle @@ -12,7 +14,7 @@ import numpy as np import pandas as pd import pytest -from scipy.sparse import csr_matrix +from scipy.sparse import csr_matrix, spmatrix import sklearn.datasets from sklearn.ensemble import VotingRegressor, VotingClassifier from smac.scenario.scenario import Scenario @@ -26,6 +28,7 @@ accuracy, log_loss, balanced_accuracy, default_metric_for_task ) from autosklearn.evaluation.abstract_evaluator import MyDummyClassifier, MyDummyRegressor +from autosklearn.util.data import default_dataset_compression_arg from autosklearn.util.logging_ import PickableLoggerAdapter import autosklearn.pipeline.util as putil from autosklearn.constants import ( @@ -35,7 +38,6 @@ REGRESSION, MULTIOUTPUT_REGRESSION, CLASSIFICATION_TASKS, - REGRESSION_TASKS, ) from smac.tae import StatusType @@ -44,18 +46,17 @@ class AutoMLStub(AutoML): - def __init__(self): - self.__class__ = AutoML + def __init__(self, classifier: bool = False): self._task = None self._dask_client = None self._is_dask_client_internally_created = False + self._classifier = classifier def __del__(self): pass def test_fit(dask_client): - X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') automl = autosklearn.automl.AutoML( seed=0, @@ -680,75 +681,6 @@ def test_fail_if_feat_type_on_pandas_input(dask_client): ) -@pytest.mark.parametrize( - 'memory_limit,precision,task', - [ - (memory_limit, precision, task) - for task in itertools.chain(CLASSIFICATION_TASKS, REGRESSION_TASKS) - for precision in (float, np.float32, np.float64, np.float128) - for memory_limit in (1, 100, None) - ] -) -def test_subsample_if_too_large(memory_limit, precision, task): - fixture = { - BINARY_CLASSIFICATION: { - 1: {float: 1310, np.float32: 2621, np.float64: 1310, np.float128: 655}, - 100: {float: 12000, np.float32: 12000, np.float64: 12000, np.float128: 12000}, - None: {float: 12000, np.float32: 12000, np.float64: 12000, np.float128: 12000}, - }, - MULTICLASS_CLASSIFICATION: { - 1: {float: 204, np.float32: 409, np.float64: 204, np.float128: 102}, - 100: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797}, - None: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797}, - }, - MULTILABEL_CLASSIFICATION: { - 1: {float: 204, np.float32: 409, np.float64: 204, np.float128: 102}, - 100: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797}, - None: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797}, - }, - REGRESSION: { - 1: {float: 655, np.float32: 1310, np.float64: 655, np.float128: 327}, - 100: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000}, - None: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000}, - }, - MULTIOUTPUT_REGRESSION: { - 1: {float: 655, np.float32: 1310, np.float64: 655, np.float128: 327}, - 100: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000}, - None: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000}, - } - } - mock = unittest.mock.Mock() - if task == BINARY_CLASSIFICATION: - X, y = sklearn.datasets.make_hastie_10_2() - elif task == MULTICLASS_CLASSIFICATION: - X, y = sklearn.datasets.load_digits(return_X_y=True) - elif task == MULTILABEL_CLASSIFICATION: - X, y_ = sklearn.datasets.load_digits(return_X_y=True) - y = np.zeros((X.shape[0], 10)) - for i, j in enumerate(y_): - y[i, j] = 1 - elif task == REGRESSION: - X, y = sklearn.datasets.make_friedman1(n_samples=5000, n_features=20) - elif task == MULTIOUTPUT_REGRESSION: - X, y = sklearn.datasets.make_friedman1(n_samples=5000, n_features=20) - y = np.vstack((y, y)).transpose() - else: - raise ValueError(task) - X = X.astype(precision) - - assert X.shape[0] == y.shape[0] - - X_new, y_new = AutoML.subsample_if_too_large(X, y, mock, 1, memory_limit, task) - assert X_new.shape[0] == fixture[task][memory_limit][precision] - if memory_limit == 1: - if precision in (np.float128, np.float64, float): - assert mock.warning.call_count == 2 - else: - assert mock.warning.call_count == 1 - else: - assert mock.warning.call_count == 0 - - def data_input_and_target_types(): n_rows = 100 @@ -1012,37 +944,188 @@ def predict(self, x): assert len(w) == 1, "One warning sould have been emmited" -@pytest.mark.parametrize("task, y", [ - (BINARY_CLASSIFICATION, np.asarray( - 9999 * [0] + 1 * [1] - )), - (MULTICLASS_CLASSIFICATION, np.asarray( - 4999 * [1] + 4999 * [2] + 1 * [3] + 1 * [4] - )), - (MULTILABEL_CLASSIFICATION, np.asarray( - 4999 * [[0, 1, 1]] + 4999 * [[1, 1, 0]] + 1 * [[1, 0, 1]] + 1 * [[0, 0, 0]] - )) +@pytest.mark.parametrize("dataset_compression", [False]) +def test_param_dataset_compression_false(dataset_compression: bool) -> None: + """ + Parameters + ---------- + dataset_compression: bool + The dataset_compression arg set as False + + Expects + ------- + * Should set the private attribute to None + """ + auto = AutoMLRegressor( + time_left_for_this_task=30, + per_run_time_limit=5, + dataset_compression=dataset_compression + ) + + assert auto._dataset_compression is None + + +@pytest.mark.parametrize("dataset_compression", [True]) +def test_construction_param_dataset_compression_true(dataset_compression: bool) -> None: + """ + Parameters + ---------- + dataset_compression: bool + The dataset_compression arg set as True + + Expects + ------- + * Should set the private attribute to the defaults + """ + auto = AutoMLRegressor( + time_left_for_this_task=30, + per_run_time_limit=5, + dataset_compression=dataset_compression + ) + + assert auto._dataset_compression == default_dataset_compression_arg + + +@pytest.mark.parametrize("dataset_compression", [{"memory_allocation": 0.2}]) +def test_construction_param_dataset_compression_valid_dict(dataset_compression: Dict) -> None: + """ + Parameters + ---------- + dataset_compression: Dict + The dataset_compression arg set partially specified + + Expects + ------- + * Should set the private attribute to the passed dataset_compression arg + defaults + """ + auto = AutoMLRegressor( + time_left_for_this_task=30, + per_run_time_limit=5, + dataset_compression=dataset_compression + ) + + expected_memory_allocation = dataset_compression["memory_allocation"] + expected_methods = default_dataset_compression_arg["methods"] + + assert auto._dataset_compression is not None + assert auto._dataset_compression["memory_allocation"] == expected_memory_allocation + assert auto._dataset_compression["methods"] == expected_methods + + +@pytest.mark.parametrize("dataset_compression", [{"methods": ["precision", "subsample"]}]) +@pytest.mark.parametrize("X", [np.ones((100, 10), dtype=int)]) +@pytest.mark.parametrize("y", [np.random.random((100,))]) +@unittest.mock.patch("autosklearn.automl.reduce_dataset_size_if_too_large") +def test_fit_performs_dataset_compression_without_precision_with_int( + mock_reduce_dataset: unittest.mock.MagicMock, + dataset_compression: Dict, + X: np.ndarray, + y: np.ndarray +) -> None: + """We can't reduce the precision of ints as we do with floats. Suppose someone + was to pass a column with `max_int64` and `min_int64`, any reduction of bits will + cause this information to be lost and not simply reduce precision as it does with + floats. + + Parameters + ---------- + mock_reduce_dataset: MagicMock + A mock function to check the parameters that were passed in + + dataset_compression: Dict + The dataset_compression arg with "precision" set in it + + X: np.ndarray + An array of ints which we can't reduce precision of + + y: np.ndarray + An array of floats as the regression target + + Expects + ------- + * Should call reduce_dataset_size_if_too_large + * "precision" should have been removed from the "methods" passed to the keyword + argument "operations" of `reduce_dataset_size_if_too_large` + """ + # We just return the data + mock_reduce_dataset.return_value = X, y + + auto = AutoMLRegressor( + time_left_for_this_task=30, # not used but required + per_run_time_limit=5, # not used but required + dataset_compression=dataset_compression + ) + + # To prevent fitting anything we use `only_return_configuration_space` + auto.fit(X, y, only_return_configuration_space=True) + + assert mock_reduce_dataset.call_count == 1 + + args, kwargs = mock_reduce_dataset.call_args + assert kwargs["operations"] == ["subsample"] + + +@pytest.mark.parametrize("dataset_compression", [True]) +@pytest.mark.parametrize("X", [ + np.empty((10, 10)), + csr_matrix(np.identity(10)), + pytest.param( + np.empty((10, 10)).tolist(), + marks=pytest.mark.xfail(reason="Converted to dataframe by InputValidator") + ), + pytest.param( + pd.DataFrame(np.empty((10, 10))), + marks=pytest.mark.xfail(reason="No pandas support yet for dataset compression") + ) ]) -def test_subsample_classification_unique_labels_stay_in_training_set(task, y): - n_samples = 10000 - X = np.random.random(size=(n_samples, 3)) - memory_limit = 1 # Force subsampling - mock = unittest.mock.Mock() - - # Make sure our test assumptions are correct - assert len(y) == n_samples, "Ensure tests are correctly setup" - - values, counts = np.unique(y, axis=0, return_counts=True) - unique_labels = [value for value, count in zip(values, counts) if count == 1] - assert len(unique_labels), "Ensure we have unique labels in the test" - - _, y_sampled = AutoML.subsample_if_too_large(X, y, - logger=mock, - seed=1, - memory_limit=memory_limit, - task=task) - - assert len(y_sampled) <= len(y), \ - "Ensure sampling took place" - assert all(label in y_sampled for label in unique_labels), \ - "All unique labels present in the return sampled set" +@pytest.mark.parametrize("y", [ + np.random.random((10, 1)), + np.random.random((10, 1)).tolist(), + pytest.param( + pd.Series(np.random.random((10,))), + marks=pytest.mark.xfail(reason="No pandas support yet for dataset compression") + ), + pytest.param( + pd.DataFrame(np.random.random((10, 10))), + marks=pytest.mark.xfail(reason="No pandas support yet for dataset compression") + ) +]) +@unittest.mock.patch("autosklearn.automl.reduce_dataset_size_if_too_large") +def test_fit_performs_dataset_compression( + mock_reduce_dataset: unittest.mock.MagicMock, + dataset_compression: bool, + X: Union[np.ndarray, spmatrix, List, pd.DataFrame], + y: Union[np.ndarray, List, pd.Series, pd.DataFrame] +) -> None: + """ + Parameters + ---------- + mock_reduce_dataset: MagicMock + A mock function to view call count + + dataset_compression: bool + Dataset compression set to True + + X: Union[np.ndarray, spmatrix, List, pd.Dataframe] + Feature to reduce + + y: Union[np.ndarray, List, pd.Series, pd.Dataframe] + Target to reduce (regression values) + + Expects + ------- + * Should call reduce_dataset_size_if_too_large + """ + # We just return the data + mock_reduce_dataset.return_value = X, y + + auto = AutoMLRegressor( + time_left_for_this_task=30, # not used but required + per_run_time_limit=5, # not used but required + dataset_compression=dataset_compression + ) + + # To prevent fitting anything we use `only_return_configuration_space` + auto.fit(X, y, only_return_configuration_space=True) + + assert mock_reduce_dataset.called diff --git a/test/test_automl/test_estimators.py b/test/test_automl/test_estimators.py index 4de0f767aa..e7275a4591 100644 --- a/test/test_automl/test_estimators.py +++ b/test/test_automl/test_estimators.py @@ -1,4 +1,4 @@ -from typing import Type, cast +from typing import Any, Dict, Type, Union, cast import copy import glob @@ -1044,21 +1044,32 @@ def test_selector_file_askl2_can_be_created(selector_path): importlib.reload(autosklearn.experimental.askl2) -def test_check_askl2_same_arguments_as_askl(): - # In case a new attribute is created, make sure it is there also in - # ASKL2 - extra_arguments = list(set( - inspect.getfullargspec(AutoSklearnEstimator.__init__).args) - set( - inspect.getfullargspec(AutoSklearn2Classifier.__init__).args)) - expected_extra_args = ['exclude', - 'include', - 'resampling_strategy_arguments', - 'get_smac_object_callback', - 'initial_configurations_via_metalearning', - 'resampling_strategy', - 'metadata_directory', - 'get_trials_callback'] - unexpected_args = set(extra_arguments) - set(expected_extra_args) +def test_check_askl2_same_arguments_as_askl() -> None: + """Check the asklearn2 has the same args as asklearn1 + + This test is useful for when adding args to asklearn1 to make sure we update asklearn2 + + Expects + ------- + * The set of arguments for AutoSklearnClassifier is the same as AutoSklearn2Classifier + except for a few expected arugments + """ + autosklearn1_classifier_args = set(inspect.getfullargspec(AutoSklearnEstimator.__init__).args) + autosklearn2_classifier_args = set(inspect.getfullargspec(AutoSklearn2Classifier.__init__).args) + extra_arguments = autosklearn1_classifier_args - autosklearn2_classifier_args + + expected_extra_args = set([ + 'exclude', + 'include', + 'resampling_strategy_arguments', + 'get_smac_object_callback', + 'initial_configurations_via_metalearning', + 'resampling_strategy', + 'metadata_directory', + 'get_trials_callback', + ]) + unexpected_args = extra_arguments - expected_extra_args + assert len(unexpected_args) == 0, unexpected_args @@ -1276,3 +1287,24 @@ def test_autosklearn_anneal(as_frame): # can be used in a meaningful way -- not meant for generalization, # hence we use the train dataset assert automl_fitted.score(X, y) > 0.75 + + +@pytest.mark.parametrize("dataset_compression", [ + False, True, {"memory_allocation": 0.2} +]) +def test_param_dataset_compression(dataset_compression: Union[bool, Dict[str, Any]]): + """We expect this does not get parsed and modified until it gets to the AutoML class, + In the meantime, it's value remains whatever was passed in. + + Parameters + ---------- + dataset_compression: Union[bool, Dict[str, Any] + The arg to pass to the estimator + + Expects + ------- + * The private attribute should be set to what was passed + """ + model = AutoSklearnClassifier(dataset_compression=dataset_compression) + + assert model.dataset_compression == dataset_compression diff --git a/test/test_data/test_target_validator.py b/test/test_data/test_target_validator.py index e864c400e5..09e075b85f 100644 --- a/test/test_data/test_target_validator.py +++ b/test/test_data/test_target_validator.py @@ -3,7 +3,7 @@ import pandas as pd import pytest -from pandas.api.types import is_numeric_dtype +from pandas.api.types import is_numeric_dtype, is_bool_dtype from scipy import sparse @@ -120,49 +120,29 @@ def input_data_targettest(request): 'list_multilabel', 'list_continuous', 'list_continuous-multioutput', - 'sparse_bsr_nonan', - 'sparse_coo_nonan', - 'sparse_csc_nonan', - 'sparse_csr_nonan', - 'sparse_lil_nonan', 'openml_204', ), indirect=True ) def test_targetvalidator_supported_types_noclassification(input_data_targettest): + y = input_data_targettest + validator = TargetValidator(is_classification=False) validator.fit(input_data_targettest) - transformed_y = validator.transform(input_data_targettest) - if sparse.issparse(input_data_targettest): - assert sparse.issparse(transformed_y) - else: - assert isinstance(transformed_y, np.ndarray) - epected_shape = np.shape(input_data_targettest) - if len(epected_shape) > 1 and epected_shape[1] == 1: - # The target should have (N,) dimensionality instead of (N, 1) - epected_shape = (epected_shape[0], ) - assert epected_shape == np.shape(transformed_y) - assert np.issubdtype(transformed_y.dtype, np.number) + + y_encoded: np.ndarray = validator.transform(y) + + # Assert the validator was fitted assert validator._is_fitted - # Because there is no classification, we do not expect a encoder + # Assert no encoder if is_classification = False assert validator.encoder is None - if hasattr(input_data_targettest, "iloc"): - np.testing.assert_array_equal( - np.ravel(input_data_targettest.to_numpy()), - np.ravel(transformed_y) - ) - elif sparse.issparse(input_data_targettest): - np.testing.assert_array_equal( - np.ravel(input_data_targettest.todense()), - np.ravel(transformed_y.todense()) - ) - else: - np.testing.assert_array_equal( - np.ravel(np.array(input_data_targettest)), - np.ravel(transformed_y) - ) + # Assert it returns y_inverse, y_encoded as ndarray + assert isinstance(y_encoded, np.ndarray) + + # Assert the return value is equal to the one passed in + np.testing.assert_array_equal(y_encoded, y) @pytest.mark.parametrize( @@ -176,66 +156,89 @@ def test_targetvalidator_supported_types_noclassification(input_data_targettest) 'numpy_multiclass', 'list_binary', 'list_multiclass', - 'sparse_bsr_nonan', - 'sparse_coo_nonan', - 'sparse_csc_nonan', - 'sparse_csr_nonan', - 'sparse_lil_nonan', 'openml_2', ), indirect=True ) def test_targetvalidator_supported_types_classification(input_data_targettest): + y = input_data_targettest # Just to remove visual clutter + validator = TargetValidator(is_classification=True) - validator.fit(input_data_targettest) - transformed_y = validator.transform(input_data_targettest) - if sparse.issparse(input_data_targettest): - assert sparse.issparse(transformed_y) - else: - assert isinstance(transformed_y, np.ndarray) - epected_shape = np.shape(input_data_targettest) - if len(epected_shape) > 1 and epected_shape[1] == 1: - # The target should have (N,) dimensionality instead of (N, 1) - epected_shape = (epected_shape[0], ) - assert epected_shape == np.shape(transformed_y) - assert np.issubdtype(transformed_y.dtype, np.number) + validator.fit(y) + + y_encoded: np.ndarray = validator.transform(y) + y_inverse: np.ndarray = validator.inverse_transform(y_encoded) + + # Assert the validator was fitted assert validator._is_fitted - # Because there is no classification, we do not expect a encoder - if not sparse.issparse(input_data_targettest): - assert validator.encoder is not None + # Assert it returns y_inverse, y_encoded as ndarray + assert isinstance(y_encoded, np.ndarray) + assert isinstance(y_inverse, np.ndarray) + + # Assert that y_encoded is numeric and not boolean + assert ( + is_numeric_dtype(y_encoded.dtype) + and not is_bool_dtype(y_encoded.dtype) + ) - # The encoding should be per column - if len(transformed_y.shape) == 1: - assert np.min(transformed_y) == 0 - assert np.max(transformed_y) == len(np.unique(transformed_y)) - 1 + # Assert dtype is presevered with y -> y_encoded -> y_inverse + def dtype(arr): + if isinstance(arr, list): + if isinstance(arr[0], list): + return type(arr[0][0]) + else: + return type(arr[0]) + elif isinstance(arr, pd.DataFrame): + return arr.dtypes[0] else: - for col in range(transformed_y.shape[1]): - assert np.min(transformed_y[:, col]) == 0 - assert np.max(transformed_y[:, col]) == len(np.unique(transformed_y[:, col])) - 1 - - # Make sure we can perform inverse transform - y_inverse = validator.inverse_transform(transformed_y) - if hasattr(input_data_targettest, 'dtype'): - # In case of numeric, we need to make sure dtype is preserved - if is_numeric_dtype(input_data_targettest.dtype): - assert y_inverse.dtype == input_data_targettest.dtype - # Then make sure every value is properly inverse-transformed - np.testing.assert_array_equal(np.array(y_inverse), np.array(input_data_targettest)) - elif hasattr(input_data_targettest, 'dtypes'): - if is_numeric_dtype(input_data_targettest.dtypes[0]): - assert y_inverse.dtype == input_data_targettest.dtypes[0] - # Then make sure every value is properly inverse-transformed - np.testing.assert_array_equal(np.array(y_inverse), - # pandas is always (N, 1) but targets are ravel() - input_data_targettest.to_numpy().reshape(-1)) + return arr.dtype + + if is_numeric_dtype(dtype(y)): + assert y_inverse.dtype == dtype(y) + + # Assert that y == y_inverse, giving back the original values + np.testing.assert_array_equal(y_inverse, np.array(y).reshape(-1)) + + # Assert the y_inverse, y_encoded have the expected shape + shape = np.shape(y) + if len(shape) == 2 and shape[1] == 1: + # For cases where y = [[1], [2], [3]], + # we expect y_inverse, y_encodedd to have been flattened to [1,2,3] + expected_shape = (shape[0], ) else: - # Sparse is not encoded, mainly because the sparse data is expected - # to be numpy of numerical type -- which currently does not require encoding - np.testing.assert_array_equal( - np.ravel(input_data_targettest.todense()), - np.ravel(transformed_y.todense()) - ) + expected_shape = shape + + assert y_encoded.shape == expected_shape + assert y_inverse.shape == expected_shape + + # These next part of the tests rely on some encoding to have taken place + # This happens when `is_classification` and not task_type = multilabel-indicator + # + # As state in TargetValidator._fit() + # > Also, encoding multilabel indicator data makes the data multiclass + # Let the user employ a MultiLabelBinarizer if needed + # + # As a result of this, we don't encode 'multilabel-indicator' labels and + # there is nothing else to check here + if validator.type_of_target == 'multilabel-indicator': + assert validator.encoder is None + + else: + assert validator.encoder is not None + + # Assert the columns have had their labels converted + if y_encoded.ndim == 1: + max_label = len(np.unique(y_encoded)) - 1 + assert np.min(y_encoded) == 0 + assert np.max(y_encoded) == max_label + + else: + for col in range(y_encoded.shape[1]): + column = y_encoded[:, col] + max_label = len(np.unique(y_encoded[:, col])) - 1 + assert np.min(column) == 0 + assert np.max(column) == max_label @pytest.mark.parametrize( @@ -416,9 +419,9 @@ def test_target_unsupported(): validator.fit(np.array([np.nan, 1, 2])) with pytest.raises(ValueError, match=r"arget values cannot contain missing/NaN values"): validator.fit(sparse.csr_matrix(np.array([1, 2, np.nan]))) - with pytest.raises(ValueError, match=r"Cannot call transform on a validator that is not fit"): + with pytest.raises(ValueError, match=r"TargetValidator must have fit\(\) called first"): validator.transform(np.array([1, 2, 3])) - with pytest.raises(ValueError, match=r"Cannot call inverse_transform on a validator that is"): + with pytest.raises(ValueError, match=r"TargetValidator must have fit\(\) called first"): validator.inverse_transform(np.array([1, 2, 3])) with pytest.raises(ValueError, match=r"Multi-dimensional classification is not yet supported"): validator._fit(np.array([[1, 2, 3], [1, 5, 6]])) diff --git a/test/test_evaluation/test_custom_splitters.py b/test/test_evaluation/test_custom_splitters.py new file mode 100644 index 0000000000..4922442228 --- /dev/null +++ b/test/test_evaluation/test_custom_splitters.py @@ -0,0 +1,50 @@ +import pytest + +import numpy as np + +from autosklearn.evaluation.splitter import CustomStratifiedShuffleSplit +from autosklearn.constants import ( + BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION +) + + +@pytest.mark.parametrize("task, X, y", [ + ( + BINARY_CLASSIFICATION, + np.asarray(10000 * [[1, 1, 1, 1, 1]]), + np.asarray(9999 * [0] + 1 * [1]) + ), + ( + MULTICLASS_CLASSIFICATION, + np.asarray(10000 * [[1, 1, 1, 1, 1]]), + np.asarray(4999 * [1] + 4999 * [2] + 1 * [3] + 1 * [4])), + ( + MULTILABEL_CLASSIFICATION, + np.asarray(10000 * [[1, 1, 1, 1, 1]]), + np.asarray(4999 * [[0, 1, 1]] + 4999 * [[1, 1, 0]] + 1 * [[1, 0, 1]] + 1 * [[0, 0, 0]]) + ) +]) +@pytest.mark.parametrize('train_size', [100, 0.5, 200, 0.75]) +def test_custom_stratified_shuffle_split_returns_unique_labels_and_maintains_size( + task, X, y, train_size +): + splitter = CustomStratifiedShuffleSplit( + train_size=train_size, + random_state=1 + ) + left_idxs, _ = next(splitter.split(X=X, y=y)) + y_sampled = y[left_idxs] + X_sampled = X[left_idxs] + + # Assert the train_size param is respected + if isinstance(train_size, float): + n_samples = int(train_size * len(y)) + else: + n_samples = train_size + + assert len(y_sampled) == n_samples + assert len(X_sampled) == n_samples + + # Assert all the unique labels are present in the training set + assert all(label in np.unique(y_sampled) for label in np.unique(y)), \ + f"{task} failed, {np.unique(y)} != {np.unique(y_sampled)}" diff --git a/test/test_util/test_data.py b/test/test_util/test_data.py new file mode 100644 index 0000000000..87548b413f --- /dev/null +++ b/test/test_util/test_data.py @@ -0,0 +1,632 @@ +from typing import Any, List, Dict, Union +from itertools import chain +import warnings + +import pytest + +import numpy as np +import pandas as pd +import sklearn.datasets +from scipy.sparse import csr_matrix, spmatrix + +from autosklearn.constants import ( + BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION, + REGRESSION, MULTIOUTPUT_REGRESSION, CLASSIFICATION_TASKS, REGRESSION_TASKS +) +from autosklearn.util.data import ( + subsample, + reduce_dataset_size_if_too_large, + reduce_precision, + reduction_mapping, + supported_precision_reductions, + validate_dataset_compression_arg, + default_dataset_compression_arg +) + + +parametrize = pytest.mark.parametrize + + +@parametrize("arg", [default_dataset_compression_arg]) +def test_validate_dataset_compression_arg_returns_original_dict(arg: Dict[str, Any]): + """ + Parameters + ---------- + arg: Dict[str, any] + + Expects + ------- + * Should validate without error + * Should return a dict identical to the default + """ + validate_arg = validate_dataset_compression_arg(arg, memory_limit=10) + assert validate_arg == arg + + +@parametrize("memory_allocation", [10, 50, 0.1, 0.5]) +def test_validate_dataset_compression_arg_returns_with_memory_allocation( + memory_allocation: Union[float, int], +): + """ + Parameters + ---------- + memory_allocation: Union[float, int] + A valid memory_allocation + + Expects + ------- + * Should validate without error + * Should not modify the memory_allocation arg + * Should populate the default methods + """ + arg = {"memory_allocation": memory_allocation} + validate_arg = validate_dataset_compression_arg(arg, memory_limit=100) + + expected_methods = default_dataset_compression_arg["methods"] + + assert validate_arg["memory_allocation"] == memory_allocation + assert validate_arg["methods"] == expected_methods + + +@parametrize("methods", [ + ["precision"], + ["precision", "subsample"], + ["precision", "precision", "subsample"] +]) +def test_validate_dataset_compression_arg_returns_with_same_methods( + methods: List[str], +): + """ + Parameters + ---------- + methods: List[str] + A valid list of methods + + Expects + ------- + * Should validate without error + * Should not modify the methods arg + * Should populate memory_allocation with the default + """ + arg = {"methods": methods} + validate_arg = validate_dataset_compression_arg(arg, memory_limit=10) + + expected_memory_allocation = default_dataset_compression_arg["memory_allocation"] + + assert validate_arg["memory_allocation"] == expected_memory_allocation + assert validate_arg["methods"] == methods + + +@parametrize("bad_arg", [[1337], "hello"]) +def test_validate_dataset_compression_arg_raises_error_with_bad_arg(bad_arg: Any): + """ + Parameters + ---------- + bad_arg: Any + An arg which is not a Mapping + + Expects + ------- + * Should raise a ValueError + """ + with pytest.raises(ValueError, match=r"Unknown type"): + validate_dataset_compression_arg(bad_arg, memory_limit=10) + + +@parametrize("key", ["hello", "hi", "hey"]) +def test_validate_dataset_compression_arg_raises_error_with_bad_key(key: str): + """ + Parameters + ---------- + key: str + A key which should not be in the parameters + + Expects + ------- + * Should raise a ValueError + """ + bad_arg = { + **default_dataset_compression_arg, + key: 1337 + } + with pytest.raises(ValueError, match=r"Unknown key"): + validate_dataset_compression_arg(bad_arg, memory_limit=10) + + +@parametrize("memory_allocation", ["hello", {}, [1, 2, 3]]) +def test_validate_dataset_compression_arg_raises_error_with_bad_memory_allocation_type( + memory_allocation: Any +): + """ + Parameters + ---------- + memory_allocation: Any + A bad type for memory_allocation + + Expects + ------- + * Should raise a ValueError + """ + bad_arg = {"memory_allocation": memory_allocation} + with pytest.raises(ValueError, match=r"key 'memory_allocation' must be an `int` or `float`"): + validate_dataset_compression_arg(bad_arg, memory_limit=10) + + +@parametrize("memory_allocation", [-0.5, 0.0, 1.0, 1.5]) +def test_validate_dataset_compression_arg_raises_error_with_bad_memory_allocation_float( + memory_allocation: float +): + """ + Parameters + ---------- + memory_allocation: Any + A bad float value for memory_allocation + + Expects + ------- + * Should raise a ValueError + """ + bad_arg = {"memory_allocation": memory_allocation} + + with pytest.raises(ValueError, match=r"key 'memory_allocation' if float must be in \(0, 1\)"): + validate_dataset_compression_arg(bad_arg, memory_limit=10) + + +@parametrize("memory_allocation, memory_limit", [ + (0, 10), (10, 10), (-20, 10), (20, 10) +]) +def test_validate_dataset_compression_arg_raises_error_with_bad_memory_allocation_int( + memory_allocation: int, + memory_limit: int +): + """ + Parameters + ---------- + memory_allocation: int + A bad float int for memory_allocation + + memory_limit: int + The memory limit + + Expects + ------- + * Should raise a ValueError + """ + bad_arg = {"memory_allocation": memory_allocation} + with pytest.raises(ValueError, match=r"key 'memory_allocation' if int must be in \(0,"): + validate_dataset_compression_arg(bad_arg, memory_limit=memory_limit) + + +@parametrize("methods", [10, {"hello", "world"}, []]) +def test_validate_dataset_compression_arg_raises_error_with_bad_methods_type(methods: Any): + """ + Parameters + ---------- + methods: int + A bad type for key methods + + Expects + ------- + * Should raise a ValueError + """ + bad_arg = {"methods": methods} + with pytest.raises(ValueError, match=r"key 'methods' must be a non-empty list"): + validate_dataset_compression_arg(bad_arg, memory_limit=10) + + +@parametrize("methods", [ + ["bad", "worse"], + ["precision", "kind_of_bad"], + ["still_bad", "precision", "subsample"] +]) +def test_validate_dataset_compression_arg_raises_error_with_bad_methods_entries(methods: Any): + """ + Parameters + ---------- + methods: int + A bad type for key methods + + Expects + ------- + * Should raise a ValueError + """ + bad_arg = {"methods": methods} + with pytest.raises(ValueError, match=r"key 'methods' can only contain"): + validate_dataset_compression_arg(bad_arg, memory_limit=10) + + +@parametrize("y", [ + np.asarray(9999 * [0] + 1 * [1]), + np.asarray(4999 * [1] + 4999 * [2] + 1 * [3] + 1 * [4]), + np.asarray(4999 * [[0, 1, 1]] + 4999 * [[1, 1, 0]] + 1 * [[1, 0, 1]] + 1 * [[0, 0, 0]]) +]) +@parametrize("random_state", list(range(5))) +def test_subsample_classification_unique_labels_stay_in_training_set(y, random_state): + n_samples = len(y) + X = np.random.random(size=(n_samples, 3)) + sample_size = 100 + + values, counts = np.unique(y, axis=0, return_counts=True) + unique_labels = [value for value, count in zip(values, counts) if count == 1] + assert len(unique_labels), "Ensure we have unique labels in the test" + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + X_sampled, y_sampled = subsample( + X, y, + random_state=random_state, + sample_size=sample_size, + is_classification=True + ) + + assert X_sampled.dtype == X.dtype and y_sampled.dtype == y.dtype + assert len(y_sampled) == sample_size + assert all(label in y_sampled for label in unique_labels), \ + f"sampled unique = {np.unique(y_sampled)}, original unique = {unique_labels}" + + +@parametrize("X", [np.asarray([[1, 1, 1]] * 30)]) +@parametrize("x_type", [list, np.ndarray, csr_matrix, pd.DataFrame]) +@parametrize("y, task", [ + (np.asarray([0] * 15 + [1] * 15), BINARY_CLASSIFICATION), + (np.asarray([0] * 10 + [1] * 10 + [2] * 10), MULTICLASS_CLASSIFICATION), + (np.asarray([[1, 0, 1]] * 30), MULTILABEL_CLASSIFICATION), + (np.asarray([1.0] * 30), REGRESSION), + (np.asarray([[1.0, 1.0, 1.0]] * 30), MULTIOUTPUT_REGRESSION), +]) +@parametrize("y_type", [list, np.ndarray, pd.DataFrame, pd.Series]) +@parametrize("random_state", [0]) +@parametrize("sample_size", [0.25, 0.5, 5, 10]) +def test_subsample_validity(X, x_type, y, y_type, random_state, sample_size, task): + """ Asserts the validity of the function with all valid types + + We want to make sure that `subsample` works correctly with all the types listed + as x_type and y_type. + + We also want to make sure it works with all kinds of target types. + + The output should maintain the types, and subsample the correct amount. + """ + assert len(X) == len(y) # Make sure our test data is correct + + if ( + y_type == pd.Series + and task in [MULTILABEL_CLASSIFICATION, MULTIOUTPUT_REGRESSION] + ): + # We can't have a pd.Series with multiple values as it's 1 dimensional + pytest.skip("Can't have pd.Series as y when task is n-dimensional") + + # Convert our data to its given x_type or y_type + def convert(arr, objtype): + if objtype == np.ndarray: + return arr + elif objtype == list: + return arr.tolist() + else: + return objtype(arr) + + X = convert(X, x_type) + y = convert(y, y_type) + + # Subsample the data, ignoring any warnings + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + X_sampled, y_sampled = subsample( + X, y, + random_state=random_state, + sample_size=sample_size, + is_classification=task in CLASSIFICATION_TASKS + ) + + # Function to get the type of an obj + def dtype(obj): + if isinstance(obj, List): + if isinstance(obj[0], List): + return type(obj[0][0]) + else: + return type(obj[0]) + + elif isinstance(obj, pd.DataFrame): + return obj.dtypes + + else: + return obj.dtype + + # Check that the types of X remain the same after subsampling + if isinstance(X, pd.DataFrame): + # Dataframe can have multiple types, one per column + assert list(dtype(X_sampled)) == list(dtype(X)) + else: + assert dtype(X_sampled) == dtype(X) + + # Check that the types of y remain the same after subsampling + if isinstance(y, pd.DataFrame): + assert list(dtype(y_sampled)) == list(dtype(y)) + else: + assert dtype(y_sampled) == dtype(y) + + # Function to get the size of an object + def size(obj): + if isinstance(obj, spmatrix): # spmatrix doesn't support __len__ + return obj.shape[0] if obj.shape[0] > 1 else obj.shape[1] + else: + return len(obj) + + # check the right amount of samples were taken + if sample_size < 1: + assert size(X_sampled) == int(sample_size * size(X)) + else: + assert size(X_sampled) == sample_size + + +@parametrize('X', [np.asarray([[0, 0, 1]] * 10)]) +@parametrize('dtype', supported_precision_reductions + [np.dtype('float32'), np.dtype('float64')]) +@parametrize('x_type', [np.ndarray, csr_matrix]) +def test_reduce_precision_correctly_reduces_precision(X, dtype, x_type): + X = X.astype(dtype) + if x_type == csr_matrix: + X = x_type(X) + + X_reduced, precision = reduce_precision(X) + + # Check the reduced precision is correctly returned + assert X_reduced.dtype == precision + + # Check that it was reduce to the correct precision + expected: Dict[type, type] = { + np.float32: np.float32, + np.float64: np.float32, + np.dtype('float32'): np.float32, + np.dtype('float64'): np.float32 + } + if hasattr(np, 'float96'): + expected[np.float96] = np.float64 + + if hasattr(np, 'float128'): + expected[np.float128] = np.float64 + + assert precision == expected[dtype] + + # Check that X's shape was not modified in any way + assert X.shape == X_reduced.shape + + # Check that the return type is the one as we passed in + assert type(X) == type(X_reduced) + + +@parametrize('X', [np.asarray([0, 0, 1]) * 10]) +@parametrize('dtype', [np.int32, np.int64, np.complex128]) +def test_reduce_precision_with_unsupported_dtypes(X, dtype): + X = X.astype(dtype) + with pytest.raises(ValueError) as err: + reduce_precision(X) + + expected = f"X.dtype = {X.dtype} not equal to any supported {supported_precision_reductions}" + assert err.value.args[0] == expected + + +@parametrize("X", [ + np.ones((100000, 10), dtype=np.float64) # Make it big for reductions to take place +]) +@parametrize("x_type", [csr_matrix, np.ndarray]) +@parametrize("dtype", supported_precision_reductions) +@parametrize('y, is_classification', [ + (np.ones((100000,)), True), + (np.ones((100000,)), False), +]) +@parametrize('memory_allocation', [0.1, 1/5.2, 1/8, 1]) +@parametrize('operations', [['precision'], ['subsample'], ['precision', 'subsample']]) +def test_reduce_dataset_reduces_size_and_precision( + X, x_type, dtype, y, is_classification, memory_allocation, operations +): + assert len(X) == len(y) + X = X.astype(dtype) + if x_type == csr_matrix: + X = x_type(X) + + random_state = 0 + memory_limit = 2 # Force reductions + + with warnings.catch_warnings(): + warnings.filterwarnings("ignore") + + X_out, y_out = reduce_dataset_size_if_too_large( + X=X, + y=y, + random_state=random_state, + memory_limit=memory_limit, + operations=operations, + memory_allocation=memory_allocation, + is_classification=is_classification, + ) + + def bytes(arr): + return arr.nbytes if isinstance(arr, np.ndarray) else arr.data.nbytes + + # If we expect some precision reduction unless at float32 already + if 'precision' in operations and dtype != np.float32: + expected = reduction_mapping[X.dtype] + assert X_out.dtype == expected + assert bytes(X_out) < bytes(X) + + # If we expect some subsampling + if 'subsample' in operations: + assert X_out.shape[0] < X.shape[0] + assert y_out.shape[0] < y.shape[0] + assert bytes(X_out) < bytes(X) + + +def test_reduce_dataset_invalid_dtype_for_precision_reduction(): + dtype = int + X = np.asarray([1, 2, 3], dtype=dtype) + + with pytest.raises(ValueError) as err: + reduce_dataset_size_if_too_large( + X=X, + y=X, + operations=['precision'], + memory_limit=1, + memory_allocation=0.1, + is_classification=False + ) + + expected_err = f"Unsupported type `{X.dtype}` for precision reduction" + assert err.value.args[0] == expected_err + + +def test_reduce_dataset_invalid_operations(): + invalid_op = "invalid" + + X = np.asarray([1, 2, 3], dtype=float) + with pytest.raises(ValueError) as err: + reduce_dataset_size_if_too_large( + X=X, + y=X, + operations=[invalid_op], + memory_limit=1, + memory_allocation=0.1, + is_classification=False + ) + + expected_err = f"Unknown operation `{invalid_op}`" + assert err.value.args[0] == expected_err + + +@parametrize("memory_allocation", [-0.5, 0.0, 1.0, 1.5]) +def test_reduce_dataset_invalid_memory_allocation_float(memory_allocation: float): + """ + Parameters + ---------- + memory_allocation: float + An invalid memory allocation as a float + + Expects + ------- + * Should raise a ValueError + """ + with pytest.raises(ValueError, match=r"memory_allocation if float must be in \(0, 1\)"): + reduce_dataset_size_if_too_large( + X=np.empty(1), + y=np.empty(1), + memory_limit=100, + is_classification=True, + memory_allocation=memory_allocation + ) + + +@parametrize("memory_allocation", [-1, 0, 100, 101]) +def test_reduce_dataset_invalid_memory_allocation_int(memory_allocation: int): + """ + Parameters + ---------- + memory_allocation: float + An invalid memory allocation as a int + + Expects + ------- + * Should raise a ValueError + """ + with pytest.raises(ValueError, match=r"memory_allocation if int must be in \(0, memory_limit"): + reduce_dataset_size_if_too_large( + X=np.empty(1), + y=np.empty(1), + is_classification=True, + memory_limit=100, + memory_allocation=memory_allocation + ) + + +@parametrize("memory_allocation", ["100", {'a': 1}, [100]]) +def test_reduce_dataset_invalid_memory_allocation_type(memory_allocation: Any): + """ + Parameters + ---------- + memory_allocation: Any + An invalid memory allocation type + + Expects + ------- + * Should raise a ValueError + """ + with pytest.raises(ValueError, match=r"Unknown type for `memory_allocation`"): + reduce_dataset_size_if_too_large( + X=np.empty(1), + y=np.empty(1), + memory_limit=100, + is_classification=True, + memory_allocation=memory_allocation + ) + + +@pytest.mark.parametrize( + 'memory_limit,precision,task', + [ + (memory_limit, precision, task) + for task in chain(CLASSIFICATION_TASKS, REGRESSION_TASKS) + for precision in (float, np.float32, np.float64, np.float128) + for memory_limit in (1, 100) + ] +) +def test_reduce_dataset_subsampling_explicit_values(memory_limit, precision, task): + random_state = 0 + fixture = { + BINARY_CLASSIFICATION: { + 1: {float: 2621, np.float32: 2621, np.float64: 2621, np.float128: 1310}, + 100: {float: 12000, np.float32: 12000, np.float64: 12000, np.float128: 12000}, + }, + MULTICLASS_CLASSIFICATION: { + 1: {float: 409, np.float32: 409, np.float64: 409, np.float128: 204}, + 100: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797}, + }, + MULTILABEL_CLASSIFICATION: { + 1: {float: 409, np.float32: 409, np.float64: 409, np.float128: 204}, + 100: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797}, + }, + REGRESSION: { + 1: {float: 1310, np.float32: 1310, np.float64: 1310, np.float128: 655}, + 100: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000}, + }, + MULTIOUTPUT_REGRESSION: { + 1: {float: 1310, np.float32: 1310, np.float64: 1310, np.float128: 655}, + 100: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000}, + } + } + + # Create the task and data + if task == BINARY_CLASSIFICATION: + X, y = sklearn.datasets.make_hastie_10_2() + elif task == MULTICLASS_CLASSIFICATION: + X, y = sklearn.datasets.load_digits(return_X_y=True) + elif task == MULTILABEL_CLASSIFICATION: + X, y_ = sklearn.datasets.load_digits(return_X_y=True) + y = np.zeros((X.shape[0], 10)) + for i, j in enumerate(y_): + y[i, j] = 1 + elif task == REGRESSION: + X, y = sklearn.datasets.make_friedman1(n_samples=5000, n_features=20) + elif task == MULTIOUTPUT_REGRESSION: + X, y = sklearn.datasets.make_friedman1(n_samples=5000, n_features=20) + y = np.vstack((y, y)).transpose() + else: + raise ValueError(task) + + # Validate the test data and make sure X and y have the same number of rows + assert X.shape[0] == y.shape[0] + + # Convert X to the dtype we are testing + X = X.astype(precision) + + # Preform the subsampling through `reduce_dataset_size_if_too_large` + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + X_new, y_new = reduce_dataset_size_if_too_large( + X=X, y=y, + random_state=random_state, + memory_limit=memory_limit, + is_classification=task in CLASSIFICATION_TASKS, + operations=['precision', 'subsample'], + memory_allocation=0.1 + ) + + # Assert the new number of samples + assert X_new.shape[0] == fixture[task][memory_limit][precision] From 1e17a07841ccfd1ee234c108ee6e018665d476a1 Mon Sep 17 00:00:00 2001 From: Lukas Strack <73999018+Louquinze@users.noreply.github.com> Date: Thu, 3 Feb 2022 09:35:59 +0100 Subject: [PATCH 023/118] Text Processing (#1300) * commit meta learning data bases * commit changed files * commit new files * fixed experimental settings * implemented last comments on old PR * adapted metalearning to last commit * add a text preprocessing example * intigrated feedback * new changes on *.csv files * reset changes * add changes for merging * add changes for merging * add changes for merging * try to merge * fixed string representation for metalearning (some sort of hot fix, maybe this needs to be fixed in a bigger scale) * fixed string representation for metalearning (some sort of hot fix, maybe this needs to be fixed in a bigger scale) * fixed string representation for metalearning (some sort of hot fix, maybe this needs to be fixed in a bigger scale) * init * init * commit changes for text preprocessing * text prepreprocessing commit * fix metalearning * fix metalearning * adapted test to new text feature * fix style guide issues * integrate PR comments * integrate PR comments * implemented the comments to the last PR * fitted operation is not in place therefore we have to assgin the fitted self.preprocessor again to it self * add first text processing tests * add first text processing tests * including comments from 01.25. * including comments from 01.28. * including comments from 01.28. * including comments from 01.28. * including comments from 01.31. --- autosklearn/data/feature_validator.py | 17 +- .../RF_None_10CV_iterative_es_if.json | 224 ++++++++-- .../RF_None_3CV_iterative_es_if.json | 224 ++++++++-- .../RF_None_5CV_iterative_es_if.json | 224 ++++++++-- .../RF_None_holdout_iterative_es_if.json | 224 ++++++++-- .../RF_SH-eta4-i_10CV_iterative_es_if.json | 224 ++++++++-- .../RF_SH-eta4-i_3CV_iterative_es_if.json | 224 ++++++++-- .../RF_SH-eta4-i_5CV_iterative_es_if.json | 224 ++++++++-- .../RF_SH-eta4-i_holdout_iterative_es_if.json | 224 ++++++++-- .../RF_None_10CV_iterative_es_if.json | 224 ++++++++-- .../RF_None_3CV_iterative_es_if.json | 224 ++++++++-- .../RF_None_5CV_iterative_es_if.json | 224 ++++++++-- .../RF_None_holdout_iterative_es_if.json | 224 ++++++++-- .../RF_SH-eta4-i_10CV_iterative_es_if.json | 224 ++++++++-- .../RF_SH-eta4-i_3CV_iterative_es_if.json | 224 ++++++++-- .../RF_SH-eta4-i_5CV_iterative_es_if.json | 224 ++++++++-- .../RF_SH-eta4-i_holdout_iterative_es_if.json | 224 ++++++++-- .../RF_None_10CV_iterative_es_if.json | 224 ++++++++-- .../RF_None_3CV_iterative_es_if.json | 224 ++++++++-- .../RF_None_5CV_iterative_es_if.json | 224 ++++++++-- .../RF_None_holdout_iterative_es_if.json | 224 ++++++++-- .../RF_SH-eta4-i_10CV_iterative_es_if.json | 224 ++++++++-- .../RF_SH-eta4-i_3CV_iterative_es_if.json | 224 ++++++++-- .../RF_SH-eta4-i_5CV_iterative_es_if.json | 224 ++++++++-- .../RF_SH-eta4-i_holdout_iterative_es_if.json | 224 ++++++++-- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 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.../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../r2_regression_dense/configurations.csv | 196 ++++----- .../r2_regression_sparse/configurations.csv | 196 ++++----- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 196 ++++----- .../configurations.csv | 196 ++++----- .../metalearning/metafeatures/metafeatures.py | 12 +- .../feature_reduction/__init__.py | 0 .../feature_reduction/truncated_svd.py | 76 ++++ .../data_preprocessing/feature_type.py | 57 ++- .../data_preprocessing/feature_type_text.py | 119 +++++ .../text_encoding/__init__.py | 116 +++++ .../text_encoding/bag_of_word_encoding.py | 113 +++++ .../bag_of_word_encoding_distinct.py | 120 +++++ .../text_encoding/tfidf_encoding.py | 115 +++++ .../40_advanced/example_text_preprocessing.py | 67 +++ test/test_automl/test_estimators.py | 2 +- test/test_data/test_feature_validator.py | 18 +- .../test_meta_base_data/configurations.csv | 258 +++++------ .../test_data_preprocessing_text.py | 95 ++++ test/test_pipeline/test_classification.py | 5 +- test/test_pipeline/test_regression.py | 3 +- 133 files changed, 22962 insertions(+), 18279 deletions(-) mode change 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autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/configurations.csv mode change 100644 => 100755 autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/configurations.csv mode change 100644 => 100755 autosklearn/metalearning/files/root_mean_squared_error_regression_dense/configurations.csv mode change 100644 => 100755 autosklearn/metalearning/files/root_mean_squared_error_regression_sparse/configurations.csv create mode 100644 autosklearn/pipeline/components/data_preprocessing/feature_reduction/__init__.py create mode 100644 autosklearn/pipeline/components/data_preprocessing/feature_reduction/truncated_svd.py create mode 100644 autosklearn/pipeline/components/data_preprocessing/feature_type_text.py create mode 100644 autosklearn/pipeline/components/data_preprocessing/text_encoding/__init__.py create mode 100644 autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding.py create mode 100644 autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding_distinct.py create mode 100644 autosklearn/pipeline/components/data_preprocessing/text_encoding/tfidf_encoding.py create mode 100644 examples/40_advanced/example_text_preprocessing.py mode change 100644 => 100755 test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv create mode 100644 test/test_pipeline/components/data_preprocessing/test_data_preprocessing_text.py diff --git a/autosklearn/data/feature_validator.py b/autosklearn/data/feature_validator.py index 05feaffe98..1a21249775 100644 --- a/autosklearn/data/feature_validator.py +++ b/autosklearn/data/feature_validator.py @@ -124,9 +124,9 @@ def fit( )) for ft in self.feat_type.values(): - if ft.lower() not in ['categorical', 'numerical']: - raise ValueError('Only `Categorical` and `Numerical` are ' - 'valid feature types, you passed `%s`' % ft) + if ft.lower() not in ['categorical', 'numerical', 'string']: + raise ValueError('Only `Categorical`, `Numerical` and `String` are ' + 'valid feature types') if X_test is not None: self._check_data(X_test) @@ -262,7 +262,7 @@ def get_feat_type_from_columns( ) -> Dict[Union[str, int], str]: """ Returns a dictionary that maps pandas dataframe columns to a feature type. - This feature type can be categorical or numerical + This feature type can be categorical, numerical or string Parameters ---------- @@ -284,8 +284,9 @@ def get_feat_type_from_columns( raise ValueError("Auto-sklearn does not yet support sparse pandas Series." f" Please convert {column} to a dense format.") elif X[column].dtype.name in ['category', 'bool']: - feat_type[column] = 'categorical' + elif X[column].dtype.name == "string": + feat_type[column] = 'string' # Move away from np.issubdtype as it causes # TypeError: data type not understood in certain pandas types elif not is_numeric_dtype(X[column]): @@ -357,12 +358,6 @@ def list_to_dataframe( # Store the dtypes and use in case of re-fit if len(self.dtypes) == 0: - # Categorical data is inferred as string. Convert to categorical. - # Warn the user about dtypes or request him to use a dataframe - for col in X_train.columns: - if X_train[col].dtype.name == 'string': - X_train[col] = X_train[col].astype('category') - self.dtypes = {col: X_train[col].dtype.name.lower() for col in X_train.columns} else: for col in X_train.columns: diff --git a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_10CV_iterative_es_if.json b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_10CV_iterative_es_if.json index 752ffd8213..5740a45656 100644 --- a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_10CV_iterative_es_if.json +++ b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_10CV_iterative_es_if.json @@ -20,7 +20,12 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.11840768063684459, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7668727846319956, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.28633349214759163, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "f1500cd": { "balancing:strategy": "weighting", @@ -42,7 +47,12 @@ "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0013138596672908974, "classifier:gradient_boosting:n_iter_no_change": 13, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "3e1a332": { "balancing:strategy": "weighting", @@ -62,7 +72,12 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json index 08a5a03a9b..c10793b4d2 100644 --- a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json +++ b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json @@ -19,7 +19,12 @@ "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1025, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + 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"data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "7e1cc2b": { "balancing:strategy": "none", @@ -579,7 +699,12 @@ "classifier:gradient_boosting:min_samples_leaf": 151, "classifier:gradient_boosting:scoring": "loss", "classifier:gradient_boosting:tol": 1e-07, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "5ac8b4c": { "balancing:strategy": "weighting", @@ -604,7 +729,12 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.009080589517127771, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "f3b27fb": { "balancing:strategy": "none", @@ -629,7 +759,12 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "classifier:gradient_boosting:n_iter_no_change": 17, "classifier:gradient_boosting:validation_fraction": 0.05025878941909691, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "d70e605": { "balancing:strategy": "none", @@ -654,7 +789,12 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.14157483812028196, "classifier:gradient_boosting:n_iter_no_change": 7, "classifier:gradient_boosting:validation_fraction": 0.044378278898149826, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "db042ae": { "balancing:strategy": "none", @@ -679,7 +819,12 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.003132312313645055, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "46148ef": { "balancing:strategy": "none", @@ -703,7 +848,12 @@ "classifier:mlp:shuffle": "True", "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "b96cfb3": { "balancing:strategy": "none", @@ -728,7 +878,12 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0032925248120968527, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "a69fc2a": { "balancing:strategy": "none", @@ -748,7 +903,12 @@ "classifier:gradient_boosting:min_samples_leaf": 2, "classifier:gradient_boosting:scoring": "loss", "classifier:gradient_boosting:tol": 1e-07, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_3CV_iterative_es_if.json b/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_3CV_iterative_es_if.json index 1bf572ad28..35859a3ae8 100644 --- a/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_3CV_iterative_es_if.json +++ b/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_3CV_iterative_es_if.json @@ -21,7 +21,12 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0004981969019895562, "classifier:gradient_boosting:n_iter_no_change": 4, "classifier:gradient_boosting:validation_fraction": 0.23374473517320618, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "a4cc318": { "balancing:strategy": "weighting", @@ -45,7 +50,12 @@ "classifier:mlp:shuffle": "True", "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "5d80c27": { "balancing:strategy": "weighting", @@ -61,7 +71,12 @@ "classifier:passive_aggressive:loss": "squared_hinge", "classifier:passive_aggressive:tol": 0.0005787736999064962, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.012192559003185645, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "772b3a8": { "balancing:strategy": "none", @@ -87,7 +102,12 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1000, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "aa1d8b0": { "balancing:strategy": "none", @@ -109,7 +129,12 @@ "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.00016137881434812206, "classifier:gradient_boosting:n_iter_no_change": 14, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "9650698": { "balancing:strategy": "weighting", @@ -130,7 +155,12 @@ "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.8072961431686058, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.06513185741134703, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "ddf8743": { "balancing:strategy": "none", @@ -710,7 +865,12 @@ "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.05399187079844309, "classifier:gradient_boosting:n_iter_no_change": 7, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_5CV_iterative_es_if.json b/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_5CV_iterative_es_if.json index 451749d104..83b82b308a 100644 --- a/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_5CV_iterative_es_if.json +++ b/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_5CV_iterative_es_if.json @@ -20,7 +20,12 @@ "classifier:gradient_boosting:tol": 1e-07, "classifier:gradient_boosting:n_iter_no_change": 20, "classifier:gradient_boosting:validation_fraction": 0.021337414482578282, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + 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"balancing:strategy": "weighting", @@ -711,7 +861,12 @@ "classifier:gradient_boosting:scoring": "loss", "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.025627338912525964, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "981e948": { "balancing:strategy": "none", @@ -731,7 +886,12 @@ "classifier:extra_trees:min_samples_split": 4, "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.026426948053594874, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json b/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json index 9da086e17f..c95d179335 100644 --- a/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json +++ b/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json @@ -21,7 +21,12 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.006280403042161129, "classifier:gradient_boosting:n_iter_no_change": 17, "classifier:gradient_boosting:validation_fraction": 0.12820455722720236, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + 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"min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "278a3b7": { "balancing:strategy": "weighting", @@ -64,7 +74,12 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.043140728717176134, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7565211582936167, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.2427714099781957, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json b/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json index 52f4d84c26..3a7005ce58 100644 --- a/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json +++ b/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json @@ -20,7 +20,12 @@ "classifier:gradient_boosting:tol": 1e-07, "classifier:gradient_boosting:n_iter_no_change": 20, "classifier:gradient_boosting:validation_fraction": 0.021337414482578282, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json b/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json index e3aead06b8..3823f941fb 100644 --- a/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json +++ b/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json @@ -22,7 +22,12 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "classifier:gradient_boosting:n_iter_no_change": 19, "classifier:gradient_boosting:validation_fraction": 0.15148736566968157, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 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"data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "73f216b": { "balancing:strategy": "none", @@ -62,7 +72,12 @@ "classifier:mlp:shuffle": "True", "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + 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"data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "5e8eb5f": { "balancing:strategy": "none", @@ -400,7 +485,12 @@ "classifier:gradient_boosting:tol": 1e-07, "classifier:gradient_boosting:n_iter_no_change": 1, "classifier:gradient_boosting:validation_fraction": 0.10464338969122808, - 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"balancing:strategy": "none", @@ -714,7 +869,12 @@ "classifier:random_forest:min_samples_leaf": 15, "classifier:random_forest:min_samples_split": 19, "classifier:random_forest:min_weight_fraction_leaf": 0.0, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_3CV_iterative_es_if.json b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_3CV_iterative_es_if.json index 1928878da9..28cf8bc1cd 100644 --- a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_3CV_iterative_es_if.json +++ b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_3CV_iterative_es_if.json @@ -19,7 +19,12 @@ "classifier:gradient_boosting:scoring": "loss", "classifier:gradient_boosting:tol": 1e-07, "classifier:gradient_boosting:n_iter_no_change": 1, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "057a961": { "balancing:strategy": "none", @@ -44,7 +49,12 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.019274771652010168, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "18b9a26": { 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"33ce15c": { "balancing:strategy": "none", @@ -674,7 +819,12 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.2974843930412059, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 817, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "7166357": { "balancing:strategy": "weighting", @@ -693,7 +843,12 @@ "classifier:extra_trees:min_samples_leaf": 1, "classifier:extra_trees:min_samples_split": 3, "classifier:extra_trees:min_weight_fraction_leaf": 0.0, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "fcfb9a5": { "balancing:strategy": "none", @@ -717,7 +872,12 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.1962257728731655, "classifier:gradient_boosting:n_iter_no_change": 5, "classifier:gradient_boosting:validation_fraction": 0.03541393128244466, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json index 8048cc14d4..d5dc0fefb1 100644 --- a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json +++ b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json @@ -20,7 +20,12 @@ "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.8406370831514932, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.20389889461388738, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + 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1 }, "e457d9c": { "balancing:strategy": "weighting", @@ -66,7 +76,12 @@ "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.10560266211666122, "classifier:gradient_boosting:n_iter_no_change": 1, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "ef55f97": { "balancing:strategy": "weighting", @@ -88,7 +103,12 @@ 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"data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7260837314772796, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.2525954332120069, - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "2e26e3e": { "balancing:strategy": "weighting", @@ -136,7 +161,12 @@ 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"data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", - "data_preprocessor:__choice__": "feature_type" + "data_preprocessor:__choice__": "feature_type", + "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, + "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 }, "c85132e": { "balancing:strategy": "weighting", @@ -45,7 +50,12 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 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+285,no_encoding,no_coalescense,,mean,minmax,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2186105871515939,fdr,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.10377482408306521,0.016255400771699312,least_squares,255,None,65,70,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type,100,bag_of_word_encoding,1,min_df_absolute,,,,,,,,,1 diff --git a/autosklearn/metalearning/metafeatures/metafeatures.py b/autosklearn/metalearning/metafeatures/metafeatures.py index 79f5626d71..9652cfc673 100644 --- a/autosklearn/metalearning/metafeatures/metafeatures.py +++ b/autosklearn/metalearning/metafeatures/metafeatures.py @@ -1082,11 +1082,19 @@ def calculate_all_metafeatures(X, y, categorical, dataset_name, logger, # TODO make sure this is done as efficient as possible (no copy for # sparse matrices because of wrong sparse format) sparse = scipy.sparse.issparse(X) + + feat_type = {key: 'categorical' if value else 'numerical' + for key, value in categorical.items()} + + # TODO make this more cohesive to the overall structure (quick bug fix) + if isinstance(X, pd.DataFrame): + for key in X.select_dtypes(include="string").columns: + feat_type[key] = "string" + DPP = FeatTypeSplit( # The difference between feat_type and categorical, is that # categorical has True/False instead of categorical/numerical - feat_type={key: 'categorical' if value else 'numerical' - for key, value in categorical.items()}, + feat_type=feat_type, force_sparse_output=True) X_transformed = DPP.fit_transform(X) categorical_transformed = {i: False for i in range(X_transformed.shape[1])} diff --git a/autosklearn/pipeline/components/data_preprocessing/feature_reduction/__init__.py b/autosklearn/pipeline/components/data_preprocessing/feature_reduction/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/autosklearn/pipeline/components/data_preprocessing/feature_reduction/truncated_svd.py b/autosklearn/pipeline/components/data_preprocessing/feature_reduction/truncated_svd.py new file mode 100644 index 0000000000..3bc4e7c002 --- /dev/null +++ b/autosklearn/pipeline/components/data_preprocessing/feature_reduction/truncated_svd.py @@ -0,0 +1,76 @@ +from typing import Dict, Optional, Tuple, Union + +from ConfigSpace.configuration_space import ConfigurationSpace +import ConfigSpace.hyperparameters as CSH + +import numpy as np + +from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE +from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT + +from sklearn.decomposition import TruncatedSVD + + +class FeatureReduction(AutoSklearnPreprocessingAlgorithm): + """ + Reduces the features created by a bag of words encoding + """ + + def __init__( + self, + n_components: Optional[int] = None, + random_state: Optional[Union[int, np.random.RandomState]] = None + ) -> None: + self.n_components = n_components + self.random_state = random_state + + def fit(self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None + ) -> 'FeatureReduction': + if X.shape[1] > self.n_components: + self.preprocessor = TruncatedSVD(n_components=self.n_components, + random_state=self.random_state) + elif X.shape[1] <= self.n_components and X.shape[1] != 1: + self.preprocessor = TruncatedSVD(n_components=X.shape[1] - 1, + random_state=self.random_state) + else: + raise ValueError("The text embedding consists only of a single dimension.\n" + "Are you sure that your text data is necessary?") + self.preprocessor.fit(X) + return self + + def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: + if self.preprocessor is None: + raise NotImplementedError() + return self.preprocessor.transform(X) + + @staticmethod + def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return {'shortname': 'TextFeatureReduction', + 'name': 'TextFeatureReduction', + 'handles_missing_values': True, + 'handles_nominal_values': True, + 'handles_numerical_features': True, + 'prefers_data_scaled': False, + 'prefers_data_normalized': False, + 'handles_regression': True, + 'handles_classification': True, + 'handles_multiclass': True, + 'handles_multilabel': True, + 'handles_multioutput': True, + 'is_deterministic': True, + 'handles_sparse': True, + 'handles_dense': True, + 'input': (DENSE, SPARSE, UNSIGNED_DATA), + 'output': (INPUT,), + 'preferred_dtype': None} + + @staticmethod + def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None + ) -> ConfigurationSpace: + cs = ConfigurationSpace() + cs.add_hyperparameter( + CSH.UniformIntegerHyperparameter("n_components", lower=1, upper=10000, + default_value=100, log=True)) + return cs diff --git a/autosklearn/pipeline/components/data_preprocessing/feature_type.py b/autosklearn/pipeline/components/data_preprocessing/feature_type.py index b268ba316a..cfd31e8c3a 100644 --- a/autosklearn/pipeline/components/data_preprocessing/feature_type.py +++ b/autosklearn/pipeline/components/data_preprocessing/feature_type.py @@ -19,6 +19,8 @@ import CategoricalPreprocessingPipeline from autosklearn.pipeline.components.data_preprocessing.feature_type_numerical \ import NumericalPreprocessingPipeline +from autosklearn.pipeline.components.data_preprocessing.feature_type_text \ + import TextPreprocessingPipeline from autosklearn.pipeline.components.base import AutoSklearnComponent, AutoSklearnChoice, \ AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT @@ -29,8 +31,8 @@ class FeatTypeSplit(AutoSklearnPreprocessingAlgorithm): - """ This component is used to apply distinct transformations to categorical and - numerical features of a dataset. It is built on top of sklearn's ColumnTransformer. + """ This component is used to apply distinct transformations to categorical, + numerical and text features of a dataset. It is built on top of sklearn's ColumnTransformer. """ def __init__( @@ -82,9 +84,23 @@ def __init__( config=None, steps=pipeline, dataset_properties=dataset_properties, include=include, exclude=exclude, random_state=random_state, init_params=init_params) + + # The pipeline that will be applied to the text features (i.e. columns) + # of the dataset + # Configuration of the data-preprocessor is different from the configuration of + # the numerical or categorical pipeline. Hence, force to None + # It is actually the call to set_hyperparameter who properly sets this argument + # TODO: Extract the child configuration space from the FeatTypeSplit to the + # pipeline if needed + self.txt_ppl = TextPreprocessingPipeline( + config=None, steps=pipeline, dataset_properties=dataset_properties, + include=include, exclude=exclude, random_state=random_state, + init_params=init_params) + self._transformers: List[Tuple[str, AutoSklearnComponent]] = [ ("categorical_transformer", self.categ_ppl), ("numerical_transformer", self.numer_ppl), + ("text_transformer", self.txt_ppl), ] if self.config: self.set_hyperparameters(self.config, init_params=init_params) @@ -96,6 +112,7 @@ def fit(self, X: SUPPORTED_FEAT_TYPES, y: Optional[SUPPORTED_TARGET_TYPES] = Non n_feats = X.shape[1] categorical_features = [] numerical_features = [] + text_features = [] if self.feat_type is not None: # Make sure that we are not missing any column! expected = set(self.feat_type.keys()) @@ -104,31 +121,28 @@ def fit(self, X: SUPPORTED_FEAT_TYPES, y: Optional[SUPPORTED_TARGET_TYPES] = Non else: columns = set(range(n_feats)) if expected != columns: - raise ValueError("Train data has columns={} yet the feat_types are feat={}".format( - expected, - columns - )) + raise ValueError(f"Train data has columns={expected} yet the" + f" feat_types are feat={columns}") categorical_features = [key for key, value in self.feat_type.items() if value.lower() == 'categorical'] numerical_features = [key for key, value in self.feat_type.items() if value.lower() == 'numerical'] + text_features = [key for key, value in self.feat_type.items() + if value.lower() == "string"] - # If no categorical features, assume we have a numerical only pipeline - if len(categorical_features) == 0: - sklearn_transf_spec: List[Tuple[str, BaseEstimator, List[Union[str, bool, int]]]] = [ - ("numerical_transformer", self.numer_ppl, [True] * n_feats) - ] - # If all features are categorical, then just the categorical transformer is used - elif len(numerical_features) == 0: sklearn_transf_spec = [ - ("categorical_transformer", self.categ_ppl, [True] * n_feats) + (name, transformer, feature_columns) + for name, transformer, feature_columns + in [ + ("text_transformer", self.txt_ppl, text_features), + ("categorical_transformer", self.categ_ppl, categorical_features), + ("numerical_transformer", self.numer_ppl, numerical_features) + ] + if len(feature_columns) > 0 ] - # For the other cases, both transformers are used else: - sklearn_transf_spec = [ - ("categorical_transformer", self.categ_ppl, categorical_features), - ("numerical_transformer", self.numer_ppl, numerical_features) - ] + # self.feature_type == None assumes numerical case + sklearn_transf_spec = [("numerical_transformer", self.numer_ppl, [True]*n_feats)] # And one last check in case feat type is None # And to make sure the final specification has all the columns @@ -136,10 +150,7 @@ def fit(self, X: SUPPORTED_FEAT_TYPES, y: Optional[SUPPORTED_TARGET_TYPES] = Non total_columns = sum([len(features) for name, ppl, features in sklearn_transf_spec]) if total_columns != n_feats: raise ValueError("Missing columns in the specification of the data validator" - " for train data={} and spec={}".format( - np.shape(X), - sklearn_transf_spec, - )) + f" for train data={np.shape(X)} and spec={sklearn_transf_spec}") self.sparse_ = sparse.issparse(X) or self.force_sparse_output self.column_transformer = sklearn.compose.ColumnTransformer( diff --git a/autosklearn/pipeline/components/data_preprocessing/feature_type_text.py b/autosklearn/pipeline/components/data_preprocessing/feature_type_text.py new file mode 100644 index 0000000000..a21980f000 --- /dev/null +++ b/autosklearn/pipeline/components/data_preprocessing/feature_type_text.py @@ -0,0 +1,119 @@ +from typing import Any, List, Dict, Optional, Tuple, Union + +from ConfigSpace.configuration_space import Configuration, ConfigurationSpace + +import numpy as np + +from sklearn.base import BaseEstimator + +from autosklearn.pipeline.components.data_preprocessing.text_encoding \ + import BagOfWordChoice +from autosklearn.pipeline.components.data_preprocessing.feature_reduction.truncated_svd import \ + FeatureReduction +from autosklearn.pipeline.base import ( + BasePipeline, + DATASET_PROPERTIES_TYPE, +) +from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT + + +class TextPreprocessingPipeline(BasePipeline): + """This class implements a pipeline for data preprocessing of text features. + It assumes that the data to be transformed is made only of text features. + The steps of this pipeline are: + 1 - Vectorize: Fits a *Vecotrizer object and apply this + 2 - text feature reduction: TruncatedSVD + + Parameters + ---------- + config : ConfigSpace.configuration_space.Configuration + The configuration to evaluate. + + random_state : Optional[int | RandomState] + If int, random_state is the seed used by the random number generator; + If RandomState instance, random_state is the random number generator; + If None, the random number generator is the RandomState instance + used by `np.random`.""" + + def __init__( + self, + config: Optional[Configuration] = None, + steps: Optional[List[Tuple[str, BaseEstimator]]] = None, + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + include: Optional[Dict[str, str]] = None, + exclude: Optional[Dict[str, str]] = None, + random_state: Optional[Union[int, np.random.RandomState]] = None, + init_params: Optional[Dict[str, Any]] = None + ) -> None: + self._output_dtype = np.int32 + super().__init__( + config, steps, dataset_properties, include, exclude, + random_state, init_params + ) + + @staticmethod + def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return {'shortname': 'txt_datapreproc', + 'name': 'text data preprocessing', + 'handles_missing_values': True, + 'handles_nominal_values': False, + 'handles_numerical_features': False, + 'prefers_data_scaled': False, + 'prefers_data_normalized': False, + 'handles_regression': True, + 'handles_classification': True, + 'handles_multiclass': True, + 'handles_multilabel': True, + 'is_deterministic': True, + 'handles_sparse': True, + 'handles_dense': True, + 'input': (DENSE, SPARSE, UNSIGNED_DATA), + 'output': (INPUT,), + 'preferred_dtype': None} + + def _get_hyperparameter_search_space( + self, + include: Optional[Dict[str, str]] = None, + exclude: Optional[Dict[str, str]] = None, + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> ConfigurationSpace: + """Create the hyperparameter configuration space. + + Parameters + ---------- + # TODO add parameter description + + Returns + ------- + cs : ConfigSpace.configuration_space.Configuration + The configuration space describing the SimpleRegressionClassifier. + """ + cs = ConfigurationSpace() + if dataset_properties is None or not isinstance(dataset_properties, dict): + dataset_properties = dict() + + cs = self._get_base_search_space( + cs=cs, dataset_properties=dataset_properties, + exclude=exclude, include=include, pipeline=self.steps) + + return cs + + def _get_pipeline_steps(self, + dataset_properties: Optional[Dict[str, str]] = None, + ) -> List[Tuple[str, BaseEstimator]]: + steps = [] + + default_dataset_properties = {} + if dataset_properties is not None and isinstance(dataset_properties, dict): + default_dataset_properties.update(dataset_properties) + + steps.extend([ + ("text_encoding", BagOfWordChoice(default_dataset_properties, + random_state=self.random_state)), + ("feature_reduction", FeatureReduction(random_state=self.random_state)) + ]) + return steps + + def _get_estimator_hyperparameter_name(self) -> str: + return "text data preprocessing" diff --git a/autosklearn/pipeline/components/data_preprocessing/text_encoding/__init__.py b/autosklearn/pipeline/components/data_preprocessing/text_encoding/__init__.py new file mode 100644 index 0000000000..949ce83298 --- /dev/null +++ b/autosklearn/pipeline/components/data_preprocessing/text_encoding/__init__.py @@ -0,0 +1,116 @@ +from collections import OrderedDict +import os + +from typing import Any, Dict, Optional + +from ConfigSpace import Configuration +from ConfigSpace.configuration_space import ConfigurationSpace +from ConfigSpace.hyperparameters import CategoricalHyperparameter + +from sklearn.base import BaseEstimator + +from ...base import AutoSklearnPreprocessingAlgorithm, find_components,\ + ThirdPartyComponents, AutoSklearnChoice, _addons + +from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE + +bow_directory = os.path.split(__file__)[0] +_bows = find_components(__package__, + bow_directory, + AutoSklearnPreprocessingAlgorithm) + +additional_components = ThirdPartyComponents(AutoSklearnPreprocessingAlgorithm) +_addons['data_preprocessing.text_encoding'] = additional_components + + +def add_bow(classifier: 'BagOfWordChoice') -> None: + additional_components.add_component(classifier) + + +class BagOfWordChoice(AutoSklearnChoice): + + @classmethod + def get_components(cls: BaseEstimator) -> Dict[str, BaseEstimator]: + components: Dict[str, BaseEstimator] = OrderedDict() + components.update(_bows) + components.update(additional_components.components) + return components + + def get_hyperparameter_search_space( + self, + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + default: Optional[str] = None, + include: Optional[Dict[str, str]] = None, + exclude: Optional[Dict[str, str]] = None, + ) -> ConfigurationSpace: + cs = ConfigurationSpace() + + if dataset_properties is None: + dataset_properties = {} + + # Compile a list of legal preprocessors for this problem + available_preprocessors = self.get_available_components( + dataset_properties=dataset_properties, + include=include, exclude=exclude) + + if len(available_preprocessors) == 0: + raise ValueError( + "No bag of word encoders found, please add any bag of word encoder" + "component.") + + if default is None: + defaults = ['bag_of_words_encoding'] + for default_ in defaults: + if default_ in available_preprocessors: + default = default_ + break + + preprocessor = CategoricalHyperparameter('__choice__', + list( + available_preprocessors.keys()), + default_value=default) + + cs.add_hyperparameter(preprocessor) + for name in available_preprocessors: + preprocessor_configuration_space = available_preprocessors[name]. \ + get_hyperparameter_search_space(dataset_properties) + parent_hyperparameter = {'parent': preprocessor, 'value': name} + cs.add_configuration_space(name, preprocessor_configuration_space, + parent_hyperparameter=parent_hyperparameter) + + self.configuration_space = cs + self.dataset_properties = dataset_properties + return cs + + def set_hyperparameters(self, configuration: Configuration, + init_params: Optional[Dict[str, Any]] = None + ) -> 'BagOfWordChoice': + new_params = {} + + params = configuration.get_dictionary() + choice = params['__choice__'] + del params['__choice__'] + + for param, value in params.items(): + param = param.replace(choice, '').replace(':', '') + new_params[param] = value + + if init_params is not None: + for param, value in init_params.items(): + # These next two lines are different than in the base class - + # they allow removing the categorical feature indicator array + # in order to not pass it to the no encoding + if choice not in param: + continue + param = param.replace(choice, '').replace(':', '') + new_params[param] = value + + new_params['random_state'] = self.random_state + + self.new_params = new_params + self.choice = self.get_components()[choice](**new_params) + + return self + + def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: + return self.choice.transform(X) diff --git a/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding.py b/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding.py new file mode 100644 index 0000000000..c66c67046b --- /dev/null +++ b/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding.py @@ -0,0 +1,113 @@ +from typing import Dict, Optional, Tuple, Union + +from ConfigSpace.configuration_space import ConfigurationSpace +import ConfigSpace.hyperparameters as CSH +from ConfigSpace import EqualsCondition + +import numpy as np +import pandas as pd +import itertools + + +from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE +from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT + +from sklearn.feature_extraction.text import CountVectorizer + + +class BagOfWordEncoder(AutoSklearnPreprocessingAlgorithm): + def __init__( + self, + ngram_range: int = 1, + min_df_choice: str = "min_df_absolute", + min_df_absolute: int = 0, + min_df_relative: float = 0.01, + random_state: Optional[Union[int, np.random.RandomState]] = None + ) -> None: + self.ngram_range = ngram_range + self.random_state = random_state + self.min_df_choice = min_df_choice + self.min_df_absolute = min_df_absolute + self.min_df_relative = min_df_relative + + def fit(self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None + ) -> 'BagOfWordEncoder': + + if isinstance(X, pd.DataFrame): + X.fillna("", inplace=True) + # define a CountVectorizer for every feature (implicitly defined by order of columns, + # maybe change the list + # to a dictionary with features as keys) + if self.min_df_choice == "min_df_absolute": + self.preprocessor = CountVectorizer(min_df=self.min_df_absolute, + ngram_range=(1, self.ngram_range)) + elif self.min_df_choice == "min_df_relative": + self.preprocessor = CountVectorizer(min_df=self.min_df_relative, + ngram_range=(1, self.ngram_range)) + else: + raise KeyError() + + all_text = itertools.chain.from_iterable(X[col] for col in X.columns) + self.preprocessor = self.preprocessor.fit(all_text) + + else: + raise ValueError("Your text data is not encoded in a pandas.DataFrame\n" + "Please make sure to use a pandas.DataFrame and ensure" + "that the text features are encoded as strings.") + return self + + def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: + X.fillna("", inplace=True) + X_transformed = None + if self.preprocessor is None: + raise NotImplementedError() + for feature in X.columns: + if X_transformed is None: + X_transformed = self.preprocessor.transform(X[feature]) + else: + X_transformed += self.preprocessor.transform(X[feature]) + return X_transformed + + @staticmethod + def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return {'shortname': 'BOW', + 'name': 'Bag Of Word Encoder', + 'handles_regression': True, + 'handles_classification': True, + 'handles_multiclass': True, + 'handles_multilabel': True, + 'handles_multioutput': True, + 'handles_sparse': True, + 'handles_dense': True, + 'input': (DENSE, SPARSE, UNSIGNED_DATA), + 'output': (INPUT,), } + + @staticmethod + def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None + ) -> ConfigurationSpace: + cs = ConfigurationSpace() + hp_ngram_range = CSH.UniformIntegerHyperparameter(name="ngram_range", lower=1, upper=3, + default_value=1) + hp_min_df_choice_bow = CSH.CategoricalHyperparameter("min_df_choice", + choices=["min_df_absolute", + "min_df_relative"]) + hp_min_df_absolute_bow = CSH.UniformIntegerHyperparameter(name="min_df_absolute", lower=0, + upper=10, + default_value=0) + hp_min_df_relative_bow = CSH.UniformFloatHyperparameter(name="min_df_relative", lower=0.01, + upper=1.0, + default_value=0.01, log=True) + cs.add_hyperparameters( + [hp_ngram_range, hp_min_df_choice_bow, hp_min_df_absolute_bow, hp_min_df_relative_bow]) + + cond_min_df_absolute_bow = EqualsCondition(hp_min_df_absolute_bow, hp_min_df_choice_bow, + "min_df_absolute") + cond_min_df_relative_bow = EqualsCondition(hp_min_df_relative_bow, hp_min_df_choice_bow, + "min_df_relative") + cs.add_conditions([cond_min_df_absolute_bow, cond_min_df_relative_bow]) + + # maybe add bigrams ... + + return cs diff --git a/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding_distinct.py b/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding_distinct.py new file mode 100644 index 0000000000..85851ca72e --- /dev/null +++ b/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding_distinct.py @@ -0,0 +1,120 @@ +from typing import Dict, Optional, Tuple, Union + +from ConfigSpace.configuration_space import ConfigurationSpace +import ConfigSpace.hyperparameters as CSH +from ConfigSpace import EqualsCondition + +import numpy as np +import pandas as pd +from scipy.sparse import hstack + +from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE +from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT + +from sklearn.feature_extraction.text import CountVectorizer + + +class BagOfWordEncoder(AutoSklearnPreprocessingAlgorithm): + def __init__( + self, + ngram_range: int = 1, + min_df_choice: str = "min_df_absolute", + min_df_absolute: int = 0, + min_df_relative: float = 0.01, + random_state: Optional[Union[int, np.random.RandomState]] = None + ) -> None: + self.ngram_range = ngram_range + self.random_state = random_state + self.min_df_choice = min_df_choice + self.min_df_absolute = min_df_absolute + self.min_df_relative = min_df_relative + + def fit(self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None + ) -> 'BagOfWordEncoder': + + if isinstance(X, pd.DataFrame): + X.fillna("", inplace=True) + if self.min_df_choice == "min_df_absolute": + + self.preprocessor = {} + + for feature in X.columns: + vectorizer = CountVectorizer(min_df=self.min_df_absolute, + ngram_range=(1, self.ngram_range)).fit(X[feature]) + self.preprocessor[feature] = vectorizer + + elif self.min_df_choice == "min_df_relative": + + self.preprocessor = {} + + for feature in X.columns: + vectorizer = CountVectorizer(min_df=self.min_df_relative, + ngram_range=(1, self.ngram_range)).fit(X[feature]) + self.preprocessor[feature] = vectorizer + else: + raise KeyError() + else: + raise ValueError("Your text data is not encoded in a pandas.DataFrame\n" + "Please make sure to use a pandas.DataFrame and ensure" + "that the text features are encoded as strings.") + return self + + def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: + X.fillna("", inplace=True) + X_new = None + if self.preprocessor is None: + raise NotImplementedError() + + for feature in self.preprocessor: + # the names in the dataframe must not change + if X_new is None: + X_new = self.preprocessor[feature].transform(X[feature]) + else: + X_transformed = self.preprocessor[feature].transform(X[feature]) + X_new = hstack([X_new, X_transformed]) + + return X_new + + @staticmethod + def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return {'shortname': 'BOW', + 'name': 'Bag Of Word Encoder', + 'handles_regression': True, + 'handles_classification': True, + 'handles_multiclass': True, + 'handles_multilabel': True, + 'handles_multioutput': True, + 'handles_sparse': True, + 'handles_dense': True, + 'input': (DENSE, SPARSE, UNSIGNED_DATA), + 'output': (INPUT,), } + + @staticmethod + def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None + ) -> ConfigurationSpace: + cs = ConfigurationSpace() + hp_ngram_range = CSH.UniformIntegerHyperparameter(name="ngram_range", lower=1, upper=3, + default_value=1) + hp_min_df_choice_bow = CSH.CategoricalHyperparameter("min_df_choice", + choices=["min_df_absolute", + "min_df_relative"]) + hp_min_df_absolute_bow = CSH.UniformIntegerHyperparameter(name="min_df_absolute", lower=0, + upper=10, + default_value=0) + hp_min_df_relative_bow = CSH.UniformFloatHyperparameter(name="min_df_relative", lower=0.01, + upper=1.0, + default_value=0.01, log=True) + cs.add_hyperparameters( + [hp_ngram_range, hp_min_df_choice_bow, hp_min_df_absolute_bow, hp_min_df_relative_bow]) + + cond_min_df_absolute_bow = EqualsCondition(hp_min_df_absolute_bow, hp_min_df_choice_bow, + "min_df_absolute") + cond_min_df_relative_bow = EqualsCondition(hp_min_df_relative_bow, hp_min_df_choice_bow, + "min_df_relative") + cs.add_conditions([cond_min_df_absolute_bow, cond_min_df_relative_bow]) + + # maybe add bigrams ... + + return cs diff --git a/autosklearn/pipeline/components/data_preprocessing/text_encoding/tfidf_encoding.py b/autosklearn/pipeline/components/data_preprocessing/text_encoding/tfidf_encoding.py new file mode 100644 index 0000000000..0b7ef239f1 --- /dev/null +++ b/autosklearn/pipeline/components/data_preprocessing/text_encoding/tfidf_encoding.py @@ -0,0 +1,115 @@ +from typing import Dict, Optional, Tuple, Union + +from ConfigSpace.configuration_space import ConfigurationSpace +import ConfigSpace.hyperparameters as CSH +from ConfigSpace import EqualsCondition + +import numpy as np +import pandas as pd +import itertools + +from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE +from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT + +from sklearn.feature_extraction.text import TfidfVectorizer + + +class TfidfEncoder(AutoSklearnPreprocessingAlgorithm): + def __init__( + self, + ngram_range: int = 1, + use_idf: bool = True, + min_df_choice: str = "min_df_absolute", + min_df_absolute: int = 0, + min_df_relative: float = 0.01, + random_state: Optional[Union[int, np.random.RandomState]] = None + ) -> None: + self.ngram_range = ngram_range + self.random_state = random_state + self.use_idf = use_idf + self.min_df_choice = min_df_choice + self.min_df_absolute = min_df_absolute + self.min_df_relative = min_df_relative + + def fit(self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None + ) -> 'TfidfEncoder': + + if isinstance(X, pd.DataFrame): + X.fillna("", inplace=True) + # define a CountVectorizer for every feature (implicitly defined by order of columns, + # maybe change the list + # to a dictionary with features as keys) + if self.min_df_choice == "min_df_absolute": + self.preprocessor = TfidfVectorizer(min_df=self.min_df_absolute, + use_idf=self.use_idf, + ngram_range=(1, self.ngram_range)) + elif self.min_df_choice == "min_df_relative": + self.preprocessor = TfidfVectorizer(min_df=self.min_df_relative, + use_idf=self.use_idf, + ngram_range=(1, self.ngram_range)) + else: + raise KeyError() + + all_text = itertools.chain.from_iterable(X[col] for col in X.columns) + self.preprocessor = self.preprocessor.fit(all_text) + + else: + raise ValueError("Your text data is not encoded in a pandas.DataFrame\n" + "Please make sure to use a pandas.DataFrame and ensure" + " that the text features are encoded as strings.") + return self + + def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: + X.fillna("", inplace=True) + X_transformed = None + if self.preprocessor is None: + raise NotImplementedError() + for feature in X.columns: + if X_transformed is None: + X_transformed = self.preprocessor.transform(X[feature]) + else: + X_transformed += self.preprocessor.transform(X[feature]) + return X_transformed + + @staticmethod + def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return {'shortname': 'RBOW', + 'name': 'Relative Bag Of Word Encoder', + 'handles_regression': True, + 'handles_classification': True, + 'handles_multiclass': True, + 'handles_multilabel': True, + 'handles_multioutput': True, + 'handles_sparse': True, + 'handles_dense': True, + 'input': (DENSE, SPARSE, UNSIGNED_DATA), + 'output': (INPUT,), } + + @staticmethod + def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None + ) -> ConfigurationSpace: + cs = ConfigurationSpace() + hp_ngram_range = CSH.UniformIntegerHyperparameter(name="ngram_range", lower=1, upper=3, + default_value=1) + hp_use_idf = CSH.CategoricalHyperparameter("use_idf", choices=[False, True]) + hp_min_df_choice = CSH.CategoricalHyperparameter("min_df_choice", + choices=["min_df_absolute", + "min_df_relative"]) + hp_min_df_absolute = CSH.UniformIntegerHyperparameter(name="min_df_absolute", lower=0, + upper=10, default_value=0) + hp_min_df_relative = CSH.UniformFloatHyperparameter(name="min_df_relative", lower=0.01, + upper=1.0, default_value=0.01, log=True) + cs.add_hyperparameters( + [hp_ngram_range, hp_use_idf, hp_min_df_choice, hp_min_df_absolute, hp_min_df_relative]) + + cond_min_df_absolute = EqualsCondition(hp_min_df_absolute, hp_min_df_choice, + "min_df_absolute") + cond_min_df_relative = EqualsCondition(hp_min_df_relative, hp_min_df_choice, + "min_df_relative") + cs.add_conditions([cond_min_df_absolute, cond_min_df_relative]) + + # maybe add bigrams ... + + return cs diff --git a/examples/40_advanced/example_text_preprocessing.py b/examples/40_advanced/example_text_preprocessing.py new file mode 100644 index 0000000000..f60188781b --- /dev/null +++ b/examples/40_advanced/example_text_preprocessing.py @@ -0,0 +1,67 @@ +# -*- encoding: utf-8 -*- +""" +================== +Text Preprocessing +================== +This example shows, how to use text features in *auto-sklearn*. *auto-sklearn* can automatically +encode text features if they are provided as string type in a pandas dataframe. + +For processing text features you need a pandas dataframe and set the desired +text columns to string and the categorical columns to category. + +*auto-sklearn* text embedding creates a bag of words count. +""" +import sklearn.metrics +import sklearn.datasets +import autosklearn.classification + +############################################################################ +# Data Loading +# ============ + +X, y = sklearn.datasets.fetch_openml(data_id=40945, return_X_y=True) + +# by default, the columns which should be strings are not formatted as such +print(f"{X.info()}\n") + +# manually convert these to string columns +X = X.astype({'name': 'string', 'ticket': 'string', 'cabin': 'string', 'boat': 'string', + 'home.dest': 'string'}) + +# now *auto-sklearn* handles the string columns with its text feature preprocessing pipeline + +X_train, X_test, y_train, y_test = \ + sklearn.model_selection.train_test_split(X, y, random_state=1) + +cls = autosklearn.classification.AutoSklearnClassifier( + time_left_for_this_task=30, + # Bellow two flags are provided to speed up calculations + # Not recommended for a real implementation + initial_configurations_via_metalearning=0, + smac_scenario_args={'runcount_limit': 1}, +) + +cls.fit(X_train, y_train, X_test, y_test) + +predictions = cls.predict(X_test) +print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions)) + + +X, y = sklearn.datasets.fetch_openml(data_id=40945, return_X_y=True, as_frame=True) +X = X.select_dtypes(exclude=['object']) + +X_train, X_test, y_train, y_test = \ + sklearn.model_selection.train_test_split(X, y, random_state=1) + +cls = autosklearn.classification.AutoSklearnClassifier( + time_left_for_this_task=30, + # Bellow two flags are provided to speed up calculations + # Not recommended for a real implementation + initial_configurations_via_metalearning=0, + smac_scenario_args={'runcount_limit': 1}, +) + +cls.fit(X_train, y_train, X_test, y_test) + +predictions = cls.predict(X_test) +print("Accuracy score without text preprocessing", sklearn.metrics.accuracy_score(y_test, predictions)) diff --git a/test/test_automl/test_estimators.py b/test/test_automl/test_estimators.py index e7275a4591..b32d1d0026 100644 --- a/test/test_automl/test_estimators.py +++ b/test/test_automl/test_estimators.py @@ -146,7 +146,7 @@ def test_feat_type_wrong_arguments(): cls.fit(X=X, y=y, feat_type=[True]*100) cls = AutoSklearnClassifier(ensemble_size=0) - expected_msg = r".*Only `Categorical` and `Numerical` are" + expected_msg = r".*Only `Categorical`, `Numerical` and `String` are" "valid feature types, you passed `Car`.*" with pytest.raises(ValueError, match=expected_msg): cls.fit(X=X, y=y, feat_type=['Car']*100) diff --git a/test/test_data/test_feature_validator.py b/test/test_data/test_feature_validator.py index 07b2670a15..012ef1a179 100644 --- a/test/test_data/test_feature_validator.py +++ b/test/test_data/test_feature_validator.py @@ -1,7 +1,7 @@ import numpy as np import pandas as pd -from pandas.api.types import is_numeric_dtype +from pandas.api.types import is_numeric_dtype, is_categorical_dtype, is_string_dtype import pytest @@ -306,8 +306,6 @@ def test_features_unsupported_calls_are_raised(): with pytest.raises(ValueError, match=r"Auto-sklearn only supports.*yet, the provided input"): validator.fit({'input1': 1, 'input2': 2}) validator = FeatureValidator() - with pytest.raises(ValueError, match=r"has unsupported dtype string"): - validator.fit(pd.DataFrame([{'A': 1, 'B': 2}], dtype='string')) with pytest.raises(ValueError, match=r"The feature dimensionality of the train and test"): validator.fit(X_train=np.array([[1, 2, 3], [4, 5, 6]]), X_test=np.array([[1, 2, 3, 4], [4, 5, 6, 7]]), @@ -323,7 +321,7 @@ def test_features_unsupported_calls_are_raised(): with pytest.raises(ValueError, match=r"feat_type must only contain strings.*"): validator.fit(np.array([[1, 2, 3], [4, 5, 6]])) validator = FeatureValidator(feat_type=['1', '2', '3']) - with pytest.raises(ValueError, match=r"Only `Categorical` and `Numerical` are.*"): + with pytest.raises(ValueError, match=r"Only `Categorical`, `Numerical` and `String` are.*"): validator.fit(np.array([[1, 2, 3], [4, 5, 6]])) @@ -418,8 +416,12 @@ def test_list_to_dataframe(openml_id): if is_numeric_dtype(X_pandas[col].dtype): # convert dtype translates 72.0 to 72. Be robust against this! assert is_numeric_dtype(transformed_X[i].dtype) + elif is_string_dtype(X_pandas[col].dtype): + assert is_string_dtype(X_pandas[col].dtype) + elif is_categorical_dtype(X_pandas[col].dtype): + assert is_categorical_dtype(X_pandas[col].dtype) else: - assert X_pandas[col].dtype.name == transformed_X[i].dtype.name, col + raise NotImplementedError # Also make sure that at testing time # this work @@ -428,8 +430,12 @@ def test_list_to_dataframe(openml_id): if is_numeric_dtype(X_pandas[col].dtype): # convert dtype translates 72.0 to 72. Be robust against this! assert is_numeric_dtype(transformed_X[i].dtype) + elif is_string_dtype(X_pandas[col].dtype): + assert is_string_dtype(X_pandas[col].dtype) + elif is_categorical_dtype(X_pandas[col].dtype): + assert is_categorical_dtype(X_pandas[col].dtype) else: - assert X_pandas[col].dtype.name == transformed_X[i].dtype.name, col + raise NotImplementedError @pytest.mark.parametrize( diff --git a/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv b/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv old mode 100644 new mode 100755 index a681712277..0336e76f01 --- a/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv +++ b/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv @@ -1,129 +1,129 @@ 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+127,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.5396632902741157e-05,0.18584665494894462,auto,255,None,6,48,13,loss,1E-07,0.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,379,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type,100,bag_of_word_encoding,1,,min_df_absolute,,,,,,,,,1 +128,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,921.894056649221,,,0.4428468266254544,rbf,-1,False,1.0295003364004332e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7588611364765459,0.25,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type,100,bag_of_word_encoding,1,,min_df_absolute,,,,,,,,,1 diff --git a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_text.py b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_text.py new file mode 100644 index 0000000000..0e39c5d7e9 --- /dev/null +++ b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_text.py @@ -0,0 +1,95 @@ +import unittest +import numpy as np +import pandas as pd + +from autosklearn.pipeline.components.data_preprocessing.text_encoding.bag_of_word_encoding import \ + BagOfWordEncoder as BOW +from autosklearn.pipeline.components.data_preprocessing.\ + text_encoding.bag_of_word_encoding_distinct import BagOfWordEncoder as BOW_distinct + + +class TextPreprocessingPipelineTest(unittest.TestCase): + + def test_fit_transform(self): + X = pd.DataFrame({"col1": ["hello world", + "This is a test"], + "col2": ["hello mars", + "This is the second column"]}).astype({"col1": "string", + "col2": "string"}) + BOW_fitted = BOW(ngram_range=1, min_df_choice="min_df_absolute", min_df_absolute=0, + min_df_relative=0, random_state=1).fit(X.copy()) + + Yt = BOW_fitted.preprocessor.vocabulary_ + words = sorted(["hello", "world", "this", "is", "test", # "a" is not added, len(...)=1 + "mars", "the", "second", "column"]) # is ignored by CountVectorizer + Y = {key: idx for idx, key in enumerate(words)} + + np.testing.assert_array_equal(Yt, Y) + + BOW_fitted = BOW_distinct(ngram_range=1, min_df_choice="min_df_absolute", min_df_absolute=0, + min_df_relative=0, random_state=1).fit(X.copy()) + + for key in BOW_fitted.preprocessor: + y = [] + for col in X[key]: + y += [word for word in col.lower().split(" ") if len(word) > 1] + y = sorted(y) + yt = sorted(BOW_fitted.preprocessor[key].vocabulary_.keys()) + np.testing.assert_array_equal(yt, y) + + def test_transform(self): + X = pd.DataFrame({"col1": ["hello world", + "this is a test"], + "col2": ["hello mars", + "this is the second column"]}).astype({"col1": "string", + "col2": "string"}) + X_t = BOW(ngram_range=1, min_df_choice="min_df_absolute", min_df_absolute=0, + min_df_relative=0, random_state=1).fit_transform(X.copy()) + + # ['column', 'hello', 'is', 'mars', 'second', 'test', 'the', 'this', 'world'] + y = np.array([[0, 2, 0, 1, 0, 0, 0, 0, 1], + [1, 0, 2, 0, 1, 1, 1, 2, 0]]) + np.testing.assert_array_equal(X_t.toarray(), y) + + X_t = BOW_distinct(ngram_range=1, min_df_choice="min_df_absolute", min_df_absolute=0, + min_df_relative=0, random_state=1).fit_transform(X.copy()) + + # 'hello', 'is', 'test', 'this', 'world', + # 'column', 'hello', 'is', 'mars', 'second', 'the', 'this' + y = np.array([[1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0], + [0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1]]) + np.testing.assert_array_equal(X_t.toarray(), y) + + def test_check_shape(self): + X = pd.DataFrame({"col1": ["hello world", + "this is test"], + "col2": ["test test", + "test test"]}).astype({"col1": "string", + "col2": "string"}) + X_t = BOW(ngram_range=1, min_df_choice="min_df_absolute", min_df_absolute=0, + min_df_relative=0, random_state=1).fit_transform(X.copy()) + + self.assertEqual(X_t.shape, (2, 5)) + + X_t = BOW_distinct(ngram_range=1, min_df_choice="min_df_absolute", min_df_absolute=0, + min_df_relative=0, random_state=1).fit_transform(X.copy()) + + self.assertEqual(X_t.shape, (2, 6)) + + def test_check_nan(self): + X = pd.DataFrame({"col1": ["hello world", + "this is test", + None], + "col2": ["test test", + "test test", + "test"]}).astype({"col1": "string", + "col2": "string"}) + X_t = BOW(ngram_range=1, min_df_choice="min_df_absolute", min_df_absolute=0, + min_df_relative=0, random_state=1).fit_transform(X.copy()) + + self.assertEqual(X_t.shape, (3, 5)) + + X_t = BOW_distinct(ngram_range=1, min_df_choice="min_df_absolute", min_df_absolute=0, + min_df_relative=0, random_state=1).fit_transform(X.copy()) + + self.assertEqual(X_t.shape, (3, 6)) diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index 82f341fde6..49267b0fee 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -573,7 +573,7 @@ def test_get_hyperparameter_search_space(self): * 7 rescaling choices * 16 classifier choices * 13 features preprocessor choices - * 168 total hyperparameters + * 183 total hyperparameters * (n_hyperparameters - 4) different conditionals for the pipeline * 53 forbidden combinations """ @@ -592,7 +592,7 @@ def test_get_hyperparameter_search_space(self): self.assertEqual(n_preprocessors, 13) hyperparameters = cs.get_hyperparameters() - self.assertEqual(len(hyperparameters), 168) + self.assertEqual(len(hyperparameters), 183) # for hp in sorted([str(h) for h in hyperparameters]): # print hp @@ -1008,6 +1008,7 @@ def _test_set_hyperparameter_choice(self, expected_key, implementation, config_d to_check = { 'numerical_transformer': implementation.choice.numer_ppl.named_steps, 'categorical_transformer': implementation.choice.categ_ppl.named_steps, + 'text_transformer': implementation.choice.txt_ppl.named_steps, } for data_type, pipeline in to_check.items(): diff --git a/test/test_pipeline/test_regression.py b/test/test_pipeline/test_regression.py index cc52109664..fccf59af67 100644 --- a/test/test_pipeline/test_regression.py +++ b/test/test_pipeline/test_regression.py @@ -288,7 +288,7 @@ def test_get_hyperparameter_search_space(self): conditions = cs.get_conditions() hyperparameters = cs.get_hyperparameters() forbiddens = cs.get_forbiddens() - self.assertEqual(156, len(hyperparameters)) + self.assertEqual(171, len(hyperparameters)) self.assertEqual(len(hyperparameters) - 3, len(conditions)) self.assertEqual(len(forbiddens), 35) @@ -514,6 +514,7 @@ def _test_set_hyperparameter_choice(self, expected_key, implementation, config_d to_check = { 'numerical_transformer': implementation.choice.numer_ppl.named_steps, 'categorical_transformer': implementation.choice.categ_ppl.named_steps, + 'text_transformer': implementation.choice.txt_ppl.named_steps, } for data_type, pipeline in to_check.items(): From 573031e75a33a9c1b0b2c50b639e62a234bbd5bc Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Thu, 3 Feb 2022 09:36:18 +0100 Subject: [PATCH 024/118] Doc: Adds documentation for the dataset compression argument from #1341 and #1250 (#1386) * Add: Doc for `dataset_compression` * Fix: Shorten line * Doc: Make more clear that the argument None still provides defaults --- autosklearn/estimators.py | 155 +++++++++++++++++--------------------- doc/conf.py | 2 +- doc/faq.rst | 6 ++ doc/manual.rst | 48 ++++++++++++ 4 files changed, 123 insertions(+), 88 deletions(-) diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 84fe97797c..070230ae94 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -156,58 +156,41 @@ def __init__( 'feature_preprocessor': ["no_preprocessing"] } - resampling_strategy : Union[str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit] = "holdout" + resampling_strategy : str | BaseCrossValidator | _RepeatedSplits | BaseShuffleSplit = "holdout" How to to handle overfitting, might need to use ``resampling_strategy_arguments`` if using ``"cv"`` based method or a Splitter object. + * **Options** + * ``"holdout"`` - Use a 67:33 (train:test) split + * ``"cv"``: perform cross validation, requires "folds" in ``resampling_strategy_arguments`` + * ``"holdout-iterative-fit"`` - Same as "holdout" but iterative fit where possible + * ``"cv-iterative-fit"``: Same as "cv" but iterative fit where possible + * ``"partial-cv"``: Same as "cv" but uses intensification. + * ``BaseCrossValidator`` - any BaseCrossValidator subclass (found in scikit-learn model_selection module) + * ``_RepeatedSplits`` - any _RepeatedSplits subclass (found in scikit-learn model_selection module) + * ``BaseShuffleSplit`` - any BaseShuffleSplit subclass (found in scikit-learn model_selection module) + If using a Splitter object that relies on the dataset retaining it's current size and order, you will need to look at the ``dataset_compression`` argument and ensure that ``"subsample"`` is not included in the applied compression ``"methods"`` or disable it entirely with ``False``. - **Options** - - * ``"holdout"``: - 67:33 (train:test) split - * ``"holdout-iterative-fit"``: - 67:33 (train:test) split, iterative fit where possible - * ``"cv"``: - crossvalidation, - requires ``"folds"`` in ``resampling_strategy_arguments`` - * ``"cv-iterative-fit"``: - crossvalidation, - calls iterative fit where possible, - requires ``"folds"`` in ``resampling_strategy_arguments`` - * 'partial-cv': - crossvalidation with intensification, - requires ``"folds"`` in ``resampling_strategy_arguments`` - * ``BaseCrossValidator`` subclass: - any BaseCrossValidator subclass (found in scikit-learn model_selection module) - * ``_RepeatedSplits`` subclass: - any _RepeatedSplits subclass (found in scikit-learn model_selection module) - * ``BaseShuffleSplit`` subclass: - any BaseShuffleSplit subclass (found in scikit-learn model_selection module) - - resampling_strategy_arguments : dict, optional if 'holdout' (train_size default=0.67) - Additional arguments for resampling_strategy: - - * ``train_size`` should be between 0.0 and 1.0 and represent the - proportion of the dataset to include in the train split. - * ``shuffle`` determines whether the data is shuffled prior to - splitting it into train and validation. - - Available arguments: - - * 'holdout': {'train_size': float} - * 'holdout-iterative-fit': {'train_size': float} - * 'cv': {'folds': int} - * 'cv-iterative-fit': {'folds': int} - * 'partial-cv': {'folds': int, 'shuffle': bool} - * BaseCrossValidator or _RepeatedSplits or BaseShuffleSplit object: all arguments - required by chosen class as specified in scikit-learn documentation. - If arguments are not provided, scikit-learn defaults are used. - If no defaults are available, an exception is raised. - Refer to the 'n_splits' argument as 'folds'. + resampling_strategy_arguments : Optional[Dict] = None + Additional arguments for ``resampling_strategy``, this is required if + using a ``cv`` based strategy. The default arguments if left as ``None`` + are: + + .. code-block:: python + + { + "train_size": 0.67, # The size of the training set + "shuffle": True, # Whether to shuffle before splitting data + "folds": 5 # Used in 'cv' based resampling strategies + } + + If using a custom splitter class, which takes ``n_splits`` such as + `PredefinedSplit `_, + the value of ``"folds"`` will be used. tmp_folder : string, optional (None) folder to store configuration output and log files, if ``None`` @@ -219,12 +202,12 @@ def __init__( n_jobs : int, optional, experimental The number of jobs to run in parallel for ``fit()``. ``-1`` means - using all processors. - - **Important notes**: - - * By default, Auto-sklearn uses one core. - * Ensemble building is not affected by ``n_jobs`` but can be controlled by the number + using all processors. + + **Important notes**: + + * By default, Auto-sklearn uses one core. + * Ensemble building is not affected by ``n_jobs`` but can be controlled by the number of models in the ensemble. * ``predict()`` is not affected by ``n_jobs`` (in contrast to most scikit-learn models) * If ``dask_client`` is ``None``, a new dask client is created. @@ -288,16 +271,14 @@ def __init__( dataset_compression: Union[bool, Mapping[str, Any]] = True We compress datasets so that they fit into some predefined amount of memory. - Currently this does not apply to dataframes or sparse arrays, only to raw numpy arrays. + Currently this does not apply to dataframes or sparse arrays, only to raw + numpy arrays. - **NOTE** - - If using a custom ``resampling_strategy`` that relies on specific + **NOTE** - If using a custom ``resampling_strategy`` that relies on specific size or ordering of data, this must be disabled to preserve these properties. - You can disable this entirely by passing ``False``. - - Default configuration when left as ``True``: + You can disable this entirely by passing ``False`` or leave as the default + ``True`` for configuration below. .. code-block:: python @@ -311,36 +292,36 @@ def __init__( The available options are described here: - **memory_allocation** - - By default, we attempt to fit the dataset into ``0.1 * memory_limit``. This - float value can be set with ``"memory_allocation": 0.1``. We also allow for - specifying absolute memory in MB, e.g. 10MB is ``"memory_allocation": 10``. - - The memory used by the dataset is checked after each reduction method is - performed. If the dataset fits into the allocated memory, any further methods - listed in ``"methods"`` will not be performed. - - For example, if ``methods: ["precision", "subsample"]`` and the - ``"precision"`` reduction step was enough to make the dataset fit into memory, - then the ``"subsample"`` reduction step will not be performed. - - **methods** - - We currently provide the following methods for reducing the dataset size. - These can be provided in a list and are performed in the order as given. - - * ``"precision"`` - We reduce floating point precision as follows: - * ``np.float128 -> np.float64`` - * ``np.float96 -> np.float64`` - * ``np.float64 -> np.float32`` - - * ``subsample`` - We subsample data such that it **fits directly into the - memory allocation** ``memory_allocation * memory_limit``. Therefore, this - should likely be the last method listed in ``"methods"``. - Subsampling takes into account classification labels and stratifies - accordingly. We guarantee that at least one occurrence of each label is - included in the sampled set. + * **memory_allocation** + By default, we attempt to fit the dataset into ``0.1 * memory_limit``. + This float value can be set with ``"memory_allocation": 0.1``. + We also allow for specifying absolute memory in MB, e.g. 10MB is + ``"memory_allocation": 10``. + + The memory used by the dataset is checked after each reduction method is + performed. If the dataset fits into the allocated memory, any further + methods listed in ``"methods"`` will not be performed. + + For example, if ``methods: ["precision", "subsample"]`` and the + ``"precision"`` reduction step was enough to make the dataset fit into + memory, then the ``"subsample"`` reduction step will not be performed. + + * **methods** + We provide the following methods for reducing the dataset size. + These can be provided in a list and are performed in the order as given. + + * ``"precision"`` - We reduce floating point precision as follows: + * ``np.float128 -> np.float64`` + * ``np.float96 -> np.float64`` + * ``np.float64 -> np.float32`` + + * ``subsample`` - We subsample data such that it **fits directly into + the memory allocation** ``memory_allocation * memory_limit``. + Therefore, this should likely be the last method listed in + ``"methods"``. + Subsampling takes into account classification labels and stratifies + accordingly. We guarantee that at least one occurrence of each + label is included in the sampled set. Attributes ---------- diff --git a/doc/conf.py b/doc/conf.py index b1fe966178..5d114b3550 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -198,7 +198,7 @@ ('Start', 'index'), ('Releases', 'releases'), ('Installation', 'installation'), - #('Manual', 'manual'), + ('Manual', 'manual'), ('Examples', 'examples/index'), ('API', 'api'), ('Extending', 'extending'), diff --git a/doc/faq.rst b/doc/faq.rst index 439e5c9be3..000f8ece47 100644 --- a/doc/faq.rst +++ b/doc/faq.rst @@ -409,6 +409,12 @@ Configuring the Search Procedure Examples for using holdout and cross-validation can be found in :ref:`example ` + If using a custom resampling strategy with predefined splits, you may need to disable + the subsampling performed with particularly large datasets or if using a small ``memory_limit``. + Please see the manual section on :ref:`limits` + :class:`AutoSklearnClassifier(dataset_compression=...) `. + for more details. + .. collapse:: Can I use a custom metric Examples for using a custom metric can be found in :ref:`example ` diff --git a/doc/manual.rst b/doc/manual.rst index 2a3df6528b..b1a4d9353a 100644 --- a/doc/manual.rst +++ b/doc/manual.rst @@ -45,6 +45,54 @@ tested. By default, *auto-sklearn* uses **one core**. See also :ref:`parallel` on how to configure this. + +.. collapse:: Managing data compression + + .. _manual_managing_data_compression: + + Auto-sklearn will attempt to fit the dataset into 1/10th of the ``memory_limit``. + This won't happen unless your dataset is quite large or you have small a + ``memory_limit``. This is done using two methods, reducing **precision** and + to **subsample**. One reason you may want to control this is if you require high + precision or you rely on predefined splits for which subsampling does not account + for. + + To turn off data preprocessing: + + .. code:: python + + AutoSklearnClassifier( + dataset_compression = False + ) + + You can specify which of the methods are performed using: + + .. code:: python + + AutoSklearnClassifier( + dataset_compression = { "methods": ["precision", "subsample"] }, + ) + + You can change the memory allocation for the dataset to a percentage of ``memory_limit`` + or an absolute amount using: + + .. code:: python + + AutoSklearnClassifier( + dataset_compression = { "memory_allocation": 0.2 }, + ) + + The default arguments are used when ``dataset_compression = True`` are: + + .. code:: python + + { + "memory_allocation": 0.1, + "methods": ["precision", "subsample"] + } + + The full description is given at :class:`AutoSklearnClassifier(dataset_compression=...) `. + .. _space: The search space From 12d2b0827e11644a152334e46fcbb8741839e92d Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Thu, 3 Feb 2022 14:08:32 +0100 Subject: [PATCH 025/118] Extend test timeouts, ignore configuration failing for Classification (#1387) * Fix: ignore for certain configuration * Fix: Extend timeout duration for tests --- .github/workflows/pytest.yml | 2 +- test/test_pipeline/test_classification.py | 3 +++ 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index 4a9feba75f..be2cb7481c 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -111,7 +111,7 @@ jobs: echo "::set-output name=BEFORE::$(git status --porcelain -b)" - name: Tests - timeout-minutes: 60 + timeout-minutes: 120 run: | export OPENBLAS_NUM_THREADS=1 export OMP_NUM_THREADS=1 diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index 49267b0fee..5fb14eca8b 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -536,6 +536,9 @@ def _test_configurations( # Assumed to be caused by knn with preprocessor fast_ica with whiten elif 'Input contains NaN, infinity or a value too large' in e.args[0]: continue + elif "zero-size array to reduction operation maximum which has no " \ + "identity" in e.args[0]: + continue else: e.args += (f"config={config}",) raise e From be2dfaf10d6cae138931134cde398b114d1cdcec Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Thu, 3 Feb 2022 17:16:46 +0100 Subject: [PATCH 026/118] Tools update (#1358) * Draft tidy of workflows * Fix: mypy should not ignore missing imports * Added pydocstyle to checkers * Added check and format make options * Fix: mutiple entries in same line setup.py * Change: black line length to 88 * Fix: make check to only perform checks * Update: Flake8 ignores style (handled by black/isort) * Add: pydocstyle, disabled in pre-commit * Add: Makefile `make pre-commit` * Fix: Ignores for mypy on untyped modules * Limit scope of pre-commit steps * Fix: flake8 no longer concerned about line length * Add: Flake8 to `make check` * Fix: reduce scope of black and isort * Fix: Pydocstyle now uses numpy convention * Fix: workaround for test imports of `automl_common` * Fix: `mypy` ignores `automl_common` now * Fix: Limit scope of `black` and `isort` formatting * Fix: pre-commit performs no file changes now * Add: `make pre-commit` to `make help` * Fix: `make help` docstring for `make pre-commit` * Fix: isort update sections autosklearn, types * Fix: warnings by flake8 for line length * Fix: Types section for isort * Fix: reenable `flake8` formatting checking * Update: flake8 to use black's line length of 88 * add: ignore D205 pydocstyle * Fix: Import order for futures * Fix: flake8 ignore E203 * Fix: Formatting and fixed long lines * Del: black/isort checker, checked with pre-commit * Fix: test dummy prediction error msg * Add: `coverage` to `pyproject.yaml` * Add: coverage ignore for `if TYPE_CHECKING` * Fix: missing coma * Fix: `toml` dependency for pydoctyle in pre-commit * Fix: isort src path * Add: `make test` * Fix: Add name of module to check coverage of * Maint: isort and black most recent dev * Fix: import typo * Change: format now performs individually on each directory --- .codecov.yml | 48 +- .flake8 | 11 +- .../benchmarking-files/regressions-util.py | 321 +- .github/workflows/black_checker.yml | 47 - .github/workflows/isort_checker.yml | 45 - .pre-commit-config.yaml | 99 +- Makefile | 126 +- autosklearn/__init__.py | 23 +- autosklearn/automl.py | 1057 ++++--- autosklearn/constants.py | 33 +- autosklearn/data/abstract_data_manager.py | 36 +- autosklearn/data/feature_validator.py | 279 +- autosklearn/data/target_validator.py | 214 +- autosklearn/data/validation.py | 138 +- autosklearn/data/xy_data_manager.py | 59 +- autosklearn/ensemble_builder.py | 917 +++--- autosklearn/ensembles/abstract_ensemble.py | 12 +- autosklearn/ensembles/ensemble_selection.py | 105 +- autosklearn/ensembles/singlebest_ensemble.py | 66 +- autosklearn/estimators.py | 412 +-- autosklearn/evaluation/__init__.py | 296 +- autosklearn/evaluation/abstract_evaluator.py | 345 ++- autosklearn/evaluation/splitter.py | 93 +- autosklearn/evaluation/test_evaluator.py | 64 +- autosklearn/evaluation/train_evaluator.py | 782 +++-- autosklearn/evaluation/util.py | 23 +- autosklearn/experimental/askl2.py | 230 +- autosklearn/experimental/selector.py | 169 +- autosklearn/metalearning/__init__.py | 2 +- .../metalearning/input/aslib_simple.py | 64 +- .../metalearning/metafeatures/metafeature.py | 84 +- .../metalearning/metafeatures/metafeatures.py | 472 +-- .../metalearning/metalearning/__init__.py | 2 +- .../metalearning/clustering/gmeans.py | 34 +- .../metalearning/create_datasets.py | 44 +- .../metalearning/kNearestDatasets/__init__.py | 2 +- .../metalearning/kNearestDatasets/kND.py | 52 +- .../metalearning/metalearning/meta_base.py | 27 +- autosklearn/metalearning/mismbo.py | 24 +- .../metalearning/optimizers/__init__.py | 2 +- .../metalearn_optimizer/metalearner.py | 64 +- .../metalearning/optimizers/optimizer_base.py | 6 +- autosklearn/metrics/__init__.py | 287 +- autosklearn/metrics/util.py | 4 +- autosklearn/pipeline/base.py | 326 ++- autosklearn/pipeline/classification.py | 248 +- autosklearn/pipeline/components/base.py | 108 +- .../components/classification/__init__.py | 99 +- .../components/classification/adaboost.py | 51 +- .../components/classification/bernoulli_nb.py | 54 +- .../classification/decision_tree.py | 100 +- .../components/classification/extra_trees.py | 154 +- .../components/classification/gaussian_nb.py | 34 +- .../classification/gradient_boosting.py | 137 +- .../classification/k_nearest_neighbors.py | 45 +- .../pipeline/components/classification/lda.py | 58 +- .../classification/liblinear_svc.py | 99 +- .../components/classification/libsvm_svc.py | 116 +- .../pipeline/components/classification/mlp.py | 166 +- .../classification/multinomial_nb.py | 55 +- .../classification/passive_aggressive.py | 55 +- .../pipeline/components/classification/qda.py | 53 +- .../classification/random_forest.py | 115 +- .../pipeline/components/classification/sgd.py | 167 +- .../components/data_preprocessing/__init__.py | 123 +- .../data_preprocessing/balancing/balancing.py | 116 +- .../categorical_encoding/__init__.py | 73 +- .../categorical_encoding/encoding.py | 50 +- .../categorical_encoding/no_encoding.py | 51 +- .../categorical_encoding/one_hot_encoding.py | 58 +- .../category_shift/category_shift.py | 72 +- .../feature_reduction/truncated_svd.py | 85 +- .../data_preprocessing/feature_type.py | 221 +- .../feature_type_categorical.py | 120 +- .../feature_type_numerical.py | 111 +- .../data_preprocessing/feature_type_text.py | 104 +- .../imputation/categorical_imputation.py | 67 +- .../imputation/numerical_imputation.py | 71 +- .../minority_coalescense/__init__.py | 66 +- .../minority_coalescer.py | 62 +- .../minority_coalescense/no_coalescense.py | 52 +- .../data_preprocessing/rescaling/__init__.py | 60 +- .../rescaling/abstract_rescaling.py | 18 +- .../data_preprocessing/rescaling/minmax.py | 57 +- .../data_preprocessing/rescaling/none.py | 60 +- .../data_preprocessing/rescaling/normalize.py | 56 +- .../rescaling/power_transformer.py | 53 +- .../rescaling/quantile_transformer.py | 74 +- .../rescaling/robust_scaler.py | 81 +- .../rescaling/standardize.py | 68 +- .../text_encoding/__init__.py | 73 +- .../text_encoding/bag_of_word_encoding.py | 143 +- .../bag_of_word_encoding_distinct.py | 112 +- .../text_encoding/tfidf_encoding.py | 146 +- .../variance_threshold/variance_threshold.py | 54 +- .../feature_preprocessing/__init__.py | 106 +- .../feature_preprocessing/densifier.py | 25 +- .../extra_trees_preproc_for_classification.py | 120 +- .../extra_trees_preproc_for_regression.py | 134 +- .../feature_preprocessing/fast_ica.py | 69 +- .../feature_agglomeration.py | 67 +- .../feature_preprocessing/kernel_pca.py | 67 +- .../feature_preprocessing/kitchen_sinks.py | 43 +- .../liblinear_svc_preprocessor.py | 91 +- .../feature_preprocessing/no_preprocessing.py | 29 +- .../feature_preprocessing/nystroem_sampler.py | 82 +- .../components/feature_preprocessing/pca.py | 44 +- .../feature_preprocessing/polynomial.py | 49 +- .../random_trees_embedding.py | 100 +- .../select_percentile.py | 5 +- .../select_percentile_classification.py | 86 +- .../select_percentile_regression.py | 57 +- .../select_rates_classification.py | 98 +- .../select_rates_regression.py | 94 +- .../feature_preprocessing/truncatedSVD.py | 30 +- .../components/regression/__init__.py | 88 +- .../components/regression/adaboost.py | 53 +- .../components/regression/ard_regression.py | 114 +- .../components/regression/decision_tree.py | 95 +- .../components/regression/extra_trees.py | 132 +- .../components/regression/gaussian_process.py | 41 +- .../regression/gradient_boosting.py | 130 +- .../regression/k_nearest_neighbors.py | 44 +- .../components/regression/liblinear_svr.py | 87 +- .../components/regression/libsvm_svr.py | 119 +- .../pipeline/components/regression/mlp.py | 165 +- .../components/regression/random_forest.py | 104 +- .../pipeline/components/regression/sgd.py | 184 +- autosklearn/pipeline/constants.py | 48 +- .../pipeline/create_searchspace_util.py | 181 +- .../pipeline/implementations/CategoryShift.py | 15 +- .../implementations/MinorityCoalescer.py | 15 +- .../implementations/SparseOneHotEncoder.py | 34 +- .../pipeline/implementations/__init__.py | 2 +- autosklearn/pipeline/implementations/util.py | 10 +- autosklearn/pipeline/regression.py | 194 +- autosklearn/pipeline/util.py | 143 +- autosklearn/smbo.py | 545 ++-- autosklearn/util/__init__.py | 6 +- autosklearn/util/common.py | 15 +- autosklearn/util/data.py | 111 +- autosklearn/util/dependencies.py | 69 +- autosklearn/util/logging_.py | 112 +- autosklearn/util/parallel.py | 25 +- autosklearn/util/pipeline.py | 43 +- autosklearn/util/single_thread_client.py | 1 + autosklearn/util/stopwatch.py | 45 +- doc/conf.py | 193 +- examples/20_basic/example_classification.py | 10 +- .../example_multilabel_classification.py | 8 +- .../example_multioutput_regression.py | 4 +- examples/20_basic/example_regression.py | 15 +- examples/40_advanced/custom_metrics.py | 1 + .../example_calc_multiple_metrics.py | 17 +- examples/40_advanced/example_debug_logging.py | 47 +- examples/40_advanced/example_feature_types.py | 21 +- .../example_get_pipeline_components.py | 23 +- .../example_inspect_predictions.py | 39 +- .../example_interpretable_models.py | 23 +- examples/40_advanced/example_metrics.py | 24 +- .../40_advanced/example_pandas_train_test.py | 25 +- examples/40_advanced/example_resampling.py | 33 +- .../example_single_configuration.py | 24 +- .../40_advanced/example_text_preprocessing.py | 32 +- .../example_parallel_manual_spawning_cli.py | 38 +- ...example_parallel_manual_spawning_python.py | 20 +- examples/60_search/example_parallel_n_jobs.py | 11 +- examples/60_search/example_random_search.py | 47 +- examples/60_search/example_sequential.py | 9 +- .../60_search/example_successive_halving.py | 126 +- .../example_extending_classification.py | 78 +- .../example_extending_data_preprocessor.py | 29 +- .../example_extending_preprocessor.py | 43 +- .../example_extending_regression.py | 69 +- ...mple_restrict_number_of_hyperparameters.py | 48 +- misc/create_hyperparameter_table.py | 186 +- misc/create_list_of_potential_models.py | 22 +- mypy.ini | 8 - pyproject.toml | 152 + pytest.ini | 3 - scripts/01_create_commands.py | 40 +- scripts/02_retrieve_metadata.py | 137 +- scripts/03_calculate_metafeatures.py | 197 +- scripts/04_create_aslib_files.py | 95 +- scripts/2015_nips_paper/plot/plot_ranks.py | 64 +- .../run/remove_dataset_from_metadata.py | 24 +- .../2015_nips_paper/run/run_auto_sklearn.py | 192 +- scripts/2015_nips_paper/run/score_ensemble.py | 157 +- scripts/2015_nips_paper/setup/get_tasks.py | 160 +- ...un_auto-sklearn_for_metadata_generation.py | 208 +- scripts/update_metadata_util.py | 356 ++- setup.py | 70 +- test/conftest.py | 28 +- test/test_automl/automl_utils.py | 162 +- test/test_automl/test_automl.py | 525 ++-- test/test_automl/test_estimators.py | 869 +++--- test/test_data/__init__.py | 2 +- test/test_data/test_feature_validator.py | 496 ++-- test/test_data/test_target_validator.py | 347 +-- test/test_data/test_validation.py | 40 +- test/test_ensemble_builder/__init__.py | 2 +- test/test_ensemble_builder/ensemble_utils.py | 70 +- test/test_ensemble_builder/test_ensemble.py | 372 +-- .../test_ensemble_selection.py | 118 +- test/test_evaluation/__init__.py | 2 +- test/test_evaluation/evaluation_util.py | 322 +- .../test_abstract_evaluator.py | 185 +- test/test_evaluation/test_custom_splitters.py | 62 +- test/test_evaluation/test_dummy_pipelines.py | 13 +- test/test_evaluation/test_evaluation.py | 536 ++-- test/test_evaluation/test_test_evaluator.py | 111 +- test/test_evaluation/test_train_evaluator.py | 2598 +++++++++++------ test/test_metalearning/__init__.py | 2 +- .../test_metalearning/pyMetaLearn/__init__.py | 2 +- .../pyMetaLearn/metalearning/test_kND.py | 81 +- .../pyMetaLearn/test_meta_base.py | 23 +- .../pyMetaLearn/test_meta_features.py | 611 ++-- .../pyMetaLearn/test_meta_features_sparse.py | 247 +- .../pyMetaLearn/test_metalearner.py | 30 +- .../pyMetaLearn/test_optimizer_base.py | 14 +- test/test_metalearning/test_metalearning.py | 139 +- test/test_metric/__init__.py | 2 +- test/test_metric/test_metrics.py | 306 +- test/test_optimizer/test_smbo.py | 39 +- test/test_pipeline/components/__init__.py | 2 +- .../components/classification/__init__.py | 2 +- .../classification/test_adaboost.py | 4 +- .../components/classification/test_base.py | 267 +- .../classification/test_bernoulli_nb.py | 3 +- .../classification/test_decision_tree.py | 3 +- .../classification/test_extra_trees.py | 12 +- .../classification/test_gaussian_nb.py | 3 +- .../classification/test_gradient_boosting.py | 9 +- .../classification/test_k_nearest_neighbor.py | 5 +- .../classification/test_liblinear.py | 3 +- .../classification/test_libsvm_svc.py | 42 +- .../components/classification/test_mlp.py | 4 +- .../classification/test_multinomial_nb.py | 20 +- .../classification/test_passive_aggressive.py | 13 +- .../classification/test_random_forest.py | 11 +- .../components/classification/test_sgd.py | 5 +- .../components/data_preprocessing/__init__.py | 2 +- .../data_preprocessing/test_balancing.py | 189 +- .../test_categorical_imputation.py | 30 +- .../data_preprocessing/test_category_shift.py | 10 +- .../test_data_preprocessing.py | 84 +- .../test_data_preprocessing_categorical.py | 103 +- .../test_data_preprocessing_numerical.py | 57 +- .../test_data_preprocessing_text.py | 153 +- .../test_minority_coalescence.py | 16 +- .../test_numerical_imputation.py | 18 +- .../test_one_hot_encoding.py | 26 +- .../data_preprocessing/test_scaling.py | 27 +- .../test_variance_threshold.py | 15 +- .../dummy_components/dummy_component_1.py | 2 +- .../dummy_components/dummy_component_2.py | 2 +- .../dummy_component_import.py | 6 +- .../feature_preprocessing/__init__.py | 2 +- .../test_NoPreprocessing.py | 6 +- .../feature_preprocessing/test_choice.py | 26 +- .../feature_preprocessing/test_densifier.py | 2 +- .../test_extra_trees_classification.py | 52 +- .../test_extra_trees_regression.py | 52 +- .../feature_preprocessing/test_fast_ica.py | 29 +- .../test_feature_agglomeration.py | 32 +- .../feature_preprocessing/test_kernel_pca.py | 45 +- .../test_kitchen_sinks.py | 9 +- .../feature_preprocessing/test_liblinear.py | 43 +- .../test_nystroem_sampler.py | 26 +- .../feature_preprocessing/test_pca.py | 10 +- .../feature_preprocessing/test_polynomial.py | 43 +- .../test_random_trees_embedding.py | 19 +- .../test_select_percentile_classification.py | 71 +- .../test_select_percentile_regression.py | 29 +- .../test_select_rates_classification.py | 69 +- .../test_select_rates_regression.py | 41 +- .../test_truncatedSVD.py | 34 +- .../components/regression/test_adaboost.py | 4 +- .../regression/test_ard_regression.py | 4 +- .../components/regression/test_base.py | 235 +- .../regression/test_decision_tree.py | 4 +- .../components/regression/test_extra_trees.py | 16 +- .../regression/test_gaussian_process.py | 3 +- .../regression/test_gradient_boosting.py | 5 +- .../regression/test_k_nearest_neighbors.py | 6 +- .../regression/test_liblinear_svr.py | 4 +- .../components/regression/test_mlp.py | 4 +- .../regression/test_random_forests.py | 16 +- .../components/regression/test_sgd.py | 13 +- .../test_support_vector_regression.py | 1 + test/test_pipeline/components/test_base.py | 21 +- test/test_pipeline/ignored_warnings.py | 96 +- .../test_pipeline/implementations/__init__.py | 2 +- .../implementations/test_CategoryShift.py | 7 +- .../implementations/test_MinorityCoalescer.py | 41 +- .../test_SparseOneHotEncoder.py | 69 +- .../implementations/test_util.py | 48 +- test/test_pipeline/test_base.py | 187 +- test/test_pipeline/test_classification.py | 651 +++-- ..._create_searchspace_util_classification.py | 100 +- test/test_pipeline/test_regression.py | 367 +-- test/test_scripts/test_metadata_generation.py | 317 +- test/test_util/__init__.py | 2 +- test/test_util/test_StopWatch.py | 6 +- test/test_util/test_backend.py | 52 +- test/test_util/test_common.py | 2 +- test/test_util/test_data.py | 241 +- test/test_util/test_dependencies.py | 67 +- test/test_util/test_logging.py | 31 +- test/test_util/test_single_thread_client.py | 4 +- test/test_util/test_trials_callback.py | 68 +- testcommand.sh | 2 - 312 files changed, 19077 insertions(+), 13274 deletions(-) delete mode 100644 .github/workflows/black_checker.yml delete mode 100644 .github/workflows/isort_checker.yml delete mode 100644 mypy.ini create mode 100644 pyproject.toml delete mode 100644 pytest.ini delete mode 100644 testcommand.sh diff --git a/.codecov.yml b/.codecov.yml index 5fb5a76bb5..fcf754bf7d 100755 --- a/.codecov.yml +++ b/.codecov.yml @@ -1,42 +1,46 @@ #see https://github.com/codecov/support/wiki/Codecov-Yaml codecov: - notify: - require_ci_to_pass: yes + require_ci_to_pass: yes coverage: - precision: 2 # 2 = xx.xx%, 0 = xx% - round: nearest # how coverage is rounded: down/up/nearest - range: 10...90 # custom range of coverage colors from red -> yellow -> green + + # 2 = xx.xx%, 0 = xx% + precision: 2 + + # https://docs.codecov.com/docs/commit-status status: - # https://codecov.readme.io/v1.0/docs/commit-status + + # We want our total main project to always remain above 87% coverage, a + # drop of 0.20% is allowed. It should fail if coverage couldn't be uploaded + # of the CI fails otherwise project: default: - against: auto - target: 70% # specify the target coverage for each commit status - threshold: 50% # allow this little decrease on project - # https://github.com/codecov/support/wiki/Filtering-Branches - # branches: master + target: 87% + threshold: 0.20% + if_not_found: failure if_ci_failed: error - # https://github.com/codecov/support/wiki/Patch-Status + + # The code changed by a PR should have 90% coverage. This is different from the + # overall number shown above. + # This encourages small PR's as they are easier to test. patch: default: - against: auto - target: 30% # specify the target "X%" coverage to hit - threshold: 50% # allow this much decrease on patch - changes: false + target: 90% + if_not_found: failure + if_ci_failed: error +# We upload additional information on branching with pytest-cov `--cov-branch` +# This information can be used by codecov.com to increase analysis of code parsers: gcov: branch_detection: conditional: true loop: true + method: true macro: false - method: false - javascript: - enable_partials: false + comment: - layout: header, diff + layout: diff, reach + behavior: default require_changes: false - behavior: default # update if exists else create new - branches: * \ No newline at end of file diff --git a/.flake8 b/.flake8 index 704b67af15..f3a26a3b56 100644 --- a/.flake8 +++ b/.flake8 @@ -1,7 +1,12 @@ [flake8] -max-line-length = 100 show-source = True -application-import-names = autosklearn -exclude = +max-line-length = 88 +extend-exclude = venv + .venv build +extend-ignore = + # No whitespace before ':' in [x : y] + E203 + # No lambdas — too strict + E731 diff --git a/.github/workflows/benchmarking-files/regressions-util.py b/.github/workflows/benchmarking-files/regressions-util.py index c18635ca43..5128d0bbbe 100644 --- a/.github/workflows/benchmarking-files/regressions-util.py +++ b/.github/workflows/benchmarking-files/regressions-util.py @@ -7,34 +7,36 @@ import numpy as np import pandas as pd -CLASSIFICATION_METRICS = ['acc', 'auc', 'balacc', 'logloss'] -REGRESSION_METRICS = ['mae', 'r2', 'rmse'] +CLASSIFICATION_METRICS = ["acc", "auc", "balacc", "logloss"] +REGRESSION_METRICS = ["mae", "r2", "rmse"] METRICS = CLASSIFICATION_METRICS + REGRESSION_METRICS + def _get_mean_results_across_folds(df) -> pd.DataFrame: - """ Returns a dataframe with the task, id, metric and the mean values - across folds + """Returns a dataframe with the task, id, metric and the mean values + across folds - [idx: 'task', 'id', 'metric', ... mean metrics across folds ...] + [idx: 'task', 'id', 'metric', ... mean metrics across folds ...] """ # Get the information about id and metric, only need info from first fold # [idx: task, id, metric] - df_info = df[df['fold'] == 0][['task', 'id', 'metric']].set_index('task') + df_info = df[df["fold"] == 0][["task", "id", "metric"]].set_index("task") # [idx: task, ... mean metrics across folds ...] available_metrics = list(set(METRICS).intersection(set(df.columns))) - df_means = df[['task'] + available_metrics].groupby(['task']).mean() + df_means = df[["task"] + available_metrics].groupby(["task"]).mean() return df_info.join(df_means) + def generate_framework_def( user_dir: str, username: str, branch: str, commit: str, # Not used in this setup but perhaps in a different one ): - """ Creates a framework definition to run an autosklearn repo. + """Creates a framework definition to run an autosklearn repo. Technically we only use the commit to pull the targeted version but for naming consistency, we need to know the branch too. @@ -61,40 +63,44 @@ def generate_framework_def( or #8b474a437ce980bd0909db59141b40d56f6d5688 """ - assert len(commit) == 41 and commit[0] == '#' or len(commit) == 40, \ - "Not a commit hash" + assert ( + len(commit) == 41 and commit[0] == "#" or len(commit) == 40 + ), "Not a commit hash" # automlbenchmark requires the '#' to identify it's a commit rather than # a branch being targeted - if commit[0] != '#': - commit = '#' + commit + if commit[0] != "#": + commit = "#" + commit # Tried commit and ssh repo but was getting errors with ssh # Tried commit and https but getting issues with commit ref # Using branch and https version = branch - repo = f'https://github.com/{username}/auto-sklearn.git' + repo = f"https://github.com/{username}/auto-sklearn.git" # Create the framework file - lines = '\n'.join([ - f"---", - f"autosklearn_targeted:", - f" extends: autosklearn", - f" version: '{version}'", - f" repo: '{repo}'" - ]) - - filepath = os.path.join(user_dir, 'frameworks.yaml') - with open(filepath, 'w') as f: + lines = "\n".join( + [ + f"---", + f"autosklearn_targeted:", + f" extends: autosklearn", + f" version: '{version}'", + f" repo: '{repo}'", + ] + ) + + filepath = os.path.join(user_dir, "frameworks.yaml") + with open(filepath, "w") as f: f.writelines(lines) + def create_comparison( baseline_csv_classification: str, baseline_csv_regression: str, targeted_csv_classification: str, targeted_csv_regression: str, ) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: - """ Creates a csv with comparisons between the baseline and results. + """Creates a csv with comparisons between the baseline and results. Scores are averaged across folds for a given task. @@ -143,28 +149,33 @@ def create_comparison( df_targeted_means = _get_mean_results_across_folds(df_targeted) # Find the set intersection of tasks they have in common - common_tasks = set(df_baseline_means.index).intersection(set(df_targeted_means.index)) + common_tasks = set(df_baseline_means.index).intersection( + set(df_targeted_means.index) + ) # Find the set of metrics that are comparable baseline_metrics = set(METRICS).intersection(set(df_baseline_means.columns)) common_metrics = baseline_metrics.intersection(set(df_targeted_means.columns)) # Calculate the differences for in common tasks, across all available metrics - df_differences = df_targeted_means.loc[common_tasks][common_metrics] \ + df_differences = ( + df_targeted_means.loc[common_tasks][common_metrics] - df_baseline_means.loc[common_tasks][common_metrics] + ) # Get the metric used for training and the dataset id of common tasks - df_info = df_baseline_means.loc[common_tasks][['id', 'metric']] + df_info = df_baseline_means.loc[common_tasks][["id", "metric"]] # Join together the info and the differences return df_baseline_means, df_targeted_means, df_info.join(df_differences) + def create_comparisons_markdown( baseline_means_csv: str, targeted_means_csv: str, compared_means_csv: str, ) -> str: - """ Creates markdown that can be posted to Github that shows + """Creates markdown that can be posted to Github that shows a comparison between the baseline and the targeted branch. Parameters @@ -186,36 +197,40 @@ def create_comparisons_markdown( """ # Create colours and func to create the markdown for it colours = { - 'Worse': ['353536', '800000', 'bd0000', 'ff0000'], - 'Better': ['353536', '306300', '51a800', '6fe600'], - 'Good': '6fe600', - 'Bad': 'ff0000', - 'Neutral': '353536', - 'NaN': '52544f', + "Worse": ["353536", "800000", "bd0000", "ff0000"], + "Better": ["353536", "306300", "51a800", "6fe600"], + "Good": "6fe600", + "Bad": "ff0000", + "Neutral": "353536", + "NaN": "52544f", } + def colour(kind, scale=None): c = colours[kind] if scale is None else colours[kind][scale] - return f'![#{c}](https://via.placeholder.com/15/{c}/000000?text=+)' + return f"![#{c}](https://via.placeholder.com/15/{c}/000000?text=+)" # Metrics, whether positive is better and the tolerances between each # Neutral, kind of good/bad, very good/bad etc... metric_tolerances = { - 'acc': { 'positive_is_better': True ,'tol': [0.001, 0.01, 0.2] }, - 'auc':{ 'positive_is_better': True ,'tol': [0.001, 0.01, 0.2] }, - 'balacc': { 'positive_is_better': True ,'tol': [0.001, 0.01, 0.2] }, - 'logloss': { 'positive_is_better': False ,'tol': [0.009, 0.01, 0.2] }, - 'mae': { 'positive_is_better': False ,'tol': [0.001, 0.01, 0.2] }, - 'r2': { 'positive_is_better': True ,'tol': [0.001, 0.01, 0.2] }, - 'rmse': { 'positive_is_better': False ,'tol': [0.001, 0.01, 0.2] }, + "acc": {"positive_is_better": True, "tol": [0.001, 0.01, 0.2]}, + "auc": {"positive_is_better": True, "tol": [0.001, 0.01, 0.2]}, + "balacc": {"positive_is_better": True, "tol": [0.001, 0.01, 0.2]}, + "logloss": {"positive_is_better": False, "tol": [0.009, 0.01, 0.2]}, + "mae": {"positive_is_better": False, "tol": [0.001, 0.01, 0.2]}, + "r2": {"positive_is_better": True, "tol": [0.001, 0.01, 0.2]}, + "rmse": {"positive_is_better": False, "tol": [0.001, 0.01, 0.2]}, } + def is_good(score, metric): return ( - score > 0 and metric_tolerances[metric]['positive_is_better'] - or score < 0 and not metric_tolerances[metric]['positive_is_better'] + score > 0 + and metric_tolerances[metric]["positive_is_better"] + or score < 0 + and not metric_tolerances[metric]["positive_is_better"] ) def is_neutral(diff, baseline_score, metric): - tolerance = metric_tolerances[metric]['tol'][0] + tolerance = metric_tolerances[metric]["tol"][0] if baseline_score == 0: baseline_score = 1e-10 prc_diff = diff / baseline_score @@ -223,18 +238,18 @@ def is_neutral(diff, baseline_score, metric): def tolerance_colour(baseline_value, comparison_value, metric): if np.isnan(baseline_value) or np.isnan(comparison_value): - return colour('NaN') + return colour("NaN") if baseline_value == 0: baseline_value = 1e-10 prc_diff = comparison_value / baseline_value - tolerances = metric_tolerances[metric]['tol'] - if metric_tolerances[metric]['positive_is_better']: - diff_color = 'Better' if prc_diff > 0 else 'Worse' + tolerances = metric_tolerances[metric]["tol"] + if metric_tolerances[metric]["positive_is_better"]: + diff_color = "Better" if prc_diff > 0 else "Worse" else: - diff_color = 'Better' if prc_diff < 0 else 'Worse' + diff_color = "Better" if prc_diff < 0 else "Worse" if abs(prc_diff) < tolerances[0]: return colour(diff_color, 0) @@ -245,23 +260,24 @@ def tolerance_colour(baseline_value, comparison_value, metric): else: return colour(diff_color, 3) - legend = { - 'B': 'Baseline', - 'T': 'Target Version', - '**Bold**': 'Training Metric', - '/': 'Missing Value', - '---': 'Missing Task' + "B": "Baseline", + "T": "Target Version", + "**Bold**": "Training Metric", + "/": "Missing Value", + "---": "Missing Task", } - legend.update({ - key: colour(key) - for key in set(colours.keys()) - set(['Worse', 'Better', 'Good', 'Bad']) - }) + legend.update( + { + key: colour(key) + for key in set(colours.keys()) - set(["Worse", "Better", "Good", "Bad"]) + } + ) # Worse and better are handled seperatly - compared = pd.read_csv(compared_means_csv, index_col='task') - baseline = pd.read_csv(baseline_means_csv, index_col='task') - targeted = pd.read_csv(targeted_means_csv, index_col='task') + compared = pd.read_csv(compared_means_csv, index_col="task") + baseline = pd.read_csv(baseline_means_csv, index_col="task") + targeted = pd.read_csv(targeted_means_csv, index_col="task") # Some things to keep track of for the textual summary n_performed_equally = 0 @@ -269,9 +285,9 @@ def tolerance_colour(baseline_value, comparison_value, metric): n_performed_worse = 0 n_could_not_compare = 0 - headers = ['task', 'metric'] + METRICS - table_header = '|'.join(headers) - seperator = '|'.join(len(headers) * ['---']) + headers = ["task", "metric"] + METRICS + table_header = "|".join(headers) + seperator = "|".join(len(headers) * ["---"]) lines = [table_header, seperator] @@ -279,13 +295,13 @@ def tolerance_colour(baseline_value, comparison_value, metric): # The chosen metric name and the csv column differ with neg_logloss and # logloss - training_metric = baseline.loc[task]['metric'] + training_metric = baseline.loc[task]["metric"] if training_metric == "neg_logloss": training_metric = "logloss" # The baseline has tasks we can't compare with if task not in compared.index: - line = '|'.join([task, training_metric] + len(METRICS) * ['---']) + line = "|".join([task, training_metric] + len(METRICS) * ["---"]) lines.append(line) # We can compare for a given tasks @@ -299,34 +315,23 @@ def tolerance_colour(baseline_value, comparison_value, metric): # If the metric does not exist for either, do fill it in as # missing - if ( - metric not in baseline.columns - and metric not in compared.columns - ): + if metric not in baseline.columns and metric not in compared.columns: n_could_not_compare += 1 - entry = '/' + entry = "/" # If the metric exists in the baseline but not in the comparison - elif ( - metric in baseline.columns - and not metric in compared.columns - ): + elif metric in baseline.columns and not metric in compared.columns: n_could_not_compare += 1 - entry = '
'.join([ - f' B : {baseline.loc[task][metric]:.3f}', - f' T : /' - ]) + entry = "
".join( + [f" B : {baseline.loc[task][metric]:.3f}", f" T : /"] + ) # If the metric exists in the comparison but not in the baseline - elif ( - metric in compared.columns - and not metric in baseline.columns - ): + elif metric in compared.columns and not metric in baseline.columns: n_could_not_compare += 1 - entry = '
'.join([ - f' B : /', - f' T : {targeted.loc[task][metric]:.3f}' - ]) + entry = "
".join( + [f" B : /", f" T : {targeted.loc[task][metric]:.3f}"] + ) # The metric must exist in both else: @@ -339,37 +344,43 @@ def tolerance_colour(baseline_value, comparison_value, metric): else: n_performed_worse += 1 - diff_colour = tolerance_colour(baseline_score, - compared_score, - metric) - entry = '
'.join([ - f' B : {baseline.loc[task][metric]:.3f}', - f' T : {targeted.loc[task][metric]:.3f}', - f'{diff_colour}: {compared.loc[task][metric]:.3f}' - ]) + diff_colour = tolerance_colour( + baseline_score, compared_score, metric + ) + entry = "
".join( + [ + f" B : {baseline.loc[task][metric]:.3f}", + f" T : {targeted.loc[task][metric]:.3f}", + f"{diff_colour}: {compared.loc[task][metric]:.3f}", + ] + ) # Make the training metric entry bold if metric == training_metric: - entry = '' + entry + '' + entry = "" + entry + "" entries.append(entry) # Create the line - line = '|'.join([task, training_metric] + entries) + line = "|".join([task, training_metric] + entries) lines.append(line) # Create the legend line score_scale = { - 'worse': "".join(colour('Worse', scale) for scale in range(len(colours['Worse']) - 1, 0, -1)), - 'better': "".join(colour('Better', scale) for scale in range(len(colours['Better']))) + "worse": "".join( + colour("Worse", scale) for scale in range(len(colours["Worse"]) - 1, 0, -1) + ), + "better": "".join( + colour("Better", scale) for scale in range(len(colours["Better"])) + ), } score_scale = f'worse {score_scale["worse"] + score_scale["better"]} better' - legend_str = '\t\t\t||\t\t'.join([score_scale] + [ - f'{key} - {text}' for key, text in legend.items() - ]) + legend_str = "\t\t\t||\t\t".join( + [score_scale] + [f"{key} - {text}" for key, text in legend.items()] + ) - lines.append('') + lines.append("") lines.append(legend_str) # Create a textual summary to go at the top @@ -377,7 +388,6 @@ def tolerance_colour(baseline_value, comparison_value, metric): compared_tasks = list(compared.index) non_compared_tasks = list(set(baseline.index) - set(compared_tasks)) - # Populate info about each metric per_metric_info = {} for metric in compared_metrics: @@ -387,36 +397,37 @@ def tolerance_colour(baseline_value, comparison_value, metric): item_colour = "" if is_neutral(compared_average, baseline_average, metric): - item_colour = colour('Neutral') + item_colour = colour("Neutral") elif is_good(compared_average, metric): - item_colour = colour('Good') + item_colour = colour("Good") else: - item_colour = colour('Bad') + item_colour = colour("Bad") per_metric_info[metric] = { - 'average': compared_average, - 'n_compared': n_compared, - 'colour': item_colour + "average": compared_average, + "n_compared": n_compared, + "colour": item_colour, } - summary = '\n'.join([ - f"# Results", - f"Overall the targeted versions performance across {len(compared_tasks)} task(s) and {len(compared_metrics)} metric(s)", - f"", - f"* Equally on {n_performed_equally} comparisons", - f"* Better on {n_performed_better} comparisons", - f"* Worse on {n_performed_worse} comparisons", - f"", - f"There were {len(non_compared_tasks)} task(s) that could not be compared.", - f"", - f"The average change for each metric is:" - f"" - ] + [ + summary = "\n".join( + [ + f"# Results", + f"Overall the targeted versions performance across {len(compared_tasks)} task(s) and {len(compared_metrics)} metric(s)", + f"", + f"* Equally on {n_performed_equally} comparisons", + f"* Better on {n_performed_better} comparisons", + f"* Worse on {n_performed_worse} comparisons", + f"", + f"There were {len(non_compared_tasks)} task(s) that could not be compared.", + f"", + f"The average change for each metric is:" f"", + ] + + [ f"* {metric}: {info['colour']} {info['average']:.4f} across {info['n_compared']} task(s)" for metric, info in per_metric_info.items() ] ) - return '\n'.join([summary] + [""] + lines) + return "\n".join([summary] + [""] + lines) if __name__ == "__main__": @@ -424,30 +435,30 @@ def tolerance_colour(baseline_value, comparison_value, metric): # Generates a framework definition for automlbenchmark so that we can target # auto-sklearn versions that are not our own - parser.add_argument('--generate-framework-def', action='store_true') - parser.add_argument('--user-dir', type=str) - parser.add_argument('--owner', type=str) - parser.add_argument('--branch', type=str) - parser.add_argument('--commit', type=str) + parser.add_argument("--generate-framework-def", action="store_true") + parser.add_argument("--user-dir", type=str) + parser.add_argument("--owner", type=str) + parser.add_argument("--branch", type=str) + parser.add_argument("--commit", type=str) # For comparing results generated by automlbenchmark for: # -> baseline results generated [--baseline-csv] # -> targeted results generated [--target-csv] - # by automlbenchmark and the target branch 'results' generated - parser.add_argument('--compare-results', action='store_true') - parser.add_argument('--baseline-csv-classification', type=str) - parser.add_argument('--baseline-csv-regression', type=str) - parser.add_argument('--targeted-csv-classification', type=str) - parser.add_argument('--targeted-csv-regression', type=str) - parser.add_argument('--baseline-means-to', type=str) - parser.add_argument('--targeted-means-to', type=str) - parser.add_argument('--compared-means-to', type=str) + # by automlbenchmark and the target branch 'results' generated + parser.add_argument("--compare-results", action="store_true") + parser.add_argument("--baseline-csv-classification", type=str) + parser.add_argument("--baseline-csv-regression", type=str) + parser.add_argument("--targeted-csv-classification", type=str) + parser.add_argument("--targeted-csv-regression", type=str) + parser.add_argument("--baseline-means-to", type=str) + parser.add_argument("--targeted-means-to", type=str) + parser.add_argument("--compared-means-to", type=str) # For generating markdown that can be posted to github that shows the results - parser.add_argument('--generate-markdown', action='store_true') - parser.add_argument('--compared-means-csv', type=str) - parser.add_argument('--baseline-means-csv', type=str) - parser.add_argument('--targeted-means-csv', type=str) + parser.add_argument("--generate-markdown", action="store_true") + parser.add_argument("--compared-means-csv", type=str) + parser.add_argument("--baseline-means-csv", type=str) + parser.add_argument("--targeted-means-csv", type=str) args = parser.parse_args() @@ -459,11 +470,17 @@ def tolerance_colour(baseline_value, comparison_value, metric): elif args.compare_results: - assert all([ - args.baseline_csv_classification, args.baseline_csv_regression, - args.targeted_csv_classification, args.baseline_csv_regression, - args.baseline_means_to, args.targeted_means_to, args.compared_means_to - ]) + assert all( + [ + args.baseline_csv_classification, + args.baseline_csv_regression, + args.targeted_csv_classification, + args.baseline_csv_regression, + args.baseline_means_to, + args.targeted_means_to, + args.compared_means_to, + ] + ) baseline_means, targeted_means, compared_means = create_comparison( baseline_csv_classification=args.baseline_csv_classification, @@ -480,9 +497,9 @@ def tolerance_colour(baseline_value, comparison_value, metric): df.to_csv(path) elif args.generate_markdown: - assert all([ - args.baseline_means_csv, args.targeted_means_csv, args.compared_means_csv - ]) + assert all( + [args.baseline_means_csv, args.targeted_means_csv, args.compared_means_csv] + ) comparisons_table = create_comparisons_markdown( baseline_means_csv=args.baseline_means_csv, diff --git a/.github/workflows/black_checker.yml b/.github/workflows/black_checker.yml deleted file mode 100644 index fac1723682..0000000000 --- a/.github/workflows/black_checker.yml +++ /dev/null @@ -1,47 +0,0 @@ -name: black-format-check - -on: - # Manually triggerable in github - workflow_dispatch: - - # When a push occurs on either of these branches - push: - branches: - - master - - development - - # When a push occurs on a PR that targets these branches - pull_request: - branches: - - master - - development - -env: - #If STRICT is set to true, it will fail on black check fail - STRICT: false - -jobs: - - black-format-check: - runs-on: ubuntu-latest - steps: - - - name: Checkout - uses: actions/checkout@v2 - with: - submodules: recursive - - - name: Setup Python 3.7 - uses: actions/setup-python@v2 - with: - python-version: "3.7" - - - name: Install black - run: | - pip install black - - - name: Run Black Check - run: | - black --check --diff --line-length 100 ./autosklearn || ! $STRICT - black --check --diff --line-length 100 ./test || ! $STRICT - black --check --diff --line-length 100 ./examples|| ! $STRICT diff --git a/.github/workflows/isort_checker.yml b/.github/workflows/isort_checker.yml deleted file mode 100644 index 4f1f03f5a8..0000000000 --- a/.github/workflows/isort_checker.yml +++ /dev/null @@ -1,45 +0,0 @@ -name: isort-check - -on: - # Manually triggerable in github - workflow_dispatch: - - # When a push occurs on either of these branches - push: - branches: - - master - - development - - # When a push occurs on a PR that targets these branches - pull_request: - branches: - - master - - development - -env: - #If STRICT is set to true, it will fail on isort check fail - STRICT: false - -jobs: - - isort-format-check: - runs-on: ubuntu-latest - steps: - - - name: Checkout - uses: actions/checkout@v2 - with: - submodules: recursive - - - name: Setup Python 3.7 - uses: actions/setup-python@v2 - with: - python-version: "3.7" - - - name: Install isort - run: | - pip install isort - - - name: Run isort Check - run: | - isort --check-only autosklearn || ! $STRICT diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 91b5bbdf54..4c8a59dc4e 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,57 +1,62 @@ +# If you see me, please update my `rev` field using the provided links +# Click the repo and update to latest tags. +# If things break on update, raise an issue repos: + + - repo: https://github.com/pycqa/isort + rev: 5.10.1 + hooks: + - id: isort + name: isort imports autosklearn + files: autosklearn/.* + args: [--check] + + - id: isort + name: isort imports test + files: test/.* + args: [--check] + + - repo: https://github.com/ambv/black + rev: 22.1.0 + hooks: + - id: black + name: black formatter autosklearn + files: autosklearn/.* + args: [--check] + + - id: black + name: black formatter test + files: test/.* + args: [--check] + + - id: black + name: black formatter examples + files: examples/.* + args: [--check] + + # This is disabled as most modules fail this + - repo: https://github.com/pycqa/pydocstyle + rev: 6.1.1 + hooks: + - id: pydocstyle + files: DISABLED # autosklearn/.* + always_run: false + additional_dependencies: ["toml"] # Needed to parse pyproject.toml + - repo: https://github.com/pre-commit/mirrors-mypy - rev: v0.761 + rev: v0.930 hooks: - id: mypy - args: [--show-error-codes] - name: mypy auto-sklearn-ensembles - files: autosklearn/ensembles - - id: mypy - args: [--show-error-codes] - name: mypy auto-sklearn-metrics - files: autosklearn/metrics - - id: mypy - args: [--show-error-codes] - name: mypy auto-sklearn-data - files: autosklearn/data - - id: mypy - args: [--show-error-codes] - name: mypy auto-sklearn-util - files: autosklearn/util - - id: mypy - args: [--show-error-codes] - name: mypy auto-sklearn-evaluation - files: autosklearn/evaluation - - id: mypy - args: [--show-error-codes] - name: mypy auto-sklearn-datapreprocessing - files: autosklearn/pipeline/components/data_preprocessing/ + name: mypy auto-sklearn + files: autosklearn/.* + - repo: https://gitlab.com/pycqa/flake8 - rev: 3.8.3 + rev: 4.0.1 hooks: - id: flake8 - name: flake8 auto-sklearn + name: flake8 autosklearn files: autosklearn/.* + - id: flake8 - name: flake8 file-order-data - files: autosklearn/data - additional_dependencies: - - flake8-import-order - - id: flake8 - name: flake8 file-order-ensemble - files: autosklearn/ensembles - additional_dependencies: - - flake8-import-order - - id: flake8 - name: flake8 file-order-metrics - files: autosklearn/metrics - additional_dependencies: - - flake8-import-order - - id: flake8 - name: flake8 file-order-util - files: autosklearn/util - additional_dependencies: - - flake8-import-order - - id: flake8 - name: flake8 autosklearn-test + name: flake8 test files: test/.* diff --git a/Makefile b/Makefile index b00a2392ec..495b1c2f24 100644 --- a/Makefile +++ b/Makefile @@ -1,32 +1,122 @@ -# simple makefile to simplify repetitive build env management tasks under posix +# NOTE: Used on linux, limited support outside of Linux +# +# A simple makefile to help with small tasks related to development of autosklearn +# These have been configured to only really run short tasks. Longer form tasks +# are usually completed in github actions. + +.PHONY: help install-dev check format pre-commit clean clean-doc clean-build build doc links examples publish test + +help: + @echo "Makefile autosklearn" + @echo "* install-dev to install all dev requirements and install pre-commit" + @echo "* check to check the source code for issues" + @echo "* format to format the code with black and isort" + @echo "* pre-commit to run the pre-commit check" + @echo "* clean to clean the dist and doc build files" + @echo "* build to build a dist" + @echo "* doc to generate and view the html files" + @echo "* linkcheck to check the documentation links" + @echo "* examples to run and generate the examples" + @echo "* publish to help publish the current branch to pypi" + @echo "* test to run the tests" PYTHON ?= python CYTHON ?= cython PYTEST ?= python -m pytest CTAGS ?= ctags +PIP ?= python -m pip +MAKE ?= make +BLACK ?= black +ISORT ?= isort +PYDOCSTYLE ?= pydocstyle +MYPY ?= mypy +PRECOMMIT ?= pre-commit +FLAKE8 ?= flake8 + +DIR := ${CURDIR} +DIST := ${CURDIR}/dist +DOCDIR := ${DIR}/doc +INDEX_HTML := file://${DOCDIR}/html/build/index.html + +install-dev: + $(PIP) install -e ".[test,examples,docs]" + pre-commit install + +check-black: + $(BLACK) autosklearn examples test --check || : + +check-isort: + $(ISORT) autosklearn test --check || : + +check-pydocstyle: + $(PYDOCSTYLE) autosklearn || : + +check-mypy: + $(MYPY) autosklearn || : + +check-flake8: + $(FLAKE8) autosklearn || : + $(FLAKE8) test || : -all: clean inplace test +# pydocstyle does not have easy ignore rules, instead, we include as they are covered +check: check-black check-isort check-mypy check-flake8 # check-pydocstyle -clean: +pre-commit: + $(PRECOMMIT) run --all-files + +format-black: + $(BLACK) autosklearn/.* + $(BLACK) test/.* + $(BLACK) examples/.* + +format-isort: + $(ISORT) autosklearn + $(ISORT) test + + +format: format-black format-isort + +clean-doc: + $(MAKE) -C ${DOCDIR} clean + +clean-build: $(PYTHON) setup.py clean - rm -rf dist + rm -rf ${DIST} -in: inplace # just a shortcut -inplace: - $(PYTHON) setup.py build_ext -i +# Clean up any builds in ./dist as well as doc +clean: clean-doc clean-build + +# Build a distribution in ./dist +build: + $(PYTHON) setup.py bdist doc: - cd ./doc - make html - cd .. + $(MAKE) -C ${DOCDIR} html-noexamples + @echo + @echo "View docs at:" + @echo ${INDEX_HTML} + +links: + $(MAKE) -C ${DOCDIR} linkcheck -test-code: in - $(PYTEST) -s -v test -test-doc: - $(PYTEST) -s -v doc/*.rst +examples: + $(MAKE) -C ${DOCDIR} html + @echo + @echo "View docs at:" + @echo ${INDEX_HTML} -test-coverage: - rm -rf coverage .coverage - $(PYTEST) -s -v --with-coverage test +# Publish to testpypi +# Will echo the commands to actually publish to be run to publish to actual PyPi +# This is done to prevent accidental publishing but provide the same conveniences +publish: clean-build build + $(PIP) install twine + $(PYTHON) -m twine upload --repository testpypi ${DIST}/* + @echo + @echo "Test with the following line:" + @echo "pip install --index-url https://test.pypi.org/simple/ auto-sklearn" + @echo + @echo "Once you have decided it works, publish to actual pypi with" + @echo "python -m twine upload dist/*" -test: test-code test-sphinxext test-doc +test: + $(PYTEST) test diff --git a/autosklearn/__init__.py b/autosklearn/__init__.py index f4769335d2..05b89a50af 100644 --- a/autosklearn/__init__.py +++ b/autosklearn/__init__.py @@ -1,27 +1,26 @@ # -*- encoding: utf-8 -*- import os -import pkg_resources import sys -from autosklearn.util import dependencies -from autosklearn.__version__ import __version__ # noqa (imported but unused) +import pkg_resources +from autosklearn.__version__ import __version__ # noqa (imported but unused) +from autosklearn.util import dependencies -requirements = pkg_resources.resource_string('autosklearn', 'requirements.txt') -requirements = requirements.decode('utf-8') +requirements = pkg_resources.resource_string("autosklearn", "requirements.txt") +requirements = requirements.decode("utf-8") dependencies.verify_packages(requirements) -if os.name != 'posix': +if os.name != "posix": raise ValueError( - 'Detected unsupported operating system: %s. Please check ' - 'the compability information of auto-sklearn: https://automl.github.io' - '/auto-sklearn/stable/installation.html#windows-osx-compability' % - sys.platform + "Detected unsupported operating system: %s. Please check " + "the compability information of auto-sklearn: https://automl.github.io" + "/auto-sklearn/stable/installation.html#windows-osx-compability" % sys.platform ) if sys.version_info < (3, 7): raise ValueError( - 'Unsupported python version %s found. Auto-sklearn requires Python ' - '3.7 or higher.' % sys.version_info + "Unsupported python version %s found. Auto-sklearn requires Python " + "3.6 or higher." % sys.version_info ) diff --git a/autosklearn/automl.py b/autosklearn/automl.py index f8057d130b..cee7c492f3 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -1,90 +1,102 @@ # -*- encoding: utf-8 -*- +from typing import Any, Dict, List, Mapping, Optional, Tuple, Union, cast + import copy -import distro import io import json -import platform import logging.handlers import multiprocessing import os +import platform import sys +import tempfile import time -from typing import Any, Dict, Mapping, Optional, List, Tuple, Union, cast -import uuid import unittest.mock -import tempfile +import uuid -from ConfigSpace.configuration_space import Configuration -from ConfigSpace.read_and_write import json as cs_json import dask import dask.distributed +import distro +import joblib import numpy as np import numpy.ma as ma import pandas as pd import pkg_resources import scipy.stats -from sklearn.base import BaseEstimator -from sklearn.ensemble import VotingRegressor -from sklearn.model_selection._split import _RepeatedSplits, \ - BaseShuffleSplit, BaseCrossValidator -from smac.runhistory.runhistory import RunInfo, RunValue -from smac.tae import StatusType -from smac.stats.stats import Stats -import joblib import sklearn.utils +from ConfigSpace.configuration_space import Configuration +from ConfigSpace.read_and_write import json as cs_json from scipy.sparse import spmatrix +from sklearn.base import BaseEstimator +from sklearn.dummy import DummyClassifier, DummyRegressor +from sklearn.ensemble import VotingRegressor +from sklearn.metrics._classification import type_of_target +from sklearn.model_selection._split import ( + BaseCrossValidator, + BaseShuffleSplit, + _RepeatedSplits, +) from sklearn.utils import check_random_state from sklearn.utils.validation import check_is_fitted -from sklearn.metrics._classification import type_of_target -from sklearn.dummy import DummyClassifier, DummyRegressor +from smac.runhistory.runhistory import RunInfo, RunValue +from smac.stats.stats import Stats +from smac.tae import StatusType from autosklearn.automl_common.common.utils.backend import Backend, create - -from autosklearn.metrics import Scorer, default_metric_for_task -from autosklearn.data.xy_data_manager import XYDataManager +from autosklearn.constants import ( + BINARY_CLASSIFICATION, + CLASSIFICATION_TASKS, + MULTICLASS_CLASSIFICATION, + MULTILABEL_CLASSIFICATION, + MULTIOUTPUT_REGRESSION, + REGRESSION, + REGRESSION_TASKS, +) from autosklearn.data.validation import ( - convert_if_sparse, - InputValidator, SUPPORTED_FEAT_TYPES, SUPPORTED_TARGET_TYPES, + InputValidator, + convert_if_sparse, ) +from autosklearn.data.xy_data_manager import XYDataManager +from autosklearn.ensemble_builder import EnsembleBuilderManager +from autosklearn.ensembles.singlebest_ensemble import SingleBest from autosklearn.evaluation import ExecuteTaFuncWithQueue, get_cost_of_crash from autosklearn.evaluation.abstract_evaluator import _fit_and_suppress_warnings from autosklearn.evaluation.train_evaluator import TrainEvaluator, _fit_with_budget -from autosklearn.metrics import calculate_metric +from autosklearn.metrics import Scorer, calculate_metric, default_metric_for_task +from autosklearn.pipeline.base import BasePipeline +from autosklearn.pipeline.components.classification import ClassifierChoice +from autosklearn.pipeline.components.data_preprocessing.categorical_encoding import ( + OHEChoice, +) +from autosklearn.pipeline.components.data_preprocessing.minority_coalescense import ( + CoalescenseChoice, +) +from autosklearn.pipeline.components.data_preprocessing.rescaling import RescalingChoice +from autosklearn.pipeline.components.feature_preprocessing import ( + FeaturePreprocessorChoice, +) +from autosklearn.pipeline.components.regression import RegressorChoice +from autosklearn.smbo import AutoMLSMBO +from autosklearn.util import RE_PATTERN, pipeline from autosklearn.util.data import ( + DatasetCompressionSpec, + default_dataset_compression_arg, reduce_dataset_size_if_too_large, supported_precision_reductions, validate_dataset_compression_arg, - default_dataset_compression_arg, - DatasetCompressionSpec, ) -from autosklearn.util.stopwatch import StopWatch from autosklearn.util.logging_ import ( + PicklableClientLogger, + get_named_client_logger, setup_logger, start_log_server, - get_named_client_logger, warnings_to, - PicklableClientLogger, ) -from autosklearn.util import pipeline, RE_PATTERN from autosklearn.util.parallel import preload_modules -from autosklearn.ensemble_builder import EnsembleBuilderManager -from autosklearn.ensembles.singlebest_ensemble import SingleBest -from autosklearn.smbo import AutoMLSMBO -from autosklearn.constants import MULTILABEL_CLASSIFICATION, MULTICLASS_CLASSIFICATION, \ - REGRESSION_TASKS, REGRESSION, BINARY_CLASSIFICATION, MULTIOUTPUT_REGRESSION, \ - CLASSIFICATION_TASKS -from autosklearn.pipeline.base import BasePipeline -from autosklearn.pipeline.components.classification import ClassifierChoice -from autosklearn.pipeline.components.regression import RegressorChoice -from autosklearn.pipeline.components.feature_preprocessing import FeaturePreprocessorChoice -from autosklearn.pipeline.components.data_preprocessing.categorical_encoding import OHEChoice -from autosklearn.pipeline.components.data_preprocessing.minority_coalescense import ( - CoalescenseChoice -) -from autosklearn.pipeline.components.data_preprocessing.rescaling import RescalingChoice from autosklearn.util.single_thread_client import SingleThreadedClient +from autosklearn.util.stopwatch import StopWatch def _model_predict( @@ -94,7 +106,7 @@ def _model_predict( batch_size: Optional[int] = None, logger: Optional[PicklableClientLogger] = None, ) -> np.ndarray: - """ Generates the predictions from a model. + """Generates the predictions from a model. This is seperated out into a seperate function to allow for multiprocessing and perform parallel predictions. @@ -149,24 +161,25 @@ def _model_predict( else: predict_func = model.predict - if batch_size is not None and hasattr(model, 'batch_size'): + if batch_size is not None and hasattr(model, "batch_size"): prediction = predict_func(X_, batch_size=batch_size) else: prediction = predict_func(X_) # Check that probability values lie between 0 and 1. if task in CLASSIFICATION_TASKS: - assert (prediction >= 0).all() and (prediction <= 1).all(), \ - f"For {model}, prediction probability not within [0, 1]!" + assert (prediction >= 0).all() and ( + prediction <= 1 + ).all(), f"For {model}, prediction probability not within [0, 1]!" - assert prediction.shape[0] == X_.shape[0], \ - f"Prediction shape {model} is {prediction.shape} while X_.shape is {X_.shape}" + assert ( + prediction.shape[0] == X_.shape[0] + ), f"Prediction shape {model} is {prediction.shape} while X_.shape is {X_.shape}" return prediction class AutoML(BaseEstimator): - def __init__( self, time_left_for_this_task, @@ -183,7 +196,7 @@ def __init__( debug_mode=False, include=None, exclude=None, - resampling_strategy='holdout-iterative-fit', + resampling_strategy="holdout-iterative-fit", resampling_strategy_arguments=None, n_jobs=None, dask_client: Optional[dask.distributed.Client] = None, @@ -195,7 +208,7 @@ def __init__( metric=None, scoring_functions=None, get_trials_callback=None, - dataset_compression: Union[bool, Mapping[str, Any]] = True + dataset_compression: Union[bool, Mapping[str, Any]] = True, ): super(AutoML, self).__init__() self.configuration_space = None @@ -205,8 +218,9 @@ def __init__( # self._tmp_dir = tmp_dir self._time_for_task = time_left_for_this_task self._per_run_time_limit = per_run_time_limit - self._initial_configurations_via_metalearning = \ + self._initial_configurations_via_metalearning = ( initial_configurations_via_metalearning + ) self._ensemble_size = ensemble_size self._ensemble_nbest = ensemble_nbest self._max_models_on_disc = max_models_on_disc @@ -217,9 +231,14 @@ def __init__( self._include = include self._exclude = exclude self._resampling_strategy = resampling_strategy - self._scoring_functions = scoring_functions if scoring_functions is not None else [] - self._resampling_strategy_arguments = resampling_strategy_arguments \ - if resampling_strategy_arguments is not None else {} + self._scoring_functions = ( + scoring_functions if scoring_functions is not None else [] + ) + self._resampling_strategy_arguments = ( + resampling_strategy_arguments + if resampling_strategy_arguments is not None + else {} + ) self._n_jobs = n_jobs self._dask_client = dask_client @@ -227,15 +246,24 @@ def __init__( self._disable_evaluator_output = disable_evaluator_output # Check arguments prior to doing anything! if not isinstance(self._disable_evaluator_output, (bool, List)): - raise ValueError('disable_evaluator_output must be of type bool ' - 'or list.') + raise ValueError( + "disable_evaluator_output must be of type bool " "or list." + ) if isinstance(self._disable_evaluator_output, List): - allowed_elements = ['model', 'cv_model', 'y_optimization', 'y_test', 'y_valid'] + allowed_elements = [ + "model", + "cv_model", + "y_optimization", + "y_test", + "y_valid", + ] for element in self._disable_evaluator_output: if element not in allowed_elements: - raise ValueError("List member '%s' for argument " - "'disable_evaluator_output' must be one " - "of " + str(allowed_elements)) + raise ValueError( + "List member '%s' for argument " + "'disable_evaluator_output' must be one " + "of " + str(allowed_elements) + ) self._get_smac_object_callback = get_smac_object_callback self._get_trials_callback = get_trials_callback self._smac_scenario_args = smac_scenario_args @@ -280,17 +308,21 @@ def __init__( # examples. Nevertheless, multi-process runs # have spawn as requirement to reduce the # possibility of a deadlock - self._multiprocessing_context = 'forkserver' + self._multiprocessing_context = "forkserver" if self._n_jobs == 1 and self._dask_client is None: - self._multiprocessing_context = 'fork' + self._multiprocessing_context = "fork" self._dask_client = SingleThreadedClient() if not isinstance(self._time_for_task, int): - raise ValueError("time_left_for_this_task not of type integer, " - "but %s" % str(type(self._time_for_task))) + raise ValueError( + "time_left_for_this_task not of type integer, " + "but %s" % str(type(self._time_for_task)) + ) if not isinstance(self._per_run_time_limit, int): - raise ValueError("per_run_time_limit not of type integer, but %s" % - str(type(self._per_run_time_limit))) + raise ValueError( + "per_run_time_limit not of type integer, but %s" + % str(type(self._per_run_time_limit)) + ) # By default try to use the TCP logging port or get a new port self._logger_port = logging.handlers.DEFAULT_TCP_LOGGING_PORT @@ -331,7 +363,7 @@ def _create_dask_client(self): def _close_dask_client(self): if ( - hasattr(self, '_is_dask_client_internally_created') + hasattr(self, "_is_dask_client_internally_created") and self._is_dask_client_internally_created and self._dask_client ): @@ -343,13 +375,13 @@ def _close_dask_client(self): del self._is_dask_client_internally_created def _get_logger(self, name): - logger_name = 'AutoML(%d):%s' % (self._seed, name) + logger_name = "AutoML(%d):%s" % (self._seed, name) # Setup the configuration for the logger # This is gonna be honored by the server # Which is created below setup_logger( - filename='%s.log' % str(logger_name), + filename="%s.log" % str(logger_name), logging_config=self.logging_config, output_dir=self._backend.temporary_directory, ) @@ -363,17 +395,17 @@ def _get_logger(self, name): context = multiprocessing.get_context(self._multiprocessing_context) preload_modules(context) self.stop_logging_server = context.Event() - port = context.Value('l') # be safe by using a long + port = context.Value("l") # be safe by using a long port.value = -1 self.logging_server = context.Process( target=start_log_server, kwargs=dict( - host='localhost', + host="localhost", logname=logger_name, event=self.stop_logging_server, port=port, - filename='%s.log' % str(logger_name), + filename="%s.log" % str(logger_name), logging_config=self.logging_config, output_dir=self._backend.temporary_directory, ), @@ -392,12 +424,12 @@ def _get_logger(self, name): return get_named_client_logger( name=logger_name, - host='localhost', + host="localhost", port=self._logger_port, ) def _clean_logger(self): - if not hasattr(self, 'stop_logging_server') or self.stop_logging_server is None: + if not hasattr(self, "stop_logging_server") or self.stop_logging_server is None: return # Clean up the logger @@ -423,20 +455,19 @@ def _stop_task(watcher, task_name): watcher.stop_task(task_name) @staticmethod - def _print_load_time(basename, time_left_for_this_task, - time_for_load_data, logger): + def _print_load_time(basename, time_left_for_this_task, time_for_load_data, logger): - time_left_after_reading = max( - 0, time_left_for_this_task - time_for_load_data) - logger.info('Remaining time after reading %s %5.2f sec' % - (basename, time_left_after_reading)) + time_left_after_reading = max(0, time_left_for_this_task - time_for_load_data) + logger.info( + "Remaining time after reading %s %5.2f sec" + % (basename, time_left_after_reading) + ) return time_for_load_data def _do_dummy_prediction(self, datamanager: XYDataManager, num_run: int) -> int: # When using partial-cv it makes no sense to do dummy predictions - if self._resampling_strategy in ['partial-cv', - 'partial-cv-iterative-fit']: + if self._resampling_strategy in ["partial-cv", "partial-cv-iterative-fit"]: return num_run self._logger.info("Starting to create dummy predictions.") @@ -451,53 +482,47 @@ def _do_dummy_prediction(self, datamanager: XYDataManager, num_run: int) -> int: # already be generated here! stats = Stats(scenario_mock) stats.start_timing() - ta = ExecuteTaFuncWithQueue(backend=self._backend, - autosklearn_seed=self._seed, - resampling_strategy=self._resampling_strategy, - initial_num_run=num_run, - stats=stats, - metric=self._metric, - memory_limit=memory_limit, - disable_file_output=self._disable_evaluator_output, - abort_on_first_run_crash=False, - cost_for_crash=get_cost_of_crash(self._metric), - port=self._logger_port, - pynisher_context=self._multiprocessing_context, - **self._resampling_strategy_arguments) - - status, cost, runtime, additional_info = ta.run(num_run, cutoff=self._time_for_task) + ta = ExecuteTaFuncWithQueue( + backend=self._backend, + autosklearn_seed=self._seed, + resampling_strategy=self._resampling_strategy, + initial_num_run=num_run, + stats=stats, + metric=self._metric, + memory_limit=memory_limit, + disable_file_output=self._disable_evaluator_output, + abort_on_first_run_crash=False, + cost_for_crash=get_cost_of_crash(self._metric), + port=self._logger_port, + pynisher_context=self._multiprocessing_context, + **self._resampling_strategy_arguments, + ) + + status, cost, runtime, additional_info = ta.run( + num_run, cutoff=self._time_for_task + ) if status == StatusType.SUCCESS: self._logger.info("Finished creating dummy predictions.") + + # Fail if dummy prediction fails. else: - if additional_info.get('exitcode') == -6: - self._logger.error( - "Dummy prediction failed with run state %s. " - "The error suggests that the provided memory limits were too tight. Please " - "increase the 'memory_limit' and try again. If this does not solve your " - "problem, please open an issue and paste the additional output. " - "Additional output: %s.", - str(status), str(additional_info), + if additional_info.get("exitcode") == -6: + msg = ( + f"Dummy prediction failed with run state {status}." + " The error suggests that the provided memory limits are too tight." + " Please increase the 'memory_limit' and try again. If this does" + " not solve your problem, please open an issue and paste the" + f" additional output. Additional output: {additional_info}" ) - # Fail if dummy prediction fails. - raise ValueError( - "Dummy prediction failed with run state %s. " - "The error suggests that the provided memory limits were too tight. Please " - "increase the 'memory_limit' and try again. If this does not solve your " - "problem, please open an issue and paste the additional output. " - "Additional output: %s." % - (str(status), str(additional_info)), - ) - else: - self._logger.error( - "Dummy prediction failed with run state %s and additional output: %s.", - str(status), str(additional_info), - ) - # Fail if dummy prediction fails. - raise ValueError( - "Dummy prediction failed with run state %s and additional output: %s." - % (str(status), str(additional_info)) + msg = ( + f" Dummy prediction failed with run state {status} and" + f" additional output: {additional_info}.", ) + + self._logger.error(msg) + raise ValueError(msg) + return num_run @classmethod @@ -620,8 +645,9 @@ def fit( if task is None: y_task = type_of_target(y) if not self._supports_task_type(y_task): - raise ValueError(f"{self.__class__.__name__} does not support" - f" task {y_task}") + raise ValueError( + f"{self.__class__.__name__} does not support" f" task {y_task}" + ) self._task = self._task_type_id(y_task) else: self._task = task @@ -675,10 +701,9 @@ def fit( memory_allocation = self._dataset_compression["memory_allocation"] # Remove precision reduction if we can't perform it - if ( - X.dtype not in supported_precision_reductions - and "precision" in cast(List[str], methods) # Removable with TypedDict - ): + if X.dtype not in supported_precision_reductions and "precision" in cast( + List[str], methods + ): # Removable with TypedDict methods = [method for method in methods if method != "precision"] with warnings_to(self._logger): @@ -689,13 +714,15 @@ def fit( is_classification=is_classification, random_state=self._seed, operations=methods, - memory_allocation=memory_allocation + memory_allocation=memory_allocation, ) # Check the re-sampling strategy try: self._check_resampling_strategy( - X=X, y=y, task=self._task, + X=X, + y=y, + task=self._task, ) except Exception as e: self._fit_cleanup() @@ -710,16 +737,16 @@ def fit( # It can be provided in the constructor, or automatically # defined in the estimator fit call if self._metric is None: - raise ValueError('No metric given.') + raise ValueError("No metric given.") if not isinstance(self._metric, Scorer): - raise ValueError('Metric must be instance of ' - 'autosklearn.metrics.Scorer.') + raise ValueError( + "Metric must be instance of " "autosklearn.metrics.Scorer." + ) # If no dask client was provided, we create one, so that we can # start a ensemble process in parallel to smbo optimize - if ( - self._dask_client is None and - (self._ensemble_size > 0 or self._n_jobs is not None and self._n_jobs > 1) + if self._dask_client is None and ( + self._ensemble_size > 0 or self._n_jobs is not None and self._n_jobs > 1 ): self._create_dask_client() else: @@ -732,78 +759,95 @@ def fit( self._feat_type = self.InputValidator.feature_validator.feat_type # Produce debug information to the logfile - self._logger.debug('Starting to print environment information') - self._logger.debug(' Python version: %s', sys.version.split('\n')) + self._logger.debug("Starting to print environment information") + self._logger.debug(" Python version: %s", sys.version.split("\n")) try: - self._logger.debug(f'\tDistribution: {distro.id()}-{distro.version()}-{distro.name()}') + self._logger.debug( + f"\tDistribution: {distro.id()}-{distro.version()}-{distro.name()}" + ) except AttributeError: pass - self._logger.debug(' System: %s', platform.system()) - self._logger.debug(' Machine: %s', platform.machine()) - self._logger.debug(' Platform: %s', platform.platform()) + self._logger.debug(" System: %s", platform.system()) + self._logger.debug(" Machine: %s", platform.machine()) + self._logger.debug(" Platform: %s", platform.platform()) # UNAME appears to leak sensible information # self._logger.debug(' uname: %s', platform.uname()) - self._logger.debug(' Version: %s', platform.version()) - self._logger.debug(' Mac version: %s', platform.mac_ver()) - requirements = pkg_resources.resource_string('autosklearn', 'requirements.txt') - requirements = requirements.decode('utf-8') - requirements = [requirement for requirement in requirements.split('\n')] + self._logger.debug(" Version: %s", platform.version()) + self._logger.debug(" Mac version: %s", platform.mac_ver()) + requirements = pkg_resources.resource_string("autosklearn", "requirements.txt") + requirements = requirements.decode("utf-8") + requirements = [requirement for requirement in requirements.split("\n")] for requirement in requirements: if not requirement: continue match = RE_PATTERN.match(requirement) if match: - name = match.group('name') + name = match.group("name") module_dist = pkg_resources.get_distribution(name) - self._logger.debug(' %s', module_dist) + self._logger.debug(" %s", module_dist) else: - raise ValueError('Unable to read requirement: %s' % requirement) - self._logger.debug('Done printing environment information') - self._logger.debug('Starting to print arguments to auto-sklearn') - self._logger.debug(' tmp_folder: %s', self._backend.context._temporary_directory) - self._logger.debug(' time_left_for_this_task: %f', self._time_for_task) - self._logger.debug(' per_run_time_limit: %f', self._per_run_time_limit) + raise ValueError("Unable to read requirement: %s" % requirement) + self._logger.debug("Done printing environment information") + self._logger.debug("Starting to print arguments to auto-sklearn") + self._logger.debug( + " tmp_folder: %s", self._backend.context._temporary_directory + ) + self._logger.debug(" time_left_for_this_task: %f", self._time_for_task) + self._logger.debug(" per_run_time_limit: %f", self._per_run_time_limit) self._logger.debug( - ' initial_configurations_via_metalearning: %d', + " initial_configurations_via_metalearning: %d", self._initial_configurations_via_metalearning, ) - self._logger.debug(' ensemble_size: %d', self._ensemble_size) - self._logger.debug(' ensemble_nbest: %f', self._ensemble_nbest) - self._logger.debug(' max_models_on_disc: %s', str(self._max_models_on_disc)) - self._logger.debug(' seed: %d', self._seed) - self._logger.debug(' memory_limit: %s', str(self._memory_limit)) - self._logger.debug(' metadata_directory: %s', self._metadata_directory) - self._logger.debug(' debug_mode: %s', self._debug_mode) - self._logger.debug(' include: %s', str(self._include)) - self._logger.debug(' exclude: %s', str(self._exclude)) - self._logger.debug(' resampling_strategy: %s', str(self._resampling_strategy)) - self._logger.debug(' resampling_strategy_arguments: %s', - str(self._resampling_strategy_arguments)) - self._logger.debug(' n_jobs: %s', str(self._n_jobs)) - self._logger.debug(' multiprocessing_context: %s', str(self._multiprocessing_context)) - self._logger.debug(' dask_client: %s', str(self._dask_client)) - self._logger.debug(' precision: %s', str(self.precision)) - self._logger.debug(' disable_evaluator_output: %s', str(self._disable_evaluator_output)) - self._logger.debug(' get_smac_objective_callback: %s', str(self._get_smac_object_callback)) - self._logger.debug(' smac_scenario_args: %s', str(self._smac_scenario_args)) - self._logger.debug(' logging_config: %s', str(self.logging_config)) - self._logger.debug(' metric: %s', str(self._metric)) - self._logger.debug('Done printing arguments to auto-sklearn') - self._logger.debug('Starting to print available components') + self._logger.debug(" ensemble_size: %d", self._ensemble_size) + self._logger.debug(" ensemble_nbest: %f", self._ensemble_nbest) + self._logger.debug(" max_models_on_disc: %s", str(self._max_models_on_disc)) + self._logger.debug(" seed: %d", self._seed) + self._logger.debug(" memory_limit: %s", str(self._memory_limit)) + self._logger.debug(" metadata_directory: %s", self._metadata_directory) + self._logger.debug(" debug_mode: %s", self._debug_mode) + self._logger.debug(" include: %s", str(self._include)) + self._logger.debug(" exclude: %s", str(self._exclude)) + self._logger.debug(" resampling_strategy: %s", str(self._resampling_strategy)) + self._logger.debug( + " resampling_strategy_arguments: %s", + str(self._resampling_strategy_arguments), + ) + self._logger.debug(" n_jobs: %s", str(self._n_jobs)) + self._logger.debug( + " multiprocessing_context: %s", str(self._multiprocessing_context) + ) + self._logger.debug(" dask_client: %s", str(self._dask_client)) + self._logger.debug(" precision: %s", str(self.precision)) + self._logger.debug( + " disable_evaluator_output: %s", str(self._disable_evaluator_output) + ) + self._logger.debug( + " get_smac_objective_callback: %s", str(self._get_smac_object_callback) + ) + self._logger.debug(" smac_scenario_args: %s", str(self._smac_scenario_args)) + self._logger.debug(" logging_config: %s", str(self.logging_config)) + self._logger.debug(" metric: %s", str(self._metric)) + self._logger.debug("Done printing arguments to auto-sklearn") + self._logger.debug("Starting to print available components") for choice in ( - ClassifierChoice, RegressorChoice, FeaturePreprocessorChoice, - OHEChoice, RescalingChoice, CoalescenseChoice, + ClassifierChoice, + RegressorChoice, + FeaturePreprocessorChoice, + OHEChoice, + RescalingChoice, + CoalescenseChoice, ): self._logger.debug( - '%s: %s', + "%s: %s", choice.__name__, choice.get_components(), ) - self._logger.debug('Done printing available components') + self._logger.debug("Done printing available components") datamanager = XYDataManager( - X, y, + X, + y, X_test=X_test, y_test=y_test, task=self._task, @@ -812,7 +856,7 @@ def fit( ) self._backend._make_internals_directory() - self._label_num = datamanager.info['label_num'] + self._label_num = datamanager.info["label_num"] # == Pickle the data manager to speed up loading self._backend.save_datamanager(datamanager) @@ -824,7 +868,8 @@ def fit( self._dataset_name, self._time_for_task, time_for_load_data, - self._logger) + self._logger, + ) # = Create a searchspace # Do this before One Hot Encoding to make sure that it creates a @@ -852,7 +897,7 @@ def fit( # Do this before calculating the meta-features to make sure that the # dummy predictions are actually included in the ensemble even if # calculating the meta-features takes very long - ensemble_task_name = 'runEnsemble' + ensemble_task_name = "runEnsemble" self._stopwatch.start_task(ensemble_task_name) elapsed_time = self._stopwatch.wall_elapsed(self._dataset_name) time_left_for_ensembles = max(0, self._time_for_task - elapsed_time) @@ -861,15 +906,19 @@ def fit( # Fit only raises error when ensemble_size is not zero but # time_left_for_ensembles is zero. if self._ensemble_size > 0: - raise ValueError("Not starting ensemble builder because there " - "is no time left. Try increasing the value " - "of time_left_for_this_task.") + raise ValueError( + "Not starting ensemble builder because there " + "is no time left. Try increasing the value " + "of time_left_for_this_task." + ) elif self._ensemble_size <= 0: - self._logger.info('Not starting ensemble builder because ' - 'ensemble size is <= 0.') + self._logger.info( + "Not starting ensemble builder because " "ensemble size is <= 0." + ) else: self._logger.info( - 'Start Ensemble with %5.2fsec time left' % time_left_for_ensembles) + "Start Ensemble with %5.2fsec time left" % time_left_for_ensembles + ) proc_ensemble = EnsembleBuilderManager( start_time=time.time(), @@ -900,26 +949,26 @@ def fit( pass # => RUN SMAC - smac_task_name = 'runSMAC' + smac_task_name = "runSMAC" self._stopwatch.start_task(smac_task_name) elapsed_time = self._stopwatch.wall_elapsed(self._dataset_name) time_left_for_smac = max(0, self._time_for_task - elapsed_time) if self._logger: - self._logger.info( - 'Start SMAC with %5.2fsec time left' % time_left_for_smac) + self._logger.info("Start SMAC with %5.2fsec time left" % time_left_for_smac) if time_left_for_smac <= 0: - self._logger.warning("Not starting SMAC because there is no time " - "left.") + self._logger.warning("Not starting SMAC because there is no time " "left.") _proc_smac = None self._budget_type = None else: - if self._per_run_time_limit is None or \ - self._per_run_time_limit > time_left_for_smac: + if ( + self._per_run_time_limit is None + or self._per_run_time_limit > time_left_for_smac + ): self._logger.warning( - 'Time limit for a single run is higher than total time ' - 'limit. Capping the limit for a single run to the total ' - 'time given to SMAC (%f)' % time_left_for_smac + "Time limit for a single run is higher than total time " + "limit. Capping the limit for a single run to the total " + "time given to SMAC (%f)" % time_left_for_smac ) per_run_time_limit = time_left_for_smac else: @@ -928,7 +977,7 @@ def fit( # Make sure that at least 2 models are created for the ensemble process num_models = time_left_for_smac // per_run_time_limit if num_models < 2: - per_run_time_limit = time_left_for_smac//2 + per_run_time_limit = time_left_for_smac // 2 self._logger.warning( "Capping the per_run_time_limit to {} to have " "time for a least 2 models in each process.".format( @@ -964,19 +1013,24 @@ def fit( port=self._logger_port, pynisher_context=self._multiprocessing_context, ensemble_callback=proc_ensemble, - trials_callback=self._get_trials_callback + trials_callback=self._get_trials_callback, ) try: - self.runhistory_, self.trajectory_, self._budget_type = \ - _proc_smac.run_smbo() + ( + self.runhistory_, + self.trajectory_, + self._budget_type, + ) = _proc_smac.run_smbo() trajectory_filename = os.path.join( self._backend.get_smac_output_directory_for_run(self._seed), - 'trajectory.json') - saveable_trajectory = \ - [list(entry[:2]) + [entry[2].get_dictionary()] + list(entry[3:]) - for entry in self.trajectory_] - with open(trajectory_filename, 'w') as fh: + "trajectory.json", + ) + saveable_trajectory = [ + list(entry[:2]) + [entry[2].get_dictionary()] + list(entry[3:]) + for entry in self.trajectory_ + ] + with open(trajectory_filename, "w") as fh: json.dump(saveable_trajectory, fh) except Exception as e: self._logger.exception(e) @@ -989,9 +1043,11 @@ def fit( self.ensemble_performance_history = list(proc_ensemble.history) if len(proc_ensemble.futures) > 0: - # Now we need to wait for the future to return as it cannot be cancelled while it - # is running: https://stackoverflow.com/a/49203129 - self._logger.info("Ensemble script still running, waiting for it to finish.") + # Now we need to wait for the future to return as it cannot be cancelled + # while it is running: https://stackoverflow.com/a/49203129 + self._logger.info( + "Ensemble script still running, waiting for it to finish." + ) result = proc_ensemble.futures.pop().result() if result: ensemble_history, _, _, _, _ = result @@ -1001,7 +1057,10 @@ def fit( # save the ensemble performance history file if len(self.ensemble_performance_history) > 0: pd.DataFrame(self.ensemble_performance_history).to_json( - os.path.join(self._backend.internals_directory, 'ensemble_history.json')) + os.path.join( + self._backend.internals_directory, "ensemble_history.json" + ) + ) if load_models: self._logger.info("Loading models...") @@ -1046,40 +1105,58 @@ def _check_resampling_strategy( """ is_split_object = isinstance( self._resampling_strategy, - (BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit) + (BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit), ) - if self._resampling_strategy not in [ - 'holdout', - 'holdout-iterative-fit', - 'cv', - 'cv-iterative-fit', - 'partial-cv', - 'partial-cv-iterative-fit', - ] and not is_split_object: - raise ValueError('Illegal resampling strategy: %s' % self._resampling_strategy) + if ( + self._resampling_strategy + not in [ + "holdout", + "holdout-iterative-fit", + "cv", + "cv-iterative-fit", + "partial-cv", + "partial-cv-iterative-fit", + ] + and not is_split_object + ): + raise ValueError( + "Illegal resampling strategy: %s" % self._resampling_strategy + ) elif is_split_object: TrainEvaluator.check_splitter_resampling_strategy( - X=X, y=y, task=task, - groups=self._resampling_strategy_arguments.get('groups', None), + X=X, + y=y, + task=task, + groups=self._resampling_strategy_arguments.get("groups", None), resampling_strategy=self._resampling_strategy, ) - elif self._resampling_strategy in [ - 'partial-cv', - 'partial-cv-iterative-fit', - ] and self._ensemble_size != 0: - raise ValueError("Resampling strategy %s cannot be used " - "together with ensembles." % self._resampling_strategy) - - elif self._resampling_strategy in [ - 'partial-cv', - 'cv', - 'cv-iterative-fit', - 'partial-cv-iterative-fit', - ] and 'folds' not in self._resampling_strategy_arguments: - self._resampling_strategy_arguments['folds'] = 5 + elif ( + self._resampling_strategy + in [ + "partial-cv", + "partial-cv-iterative-fit", + ] + and self._ensemble_size != 0 + ): + raise ValueError( + "Resampling strategy %s cannot be used " + "together with ensembles." % self._resampling_strategy + ) + + elif ( + self._resampling_strategy + in [ + "partial-cv", + "cv", + "cv-iterative-fit", + "partial-cv-iterative-fit", + ] + and "folds" not in self._resampling_strategy_arguments + ): + self._resampling_strategy_arguments["folds"] = 5 return @@ -1089,8 +1166,10 @@ def refit(self, X, y): # Make sure input data is valid if self.InputValidator is None or not self.InputValidator._is_fitted: - raise ValueError("refit() is only supported after calling fit. Kindly call first " - "the estimator fit() method.") + raise ValueError( + "refit() is only supported after calling fit. Kindly call first " + "the estimator fit() method." + ) X, y = self.InputValidator.transform(X, y) if self.models_ is None or len(self.models_) == 0 or self.ensemble_ is None: @@ -1142,7 +1221,7 @@ def fit_pipeline( X: SUPPORTED_FEAT_TYPES, y: Union[SUPPORTED_TARGET_TYPES, spmatrix], is_classification: bool, - config: Union[Configuration, Dict[str, Union[str, float, int]]], + config: Union[Configuration, Dict[str, Union[str, float, int]]], task: Optional[int] = None, dataset_name: Optional[str] = None, X_test: Optional[SUPPORTED_FEAT_TYPES] = None, @@ -1150,7 +1229,7 @@ def fit_pipeline( feat_type: Optional[List[str]] = None, **kwargs: Dict, ) -> Tuple[Optional[BasePipeline], RunInfo, RunValue]: - """ Fits and individual pipeline configuration and returns + """Fits and individual pipeline configuration and returns the result to the user. The Estimator constraints are honored, for example the resampling @@ -1170,8 +1249,8 @@ def fit_pipeline( y_test: array-like If provided, the testing performance will be tracked on this labels config: Union[Configuration, Dict[str, Union[str, float, int]]] - A configuration object used to define the pipeline steps. If a dictionary is passed, - a configuration is created based on this dictionary. + A configuration object used to define the pipeline steps. + If a dict is passed, a configuration is created based on this dict. dataset_name: Optional[str] A string to tag and identify the Auto-Sklearn run is_classification: bool @@ -1202,8 +1281,9 @@ def fit_pipeline( if task is None: y_task = type_of_target(y) if not self._supports_task_type(y_task): - raise ValueError(f"{self.__class__.__name__} does not support" - f" task {y_task}") + raise ValueError( + f"{self.__class__.__name__} does not support" f" task {y_task}" + ) self._task = self._task_type_id(y_task) else: self._task = task @@ -1217,12 +1297,16 @@ def fit_pipeline( # dataset if self.configuration_space is None: self.configuration_space = self.fit( - X=X, y=y, - dataset_name=dataset_name if dataset_name is not None else self._dataset_name, + X=X, + y=y, + dataset_name=dataset_name + if dataset_name is not None + else self._dataset_name, X_test=X_test, y_test=y_test, feat_type=feat_type, - only_return_configuration_space=True) + only_return_configuration_space=True, + ) # We do not want to overwrite existing runs self.num_run += 1 @@ -1231,25 +1315,25 @@ def fit_pipeline( config.config_id = self.num_run # Prepare missing components to the TAE function call - if 'include' not in kwargs: - kwargs['include'] = self._include - if 'exclude' not in kwargs: - kwargs['exclude'] = self._exclude - if 'memory_limit' not in kwargs: - kwargs['memory_limit'] = self._memory_limit - if 'resampling_strategy' not in kwargs: - kwargs['resampling_strategy'] = self._resampling_strategy - if 'metric' not in kwargs: - kwargs['metric'] = self._metric - if 'disable_file_output' not in kwargs: - kwargs['disable_file_output'] = self._disable_evaluator_output - if 'pynisher_context' not in kwargs: - kwargs['pynisher_context'] = self._multiprocessing_context - if 'stats' not in kwargs: + if "include" not in kwargs: + kwargs["include"] = self._include + if "exclude" not in kwargs: + kwargs["exclude"] = self._exclude + if "memory_limit" not in kwargs: + kwargs["memory_limit"] = self._memory_limit + if "resampling_strategy" not in kwargs: + kwargs["resampling_strategy"] = self._resampling_strategy + if "metric" not in kwargs: + kwargs["metric"] = self._metric + if "disable_file_output" not in kwargs: + kwargs["disable_file_output"] = self._disable_evaluator_output + if "pynisher_context" not in kwargs: + kwargs["pynisher_context"] = self._multiprocessing_context + if "stats" not in kwargs: scenario_mock = unittest.mock.Mock() scenario_mock.wallclock_limit = self._time_for_task - kwargs['stats'] = Stats(scenario_mock) - kwargs['stats'].start_timing() + kwargs["stats"] = Stats(scenario_mock) + kwargs["stats"].start_timing() # Fit a pipeline, which will be stored on disk # which we can later load via the backend @@ -1257,10 +1341,10 @@ def fit_pipeline( backend=self._backend, autosklearn_seed=self._seed, abort_on_first_run_crash=False, - cost_for_crash=get_cost_of_crash(kwargs['metric']), + cost_for_crash=get_cost_of_crash(kwargs["metric"]), port=self._logger_port, **kwargs, - **self._resampling_strategy_arguments + **self._resampling_strategy_arguments, ) run_info, run_value = ta.run_wrapper( @@ -1269,16 +1353,16 @@ def fit_pipeline( instance=None, instance_specific=None, seed=self._seed, - cutoff=kwargs.pop('cutoff', self._per_run_time_limit), + cutoff=kwargs.pop("cutoff", self._per_run_time_limit), capped=False, ) ) pipeline = None - if kwargs['disable_file_output'] or kwargs['resampling_strategy'] == 'test': + if kwargs["disable_file_output"] or kwargs["resampling_strategy"] == "test": self._logger.warning("File output is disabled. No pipeline can returned") elif run_value.status == StatusType.SUCCESS: - if kwargs['resampling_strategy'] in ('cv', 'cv-iterative-fit'): + if kwargs["resampling_strategy"] in ("cv", "cv-iterative-fit"): load_function = self._backend.load_cv_model_by_seed_and_id_and_budget else: load_function = self._backend.load_model_by_seed_and_id_and_budget @@ -1309,13 +1393,14 @@ def predict(self, X, batch_size=None, n_jobs=1): processes. """ if ( - self._resampling_strategy not in ( - 'holdout', 'holdout-iterative-fit', 'cv', 'cv-iterative-fit') + self._resampling_strategy + not in ("holdout", "holdout-iterative-fit", "cv", "cv-iterative-fit") and not self._can_predict ): raise NotImplementedError( - 'Predict is currently not implemented for resampling ' - 'strategy %s, please call refit().' % self._resampling_strategy) + "Predict is currently not implemented for resampling " + "strategy %s, please call refit()." % self._resampling_strategy + ) if self.models_ is None or len(self.models_) == 0 or self.ensemble_ is None: self._load_models() @@ -1324,13 +1409,17 @@ def predict(self, X, batch_size=None, n_jobs=1): # In such cases, raise error because predict and predict_proba cannot # be called. if self.ensemble_ is None: - raise ValueError("Predict and predict_proba can only be called " - "if 'ensemble_size != 0'") + raise ValueError( + "Predict and predict_proba can only be called " + "if 'ensemble_size != 0'" + ) # Make sure that input is valid if self.InputValidator is None or not self.InputValidator._is_fitted: - raise ValueError("predict() can only be called after performing fit(). Kindly call " - "the estimator fit() method first.") + raise ValueError( + "predict() can only be called after performing fit(). Kindly call " + "the estimator fit() method first." + ) X = self.InputValidator.feature_validator.transform(X) # Parallelize predictions across models with n_jobs processes. @@ -1352,24 +1441,26 @@ def predict(self, X, batch_size=None, n_jobs=1): check_is_fitted(list(self.cv_models_.values())[0]) models = self.cv_models_ except sklearn.exceptions.NotFittedError: - raise ValueError('Found no fitted models!') + raise ValueError("Found no fitted models!") all_predictions = joblib.Parallel(n_jobs=n_jobs)( joblib.delayed(_model_predict)( - model=models[identifier], - X=X, - task=self._task, - batch_size=batch_size + model=models[identifier], X=X, task=self._task, batch_size=batch_size ) for identifier in self.ensemble_.get_selected_model_identifiers() ) if len(all_predictions) == 0: - raise ValueError('Something went wrong generating the predictions. ' - 'The ensemble should consist of the following ' - 'models: %s, the following models were loaded: ' - '%s' % (str(list(self.ensemble_indices_.keys())), - str(list(self.models_.keys())))) + raise ValueError( + "Something went wrong generating the predictions. " + "The ensemble should consist of the following " + "models: %s, the following models were loaded: " + "%s" + % ( + str(list(self.ensemble_indices_.keys())), + str(list(self.models_.keys())), + ) + ) predictions = self.ensemble_.predict(all_predictions) @@ -1381,23 +1472,33 @@ def predict(self, X, batch_size=None, n_jobs=1): return predictions - def fit_ensemble(self, y, task=None, precision=32, - dataset_name=None, ensemble_nbest=None, - ensemble_size=None): + def fit_ensemble( + self, + y, + task=None, + precision=32, + dataset_name=None, + ensemble_nbest=None, + ensemble_size=None, + ): # AutoSklearn does not handle sparse y for now y = convert_if_sparse(y) - if self._resampling_strategy in ['partial-cv', 'partial-cv-iterative-fit']: - raise ValueError('Cannot call fit_ensemble with resampling ' - 'strategy %s.' % self._resampling_strategy) + if self._resampling_strategy in ["partial-cv", "partial-cv-iterative-fit"]: + raise ValueError( + "Cannot call fit_ensemble with resampling " + "strategy %s." % self._resampling_strategy + ) if self._logger is None: self._logger = self._get_logger(dataset_name) # Make sure that input is valid if self.InputValidator is None or not self.InputValidator._is_fitted: - raise ValueError("fit_ensemble() can only be called after fit. Please call the " - "estimator fit() method prior to fit_ensemble().") + raise ValueError( + "fit_ensemble() can only be called after fit. Please call the " + "estimator fit() method prior to fit_ensemble()." + ) y = self.InputValidator.target_validator.transform(y) # Create a client if needed @@ -1432,8 +1533,10 @@ def fit_ensemble(self, y, task=None, precision=32, future = manager.futures.pop() result = future.result() if result is None: - raise ValueError("Error building the ensemble - please check the log file and command " - "line output for error messages.") + raise ValueError( + "Error building the ensemble - please check the log file and command " + "line output for error messages." + ) self.ensemble_performance_history, _, _, _, _ = result self._load_models() @@ -1450,29 +1553,34 @@ def _load_models(self): if self.ensemble_: identifiers = self.ensemble_.get_selected_model_identifiers() self.models_ = self._backend.load_models_by_identifiers(identifiers) - if self._resampling_strategy in ('cv', 'cv-iterative-fit'): - self.cv_models_ = self._backend.load_cv_models_by_identifiers(identifiers) + if self._resampling_strategy in ("cv", "cv-iterative-fit"): + self.cv_models_ = self._backend.load_cv_models_by_identifiers( + identifiers + ) else: self.cv_models_ = None + if len(self.models_) == 0 and self._resampling_strategy not in [ + "partial-cv", + "partial-cv-iterative-fit", + ]: + raise ValueError("No models fitted!") if ( - len(self.models_) == 0 and - self._resampling_strategy not in ['partial-cv', 'partial-cv-iterative-fit'] - ): - raise ValueError('No models fitted!') - if ( - self._resampling_strategy in ['cv', 'cv-iterative-fit'] + self._resampling_strategy in ["cv", "cv-iterative-fit"] and len(self.cv_models_) == 0 ): - raise ValueError('No models fitted!') + raise ValueError("No models fitted!") - elif self._disable_evaluator_output is False or \ - (isinstance(self._disable_evaluator_output, List) and - 'model' not in self._disable_evaluator_output): + elif self._disable_evaluator_output is False or ( + isinstance(self._disable_evaluator_output, List) + and "model" not in self._disable_evaluator_output + ): model_names = self._backend.list_all_models(self._seed) - if len(model_names) == 0 and self._resampling_strategy not in \ - ['partial-cv', 'partial-cv-iterative-fit']: - raise ValueError('No models fitted!') + if len(model_names) == 0 and self._resampling_strategy not in [ + "partial-cv", + "partial-cv-iterative-fit", + ]: + raise ValueError("No models fitted!") self.models_ = [] @@ -1522,8 +1630,10 @@ def score(self, X, y): # Make sure that input is valid if self.InputValidator is None or not self.InputValidator._is_fitted: - raise ValueError("score() is only supported after calling fit. Kindly call first " - "the estimator fit() method.") + raise ValueError( + "score() is only supported after calling fit. Kindly call first " + "the estimator fit() method." + ) y = self.InputValidator.target_validator.transform(y) # Encode the prediction using the input validator @@ -1534,10 +1644,12 @@ def score(self, X, y): # same representation domain prediction = self.InputValidator.target_validator.transform(prediction) - return calculate_metric(solution=y, - prediction=prediction, - task_type=self._task, - metric=self._metric, ) + return calculate_metric( + solution=y, + prediction=prediction, + task_type=self._task, + metric=self._metric, + ) def _get_runhistory_models_performance(self): metric = self._metric @@ -1549,20 +1661,24 @@ def _get_runhistory_models_performance(self): continue # Alternatively, it is possible to also obtain the start time with # ``run_value.starttime`` - endtime = pd.Timestamp(time.strftime('%Y-%m-%d %H:%M:%S', - time.localtime(run_value.endtime))) + endtime = pd.Timestamp( + time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(run_value.endtime)) + ) val_score = metric._optimum - (metric._sign * run_value.cost) - train_score = metric._optimum - (metric._sign * run_value.additional_info['train_loss']) + train_score = metric._optimum - ( + metric._sign * run_value.additional_info["train_loss"] + ) scores = { - 'Timestamp': endtime, - 'single_best_optimization_score': val_score, - 'single_best_train_score': train_score, + "Timestamp": endtime, + "single_best_optimization_score": val_score, + "single_best_train_score": train_score, } # Append test-scores, if data for test_loss are available. # This is the case, if X_test and y_test where provided. - if 'test_loss' in run_value.additional_info: - test_score = metric._optimum - (metric._sign * - run_value.additional_info['test_loss']) + if "test_loss" in run_value.additional_info: + test_score = metric._optimum - ( + metric._sign * run_value.additional_info["test_loss"] + ) scores["single_best_test_score"] = test_score performance_list.append(scores) @@ -1571,13 +1687,17 @@ def _get_runhistory_models_performance(self): @property def performance_over_time_(self): individual_performance_frame = self._get_runhistory_models_performance() - best_values = pd.Series({'single_best_optimization_score': -np.inf, - 'single_best_test_score': -np.inf, - 'single_best_train_score': -np.inf}) + best_values = pd.Series( + { + "single_best_optimization_score": -np.inf, + "single_best_test_score": -np.inf, + "single_best_train_score": -np.inf, + } + ) for idx in individual_performance_frame.index: if ( - individual_performance_frame.loc[idx, 'single_best_optimization_score'] - > best_values['single_best_optimization_score'] + individual_performance_frame.loc[idx, "single_best_optimization_score"] + > best_values["single_best_optimization_score"] ): best_values = individual_performance_frame.loc[idx] individual_performance_frame.loc[idx] = best_values @@ -1586,21 +1706,27 @@ def performance_over_time_(self): if self._ensemble_size != 0: ensemble_performance_frame = pd.DataFrame(self.ensemble_performance_history) - best_values = pd.Series({'ensemble_optimization_score': -np.inf, - 'ensemble_test_score': -np.inf}) + best_values = pd.Series( + {"ensemble_optimization_score": -np.inf, "ensemble_test_score": -np.inf} + ) for idx in ensemble_performance_frame.index: if ( - ensemble_performance_frame.loc[idx, 'ensemble_optimization_score'] - > best_values['ensemble_optimization_score'] + ensemble_performance_frame.loc[idx, "ensemble_optimization_score"] + > best_values["ensemble_optimization_score"] ): best_values = ensemble_performance_frame.loc[idx] ensemble_performance_frame.loc[idx] = best_values - performance_over_time = pd.merge( - ensemble_performance_frame, - individual_performance_frame, - on="Timestamp", how='outer' - ).sort_values('Timestamp').fillna(method='ffill') + performance_over_time = ( + pd.merge( + ensemble_performance_frame, + individual_performance_frame, + on="Timestamp", + how="outer", + ) + .sort_values("Timestamp") + .fillna(method="ffill") + ) return performance_over_time @@ -1623,8 +1749,8 @@ def cv_results_(self): # TODO: add those arguments # TODO remove this restriction! - if self._resampling_strategy in ['partial-cv', 'partial-cv-iterative-fit']: - raise ValueError('Cannot call cv_results when using partial-cv!') + if self._resampling_strategy in ["partial-cv", "partial-cv-iterative-fit"]: + raise ValueError("Cannot call cv_results when using partial-cv!") parameter_dictionaries = dict() masks = dict() @@ -1659,19 +1785,19 @@ def cv_results_(self): s = run_value.status if s == StatusType.SUCCESS: - status.append('Success') + status.append("Success") elif s == StatusType.DONOTADVANCE: - status.append('Success (but do not advance to higher budget)') + status.append("Success (but do not advance to higher budget)") elif s == StatusType.TIMEOUT: - status.append('Timeout') + status.append("Timeout") elif s == StatusType.CRASHED: - status.append('Crash') + status.append("Crash") elif s == StatusType.ABORT: - status.append('Abort') + status.append("Abort") elif s == StatusType.MEMOUT: - status.append('Memout') - # TODO remove StatusType.RUNNING at some point in the future when the new SMAC 0.13.2 - # is the new minimum required version! + status.append("Memout") + # TODO remove StatusType.RUNNING at some point in the future when the new + # SMAC 0.13.2 is the new minimum required version! elif s in (StatusType.STOP, StatusType.RUNNING): continue else: @@ -1679,7 +1805,9 @@ def cv_results_(self): param_dict = config.get_dictionary() params.append(param_dict) - mean_test_score.append(self._metric._optimum - (self._metric._sign * run_value.cost)) + mean_test_score.append( + self._metric._optimum - (self._metric._sign * run_value.cost) + ) mean_fit_time.append(run_value.time) budgets.append(run_key.budget) @@ -1705,69 +1833,79 @@ def cv_results_(self): metric_dict[metric.name].append(metric_value) metric_mask[metric.name].append(mask_value) - results['mean_test_score'] = np.array(mean_test_score) + results["mean_test_score"] = np.array(mean_test_score) for name in metric_name: - masked_array = ma.MaskedArray(metric_dict[name], - metric_mask[name]) - results['metric_%s' % name] = masked_array + masked_array = ma.MaskedArray(metric_dict[name], metric_mask[name]) + results["metric_%s" % name] = masked_array - results['mean_fit_time'] = np.array(mean_fit_time) - results['params'] = params - rank_order = -1 * self._metric._sign * results['mean_test_score'] - results['rank_test_scores'] = scipy.stats.rankdata(rank_order, method='min') - results['status'] = status - results['budgets'] = budgets + results["mean_fit_time"] = np.array(mean_fit_time) + results["params"] = params + rank_order = -1 * self._metric._sign * results["mean_test_score"] + results["rank_test_scores"] = scipy.stats.rankdata(rank_order, method="min") + results["status"] = status + results["budgets"] = budgets for hp_name in hp_names: - masked_array = ma.MaskedArray(parameter_dictionaries[hp_name], - masks[hp_name]) - results['param_%s' % hp_name] = masked_array + masked_array = ma.MaskedArray( + parameter_dictionaries[hp_name], masks[hp_name] + ) + results["param_%s" % hp_name] = masked_array return results def sprint_statistics(self): cv_results = self.cv_results_ sio = io.StringIO() - sio.write('auto-sklearn results:\n') - sio.write(' Dataset name: %s\n' % self._dataset_name) - sio.write(' Metric: %s\n' % self._metric) - idx_success = np.where(np.array( - [status in ['Success', 'Success (but do not advance to higher budget)'] - for status in cv_results['status']] - ))[0] + sio.write("auto-sklearn results:\n") + sio.write(" Dataset name: %s\n" % self._dataset_name) + sio.write(" Metric: %s\n" % self._metric) + idx_success = np.where( + np.array( + [ + status + in ["Success", "Success (but do not advance to higher budget)"] + for status in cv_results["status"] + ] + ) + )[0] if len(idx_success) > 0: if not self._metric._optimum: - idx_best_run = np.argmin(cv_results['mean_test_score'][idx_success]) + idx_best_run = np.argmin(cv_results["mean_test_score"][idx_success]) else: - idx_best_run = np.argmax(cv_results['mean_test_score'][idx_success]) - best_score = cv_results['mean_test_score'][idx_success][idx_best_run] - sio.write(' Best validation score: %f\n' % best_score) - num_runs = len(cv_results['status']) - sio.write(' Number of target algorithm runs: %d\n' % num_runs) - num_success = sum([ - s in ['Success', 'Success (but do not advance to higher budget)'] - for s in cv_results['status'] - ]) - sio.write(' Number of successful target algorithm runs: %d\n' % num_success) - num_crash = sum([s == 'Crash' for s in cv_results['status']]) - sio.write(' Number of crashed target algorithm runs: %d\n' % num_crash) - num_timeout = sum([s == 'Timeout' for s in cv_results['status']]) - sio.write(' Number of target algorithms that exceeded the time ' - 'limit: %d\n' % num_timeout) - num_memout = sum([s == 'Memout' for s in cv_results['status']]) - sio.write(' Number of target algorithms that exceeded the memory ' - 'limit: %d\n' % num_memout) + idx_best_run = np.argmax(cv_results["mean_test_score"][idx_success]) + best_score = cv_results["mean_test_score"][idx_success][idx_best_run] + sio.write(" Best validation score: %f\n" % best_score) + num_runs = len(cv_results["status"]) + sio.write(" Number of target algorithm runs: %d\n" % num_runs) + num_success = sum( + [ + s in ["Success", "Success (but do not advance to higher budget)"] + for s in cv_results["status"] + ] + ) + sio.write(" Number of successful target algorithm runs: %d\n" % num_success) + num_crash = sum([s == "Crash" for s in cv_results["status"]]) + sio.write(" Number of crashed target algorithm runs: %d\n" % num_crash) + num_timeout = sum([s == "Timeout" for s in cv_results["status"]]) + sio.write( + " Number of target algorithms that exceeded the time " + "limit: %d\n" % num_timeout + ) + num_memout = sum([s == "Memout" for s in cv_results["status"]]) + sio.write( + " Number of target algorithms that exceeded the memory " + "limit: %d\n" % num_memout + ) return sio.getvalue() def get_models_with_weights(self): - if self.models_ is None or len(self.models_) == 0 or \ - self.ensemble_ is None: + if self.models_ is None or len(self.models_) == 0 or self.ensemble_ is None: self._load_models() return self.ensemble_.get_models_with_weights(self.models_) def show_models(self) -> Dict[int, Any]: - """ Returns a dictionary containing dictionaries of ensemble models. + """Returns a dictionary containing dictionaries of ensemble models. Each model in the ensemble can be accessed by giving its ``model_id`` as key. @@ -1821,11 +1959,12 @@ def show_models(self) -> Dict[int, Any]: 'rank': 2, 'cost': 0.4550418898836528, 'ensemble_weight': 0.3, - 'data_preprocessor': , + 'data_preprocessor': , 'feature_preprocessor': , 'regressor': , - 'sklearn_regressor': ARDRegression(alpha_1=0.0003701926442639788,...) - }... + 'sklearn_regressor': ARDRegression(alpha_1=0.027,...) + }, + ... } Returns @@ -1833,8 +1972,7 @@ def show_models(self) -> Dict[int, Any]: Dict(int, Any) : dictionary of length = number of models in the ensemble A dictionary of models in the ensemble, where ``model_id`` is the key. - """ - + """ # noqa: E501 ensemble_dict = {} def has_key(rv, key): @@ -1842,26 +1980,25 @@ def has_key(rv, key): table_dict = {} for rkey, rval in self.runhistory_.data.items(): - if has_key(rval, 'num_run'): - model_id = rval.additional_info['num_run'] - table_dict[model_id] = { - 'model_id': model_id, - 'cost': rval.cost - } + if has_key(rval, "num_run"): + model_id = rval.additional_info["num_run"] + table_dict[model_id] = {"model_id": model_id, "cost": rval.cost} # Checking if the dictionary is empty if not table_dict: - raise RuntimeError('No model found. Try increasing \'time_left_for_this_task\'.') + raise RuntimeError( + "No model found. Try increasing 'time_left_for_this_task'." + ) for i, weight in enumerate(self.ensemble_.weights_): (_, model_id, _) = self.ensemble_.identifiers_[i] - table_dict[model_id]['ensemble_weight'] = weight + table_dict[model_id]["ensemble_weight"] = weight - table = pd.DataFrame.from_dict(table_dict, orient='index') - table.sort_values(by='cost', inplace=True) + table = pd.DataFrame.from_dict(table_dict, orient="index") + table.sort_values(by="cost", inplace=True) - # Checking which resampling strategy is chosen and selecting the appropriate models - is_cv = (self._resampling_strategy == "cv") + # Check which resampling strategy is chosen and selecting the appropriate models + is_cv = self._resampling_strategy == "cv" models = self.cv_models_ if is_cv else self.models_ rank = 1 # Initializing rank for the first model @@ -1869,23 +2006,23 @@ def has_key(rv, key): model_dict = {} # Declaring model dictionary # Inserting model_id, rank, cost and ensemble weight - model_dict['model_id'] = table.loc[model_id]['model_id'].astype(int) - model_dict['rank'] = rank - model_dict['cost'] = table.loc[model_id]['cost'] - model_dict['ensemble_weight'] = table.loc[model_id]['ensemble_weight'] + model_dict["model_id"] = table.loc[model_id]["model_id"].astype(int) + model_dict["rank"] = rank + model_dict["cost"] = table.loc[model_id]["cost"] + model_dict["ensemble_weight"] = table.loc[model_id]["ensemble_weight"] rank += 1 # Incrementing rank by 1 for the next model # The steps in the models pipeline are as follows: # 'data_preprocessor': DataPreprocessor, # 'balancing': Balancing, # 'feature_preprocessor': FeaturePreprocessorChoice, - # 'classifier'/'regressor': ClassifierChoice/RegressorChoice (autosklearn wrapped model) + # 'classifier'/'regressor': ClassifierChoice/RegressorChoice (wrapped model) # For 'cv' (cross validation) strategy if is_cv: # Voting model created by cross validation cv_voting_ensemble = model - model_dict['voting_model'] = cv_voting_ensemble + model_dict["voting_model"] = cv_voting_ensemble # List of models, each trained on one cv fold cv_models = [] @@ -1894,9 +2031,11 @@ def has_key(rv, key): # Adding sklearn model to the model dictionary model_type, autosklearn_wrapped_model = cv_model.steps[-1] - estimator[f'sklearn_{model_type}'] = autosklearn_wrapped_model.choice.estimator + estimator[ + f"sklearn_{model_type}" + ] = autosklearn_wrapped_model.choice.estimator cv_models.append(estimator) - model_dict['estimators'] = cv_models + model_dict["estimators"] = cv_models # For any other strategy else: @@ -1905,7 +2044,9 @@ def has_key(rv, key): # Adding sklearn model to the model dictionary model_type, autosklearn_wrapped_model = model.steps[-1] - model_dict[f'sklearn_{model_type}'] = autosklearn_wrapped_model.choice.estimator + model_dict[ + f"sklearn_{model_type}" + ] = autosklearn_wrapped_model.choice.estimator # Insterting model_dict in the ensemble dictionary ensemble_dict[model_id] = model_dict @@ -1920,21 +2061,20 @@ def _create_search_space( include: Optional[Dict[str, List[str]]] = None, exclude: Optional[Dict[str, List[str]]] = None, ): - task_name = 'CreateConfigSpace' + task_name = "CreateConfigSpace" self._stopwatch.start_task(task_name) - configspace_path = os.path.join(tmp_dir, 'space.json') + configspace_path = os.path.join(tmp_dir, "space.json") configuration_space = pipeline.get_configuration_space( datamanager.info, include=include, exclude=exclude, ) configuration_space = self.configuration_space_created_hook( - datamanager, configuration_space) + datamanager, configuration_space + ) backend.write_txt_file( - configspace_path, - cs_json.write(configuration_space), - 'Configuration space' + configspace_path, cs_json.write(configuration_space), "Configuration space" ) self._stopwatch.stop_task(task_name) @@ -1960,9 +2100,9 @@ def __del__(self): class AutoMLClassifier(AutoML): _task_mapping = { - 'multilabel-indicator': MULTILABEL_CLASSIFICATION, - 'multiclass': MULTICLASS_CLASSIFICATION, - 'binary': BINARY_CLASSIFICATION, + "multilabel-indicator": MULTILABEL_CLASSIFICATION, + "multiclass": MULTICLASS_CLASSIFICATION, + "binary": BINARY_CLASSIFICATION, } @classmethod @@ -1985,7 +2125,8 @@ def fit( load_models: bool = True, ): return super().fit( - X, y, + X, + y, X_test=X_test, y_test=y_test, feat_type=feat_type, @@ -1999,7 +2140,7 @@ def fit_pipeline( self, X: SUPPORTED_FEAT_TYPES, y: Union[SUPPORTED_TARGET_TYPES, spmatrix], - config: Union[Configuration, Dict[str, Union[str, float, int]]], + config: Union[Configuration, Dict[str, Union[str, float, int]]], dataset_name: Optional[str] = None, X_test: Optional[SUPPORTED_FEAT_TYPES] = None, y_test: Optional[Union[SUPPORTED_TARGET_TYPES, spmatrix]] = None, @@ -2007,8 +2148,10 @@ def fit_pipeline( **kwargs, ) -> Tuple[Optional[BasePipeline], RunInfo, RunValue]: return super().fit_pipeline( - X=X, y=y, - X_test=X_test, y_test=y_test, + X=X, + y=y, + X_test=X_test, + y_test=y_test, dataset_name=dataset_name, config=config, is_classification=True, @@ -2017,12 +2160,15 @@ def fit_pipeline( ) def predict(self, X, batch_size=None, n_jobs=1): - predicted_probabilities = super().predict(X, batch_size=batch_size, - n_jobs=n_jobs) + predicted_probabilities = super().predict( + X, batch_size=batch_size, n_jobs=n_jobs + ) if self.InputValidator is None or not self.InputValidator._is_fitted: - raise ValueError("predict() is only supported after calling fit. Kindly call first " - "the estimator fit() method.") + raise ValueError( + "predict() is only supported after calling fit. Kindly call first " + "the estimator fit() method." + ) if self.InputValidator.target_validator.is_single_column_target(): predicted_indexes = np.argmax(predicted_probabilities, axis=1) else: @@ -2037,9 +2183,9 @@ def predict_proba(self, X, batch_size=None, n_jobs=1): class AutoMLRegressor(AutoML): _task_mapping = { - 'continuous-multioutput': MULTIOUTPUT_REGRESSION, - 'continuous': REGRESSION, - 'multiclass': REGRESSION, + "continuous-multioutput": MULTIOUTPUT_REGRESSION, + "continuous": REGRESSION, + "multiclass": REGRESSION, } @classmethod @@ -2062,7 +2208,8 @@ def fit( load_models: bool = True, ): return super().fit( - X, y, + X, + y, X_test=X_test, y_test=y_test, feat_type=feat_type, @@ -2076,7 +2223,7 @@ def fit_pipeline( self, X: SUPPORTED_FEAT_TYPES, y: Union[SUPPORTED_TARGET_TYPES, spmatrix], - config: Union[Configuration, Dict[str, Union[str, float, int]]], + config: Union[Configuration, Dict[str, Union[str, float, int]]], dataset_name: Optional[str] = None, X_test: Optional[SUPPORTED_FEAT_TYPES] = None, y_test: Optional[Union[SUPPORTED_TARGET_TYPES, spmatrix]] = None, @@ -2084,8 +2231,10 @@ def fit_pipeline( **kwargs: Dict, ) -> Tuple[Optional[BasePipeline], RunInfo, RunValue]: return super().fit_pipeline( - X=X, y=y, - X_test=X_test, y_test=y_test, + X=X, + y=y, + X_test=X_test, + y_test=y_test, config=config, feat_type=feat_type, dataset_name=dataset_name, diff --git a/autosklearn/constants.py b/autosklearn/constants.py index 60a025999e..2db372925c 100644 --- a/autosklearn/constants.py +++ b/autosklearn/constants.py @@ -7,21 +7,26 @@ MULTIOUTPUT_REGRESSION = 5 REGRESSION_TASKS = [REGRESSION, MULTIOUTPUT_REGRESSION] -CLASSIFICATION_TASKS = [BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, - MULTILABEL_CLASSIFICATION] +CLASSIFICATION_TASKS = [ + BINARY_CLASSIFICATION, + MULTICLASS_CLASSIFICATION, + MULTILABEL_CLASSIFICATION, +] TASK_TYPES = REGRESSION_TASKS + CLASSIFICATION_TASKS -TASK_TYPES_TO_STRING = \ - {BINARY_CLASSIFICATION: 'binary.classification', - MULTICLASS_CLASSIFICATION: 'multiclass.classification', - MULTILABEL_CLASSIFICATION: 'multilabel.classification', - REGRESSION: 'regression', - MULTIOUTPUT_REGRESSION: 'multioutput.regression'} +TASK_TYPES_TO_STRING = { + BINARY_CLASSIFICATION: "binary.classification", + MULTICLASS_CLASSIFICATION: "multiclass.classification", + MULTILABEL_CLASSIFICATION: "multilabel.classification", + REGRESSION: "regression", + MULTIOUTPUT_REGRESSION: "multioutput.regression", +} -STRING_TO_TASK_TYPES = \ - {'binary.classification': BINARY_CLASSIFICATION, - 'multiclass.classification': MULTICLASS_CLASSIFICATION, - 'multilabel.classification': MULTILABEL_CLASSIFICATION, - 'regression': REGRESSION, - 'multioutput.regression': MULTIOUTPUT_REGRESSION} +STRING_TO_TASK_TYPES = { + "binary.classification": BINARY_CLASSIFICATION, + "multiclass.classification": MULTICLASS_CLASSIFICATION, + "multilabel.classification": MULTILABEL_CLASSIFICATION, + "regression": REGRESSION, + "multioutput.regression": MULTIOUTPUT_REGRESSION, +} diff --git a/autosklearn/data/abstract_data_manager.py b/autosklearn/data/abstract_data_manager.py index 739e707334..0837d59ad0 100644 --- a/autosklearn/data/abstract_data_manager.py +++ b/autosklearn/data/abstract_data_manager.py @@ -2,14 +2,14 @@ from typing import Any, Dict, Union import numpy as np - import scipy.sparse -from autosklearn.pipeline.components.data_preprocessing.feature_type \ - import FeatTypeSplit +from autosklearn.pipeline.components.data_preprocessing.feature_type import ( + FeatTypeSplit, +) -class AbstractDataManager(): +class AbstractDataManager: __metaclass__ = abc.ABCMeta def __init__(self, name: str): @@ -47,22 +47,26 @@ def encoder(self, value: FeatTypeSplit) -> FeatTypeSplit: self._encoder = value def __repr__(self) -> str: - return 'DataManager : ' + self.name + return "DataManager : " + self.name def __str__(self) -> str: - val = 'DataManager : ' + self.name + '\ninfo:\n' + val = "DataManager : " + self.name + "\ninfo:\n" for item in self.info: - val = val + '\t' + item + ' = ' + str(self.info[item]) + '\n' - val = val + 'data:\n' + val = val + "\t" + item + " = " + str(self.info[item]) + "\n" + val = val + "data:\n" for subset in self.data: - val = val + '\t%s = %s %s %s\n' % (subset, type(self.data[subset]), - str(self.data[subset].shape), - str(self.data[subset].dtype)) + val = val + "\t%s = %s %s %s\n" % ( + subset, + type(self.data[subset]), + str(self.data[subset].shape), + str(self.data[subset].dtype), + ) if isinstance(self.data[subset], scipy.sparse.spmatrix): - val = val + '\tdensity: %f\n' % \ - (float(len(self.data[subset].data)) / - self.data[subset].shape[0] / - self.data[subset].shape[1]) - val = val + 'feat_type:\t' + str(self.feat_type) + '\n' + val = val + "\tdensity: %f\n" % ( + float(len(self.data[subset].data)) + / self.data[subset].shape[0] + / self.data[subset].shape[1] + ) + val = val + "feat_type:\t" + str(self.feat_type) + "\n" return val diff --git a/autosklearn/data/feature_validator.py b/autosklearn/data/feature_validator.py index 1a21249775..0b7ae8e8b1 100644 --- a/autosklearn/data/feature_validator.py +++ b/autosklearn/data/feature_validator.py @@ -1,19 +1,16 @@ -import logging from typing import Dict, List, Optional, Tuple, Union, cast -import numpy as np +import logging +import numpy as np import pandas as pd from pandas.api.types import is_numeric_dtype, is_sparse - from scipy.sparse import csr_matrix, spmatrix - from sklearn.base import BaseEstimator from sklearn.exceptions import NotFittedError from autosklearn.util.logging_ import PickableLoggerAdapter - SUPPORTED_FEAT_TYPES = Union[List, pd.DataFrame, np.ndarray, spmatrix] @@ -26,34 +23,37 @@ class FeatureValidator(BaseEstimator): Attributes ---------- - feat_type: Optional[List[str]] - In case the dataset is not a pandas DataFrame: - + If provided, this list indicates which columns should be treated as categorical - it is internally transformed into a dictionary that indicates a mapping from - column index to categorical/numerical - + If not provided, by default all columns are treated as numerical - If the input dataset is of type pandas dataframe, this argument - must be none, as the column type will be inferred from the pandas dtypes. - - data_type: - Class name of the data type provided during fit. + feat_type: Optional[List[str]] = None + In case the dataset is not a pandas DataFrame: + + If provided, this list indicates which columns should be treated as + categorical it is internally transformed into a dictionary that + indicates a mapping from column index to categorical/numerical. + + If not provided, by default all columns are treated as numerical + + If the input dataset is of type pandas dataframe, this argument + must be none, as the column type will be inferred from the pandas dtypes. + + data_type: + Class name of the data type provided during fit. """ - def __init__(self, - feat_type: Optional[List[str]] = None, - logger: Optional[PickableLoggerAdapter] = None, - ) -> None: + + def __init__( + self, + feat_type: Optional[List[str]] = None, + logger: Optional[PickableLoggerAdapter] = None, + ) -> None: # If a dataframe was provided, we populate # this attribute with a mapping from column to {numerical | categorical} - self.feat_type: Optional[ - Dict[Union[str, int], str] - ] = None + self.feat_type: Optional[Dict[Union[str, int], str]] = None if feat_type is not None: if isinstance(feat_type, dict): self.feat_type = feat_type elif not isinstance(feat_type, List): - raise ValueError("Auto-Sklearn expects a list of categorical/" - "numerical feature types, yet a" - " {} was provided".format(type(feat_type))) + raise ValueError( + "Auto-Sklearn expects a list of categorical/" + "numerical feature types, yet a" + " {} was provided".format(type(feat_type)) + ) else: # Convert to a dictionary which will be passed to the ColumnTransformer @@ -72,7 +72,7 @@ def fit( self, X_train: SUPPORTED_FEAT_TYPES, X_test: Optional[SUPPORTED_FEAT_TYPES] = None, - ) -> 'FeatureValidator': + ) -> "FeatureValidator": """ Validates input data to Auto-Sklearn. The supported data types are List, numpy arrays and pandas DataFrames. @@ -86,7 +86,6 @@ def fit( X_test: Optional[SUPPORTED_FEAT_TYPES] A hold out set of data used for checking """ - # If a list was provided, it will be converted to pandas if isinstance(X_train, List): X_train, X_test = self.list_to_dataframe(X_train, X_test) @@ -96,47 +95,56 @@ def fit( # Handle categorical feature identification for the pipeline if hasattr(X_train, "iloc"): if self.feat_type is not None: - raise ValueError("When providing a DataFrame to Auto-Sklearn, we extract " - "the feature types from the DataFrame.dtypes. That is, " - "providing the option feat_type to the fit method is not " - "supported when using a Dataframe. Please make sure that the " - "type of each column in your DataFrame is properly set. " - "More details about having the correct data type in your " - "DataFrame can be seen in " - "https://pandas.pydata.org/pandas-docs/stable/reference" - "/api/pandas.DataFrame.astype.html") + raise ValueError( + "When providing a DataFrame to Auto-Sklearn, we extract " + "the feature types from the DataFrame.dtypes. That is, " + "providing the option feat_type to the fit method is not " + "supported when using a Dataframe. Please make sure that the " + "type of each column in your DataFrame is properly set. " + "More details about having the correct data type in your " + "DataFrame can be seen in " + "https://pandas.pydata.org/pandas-docs/stable/reference" + "/api/pandas.DataFrame.astype.html" + ) else: self.feat_type = self.get_feat_type_from_columns(X_train) else: # Numpy array was provided if self.feat_type is None: # Assume numerical columns if a numpy array has no feature types - self.feat_type = {i: 'numerical' for i in range(np.shape(X_train)[1])} + self.feat_type = {i: "numerical" for i in range(np.shape(X_train)[1])} else: # Check The feat type provided if len(self.feat_type) != np.shape(X_train)[1]: - raise ValueError('Array feat_type does not have same number of ' - 'variables as X has features. %d vs %d.' % - (len(self.feat_type), np.shape(X_train)[1])) + raise ValueError( + "Array feat_type does not have same number of " + "variables as X has features. %d vs %d." + % (len(self.feat_type), np.shape(X_train)[1]) + ) if not all([isinstance(f, str) for f in self.feat_type.values()]): - raise ValueError("feat_type must only contain strings: {}".format( - list(self.feat_type.values()), - )) + raise ValueError( + "feat_type must only contain strings: {}".format( + list(self.feat_type.values()), + ) + ) for ft in self.feat_type.values(): - if ft.lower() not in ['categorical', 'numerical', 'string']: - raise ValueError('Only `Categorical`, `Numerical` and `String` are ' - 'valid feature types') + if ft.lower() not in ["categorical", "numerical", "string"]: + raise ValueError( + "Only `Categorical`, `Numerical` and `String` are " + "valid feature types" + ) if X_test is not None: self._check_data(X_test) if np.shape(X_train)[1] != np.shape(X_test)[1]: - raise ValueError("The feature dimensionality of the train and test " - "data does not match train({}) != test({})".format( - np.shape(X_train)[1], - np.shape(X_test)[1] - )) + raise ValueError( + "The feature dimensionality of the train and test " + "data does not match train({}) != test({})".format( + np.shape(X_train)[1], np.shape(X_test)[1] + ) + ) self._is_fitted = True @@ -162,7 +170,9 @@ def transform( The transformed array """ if not self._is_fitted: - raise NotFittedError("Cannot call transform on a validator that is not fitted") + raise NotFittedError( + "Cannot call transform on a validator that is not fitted" + ) # If a list was provided, it will be converted to pandas if isinstance(X, List): @@ -177,9 +187,11 @@ def transform( # Not all sparse format support index sorting if isinstance(X_transformed, spmatrix): if not isinstance(X_transformed, csr_matrix): - self.logger.warning(f"Sparse data provided is of type {type(X_transformed)} " - "yet Auto-Sklearn only support csr_matrix. Auto-sklearn " - "will convert the provided data to the csr_matrix format.") + self.logger.warning( + f"Sparse data provided is of type {type(X_transformed)} " + "yet Auto-Sklearn only support csr_matrix. Auto-sklearn " + "will convert the provided data to the csr_matrix format." + ) X_transformed = X_transformed.tocsr(copy=False) X_transformed.sort_indices() @@ -195,45 +207,42 @@ def _check_data( Parameters ---------- - X: SUPPORTED_FEAT_TYPES - A set of features that are going to be validated (type and dimensionality - checks) and a encoder fitted in the case the data needs encoding + X: SUPPORTED_FEAT_TYPES + A set of features that are going to be validated (type and dimensionality) + and a encoder fitted in the case the data needs encoding """ - # We consider columns that are all nan in a pandas frame as category - if hasattr(X, 'columns'): + if hasattr(X, "columns"): for column in cast(pd.DataFrame, X).columns: if X[column].isna().all(): - X[column] = X[column].astype('category') + X[column] = X[column].astype("category") - if not isinstance(X, (np.ndarray, pd.DataFrame)) and not isinstance(X, spmatrix): - raise ValueError("Auto-sklearn only supports Numpy arrays, Pandas DataFrames," - " scipy sparse and Python Lists, yet, the provided input is" - " of type {}".format( - type(X) - )) + if not isinstance(X, (np.ndarray, pd.DataFrame)) and not isinstance( + X, spmatrix + ): + raise ValueError( + "Auto-sklearn only supports Numpy arrays, Pandas DataFrames," + " scipy sparse and Python Lists, yet, the provided input is" + " of type {}".format(type(X)) + ) if self.data_type is None: self.data_type = type(X) if self.data_type != type(X): - self.logger.warning("Auto-sklearn previously received features of type %s " - "yet the current features have type %s. Changing the dtype " - "of inputs to an estimator might cause problems" % ( - str(self.data_type), - str(type(X)), - ), - ) + self.logger.warning( + f"Auto-sklearn previously received features of type {self.data_type} " + f"yet the current features have type {type(X)}. Changing the dtype " + "of inputs to an estimator might cause problems" + ) # Do not support category/string numpy data. Only numbers if hasattr(X, "dtype"): if not np.issubdtype(X.dtype.type, np.number): # type: ignore[union-attr] raise ValueError( - "When providing a numpy array to Auto-sklearn, the only valid " - "dtypes are numerical ones. The provided data type {} is not supported." - "".format( - X.dtype.type, # type: ignore[union-attr] - ) + "When providing a numpy array to Auto-sklearn, the only valid" + f" dtypes are numerical ones. The provided data type {X.dtype.type}" + " is not supported." ) # Then for Pandas, we do not support Nan in categorical columns @@ -247,12 +256,14 @@ def _check_data( # To support list, we need to support object inference. # In extreme cases, the train column might be all integer, # and the test column might be float. - self.logger.warning("Changing the dtype of the features after fit() is " - "not recommended. Fit() method was called with " - "{} whereas the new features have {} as type".format( - self.dtypes, - dtypes, - )) + self.logger.warning( + "Changing the dtype of the features after fit() is " + "not recommended. Fit() method was called with " + "{} whereas the new features have {} as type".format( + self.dtypes, + dtypes, + ) + ) else: self.dtypes = dtypes @@ -266,53 +277,52 @@ def get_feat_type_from_columns( Parameters ---------- - X: pd.DataFrame - A set of features that are going to be validated (type and dimensionality - checks) and a encoder fitted in the case the data needs encoding + X: pd.DataFrame + A set of features that are going to be validated (type and dimensionality + checks) and a encoder fitted in the case the data needs encoding + Returns ------- - feat_type: - dictionary with column to feature type mapping + feat_type: + dictionary with column to feature type mapping """ - # Also, register the feature types for the estimator feat_type = {} # Make sure each column is a valid type for i, column in enumerate(X.columns): if is_sparse(X[column]): - raise ValueError("Auto-sklearn does not yet support sparse pandas Series." - f" Please convert {column} to a dense format.") - elif X[column].dtype.name in ['category', 'bool']: - feat_type[column] = 'categorical' + raise ValueError( + "Auto-sklearn does not yet support sparse pandas Series." + f" Please convert {column} to a dense format." + ) + elif X[column].dtype.name in ["category", "bool"]: + feat_type[column] = "categorical" elif X[column].dtype.name == "string": - feat_type[column] = 'string' + feat_type[column] = "string" # Move away from np.issubdtype as it causes # TypeError: data type not understood in certain pandas types elif not is_numeric_dtype(X[column]): - if X[column].dtype.name == 'object': + if X[column].dtype.name == "object": raise ValueError( - "Input Column {} has invalid type object. " + f"Input Column {column} has invalid type object. " "Cast it to a valid dtype before using it in Auto-Sklearn. " "Valid types are numerical, categorical or boolean. " "You can cast it to a valid dtype using " "pandas.Series.astype ." "If working with string objects, the following " "tutorial illustrates how to work with text data: " - "https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html".format( # noqa: E501 - column, - ) + "https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html" # noqa: E501 ) elif pd.core.dtypes.common.is_datetime_or_timedelta_dtype( X[column].dtype ): raise ValueError( - "Auto-sklearn does not support time and/or date datatype as given " - "in column {}. Please convert the time information to a numerical value " - "first. One example on how to do this can be found on " - "https://stats.stackexchange.com/questions/311494/".format( - column, - ) + "Auto-sklearn does not support time and/or date datatype as " + f"given in column {column}. Please convert the time " + " information to a numerical value first. One example on how to" + " do this can be found on " + " https://stats.stackexchange.com/questions/311494/" ) else: raise ValueError( @@ -325,7 +335,7 @@ def get_feat_type_from_columns( ) ) else: - feat_type[column] = 'numerical' + feat_type[column] = "numerical" return feat_type def list_to_dataframe( @@ -334,31 +344,32 @@ def list_to_dataframe( X_test: Optional[SUPPORTED_FEAT_TYPES] = None, ) -> Tuple[pd.DataFrame, Optional[pd.DataFrame]]: """ - Converts a list to a pandas DataFrame. In this process, column types are inferred. + Converts a list to a DataFrame. In this process, column types are inferred. If test data is provided, we proactively match it to train data Parameters ---------- - X_train: SUPPORTED_FEAT_TYPES - A set of features that are going to be validated (type and dimensionality - checks) and a encoder fitted in the case the data needs encoding - X_test: Optional[SUPPORTED_FEAT_TYPES] - A hold out set of data used for checking + X_train: SUPPORTED_FEAT_TYPES + A set of features that are going to be validated (type and dimensionality + checks) and a encoder fitted in the case the data needs encoding + + X_test: Optional[SUPPORTED_FEAT_TYPES] + A hold out set of data used for checking + Returns ------- - pd.DataFrame: - transformed train data from list to pandas DataFrame - pd.DataFrame: - transformed test data from list to pandas DataFrame + Union[pd.DataFrame, pd.DataFrame]: + transformed (train, test) data from list to pandas DataFrame """ - # If a list was provided, it will be converted to pandas X_train = pd.DataFrame(data=X_train).convert_dtypes() # Store the dtypes and use in case of re-fit if len(self.dtypes) == 0: - self.dtypes = {col: X_train[col].dtype.name.lower() for col in X_train.columns} + self.dtypes = { + col: X_train[col].dtype.name.lower() for col in X_train.columns + } else: for col in X_train.columns: # Try to convert to the original dtype used to fit the validator @@ -367,25 +378,31 @@ def list_to_dataframe( try: X_train[col] = X_train[col].astype(self.dtypes[col]) except Exception as e: - self.logger.warning(f"Failed to format column {col} as {self.dtypes[col]}: {e}") + self.logger.warning( + f"Failed to format column {col} as {self.dtypes[col]}: {e}" + ) self.dtypes[col] = X_train[col].dtype.name.lower() - self.logger.warning("The provided feature types to autosklearn are of type list." - "Features have been interpreted as: {}".format( - [(col, t) for col, t in zip(X_train.columns, X_train.dtypes)] - )) + self.logger.warning( + "The provided feature types to autosklearn are of type list." + "Features have been interpreted as: {}".format( + [(col, t) for col, t in zip(X_train.columns, X_train.dtypes)] + ) + ) if X_test is not None: if not isinstance(X_test, List): - self.logger.warning("Train features are a list while the provided test data" - "is {}. X_test will be casted as DataFrame.".format( - type(X_test) - )) + self.logger.warning( + "Train features are a list while the provided test data" + "is {}. X_test will be casted as DataFrame.".format(type(X_test)) + ) X_test = pd.DataFrame(data=X_test) for col in X_test.columns: try: X_test[col] = X_test[col].astype(self.dtypes[col]) except Exception as e: - self.logger.warning(f"Failed to format column {col} as {self.dtypes[col]}: {e}") + self.logger.warning( + f"Failed to format column {col} as {self.dtypes[col]}: {e}" + ) self.dtypes[col] = X_test[col].dtype.name.lower() return X_train, X_test diff --git a/autosklearn/data/target_validator.py b/autosklearn/data/target_validator.py index 9f6d2e74b5..030a40b9b0 100644 --- a/autosklearn/data/target_validator.py +++ b/autosklearn/data/target_validator.py @@ -1,16 +1,14 @@ +from typing import List, Optional, Type, Union, cast + import logging import warnings -from typing import List, Optional, Type, Union, cast import numpy as np - import pandas as pd +import sklearn.utils from pandas.api.types import is_numeric_dtype from pandas.core.dtypes.base import ExtensionDtype - from scipy.sparse import spmatrix - -import sklearn.utils from sklearn.base import BaseEstimator from sklearn.exceptions import NotFittedError from sklearn.preprocessing import OrdinalEncoder @@ -18,29 +16,34 @@ from autosklearn.util.logging_ import PickableLoggerAdapter - SUPPORTED_TARGET_TYPES = Union[List, pd.Series, pd.DataFrame, np.ndarray, spmatrix] class TargetValidator(BaseEstimator): - """ - A class to pre-process targets. It validates the data provided during fit (to make sure - it matches Sklearn expectation) as well as encoding the targets in case of classification + """A class to pre-process targets. + + It validates the data provided during fit to make sure it matches Sklearn + expectation as well as encoding the targets in case of classification + Attributes ---------- - is_classification: bool - A bool that indicates if the validator should operate in classification mode. - During classification, the targets are encoded. - encoder: Optional[BaseEstimator] - Host a encoder object if the data requires transformation (for example, - if provided a categorical column in a pandas DataFrame) - enc_columns: List[str] - List of columns that where encoded + is_classification: bool + A bool that indicates if the validator should operate in classification mode. + During classification, the targets are encoded. + + encoder: Optional[BaseEstimator] + Host a encoder object if the data requires transformation (for example, if + provided a categorical column in a pandas DataFrame). + + enc_columns: List[str] + List of columns that where encoded """ - def __init__(self, - is_classification: bool = False, - logger: Optional[PickableLoggerAdapter] = None, - ) -> None: + + def __init__( + self, + is_classification: bool = False, + logger: Optional[PickableLoggerAdapter] = None, + ) -> None: self.is_classification = is_classification self.data_type = None # type: Optional[type] @@ -66,7 +69,7 @@ def fit( self, y_train: Union[List, np.ndarray, pd.Series, pd.DataFrame], y_test: Optional[Union[List, np.ndarray, pd.Series, pd.DataFrame]] = None, - ) -> 'TargetValidator': + ) -> "TargetValidator": """ Validates and fit a categorical encoder (if needed) to the targets The supported data types are List, numpy arrays and pandas DataFrames. @@ -87,31 +90,40 @@ def fit( self._check_data(y_test) if len(shape) != len(np.shape(y_test)) or ( - len(shape) > 1 and (shape[1] != np.shape(y_test)[1])): - raise ValueError("The dimensionality of the train and test" - " targets do not match" - f" train {np.shape(y_train)}" - f" != test {np.shape(y_test)}") + len(shape) > 1 and (shape[1] != np.shape(y_test)[1]) + ): + raise ValueError( + "The dimensionality of the train and test" + " targets do not match" + f" train {np.shape(y_train)}" + f" != test {np.shape(y_test)}" + ) if isinstance(y_train, pd.DataFrame): if not isinstance(y_test, pd.DataFrame): y_test = pd.DataFrame(y_test) if y_train.columns.tolist() != y_test.columns.tolist(): - raise ValueError("Train and test targets must both have the" - f" same columns, yet y={y_train.columns}" - f" and y_test={y_test.columns}") + raise ValueError( + "Train and test targets must both have the" + f" same columns, yet y={y_train.columns}" + f" and y_test={y_test.columns}" + ) if list(y_train.dtypes) != list(y_test.dtypes): - raise ValueError("Train and test targets must both have the same dtypes") + raise ValueError( + "Train and test targets must both have the same dtypes" + ) if self.out_dimensionality is None: self.out_dimensionality = 1 if len(shape) == 1 else shape[1] else: _n_outputs = 1 if len(shape) == 1 else shape[1] if self.out_dimensionality != _n_outputs: - raise ValueError('Number of outputs changed from %d to %d!' % - (self.out_dimensionality, _n_outputs)) + raise ValueError( + "Number of outputs changed from %d to %d!" + % (self.out_dimensionality, _n_outputs) + ) # Fit on the training data self._fit(y_train, y_test) @@ -124,7 +136,7 @@ def _fit( self, y_train: Union[List, np.ndarray, pd.Series, pd.DataFrame], y_test: Optional[Union[List, np.ndarray, pd.Series, pd.DataFrame]] = None, - ) -> 'TargetValidator': + ) -> "TargetValidator": """ If dealing with classification, this utility encodes the targets. @@ -139,7 +151,7 @@ def _fit( y_test: Optional[SUPPORTED_TARGET_TYPES] A holdout set of labels """ - if not self.is_classification or self.type_of_target == 'multilabel-indicator': + if not self.is_classification or self.type_of_target == "multilabel-indicator": # Only fit an encoder for classification tasks # Also, encoding multilabel indicator data makes the data multiclass # Let the user employ a MultiLabelBinarizer if needed @@ -150,16 +162,17 @@ def _fit( shape = np.shape(y_train) ndim = len(shape) if ndim > 1 and shape[1] != 1: - # We should not reach this if statement as we check for type of targets before - raise ValueError("Multi-dimensional classification is not yet" - " supported. Encoding multidimensional data" - " converts multiple columns to a 1 dimensional encoding." - f" Data involved = {shape}/{self.type_of_target}") + # We should not reach this if statement, we check for type of targets before + raise ValueError( + "Multi-dimensional classification is not yet" + " supported. Encoding multidimensional data" + " converts multiple columns to a 1 dimensional encoding." + f" Data involved = {shape}/{self.type_of_target}" + ) # Creat the encoder self.encoder = OrdinalEncoder( - handle_unknown='use_encoded_value', - unknown_value=-1 + handle_unknown="use_encoded_value", unknown_value=-1 ) # Clear typing to just numpy arrays and pandas @@ -172,10 +185,12 @@ def _fit( # inverse_transform to try corretly restore it's dtype if isinstance(y, pd.Series): if isinstance(y.dtype, ExtensionDtype): - warnings.warn("Fitting transformer with a pandas series which" - f" has the dtype {y.dtype}. Inverse transform" - " may not be able preserve dtype when converting" - " to np.ndarray") + warnings.warn( + "Fitting transformer with a pandas series which" + f" has the dtype {y.dtype}. Inverse transform" + " may not be able preserve dtype when converting" + " to np.ndarray" + ) if is_numeric_dtype(y.dtype): self.dtype = y.dtype elif isinstance(y, pd.DataFrame): @@ -187,7 +202,7 @@ def _fit( # Merge y_test and y_train for encoding if y_test is not None: - if isinstance(y, (pd.Series, pd.DataFrame)): + if isinstance(y, (pd.Series, pd.DataFrame)): if isinstance(y, pd.Series): y_test = pd.Series(y_test) else: @@ -257,7 +272,7 @@ def transform( y_transformed = sklearn.utils.check_array( y_transformed, force_all_finite=True, - accept_sparse='csr', + accept_sparse="csr", ensure_2d=False, ) @@ -271,7 +286,7 @@ def inverse_transform( self, y: Union[List, pd.Series, pd.DataFrame, np.ndarray], ) -> np.ndarray: - """ Revert any encoding transformation done on a target array. + """Revert any encoding transformation done on a target array. Parameters ---------- @@ -318,9 +333,7 @@ def inverse_transform( return y_inv def is_single_column_target(self) -> bool: - """ - Output is encoded with a single column encoding - """ + """Output is encoded with a single column encoding""" return self.out_dimensionality == 1 def _check_data( @@ -332,38 +345,41 @@ def _check_data( Parameters ---------- - y: Union[np.ndarray, pd.DataFrame, pd.Series] - A set of features whose dimensionality and data type is going to be checked + y: Union[np.ndarray, pd.DataFrame, pd.Series] + A set of features whose dimensionality and data type is going to be checked """ - if not isinstance( - y, (np.ndarray, pd.DataFrame, List, pd.Series)) and not isinstance(y, spmatrix): - raise ValueError("Auto-sklearn only supports Numpy arrays, Pandas DataFrames," - " pd.Series, sparse data and Python Lists as targets, yet, " - "the provided input is of type {}".format( - type(y) - )) + y, (np.ndarray, pd.DataFrame, List, pd.Series) + ) and not isinstance(y, spmatrix): + raise ValueError( + "Auto-sklearn only supports Numpy arrays, Pandas DataFrames," + " pd.Series, sparse data and Python Lists as targets, yet, " + "the provided input is of type {}".format(type(y)) + ) if isinstance(y, spmatrix) and not np.issubdtype(y.dtype.type, np.number): - raise ValueError("When providing a sparse matrix as targets, the only supported " - "values are numerical. Please consider using a dense" - " instead." - ) + raise ValueError( + "When providing a sparse matrix as targets, the only supported " + "values are numerical. Please consider using a dense" + " instead." + ) if self.data_type is None: self.data_type = type(y) if self.data_type != type(y): - self.logger.warning("Auto-sklearn previously received targets of type %s " - "yet the current features have type %s. Changing the dtype " - "of inputs to an estimator might cause problems" % ( - str(self.data_type), - str(type(y)), - ), - ) + self.logger.warning( + "Auto-sklearn previously received targets of type %s " + "yet the current features have type %s. Changing the dtype " + "of inputs to an estimator might cause problems" + % ( + str(self.data_type), + str(type(y)), + ), + ) # No Nan is supported has_nan_values = False - if hasattr(y, 'iloc'): + if hasattr(y, "iloc"): has_nan_values = cast(pd.DataFrame, y).isnull().values.any() if isinstance(y, spmatrix): @@ -374,34 +390,39 @@ def _check_data( # but NaN, are not equal to themselves: has_nan_values = not np.array_equal(y, y) if has_nan_values: - raise ValueError("Target values cannot contain missing/NaN values. " - "This is not supported by scikit-learn. " - ) + raise ValueError( + "Target values cannot contain missing/NaN values. " + "This is not supported by scikit-learn. " + ) # Pandas Series is not supported for multi-label indicator # This format checks are done by type of target try: self.type_of_target = type_of_target(y) except Exception as e: - raise ValueError("The provided data could not be interpreted by Sklearn. " - "While determining the type of the targets via type_of_target " - "run into exception: {}.".format(e)) - - supported_output_types = ('binary', - 'continuous', - 'continuous-multioutput', - 'multiclass', - 'multilabel-indicator', - # Notice unknown/multiclass-multioutput are not supported - # This can only happen during testing only as estimators - # should filter out unsupported types. - ) + raise ValueError( + "The provided data could not be interpreted by Sklearn. " + "While determining the type of the targets via type_of_target " + "run into exception: {}.".format(e) + ) + + supported_output_types = ( + "binary", + "continuous", + "continuous-multioutput", + "multiclass", + "multilabel-indicator", + # Notice unknown/multiclass-multioutput are not supported + # This can only happen during testing only as estimators + # should filter out unsupported types. + ) if self.type_of_target not in supported_output_types: - raise ValueError("Provided targets are not supported by Auto-Sklearn. " - "Provided type is {} whereas supported types are {}.".format( - self.type_of_target, - supported_output_types - )) + raise ValueError( + "Provided targets are not supported by Auto-Sklearn. " + "Provided type is {} whereas supported types are {}.".format( + self.type_of_target, supported_output_types + ) + ) @property def classes_(self) -> np.ndarray: @@ -410,10 +431,11 @@ def classes_(self) -> np.ndarray: which consist of a ndarray of shape (n_classes,) where n_classes are the number of classes seen while fitting a encoder to the targets. + Returns ------- - classes_: np.ndarray - The unique classes seen during encoding of a classifier + classes_: np.ndarray + The unique classes seen during encoding of a classifier """ if self.encoder is None: return np.array([]) diff --git a/autosklearn/data/validation.py b/autosklearn/data/validation.py index d06082258d..89aaca85c0 100644 --- a/autosklearn/data/validation.py +++ b/autosklearn/data/validation.py @@ -1,23 +1,21 @@ # -*- encoding: utf-8 -*- -import logging from typing import List, Optional, Tuple, Union -import numpy as np +import logging +import numpy as np import pandas as pd - from scipy.sparse import spmatrix - from sklearn.base import BaseEstimator from sklearn.exceptions import NotFittedError -from autosklearn.data.feature_validator import FeatureValidator, SUPPORTED_FEAT_TYPES +from autosklearn.data.feature_validator import SUPPORTED_FEAT_TYPES, FeatureValidator from autosklearn.data.target_validator import SUPPORTED_TARGET_TYPES, TargetValidator from autosklearn.util.logging_ import get_named_client_logger def convert_if_sparse( - y: SUPPORTED_TARGET_TYPES + y: SUPPORTED_TARGET_TYPES, ) -> Union[np.ndarray, List, pd.DataFrame, pd.Series]: """If the labels `y` are sparse, it will convert it to its dense representation @@ -51,26 +49,32 @@ class InputValidator(BaseEstimator): This class also perform checks for data integrity and flags the user via informative errors. + Attributes ---------- - feat_type: Optional[List[str]] - In case the dataset is not a pandas DataFrame: - + If provided, this list indicates which columns should be treated as categorical - it is internally transformed into a dictionary that indicates a mapping from - column index to categorical/numerical - + If not provided, by default all columns are treated as numerical - If the input dataset is of type pandas dataframe, this argument - must be none, as the column type will be inferred from the pandas dtypes. - is_classification: bool - For classification task, this flag indicates that the target data - should be encoded - feature_validator: FeatureValidator - A FeatureValidator instance used to validate and encode feature columns to match - sklearn expectations on the data - target_validator: TargetValidator - A TargetValidator instance used to validate and encode (in case of classification) - the target values + feat_type: Optional[List[str]] = None + In case the dataset is not a pandas DataFrame: + + If provided, this list indicates which columns should be treated as + categorical it is internally transformed into a dictionary that + indicates a mapping from column index to categorical/numerical. + + If not provided, by default all columns are treated as numerical + + If the input dataset is of type pandas dataframe, this argument + must be none, as the column type will be inferred from the pandas dtypes. + + is_classification: bool + For classification task, this flag indicates that the target data + should be encoded + + feature_validator: FeatureValidator + A FeatureValidator instance used to validate and encode feature columns to match + sklearn expectations on the data + + target_validator: TargetValidator + A TargetValidator instance used for classification to validate and encode the + target values """ + def __init__( self, feat_type: Optional[List[str]] = None, @@ -82,16 +86,18 @@ def __init__( self.logger_port = logger_port if self.logger_port is not None: self.logger = get_named_client_logger( - name='Validation', + name="Validation", port=self.logger_port, ) else: - self.logger = logging.getLogger('Validation') - - self.feature_validator = FeatureValidator(feat_type=self.feat_type, - logger=self.logger) - self.target_validator = TargetValidator(is_classification=self.is_classification, - logger=self.logger) + self.logger = logging.getLogger("Validation") + + self.feature_validator = FeatureValidator( + feat_type=self.feat_type, logger=self.logger + ) + self.target_validator = TargetValidator( + is_classification=self.is_classification, logger=self.logger + ) self._is_fitted = False def fit( @@ -106,45 +112,55 @@ def fit( a encoder for targets in the case of classification. Specifically: For features: - + Valid data types are enforced (List, np.ndarray, pd.DataFrame, pd.Series, scipy - sparse) as well as dimensionality checks - + If the provided data is a pandas DataFrame with categorical/boolean/int columns, - such columns will be encoded using an Ordinal Encoder + Valid data types are enforced (List, np.ndarray, pd.DataFrame, pd.Series, scipy + sparse) as well as dimensionality checks + + If the provided data is a pandas DataFrame with categorical/boolean/int columns, + such columns will be encoded using an Ordinal Encoder + For targets: - + Checks for dimensionality as well as missing values are performed. - + If performing a classification task, the data is going to be encoded + * Checks for dimensionality as well as missing values are performed. + * If performing a classification task, the data is going to be encoded Parameters ---------- - X_train: SUPPORTED_FEAT_TYPES - A set of features that are going to be validated (type and dimensionality - checks). If this data contains categorical columns, an encoder is going to - be instantiated and trained with this data. - y_train: SUPPORTED_TARGET_TYPES - A set of targets that are going to be encoded if the task is for classification - X_test: Optional[SUPPORTED_FEAT_TYPES] - A hold out set of features used for checking - y_test: SUPPORTED_TARGET_TYPES - A hold out set of targets used for checking. Additionally, if the current task - is a classification task, this y_test categories are also going to be used to - fit a pre-processing encoding (to prevent errors on unseen classes). + X_train: SUPPORTED_FEAT_TYPES + A set of features that are going to be validated (type and dimensionality + checks). If this data contains categorical columns, an encoder is going to + be instantiated and trained with this data. + + y_train: SUPPORTED_TARGET_TYPES + A set of targets to encoded if the task is for classification. + + X_test: Optional[SUPPORTED_FEAT_TYPES] + A hold out set of features used for checking + + y_test: SUPPORTED_TARGET_TYPES + A hold out set of targets used for checking. Additionally, if the current + task is a classification task, this y_test categories are also going to be + used to fit a pre-processing encoding (to prevent errors on unseen classes). + Returns ------- - self + self """ # Check that the data is valid if np.shape(X_train)[0] != np.shape(y_train)[0]: - raise ValueError("Inconsistent number of train datapoints for features and targets," - " {} for features and {} for targets".format( - np.shape(X_train)[0], - np.shape(y_train)[0], - )) + raise ValueError( + "Inconsistent number of train datapoints for features and targets," + " {} for features and {} for targets".format( + np.shape(X_train)[0], + np.shape(y_train)[0], + ) + ) if X_test is not None and np.shape(X_test)[0] != np.shape(y_test)[0]: - raise ValueError("Inconsistent number of test datapoints for features and targets," - " {} for features and {} for targets".format( - np.shape(X_test)[0], - np.shape(y_test)[0], - )) + raise ValueError( + "Inconsistent number of test datapoints for features and targets," + " {} for features and {} for targets".format( + np.shape(X_test)[0], + np.shape(y_test)[0], + ) + ) self.feature_validator.fit(X_train, X_test) self.target_validator.fit(y_train, y_test) @@ -175,7 +191,9 @@ def transform( The transformed targets array """ if not self._is_fitted: - raise NotFittedError("Cannot call transform on a validator that is not fitted") + raise NotFittedError( + "Cannot call transform on a validator that is not fitted" + ) X_transformed = self.feature_validator.transform(X) if y is not None: diff --git a/autosklearn/data/xy_data_manager.py b/autosklearn/data/xy_data_manager.py index 4c539157ee..d8cd467214 100644 --- a/autosklearn/data/xy_data_manager.py +++ b/autosklearn/data/xy_data_manager.py @@ -2,9 +2,7 @@ from typing import Dict, Optional, Union, cast import numpy as np - import pandas as pd - from scipy import sparse from autosklearn.constants import ( @@ -15,14 +13,10 @@ REGRESSION, ) from autosklearn.data.abstract_data_manager import AbstractDataManager -from autosklearn.data.validation import ( - SUPPORTED_FEAT_TYPES, - SUPPORTED_TARGET_TYPES, -) +from autosklearn.data.validation import SUPPORTED_FEAT_TYPES, SUPPORTED_TARGET_TYPES class XYDataManager(AbstractDataManager): - def __init__( self, X: SUPPORTED_FEAT_TYPES, @@ -31,52 +25,59 @@ def __init__( y_test: Optional[SUPPORTED_TARGET_TYPES], task: int, feat_type: Dict[Union[str, int], str], - dataset_name: str + dataset_name: str, ): super(XYDataManager, self).__init__(dataset_name) - self.info['task'] = task + self.info["task"] = task if sparse.issparse(X): - self.info['is_sparse'] = 1 - self.info['has_missing'] = np.all(np.isfinite(cast(sparse.csr_matrix, X).data)) + self.info["is_sparse"] = 1 + self.info["has_missing"] = np.all( + np.isfinite(cast(sparse.csr_matrix, X).data) + ) else: - self.info['is_sparse'] = 0 - if hasattr(X, 'iloc'): - self.info['has_missing'] = cast(pd.DataFrame, X).isnull().values.any() + self.info["is_sparse"] = 0 + if hasattr(X, "iloc"): + self.info["has_missing"] = cast(pd.DataFrame, X).isnull().values.any() else: - self.info['has_missing'] = np.all(np.isfinite(X)) + self.info["has_missing"] = np.all(np.isfinite(X)) label_num = { REGRESSION: 1, BINARY_CLASSIFICATION: 2, MULTIOUTPUT_REGRESSION: np.shape(y)[-1], MULTICLASS_CLASSIFICATION: len(np.unique(y)), - MULTILABEL_CLASSIFICATION: np.shape(y)[-1] + MULTILABEL_CLASSIFICATION: np.shape(y)[-1], } - self.info['label_num'] = label_num[task] + self.info["label_num"] = label_num[task] - self.data['X_train'] = X - self.data['Y_train'] = y + self.data["X_train"] = X + self.data["Y_train"] = y if X_test is not None: - self.data['X_test'] = X_test + self.data["X_test"] = X_test if y_test is not None: - self.data['Y_test'] = y_test + self.data["Y_test"] = y_test if isinstance(feat_type, dict): self.feat_type = feat_type else: - raise ValueError("Unsupported feat_type provided. We expect the user to " - "provide a Dict[str, str] mapping from column to categorical/ " - "numerical.") + raise ValueError( + "Unsupported feat_type provided. We expect the user to " + "provide a Dict[str, str] mapping from column to categorical/ " + "numerical." + ) # TODO: try to guess task type! if len(np.shape(y)) > 2: - raise ValueError('y must not have more than two dimensions, ' - 'but has %d.' % len(np.shape(y))) + raise ValueError( + "y must not have more than two dimensions, " + "but has %d." % len(np.shape(y)) + ) if np.shape(X)[0] != np.shape(y)[0]: - raise ValueError('X and y must have the same number of ' - 'datapoints, but have %d and %d.' % (np.shape(X)[0], - np.shape(y)[0])) + raise ValueError( + "X and y must have the same number of " + "datapoints, but have %d and %d." % (np.shape(X)[0], np.shape(y)[0]) + ) diff --git a/autosklearn/ensemble_builder.py b/autosklearn/ensemble_builder.py index e337726b0e..3707ce84c9 100644 --- a/autosklearn/ensemble_builder.py +++ b/autosklearn/ensemble_builder.py @@ -1,21 +1,21 @@ # -*- encoding: utf-8 -*- +from typing import List, Optional, Tuple, Union + import glob import gzip -import math -import numbers import logging.handlers +import math import multiprocessing +import numbers import os import pickle import re import shutil import time import traceback -from typing import List, Optional, Tuple, Union import zlib import dask.distributed - import numpy as np import pandas as pd import pynisher @@ -24,12 +24,13 @@ from smac.runhistory.runhistory import RunInfo, RunValue from smac.tae.base import StatusType +from autosklearn.automl_common.common.ensemble_building.abstract_ensemble import ( # noqa: E501 + AbstractEnsemble, +) from autosklearn.automl_common.common.utils.backend import Backend -from autosklearn.automl_common.common.ensemble_building.abstract_ensemble import AbstractEnsemble - from autosklearn.constants import BINARY_CLASSIFICATION -from autosklearn.metrics import calculate_score, calculate_loss, Scorer from autosklearn.ensembles.ensemble_selection import EnsembleSelection +from autosklearn.metrics import Scorer, calculate_loss, calculate_score from autosklearn.util.logging_ import get_named_client_logger from autosklearn.util.parallel import preload_modules @@ -37,7 +38,7 @@ Y_VALID = 1 Y_TEST = 2 -MODEL_FN_RE = r'_([0-9]*)_([0-9]*)_([0-9]{1,3}\.[0-9]*)\.npy' +MODEL_FN_RE = r"_([0-9]*)_([0-9]*)_([0-9]{1,3}\.[0-9]*)\.npy" class EnsembleBuilderManager(IncorporateRunResultCallback): @@ -59,62 +60,78 @@ def __init__( ensemble_memory_limit: Optional[int], random_state: Union[int, np.random.RandomState], logger_port: int = logging.handlers.DEFAULT_TCP_LOGGING_PORT, - pynisher_context: str = 'fork', + pynisher_context: str = "fork", ): - """ SMAC callback to handle ensemble building + """SMAC callback to handle ensemble building Parameters ---------- start_time: int - the time when this job was started, to account for any latency in job allocation + the time when this job was started, to account for any latency in job + allocation. + time_left_for_ensemble: int - How much time is left for the task. Job should finish within this allocated time + How much time is left for the task. Job should finish within this + allocated time + backend: util.backend.Backend backend to write and read files + dataset_name: str name of dataset + task_type: int type of ML task + metric: str name of metric to compute the loss of the given predictions + ensemble_size: int - maximal size of ensemble (passed to autosklearn.ensemble.ensemble_selection) + maximal size of ensemble + ensemble_nbest: int/float if int: consider only the n best prediction if float: consider only this fraction of the best models Both wrt to validation predictions If performance_range_threshold > 0, might return less models + max_models_on_disc: int Defines the maximum number of models that are kept in the disc. - If int, it must be greater or equal than 1, and dictates the max number of - models to keep. - If float, it will be interpreted as the max megabytes allowed of disc space. That - is, if the number of ensemble candidates require more disc space than this float - value, the worst models will be deleted to keep within this budget. - Models and predictions of the worst-performing models will be deleted then. - If None, the feature is disabled. - It defines an upper bound on the models that can be used in the ensemble. + + If int, it must be greater or equal than 1, and dictates the max + number of models to keep. + + If float, it will be interpreted as the max megabytes allowed of + disc space. That is, if the number of ensemble candidates require more + disc space than this float value, the worst models will be deleted to + keep within this budget. Models and predictions of the worst-performing + models will be deleted then. + + If None, the feature is disabled. It defines an upper bound on the + models that can be used in the ensemble. + seed: int random seed + max_iterations: int maximal number of iterations to run this script (default None --> deactivated) + precision: [16,32,64,128] precision of floats to read the predictions + memory_limit: Optional[int] memory limit in mb. If ``None``, no memory limit is enforced. + read_at_most: int read at most n new prediction files in each iteration + logger_port: int port that receives logging records + pynisher_context: str The multiprocessing context for pynisher. One of spawn/fork/forkserver. - Returns - ------- - List[Tuple[int, float, float, float]]: - A list with the performance history of this ensemble, of the form - [[pandas_timestamp, train_performance, val_performance, test_performance], ...] """ self.start_time = start_time self.time_left_for_ensembles = time_left_for_ensembles @@ -148,26 +165,31 @@ def __init__( def __call__( self, - smbo: 'SMBO', + smbo: "SMBO", run_info: RunInfo, result: RunValue, time_left: float, ): + """ + Returns + ------- + List[Tuple[int, float, float, float]]: + A list with the performance history of this ensemble, of the form + [(pandas_timestamp, train_performance, val_performance, test_performance)] + """ if result.status in (StatusType.STOP, StatusType.ABORT) or smbo._stop: return self.build_ensemble(smbo.tae_runner.client) def build_ensemble( - self, - dask_client: dask.distributed.Client, - unit_test: bool = False + self, dask_client: dask.distributed.Client, unit_test: bool = False ) -> None: # The second criteria is elapsed time elapsed_time = time.time() - self.start_time logger = get_named_client_logger( - name='EnsembleBuilder', + name="EnsembleBuilder", port=self.logger_port, ) @@ -181,10 +203,8 @@ def build_ensemble( return if self.max_iterations is not None and self.max_iterations <= self.iteration: logger.info( - "Terminate ensemble building because of max iterations: {} of {}".format( - self.max_iterations, - self.iteration - ) + "Terminate ensemble building because of max iterations:" + f" {self.max_iterations} of {self.iteration}" ) return @@ -193,11 +213,13 @@ def build_ensemble( result = self.futures.pop().result() if result: ensemble_history, self.ensemble_nbest, _, _, _ = result - logger.debug("iteration={} @ elapsed_time={} has history={}".format( - self.iteration, - elapsed_time, - ensemble_history, - )) + logger.debug( + "iteration={} @ elapsed_time={} has history={}".format( + self.iteration, + elapsed_time, + ensemble_history, + ) + ) self.history.extend(ensemble_history) # Only submit new jobs if the previous ensemble job finished @@ -215,28 +237,30 @@ def build_ensemble( # see it in the dask diagnostic dashboard # Notice that the forked ensemble_builder_process will # wait for the below function to be done - self.futures.append(dask_client.submit( - fit_and_return_ensemble, - backend=self.backend, - dataset_name=self.dataset_name, - task_type=self.task, - metric=self.metric, - ensemble_size=self.ensemble_size, - ensemble_nbest=self.ensemble_nbest, - max_models_on_disc=self.max_models_on_disc, - seed=self.seed, - precision=self.precision, - memory_limit=self.ensemble_memory_limit, - read_at_most=self.read_at_most, - random_state=self.random_state, - end_at=self.start_time + self.time_left_for_ensembles, - iteration=self.iteration, - return_predictions=False, - priority=100, - pynisher_context=self.pynisher_context, - logger_port=self.logger_port, - unit_test=unit_test, - )) + self.futures.append( + dask_client.submit( + fit_and_return_ensemble, + backend=self.backend, + dataset_name=self.dataset_name, + task_type=self.task, + metric=self.metric, + ensemble_size=self.ensemble_size, + ensemble_nbest=self.ensemble_nbest, + max_models_on_disc=self.max_models_on_disc, + seed=self.seed, + precision=self.precision, + memory_limit=self.ensemble_memory_limit, + read_at_most=self.read_at_most, + random_state=self.random_state, + end_at=self.start_time + self.time_left_for_ensembles, + iteration=self.iteration, + return_predictions=False, + priority=100, + pynisher_context=self.pynisher_context, + logger_port=self.logger_port, + unit_test=unit_test, + ) + ) logger.info( "{}/{} Started Ensemble builder job at {} for iteration {}.".format( @@ -276,11 +300,11 @@ def fit_and_return_ensemble( memory_limit: Optional[int] = None, random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> Tuple[ - List[Tuple[int, float, float, float]], - int, - Optional[np.ndarray], - Optional[np.ndarray], - Optional[np.ndarray], + List[Tuple[int, float, float, float]], + int, + Optional[np.ndarray], + Optional[np.ndarray], + Optional[np.ndarray], ]: """ @@ -291,60 +315,79 @@ def fit_and_return_ensemble( ---------- backend: util.backend.Backend backend to write and read files + dataset_name: str name of dataset + metric: str name of metric to compute the loss of the given predictions + task_type: int type of ML task + ensemble_size: int maximal size of ensemble (passed to autosklearn.ensemble.ensemble_selection) + ensemble_nbest: int/float if int: consider only the n best prediction if float: consider only this fraction of the best models Both wrt to validation predictions If performance_range_threshold > 0, might return less models + max_models_on_disc: int Defines the maximum number of models that are kept in the disc. + If int, it must be greater or equal than 1, and dictates the max number of models to keep. - If float, it will be interpreted as the max megabytes allowed of disc space. That - is, if the number of ensemble candidates require more disc space than this float - value, the worst models will be deleted to keep within this budget. - Models and predictions of the worst-performing models will be deleted then. + + If float, it will be interpreted as the max megabytes allowed of disc space. + That is, if the number of ensemble candidates require more disc space than + this float value, the worst models will be deleted to keep within this + budget. Models and predictions of the worst-performing models will be + deleted then. + If None, the feature is disabled. It defines an upper bound on the models that can be used in the ensemble. + seed: int random seed + precision: [16,32,64,128] precision of floats to read the predictions + read_at_most: int read at most n new prediction files in each iteration + end_at: float - At what time the job must finish. Needs to be the endtime and not the time left - because we do not know when dask schedules the job. + At what time the job must finish. Needs to be the endtime and not the + time left because we do not know when dask schedules the job. + iteration: int The current iteration + pynisher_context: str Context to use for multiprocessing, can be either fork, spawn or forkserver. + logger_port: int = DEFAULT_TCP_LOGGING_PORT The port where the logging server is listening to. + unit_test: bool = False - Turn on unit testing mode. This currently makes fit_ensemble raise a MemoryError. - Having this is very bad coding style, but I did not find a way to make - unittest.mock work through the pynisher with all spawn contexts. If you know a - better solution, please let us know by opening an issue. + Turn on unit testing mode. This currently makes fit_ensemble raise a + MemoryError. Having this is very bad coding style, but I did not find a way + to make unittest.mock work through the pynisher with all spawn contexts. + If you know a better solution, please let us know by opening an issue. + memory_limit: Optional[int] = None memory limit in mb. If ``None``, no memory limit is enforced. + random_state: Optional[int | RandomState] = None A random state used for the ensemble selection process. Returns ------- - List[Tuple[int, float, float, float]] - A list with the performance history of this ensemble, of the form - [[pandas_timestamp, train_performance, val_performance, test_performance], ...] - + List[Tuple[int, float, float, float]] + A list with the performance history of this ensemble, of the form + [(pandas_timestamp, train_performance, val_performance, test_performance)] """ result = EnsembleBuilder( backend=backend, @@ -390,58 +433,58 @@ def __init__( unit_test: bool = False, ): """ - Constructor - - Parameters - ---------- - backend: util.backend.Backend - backend to write and read files - dataset_name: str - name of dataset - task_type: int - type of ML task - metric: str - name of metric to compute the loss of the given predictions - ensemble_size: int = 10 - maximal size of ensemble (passed to autosklearn.ensemble.ensemble_selection) - ensemble_nbest: int | float = 100 - if int: consider only the n best prediction - if float: consider only this fraction of the best models - Both with respect to the validation predictions - If performance_range_threshold > 0, might return less models - max_models_on_disc: int = 100 - Defines the maximum number of models that are kept in the disc. - If int, it must be greater or equal than 1, and dictates the max number of - models to keep. - If float, it will be interpreted as the max megabytes allowed of disc space. That - is, if the number of ensemble candidates require more disc space than this float - value, the worst models will be deleted to keep within this budget. - Models and predictions of the worst-performing models will be deleted then. - If None, the feature is disabled. - It defines an upper bound on the models that can be used in the ensemble. - performance_range_threshold: float = 0 - Keep only models that are better than: - dummy + (best - dummy)*performance_range_threshold - E.g dummy=2, best=4, thresh=0.5 --> only consider models with loss > 3 - Will at most return the minimum between ensemble_nbest models, - and max_models_on_disc. Might return less - seed: int = 1 - random seed that is used as part of the filename - precision: int in [16,32,64,128] = 32 - precision of floats to read the predictions - memory_limit: Optional[int] = 1024 - memory limit in mb. If ``None``, no memory limit is enforced. - read_at_most: int = 5 - read at most n new prediction files in each iteration - logger_port: int = DEFAULT_TCP_LOGGING_PORT - port that receives logging records - random_state: Optional[int | RandomState] = None - An int or RandomState object used for generating the ensemble. - unit_test: bool = False - Turn on unit testing mode. This currently makes fit_ensemble raise a MemoryError. - Having this is very bad coding style, but I did not find a way to make - unittest.mock work through the pynisher with all spawn contexts. If you know a - better solution, please let us know by opening an issue. + Constructor + + Parameters + ---------- + backend: util.backend.Backend + backend to write and read files + dataset_name: str + name of dataset + task_type: int + type of ML task + metric: str + name of metric to compute the loss of the given predictions + ensemble_size: int = 10 + maximal size of ensemble (passed to autosklearn.ensemble.ensemble_selection) + ensemble_nbest: int | float = 100 + if int: consider only the n best prediction + if float: consider only this fraction of the best models + Both with respect to the validation predictions + If performance_range_threshold > 0, might return less models + max_models_on_disc: int = 100 + Defines the maximum number of models that are kept in the disc. + If int, it must be greater or equal than 1, and dictates the max number of + models to keep. + If float, it will be interpreted as the max megabytes allowed of disc space. + That is, if the number of ensemble candidates require more disc space than + this float value, the worst models are deleted to keep within this budget. + Models and predictions of the worst-performing models will be deleted then. + If None, the feature is disabled. + It defines an upper bound on the models that can be used in the ensemble. + performance_range_threshold: float = 0 + Keep only models that are better than: + dummy + (best - dummy)*performance_range_threshold + E.g dummy=2, best=4, thresh=0.5 --> only consider models with loss > 3 + Will at most return the minimum between ensemble_nbest models, + and max_models_on_disc. Might return less + seed: int = 1 + random seed that is used as part of the filename + precision: int in [16,32,64,128] = 32 + precision of floats to read the predictions + memory_limit: Optional[int] = 1024 + memory limit in mb. If ``None``, no memory limit is enforced. + read_at_most: int = 5 + read at most n new prediction files in each iteration + logger_port: int = DEFAULT_TCP_LOGGING_PORT + port that receives logging records + random_state: Optional[int | RandomState] = None + An int or RandomState object used for generating the ensemble. + unit_test: bool = False + Turn on unit testing mode. This currently makes fit_ensemble raise + a MemoryError. Having this is very bad coding style, but I did not find a + way to make unittest.mock work through the pynisher with all spawn contexts. + If you know a better solution, please let us know by opening an issue. """ super(EnsembleBuilder, self).__init__() @@ -454,13 +497,15 @@ def __init__( self.performance_range_threshold = performance_range_threshold if isinstance(ensemble_nbest, numbers.Integral) and ensemble_nbest < 1: - raise ValueError("Integer ensemble_nbest has to be larger 1: %s" % - ensemble_nbest) + raise ValueError( + "Integer ensemble_nbest has to be larger 1: %s" % ensemble_nbest + ) elif not isinstance(ensemble_nbest, numbers.Integral): if ensemble_nbest < 0 or ensemble_nbest > 1: raise ValueError( - "Float ensemble_nbest best has to be >= 0 and <= 1: %s" % - ensemble_nbest) + "Float ensemble_nbest best has to be >= 0 and <= 1: %s" + % ensemble_nbest + ) self.ensemble_nbest = ensemble_nbest @@ -469,9 +514,7 @@ def __init__( # max number of ensemble models. max_resident_models keeps the # maximum number of models in disc if max_models_on_disc is not None and max_models_on_disc < 0: - raise ValueError( - "max_models_on_disc has to be a positive number or None" - ) + raise ValueError("max_models_on_disc has to be a positive number or None") self.max_models_on_disc = max_models_on_disc self.max_resident_models = None @@ -485,13 +528,15 @@ def __init__( # Setup the logger self.logger_port = logger_port self.logger = get_named_client_logger( - name='EnsembleBuilder', + name="EnsembleBuilder", port=self.logger_port, ) if ensemble_nbest == 1: - self.logger.debug("Behaviour depends on int/float: %s, %s (ensemble_nbest, type)" % - (ensemble_nbest, type(ensemble_nbest))) + self.logger.debug( + "Behaviour depends on int/float: %s, %s (ensemble_nbest, type)" + % (ensemble_nbest, type(ensemble_nbest)) + ) self.start_time = 0 self.model_fn_re = re.compile(MODEL_FN_RE) @@ -528,8 +573,7 @@ def __init__( # we save the state of this dictionary to memory # and read it if available self.ensemble_memory_file = os.path.join( - self.backend.internals_directory, - 'ensemble_read_preds.pkl' + self.backend.internals_directory, "ensemble_read_preds.pkl" ) if os.path.exists(self.ensemble_memory_file): try: @@ -537,15 +581,12 @@ def __init__( self.read_preds, self.last_hash = pickle.load(memory) except Exception as e: self.logger.warning( - "Could not load the previous iterations of ensemble_builder predictions." - "This might impact the quality of the run. Exception={} {}".format( - e, - traceback.format_exc(), - ) + "Could not load the previous iterations of ensemble_builder" + " predictions. This might impact the quality of the run." + f" Exception={e} {traceback.format_exc()}" ) self.ensemble_loss_file = os.path.join( - self.backend.internals_directory, - 'ensemble_read_losses.pkl' + self.backend.internals_directory, "ensemble_read_losses.pkl" ) if os.path.exists(self.ensemble_loss_file): try: @@ -560,17 +601,17 @@ def __init__( ) ) - # hidden feature which can be activated via an environment variable. This keeps all - # models and predictions which have ever been a candidate. This is necessary to post-hoc - # compute the whole ensemble building trajectory. + # hidden feature which can be activated via an environment variable. + # This keeps all models and predictions which have ever been a candidate. + # This is necessary to post-hoc compute the whole ensemble building trajectory. self._has_been_candidate = set() self.validation_performance_ = np.inf # Track the ensemble performance datamanager = self.backend.load_datamanager() - self.y_valid = datamanager.data.get('Y_valid') - self.y_test = datamanager.data.get('Y_test') + self.y_valid = datamanager.data.get("Y_valid") + self.y_test = datamanager.data.get("Y_test") del datamanager self.ensemble_history = [] @@ -585,12 +626,12 @@ def run( ): if time_left is None and end_at is None: - raise ValueError('Must provide either time_left or end_at.') + raise ValueError("Must provide either time_left or end_at.") elif time_left is not None and end_at is not None: - raise ValueError('Cannot provide both time_left and end_at.') + raise ValueError("Cannot provide both time_left and end_at.") self.logger = get_named_client_logger( - name='EnsembleBuilder', + name="EnsembleBuilder", port=self.logger_port, ) @@ -624,37 +665,44 @@ def run( # if ensemble script died because of memory error, # reduce nbest to reduce memory consumption and try it again - # ATTENTION: main will start from scratch; # all data structures are empty again + # ATTENTION: main will start from scratch; + # all data structures are empty again try: os.remove(self.ensemble_memory_file) except: # noqa E722 pass - if isinstance(self.ensemble_nbest, numbers.Integral) and self.ensemble_nbest <= 1: + if ( + isinstance(self.ensemble_nbest, numbers.Integral) + and self.ensemble_nbest <= 1 + ): if self.read_at_most == 1: self.logger.error( - "Memory Exception -- Unable to further reduce the number of ensemble " - "members and can no further limit the number of ensemble members " - "loaded per iteration -- please restart Auto-sklearn with a higher " - "value for the argument `memory_limit` (current limit is %s MB). " - "The ensemble builder will keep running to delete files from disk in " - "case this was enabled.", self.memory_limit + "Memory Exception -- Unable to further reduce the number" + " of ensemble members and can no further limit the number" + " of ensemble members loaded per iteration, please restart" + " Auto-sklearn with a higher value for the argument" + f" `memory_limit` (current limit is {self.memory_limit}MB)." + " The ensemble builder will keep running to delete files" + " from disk in case this was enabled.", ) self.ensemble_nbest = 0 else: self.read_at_most = 1 self.logger.warning( - "Memory Exception -- Unable to further reduce the number of ensemble " - "members -- Now reducing the number of predictions per call to read " - "at most to 1." + "Memory Exception -- Unable to further reduce the number of" + " ensemble members. Now reducing the number of predictions" + " per call to read at most to 1." ) else: if isinstance(self.ensemble_nbest, numbers.Integral): self.ensemble_nbest = max(1, int(self.ensemble_nbest / 2)) else: self.ensemble_nbest = self.ensemble_nbest / 2 - self.logger.warning("Memory Exception -- restart with " - "less ensemble_nbest: %d" % self.ensemble_nbest) + self.logger.warning( + "Memory Exception -- restart with " + "less ensemble_nbest: %d" % self.ensemble_nbest + ) return [], self.ensemble_nbest, None, None, None else: return safe_ensemble_script.result @@ -667,7 +715,7 @@ def main(self, time_left, iteration, return_predictions): # the logger configuration. So we have to set it up # accordingly self.logger = get_named_client_logger( - name='EnsembleBuilder', + name="EnsembleBuilder", port=self.logger_port, ) @@ -676,7 +724,7 @@ def main(self, time_left, iteration, return_predictions): used_time = time.time() - self.start_time self.logger.debug( - 'Starting iteration %d, time left: %f', + "Starting iteration %d, time left: %f", iteration, time_left - used_time, ) @@ -684,7 +732,13 @@ def main(self, time_left, iteration, return_predictions): # populates self.read_preds and self.read_losses if not self.compute_loss_per_model(): if return_predictions: - return self.ensemble_history, self.ensemble_nbest, train_pred, valid_pred, test_pred + return ( + self.ensemble_history, + self.ensemble_nbest, + train_pred, + valid_pred, + test_pred, + ) else: return self.ensemble_history, self.ensemble_nbest, None, None, None @@ -693,22 +747,40 @@ def main(self, time_left, iteration, return_predictions): candidate_models = self.get_n_best_preds() if not candidate_models: # no candidates yet if return_predictions: - return self.ensemble_history, self.ensemble_nbest, train_pred, valid_pred, test_pred + return ( + self.ensemble_history, + self.ensemble_nbest, + train_pred, + valid_pred, + test_pred, + ) else: return self.ensemble_history, self.ensemble_nbest, None, None, None # populates predictions in self.read_preds # reduces selected models if file reading failed - n_sel_valid, n_sel_test = self. \ - get_valid_test_preds(selected_keys=candidate_models) + n_sel_valid, n_sel_test = self.get_valid_test_preds( + selected_keys=candidate_models + ) # If valid/test predictions loaded, then reduce candidate models to this set - if len(n_sel_test) != 0 and len(n_sel_valid) != 0 \ - and len(set(n_sel_valid).intersection(set(n_sel_test))) == 0: + if ( + len(n_sel_test) != 0 + and len(n_sel_valid) != 0 + and len(set(n_sel_valid).intersection(set(n_sel_test))) == 0 + ): # Both n_sel_* have entries, but there is no overlap, this is critical - self.logger.error("n_sel_valid and n_sel_test are not empty, but do not overlap") + self.logger.error( + "n_sel_valid and n_sel_test are not empty, but do not overlap" + ) if return_predictions: - return self.ensemble_history, self.ensemble_nbest, train_pred, valid_pred, test_pred + return ( + self.ensemble_history, + self.ensemble_nbest, + train_pred, + valid_pred, + test_pred, + ) else: return self.ensemble_history, self.ensemble_nbest, None, None, None @@ -716,24 +788,31 @@ def main(self, time_left, iteration, return_predictions): # then ensure candidate_models AND n_sel_test are sorted the same candidate_models_set = set(candidate_models) if candidate_models_set.intersection(n_sel_valid).intersection(n_sel_test): - candidate_models = sorted(list(candidate_models_set.intersection( - n_sel_valid).intersection(n_sel_test))) + candidate_models = sorted( + list( + candidate_models_set.intersection(n_sel_valid).intersection( + n_sel_test + ) + ) + ) n_sel_test = candidate_models n_sel_valid = candidate_models elif candidate_models_set.intersection(n_sel_valid): - candidate_models = sorted(list(candidate_models_set.intersection( - n_sel_valid))) + candidate_models = sorted( + list(candidate_models_set.intersection(n_sel_valid)) + ) n_sel_valid = candidate_models elif candidate_models_set.intersection(n_sel_test): - candidate_models = sorted(list(candidate_models_set.intersection( - n_sel_test))) + candidate_models = sorted( + list(candidate_models_set.intersection(n_sel_test)) + ) n_sel_test = candidate_models else: # This has to be the case n_sel_test = [] n_sel_valid = [] - if os.environ.get('ENSEMBLE_KEEP_ALL_CANDIDATES'): + if os.environ.get("ENSEMBLE_KEEP_ALL_CANDIDATES"): for candidate in candidate_models: self._has_been_candidate.add(candidate) @@ -744,8 +823,9 @@ def main(self, time_left, iteration, return_predictions): if ensemble is not None and self.SAVE2DISC: self.backend.save_ensemble(ensemble, iteration, self.seed) - # Delete files of non-candidate models - can only be done after fitting the ensemble and - # saving it to disc so we do not accidentally delete models in the previous ensemble + # Delete files of non-candidate models - can only be done after fitting the + # ensemble and saving it to disc so we do not accidentally delete models in + # the previous ensemble if self.max_resident_models is not None: self._delete_excess_models(selected_keys=candidate_models) @@ -754,39 +834,47 @@ def main(self, time_left, iteration, return_predictions): pickle.dump(self.read_losses, memory) if ensemble is not None: - train_pred = self.predict(set_="train", - ensemble=ensemble, - selected_keys=candidate_models, - n_preds=len(candidate_models), - index_run=iteration) + train_pred = self.predict( + set_="train", + ensemble=ensemble, + selected_keys=candidate_models, + n_preds=len(candidate_models), + index_run=iteration, + ) # We can't use candidate_models here, as n_sel_* might be empty - valid_pred = self.predict(set_="valid", - ensemble=ensemble, - selected_keys=n_sel_valid, - n_preds=len(candidate_models), - index_run=iteration) + valid_pred = self.predict( + set_="valid", + ensemble=ensemble, + selected_keys=n_sel_valid, + n_preds=len(candidate_models), + index_run=iteration, + ) # TODO if predictions fails, build the model again during the # next iteration! - test_pred = self.predict(set_="test", - ensemble=ensemble, - selected_keys=n_sel_test, - n_preds=len(candidate_models), - index_run=iteration) + test_pred = self.predict( + set_="test", + ensemble=ensemble, + selected_keys=n_sel_test, + n_preds=len(candidate_models), + index_run=iteration, + ) # Add a score to run history to see ensemble progress - self._add_ensemble_trajectory( - train_pred, - valid_pred, - test_pred - ) + self._add_ensemble_trajectory(train_pred, valid_pred, test_pred) - # The loaded predictions and the hash can only be saved after the ensemble has been + # The loaded predictions and hash can only be saved after the ensemble has been # built, because the hash is computed during the construction of the ensemble with open(self.ensemble_memory_file, "wb") as memory: pickle.dump((self.read_preds, self.last_hash), memory) if return_predictions: - return self.ensemble_history, self.ensemble_nbest, train_pred, valid_pred, test_pred + return ( + self.ensemble_history, + self.ensemble_nbest, + train_pred, + valid_pred, + test_pred, + ) else: return self.ensemble_history, self.ensemble_nbest, None, None, None @@ -803,10 +891,14 @@ def get_disk_consumption(self, pred_path): _budget = float(match.group(3)) stored_files_for_run = os.listdir( - self.backend.get_numrun_directory(_seed, _num_run, _budget)) + self.backend.get_numrun_directory(_seed, _num_run, _budget) + ) stored_files_for_run = [ - os.path.join(self.backend.get_numrun_directory(_seed, _num_run, _budget), file_name) - for file_name in stored_files_for_run] + os.path.join( + self.backend.get_numrun_directory(_seed, _num_run, _budget), file_name + ) + for file_name in stored_files_for_run + ] this_model_cost = sum([os.path.getsize(path) for path in stored_files_for_run]) # get the megabytes @@ -814,8 +906,8 @@ def get_disk_consumption(self, pred_path): def compute_loss_per_model(self): """ - Compute the loss of the predictions on ensemble building data set; - populates self.read_preds and self.read_losses + Compute the loss of the predictions on ensemble building data set; + populates self.read_preds and self.read_losses """ self.logger.debug("Read ensemble data set predictions") @@ -832,17 +924,21 @@ def compute_loss_per_model(self): pred_path = os.path.join( glob.escape(self.backend.get_runs_directory()), - '%d_*_*' % self.seed, - 'predictions_ensemble_%s_*_*.npy*' % self.seed, + "%d_*_*" % self.seed, + "predictions_ensemble_%s_*_*.npy*" % self.seed, ) y_ens_files = glob.glob(pred_path) - y_ens_files = [y_ens_file for y_ens_file in y_ens_files - if y_ens_file.endswith('.npy') or y_ens_file.endswith('.npy.gz')] + y_ens_files = [ + y_ens_file + for y_ens_file in y_ens_files + if y_ens_file.endswith(".npy") or y_ens_file.endswith(".npy.gz") + ] self.y_ens_files = y_ens_files # no validation predictions so far -- no files if len(self.y_ens_files) == 0: - self.logger.debug("Found no prediction files on ensemble data set:" - " %s" % pred_path) + self.logger.debug( + "Found no prediction files on ensemble data set:" " %s" % pred_path + ) return False # First sort files chronologically @@ -858,15 +954,18 @@ def compute_loss_per_model(self): n_read_files = 0 # Now read file wrt to num_run - for y_ens_fn, match, _seed, _num_run, _budget, mtime in \ - sorted(to_read, key=lambda x: x[5]): + for y_ens_fn, match, _seed, _num_run, _budget, mtime in sorted( + to_read, key=lambda x: x[5] + ): if self.read_at_most and n_read_files >= self.read_at_most: # limit the number of files that will be read # to limit memory consumption break if not y_ens_fn.endswith(".npy") and not y_ens_fn.endswith(".npy.gz"): - self.logger.info('Error loading file (not .npy or .npy.gz): %s', y_ens_fn) + self.logger.info( + "Error loading file (not .npy or .npy.gz): %s", y_ens_fn + ) continue if not self.read_losses.get(y_ens_fn): @@ -884,7 +983,7 @@ def compute_loss_per_model(self): # 1 - loaded and in memory # 2 - loaded but dropped again # 3 - deleted from disk due to space constraints - "loaded": 0 + "loaded": 0, } if not self.read_preds.get(y_ens_fn): self.read_preds[y_ens_fn] = { @@ -900,16 +999,18 @@ def compute_loss_per_model(self): # actually read the predictions and compute their respective loss try: y_ensemble = self._read_np_fn(y_ens_fn) - loss = calculate_loss(solution=self.y_true_ensemble, - prediction=y_ensemble, - task_type=self.task_type, - metric=self.metric, - scoring_functions=None) + loss = calculate_loss( + solution=self.y_true_ensemble, + prediction=y_ensemble, + task_type=self.task_type, + metric=self.metric, + scoring_functions=None, + ) if np.isfinite(self.read_losses[y_ens_fn]["ens_loss"]): self.logger.debug( - 'Changing ensemble loss for file %s from %f to %f ' - 'because file modification time changed? %f - %f', + "Changing ensemble loss for file %s from %f to %f " + "because file modification time changed? %f - %f", y_ens_fn, self.read_losses[y_ens_fn]["ens_loss"], loss, @@ -923,39 +1024,38 @@ def compute_loss_per_model(self): # To save memory, we just compute the loss. self.read_losses[y_ens_fn]["mtime_ens"] = os.path.getmtime(y_ens_fn) self.read_losses[y_ens_fn]["loaded"] = 2 - self.read_losses[y_ens_fn]["disc_space_cost_mb"] = self.get_disk_consumption( - y_ens_fn - ) + self.read_losses[y_ens_fn][ + "disc_space_cost_mb" + ] = self.get_disk_consumption(y_ens_fn) n_read_files += 1 except Exception: self.logger.warning( - 'Error loading %s: %s', + "Error loading %s: %s", y_ens_fn, traceback.format_exc(), ) self.read_losses[y_ens_fn]["ens_loss"] = np.inf self.logger.debug( - 'Done reading %d new prediction files. Loaded %d predictions in ' - 'total.', + "Done reading %d new prediction files. Loaded %d predictions in " "total.", n_read_files, - np.sum([pred["loaded"] > 0 for pred in self.read_losses.values()]) + np.sum([pred["loaded"] > 0 for pred in self.read_losses.values()]), ) return True def get_n_best_preds(self): """ - get best n predictions (i.e., keys of self.read_losses) - according to the loss on the "ensemble set" - n: self.ensemble_nbest - - Side effects: - ->Define the n-best models to use in ensemble - ->Only the best models are loaded - ->Any model that is not best is candidate to deletion - if max models in disc is exceeded. + get best n predictions (i.e., keys of self.read_losses) + according to the loss on the "ensemble set" + n: self.ensemble_nbest + + Side effects: + ->Define the n-best models to use in ensemble + ->Only the best models are loaded + ->Any model that is not best is candidate to deletion + if max models in disc is exceeded. """ sorted_keys = self._get_list_of_sorted_preds() @@ -982,31 +1082,39 @@ def get_n_best_preds(self): # no model left; try to use dummy loss (num_run==0) # log warning when there are other models but not better than dummy model if num_keys > num_dummy: - self.logger.warning("No models better than random - using Dummy loss!" - "Number of models besides current dummy model: %d. " - "Number of dummy models: %d", - num_keys - 1, - num_dummy) + self.logger.warning( + "No models better than random - using Dummy loss!" + "Number of models besides current dummy model: %d. " + "Number of dummy models: %d", + num_keys - 1, + num_dummy, + ) sorted_keys = [ - (k, v["ens_loss"], v["num_run"]) for k, v in self.read_losses.items() + (k, v["ens_loss"], v["num_run"]) + for k, v in self.read_losses.items() if v["seed"] == self.seed and v["num_run"] == 1 ] # reload predictions if losses changed over time and a model is # considered to be in the top models again! if not isinstance(self.ensemble_nbest, numbers.Integral): # Transform to number of models to keep. Keep at least one - keep_nbest = max(1, min(len(sorted_keys), - int(len(sorted_keys) * self.ensemble_nbest))) + keep_nbest = max( + 1, min(len(sorted_keys), int(len(sorted_keys) * self.ensemble_nbest)) + ) self.logger.debug( "Library pruning: using only top %f percent of the models for ensemble " "(%d out of %d)", - self.ensemble_nbest * 100, keep_nbest, len(sorted_keys) + self.ensemble_nbest * 100, + keep_nbest, + len(sorted_keys), ) else: # Keep only at most ensemble_nbest keep_nbest = min(self.ensemble_nbest, len(sorted_keys)) - self.logger.debug("Library Pruning: using for ensemble only " - " %d (out of %d) models" % (keep_nbest, len(sorted_keys))) + self.logger.debug( + "Library Pruning: using for ensemble only " + " %d (out of %d) models" % (keep_nbest, len(sorted_keys)) + ) # If max_models_on_disc is None, do nothing # One can only read at most max_models_on_disc models @@ -1016,21 +1124,28 @@ def get_n_best_preds(self): [ v["ens_loss"], v["disc_space_cost_mb"], - ] for v in self.read_losses.values() if v["disc_space_cost_mb"] is not None + ] + for v in self.read_losses.values() + if v["disc_space_cost_mb"] is not None ] max_consumption = max(c[1] for c in consumption) # We are pessimistic with the consumption limit indicated by # max_models_on_disc by 1 model. Such model is assumed to spend # max_consumption megabytes - if (sum(c[1] for c in consumption) + max_consumption) > self.max_models_on_disc: + if ( + sum(c[1] for c in consumption) + max_consumption + ) > self.max_models_on_disc: # just leave the best -- smaller is better! # This list is in descending order, to preserve the best models - sorted_cum_consumption = np.cumsum([ - c[1] for c in list(sorted(consumption)) - ]) + max_consumption - max_models = np.argmax(sorted_cum_consumption > self.max_models_on_disc) + sorted_cum_consumption = ( + np.cumsum([c[1] for c in list(sorted(consumption))]) + + max_consumption + ) + max_models = np.argmax( + sorted_cum_consumption > self.max_models_on_disc + ) # Make sure that at least 1 model survives self.max_resident_models = max(1, max_models) @@ -1040,7 +1155,7 @@ def get_n_best_preds(self): self.max_models_on_disc, (sum(c[1] for c in consumption) + max_consumption), max_consumption, - self.max_resident_models + self.max_resident_models, ) ) else: @@ -1048,11 +1163,15 @@ def get_n_best_preds(self): else: self.max_resident_models = self.max_models_on_disc - if self.max_resident_models is not None and keep_nbest > self.max_resident_models: + if ( + self.max_resident_models is not None + and keep_nbest > self.max_resident_models + ): self.logger.debug( "Restricting the number of models to %d instead of %d due to argument " "max_models_on_disc", - self.max_resident_models, keep_nbest, + self.max_resident_models, + keep_nbest, ) keep_nbest = self.max_resident_models @@ -1069,9 +1188,12 @@ def get_n_best_preds(self): # but always keep at least one model current_loss = sorted_keys[i][1] if current_loss >= worst_loss: - self.logger.debug("Dynamic Performance range: " - "Further reduce from %d to %d models", - keep_nbest, max(1, i)) + self.logger.debug( + "Dynamic Performance range: " + "Further reduce from %d to %d models", + keep_nbest, + max(1, i), + ) keep_nbest = max(1, i) break ensemble_n_best = keep_nbest @@ -1085,38 +1207,33 @@ def get_n_best_preds(self): self.read_preds[k][Y_ENSEMBLE] = None self.read_preds[k][Y_VALID] = None self.read_preds[k][Y_TEST] = None - if self.read_losses[k]['loaded'] == 1: + if self.read_losses[k]["loaded"] == 1: self.logger.debug( - 'Dropping model %s (%d,%d) with loss %f.', + "Dropping model %s (%d,%d) with loss %f.", k, - self.read_losses[k]['seed'], - self.read_losses[k]['num_run'], - self.read_losses[k]['ens_loss'], + self.read_losses[k]["seed"], + self.read_losses[k]["num_run"], + self.read_losses[k]["ens_loss"], ) - self.read_losses[k]['loaded'] = 2 + self.read_losses[k]["loaded"] = 2 # Load the predictions for the winning for k in sorted_keys[:ensemble_n_best]: if ( - ( - k not in self.read_preds or - self.read_preds[k][Y_ENSEMBLE] is None - ) - and self.read_losses[k]['loaded'] != 3 - ): + k not in self.read_preds or self.read_preds[k][Y_ENSEMBLE] is None + ) and self.read_losses[k]["loaded"] != 3: self.read_preds[k][Y_ENSEMBLE] = self._read_np_fn(k) # No need to load valid and test here because they are loaded # only if the model ends up in the ensemble - self.read_losses[k]['loaded'] = 1 + self.read_losses[k]["loaded"] = 1 # return keys of self.read_losses with lowest losses return sorted_keys[:ensemble_n_best] - def get_valid_test_preds(self, selected_keys: List[str]) -> Tuple[List[str], List[str]]: - """ - get valid and test predictions from disc - and store them in self.read_preds - + def get_valid_test_preds( + self, selected_keys: List[str] + ) -> Tuple[List[str], List[str]]: + """Get valid and test predictions from disc and store them in self.read_preds Parameters --------- selected_keys: list @@ -1135,35 +1252,47 @@ def get_valid_test_preds(self, selected_keys: List[str]) -> Tuple[List[str], Lis valid_fn = glob.glob( os.path.join( glob.escape(self.backend.get_runs_directory()), - '%d_%d_%s' % ( + "%d_%d_%s" + % ( self.read_losses[k]["seed"], self.read_losses[k]["num_run"], self.read_losses[k]["budget"], ), - 'predictions_valid_%d_%d_%s.npy*' % ( + "predictions_valid_%d_%d_%s.npy*" + % ( self.read_losses[k]["seed"], self.read_losses[k]["num_run"], self.read_losses[k]["budget"], - ) + ), ) ) - valid_fn = [vfn for vfn in valid_fn if vfn.endswith('.npy') or vfn.endswith('.npy.gz')] + valid_fn = [ + vfn + for vfn in valid_fn + if vfn.endswith(".npy") or vfn.endswith(".npy.gz") + ] test_fn = glob.glob( os.path.join( glob.escape(self.backend.get_runs_directory()), - '%d_%d_%s' % ( + "%d_%d_%s" + % ( self.read_losses[k]["seed"], self.read_losses[k]["num_run"], self.read_losses[k]["budget"], ), - 'predictions_test_%d_%d_%s.npy*' % ( + "predictions_test_%d_%d_%s.npy*" + % ( self.read_losses[k]["seed"], self.read_losses[k]["num_run"], - self.read_losses[k]["budget"] - ) + self.read_losses[k]["budget"], + ), ) ) - test_fn = [tfn for tfn in test_fn if tfn.endswith('.npy') or tfn.endswith('.npy.gz')] + test_fn = [ + tfn + for tfn in test_fn + if tfn.endswith(".npy") or tfn.endswith(".npy.gz") + ] if len(valid_fn) == 0: # self.logger.debug("Not found validation prediction file " @@ -1185,8 +1314,9 @@ def get_valid_test_preds(self, selected_keys: List[str]) -> Tuple[List[str], Lis success_keys_valid.append(k) self.read_losses[k]["mtime_valid"] = os.path.getmtime(valid_fn) except Exception: - self.logger.warning('Error loading %s: %s', - valid_fn, traceback.format_exc()) + self.logger.warning( + "Error loading %s: %s", valid_fn, traceback.format_exc() + ) if len(test_fn) == 0: # self.logger.debug("Not found test prediction file (although " @@ -1208,26 +1338,24 @@ def get_valid_test_preds(self, selected_keys: List[str]) -> Tuple[List[str], Lis success_keys_test.append(k) self.read_losses[k]["mtime_test"] = os.path.getmtime(test_fn) except Exception: - self.logger.warning('Error loading %s: %s', - test_fn, traceback.format_exc()) + self.logger.warning( + "Error loading %s: %s", test_fn, traceback.format_exc() + ) return success_keys_valid, success_keys_test def fit_ensemble(self, selected_keys: list): """ - fit ensemble - - Parameters - --------- - selected_keys: list - list of selected keys of self.read_losses + Parameters + --------- + selected_keys: list + list of selected keys of self.read_losses - Returns - ------- - ensemble: EnsembleSelection - trained Ensemble + Returns + ------- + ensemble: EnsembleSelection + trained Ensemble """ - if self.unit_test: raise MemoryError() @@ -1238,13 +1366,16 @@ def fit_ensemble(self, selected_keys: list): self.read_losses[k]["num_run"], self.read_losses[k]["budget"], ) - for k in selected_keys] + for k in selected_keys + ] # check hash if ensemble training data changed - current_hash = "".join([ - str(zlib.adler32(predictions_train[i].data.tobytes())) - for i in range(len(predictions_train)) - ]) + current_hash = "".join( + [ + str(zlib.adler32(predictions_train[i].data.tobytes())) + for i in range(len(predictions_train)) + ] + ) if self.last_hash == current_hash: self.logger.debug( "No new model predictions selected -- skip ensemble building " @@ -1268,8 +1399,7 @@ def fit_ensemble(self, selected_keys: list): len(predictions_train), ) start_time = time.time() - ensemble.fit(predictions_train, self.y_true_ensemble, - include_num_runs) + ensemble.fit(predictions_train, self.y_true_ensemble, include_num_runs) end_time = time.time() self.logger.debug( "Fitting the ensemble took %.2f seconds.", @@ -1282,10 +1412,10 @@ def fit_ensemble(self, selected_keys: list): ) except ValueError: - self.logger.error('Caught ValueError: %s', traceback.format_exc()) + self.logger.error("Caught ValueError: %s", traceback.format_exc()) return None except IndexError: - self.logger.error('Caught IndexError: %s' + traceback.format_exc()) + self.logger.error("Caught IndexError: %s" + traceback.format_exc()) return None finally: # Explicitly free memory @@ -1293,37 +1423,39 @@ def fit_ensemble(self, selected_keys: list): return ensemble - def predict(self, set_: str, - ensemble: AbstractEnsemble, - selected_keys: list, - n_preds: int, - index_run: int): - """ - save preditions on ensemble, validation and test data on disc - - Parameters - ---------- - set_: ["valid","test"] - data split name - ensemble: EnsembleSelection - trained Ensemble - selected_keys: list - list of selected keys of self.read_losses - n_preds: int - number of prediction models used for ensemble building - same number of predictions on valid and test are necessary - index_run: int - n-th time that ensemble predictions are written to disc - - Return - ------ - y: np.ndarray + def predict( + self, + set_: str, + ensemble: AbstractEnsemble, + selected_keys: list, + n_preds: int, + index_run: int, + ): + """Save preditions on ensemble, validation and test data on disc + + Parameters + ---------- + set_: ["valid","test"] + data split name + ensemble: EnsembleSelection + trained Ensemble + selected_keys: list + list of selected keys of self.read_losses + n_preds: int + number of prediction models used for ensemble building + same number of predictions on valid and test are necessary + index_run: int + n-th time that ensemble predictions are written to disc + + Return + ------ + y: np.ndarray """ self.logger.debug("Predicting the %s set with the ensemble!", set_) - if set_ == 'valid': + if set_ == "valid": pred_set = Y_VALID - elif set_ == 'test': + elif set_ == "test": pred_set = Y_TEST else: pred_set = Y_ENSEMBLE @@ -1364,79 +1496,82 @@ def _add_ensemble_trajectory(self, train_pred, valid_pred, test_pred): train_pred = np.vstack( ((1 - train_pred).reshape((1, -1)), train_pred.reshape((1, -1))) ).transpose() - if valid_pred is not None and (len(valid_pred.shape) == 1 or valid_pred.shape[1] == 1): + if valid_pred is not None and ( + len(valid_pred.shape) == 1 or valid_pred.shape[1] == 1 + ): valid_pred = np.vstack( ((1 - valid_pred).reshape((1, -1)), valid_pred.reshape((1, -1))) ).transpose() - if test_pred is not None and (len(test_pred.shape) == 1 or test_pred.shape[1] == 1): + if test_pred is not None and ( + len(test_pred.shape) == 1 or test_pred.shape[1] == 1 + ): test_pred = np.vstack( ((1 - test_pred).reshape((1, -1)), test_pred.reshape((1, -1))) ).transpose() performance_stamp = { - 'Timestamp': pd.Timestamp.now(), - 'ensemble_optimization_score': calculate_score( + "Timestamp": pd.Timestamp.now(), + "ensemble_optimization_score": calculate_score( solution=self.y_true_ensemble, prediction=train_pred, task_type=self.task_type, metric=self.metric, - scoring_functions=None - ) + scoring_functions=None, + ), } if valid_pred is not None: # TODO: valid_pred are a legacy from competition manager # and this if never happens. Re-evaluate Y_valid support - performance_stamp['ensemble_val_score'] = calculate_score( + performance_stamp["ensemble_val_score"] = calculate_score( solution=self.y_valid, prediction=valid_pred, task_type=self.task_type, metric=self.metric, - scoring_functions=None + scoring_functions=None, ) # In case test_pred was provided if test_pred is not None: - performance_stamp['ensemble_test_score'] = calculate_score( + performance_stamp["ensemble_test_score"] = calculate_score( solution=self.y_test, prediction=test_pred, task_type=self.task_type, metric=self.metric, - scoring_functions=None + scoring_functions=None, ) self.ensemble_history.append(performance_stamp) def _get_list_of_sorted_preds(self): """ - Returns a list of sorted predictions in descending order - Losses are taken from self.read_losses. + Returns a list of sorted predictions in descending order + Losses are taken from self.read_losses. - Parameters - ---------- - None + Parameters + ---------- + None - Return - ------ - sorted_keys: list + Return + ------ + sorted_keys: list """ # Sort by loss - smaller is better! - sorted_keys = list(sorted( - [ - (k, v["ens_loss"], v["num_run"]) - for k, v in self.read_losses.items() - ], - # Sort by loss as priority 1 and then by num_run on a ascending order - # We want small num_run first - key=lambda x: (x[1], x[2]), - )) + sorted_keys = list( + sorted( + [(k, v["ens_loss"], v["num_run"]) for k, v in self.read_losses.items()], + # Sort by loss as priority 1 and then by num_run on a ascending order + # We want small num_run first + key=lambda x: (x[1], x[2]), + ) + ) return sorted_keys def _delete_excess_models(self, selected_keys: List[str]): """ - Deletes models excess models on disc. self.max_models_on_disc - defines the upper limit on how many models to keep. - Any additional model with a worst loss than the top - self.max_models_on_disc is deleted. + Deletes models excess models on disc. self.max_models_on_disc + defines the upper limit on how many models to keep. + Any additional model with a worst loss than the top + self.max_models_on_disc is deleted. """ @@ -1461,8 +1596,8 @@ def _delete_excess_models(self, selected_keys: List[str]): numrun_dir = self.backend.get_numrun_directory(_seed, _num_run, _budget) try: - os.rename(numrun_dir, numrun_dir + '.old') - shutil.rmtree(numrun_dir + '.old') + os.rename(numrun_dir, numrun_dir + ".old") + shutil.rmtree(numrun_dir + ".old") self.logger.info("Deleted files of non-candidate model %s", pred_path) self.read_losses[pred_path]["disc_space_cost_mb"] = None self.read_losses[pred_path]["loaded"] = 3 @@ -1470,7 +1605,9 @@ def _delete_excess_models(self, selected_keys: List[str]): except Exception as e: self.logger.error( "Failed to delete files of non-candidate model %s due" - " to error %s", pred_path, e + " to error %s", + pred_path, + e, ) def _read_np_fn(self, path): @@ -1478,9 +1615,7 @@ def _read_np_fn(self, path): # Support for string precision if isinstance(self.precision, str): precision = int(self.precision) - self.logger.warning("Interpreted str-precision as {}".format( - precision - )) + self.logger.warning("Interpreted str-precision as {}".format(precision)) else: precision = self.precision @@ -1490,7 +1625,7 @@ def _read_np_fn(self, path): open_method = open else: raise ValueError("Unknown filetype %s" % path) - with open_method(path, 'rb') as fp: + with open_method(path, "rb") as fp: if precision == 16: predictions = np.load(fp, allow_pickle=True).astype(dtype=np.float16) elif precision == 32: diff --git a/autosklearn/ensembles/abstract_ensemble.py b/autosklearn/ensembles/abstract_ensemble.py index 752131407f..24d352ab5e 100644 --- a/autosklearn/ensembles/abstract_ensemble.py +++ b/autosklearn/ensembles/abstract_ensemble.py @@ -15,7 +15,7 @@ def fit( base_models_predictions: np.ndarray, true_targets: np.ndarray, model_identifiers: List[Tuple[int, int, float]], - ) -> 'AbstractEnsemble': + ) -> "AbstractEnsemble": """Fit an ensemble given predictions of base models and targets. Ensemble building maximizes performance (in contrast to @@ -23,7 +23,8 @@ def fit( Parameters ---------- - base_models_predictions : array of shape = [n_base_models, n_data_points, n_targets] + base_models_predictions: np.ndarray + shape = (n_base_models, n_data_points, n_targets) n_targets is the number of classes in case of classification, n_targets is 0 or 1 in case of regression @@ -40,12 +41,15 @@ def fit( pass @abstractmethod - def predict(self, base_models_predictions: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray: + def predict( + self, base_models_predictions: Union[np.ndarray, List[np.ndarray]] + ) -> np.ndarray: """Create ensemble predictions from the base model predictions. Parameters ---------- - base_models_predictions : array of shape = [n_base_models, n_data_points, n_targets] + base_models_predictions : np.ndarray + shape = (n_base_models, n_data_points, n_targets) Same as in the fit method. Returns diff --git a/autosklearn/ensembles/ensemble_selection.py b/autosklearn/ensembles/ensemble_selection.py index 1546c763c2..3ae216da01 100644 --- a/autosklearn/ensembles/ensemble_selection.py +++ b/autosklearn/ensembles/ensemble_selection.py @@ -1,9 +1,9 @@ +from typing import Any, Dict, List, Optional, Tuple, Union, cast + import random from collections import Counter -from typing import Any, Dict, List, Optional, Tuple, Union, cast import numpy as np - from sklearn.utils import check_random_state from autosklearn.constants import TASK_TYPES @@ -19,10 +19,10 @@ def __init__( task_type: int, metric: Scorer, bagging: bool = False, - mode: str = 'fast', + mode: str = "fast", random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> None: - """ An ensemble of selected algorithms + """An ensemble of selected algorithms Fitting an EnsembleSelection generates an ensemble from the the models generated during the search process. Can be further used for prediction. @@ -79,17 +79,19 @@ def fit( ) -> AbstractEnsemble: self.ensemble_size = int(self.ensemble_size) if self.ensemble_size < 1: - raise ValueError('Ensemble size cannot be less than one!') + raise ValueError("Ensemble size cannot be less than one!") if self.task_type not in TASK_TYPES: - raise ValueError('Unknown task type %s.' % self.task_type) + raise ValueError("Unknown task type %s." % self.task_type) if not isinstance(self.metric, Scorer): - raise ValueError("The provided metric must be an instance of Scorer, " - "nevertheless it is {}({})".format( - self.metric, - type(self.metric), - )) - if self.mode not in ('fast', 'slow'): - raise ValueError('Unknown mode %s' % self.mode) + raise ValueError( + "The provided metric must be an instance of Scorer, " + "nevertheless it is {}({})".format( + self.metric, + type(self.metric), + ) + ) + if self.mode not in ("fast", "slow"): + raise ValueError("Unknown mode %s" % self.mode) if self.bagging: self._bagging(predictions, labels) @@ -104,7 +106,7 @@ def _fit( predictions: List[np.ndarray], labels: np.ndarray, ) -> AbstractEnsemble: - if self.mode == 'fast': + if self.mode == "fast": self._fast(predictions, labels) else: self._slow(predictions, labels) @@ -149,18 +151,17 @@ def _fast( # Memory-efficient averaging! for j, pred in enumerate(predictions): # fant_ensemble_prediction is the prediction of the current ensemble - # and should be ([predictions[selected_prev_iterations] + predictions[j])/(s+1) - # We overwrite the contents of fant_ensemble_prediction - # directly with weighted_ensemble_prediction + new_prediction and then scale for avg - np.add( - weighted_ensemble_prediction, - pred, - out=fant_ensemble_prediction - ) + # and should be + # + # ([predictions[selected_prev_iterations] + predictions[j])/(s+1) + # + # We overwrite the contents of fant_ensemble_prediction directly with + # weighted_ensemble_prediction + new_prediction and then scale for avg + np.add(weighted_ensemble_prediction, pred, out=fant_ensemble_prediction) np.multiply( fant_ensemble_prediction, - (1. / float(s + 1)), - out=fant_ensemble_prediction + (1.0 / float(s + 1)), + out=fant_ensemble_prediction, ) # calculate_loss is versatile and can return a dict of losses @@ -172,8 +173,8 @@ def _fast( prediction=fant_ensemble_prediction, task_type=self.task_type, metric=self.metric, - scoring_functions=None - ) + scoring_functions=None, + ), ) all_best = np.argwhere(losses == np.nanmin(losses)).flatten() @@ -192,11 +193,7 @@ def _fast( self.trajectory_ = trajectory self.train_loss_ = trajectory[-1] - def _slow( - self, - predictions: List[np.ndarray], - labels: np.ndarray - ) -> None: + def _slow(self, predictions: List[np.ndarray], labels: np.ndarray) -> None: """Rich Caruana's ensemble selection method.""" self.num_input_models_ = len(predictions) @@ -223,8 +220,8 @@ def _slow( prediction=ensemble_prediction, task_type=self.task_type, metric=self.metric, - scoring_functions=None - ) + scoring_functions=None, + ), ) ensemble.pop() best = np.nanargmin(losses) @@ -269,7 +266,7 @@ def _bagging( n_bags: int = 20, ) -> np.ndarray: """Rich Caruana's ensemble selection method with bagging.""" - raise ValueError('Bagging might not work with class-based interface!') + raise ValueError("Bagging might not work with class-based interface!") n_models = predictions.shape[0] bag_size = int(n_models * fraction) @@ -308,30 +305,34 @@ def predict(self, predictions: Union[np.ndarray, List[np.ndarray]]) -> np.ndarra # If none of the above applies, then something must have gone wrong. else: - raise ValueError("The dimensions of ensemble predictions" - " and ensemble weights do not match!") + raise ValueError( + "The dimensions of ensemble predictions" + " and ensemble weights do not match!" + ) del tmp_predictions return average def __str__(self) -> str: - trajectory_str = ' '.join([ - f'{id}: {perf:.5f}' - for id, perf in enumerate(self.trajectory_) - ]) - identifiers_str = ' '.join([ - f'{identifier}' - for idx, identifier in enumerate(self.identifiers_) - if self.weights_[idx] > 0 - ]) - return ("Ensemble Selection:\n" - f"\tTrajectory: {trajectory_str}\n" - f"\tMembers: {self.indices_}\n" - f"\tWeights: {self.weights_}\n" - f"\tIdentifiers: {identifiers_str}\n") + trajectory_str = " ".join( + [f"{id}: {perf:.5f}" for id, perf in enumerate(self.trajectory_)] + ) + identifiers_str = " ".join( + [ + f"{identifier}" + for idx, identifier in enumerate(self.identifiers_) + if self.weights_[idx] > 0 + ] + ) + return ( + "Ensemble Selection:\n" + f"\tTrajectory: {trajectory_str}\n" + f"\tMembers: {self.indices_}\n" + f"\tWeights: {self.weights_}\n" + f"\tIdentifiers: {identifiers_str}\n" + ) def get_models_with_weights( - self, - models: BasePipeline + self, models: BasePipeline ) -> List[Tuple[float, BasePipeline]]: output = [] for i, weight in enumerate(self.weights_): diff --git a/autosklearn/ensembles/singlebest_ensemble.py b/autosklearn/ensembles/singlebest_ensemble.py index e10eee978f..58e026dff2 100644 --- a/autosklearn/ensembles/singlebest_ensemble.py +++ b/autosklearn/ensembles/singlebest_ensemble.py @@ -1,8 +1,8 @@ -import os from typing import List, Tuple, Union -import numpy as np +import os +import numpy as np from smac.runhistory.runhistory import RunHistory from autosklearn.automl_common.common.utils.backend import Backend @@ -20,6 +20,7 @@ class SingleBest(AbstractEnsemble): object, to comply with the expected interface of an AbstractEnsemble. """ + def __init__( self, metric: Scorer, @@ -38,12 +39,10 @@ def __init__( self.identifiers_ = self.get_identifiers_from_run_history() def get_identifiers_from_run_history(self) -> List[Tuple[int, int, float]]: - """ - This method parses the run history, to identify - the best performing model + """Parses the run history, to identify the best performing model - It populates the identifiers attribute, which is used - by the backend to access the actual model + Populates the identifiers attribute, which is used by the backend to access + the actual model. """ best_model_identifier = [] best_model_score = self.metric._worst_possible_result @@ -52,35 +51,38 @@ def get_identifiers_from_run_history(self) -> List[Tuple[int, int, float]]: run_value = self.run_history.data[run_key] score = self.metric._optimum - (self.metric._sign * run_value.cost) - if (score > best_model_score and self.metric._sign > 0) \ - or (score < best_model_score and self.metric._sign < 0): + if (score > best_model_score and self.metric._sign > 0) or ( + score < best_model_score and self.metric._sign < 0 + ): # Make sure that the individual best model actually exists model_dir = self.backend.get_numrun_directory( self.seed, - run_value.additional_info['num_run'], + run_value.additional_info["num_run"], run_key.budget, ) model_file_name = self.backend.get_model_filename( self.seed, - run_value.additional_info['num_run'], + run_value.additional_info["num_run"], run_key.budget, ) file_path = os.path.join(model_dir, model_file_name) if not os.path.exists(file_path): continue - best_model_identifier = [( - self.seed, - run_value.additional_info['num_run'], - run_key.budget, - )] + best_model_identifier = [ + ( + self.seed, + run_value.additional_info["num_run"], + run_key.budget, + ) + ] best_model_score = score if not best_model_identifier: raise ValueError( - "No valid model found in run history. This means smac was not able to fit" - " a valid model. Please check the log file for errors." + "No valid model found in run history. This means smac was not able to" + " fit a valid model. Please check the log file for errors." ) return best_model_identifier @@ -89,15 +91,25 @@ def predict(self, predictions: Union[np.ndarray, List[np.ndarray]]) -> np.ndarra return predictions[0] def __str__(self) -> str: - return 'Single Model Selection:\n\tMembers: %s' \ - '\n\tWeights: %s\n\tIdentifiers: %s' % \ - (self.indices_, self.weights_, - ' '.join([str(identifier) for idx, identifier in - enumerate(self.identifiers_) - if self.weights_[idx] > 0])) - - def get_models_with_weights(self, models: BasePipeline - ) -> List[Tuple[float, BasePipeline]]: + return ( + "Single Model Selection:\n\tMembers: %s" + "\n\tWeights: %s\n\tIdentifiers: %s" + % ( + self.indices_, + self.weights_, + " ".join( + [ + str(identifier) + for idx, identifier in enumerate(self.identifiers_) + if self.weights_[idx] > 0 + ] + ), + ) + ) + + def get_models_with_weights( + self, models: BasePipeline + ) -> List[Tuple[float, BasePipeline]]: output = [] for i, weight in enumerate(self.weights_): if weight > 0.0: diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 070230ae94..491309a7b8 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -1,29 +1,28 @@ # -*- encoding: utf-8 -*- -from typing import Any, Optional, Dict, List, Mapping, Tuple, Union, Iterable -from typing_extensions import Literal +from typing import Any, Dict, Iterable, List, Mapping, Optional, Tuple, Union -from ConfigSpace.configuration_space import Configuration, ConfigurationSpace import dask.distributed import joblib import numpy as np import pandas as pd +from ConfigSpace.configuration_space import Configuration, ConfigurationSpace from scipy.sparse import spmatrix from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin from sklearn.utils.multiclass import type_of_target from smac.runhistory.runhistory import RunInfo, RunValue +from typing_extensions import Literal +from autosklearn.automl import AutoML, AutoMLClassifier, AutoMLRegressor from autosklearn.data.validation import ( - convert_if_sparse, SUPPORTED_FEAT_TYPES, SUPPORTED_TARGET_TYPES, + convert_if_sparse, ) -from autosklearn.pipeline.base import BasePipeline -from autosklearn.automl import AutoMLClassifier, AutoMLRegressor, AutoML from autosklearn.metrics import Scorer +from autosklearn.pipeline.base import BasePipeline class AutoSklearnEstimator(BaseEstimator): - def __init__( self, time_left_for_this_task=3600, @@ -36,7 +35,7 @@ def __init__( memory_limit=3072, include: Optional[Dict[str, List[str]]] = None, exclude: Optional[Dict[str, List[str]]] = None, - resampling_strategy='holdout', + resampling_strategy="holdout", resampling_strategy_arguments=None, tmp_folder=None, delete_tmp_folder_after_terminate=True, @@ -51,7 +50,7 @@ def __init__( scoring_functions: Optional[List[Scorer]] = None, load_models: bool = True, get_trials_callback=None, - dataset_compression: Union[bool, Mapping[str, Any]] = True + dataset_compression: Union[bool, Mapping[str, Any]] = True, ): """ Parameters @@ -339,11 +338,12 @@ def __init__( """ # noqa (links are too long) # Raise error if the given total time budget is less than 30 seconds. if time_left_for_this_task < 30: - raise ValueError("Time left for this task must be at least " - "30 seconds. ") + raise ValueError("Time left for this task must be at least " "30 seconds. ") self.time_left_for_this_task = time_left_for_this_task self.per_run_time_limit = per_run_time_limit - self.initial_configurations_via_metalearning = initial_configurations_via_metalearning + self.initial_configurations_via_metalearning = ( + initial_configurations_via_metalearning + ) self.ensemble_size = ensemble_size self.ensemble_nbest = ensemble_nbest self.max_models_on_disc = max_models_on_disc @@ -388,12 +388,13 @@ def __getstate__(self): def build_automl(self): + initial_configs = self.initial_configurations_via_metalearning automl = self._get_automl_class()( temporary_directory=self.tmp_folder, delete_tmp_folder_after_terminate=self.delete_tmp_folder_after_terminate, time_left_for_this_task=self.time_left_for_this_task, per_run_time_limit=self.per_run_time_limit, - initial_configurations_via_metalearning=self.initial_configurations_via_metalearning, + initial_configurations_via_metalearning=initial_configs, ensemble_size=self.ensemble_size, ensemble_nbest=self.ensemble_nbest, max_models_on_disc=self.max_models_on_disc, @@ -413,7 +414,7 @@ def build_automl(self): metric=self.metric, scoring_functions=self.scoring_functions, get_trials_callback=self.get_trials_callback, - dataset_compression=self.dataset_compression + dataset_compression=self.dataset_compression, ) return automl @@ -434,7 +435,7 @@ def fit_pipeline( self, X: SUPPORTED_FEAT_TYPES, y: Union[SUPPORTED_TARGET_TYPES, spmatrix], - config: Union[Configuration, Dict[str, Union[str, float, int]]], + config: Union[Configuration, Dict[str, Union[str, float, int]]], dataset_name: Optional[str] = None, X_test: Optional[SUPPORTED_FEAT_TYPES] = None, y_test: Optional[Union[SUPPORTED_TARGET_TYPES, spmatrix]] = None, @@ -442,7 +443,7 @@ def fit_pipeline( *args, **kwargs: Dict, ) -> Tuple[Optional[BasePipeline], RunInfo, RunValue]: - """ Fits and individual pipeline configuration and returns + """Fits and individual pipeline configuration and returns the result to the user. The Estimator constraints are honored, for example the resampling @@ -451,7 +452,8 @@ def fit_pipeline( arguments are redirected to the TAE evaluation function, which allows for further customization while building a pipeline. - Any additional argument provided is directly passed to the worker exercising the run. + Any additional argument provided is directly passed to the + worker exercising the run. Parameters ---------- @@ -465,7 +467,7 @@ def fit_pipeline( If provided, the testing performance will be tracked on this labels config: Union[Configuration, Dict[str, Union[str, float, int]]] A configuration object used to define the pipeline steps. - If a dictionary is passed, a configuration is created based on this dictionary. + If a dict is passed, a configuration is created based on this dict. dataset_name: Optional[str] Name that will be used to tag the Auto-Sklearn run and identify the Auto-Sklearn run @@ -489,16 +491,27 @@ def fit_pipeline( """ if self.automl_ is None: self.automl_ = self.build_automl() - return self.automl_.fit_pipeline(X=X, y=y, - dataset_name=dataset_name, - config=config, - feat_type=feat_type, - X_test=X_test, y_test=y_test, - *args, **kwargs) - - def fit_ensemble(self, y, task=None, precision=32, - dataset_name=None, ensemble_nbest=None, - ensemble_size=None): + return self.automl_.fit_pipeline( + X=X, + y=y, + dataset_name=dataset_name, + config=config, + feat_type=feat_type, + X_test=X_test, + y_test=y_test, + *args, + **kwargs, + ) + + def fit_ensemble( + self, + y, + task=None, + precision=32, + dataset_name=None, + ensemble_nbest=None, + ensemble_size=None, + ): """Fit an ensemble to models trained during an optimization process. All parameters are ``None`` by default. If no other value is given, @@ -584,30 +597,42 @@ def predict(self, X, batch_size=None, n_jobs=1): return self.automl_.predict(X, batch_size=batch_size, n_jobs=n_jobs) def predict_proba(self, X, batch_size=None, n_jobs=1): - return self.automl_.predict_proba( - X, batch_size=batch_size, n_jobs=n_jobs) + return self.automl_.predict_proba(X, batch_size=batch_size, n_jobs=n_jobs) def score(self, X, y): return self.automl_.score(X, y) def show_models(self): - """ Returns a dictionary containing dictionaries of ensemble models. + """Returns a dictionary containing dictionaries of ensemble models. Each model in the ensemble can be accessed by giving its ``model_id`` as key. A model dictionary contains the following: * ``"model_id"`` - The id given to a model by ``autosklearn``. + * ``"rank"`` - The rank of the model based on it's ``"cost"``. + * ``"cost"`` - The loss of the model on the validation set. + * ``"ensemble_weight"`` - The weight given to the model in the ensemble. + * ``"voting_model"`` - The ``cv_voting_ensemble`` model (for 'cv' resampling). - * ``"estimators"`` - List of models (dicts) in ``cv_voting_ensemble`` (for 'cv' resampling). + + * ``"estimators"`` - List of models (dicts) in ``cv_voting_ensemble`` + ('cv' resampling). + * ``"data_preprocessor"`` - The preprocessor used on the data. + * ``"balancing"`` - The balancing used on the data (for classification). + * ``"feature_preprocessor"`` - The preprocessor for features types. - * ``"classifier"`` or ``"regressor"`` - The autosklearn wrapped classifier or regressor. - * ``"sklearn_classifier"`` or ``"sklearn_regressor"`` - The sklearn classifier or regressor. + + * ``"classifier"`` / ``"regressor"`` + - The autosklearn wrapped classifier or regressor. + + * ``"sklearn_classifier"`` or ``"sklearn_regressor"`` + - The sklearn classifier or regressor. **Example** @@ -657,7 +682,7 @@ def show_models(self): Dict(int, Any) : dictionary of length = number of models in the ensemble A dictionary of models in the ensemble, where ``model_id`` is the key. - """ + """ # noqa: E501 return self.automl_.show_models() @@ -709,12 +734,12 @@ def leaderboard( self, detailed: bool = False, ensemble_only: bool = True, - top_k: Union[int, Literal['all']] = 'all', - sort_by: str = 'cost', - sort_order: Literal['auto', 'ascending', 'descending'] = 'auto', - include: Optional[Union[str, Iterable[str]]] = None + top_k: Union[int, Literal["all"]] = "all", + sort_by: str = "cost", + sort_order: Literal["auto", "ascending", "descending"] = "auto", + include: Optional[Union[str, Iterable[str]]] = None, ) -> pd.DataFrame: - """ Returns a pandas table of results for all evaluated models. + """Returns a pandas table of results for all evaluated models. Gives an overview of all models trained during the search process along with various statistics about their training. @@ -789,46 +814,53 @@ def leaderboard( # Validation of top_k if ( not (isinstance(top_k, str) or isinstance(top_k, int)) - or (isinstance(top_k, str) and top_k != 'all') + or (isinstance(top_k, str) and top_k != "all") or (isinstance(top_k, int) and top_k <= 0) ): - raise ValueError(f"top_k={top_k} must be a positive integer or pass" - " `top_k`='all' to view results for all models") + raise ValueError( + f"top_k={top_k} must be a positive integer or pass" + " `top_k`='all' to view results for all models" + ) # Validate columns to include if isinstance(include, str): include = [include] - if include == ['model_id']: - raise ValueError('Must provide more than just `model_id`') + if include == ["model_id"]: + raise ValueError("Must provide more than just `model_id`") if include is not None: columns = [*include] # 'model_id' should always be present as it is the unique index # used for pandas - if 'model_id' not in columns: - columns.append('model_id') + if "model_id" not in columns: + columns.append("model_id") - invalid_include_items = set(columns) - set(column_types['all']) + invalid_include_items = set(columns) - set(column_types["all"]) if len(invalid_include_items) != 0: - raise ValueError(f"Values {invalid_include_items} are not known" - f" columns to include, must be contained in " - f"{column_types['all']}") + raise ValueError( + f"Values {invalid_include_items} are not known" + f" columns to include, must be contained in " + f"{column_types['all']}" + ) elif detailed: - columns = column_types['all'] + columns = column_types["all"] else: - columns = column_types['simple'] + columns = column_types["simple"] # Validation of sorting - if sort_by not in column_types['all']: - raise ValueError(f"sort_by='{sort_by}' must be one of included " - f"columns {set(column_types['all'])}") + if sort_by not in column_types["all"]: + raise ValueError( + f"sort_by='{sort_by}' must be one of included " + f"columns {set(column_types['all'])}" + ) - valid_sort_orders = ['auto', 'ascending', 'descending'] + valid_sort_orders = ["auto", "ascending", "descending"] if not (isinstance(sort_order, str) and sort_order in valid_sort_orders): - raise ValueError(f"`sort_order` = {sort_order} must be a str in " - f"{valid_sort_orders}") + raise ValueError( + f"`sort_order` = {sort_order} must be a str in " f"{valid_sort_orders}" + ) # To get all the models that were optmized, we collect what we can from # runhistory first. @@ -836,29 +868,31 @@ def has_key(rv, key): return rv.additional_info and key in rv.additional_info model_runs = { - rval.additional_info['num_run']: { - 'model_id': rval.additional_info['num_run'], - 'seed': rkey.seed, - 'budget': rkey.budget, - 'duration': rval.time, - 'config_id': rkey.config_id, - 'start_time': rval.starttime, - 'end_time': rval.endtime, - 'status': str(rval.status), - 'cost': rval.cost, - 'train_loss': rval.additional_info['train_loss'] - if has_key(rval, 'train_loss') else None, - 'config_origin': rval.additional_info['configuration_origin'] - if has_key(rval, 'configuration_origin') else None + rval.additional_info["num_run"]: { + "model_id": rval.additional_info["num_run"], + "seed": rkey.seed, + "budget": rkey.budget, + "duration": rval.time, + "config_id": rkey.config_id, + "start_time": rval.starttime, + "end_time": rval.endtime, + "status": str(rval.status), + "cost": rval.cost, + "train_loss": rval.additional_info["train_loss"] + if has_key(rval, "train_loss") + else None, + "config_origin": rval.additional_info["configuration_origin"] + if has_key(rval, "configuration_origin") + else None, } for rkey, rval in self.automl_.runhistory_.data.items() - if has_key(rval, 'num_run') + if has_key(rval, "num_run") } # Next we get some info about the model itself model_class_strings = { - AutoMLClassifier: 'classifier', - AutoMLRegressor: 'regressor' + AutoMLClassifier: "classifier", + AutoMLRegressor: "regressor", } model_type = model_class_strings.get(self._get_automl_class(), None) if model_type is None: @@ -868,21 +902,25 @@ def has_key(rv, key): configurations = self.automl_.runhistory_.ids_config for model_id, run_info in model_runs.items(): - config_id = run_info['config_id'] + config_id = run_info["config_id"] run_config = configurations[config_id]._values - run_info.update({ - 'balancing_strategy': run_config.get('balancing:strategy', None), - 'type': run_config[f'{model_type}:__choice__'], - 'data_preprocessors': [ - value for key, value in run_config.items() - if 'data_preprocessing' in key and '__choice__' in key - ], - 'feature_preprocessors': [ - value for key, value in run_config.items() - if 'feature_preprocessor' in key and '__choice__' in key - ] - }) + run_info.update( + { + "balancing_strategy": run_config.get("balancing:strategy", None), + "type": run_config[f"{model_type}:__choice__"], + "data_preprocessors": [ + value + for key, value in run_config.items() + if "data_preprocessing" in key and "__choice__" in key + ], + "feature_preprocessors": [ + value + for key, value in run_config.items() + if "feature_preprocessor" in key and "__choice__" in key + ], + } + ) # Get the models ensemble weight if it has one # TODO both implementing classes of AbstractEnsemble have a property @@ -892,7 +930,7 @@ def has_key(rv, key): # tied together by ordering, might be better to store as tuple for i, weight in enumerate(self.automl_.ensemble_.weights_): (_, model_id, _) = self.automl_.ensemble_.identifiers_[i] - model_runs[model_id]['ensemble_weight'] = weight + model_runs[model_id]["ensemble_weight"] = weight # Filter out non-ensemble members if needed, else fill in a default # value of 0 if it's missing @@ -900,65 +938,70 @@ def has_key(rv, key): model_runs = { model_id: info for model_id, info in model_runs.items() - if ('ensemble_weight' in info and info['ensemble_weight'] > 0) + if ("ensemble_weight" in info and info["ensemble_weight"] > 0) } else: for model_id, info in model_runs.items(): - if 'ensemble_weight' not in info: - info['ensemble_weight'] = 0 + if "ensemble_weight" not in info: + info["ensemble_weight"] = 0 # `rank` relies on `cost` so we include `cost` # We drop it later if it's not requested - if 'rank' in columns and 'cost' not in columns: - columns = [*columns, 'cost'] + if "rank" in columns and "cost" not in columns: + columns = [*columns, "cost"] # Finally, convert into a tabular format by converting the dict into # column wise orientation. - dataframe = pd.DataFrame({ - col: [run_info[col] for run_info in model_runs.values()] - for col in columns if col != 'rank' - }) + dataframe = pd.DataFrame( + { + col: [run_info[col] for run_info in model_runs.values()] + for col in columns + if col != "rank" + } + ) # Give it an index, even if not in the `include` - dataframe.set_index('model_id', inplace=True) + dataframe.set_index("model_id", inplace=True) # Add the `rank` column if needed, dropping `cost` if it's not # requested by the user - if 'rank' in columns: - dataframe.sort_values(by='cost', ascending=True, inplace=True) - dataframe.insert(column='rank', - value=range(1, len(dataframe) + 1), - loc=list(columns).index('rank') - 1) # account for `model_id` + if "rank" in columns: + dataframe.sort_values(by="cost", ascending=True, inplace=True) + dataframe.insert( + column="rank", + value=range(1, len(dataframe) + 1), + loc=list(columns).index("rank") - 1, + ) # account for `model_id` - if 'cost' not in columns: - dataframe.drop('cost', inplace=True) + if "cost" not in columns: + dataframe.drop("cost", inplace=True) # Decide on the sort order depending on what it gets sorted by - descending_columns = ['ensemble_weight', 'duration'] - if sort_order == 'auto': + descending_columns = ["ensemble_weight", "duration"] + if sort_order == "auto": ascending_param = False if sort_by in descending_columns else True else: - ascending_param = False if sort_order == 'descending' else True + ascending_param = False if sort_order == "descending" else True # Sort by the given column name, defaulting to 'model_id' if not present if sort_by not in dataframe.columns: - self.automl_._logger.warning(f"sort_by = '{sort_by}' was not present" - ", defaulting to sort on the index " - "'model_id'") - sort_by = 'model_id' + self.automl_._logger.warning( + f"sort_by = '{sort_by}' was not present" + ", defaulting to sort on the index " + "'model_id'" + ) + sort_by = "model_id" # Cost can be the same but leave rank all over the place - if 'rank' in columns and sort_by == 'cost': - dataframe.sort_values(by=[sort_by, 'rank'], - ascending=[ascending_param, True], - inplace=True) + if "rank" in columns and sort_by == "cost": + dataframe.sort_values( + by=[sort_by, "rank"], ascending=[ascending_param, True], inplace=True + ) else: - dataframe.sort_values(by=sort_by, - ascending=ascending_param, - inplace=True) + dataframe.sort_values(by=sort_by, ascending=ascending_param, inplace=True) # Lastly, just grab the top_k - if top_k == 'all' or top_k >= len(dataframe): + if top_k == "all" or top_k >= len(dataframe): top_k = len(dataframe) dataframe = dataframe.head(top_k) @@ -966,18 +1009,29 @@ def has_key(rv, key): return dataframe @staticmethod - def _leaderboard_columns() -> Dict[Literal['all', 'simple', 'detailed'], List[str]]: + def _leaderboard_columns() -> Dict[Literal["all", "simple", "detailed"], List[str]]: all = [ - "model_id", "rank", "ensemble_weight", "type", "cost", "duration", - "config_id", "train_loss", "seed", "start_time", "end_time", - "budget", "status", "data_preprocessors", "feature_preprocessors", - "balancing_strategy", "config_origin" - ] - simple = [ - "model_id", "rank", "ensemble_weight", "type", "cost", "duration" + "model_id", + "rank", + "ensemble_weight", + "type", + "cost", + "duration", + "config_id", + "train_loss", + "seed", + "start_time", + "end_time", + "budget", + "status", + "data_preprocessors", + "feature_preprocessors", + "balancing_strategy", + "config_origin", ] + simple = ["model_id", "rank", "ensemble_weight", "type", "cost", "duration"] detailed = all - return {'all': all, 'detailed': detailed, 'simple': simple} + return {"all": all, "detailed": detailed, "simple": simple} def _get_automl_class(self): raise NotImplementedError() @@ -1012,23 +1066,25 @@ def get_configuration_space( if self.automl_ is None: self.automl_ = self.build_automl() - return self.automl_.fit( - X, y, - X_test=X_test, y_test=y_test, - dataset_name=dataset_name, - feat_type=feat_type, - only_return_configuration_space=True, - ) if self.automl_.configuration_space is None else self.automl_.configuration_space + return ( + self.automl_.fit( + X, + y, + X_test=X_test, + y_test=y_test, + dataset_name=dataset_name, + feat_type=feat_type, + only_return_configuration_space=True, + ) + if self.automl_.configuration_space is None + else self.automl_.configuration_space + ) class AutoSklearnClassifier(AutoSklearnEstimator, ClassifierMixin): - """This class implements the classification task. """ + """This class implements the classification task.""" - def fit(self, X, y, - X_test=None, - y_test=None, - feat_type=None, - dataset_name=None): + def fit(self, X, y, X_test=None, y_test=None, feat_type=None, dataset_name=None): """Fit *auto-sklearn* to given training set (X, y). Fit both optimizes the machine learning models and builds an ensemble @@ -1075,18 +1131,16 @@ def fit(self, X, y, # type of data is compatible with auto-sklearn. Legal target # types are: binary, multiclass, multilabel-indicator. target_type = type_of_target(y) - supported_types = ['binary', 'multiclass', 'multilabel-indicator'] + supported_types = ["binary", "multiclass", "multilabel-indicator"] if target_type not in supported_types: - raise ValueError("Classification with data of type {} is " - "not supported. Supported types are {}. " - "You can find more information about scikit-learn " - "data types in: " - "https://scikit-learn.org/stable/modules/multiclass.html" - "".format( - target_type, - supported_types - ) - ) + raise ValueError( + "Classification with data of type {} is " + "not supported. Supported types are {}. " + "You can find more information about scikit-learn " + "data types in: " + "https://scikit-learn.org/stable/modules/multiclass.html" + "".format(target_type, supported_types) + ) # remember target type for using in predict_proba later. self.target_type = target_type @@ -1138,22 +1192,19 @@ def predict_proba(self, X, batch_size=None, n_jobs=1): y : array of shape = [n_samples, n_classes] or [n_samples, n_labels] The predicted class probabilities. """ - pred_proba = super().predict_proba( - X, batch_size=batch_size, n_jobs=n_jobs) + pred_proba = super().predict_proba(X, batch_size=batch_size, n_jobs=n_jobs) # Check if all probabilities sum up to 1. # Assert only if target type is not multilabel-indicator. - if self.target_type not in ['multilabel-indicator']: - assert( - np.allclose( - np.sum(pred_proba, axis=1), - np.ones_like(pred_proba[:, 0])) + if self.target_type not in ["multilabel-indicator"]: + assert np.allclose( + np.sum(pred_proba, axis=1), np.ones_like(pred_proba[:, 0]) ), "prediction probability does not sum up to 1!" # Check that all probability values lie between 0 and 1. - assert( - (pred_proba >= 0).all() and (pred_proba <= 1).all() - ), "found prediction probability value outside of [0, 1]!" + assert (pred_proba >= 0).all() and ( + pred_proba <= 1 + ).all(), "found prediction probability value outside of [0, 1]!" return pred_proba @@ -1167,11 +1218,7 @@ class AutoSklearnRegressor(AutoSklearnEstimator, RegressorMixin): """ - def fit(self, X, y, - X_test=None, - y_test=None, - feat_type=None, - dataset_name=None): + def fit(self, X, y, X_test=None, y_test=None, feat_type=None, dataset_name=None): """Fit *Auto-sklearn* to given training set (X, y). Fit both optimizes the machine learning models and builds an ensemble @@ -1219,18 +1266,21 @@ def fit(self, X, y, y = convert_if_sparse(y) target_type = type_of_target(y) - supported_types = ['continuous', 'binary', 'multiclass', 'continuous-multioutput'] + supported_types = [ + "continuous", + "binary", + "multiclass", + "continuous-multioutput", + ] if target_type not in supported_types: - raise ValueError("Regression with data of type {} is " - "not supported. Supported types are {}. " - "You can find more information about scikit-learn " - "data types in: " - "https://scikit-learn.org/stable/modules/multiclass.html" - "".format( - target_type, - supported_types - ) - ) + raise ValueError( + "Regression with data of type {} is " + "not supported. Supported types are {}. " + "You can find more information about scikit-learn " + "data types in: " + "https://scikit-learn.org/stable/modules/multiclass.html" + "".format(target_type, supported_types) + ) # Fit is supposed to be idempotent! # But not if we use share_mode. diff --git a/autosklearn/evaluation/__init__.py b/autosklearn/evaluation/__init__.py index 506cf51441..51ad69fbb3 100644 --- a/autosklearn/evaluation/__init__.py +++ b/autosklearn/evaluation/__init__.py @@ -1,42 +1,42 @@ # -*- encoding: utf-8 -*- +from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union, cast + import functools -import logging import json +import logging import math import multiprocessing -from queue import Empty import time import traceback -from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union, cast +from queue import Empty -from ConfigSpace import Configuration import numpy as np import pynisher +from ConfigSpace import Configuration +from sklearn.model_selection._split import ( + BaseCrossValidator, + BaseShuffleSplit, + _RepeatedSplits, +) from smac.runhistory.runhistory import RunInfo, RunValue from smac.stats.stats import Stats from smac.tae import StatusType, TAEAbortException from smac.tae.execute_func import AbstractTAFunc -from sklearn.model_selection._split import _RepeatedSplits, BaseShuffleSplit,\ - BaseCrossValidator - -from autosklearn.automl_common.common.utils.backend import Backend - -from autosklearn.metrics import Scorer -import autosklearn.evaluation.train_evaluator import autosklearn.evaluation.test_evaluator +import autosklearn.evaluation.train_evaluator import autosklearn.evaluation.util import autosklearn.pipeline.components +from autosklearn.automl_common.common.utils.backend import Backend from autosklearn.evaluation.train_evaluator import TYPE_ADDITIONAL_INFO +from autosklearn.metrics import Scorer from autosklearn.util.logging_ import PickableLoggerAdapter, get_named_client_logger from autosklearn.util.parallel import preload_modules def fit_predict_try_except_decorator( - ta: Callable, - queue: multiprocessing.Queue, - cost_for_crash: float, - **kwargs: Any) -> None: + ta: Callable, queue: multiprocessing.Queue, cost_for_crash: float, **kwargs: Any +) -> None: try: return ta(queue=queue, **kwargs) @@ -48,7 +48,8 @@ def fit_predict_try_except_decorator( exception_traceback = traceback.format_exc() error_message = repr(e) - # Printing stuff to stdout just in case the queue doesn't work, which happened with the + # Printing stuff to stdout just in case the queue doesn't work, + # which happened with the # following traceback: # File "auto-sklearn/autosklearn/evaluation/__init__.py", line 29, in fit_predict_try_except_decorator # noqa E501 # return ta(queue=queue, **kwargs) @@ -64,14 +65,23 @@ def fit_predict_try_except_decorator( # self._thread.start() # File "miniconda/3-4.5.4/envs/autosklearn/lib/python3.7/threading.py", line 847, in start # noqa E501 # RuntimeError: can't start new thread - print("Exception handling in `fit_predict_try_except_decorator`: " - "traceback: %s \nerror message: %s" % (exception_traceback, error_message)) - - queue.put({'loss': cost_for_crash, - 'additional_run_info': {'traceback': exception_traceback, - 'error': error_message}, - 'status': StatusType.CRASHED, - 'final_queue_element': True}, block=True) + print( + "Exception handling in `fit_predict_try_except_decorator`: " + "traceback: %s \nerror message: %s" % (exception_traceback, error_message) + ) + + queue.put( + { + "loss": cost_for_crash, + "additional_run_info": { + "traceback": exception_traceback, + "error": error_message, + }, + "status": StatusType.CRASHED, + "final_queue_element": True, + }, + block=True, + ) queue.close() @@ -94,8 +104,9 @@ def get_cost_of_crash(metric: Scorer) -> float: return worst_possible_result -def _encode_exit_status(exit_status: Union[str, int, Type[BaseException]] - ) -> Union[str, int]: +def _encode_exit_status( + exit_status: Union[str, int, Type[BaseException]] +) -> Union[str, int]: try: # If it can be dumped, then it is int exit_status = cast(int, exit_status) @@ -108,12 +119,13 @@ def _encode_exit_status(exit_status: Union[str, int, Type[BaseException]] # TODO potentially log all inputs to this class to pickle them in order to do # easier debugging of potential crashes class ExecuteTaFuncWithQueue(AbstractTAFunc): - def __init__( self, backend: Backend, autosklearn_seed: int, - resampling_strategy: Union[str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit], + resampling_strategy: Union[ + str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit + ], metric: Scorer, cost_for_crash: float, abort_on_first_run_crash: bool, @@ -121,7 +133,7 @@ def __init__( pynisher_context: str, initial_num_run: int = 1, stats: Optional[Stats] = None, - run_obj: str = 'quality', + run_obj: str = "quality", par_factor: int = 1, scoring_functions: Optional[List[Scorer]] = None, output_y_hat_optimization: bool = True, @@ -135,26 +147,29 @@ def __init__( **resampling_strategy_args: Any, ): - if resampling_strategy == 'holdout': + if resampling_strategy == "holdout": eval_function = autosklearn.evaluation.train_evaluator.eval_holdout - elif resampling_strategy == 'holdout-iterative-fit': - eval_function = autosklearn.evaluation.train_evaluator.eval_iterative_holdout - elif resampling_strategy == 'cv-iterative-fit': + elif resampling_strategy == "holdout-iterative-fit": + eval_function = ( + autosklearn.evaluation.train_evaluator.eval_iterative_holdout + ) + elif resampling_strategy == "cv-iterative-fit": eval_function = autosklearn.evaluation.train_evaluator.eval_iterative_cv - elif resampling_strategy == 'cv' or isinstance(resampling_strategy, ( - BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit) + elif resampling_strategy == "cv" or isinstance( + resampling_strategy, (BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit) ): eval_function = autosklearn.evaluation.train_evaluator.eval_cv - elif resampling_strategy == 'partial-cv': + elif resampling_strategy == "partial-cv": eval_function = autosklearn.evaluation.train_evaluator.eval_partial_cv - elif resampling_strategy == 'partial-cv-iterative-fit': - eval_function = autosklearn.evaluation.train_evaluator.eval_partial_cv_iterative - elif resampling_strategy == 'test': + elif resampling_strategy == "partial-cv-iterative-fit": + eval_function = ( + autosklearn.evaluation.train_evaluator.eval_partial_cv_iterative + ) + elif resampling_strategy == "test": eval_function = autosklearn.evaluation.test_evaluator.eval_t output_y_hat_optimization = False else: - raise ValueError('Unknown resampling strategy %s' % - resampling_strategy) + raise ValueError("Unknown resampling strategy %s" % resampling_strategy) self.worst_possible_result = cost_for_crash @@ -181,7 +196,7 @@ def __init__( self.resampling_strategy = resampling_strategy self.resampling_strategy_args = resampling_strategy_args self.scoring_functions = scoring_functions - # TODO deactivate output_y_hat_optimization and let the respective evaluator decide + # TODO deactivate output_y_hat_optimization and let respective evaluator decide self.output_y_hat_optimization = output_y_hat_optimization self.include = include self.exclude = exclude @@ -194,11 +209,11 @@ def __init__( self.memory_limit = memory_limit dm = self.backend.load_datamanager() - if 'X_valid' in dm.data and 'Y_valid' in dm.data: + if "X_valid" in dm.data and "Y_valid" in dm.data: self._get_validation_loss = True else: self._get_validation_loss = False - if 'X_test' in dm.data and 'Y_test' in dm.data: + if "X_test" in dm.data and "Y_test" in dm.data: self._get_test_loss = True else: self._get_test_loss = False @@ -206,7 +221,9 @@ def __init__( self.port = port self.pynisher_context = pynisher_context if self.port is None: - self.logger: Union[logging.Logger, PickableLoggerAdapter] = logging.getLogger("TAE") + self.logger: Union[ + logging.Logger, PickableLoggerAdapter + ] = logging.getLogger("TAE") else: self.logger = get_named_client_logger( name="TAE", @@ -236,18 +253,23 @@ def run_wrapper( if self.budget_type is None: if run_info.budget != 0: raise ValueError( - 'If budget_type is None, budget must be.0, but is %f' % run_info.budget + "If budget_type is None, budget must be.0, but is %f" + % run_info.budget ) else: if run_info.budget == 0: run_info = run_info._replace(budget=100) elif run_info.budget <= 0 or run_info.budget > 100: - raise ValueError('Illegal value for budget, must be >0 and <=100, but is %f' % - run_info.budget) - if self.budget_type not in ('subsample', 'iterations', 'mixed'): - raise ValueError("Illegal value for budget type, must be one of " - "('subsample', 'iterations', 'mixed'), but is : %s" % - self.budget_type) + raise ValueError( + "Illegal value for budget, must be >0 and <=100, but is %f" + % run_info.budget + ) + if self.budget_type not in ("subsample", "iterations", "mixed"): + raise ValueError( + "Illegal value for budget type, must be one of " + "('subsample', 'iterations', 'mixed'), but is : %s" + % self.budget_type + ) remaining_time = self.stats.get_remaing_time_budget() @@ -255,11 +277,15 @@ def run_wrapper( run_info = run_info._replace(cutoff=int(remaining_time - 5)) config_id = ( - run_info.config if isinstance(run_info.config, int) else run_info.config.config_id + run_info.config + if isinstance(run_info.config, int) + else run_info.config.config_id ) if run_info.cutoff < 1.0: - self.logger.info("Not starting configuration %d because time is up" % config_id) + self.logger.info( + "Not starting configuration %d because time is up" % config_id + ) return run_info, RunValue( status=StatusType.STOP, cost=self.worst_possible_result, @@ -268,9 +294,8 @@ def run_wrapper( starttime=time.time(), endtime=time.time(), ) - elif ( - run_info.cutoff != int(np.ceil(run_info.cutoff)) - and not isinstance(run_info.cutoff, int) + elif run_info.cutoff != int(np.ceil(run_info.cutoff)) and not isinstance( + run_info.cutoff, int ): run_info = run_info._replace(cutoff=int(np.ceil(run_info.cutoff))) @@ -285,7 +310,9 @@ def run( seed: int = 12345, budget: float = 0.0, instance_specific: Optional[str] = None, - ) -> Tuple[StatusType, float, float, Dict[str, Union[int, float, str, Dict, List, Tuple]]]: + ) -> Tuple[ + StatusType, float, float, Dict[str, Union[int, float, str, Dict, List, Tuple]] + ]: # Additional information of each of the tae executions # Defined upfront for mypy @@ -295,14 +322,16 @@ def run( preload_modules(context) queue = context.Queue() - if not (instance_specific is None or instance_specific == '0'): + if not (instance_specific is None or instance_specific == "0"): raise ValueError(instance_specific) - init_params = {'instance': instance} + init_params = {"instance": instance} if self.init_params is not None: init_params.update(self.init_params) if self.port is None: - logger: Union[logging.Logger, PickableLoggerAdapter] = logging.getLogger("pynisher") + logger: Union[logging.Logger, PickableLoggerAdapter] = logging.getLogger( + "pynisher" + ) else: logger = get_named_client_logger( name="pynisher", @@ -341,9 +370,9 @@ def run( additional_components=autosklearn.pipeline.components.base._addons, ) - if self.resampling_strategy != 'test': - obj_kwargs['resampling_strategy'] = self.resampling_strategy - obj_kwargs['resampling_strategy_args'] = self.resampling_strategy_args + if self.resampling_strategy != "test": + obj_kwargs["resampling_strategy"] = self.resampling_strategy + obj_kwargs["resampling_strategy_args"] = self.resampling_strategy_args try: obj = pynisher.enforce_limits(**arguments)(self.ta) @@ -351,31 +380,38 @@ def run( except Exception as e: exception_traceback = traceback.format_exc() error_message = repr(e) - additional_run_info.update({ - 'traceback': exception_traceback, - 'error': error_message - }) - return StatusType.CRASHED, self.worst_possible_result, 0.0, additional_run_info + additional_run_info.update( + {"traceback": exception_traceback, "error": error_message} + ) + return ( + StatusType.CRASHED, + self.worst_possible_result, + 0.0, + additional_run_info, + ) - if obj.exit_status in (pynisher.TimeoutException, pynisher.MemorylimitException): + if obj.exit_status in ( + pynisher.TimeoutException, + pynisher.MemorylimitException, + ): # Even if the pynisher thinks that a timeout or memout occured, # it can be that the target algorithm wrote something into the queue # - then we treat it as a succesful run try: info = autosklearn.evaluation.util.read_queue(queue) - result = info[-1]['loss'] - status = info[-1]['status'] - additional_run_info = info[-1]['additional_run_info'] + result = info[-1]["loss"] + status = info[-1]["status"] + additional_run_info = info[-1]["additional_run_info"] if obj.stdout: - additional_run_info['subprocess_stdout'] = obj.stdout + additional_run_info["subprocess_stdout"] = obj.stdout if obj.stderr: - additional_run_info['subprocess_stderr'] = obj.stderr + additional_run_info["subprocess_stderr"] = obj.stderr if obj.exit_status is pynisher.TimeoutException: - additional_run_info['info'] = 'Run stopped because of timeout.' + additional_run_info["info"] = "Run stopped because of timeout." elif obj.exit_status is pynisher.MemorylimitException: - additional_run_info['info'] = 'Run stopped because of memout.' + additional_run_info["info"] = "Run stopped because of memout." if status in [StatusType.SUCCESS, StatusType.DONOTADVANCE]: cost = result @@ -386,11 +422,13 @@ def run( info = None if obj.exit_status is pynisher.TimeoutException: status = StatusType.TIMEOUT - additional_run_info = {'error': 'Timeout'} + additional_run_info = {"error": "Timeout"} elif obj.exit_status is pynisher.MemorylimitException: status = StatusType.MEMOUT additional_run_info = { - "error": "Memout (used more than {} MB).".format(self.memory_limit) + "error": "Memout (used more than {} MB).".format( + self.memory_limit + ) } else: raise ValueError(obj.exit_status) @@ -400,99 +438,111 @@ def run( info = None status = StatusType.ABORT cost = self.worst_possible_result - additional_run_info = {'error': 'Your configuration of ' - 'auto-sklearn does not work!', - 'exit_status': _encode_exit_status(obj.exit_status), - 'subprocess_stdout': obj.stdout, - 'subprocess_stderr': obj.stderr, - } + additional_run_info = { + "error": "Your configuration of " "auto-sklearn does not work!", + "exit_status": _encode_exit_status(obj.exit_status), + "subprocess_stdout": obj.stdout, + "subprocess_stderr": obj.stderr, + } else: try: info = autosklearn.evaluation.util.read_queue(queue) - result = info[-1]['loss'] - status = info[-1]['status'] - additional_run_info = info[-1]['additional_run_info'] + result = info[-1]["loss"] + status = info[-1]["status"] + additional_run_info = info[-1]["additional_run_info"] if obj.exit_status == 0: cost = result else: status = StatusType.CRASHED cost = self.worst_possible_result - additional_run_info['info'] = 'Run treated as crashed ' \ - 'because the pynisher exit ' \ - 'status %s is unknown.' % \ - str(obj.exit_status) - additional_run_info['exit_status'] = _encode_exit_status(obj.exit_status) - additional_run_info['subprocess_stdout'] = obj.stdout - additional_run_info['subprocess_stderr'] = obj.stderr + additional_run_info["info"] = ( + "Run treated as crashed " + "because the pynisher exit " + "status %s is unknown." % str(obj.exit_status) + ) + additional_run_info["exit_status"] = _encode_exit_status( + obj.exit_status + ) + additional_run_info["subprocess_stdout"] = obj.stdout + additional_run_info["subprocess_stderr"] = obj.stderr except Empty: info = None additional_run_info = { - 'error': 'Result queue is empty', - 'exit_status': _encode_exit_status(obj.exit_status), - 'subprocess_stdout': obj.stdout, - 'subprocess_stderr': obj.stderr, - 'exitcode': obj.exitcode + "error": "Result queue is empty", + "exit_status": _encode_exit_status(obj.exit_status), + "subprocess_stdout": obj.stdout, + "subprocess_stderr": obj.stderr, + "exitcode": obj.exitcode, } status = StatusType.CRASHED cost = self.worst_possible_result if ( - (self.budget_type is None or budget == 0) - and status == StatusType.DONOTADVANCE - ): + self.budget_type is None or budget == 0 + ) and status == StatusType.DONOTADVANCE: status = StatusType.SUCCESS if not isinstance(additional_run_info, dict): - additional_run_info = {'message': additional_run_info} + additional_run_info = {"message": additional_run_info} if ( info is not None - and self.resampling_strategy in ('holdout-iterative-fit', 'cv-iterative-fit') + and self.resampling_strategy + in ("holdout-iterative-fit", "cv-iterative-fit") and status != StatusType.CRASHED ): learning_curve = autosklearn.evaluation.util.extract_learning_curve(info) learning_curve_runtime = autosklearn.evaluation.util.extract_learning_curve( - info, 'duration' + info, "duration" ) if len(learning_curve) > 1: - additional_run_info['learning_curve'] = learning_curve - additional_run_info['learning_curve_runtime'] = learning_curve_runtime + additional_run_info["learning_curve"] = learning_curve + additional_run_info["learning_curve_runtime"] = learning_curve_runtime train_learning_curve = autosklearn.evaluation.util.extract_learning_curve( - info, 'train_loss' + info, "train_loss" ) if len(train_learning_curve) > 1: - additional_run_info['train_learning_curve'] = train_learning_curve - additional_run_info['learning_curve_runtime'] = learning_curve_runtime + additional_run_info["train_learning_curve"] = train_learning_curve + additional_run_info["learning_curve_runtime"] = learning_curve_runtime if self._get_validation_loss: - validation_learning_curve = autosklearn.evaluation.util.extract_learning_curve( - info, 'validation_loss', + validation_learning_curve = ( + autosklearn.evaluation.util.extract_learning_curve( + info, + "validation_loss", + ) ) if len(validation_learning_curve) > 1: - additional_run_info['validation_learning_curve'] = \ - validation_learning_curve additional_run_info[ - 'learning_curve_runtime'] = learning_curve_runtime + "validation_learning_curve" + ] = validation_learning_curve + additional_run_info[ + "learning_curve_runtime" + ] = learning_curve_runtime if self._get_test_loss: - test_learning_curve = autosklearn.evaluation.util.extract_learning_curve( - info, 'test_loss', + test_learning_curve = ( + autosklearn.evaluation.util.extract_learning_curve( + info, + "test_loss", + ) ) if len(test_learning_curve) > 1: - additional_run_info['test_learning_curve'] = test_learning_curve + additional_run_info["test_learning_curve"] = test_learning_curve additional_run_info[ - 'learning_curve_runtime'] = learning_curve_runtime + "learning_curve_runtime" + ] = learning_curve_runtime if isinstance(config, int): - origin = 'DUMMY' + origin = "DUMMY" config_id = config else: - origin = getattr(config, 'origin', 'UNKNOWN') + origin = getattr(config, "origin", "UNKNOWN") config_id = config.config_id - additional_run_info['configuration_origin'] = origin + additional_run_info["configuration_origin"] = origin runtime = float(obj.wall_clock_time) diff --git a/autosklearn/evaluation/abstract_evaluator.py b/autosklearn/evaluation/abstract_evaluator.py index 36d51d7e0d..bc0be0e8d8 100644 --- a/autosklearn/evaluation/abstract_evaluator.py +++ b/autosklearn/evaluation/abstract_evaluator.py @@ -1,43 +1,36 @@ +from typing import Any, Dict, List, Optional, TextIO, Tuple, Type, Union, cast + import logging import multiprocessing import time import warnings -from typing import Any, Dict, List, Optional, TextIO, Tuple, Type, Union, cast import numpy as np - +from ConfigSpace import Configuration from sklearn.base import BaseEstimator from sklearn.dummy import DummyClassifier, DummyRegressor from sklearn.ensemble import VotingClassifier, VotingRegressor - from smac.tae import StatusType - from threadpoolctl import threadpool_limits -from autosklearn.automl_common.common.utils.backend import Backend - import autosklearn.pipeline.classification import autosklearn.pipeline.regression -from autosklearn.pipeline.components.base import ThirdPartyComponents, _addons +from autosklearn.automl_common.common.utils.backend import Backend from autosklearn.constants import ( CLASSIFICATION_TASKS, - REGRESSION_TASKS, - MULTILABEL_CLASSIFICATION, MULTICLASS_CLASSIFICATION, - MULTIOUTPUT_REGRESSION + MULTILABEL_CLASSIFICATION, + MULTIOUTPUT_REGRESSION, + REGRESSION_TASKS, ) +from autosklearn.metrics import Scorer, calculate_loss +from autosklearn.pipeline.components.base import ThirdPartyComponents, _addons from autosklearn.pipeline.implementations.util import ( - convert_multioutput_multiclass_to_multilabel + convert_multioutput_multiclass_to_multilabel, ) -from autosklearn.metrics import calculate_loss, Scorer from autosklearn.util.logging_ import PicklableClientLogger, get_named_client_logger -from ConfigSpace import Configuration - - -__all__ = [ - 'AbstractEvaluator' -] +__all__ = ["AbstractEvaluator"] # General TYPE definitions for numpy @@ -66,37 +59,39 @@ def __init__( self.exclude = exclude def pre_transform( - self, - X: np.ndarray, - y: np.ndarray, - fit_params: Optional[Dict[str, Any]] = None + self, X: np.ndarray, y: np.ndarray, fit_params: Optional[Dict[str, Any]] = None ) -> Tuple[np.ndarray, Dict[str, Any]]: # pylint: disable=R0201 if fit_params is None: fit_params = {} return X, fit_params - def fit(self, X: np.ndarray, y: np.ndarray, - sample_weight: Optional[Union[np.ndarray, List]] = None - ) -> DummyClassifier: - return super(MyDummyClassifier, self).fit(np.ones((X.shape[0], 1)), y, - sample_weight=sample_weight) + def fit( + self, + X: np.ndarray, + y: np.ndarray, + sample_weight: Optional[Union[np.ndarray, List]] = None, + ) -> DummyClassifier: + return super(MyDummyClassifier, self).fit( + np.ones((X.shape[0], 1)), y, sample_weight=sample_weight + ) - def fit_estimator(self, X: np.ndarray, y: np.ndarray, - fit_params: Optional[Dict[str, Any]] = None) -> DummyClassifier: + def fit_estimator( + self, X: np.ndarray, y: np.ndarray, fit_params: Optional[Dict[str, Any]] = None + ) -> DummyClassifier: return self.fit(X, y) - def predict_proba(self, X: np.ndarray, batch_size: int = 1000 - ) -> np.ndarray: + def predict_proba(self, X: np.ndarray, batch_size: int = 1000) -> np.ndarray: new_X = np.ones((X.shape[0], 1)) probas = super(MyDummyClassifier, self).predict_proba(new_X) - probas = convert_multioutput_multiclass_to_multilabel(probas).astype( - np.float32) + probas = convert_multioutput_multiclass_to_multilabel(probas).astype(np.float32) return probas def estimator_supports_iterative_fit(self) -> bool: # pylint: disable=R0201 return False - def get_additional_run_info(self) -> Optional[TYPE_ADDITIONAL_INFO]: # pylint: disable=R0201 + def get_additional_run_info( + self, + ) -> Optional[TYPE_ADDITIONAL_INFO]: # pylint: disable=R0201 return None @@ -112,9 +107,9 @@ def __init__( ): self.config = config if config == 1: - super(MyDummyRegressor, self).__init__(strategy='mean') + super(MyDummyRegressor, self).__init__(strategy="mean") else: - super(MyDummyRegressor, self).__init__(strategy='median') + super(MyDummyRegressor, self).__init__(strategy="median") self.random_state = random_state self.init_params = init_params self.dataset_properties = dataset_properties @@ -122,23 +117,25 @@ def __init__( self.exclude = exclude def pre_transform( - self, - X: np.ndarray, - y: np.ndarray, - fit_params: Optional[Dict[str, Any]] = None + self, X: np.ndarray, y: np.ndarray, fit_params: Optional[Dict[str, Any]] = None ) -> Tuple[np.ndarray, Dict[str, Any]]: # pylint: disable=R0201 if fit_params is None: fit_params = {} return X, fit_params - def fit(self, X: np.ndarray, y: np.ndarray, - sample_weight: Optional[Union[np.ndarray, List]] = None - ) -> DummyRegressor: - return super(MyDummyRegressor, self).fit(np.ones((X.shape[0], 1)), y, - sample_weight=sample_weight) + def fit( + self, + X: np.ndarray, + y: np.ndarray, + sample_weight: Optional[Union[np.ndarray, List]] = None, + ) -> DummyRegressor: + return super(MyDummyRegressor, self).fit( + np.ones((X.shape[0], 1)), y, sample_weight=sample_weight + ) - def fit_estimator(self, X: np.ndarray, y: np.ndarray, - fit_params: Optional[Dict[str, Any]] = None) -> DummyRegressor: + def fit_estimator( + self, X: np.ndarray, y: np.ndarray, fit_params: Optional[Dict[str, Any]] = None + ) -> DummyRegressor: return self.fit(X, y) def predict(self, X: np.ndarray, batch_size: int = 1000) -> np.ndarray: @@ -148,7 +145,9 @@ def predict(self, X: np.ndarray, batch_size: int = 1000) -> np.ndarray: def estimator_supports_iterative_fit(self) -> bool: # pylint: disable=R0201 return False - def get_additional_run_info(self) -> Optional[TYPE_ADDITIONAL_INFO]: # pylint: disable=R0201 + def get_additional_run_info( + self, + ) -> Optional[TYPE_ADDITIONAL_INFO]: # pylint: disable=R0201 return None @@ -156,7 +155,7 @@ def _fit_and_suppress_warnings( logger: Union[logging.Logger, PicklableClientLogger], model: BaseEstimator, X: np.ndarray, - y: np.ndarray + y: np.ndarray, ) -> BaseEstimator: def send_warnings_to_log( message: Union[Warning, str], @@ -166,8 +165,7 @@ def send_warnings_to_log( file: Optional[TextIO] = None, line: Optional[str] = None, ) -> None: - logger.debug('%s:%s: %s:%s' % - (filename, lineno, str(category), message)) + logger.debug("%s:%s: %s:%s" % (filename, lineno, str(category), message)) return with warnings.catch_warnings(): @@ -212,13 +210,13 @@ def __init__( self.include = include self.exclude = exclude - self.X_valid = self.datamanager.data.get('X_valid') - self.y_valid = self.datamanager.data.get('Y_valid') - self.X_test = self.datamanager.data.get('X_test') - self.y_test = self.datamanager.data.get('Y_test') + self.X_valid = self.datamanager.data.get("X_valid") + self.y_valid = self.datamanager.data.get("Y_valid") + self.X_test = self.datamanager.data.get("X_test") + self.y_test = self.datamanager.data.get("Y_test") self.metric = metric - self.task_type = self.datamanager.info['task'] + self.task_type = self.datamanager.info["task"] self.seed = seed self.output_y_hat_optimization = output_y_hat_optimization @@ -227,25 +225,26 @@ def __init__( if isinstance(disable_file_output, (bool, list)): self.disable_file_output: Union[bool, List[str]] = disable_file_output else: - raise ValueError('disable_file_output should be either a bool or a list') + raise ValueError("disable_file_output should be either a bool or a list") if self.task_type in REGRESSION_TASKS: if not isinstance(self.configuration, Configuration): self.model_class = MyDummyRegressor else: - self.model_class = \ + self.model_class = ( autosklearn.pipeline.regression.SimpleRegressionPipeline + ) self.predict_function = self._predict_regression else: if not isinstance(self.configuration, Configuration): self.model_class = MyDummyClassifier else: - self.model_class = autosklearn.pipeline.classification.SimpleClassificationPipeline + self.model_class = ( + autosklearn.pipeline.classification.SimpleClassificationPipeline + ) self.predict_function = self._predict_proba - self._init_params = { - 'data_preprocessor:feat_type': self.datamanager.feat_type - } + self._init_params = {"data_preprocessor:feat_type": self.datamanager.feat_type} if init_params is not None: self._init_params.update(init_params) @@ -254,8 +253,11 @@ def __init__( num_run = 0 self.num_run = num_run - logger_name = '%s(%d):%s' % (self.__class__.__name__.split('.')[-1], - self.seed, self.datamanager.name) + logger_name = "%s(%d):%s" % ( + self.__class__.__name__.split(".")[-1], + self.seed, + self.datamanager.name, + ) if self.port is None: self.logger = logging.getLogger(__name__) @@ -271,12 +273,14 @@ def __init__( self.budget = budget self.budget_type = budget_type - # Add 3rd-party components to the list of 3rd-party components in case this wasn't done - # before (this happens if we run in parallel and the components are only passed to the - # AbstractEvaluator via the TAE and are not there yet because the worker is in its own - # process). + # Add 3rd-party components to the list of 3rd-party components in case this + # wasn't done before (this happens if we run in parallel and the components + # are only passed to the AbstractEvaluator via the TAE and are not there + # yet because the worker is in its own process). for key in additional_components: - for component_name, component in additional_components[key].components.items(): + for component_name, component in additional_components[ + key + ].components.items(): if component_name not in _addons[key].components: _addons[key].add_component(component) @@ -285,34 +289,41 @@ def __init__( def _get_model(self) -> BaseEstimator: if not isinstance(self.configuration, Configuration): - model = self.model_class(config=self.configuration, - random_state=self.seed, - init_params=self._init_params) + model = self.model_class( + config=self.configuration, + random_state=self.seed, + init_params=self._init_params, + ) else: if self.task_type in REGRESSION_TASKS: dataset_properties = { - 'task': self.task_type, - 'sparse': self.datamanager.info['is_sparse'] == 1, - 'multioutput': self.task_type == MULTIOUTPUT_REGRESSION, + "task": self.task_type, + "sparse": self.datamanager.info["is_sparse"] == 1, + "multioutput": self.task_type == MULTIOUTPUT_REGRESSION, } else: dataset_properties = { - 'task': self.task_type, - 'sparse': self.datamanager.info['is_sparse'] == 1, - 'multilabel': self.task_type == MULTILABEL_CLASSIFICATION, - 'multiclass': self.task_type == MULTICLASS_CLASSIFICATION, + "task": self.task_type, + "sparse": self.datamanager.info["is_sparse"] == 1, + "multilabel": self.task_type == MULTILABEL_CLASSIFICATION, + "multiclass": self.task_type == MULTICLASS_CLASSIFICATION, } - model = self.model_class(config=self.configuration, - dataset_properties=dataset_properties, - random_state=self.seed, - include=self.include, - exclude=self.exclude, - init_params=self._init_params) + model = self.model_class( + config=self.configuration, + dataset_properties=dataset_properties, + random_state=self.seed, + include=self.include, + exclude=self.exclude, + init_params=self._init_params, + ) return model - def _loss(self, y_true: np.ndarray, y_hat: np.ndarray, - scoring_functions: Optional[List[Scorer]] = None - ) -> Union[float, Dict[str, float]]: + def _loss( + self, + y_true: np.ndarray, + y_hat: np.ndarray, + scoring_functions: Optional[List[Scorer]] = None, + ) -> Union[float, Dict[str, float]]: """Auto-sklearn follows a minimization goal. The calculate_loss internally translate a score function to a minimization problem. @@ -324,9 +335,7 @@ def _loss(self, y_true: np.ndarray, y_hat: np.ndarray, y_true """ scoring_functions = ( - self.scoring_functions - if scoring_functions is None - else scoring_functions + self.scoring_functions if scoring_functions is None else scoring_functions ) if not isinstance(self.configuration, Configuration): if scoring_functions: @@ -335,8 +344,12 @@ def _loss(self, y_true: np.ndarray, y_hat: np.ndarray, return self.metric._worst_possible_result return calculate_loss( - y_true, y_hat, self.task_type, self.metric, - scoring_functions=scoring_functions) + y_true, + y_hat, + self.task_type, + self.metric, + scoring_functions=scoring_functions, + ) def finish_up( self, @@ -349,28 +362,35 @@ def finish_up( file_output: bool, final_call: bool, status: StatusType, - ) -> Tuple[float, Union[float, Dict[str, float]], int, - Dict[str, Union[str, int, float, Dict, List, Tuple]]]: - """This function does everything necessary after the fitting is done: + ) -> Tuple[ + float, + Union[float, Dict[str, float]], + int, + Dict[str, Union[str, int, float, Dict, List, Tuple]], + ]: + """Do everything necessary after the fitting is done: * predicting * saving the files for the ensembles_statistics * generate output for SMAC We use it as the signal handler so we can recycle the code for the - normal usecase and when the runsolver kills us here :)""" - + normal usecase and when the runsolver kills us here :) + """ self.duration = time.time() - self.starttime if file_output: file_out_loss, additional_run_info_ = self.file_output( - opt_pred, valid_pred, test_pred, + opt_pred, + valid_pred, + test_pred, ) else: file_out_loss = None additional_run_info_ = {} validation_loss, test_loss = self.calculate_auxiliary_losses( - valid_pred, test_pred, + valid_pred, + test_pred, ) if file_out_loss is not None: @@ -382,25 +402,25 @@ def finish_up( else: loss_ = {} - additional_run_info = ( - {} if additional_run_info is None else additional_run_info - ) + additional_run_info = {} if additional_run_info is None else additional_run_info for metric_name, value in loss_.items(): additional_run_info[metric_name] = value - additional_run_info['duration'] = self.duration - additional_run_info['num_run'] = self.num_run + additional_run_info["duration"] = self.duration + additional_run_info["num_run"] = self.num_run if train_loss is not None: - additional_run_info['train_loss'] = train_loss + additional_run_info["train_loss"] = train_loss if validation_loss is not None: - additional_run_info['validation_loss'] = validation_loss + additional_run_info["validation_loss"] = validation_loss if test_loss is not None: - additional_run_info['test_loss'] = test_loss + additional_run_info["test_loss"] = test_loss - rval_dict = {'loss': loss, - 'additional_run_info': additional_run_info, - 'status': status} + rval_dict = { + "loss": loss, + "additional_run_info": additional_run_info, + "status": status, + } if final_call: - rval_dict['final_queue_element'] = True + rval_dict["final_queue_element"] = True self.queue.put(rval_dict) return self.duration, loss_, self.seed, additional_run_info_ @@ -413,7 +433,8 @@ def calculate_auxiliary_losses( if Y_valid_pred is not None: if self.y_valid is not None: validation_loss: Optional[Union[float, Dict[str, float]]] = self._loss( - self.y_valid, Y_valid_pred) + self.y_valid, Y_valid_pred + ) if isinstance(validation_loss, dict): validation_loss = validation_loss[self.metric.name] else: @@ -424,7 +445,8 @@ def calculate_auxiliary_losses( if Y_test_pred is not None: if self.y_test is not None: test_loss: Optional[Union[float, Dict[str, float]]] = self._loss( - self.y_test, Y_test_pred) + self.y_test, Y_test_pred + ) if isinstance(test_loss, dict): test_loss = test_loss[self.metric.name] else: @@ -451,27 +473,24 @@ def file_output( return ( 1.0, { - 'error': - "Targets %s and prediction %s don't have " - "the same length. Probably training didn't " - "finish" % (np.shape(self.Y_optimization), Y_optimization_pred.shape) - }, + "error": "Targets %s and prediction %s don't have " + "the same length. Probably training didn't " + "finish" + % (np.shape(self.Y_optimization), Y_optimization_pred.shape) + }, ) # Abort if predictions contain NaNs for y, s in [ # Y_train_pred deleted here. Fix unittest accordingly. - [Y_optimization_pred, 'optimization'], - [Y_valid_pred, 'validation'], - [Y_test_pred, 'test'] + [Y_optimization_pred, "optimization"], + [Y_valid_pred, "validation"], + [Y_test_pred, "test"], ]: if y is not None and not np.all(np.isfinite(y)): return ( 1.0, - { - 'error': - 'Model predictions for %s set contains NaNs.' % s - }, + {"error": "Model predictions for %s set contains NaNs." % s}, ) # Abort if we don't want to output anything. @@ -489,17 +508,20 @@ def file_output( self.disable_file_output = cast(List, self.disable_file_output) # This file can be written independently of the others down bellow - if ('y_optimization' not in self.disable_file_output): + if "y_optimization" not in self.disable_file_output: if self.output_y_hat_optimization: self.backend.save_targets_ensemble(self.Y_optimization) models: Optional[BaseEstimator] = None - if hasattr(self, 'models'): - if len(self.models) > 0 and self.models[0] is not None: # type: ignore[attr-defined] - if ('models' not in self.disable_file_output): + if hasattr(self, "models"): + if len(self.models) > 0 and self.models[0] is not None: + if "models" not in self.disable_file_output: if self.task_type in CLASSIFICATION_TASKS: - models = VotingClassifier(estimators=None, voting='soft', ) + models = VotingClassifier( + estimators=None, + voting="soft", + ) else: models = VotingRegressor(estimators=None) # Mypy cannot understand hasattr yet @@ -509,24 +531,30 @@ def file_output( seed=self.seed, idx=self.num_run, budget=self.budget, - model=self.model if 'model' not in self.disable_file_output else None, - cv_model=models if 'cv_model' not in self.disable_file_output else None, + model=self.model if "model" not in self.disable_file_output else None, + cv_model=models if "cv_model" not in self.disable_file_output else None, ensemble_predictions=( - Y_optimization_pred if 'y_optimization' not in self.disable_file_output else None + Y_optimization_pred + if "y_optimization" not in self.disable_file_output + else None ), valid_predictions=( - Y_valid_pred if 'y_valid' not in self.disable_file_output else None + Y_valid_pred if "y_valid" not in self.disable_file_output else None ), test_predictions=( - Y_test_pred if 'y_test' not in self.disable_file_output else None + Y_test_pred if "y_test" not in self.disable_file_output else None ), ) return None, {} - def _predict_proba(self, X: np.ndarray, model: BaseEstimator, - task_type: int, Y_train: Optional[np.ndarray] = None, - ) -> np.ndarray: + def _predict_proba( + self, + X: np.ndarray, + model: BaseEstimator, + task_type: int, + Y_train: Optional[np.ndarray] = None, + ) -> np.ndarray: def send_warnings_to_log( message: Union[Warning, str], category: Type[Warning], @@ -535,8 +563,9 @@ def send_warnings_to_log( file: Optional[TextIO] = None, line: Optional[str] = None, ) -> None: - self.logger.debug('%s:%s: %s:%s' % - (filename, lineno, str(category), message)) + self.logger.debug( + "%s:%s: %s:%s" % (filename, lineno, str(category), message) + ) return with warnings.catch_warnings(): @@ -549,8 +578,13 @@ def send_warnings_to_log( Y_pred = self._ensure_prediction_array_sizes(Y_pred, Y_train) return Y_pred - def _predict_regression(self, X: np.ndarray, model: BaseEstimator, - task_type: int, Y_train: Optional[np.ndarray] = None) -> np.ndarray: + def _predict_regression( + self, + X: np.ndarray, + model: BaseEstimator, + task_type: int, + Y_train: Optional[np.ndarray] = None, + ) -> np.ndarray: def send_warnings_to_log( message: Union[Warning, str], category: Type[Warning], @@ -559,8 +593,9 @@ def send_warnings_to_log( file: Optional[TextIO] = None, line: Optional[str] = None, ) -> None: - self.logger.debug('%s:%s: %s:%s' % - (filename, lineno, str(category), message)) + self.logger.debug( + "%s:%s: %s:%s" % (filename, lineno, str(category), message) + ) return with warnings.catch_warnings(): @@ -572,14 +607,17 @@ def send_warnings_to_log( return Y_pred - def _ensure_prediction_array_sizes(self, prediction: np.ndarray, Y_train: np.ndarray - ) -> np.ndarray: - num_classes = self.datamanager.info['label_num'] + def _ensure_prediction_array_sizes( + self, prediction: np.ndarray, Y_train: np.ndarray + ) -> np.ndarray: + num_classes = self.datamanager.info["label_num"] - if self.task_type == MULTICLASS_CLASSIFICATION and \ - prediction.shape[1] < num_classes: + if ( + self.task_type == MULTICLASS_CLASSIFICATION + and prediction.shape[1] < num_classes + ): if Y_train is None: - raise ValueError('Y_train must not be None!') + raise ValueError("Y_train must not be None!") classes = list(np.unique(Y_train)) mapping = dict() @@ -587,8 +625,9 @@ def _ensure_prediction_array_sizes(self, prediction: np.ndarray, Y_train: np.nda if class_number in classes: index = classes.index(class_number) mapping[index] = class_number - new_predictions = np.zeros((prediction.shape[0], num_classes), - dtype=np.float32) + new_predictions = np.zeros( + (prediction.shape[0], num_classes), dtype=np.float32 + ) for index in mapping: class_index = mapping[index] diff --git a/autosklearn/evaluation/splitter.py b/autosklearn/evaluation/splitter.py index a18e29e08a..586d92c88f 100644 --- a/autosklearn/evaluation/splitter.py +++ b/autosklearn/evaluation/splitter.py @@ -1,31 +1,30 @@ import warnings import numpy as np - -from sklearn.model_selection import StratifiedShuffleSplit, StratifiedKFold +from sklearn.model_selection import StratifiedKFold, StratifiedShuffleSplit from sklearn.model_selection._split import _validate_shuffle_split -from sklearn.utils import indexable, check_random_state -from sklearn.utils import _approximate_mode -from sklearn.utils.validation import _num_samples, column_or_1d -from sklearn.utils.validation import check_array +from sklearn.utils import _approximate_mode, check_random_state, indexable from sklearn.utils.multiclass import type_of_target +from sklearn.utils.validation import _num_samples, check_array, column_or_1d class CustomStratifiedShuffleSplit(StratifiedShuffleSplit): - """Stratified ShuffleSplit cross-validator that deals with classes with too few samples - """ + """Splitter that deals with classes with too few samples""" def _iter_indices(self, X, y, groups=None): # type: ignore n_samples = _num_samples(X) y = check_array(y, ensure_2d=False, dtype=None) n_train, n_test = _validate_shuffle_split( - n_samples, self.test_size, self.train_size, - default_test_size=self._default_test_size) + n_samples, + self.test_size, + self.train_size, + default_test_size=self._default_test_size, + ) if y.ndim == 2: # for multi-label y, map each distinct row to a string repr # using join because str(row) uses an ellipsis if len(row) > 1000 - y = np.array([' '.join(row.astype('str')) for row in y]) + y = np.array([" ".join(row.astype("str")) for row in y]) classes, y_indices = np.unique(y, return_inverse=True) n_classes = classes.shape[0] @@ -33,18 +32,21 @@ def _iter_indices(self, X, y, groups=None): # type: ignore class_counts = np.bincount(y_indices) if n_train < n_classes: - raise ValueError('The train_size = %d should be greater or ' - 'equal to the number of classes = %d' % - (n_train, n_classes)) + raise ValueError( + "The train_size = %d should be greater or " + "equal to the number of classes = %d" % (n_train, n_classes) + ) if n_test < n_classes: - raise ValueError('The test_size = %d should be greater or ' - 'equal to the number of classes = %d' % - (n_test, n_classes)) + raise ValueError( + "The test_size = %d should be greater or " + "equal to the number of classes = %d" % (n_test, n_classes) + ) # Find the sorted list of instances for each class: # (np.unique above performs a sort, so code is O(n logn) already) - class_indices = np.split(np.argsort(y_indices, kind='mergesort'), - np.cumsum(class_counts)[:-1]) + class_indices = np.split( + np.argsort(y_indices, kind="mergesort"), np.cumsum(class_counts)[:-1] + ) rng = check_random_state(self.random_state) @@ -62,18 +64,18 @@ def _iter_indices(self, X, y, groups=None): # type: ignore # Each list n_i, t_i represent the list of class in the # training_set and test_set resepectively. # - # n_i = [100, 100, 0, 3] # 100 instance of class '0', 0 instance of class '2' - # t_i = [300, 300, 1, 3] # 300 instances of class '0', 1 instance of class '2' + # n_i = [100, 100, 0, 3] # 100 of class '0', 0 of class '2' + # t_i = [300, 300, 1, 3] # 300 of class '0', 1 of class '2' # # To support unique labels such as class '2', which only has one sample # between both n_i and t_i, we need to make sure that n_i has at least # one sample of all classes. There is also the extra check to ensure # that the sizes stay the same. # - # n_i = [ 99, 100, 1, 3] # 100 instance of class '0', 0 instance of class '2' + # n_i = [ 99, 100, 1, 3] # 100 of class '0', 0 of class '2' # | ^ # v | - # t_i = [301, 300, 0, 3] # 300 instances of class '0', 1 instance of class '2' + # t_i = [301, 300, 0, 3] # 300 of class '0', 1 of class '2' # for i, class_count in enumerate(n_i): if class_count == 0: @@ -82,20 +84,21 @@ def _iter_indices(self, X, y, groups=None): # type: ignore j = np.argmax(n_i) if n_i[j] == 1: - warnings.warn("Can't respect size requirements for split.", - " The training set must contain all of the unique" - " labels that exist in the dataset.") + warnings.warn( + "Can't respect size requirements for split.", + " The training set must contain all of the unique" + " labels that exist in the dataset.", + ) else: n_i[j] -= 1 t_i[j] += 1 for i in range(n_classes): permutation = rng.permutation(class_counts[i]) - perm_indices_class_i = class_indices[i].take(permutation, - mode='clip') + perm_indices_class_i = class_indices[i].take(permutation, mode="clip") - train.extend(perm_indices_class_i[:n_i[i]]) - test.extend(perm_indices_class_i[n_i[i]:n_i[i] + t_i[i]]) + train.extend(perm_indices_class_i[: n_i[i]]) + test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]) train = rng.permutation(train) test = rng.permutation(test) @@ -112,11 +115,13 @@ def _make_test_folds(self, X, y=None): # type: ignore rng = check_random_state(self.random_state) y = np.asarray(y) type_of_target_y = type_of_target(y) - allowed_target_types = ('binary', 'multiclass') + allowed_target_types = ("binary", "multiclass") if type_of_target_y not in allowed_target_types: raise ValueError( - 'Supported target types are: {}. Got {!r} instead.'.format( - allowed_target_types, type_of_target_y)) + "Supported target types are: {}. Got {!r} instead.".format( + allowed_target_types, type_of_target_y + ) + ) y = column_or_1d(y) @@ -134,13 +139,16 @@ def _make_test_folds(self, X, y=None): # type: ignore # counts, but that code is unreadable.) y_order = np.sort(y_encoded) allocation = np.asarray( - [np.bincount(y_order[i::self.n_splits], minlength=n_classes) - for i in range(self.n_splits)]) + [ + np.bincount(y_order[i :: self.n_splits], minlength=n_classes) + for i in range(self.n_splits) + ] + ) # To maintain the data order dependencies as best as possible within # the stratification constraint, we assign samples from each class in # blocks (and then mess that up when shuffle=True). - test_folds = np.empty(len(y), dtype='i') + test_folds = np.empty(len(y), dtype="i") for k in range(n_classes): # since the kth column of allocation stores the number of samples # of class k in each test set, this generates blocks of fold @@ -157,12 +165,11 @@ def split(self, X, y=None, groups=None): # type: ignore n_samples = _num_samples(X) if self.n_splits > n_samples: raise ValueError( - ("Cannot have number of splits n_splits={0} greater" - " than the number of samples: n_samples={1}.") - .format(self.n_splits, n_samples)) + f"Cannot have number of splits n_splits={self.n_splits} greater" + f" than the number of samples: n_samples={n_samples}." + ) for train, test in super().split(X, y, groups): - # print(len(np.unique(y)), len(np.unique(y[train])), len(np.unique(y[test]))) all_classes = np.unique(y) train_classes = np.unique(y[train]) train = list(train) @@ -179,11 +186,5 @@ def split(self, X, y=None, groups=None): # type: ignore # print(len(train), len(test)) train = np.array(train, dtype=int) test = np.array(test, dtype=int) - # print( - # len(np.unique(y)), - # len(np.unique(y[train])), - # len(np.unique(y[test])), - # len(train), len(test), - # ) yield train, test diff --git a/autosklearn/evaluation/test_evaluator.py b/autosklearn/evaluation/test_evaluator.py index 181ebce233..4b6cf8452c 100644 --- a/autosklearn/evaluation/test_evaluator.py +++ b/autosklearn/evaluation/test_evaluator.py @@ -1,31 +1,24 @@ # -*- encoding: utf-8 -*- -import multiprocessing from typing import Any, Dict, List, Optional, Tuple, Union -from ConfigSpace import Configuration +import multiprocessing import numpy as np - +from ConfigSpace import Configuration from smac.tae import StatusType from autosklearn.automl_common.common.utils.backend import Backend - from autosklearn.evaluation.abstract_evaluator import ( AbstractEvaluator, _fit_and_suppress_warnings, ) +from autosklearn.metrics import Scorer, calculate_loss from autosklearn.pipeline.components.base import ThirdPartyComponents -from autosklearn.metrics import calculate_loss, Scorer - -__all__ = [ - 'eval_t', - 'TestEvaluator' -] +__all__ = ["eval_t", "TestEvaluator"] class TestEvaluator(AbstractEvaluator): - def __init__( self, backend: Backend, @@ -55,15 +48,15 @@ def __init__( include=include, exclude=exclude, disable_file_output=disable_file_output, - init_params=init_params + init_params=init_params, ) self.configuration = configuration - self.X_train = self.datamanager.data['X_train'] - self.Y_train = self.datamanager.data['Y_train'] + self.X_train = self.datamanager.data["X_train"] + self.Y_train = self.datamanager.data["Y_train"] - self.X_test = self.datamanager.data.get('X_test') - self.Y_test = self.datamanager.data.get('Y_test') + self.X_test = self.datamanager.data.get("X_test") + self.Y_test = self.datamanager.data.get("Y_test") self.model = self._get_model() @@ -87,23 +80,27 @@ def predict_and_loss( ) -> Tuple[Union[Dict[str, float], float], np.array, Any, Any]: if train: - Y_pred = self.predict_function(self.X_train, self.model, - self.task_type, self.Y_train) + Y_pred = self.predict_function( + self.X_train, self.model, self.task_type, self.Y_train + ) err = calculate_loss( solution=self.Y_train, prediction=Y_pred, task_type=self.task_type, metric=self.metric, - scoring_functions=self.scoring_functions) + scoring_functions=self.scoring_functions, + ) else: - Y_pred = self.predict_function(self.X_test, self.model, - self.task_type, self.Y_train) + Y_pred = self.predict_function( + self.X_test, self.model, self.task_type, self.Y_train + ) err = calculate_loss( solution=self.Y_test, prediction=Y_pred, task_type=self.task_type, metric=self.metric, - scoring_functions=self.scoring_functions) + scoring_functions=self.scoring_functions, + ) return err, Y_pred, None, None @@ -129,14 +126,19 @@ def eval_t( budget: Optional[float] = None, budget_type: Optional[str] = None, ) -> None: - evaluator = TestEvaluator(configuration=config, - backend=backend, metric=metric, seed=seed, - port=port, - queue=queue, - scoring_functions=scoring_functions, - include=include, exclude=exclude, - disable_file_output=disable_file_output, - additional_components=additional_components, - init_params=init_params,) + evaluator = TestEvaluator( + configuration=config, + backend=backend, + metric=metric, + seed=seed, + port=port, + queue=queue, + scoring_functions=scoring_functions, + include=include, + exclude=exclude, + disable_file_output=disable_file_output, + additional_components=additional_components, + init_params=init_params, + ) evaluator.fit_predict_and_loss() diff --git a/autosklearn/evaluation/train_evaluator.py b/autosklearn/evaluation/train_evaluator.py index 558fdd3b67..7a047d3e10 100644 --- a/autosklearn/evaluation/train_evaluator.py +++ b/autosklearn/evaluation/train_evaluator.py @@ -1,54 +1,64 @@ -import logging -import multiprocessing -import warnings from typing import Any, Dict, List, Optional, Tuple, Union, cast import copy import json - -from ConfigSpace import Configuration +import logging +import multiprocessing +import warnings import numpy as np - -from smac.tae import TAEAbortException, StatusType - +from ConfigSpace import Configuration from sklearn.base import BaseEstimator -from sklearn.model_selection import ShuffleSplit, StratifiedShuffleSplit, KFold, \ - StratifiedKFold, train_test_split, BaseCrossValidator, PredefinedSplit -from sklearn.model_selection._split import _RepeatedSplits, BaseShuffleSplit +from sklearn.model_selection import ( + BaseCrossValidator, + KFold, + PredefinedSplit, + ShuffleSplit, + StratifiedKFold, + StratifiedShuffleSplit, + train_test_split, +) +from sklearn.model_selection._split import BaseShuffleSplit, _RepeatedSplits +from smac.tae import StatusType, TAEAbortException from autosklearn.automl_common.common.utils.backend import Backend - -from autosklearn.evaluation.abstract_evaluator import ( - AbstractEvaluator, - TYPE_ADDITIONAL_INFO, - _fit_and_suppress_warnings, -) -from autosklearn.evaluation.splitter import CustomStratifiedShuffleSplit, CustomStratifiedKFold -from autosklearn.data.abstract_data_manager import AbstractDataManager from autosklearn.constants import ( CLASSIFICATION_TASKS, MULTILABEL_CLASSIFICATION, + MULTIOUTPUT_REGRESSION, REGRESSION_TASKS, - MULTIOUTPUT_REGRESSION ) -from autosklearn.data.validation import ( - SUPPORTED_FEAT_TYPES, - SUPPORTED_TARGET_TYPES, - ) -from autosklearn.pipeline.base import PIPELINE_DATA_DTYPE -from autosklearn.pipeline.components.base import IterativeComponent, ThirdPartyComponents +from autosklearn.data.abstract_data_manager import AbstractDataManager +from autosklearn.data.validation import SUPPORTED_FEAT_TYPES, SUPPORTED_TARGET_TYPES +from autosklearn.evaluation.abstract_evaluator import ( + TYPE_ADDITIONAL_INFO, + AbstractEvaluator, + _fit_and_suppress_warnings, +) +from autosklearn.evaluation.splitter import ( + CustomStratifiedKFold, + CustomStratifiedShuffleSplit, +) from autosklearn.metrics import Scorer +from autosklearn.pipeline.base import PIPELINE_DATA_DTYPE +from autosklearn.pipeline.components.base import ( + IterativeComponent, + ThirdPartyComponents, +) from autosklearn.util.logging_ import PicklableClientLogger - -__all__ = ['TrainEvaluator', 'eval_holdout', 'eval_iterative_holdout', - 'eval_cv', 'eval_partial_cv', 'eval_partial_cv_iterative'] +__all__ = [ + "TrainEvaluator", + "eval_holdout", + "eval_iterative_holdout", + "eval_cv", + "eval_partial_cv", + "eval_partial_cv_iterative", +] def _get_y_array(y: SUPPORTED_TARGET_TYPES, task_type: int) -> SUPPORTED_TARGET_TYPES: - if task_type in CLASSIFICATION_TASKS and task_type != \ - MULTILABEL_CLASSIFICATION: + if task_type in CLASSIFICATION_TASKS and task_type != MULTILABEL_CLASSIFICATION: return y.ravel() else: return y @@ -58,29 +68,26 @@ def subsample_indices( train_indices: List[int], subsample: Optional[float], task_type: int, - Y_train: SUPPORTED_TARGET_TYPES + Y_train: SUPPORTED_TARGET_TYPES, ) -> List[int]: if not isinstance(subsample, float): raise ValueError( - 'Subsample must be of type float, but is of type %s' - % type(subsample) + "Subsample must be of type float, but is of type %s" % type(subsample) ) elif subsample > 1: - raise ValueError( - 'Subsample must not be larger than 1, but is %f' - % subsample - ) + raise ValueError("Subsample must not be larger than 1, but is %f" % subsample) if subsample is not None and subsample < 1: # Only subsample if there are more indices given to this method than # required to subsample because otherwise scikit-learn will complain if task_type in CLASSIFICATION_TASKS and task_type != MULTILABEL_CLASSIFICATION: - stratify: Optional[ - SUPPORTED_TARGET_TYPES - ] = Y_train.iloc[train_indices] if hasattr( - Y_train, 'iloc') else Y_train[train_indices] + stratify: Optional[SUPPORTED_TARGET_TYPES] = ( + Y_train.iloc[train_indices] + if hasattr(Y_train, "iloc") + else Y_train[train_indices] + ) else: stratify = None @@ -109,40 +116,55 @@ def _fit_with_budget( task_type: int, ) -> None: if ( - budget_type == 'iterations' - or budget_type == 'mixed' and model.estimator_supports_iterative_fit() + budget_type == "iterations" + or budget_type == "mixed" + and model.estimator_supports_iterative_fit() ): if model.estimator_supports_iterative_fit(): budget_factor = model.get_max_iter() Xt, fit_params = model.fit_transformer( - X_train.iloc[train_indices] if hasattr(X_train, 'iloc') else X_train[train_indices], - Y_train.iloc[train_indices] if hasattr(Y_train, 'iloc') else Y_train[train_indices], + X_train.iloc[train_indices] + if hasattr(X_train, "iloc") + else X_train[train_indices], + Y_train.iloc[train_indices] + if hasattr(Y_train, "iloc") + else Y_train[train_indices], ) n_iter = int(np.ceil(budget / 100 * budget_factor)) model.iterative_fit( Xt, - Y_train.iloc[train_indices] if hasattr(Y_train, 'iloc') else Y_train[train_indices], + Y_train.iloc[train_indices] + if hasattr(Y_train, "iloc") + else Y_train[train_indices], n_iter=n_iter, refit=True, - **fit_params + **fit_params, ) else: _fit_and_suppress_warnings( logger, model, - X_train.iloc[train_indices] if hasattr(X_train, 'iloc') else X_train[train_indices], - Y_train.iloc[train_indices] if hasattr(Y_train, 'iloc') else Y_train[train_indices], + X_train.iloc[train_indices] + if hasattr(X_train, "iloc") + else X_train[train_indices], + Y_train.iloc[train_indices] + if hasattr(Y_train, "iloc") + else Y_train[train_indices], ) elif ( - budget_type == 'subsample' - or budget_type == 'mixed' and not model.estimator_supports_iterative_fit() + budget_type == "subsample" + or budget_type == "mixed" + and not model.estimator_supports_iterative_fit() ): subsample = budget / 100 train_indices_subset = subsample_indices( - train_indices, subsample, task_type, Y_train, + train_indices, + subsample, + task_type, + Y_train, ) _fit_and_suppress_warnings( logger, @@ -167,9 +189,12 @@ def __init__( scoring_functions: Optional[List[Scorer]] = None, seed: int = 1, output_y_hat_optimization: bool = True, - resampling_strategy: Optional[Union[str, BaseCrossValidator, - _RepeatedSplits, BaseShuffleSplit]] = None, - resampling_strategy_args: Optional[Dict[str, Optional[Union[float, int, str]]]] = None, + resampling_strategy: Optional[ + Union[str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit] + ] = None, + resampling_strategy_args: Optional[ + Dict[str, Optional[Union[float, int, str]]] + ] = None, num_run: Optional[int] = None, budget: Optional[float] = None, budget_type: Optional[str] = None, @@ -206,15 +231,17 @@ def __init__( self.resampling_strategy_args = resampling_strategy_args self.splitter = self.get_splitter(self.datamanager) self.num_cv_folds = self.splitter.get_n_splits( - groups=self.resampling_strategy_args.get('groups') + groups=self.resampling_strategy_args.get("groups") ) - self.X_train = self.datamanager.data['X_train'] - self.Y_train = self.datamanager.data['Y_train'] + self.X_train = self.datamanager.data["X_train"] + self.Y_train = self.datamanager.data["Y_train"] self.Y_optimization: Optional[SUPPORTED_TARGET_TYPES] = None self.Y_targets = [None] * self.num_cv_folds self.Y_train_targets = np.ones(self.Y_train.shape) * np.NaN self.models = [None] * self.num_cv_folds - self.indices: List[Optional[Tuple[List[int], List[int]]]] = [None] * self.num_cv_folds + self.indices: List[Optional[Tuple[List[int], List[int]]]] = [ + None + ] * self.num_cv_folds # Necessary for full CV. Makes full CV not write predictions if only # a subset of folds is evaluated but time is up. Complicated, because @@ -225,8 +252,8 @@ def __init__( def fit_predict_and_loss(self, iterative: bool = False) -> None: """Fit, predict and compute the loss for cross-validation and - holdout (both iterative and non-iterative)""" - + holdout (both iterative and non-iterative) + """ # Define beforehand for mypy additional_run_info: Optional[TYPE_ADDITIONAL_INFO] = None @@ -234,14 +261,18 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: if self.num_cv_folds == 1: for train_split, test_split in self.splitter.split( - self.X_train, self.Y_train, - groups=self.resampling_strategy_args.get('groups') + self.X_train, + self.Y_train, + groups=self.resampling_strategy_args.get("groups"), ): self.Y_optimization = self.Y_train[test_split] self.Y_actual_train = self.Y_train[train_split] - self._partial_fit_and_predict_iterative(0, train_indices=train_split, - test_indices=test_split, - add_model_to_self=True) + self._partial_fit_and_predict_iterative( + 0, + train_indices=train_split, + test_indices=test_split, + add_model_to_self=True, + ) else: # Test if the model allows for an iterative fit, if not, @@ -266,16 +297,24 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: total_n_iterations = [0] * self.num_cv_folds # model.estimator_supports_iterative_fit -> true # After the if above, we know estimator support iterative fit - model_max_iter = [cast(IterativeComponent, model).get_max_iter() - for model in self.models] - - if self.budget_type in ['iterations', 'mixed'] and self.budget is None: - raise ValueError(f"When budget type is {self.budget_type} the budget " - "can not be None") + model_max_iter = [ + cast(IterativeComponent, model).get_max_iter() + for model in self.models + ] + + if self.budget_type in ["iterations", "mixed"] and self.budget is None: + raise ValueError( + f"When budget type is {self.budget_type} the budget " + "can not be None" + ) - if self.budget_type in ['iterations', 'mixed'] and cast(float, self.budget) > 0: + if ( + self.budget_type in ["iterations", "mixed"] + and cast(float, self.budget) > 0 + ): max_n_iter_budget = int( - np.ceil(cast(float, self.budget) / 100 * model_max_iter[0])) + np.ceil(cast(float, self.budget) / 100 * model_max_iter[0]) + ) max_iter = min(model_max_iter[0], max_n_iter_budget) else: max_iter = model_max_iter[0] @@ -283,7 +322,9 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: models_current_iters = [0] * self.num_cv_folds Xt_array = [None] * self.num_cv_folds - fit_params_array = [{}] * self.num_cv_folds # type: List[Dict[str, Any]] + fit_params_array = [ + {} + ] * self.num_cv_folds # type: List[Dict[str, Any]] y = _get_y_array(self.Y_train, self.task_type) @@ -300,26 +341,33 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: splitter = self.get_splitter(self.datamanager) - for i, (train_indices, test_indices) in enumerate(splitter.split( - self.X_train, y, - groups=self.resampling_strategy_args.get('groups') - )): + for i, (train_indices, test_indices) in enumerate( + splitter.split( + self.X_train, + y, + groups=self.resampling_strategy_args.get("groups"), + ) + ): if converged[i]: continue model = self.models[i] if iterations[i] == 1: - self.Y_train_targets[train_indices] = \ - self.Y_train.iloc[train_indices] if hasattr( - self.Y_train, 'iloc') else self.Y_train[train_indices] + self.Y_train_targets[train_indices] = ( + self.Y_train.iloc[train_indices] + if hasattr(self.Y_train, "iloc") + else self.Y_train[train_indices] + ) self.Y_targets[i] = self.Y_train[test_indices] Xt, fit_params = model.fit_transformer( - self.X_train.iloc[train_indices] if hasattr( - self.X_train, 'iloc') else self.X_train[train_indices], - self.Y_train.iloc[train_indices] if hasattr( - self.Y_train, 'iloc') else self.Y_train[train_indices], + self.X_train.iloc[train_indices] + if hasattr(self.X_train, "iloc") + else self.X_train[train_indices], + self.Y_train.iloc[train_indices] + if hasattr(self.Y_train, "iloc") + else self.Y_train[train_indices], ) Xt_array[i] = Xt fit_params_array[i] = fit_params @@ -328,17 +376,14 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: model.iterative_fit( Xt_array[i], - self.Y_train.iloc[train_indices] if hasattr( - self.Y_train, 'iloc') else self.Y_train[train_indices], - n_iter=n_iter, **fit_params_array[i] + self.Y_train.iloc[train_indices] + if hasattr(self.Y_train, "iloc") + else self.Y_train[train_indices], + n_iter=n_iter, + **fit_params_array[i], ) - ( - train_pred, - opt_pred, - valid_pred, - test_pred - ) = self._predict( + (train_pred, opt_pred, valid_pred, test_pred) = self._predict( model, train_indices=train_indices, test_indices=test_indices, @@ -353,13 +398,14 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: # Compute train loss of this fold and store it. train_loss could # either be a scalar or a dict of scalars with metrics as keys. train_loss = self._loss( - self.Y_train.iloc[train_indices] if hasattr( - self.Y_train, 'iloc') else self.Y_train[train_indices], + self.Y_train.iloc[train_indices] + if hasattr(self.Y_train, "iloc") + else self.Y_train[train_indices], train_pred, ) train_losses[i] = train_loss - # number of training data points for this fold. Used for weighting - # the average. + # Number of training data points for this fold. + # Used for weighting the average. train_fold_weights[i] = len(train_indices) # Compute validation loss of this fold and store it. @@ -382,8 +428,8 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: iterations[i] = iterations[i] + 1 - # Compute weights of each fold based on the number of samples in each - # fold. + # Compute weights of each fold based on the number of samples + # in each fold. train_fold_weights_percentage = [ w / sum(train_fold_weights) for w in train_fold_weights ] @@ -395,12 +441,17 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: # dicts, then train_loss is computed using the target metric # (self.metric). if all(isinstance(elem, dict) for elem in train_losses): - train_loss = np.average([train_losses[i][str(self.metric)] - for i in range(self.num_cv_folds)], - weights=train_fold_weights_percentage, - ) + train_loss = np.average( + [ + train_losses[i][str(self.metric)] + for i in range(self.num_cv_folds) + ], + weights=train_fold_weights_percentage, + ) else: - train_loss = np.average(train_losses, weights=train_fold_weights_percentage) + train_loss = np.average( + train_losses, weights=train_fold_weights_percentage + ) # if all_scoring_function is true, return a dict of opt_loss. # Otherwise, return a scalar. @@ -415,23 +466,36 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: weights=opt_fold_weights_percentage, ) else: - opt_loss = np.average(opt_losses, weights=opt_fold_weights_percentage) + opt_loss = np.average( + opt_losses, weights=opt_fold_weights_percentage + ) Y_targets = self.Y_targets Y_train_targets = self.Y_train_targets Y_optimization_preds = np.concatenate( - [Y_optimization_pred[i] for i in range(self.num_cv_folds) - if Y_optimization_pred[i] is not None]) - Y_targets = np.concatenate([ - Y_targets[i] for i in range(self.num_cv_folds) - if Y_targets[i] is not None - ]) + [ + Y_optimization_pred[i] + for i in range(self.num_cv_folds) + if Y_optimization_pred[i] is not None + ] + ) + Y_targets = np.concatenate( + [ + Y_targets[i] + for i in range(self.num_cv_folds) + if Y_targets[i] is not None + ] + ) if self.X_valid is not None: - Y_valid_preds = np.array([Y_valid_pred[i] - for i in range(self.num_cv_folds) - if Y_valid_pred[i] is not None]) + Y_valid_preds = np.array( + [ + Y_valid_pred[i] + for i in range(self.num_cv_folds) + if Y_valid_pred[i] is not None + ] + ) # Average the predictions of several models if len(Y_valid_preds.shape) == 3: Y_valid_preds = np.nanmean(Y_valid_preds, axis=0) @@ -439,9 +503,13 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: Y_valid_preds = None if self.X_test is not None: - Y_test_preds = np.array([Y_test_pred[i] - for i in range(self.num_cv_folds) - if Y_test_pred[i] is not None]) + Y_test_preds = np.array( + [ + Y_test_pred[i] + for i in range(self.num_cv_folds) + if Y_test_pred[i] is not None + ] + ) # Average the predictions of several models if len(Y_test_preds.shape) == 3: Y_test_preds = np.nanmean(Y_test_preds, axis=0) @@ -453,8 +521,12 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: self.model = self._get_model() status = StatusType.DONOTADVANCE - if any([model_current_iter == max_iter - for model_current_iter in models_current_iters]): + if any( + [ + model_current_iter == max_iter + for model_current_iter in models_current_iters + ] + ): status = StatusType.SUCCESS self.finish_up( loss=opt_loss, @@ -488,10 +560,11 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: # TODO: mention that no additional run info is possible in this # case! -> maybe remove full CV from the train evaluator anyway and # make the user implement this! - for i, (train_split, test_split) in enumerate(self.splitter.split( - self.X_train, y, - groups=self.resampling_strategy_args.get('groups') - )): + for i, (train_split, test_split) in enumerate( + self.splitter.split( + self.X_train, y, groups=self.resampling_strategy_args.get("groups") + ) + ): # TODO add check that split is actually an integer array, # not a boolean array (to allow indexed assignement of @@ -504,11 +577,11 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: valid_pred, test_pred, additional_run_info, - ) = ( - self._partial_fit_and_predict_standard( - i, train_indices=train_split, test_indices=test_split, - add_model_to_self=self.num_cv_folds == 1, - ) + ) = self._partial_fit_and_predict_standard( + i, + train_indices=train_split, + test_indices=test_split, + add_model_to_self=self.num_cv_folds == 1, ) else: ( @@ -517,11 +590,11 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: valid_pred, test_pred, additional_run_info, - ) = ( - self._partial_fit_and_predict_budget( - i, train_indices=train_split, test_indices=test_split, - add_model_to_self=self.num_cv_folds == 1, - ) + ) = self._partial_fit_and_predict_budget( + i, + train_indices=train_split, + test_indices=test_split, + add_model_to_self=self.num_cv_folds == 1, ) if ( @@ -531,8 +604,8 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: ): raise TAEAbortException( 'Found additional run info "%s" in fold %d, ' - 'but cannot handle additional run info if fold >= 1.' % - (additional_run_info, i) + "but cannot handle additional run info if fold >= 1." + % (additional_run_info, i) ) Y_train_pred[i] = train_pred @@ -564,16 +637,21 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: # Compute weights of each fold based on the number of samples in each # fold. - train_fold_weights = [w / sum(train_fold_weights) for w in train_fold_weights] + train_fold_weights = [ + w / sum(train_fold_weights) for w in train_fold_weights + ] opt_fold_weights = [w / sum(opt_fold_weights) for w in opt_fold_weights] # train_losses is a list of either scalars or dicts. If it contains dicts, # then train_loss is computed using the target metric (self.metric). if all(isinstance(elem, dict) for elem in train_losses): - train_loss = np.average([train_losses[i][str(self.metric)] - for i in range(self.num_cv_folds)], - weights=train_fold_weights, - ) + train_loss = np.average( + [ + train_losses[i][str(self.metric)] + for i in range(self.num_cv_folds) + ], + weights=train_fold_weights, + ) else: train_loss = np.average(train_losses, weights=train_fold_weights) @@ -582,10 +660,10 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: if self.scoring_functions: opt_loss = {} for metric in opt_losses[0].keys(): - opt_loss[metric] = np.average([opt_losses[i][metric] - for i in range(self.num_cv_folds)], - weights=opt_fold_weights, - ) + opt_loss[metric] = np.average( + [opt_losses[i][metric] for i in range(self.num_cv_folds)], + weights=opt_fold_weights, + ) else: opt_loss = np.average(opt_losses, weights=opt_fold_weights) @@ -593,23 +671,40 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: Y_train_targets = self.Y_train_targets Y_optimization_pred = np.concatenate( - [Y_optimization_pred[i] for i in range(self.num_cv_folds) - if Y_optimization_pred[i] is not None]) - Y_targets = np.concatenate([Y_targets[i] for i in range(self.num_cv_folds) - if Y_targets[i] is not None]) + [ + Y_optimization_pred[i] + for i in range(self.num_cv_folds) + if Y_optimization_pred[i] is not None + ] + ) + Y_targets = np.concatenate( + [ + Y_targets[i] + for i in range(self.num_cv_folds) + if Y_targets[i] is not None + ] + ) if self.X_valid is not None: - Y_valid_pred = np.array([Y_valid_pred[i] - for i in range(self.num_cv_folds) - if Y_valid_pred[i] is not None]) + Y_valid_pred = np.array( + [ + Y_valid_pred[i] + for i in range(self.num_cv_folds) + if Y_valid_pred[i] is not None + ] + ) # Average the predictions of several models if len(np.shape(Y_valid_pred)) == 3: Y_valid_pred = np.nanmean(Y_valid_pred, axis=0) if self.X_test is not None: - Y_test_pred = np.array([Y_test_pred[i] - for i in range(self.num_cv_folds) - if Y_test_pred[i] is not None]) + Y_test_pred = np.array( + [ + Y_test_pred[i] + for i in range(self.num_cv_folds) + if Y_test_pred[i] is not None + ] + ) # Average the predictions of several models if len(np.shape(Y_test_pred)) == 3: Y_test_pred = np.nanmean(Y_test_pred, axis=0) @@ -625,8 +720,8 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: # TODO check if there might be reasons for do-not-advance here! status = StatusType.SUCCESS elif ( - self.budget_type == 'iterations' - or self.budget_type == 'mixed' + self.budget_type == "iterations" + or self.budget_type == "mixed" and self.model.estimator_supports_iterative_fit() ): budget_factor = self.model.get_max_iter() @@ -661,19 +756,21 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: ) def partial_fit_predict_and_loss(self, fold: int, iterative: bool = False) -> None: - """Fit, predict and compute the loss for eval_partial_cv (both iterative and normal)""" - + """Fit, predict and get loss for eval_partial_cv (iterative and normal)""" if fold > self.num_cv_folds: - raise ValueError('Cannot evaluate a fold %d which is higher than ' - 'the number of folds %d.' % (fold, self.num_cv_folds)) + raise ValueError( + "Cannot evaluate a fold %d which is higher than " + "the number of folds %d." % (fold, self.num_cv_folds) + ) if self.budget_type is not None: raise NotImplementedError() y = _get_y_array(self.Y_train, self.task_type) - for i, (train_split, test_split) in enumerate(self.splitter.split( - self.X_train, y, - groups=self.resampling_strategy_args.get('groups') - )): + for i, (train_split, test_split) in enumerate( + self.splitter.split( + self.X_train, y, groups=self.resampling_strategy_args.get("groups") + ) + ): if i != fold: continue else: @@ -685,18 +782,25 @@ def partial_fit_predict_and_loss(self, fold: int, iterative: bool = False) -> No if iterative: self._partial_fit_and_predict_iterative( - fold, train_indices=train_split, test_indices=test_split, - add_model_to_self=True) + fold, + train_indices=train_split, + test_indices=test_split, + add_model_to_self=True, + ) elif self.budget_type is not None: raise NotImplementedError() else: - train_pred, opt_pred, valid_pred, test_pred, additional_run_info = ( - self._partial_fit_and_predict_standard( - fold, - train_indices=train_split, - test_indices=test_split, - add_model_to_self=True, - ) + ( + train_pred, + opt_pred, + valid_pred, + test_pred, + additional_run_info, + ) = self._partial_fit_and_predict_standard( + fold, + train_indices=train_split, + test_indices=test_split, + add_model_to_self=True, ) train_loss = self._loss(self.Y_actual_train, train_pred) loss = self._loss(self.Y_targets[fold], opt_pred) @@ -720,15 +824,19 @@ def partial_fit_predict_and_loss(self, fold: int, iterative: bool = False) -> No file_output=False, final_call=True, additional_run_info=None, - status=status + status=status, ) - def _partial_fit_and_predict_iterative(self, fold: int, train_indices: List[int], - test_indices: List[int], - add_model_to_self: bool) -> None: + def _partial_fit_and_predict_iterative( + self, + fold: int, + train_indices: List[int], + test_indices: List[int], + add_model_to_self: bool, + ) -> None: model = self._get_model() - self.indices[fold] = ((train_indices, test_indices)) + self.indices[fold] = (train_indices, test_indices) # Do only output the files in the case of iterative holdout, # In case of iterative partial cv, no file output is needed @@ -737,14 +845,19 @@ def _partial_fit_and_predict_iterative(self, fold: int, train_indices: List[int] if model.estimator_supports_iterative_fit(): Xt, fit_params = model.fit_transformer( - self.X_train.iloc[train_indices] if hasattr( - self.Y_train, 'iloc') else self.X_train[train_indices], - self.Y_train.iloc[train_indices] if hasattr( - self.Y_train, 'iloc') else self.Y_train[train_indices], + self.X_train.iloc[train_indices] + if hasattr(self.Y_train, "iloc") + else self.X_train[train_indices], + self.Y_train.iloc[train_indices] + if hasattr(self.Y_train, "iloc") + else self.Y_train[train_indices], ) - self.Y_train_targets[train_indices] = self.Y_train.iloc[train_indices] if hasattr( - self.Y_train, 'iloc') else self.Y_train[train_indices] + self.Y_train_targets[train_indices] = ( + self.Y_train.iloc[train_indices] + if hasattr(self.Y_train, "iloc") + else self.Y_train[train_indices] + ) iteration = 1 total_n_iteration = 0 @@ -760,19 +873,21 @@ def _partial_fit_and_predict_iterative(self, fold: int, train_indices: List[int] while ( not model.configuration_fully_fitted() and model_current_iter < max_iter ): - n_iter = int(2**iteration/2) if iteration > 1 else 2 + n_iter = int(2**iteration / 2) if iteration > 1 else 2 total_n_iteration += n_iter model.iterative_fit( Xt, - self.Y_train.iloc[train_indices] if hasattr( - self.Y_train, 'iloc') else self.Y_train[train_indices], - n_iter=n_iter, **fit_params + self.Y_train.iloc[train_indices] + if hasattr(self.Y_train, "iloc") + else self.Y_train[train_indices], + n_iter=n_iter, + **fit_params, ) ( Y_train_pred, Y_optimization_pred, Y_valid_pred, - Y_test_pred + Y_test_pred, ) = self._predict( model, train_indices=train_indices, @@ -783,9 +898,10 @@ def _partial_fit_and_predict_iterative(self, fold: int, train_indices: List[int] self.model = model train_loss = self._loss( - self.Y_train.iloc[train_indices] if hasattr( - self.Y_train, 'iloc') else self.Y_train[train_indices], - Y_train_pred + self.Y_train.iloc[train_indices] + if hasattr(self.Y_train, "iloc") + else self.Y_train[train_indices], + Y_train_pred, ) loss = self._loss(self.Y_train[test_indices], Y_optimization_pred) additional_run_info = model.get_additional_run_info() @@ -822,13 +938,15 @@ def _partial_fit_and_predict_iterative(self, fold: int, train_indices: List[int] Y_optimization_pred, Y_valid_pred, Y_test_pred, - additional_run_info - ) = self._partial_fit_and_predict_standard(fold, train_indices, test_indices, - add_model_to_self) + additional_run_info, + ) = self._partial_fit_and_predict_standard( + fold, train_indices, test_indices, add_model_to_self + ) train_loss = self._loss( - self.Y_train.iloc[train_indices] if hasattr( - self.Y_train, 'iloc') else self.Y_train[train_indices], - Y_train_pred + self.Y_train.iloc[train_indices] + if hasattr(self.Y_train, "iloc") + else self.Y_train[train_indices], + Y_train_pred, ) loss = self._loss(self.Y_train[test_indices], Y_optimization_pred) if self.model.estimator_supports_iterative_fit(): @@ -855,25 +973,30 @@ def _partial_fit_and_predict_iterative(self, fold: int, train_indices: List[int] def _partial_fit_and_predict_standard( self, - fold: int, train_indices: List[int], + fold: int, + train_indices: List[int], test_indices: List[int], - add_model_to_self: bool = False - ) -> Tuple[PIPELINE_DATA_DTYPE, # train_pred - PIPELINE_DATA_DTYPE, # opt_pred - PIPELINE_DATA_DTYPE, # valid_pred - PIPELINE_DATA_DTYPE, # test_pred - TYPE_ADDITIONAL_INFO]: + add_model_to_self: bool = False, + ) -> Tuple[ + PIPELINE_DATA_DTYPE, # train_pred + PIPELINE_DATA_DTYPE, # opt_pred + PIPELINE_DATA_DTYPE, # valid_pred + PIPELINE_DATA_DTYPE, # test_pred + TYPE_ADDITIONAL_INFO, + ]: model = self._get_model() - self.indices[fold] = ((train_indices, test_indices)) + self.indices[fold] = (train_indices, test_indices) _fit_and_suppress_warnings( self.logger, model, - self.X_train.iloc[train_indices] if hasattr( - self.X_train, 'iloc') else self.X_train[train_indices], - self.Y_train.iloc[train_indices] if hasattr( - self.Y_train, 'iloc') else self.Y_train[train_indices], + self.X_train.iloc[train_indices] + if hasattr(self.X_train, "iloc") + else self.X_train[train_indices], + self.Y_train.iloc[train_indices] + if hasattr(self.Y_train, "iloc") + else self.Y_train[train_indices], ) if add_model_to_self: @@ -881,10 +1004,16 @@ def _partial_fit_and_predict_standard( else: self.models[fold] = model - self.Y_targets[fold] = self.Y_train.iloc[test_indices] if hasattr( - self.Y_train, 'iloc') else self.Y_train[test_indices] - self.Y_train_targets[train_indices] = self.Y_train.iloc[train_indices] if hasattr( - self.Y_train, 'iloc') else self.Y_train[train_indices] + self.Y_targets[fold] = ( + self.Y_train.iloc[test_indices] + if hasattr(self.Y_train, "iloc") + else self.Y_train[test_indices] + ) + self.Y_train_targets[train_indices] = ( + self.Y_train.iloc[train_indices] + if hasattr(self.Y_train, "iloc") + else self.Y_train[train_indices] + ) train_pred, opt_pred, valid_pred, test_pred = self._predict( model=model, @@ -902,24 +1031,30 @@ def _partial_fit_and_predict_standard( def _partial_fit_and_predict_budget( self, - fold: int, train_indices: List[int], + fold: int, + train_indices: List[int], test_indices: List[int], add_model_to_self: bool = False, - ) -> Tuple[PIPELINE_DATA_DTYPE, # train_pred - PIPELINE_DATA_DTYPE, # opt_pred - PIPELINE_DATA_DTYPE, # valid_pred - PIPELINE_DATA_DTYPE, # test_pred - TYPE_ADDITIONAL_INFO]: + ) -> Tuple[ + PIPELINE_DATA_DTYPE, # train_pred + PIPELINE_DATA_DTYPE, # opt_pred + PIPELINE_DATA_DTYPE, # valid_pred + PIPELINE_DATA_DTYPE, # test_pred + TYPE_ADDITIONAL_INFO, + ]: # This function is only called in the event budget is not None # Add this statement for mypy assert self.budget is not None model = self._get_model() - self.indices[fold] = ((train_indices, test_indices)) + self.indices[fold] = (train_indices, test_indices) self.Y_targets[fold] = self.Y_train[test_indices] - self.Y_train_targets[train_indices] = self.Y_train.iloc[train_indices] if hasattr( - self.Y_train, 'iloc') else self.Y_train[train_indices], + self.Y_train_targets[train_indices] = ( + self.Y_train.iloc[train_indices] + if hasattr(self.Y_train, "iloc") + else self.Y_train[train_indices], + ) _fit_with_budget( X_train=self.X_train, @@ -952,93 +1087,110 @@ def _partial_fit_and_predict_budget( additional_run_info, ) - def _predict(self, model: BaseEstimator, test_indices: List[int], - train_indices: List[int]) -> Tuple[PIPELINE_DATA_DTYPE, - PIPELINE_DATA_DTYPE, - PIPELINE_DATA_DTYPE, - PIPELINE_DATA_DTYPE]: + def _predict( + self, model: BaseEstimator, test_indices: List[int], train_indices: List[int] + ) -> Tuple[ + PIPELINE_DATA_DTYPE, + PIPELINE_DATA_DTYPE, + PIPELINE_DATA_DTYPE, + PIPELINE_DATA_DTYPE, + ]: train_pred = self.predict_function( - self.X_train.iloc[train_indices] if hasattr( - self.X_train, 'iloc') else self.X_train[train_indices], - model, self.task_type, - self.Y_train.iloc[train_indices] if hasattr( - self.Y_train, 'iloc') else self.Y_train[train_indices] + self.X_train.iloc[train_indices] + if hasattr(self.X_train, "iloc") + else self.X_train[train_indices], + model, + self.task_type, + self.Y_train.iloc[train_indices] + if hasattr(self.Y_train, "iloc") + else self.Y_train[train_indices], ) opt_pred = self.predict_function( - self.X_train.iloc[test_indices] if hasattr( - self.X_train, 'iloc') else self.X_train[test_indices], - model, self.task_type, - self.Y_train.iloc[train_indices] if hasattr( - self.Y_train, 'iloc') else self.Y_train[train_indices] + self.X_train.iloc[test_indices] + if hasattr(self.X_train, "iloc") + else self.X_train[test_indices], + model, + self.task_type, + self.Y_train.iloc[train_indices] + if hasattr(self.Y_train, "iloc") + else self.Y_train[train_indices], ) if self.X_valid is not None: X_valid = self.X_valid.copy() - valid_pred = self.predict_function(X_valid, model, - self.task_type, - self.Y_train[train_indices]) + valid_pred = self.predict_function( + X_valid, model, self.task_type, self.Y_train[train_indices] + ) else: valid_pred = None if self.X_test is not None: X_test = self.X_test.copy() test_pred = self.predict_function( - X_test, model, + X_test, + model, self.task_type, - self.Y_train.iloc[train_indices] if hasattr( - self.Y_train, 'iloc') else self.Y_train[train_indices] + self.Y_train.iloc[train_indices] + if hasattr(self.Y_train, "iloc") + else self.Y_train[train_indices], ) else: test_pred = None return train_pred, opt_pred, valid_pred, test_pred - def get_splitter(self, D: AbstractDataManager) -> Union[BaseCrossValidator, _RepeatedSplits, - BaseShuffleSplit]: + def get_splitter( + self, D: AbstractDataManager + ) -> Union[BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit]: if self.resampling_strategy_args is None: self.resampling_strategy_args = {} - if ( - self.resampling_strategy is not None - and not isinstance(self.resampling_strategy, str) + if self.resampling_strategy is not None and not isinstance( + self.resampling_strategy, str ): - if 'groups' not in self.resampling_strategy_args: - self.resampling_strategy_args['groups'] = None + if "groups" not in self.resampling_strategy_args: + self.resampling_strategy_args["groups"] = None - if isinstance(self.resampling_strategy, (BaseCrossValidator, - _RepeatedSplits, - BaseShuffleSplit)): + if isinstance( + self.resampling_strategy, + (BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit), + ): self.check_splitter_resampling_strategy( - X=D.data['X_train'], y=D.data['Y_train'], - groups=self.resampling_strategy_args.get('groups'), - task=D.info['task'], + X=D.data["X_train"], + y=D.data["Y_train"], + groups=self.resampling_strategy_args.get("groups"), + task=D.info["task"], resampling_strategy=self.resampling_strategy, ) return self.resampling_strategy # If it got to this point, we are dealing with a non-supported # re-sampling strategy - raise ValueError("Unsupported resampling strategy {}/{} provided".format( - self.resampling_strategy, - type(self.resampling_strategy), - )) + raise ValueError( + "Unsupported resampling strategy {}/{} provided".format( + self.resampling_strategy, + type(self.resampling_strategy), + ) + ) - y = D.data['Y_train'] - shuffle = self.resampling_strategy_args.get('shuffle', True) + y = D.data["Y_train"] + shuffle = self.resampling_strategy_args.get("shuffle", True) train_size = 0.67 if self.resampling_strategy_args: - train_size_from_user = self.resampling_strategy_args.get('train_size') + train_size_from_user = self.resampling_strategy_args.get("train_size") if train_size_from_user is not None: train_size = float(train_size_from_user) test_size = float("%.4f" % (1 - train_size)) - if D.info['task'] in CLASSIFICATION_TASKS and D.info['task'] != MULTILABEL_CLASSIFICATION: + if ( + D.info["task"] in CLASSIFICATION_TASKS + and D.info["task"] != MULTILABEL_CLASSIFICATION + ): y = y.ravel() - if self.resampling_strategy in ['holdout', - 'holdout-iterative-fit']: + if self.resampling_strategy in ["holdout", "holdout-iterative-fit"]: if shuffle: try: @@ -1050,7 +1202,7 @@ def get_splitter(self, D: AbstractDataManager) -> Union[BaseCrossValidator, _Rep test_cv = copy.deepcopy(cv) next(test_cv.split(y, y)) except ValueError as e: - if 'The least populated class in y has only' in e.args[0]: + if "The least populated class in y has only" in e.args[0]: cv = CustomStratifiedShuffleSplit( n_splits=1, test_size=test_size, @@ -1064,14 +1216,18 @@ def get_splitter(self, D: AbstractDataManager) -> Union[BaseCrossValidator, _Rep test_fold[:tmp_train_size] = -1 cv = PredefinedSplit(test_fold=test_fold) cv.n_splits = 1 # As sklearn is inconsistent here - elif self.resampling_strategy in ['cv', 'cv-iterative-fit', 'partial-cv', - 'partial-cv-iterative-fit']: + elif self.resampling_strategy in [ + "cv", + "cv-iterative-fit", + "partial-cv", + "partial-cv-iterative-fit", + ]: if shuffle: try: with warnings.catch_warnings(): - warnings.simplefilter('error') + warnings.simplefilter("error") cv = StratifiedKFold( - n_splits=self.resampling_strategy_args['folds'], + n_splits=self.resampling_strategy_args["folds"], shuffle=shuffle, random_state=1, ) @@ -1079,37 +1235,39 @@ def get_splitter(self, D: AbstractDataManager) -> Union[BaseCrossValidator, _Rep next(test_cv.split(y, y)) except UserWarning as e: print(e) - if 'The least populated class in y has only' in e.args[0]: + if "The least populated class in y has only" in e.args[0]: cv = CustomStratifiedKFold( - n_splits=self.resampling_strategy_args['folds'], + n_splits=self.resampling_strategy_args["folds"], shuffle=shuffle, random_state=1, ) else: raise e else: - cv = KFold(n_splits=self.resampling_strategy_args['folds'], - shuffle=shuffle) + cv = KFold( + n_splits=self.resampling_strategy_args["folds"], shuffle=shuffle + ) else: raise ValueError(self.resampling_strategy) else: - if self.resampling_strategy in ['holdout', - 'holdout-iterative-fit']: + if self.resampling_strategy in ["holdout", "holdout-iterative-fit"]: # TODO shuffle not taken into account for this if shuffle: - cv = ShuffleSplit(n_splits=1, test_size=test_size, - random_state=1) + cv = ShuffleSplit(n_splits=1, test_size=test_size, random_state=1) else: tmp_train_size = int(np.floor(train_size * y.shape[0])) test_fold = np.zeros(y.shape[0]) test_fold[:tmp_train_size] = -1 cv = PredefinedSplit(test_fold=test_fold) cv.n_splits = 1 # As sklearn is inconsistent here - elif self.resampling_strategy in ['cv', 'partial-cv', - 'partial-cv-iterative-fit']: + elif self.resampling_strategy in [ + "cv", + "partial-cv", + "partial-cv-iterative-fit", + ]: random_state = 1 if shuffle else None cv = KFold( - n_splits=self.resampling_strategy_args['folds'], + n_splits=self.resampling_strategy_args["folds"], shuffle=shuffle, random_state=random_state, ) @@ -1124,16 +1282,14 @@ def check_splitter_resampling_strategy( y: np.ndarray, task: int, groups: Any, - resampling_strategy: Union[BaseCrossValidator, _RepeatedSplits, - BaseShuffleSplit], + resampling_strategy: Union[ + BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit + ], ) -> None: if ( task in CLASSIFICATION_TASKS and task != MULTILABEL_CLASSIFICATION - or ( - task in REGRESSION_TASKS - and task != MULTIOUTPUT_REGRESSION - ) + or (task in REGRESSION_TASKS and task != MULTIOUTPUT_REGRESSION) ): y = y.ravel() @@ -1141,12 +1297,14 @@ def check_splitter_resampling_strategy( resampling_strategy.get_n_splits(X=X, y=y, groups=groups) next(resampling_strategy.split(X=X, y=y, groups=groups)) except Exception as e: - raise ValueError("Unsupported resampling strategy " - "{}/{} cause exception: {}".format( - resampling_strategy, - groups, - str(e), - )) + raise ValueError( + "Unsupported resampling strategy " + "{}/{} cause exception: {}".format( + resampling_strategy, + groups, + str(e), + ) + ) # create closure for evaluating an algorithm @@ -1154,7 +1312,9 @@ def eval_holdout( queue: multiprocessing.Queue, config: Union[int, Configuration], backend: Backend, - resampling_strategy: Union[str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit], + resampling_strategy: Union[ + str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit + ], resampling_strategy_args: Dict[str, Optional[Union[float, int, str]]], metric: Scorer, seed: int, @@ -1199,7 +1359,9 @@ def eval_iterative_holdout( queue: multiprocessing.Queue, config: Union[int, Configuration], backend: Backend, - resampling_strategy: Union[str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit], + resampling_strategy: Union[ + str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit + ], resampling_strategy_args: Dict[str, Optional[Union[float, int, str]]], metric: Scorer, seed: int, @@ -1236,7 +1398,7 @@ def eval_iterative_holdout( additional_components=additional_components, init_params=init_params, budget=budget, - budget_type=budget_type + budget_type=budget_type, ) @@ -1244,7 +1406,9 @@ def eval_partial_cv( queue: multiprocessing.Queue, config: Union[int, Configuration], backend: Backend, - resampling_strategy: Union[str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit], + resampling_strategy: Union[ + str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit + ], resampling_strategy_args: Dict[str, Optional[Union[float, int, str]]], metric: Scorer, seed: int, @@ -1265,7 +1429,7 @@ def eval_partial_cv( if budget_type is not None: raise NotImplementedError() instance_dict: Dict[str, int] = json.loads(instance) if instance is not None else {} - fold = instance_dict['fold'] + fold = instance_dict["fold"] evaluator = TrainEvaluator( backend=backend, @@ -1295,7 +1459,9 @@ def eval_partial_cv_iterative( queue: multiprocessing.Queue, config: Union[int, Configuration], backend: Backend, - resampling_strategy: Union[str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit], + resampling_strategy: Union[ + str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit + ], resampling_strategy_args: Dict[str, Optional[Union[float, int, str]]], metric: Scorer, seed: int, @@ -1341,7 +1507,9 @@ def eval_cv( queue: multiprocessing.Queue, config: Union[int, Configuration], backend: Backend, - resampling_strategy: Union[str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit], + resampling_strategy: Union[ + str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit + ], resampling_strategy_args: Dict[str, Optional[Union[float, int, str]]], metric: Scorer, seed: int, @@ -1387,7 +1555,9 @@ def eval_iterative_cv( queue: multiprocessing.Queue, config: Union[int, Configuration], backend: Backend, - resampling_strategy: Union[str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit], + resampling_strategy: Union[ + str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit + ], resampling_strategy_args: Dict[str, Optional[Union[float, int, str]]], metric: Scorer, seed: int, diff --git a/autosklearn/evaluation/util.py b/autosklearn/evaluation/util.py index e7483cbd26..c249c8be1c 100644 --- a/autosklearn/evaluation/util.py +++ b/autosklearn/evaluation/util.py @@ -3,14 +3,12 @@ import multiprocessing import queue +__all__ = ["read_queue"] -__all__ = [ - 'read_queue' -] - -def read_queue(queue_: multiprocessing.Queue - ) -> List[Dict[str, Union[str, bool, int, float, List, Dict, Tuple]]]: +def read_queue( + queue_: multiprocessing.Queue, +) -> List[Dict[str, Union[str, bool, int, float, List, Dict, Tuple]]]: stack = [] while True: try: @@ -21,8 +19,8 @@ def read_queue(queue_: multiprocessing.Queue # Check if there is a special placeholder value which tells us that # we don't have to wait until the queue times out in order to # retrieve the final value! - if 'final_queue_element' in rval: - del rval['final_queue_element'] + if "final_queue_element" in rval: + del rval["final_queue_element"] do_break = True else: do_break = False @@ -46,12 +44,13 @@ def empty_queue(queue_: multiprocessing.Queue) -> None: queue_.close() -def extract_learning_curve(stack: List[Dict[str, Any]], - key: Optional[str] = None) -> List[float]: +def extract_learning_curve( + stack: List[Dict[str, Any]], key: Optional[str] = None +) -> List[float]: learning_curve = [] for entry in stack: if key: - learning_curve.append(entry['additional_run_info'][key]) + learning_curve.append(entry["additional_run_info"][key]) else: - learning_curve.append(entry['loss']) + learning_curve.append(entry["loss"]) return list(learning_curve) diff --git a/autosklearn/experimental/askl2.py b/autosklearn/experimental/askl2.py index 749dfc6611..7068270a8e 100644 --- a/autosklearn/experimental/askl2.py +++ b/autosklearn/experimental/askl2.py @@ -1,59 +1,65 @@ +from typing import Any, Dict, List, Mapping, Optional, Union + import hashlib import json import os import pathlib import pickle -from typing import Any, Dict, List, Optional, Union, Mapping import dask.distributed -import scipy.sparse - -from ConfigSpace import Configuration import numpy as np import pandas as pd +import scipy.sparse import sklearn +from ConfigSpace import Configuration import autosklearn -from autosklearn.classification import AutoSklearnClassifier import autosklearn.experimental.selector -from autosklearn.metrics import Scorer, balanced_accuracy, roc_auc, log_loss, accuracy +from autosklearn.classification import AutoSklearnClassifier +from autosklearn.metrics import Scorer, accuracy, balanced_accuracy, log_loss, roc_auc metrics = (balanced_accuracy, roc_auc, log_loss) selector_files = {} this_directory = pathlib.Path(__file__).resolve().parent for metric in metrics: - training_data_file = this_directory / metric.name / 'askl2_training_data.json' + training_data_file = this_directory / metric.name / "askl2_training_data.json" with open(training_data_file) as fh: training_data = json.load(fh) fh.seek(0) m = hashlib.md5() - m.update(fh.read().encode('utf8')) + m.update(fh.read().encode("utf8")) training_data_hash = m.hexdigest()[:10] selector_filename = "askl2_selector_%s_%s_%s_%s.pkl" % ( autosklearn.__version__, sklearn.__version__, metric.name, - training_data_hash + training_data_hash, ) - selector_directory = os.environ.get('XDG_CACHE_HOME') + selector_directory = os.environ.get("XDG_CACHE_HOME") if selector_directory is None: selector_directory = pathlib.Path.home() - selector_directory = pathlib.Path(selector_directory).joinpath('auto-sklearn').expanduser() + selector_directory = ( + pathlib.Path(selector_directory).joinpath("auto-sklearn").expanduser() + ) selector_files[metric.name] = selector_directory / selector_filename - metafeatures = pd.DataFrame(training_data['metafeatures']) - strategies = training_data['strategies'] - y_values = pd.DataFrame(training_data['y_values'], columns=strategies, index=metafeatures.index) - minima_for_methods = training_data['minima_for_methods'] - maxima_for_methods = training_data['maxima_for_methods'] - default_strategies = training_data['tie_break_order'] + metafeatures = pd.DataFrame(training_data["metafeatures"]) + strategies = training_data["strategies"] + y_values = pd.DataFrame( + training_data["y_values"], columns=strategies, index=metafeatures.index + ) + minima_for_methods = training_data["minima_for_methods"] + maxima_for_methods = training_data["maxima_for_methods"] + default_strategies = training_data["tie_break_order"] if not selector_files[metric.name].exists(): selector = autosklearn.experimental.selector.OVORF( - configuration=training_data['configuration'], + configuration=training_data["configuration"], random_state=np.random.RandomState(1), n_estimators=500, tie_break_order=default_strategies, ) - selector = autosklearn.experimental.selector.FallbackWrapper(selector, default_strategies) + selector = autosklearn.experimental.selector.FallbackWrapper( + selector, default_strategies + ) selector.fit( X=metafeatures, y=y_values, @@ -63,12 +69,14 @@ selector_files[metric.name].parent.mkdir(exist_ok=True, parents=True) try: - with open(selector_files[metric.name], 'wb') as fh: + with open(selector_files[metric.name], "wb") as fh: pickle.dump(selector, fh) except Exception as e: - print("AutoSklearn2Classifier needs to create a selector file under " - "the user's home directory or XDG_CACHE_HOME. Nevertheless " - "the path {} is not writable.".format(selector_files[metric.name])) + print( + "AutoSklearn2Classifier needs to create a selector file under " + "the user's home directory or XDG_CACHE_HOME. Nevertheless " + "the path {} is not writable.".format(selector_files[metric.name]) + ) raise e @@ -87,9 +95,9 @@ def __call__( dask_client, ): from smac.facade.smac_ac_facade import SMAC4AC + from smac.intensification.simple_intensifier import SimpleIntensifier from smac.runhistory.runhistory2epm import RunHistory2EPM4LogCost from smac.scenario.scenario import Scenario - from smac.intensification.simple_intensifier import SimpleIntensifier scenario = Scenario(scenario_dict) @@ -151,7 +159,7 @@ def __call__( pass rh2EPM = RunHistory2EPM4LogCost - ta_kwargs['budget_type'] = self.budget_type + ta_kwargs["budget_type"] = self.budget_type smac4ac = SMAC4AC( scenario=scenario, @@ -163,10 +171,10 @@ def __call__( run_id=seed, intensifier=SuccessiveHalving, intensifier_kwargs={ - 'initial_budget': self.initial_budget, - 'max_budget': 100, - 'eta': self.eta, - 'min_chall': 1, + "initial_budget": self.initial_budget, + "max_budget": 100, + "eta": self.eta, + "min_chall": 1, }, dask_client=dask_client, n_jobs=n_jobs, @@ -178,7 +186,6 @@ def __call__( class AutoSklearn2Classifier(AutoSklearnClassifier): - def __init__( self, time_left_for_this_task: int = 3600, @@ -198,7 +205,7 @@ def __init__( metric: Optional[Scorer] = None, scoring_functions: Optional[List[Scorer]] = None, load_models: bool = True, - dataset_compression: Union[bool, Mapping[str, Any]] = True + dataset_compression: Union[bool, Mapping[str, Any]] = True, ): """ @@ -240,11 +247,11 @@ def __init__( Memory limit in MB for the machine learning algorithm. `auto-sklearn` will stop fitting the machine learning algorithm if it tries to allocate more than ``memory_limit`` MB. - - **Important notes:** - + + **Important notes:** + * If ``None`` is provided, no memory limit is set. - * In case of multi-processing, ``memory_limit`` will be *per job*, so the total usage is + * In case of multi-processing, ``memory_limit`` will be *per job*, so the total usage is ``n_jobs x memory_limit``. * The memory limit also applies to the ensemble creation process. @@ -258,12 +265,12 @@ def __init__( n_jobs : int, optional, experimental The number of jobs to run in parallel for ``fit()``. ``-1`` means - using all processors. - - **Important notes**: - - * By default, Auto-sklearn uses one core. - * Ensemble building is not affected by ``n_jobs`` but can be controlled by the number + using all processors. + + **Important notes**: + + * By default, Auto-sklearn uses one core. + * Ensemble building is not affected by ``n_jobs`` but can be controlled by the number of models in the ensemble. * ``predict()`` is not affected by ``n_jobs`` (in contrast to most scikit-learn models) * If ``dask_client`` is ``None``, a new dask client is created. @@ -319,11 +326,18 @@ def __init__( """ # noqa (links are too long) include_estimators = [ - 'extra_trees', 'passive_aggressive', 'random_forest', 'sgd', 'gradient_boosting', 'mlp', + "extra_trees", + "passive_aggressive", + "random_forest", + "sgd", + "gradient_boosting", + "mlp", ] include_preprocessors = ["no_preprocessing"] - include = {'classifier': include_estimators, - 'feature_preprocessor': include_preprocessors} + include = { + "classifier": include_estimators, + "feature_preprocessor": include_preprocessors, + } super().__init__( time_left_for_this_task=time_left_for_this_task, per_run_time_limit=per_run_time_limit, @@ -351,31 +365,40 @@ def __init__( load_models=load_models, ) - def fit(self, X, y, - X_test=None, - y_test=None, - metric=None, - feat_type=None, - dataset_name=None): + def fit( + self, + X, + y, + X_test=None, + y_test=None, + metric=None, + feat_type=None, + dataset_name=None, + ): # TODO - # regularly check https://github.com/scikit-learn/scikit-learn/issues/15336 whether - # histogram gradient boosting in scikit-learn finally support sparse data + # regularly check https://github.com/scikit-learn/scikit-learn/issues/15336 + # whether histogram gradient boosting in scikit-learn finally support + # sparse data is_sparse = scipy.sparse.issparse(X) if is_sparse: include_estimators = [ - 'extra_trees', 'passive_aggressive', 'random_forest', 'sgd', 'mlp', + "extra_trees", + "passive_aggressive", + "random_forest", + "sgd", + "mlp", ] else: include_estimators = [ - 'extra_trees', - 'passive_aggressive', - 'random_forest', - 'sgd', - 'gradient_boosting', - 'mlp', + "extra_trees", + "passive_aggressive", + "random_forest", + "sgd", + "gradient_boosting", + "mlp", ] - self.include['classifier'] = include_estimators + self.include["classifier"] = include_estimators if self.metric is None: if len(y.shape) == 1 or y.shape[1] == 1: @@ -387,71 +410,76 @@ def fit(self, X, y, metric_name = self.metric.name selector_file = selector_files[metric_name] else: - metric_name = 'balanced_accuracy' + metric_name = "balanced_accuracy" selector_file = selector_files[metric_name] - with open(selector_file, 'rb') as fh: + with open(selector_file, "rb") as fh: selector = pickle.load(fh) - metafeatures = pd.DataFrame({dataset_name: [X.shape[1], X.shape[0]]}).transpose() + metafeatures = pd.DataFrame( + {dataset_name: [X.shape[1], X.shape[0]]} + ).transpose() selection = np.argmax(selector.predict(metafeatures)) automl_policy = strategies[selection] setting = { - 'RF_None_holdout_iterative_es_if': { - 'resampling_strategy': 'holdout-iterative-fit', - 'fidelity': None, + "RF_None_holdout_iterative_es_if": { + "resampling_strategy": "holdout-iterative-fit", + "fidelity": None, + }, + "RF_None_3CV_iterative_es_if": { + "resampling_strategy": "cv-iterative-fit", + "folds": 3, + "fidelity": None, }, - 'RF_None_3CV_iterative_es_if': { - 'resampling_strategy': 'cv-iterative-fit', - 'folds': 3, - 'fidelity': None, + "RF_None_5CV_iterative_es_if": { + "resampling_strategy": "cv-iterative-fit", + "folds": 5, + "fidelity": None, }, - 'RF_None_5CV_iterative_es_if': { - 'resampling_strategy': 'cv-iterative-fit', - 'folds': 5, - 'fidelity': None, + "RF_None_10CV_iterative_es_if": { + "resampling_strategy": "cv-iterative-fit", + "folds": 10, + "fidelity": None, }, - 'RF_None_10CV_iterative_es_if': { - 'resampling_strategy': 'cv-iterative-fit', - 'folds': 10, - 'fidelity': None, + "RF_SH-eta4-i_holdout_iterative_es_if": { + "resampling_strategy": "holdout-iterative-fit", + "fidelity": "SH", }, - 'RF_SH-eta4-i_holdout_iterative_es_if': { - 'resampling_strategy': 'holdout-iterative-fit', - 'fidelity': 'SH', + "RF_SH-eta4-i_3CV_iterative_es_if": { + "resampling_strategy": "cv-iterative-fit", + "folds": 3, + "fidelity": "SH", }, - 'RF_SH-eta4-i_3CV_iterative_es_if': { - 'resampling_strategy': 'cv-iterative-fit', - 'folds': 3, - 'fidelity': 'SH', + "RF_SH-eta4-i_5CV_iterative_es_if": { + "resampling_strategy": "cv-iterative-fit", + "folds": 5, + "fidelity": "SH", }, - 'RF_SH-eta4-i_5CV_iterative_es_if': { - 'resampling_strategy': 'cv-iterative-fit', - 'folds': 5, - 'fidelity': 'SH', + "RF_SH-eta4-i_10CV_iterative_es_if": { + "resampling_strategy": "cv-iterative-fit", + "folds": 10, + "fidelity": "SH", }, - 'RF_SH-eta4-i_10CV_iterative_es_if': { - 'resampling_strategy': 'cv-iterative-fit', - 'folds': 10, - 'fidelity': 'SH', - } }[automl_policy] - resampling_strategy = setting['resampling_strategy'] - if resampling_strategy == 'cv-iterative-fit': - resampling_strategy_kwargs = {'folds': setting['folds']} + resampling_strategy = setting["resampling_strategy"] + if resampling_strategy == "cv-iterative-fit": + resampling_strategy_kwargs = {"folds": setting["folds"]} else: resampling_strategy_kwargs = None portfolio_file = ( - this_directory / metric_name / 'askl2_portfolios' / ('%s.json' % automl_policy) + this_directory + / metric_name + / "askl2_portfolios" + / ("%s.json" % automl_policy) ) with open(portfolio_file) as fh: portfolio_json = json.load(fh) - portfolio = portfolio_json['portfolio'] + portfolio = portfolio_json["portfolio"] - if setting['fidelity'] == 'SH': - smac_callback = SHObjectCallback('iterations', 4, 5.0, portfolio) + if setting["fidelity"] == "SH": + smac_callback = SHObjectCallback("iterations", 4, 5.0, portfolio) else: smac_callback = SmacObjectCallback(portfolio) diff --git a/autosklearn/experimental/selector.py b/autosklearn/experimental/selector.py index 3ef681bef8..125cba6125 100644 --- a/autosklearn/experimental/selector.py +++ b/autosklearn/experimental/selector.py @@ -1,6 +1,7 @@ +import typing + import copy import itertools -import typing import numpy as np import pandas as pd @@ -9,7 +10,6 @@ class AbstractSelector: - def fit( self, X: pd.DataFrame, @@ -19,18 +19,22 @@ def fit( ) -> None: raise NotImplementedError() - def predict(self, X: pd.DataFrame, y: typing.Optional[pd.DataFrame] = None) -> pd.DataFrame: + def predict( + self, X: pd.DataFrame, y: typing.Optional[pd.DataFrame] = None + ) -> pd.DataFrame: prediction = self._predict(X, y) for col, series in prediction.iteritems(): assert series.dtype == float, (col, series) np.testing.assert_array_almost_equal( - prediction.sum(axis='columns').to_numpy(), + prediction.sum(axis="columns").to_numpy(), np.ones(X.shape[0]), err_msg=prediction.to_csv(), ) return prediction - def _predict(self, X: pd.DataFrame, y: typing.Optional[pd.DataFrame]) -> pd.DataFrame: + def _predict( + self, X: pd.DataFrame, y: typing.Optional[pd.DataFrame] + ) -> pd.DataFrame: raise NotImplementedError() @@ -68,43 +72,60 @@ def fit( weights[i] = dict() for j in range(i + 1, len(target_indices)): - if self.configuration['normalization'] in ('all', 'binary', 'y', 'all1', - 'binary1'): + if self.configuration["normalization"] in ( + "all", + "binary", + "y", + "all1", + "binary1", + ): minimum2 = np.ones(len(X)) * np.inf maximum2 = np.zeros(len(X)) - if self.configuration['normalization'] in ('all', 'all1'): + if self.configuration["normalization"] in ("all", "all1"): for idx, task_id in enumerate(X.index): for method_id in range(len(target_indices)): - minimum2[idx] = np.nanmin(( - minimum2[idx], - minima[task_id][self.strategies_[method_id]] - )) - maximum2[idx] = np.nanmax(( - maximum2[idx], - maxima[task_id][self.strategies_[method_id]] - )) - if self.configuration['normalization'] == 'all1': + minimum2[idx] = np.nanmin( + ( + minimum2[idx], + minima[task_id][self.strategies_[method_id]], + ) + ) + maximum2[idx] = np.nanmax( + ( + maximum2[idx], + maxima[task_id][self.strategies_[method_id]], + ) + ) + if self.configuration["normalization"] == "all1": maximum2 = np.ones_like(maximum2) - elif self.configuration['normalization'] in ('binary', 'binary1'): + elif self.configuration["normalization"] in ("binary", "binary1"): for idx, task_id in enumerate(X.index): for method_id in (i, j): - minimum2[idx] = np.nanmin(( - minimum2[idx], - minima[task_id][self.strategies_[method_id]] - )) - maximum2[idx] = np.nanmax(( - maximum2[idx], - maxima[task_id][self.strategies_[method_id]] - )) - if self.configuration['normalization'] == 'binary1': + minimum2[idx] = np.nanmin( + ( + minimum2[idx], + minima[task_id][self.strategies_[method_id]], + ) + ) + maximum2[idx] = np.nanmax( + ( + maximum2[idx], + maxima[task_id][self.strategies_[method_id]], + ) + ) + if self.configuration["normalization"] == "binary1": maximum2 = np.ones_like(maximum2) - elif self.configuration['normalization'] == 'y': + elif self.configuration["normalization"] == "y": for idx, task_id in enumerate(X.index): - minimum2[idx] = np.nanmin((minimum2[idx], y_pd.loc[task_id].min())) - maximum2[idx] = np.nanmax((maximum2[idx], y_pd.loc[task_id].max())) + minimum2[idx] = np.nanmin( + (minimum2[idx], y_pd.loc[task_id].min()) + ) + maximum2[idx] = np.nanmax( + (maximum2[idx], y_pd.loc[task_id].max()) + ) else: - raise ValueError(self.configuration['normalization']) + raise ValueError(self.configuration["normalization"]) y_i_j = y[:, i] < y[:, j] mask = np.isfinite(y[:, i]) & np.isfinite(y[:, j]) @@ -121,7 +142,7 @@ def fit( weights_i_j = np.abs(normalized_y_i - normalized_y_j) - elif self.configuration['normalization'] == 'rank': + elif self.configuration["normalization"] == "rank": y_i_j = y[:, i] < y[:, j] mask = np.isfinite(y[:, i]) & np.isfinite(y[:, j]) X_ = X.to_numpy()[mask] @@ -129,7 +150,7 @@ def fit( ranks = scipy.stats.rankdata(y[mask], axis=1) weights_i_j = np.abs(ranks[:, i] - ranks[:, j]) - elif self.configuration['normalization'] == 'None': + elif self.configuration["normalization"] == "None": y_i_j = y[:, i] < y[:, j] mask = np.isfinite(y[:, i]) & np.isfinite(y[:, j]) X_ = X.to_numpy()[mask] @@ -137,7 +158,7 @@ def fit( weights_i_j = np.ones_like(y_i_j).astype(int) else: - raise ValueError(self.configuration['normalization']) + raise ValueError(self.configuration["normalization"]) if len(y_i_j) == 0: models[i][j] = None @@ -148,21 +169,25 @@ def fit( n_zeros = int(np.ceil(len(y_i_j) / 2)) n_ones = int(np.floor(len(y_i_j) / 2)) import sklearn.dummy - base_model = sklearn.dummy.DummyClassifier(strategy='constant', - constant=y_i_j[0]) + + base_model = sklearn.dummy.DummyClassifier( + strategy="constant", constant=y_i_j[0] + ) base_model.fit( X_, np.array(([[0]] * n_zeros) + ([[1]] * n_ones)).flatten(), sample_weight=weights_i_j, ) else: - if self.configuration.get('max_depth') == 0: + if self.configuration.get("max_depth") == 0: import sklearn.dummy + loss_i = np.sum((y_i_j == 0) * weights_i_j) loss_j = np.sum((y_i_j == 1) * weights_i_j) base_model = sklearn.dummy.DummyClassifier( - strategy='constant', constant=1 if loss_i < loss_j else 0, + strategy="constant", + constant=1 if loss_i < loss_j else 0, ) base_model.fit( X_, @@ -171,7 +196,11 @@ def fit( ) else: base_model = self.fit_pairwise_model( - X_, y_i_j, weights_i_j, self.rng, self.configuration, + X_, + y_i_j, + weights_i_j, + self.rng, + self.configuration, ) models[i][j] = base_model weights[i][j] = weights_i_j @@ -179,7 +208,9 @@ def fit( self.weights_ = weights self.target_indices = target_indices - def _predict(self, X: pd.DataFrame, y: typing.Optional[pd.DataFrame]) -> pd.DataFrame: + def _predict( + self, X: pd.DataFrame, y: typing.Optional[pd.DataFrame] + ) -> pd.DataFrame: if y is not None: raise ValueError("y must not be provided") @@ -193,7 +224,9 @@ def _predict(self, X: pd.DataFrame, y: typing.Optional[pd.DataFrame]) -> pd.Data raw_probas[(i, j)] = self.models[i][j].predict_proba(X) if len(raw_predictions) == 0: - predictions = pd.DataFrame(0, index=X.index, columns=self.strategies_).astype(float) + predictions = pd.DataFrame( + 0, index=X.index, columns=self.strategies_ + ).astype(float) predictions.iloc[:, self.single_strategy_idx] = 1.0 return predictions @@ -203,21 +236,21 @@ def _predict(self, X: pd.DataFrame, y: typing.Optional[pd.DataFrame]) -> pd.Data for i in range(len(self.target_indices)): for j in range(i + 1, len(self.target_indices)): if (i, j) in raw_predictions: - if self.configuration['prediction'] == 'soft': + if self.configuration["prediction"] == "soft": if raw_probas[(i, j)].shape[1] == 1: proba = raw_probas[(i, j)][x_idx][0] else: proba = raw_probas[(i, j)][x_idx][1] wins[i] += proba wins[j] += 1 - proba - elif self.configuration['prediction'] == 'hard': + elif self.configuration["prediction"] == "hard": prediction = raw_predictions[(i, j)][x_idx] if prediction == 1: wins[i] += 1 else: wins[j] += 1 else: - raise ValueError(self.configuration['prediction']) + raise ValueError(self.configuration["prediction"]) n_prev = np.inf # Tie breaking @@ -236,7 +269,9 @@ def _predict(self, X: pd.DataFrame, y: typing.Optional[pd.DataFrame]) -> pd.Data hit = True break if not hit: - wins[int(self.rng.choice(np.argwhere(most_wins_mask).flatten()))] += 1 + wins[ + int(self.rng.choice(np.argwhere(most_wins_mask).flatten())) + ] += 1 elif np.sum(most_wins_mask) > 1: n_prev = np.sum(most_wins_mask) where = np.argwhere(most_wins_mask).flatten() @@ -250,10 +285,9 @@ def _predict(self, X: pd.DataFrame, y: typing.Optional[pd.DataFrame]) -> pd.Data else: method_i = self.strategies_[i] method_j = self.strategies_[j] - if ( - self.tie_break_order.index(method_i) - < self.tie_break_order.index(method_j) - ): + if self.tie_break_order.index( + method_i + ) < self.tie_break_order.index(method_j): wins[i] += 1 else: wins[j] += 1 @@ -288,18 +322,17 @@ def fit_pairwise_model(self, X, y, weights, rng, configuration): base_model = sklearn.ensemble.RandomForestClassifier( random_state=rng, n_estimators=self.n_estimators, - bootstrap=True if configuration['bootstrap'] == 'True' else False, - min_samples_split=configuration['min_samples_split'], - min_samples_leaf=configuration['min_samples_leaf'], - max_features=int(configuration['max_features']), - max_depth=configuration['max_depth'], + bootstrap=True if configuration["bootstrap"] == "True" else False, + min_samples_split=configuration["min_samples_split"], + min_samples_leaf=configuration["min_samples_leaf"], + max_features=int(configuration["max_features"]), + max_depth=configuration["max_depth"], ) base_model.fit(X, y, sample_weight=weights) return base_model class FallbackWrapper(AbstractSelector): - def __init__(self, selector, default_strategies: typing.List[str]): self.selector = selector self.default_strategies = default_strategies @@ -313,16 +346,19 @@ def fit( ) -> None: self.X_ = X self.strategies_ = y.columns - self.rval_ = np.array([ - ( - len(self.strategies_) - self.default_strategies.index(strategy) - 1 - ) / (len(self.strategies_) - 1) - for strategy in self.strategies_ - ]) + self.rval_ = np.array( + [ + (len(self.strategies_) - self.default_strategies.index(strategy) - 1) + / (len(self.strategies_) - 1) + for strategy in self.strategies_ + ] + ) self.rval_ = self.rval_ / np.sum(self.rval_) self.selector.fit(X, y, minima, maxima) - def _predict(self, X: pd.DataFrame, y: typing.Optional[pd.DataFrame]) -> pd.DataFrame: + def _predict( + self, X: pd.DataFrame, y: typing.Optional[pd.DataFrame] + ) -> pd.DataFrame: if y is not None: prediction = self.selector.predict(X, y) @@ -338,8 +374,11 @@ def _predict(self, X: pd.DataFrame, y: typing.Optional[pd.DataFrame]) -> pd.Data counter += 1 if counter == 0: - prediction.loc[task_id] = pd.Series({ - strategy: value for strategy, value in zip(self.strategies_, self.rval_) - }) + prediction.loc[task_id] = pd.Series( + { + strategy: value + for strategy, value in zip(self.strategies_, self.rval_) + } + ) return prediction diff --git a/autosklearn/metalearning/__init__.py b/autosklearn/metalearning/__init__.py index cc3cd7becd..e298f0f075 100644 --- a/autosklearn/metalearning/__init__.py +++ b/autosklearn/metalearning/__init__.py @@ -1,2 +1,2 @@ # -*- encoding: utf-8 -*- -__author__ = 'feurerm' +__author__ = "feurerm" diff --git a/autosklearn/metalearning/input/aslib_simple.py b/autosklearn/metalearning/input/aslib_simple.py index 7bac637c50..833242729d 100644 --- a/autosklearn/metalearning/input/aslib_simple.py +++ b/autosklearn/metalearning/input/aslib_simple.py @@ -1,7 +1,7 @@ -from collections import defaultdict, OrderedDict import csv import logging import os +from collections import OrderedDict, defaultdict import arff import pandas as pd @@ -24,7 +24,7 @@ def __init__(self, directory): # "feature_runstatus.arff": self._read_feature_runstatus, # "ground_truth.arff": self._read_ground_truth, # "cv.arff": self._read_cv, - "configurations.csv": self._read_configurations + "configurations.csv": self._read_configurations, } self.found_files = [] @@ -33,24 +33,28 @@ def __init__(self, directory): self._read_files() def _find_files(self): - ''' - find all expected files in self.dir_ - fills self.found_files - ''' + """ + find all expected files in self.dir_ + fills self.found_files + """ expected = [ # "description.txt", "algorithm_runs.arff", "feature_values.arff", # "feature_runstatus.arff", ] - optional = ["ground_truth.arff", "feature_costs.arff", "citation.bib", - "cv.arff", "configurations.csv"] + optional = [ + "ground_truth.arff", + "feature_costs.arff", + "citation.bib", + "cv.arff", + "configurations.csv", + ] for expected_file in expected: full_path = os.path.join(self.dir_, expected_file) if not os.path.isfile(full_path): - self.logger.error( - "Not found: %s (has to be added)" % (full_path)) + self.logger.error("Not found: %s (has to be added)" % (full_path)) else: self.found_files.append(full_path) @@ -64,10 +68,10 @@ def _find_files(self): self.found_files.append(full_path) def _read_files(self): - ''' - iterates over all found files (self.found_files) and - calls the corresponding function to validate file - ''' + """ + iterates over all found files (self.found_files) and + calls the corresponding function to validate file + """ for file_ in self.found_files: read_func = self.read_funcs.get(os.path.basename(file_)) if read_func: @@ -79,15 +83,18 @@ def _read_algorithm_runs(self, filename): if arff_dict["attributes"][0][0].upper() != "INSTANCE_ID": self.logger.error( - "instance_id as first attribute is missing in %s" % (filename)) + "instance_id as first attribute is missing in %s" % (filename) + ) if arff_dict["attributes"][1][0].upper() != "REPETITION": self.logger.error( - "repetition as second attribute is missing in %s" % (filename)) + "repetition as second attribute is missing in %s" % (filename) + ) if arff_dict["attributes"][2][0].upper() != "ALGORITHM": self.logger.error( - "algorithm as third attribute is missing in %s" % (filename)) + "algorithm as third attribute is missing in %s" % (filename) + ) - performance_measures = [pm[0] for pm in arff_dict['attributes'][3:-1]] + performance_measures = [pm[0] for pm in arff_dict["attributes"][3:-1]] measure_instance_algorithm_triples = defaultdict(lambda: defaultdict(dict)) for data in arff_dict["data"]: @@ -97,18 +104,20 @@ def _read_algorithm_runs(self, filename): perf_list = data[3:-1] status = data[-1] - if status != 'ok': + if status != "ok": continue for i, performance_measure in enumerate(performance_measures): - measure_instance_algorithm_triples[performance_measure][ - inst_name][algorithm] = perf_list[i] + measure_instance_algorithm_triples[performance_measure][inst_name][ + algorithm + ] = perf_list[i] # TODO: this does not support any repetitions! measure_algorithm_matrices = OrderedDict() for pm in performance_measures: measure_algorithm_matrices[pm] = pd.DataFrame( - measure_instance_algorithm_triples[pm]).transpose() + measure_instance_algorithm_triples[pm] + ).transpose() self.algorithm_runs = measure_algorithm_matrices @@ -122,9 +131,10 @@ def _read_feature_values(self, filename): # repetition = data[1] features = data[2:] - metafeatures[inst_name] = {feature[0]: feature_value - for feature, feature_value in - zip(arff_dict['attributes'][2:], features)} + metafeatures[inst_name] = { + feature[0]: feature_value + for feature, feature_value in zip(arff_dict["attributes"][2:], features) + } self.metafeatures = pd.DataFrame(metafeatures).transpose() @@ -135,9 +145,9 @@ def _read_configurations(self, filename): configurations = dict() for line in csv_reader: configuration = dict() - algorithm_id = line['idx'] + algorithm_id = line["idx"] for hp_name, value in line.items(): - if not value or hp_name == 'idx': + if not value or hp_name == "idx": continue try: diff --git a/autosklearn/metalearning/metafeatures/metafeature.py b/autosklearn/metalearning/metafeatures/metafeature.py index 821a5033f4..033b76116b 100644 --- a/autosklearn/metalearning/metafeatures/metafeature.py +++ b/autosklearn/metalearning/metafeatures/metafeature.py @@ -1,6 +1,7 @@ from abc import ABCMeta, abstractmethod -from io import StringIO + import time +from io import StringIO import arff import scipy.sparse @@ -33,8 +34,15 @@ def __call__(self, X, y, logger, categorical=None): comment = "Memory Error" endtime = time.time() - return MetaFeatureValue(self.__class__.__name__, self.type_, - 0, 0, value, endtime-starttime, comment=comment) + return MetaFeatureValue( + self.__class__.__name__, + self.type_, + 0, + 0, + value, + endtime - starttime, + comment=comment, + ) class MetaFeature(AbstractMetaFeature): @@ -65,15 +73,26 @@ def to_arff_row(self): else: value = "?" - return [self.name, self.type_, self.fold, - self.repeat, value, self.time, self.comment] + return [ + self.name, + self.type_, + self.fold, + self.repeat, + value, + self.time, + self.comment, + ] def __repr__(self): - repr = "%s (type: %s, fold: %d, repeat: %d, value: %s, time: %3.3f, " \ - "comment: %s)" - repr = repr % tuple(self.to_arff_row()[:4] + - [str(self.to_arff_row()[4])] + - self.to_arff_row()[5:]) + repr = ( + "%s (type: %s, fold: %d, repeat: %d, value: %s, time: %3.3f, " + "comment: %s)" + ) + repr = repr % tuple( + self.to_arff_row()[:4] + + [str(self.to_arff_row()[4])] + + self.to_arff_row()[5:] + ) return repr @@ -84,19 +103,21 @@ def __init__(self, dataset_name, metafeature_values): def _get_arff(self): output = dict() - output['relation'] = "metafeatures_%s" % (self.dataset_name) - output['description'] = "" - output['attributes'] = [('name', 'STRING'), - ('type', 'STRING'), - ('fold', 'NUMERIC'), - ('repeat', 'NUMERIC'), - ('value', 'NUMERIC'), - ('time', 'NUMERIC'), - ('comment', 'STRING')] - output['data'] = [] + output["relation"] = "metafeatures_%s" % (self.dataset_name) + output["description"] = "" + output["attributes"] = [ + ("name", "STRING"), + ("type", "STRING"), + ("fold", "NUMERIC"), + ("repeat", "NUMERIC"), + ("value", "NUMERIC"), + ("time", "NUMERIC"), + ("comment", "STRING"), + ] + output["data"] = [] for key in sorted(self.metafeature_values): - output['data'].append(self.metafeature_values[key].to_arff_row()) + output["data"].append(self.metafeature_values[key].to_arff_row()) return output def dumps(self): @@ -120,9 +141,9 @@ def load(cls, path_or_filehandle): else: input = arff.load(path_or_filehandle) - dataset_name = input['relation'].replace('metafeatures_', '') + dataset_name = input["relation"].replace("metafeatures_", "") metafeature_values = [] - for item in input['data']: + for item in input["data"]: mf = MetaFeatureValue(*item) metafeature_values.append(mf) @@ -135,13 +156,18 @@ def __repr__(self, verbosity=0): if verbosity == 0 and self.metafeature_values[name].type_ != "METAFEATURE": continue if verbosity == 0: - repr.write(" %s: %s\n" % - (str(name), str(self.metafeature_values[name].value))) + repr.write( + " %s: %s\n" % (str(name), str(self.metafeature_values[name].value)) + ) elif verbosity >= 1: - repr.write(" %s: %10s (%10fs)\n" % - (str(name), str(self.metafeature_values[ - name].value)[:10], - self.metafeature_values[name].time)) + repr.write( + " %s: %10s (%10fs)\n" + % ( + str(name), + str(self.metafeature_values[name].value)[:10], + self.metafeature_values[name].time, + ) + ) # Add the reason for a crash if one happened! if verbosity > 1 and self.metafeature_values[name].comment: diff --git a/autosklearn/metalearning/metafeatures/metafeatures.py b/autosklearn/metalearning/metafeatures/metafeatures.py index 9652cfc673..3c95fbf22f 100644 --- a/autosklearn/metalearning/metafeatures/metafeatures.py +++ b/autosklearn/metalearning/metafeatures/metafeatures.py @@ -1,22 +1,22 @@ -from collections import defaultdict, OrderedDict, deque import copy +from collections import OrderedDict, defaultdict, deque import numpy as np - import pandas as pd - +import scipy.sparse import scipy.stats from scipy.linalg import LinAlgError -import scipy.sparse # TODO use balanced accuracy! from sklearn.multiclass import OneVsRestClassifier from sklearn.utils import check_array from sklearn.utils.multiclass import type_of_target -from autosklearn.pipeline.components.data_preprocessing.feature_type \ - import FeatTypeSplit -from .metafeature import MetaFeature, HelperFunction, DatasetMetafeatures +from autosklearn.pipeline.components.data_preprocessing.feature_type import ( + FeatTypeSplit, +) + +from .metafeature import DatasetMetafeatures, HelperFunction, MetaFeature # TODO Allow multiple dependencies for a metafeature @@ -62,10 +62,12 @@ def define(self, name): """Decorator for adding helper functions to a "dictionary". This behaves like a function decorating a function, not a class decorating a function""" + def wrapper(metafeature_class): instance = metafeature_class() self.__setitem__(name, instance) return instance + return wrapper @@ -107,19 +109,20 @@ def is_calculated(self, key): return key in self.values def get_dependency(self, name): - """Return the dependency of metafeature "name". - """ + """Return the dependency of metafeature "name".""" return self.dependencies.get(name) def define(self, name, dependency=None): """Decorator for adding metafeature functions to a "dictionary" of metafeatures. This behaves like a function decorating a function, not a class decorating a function""" + def wrapper(metafeature_class): instance = metafeature_class() self.__setitem__(name, instance) self.dependencies[name] = dependency return instance + return wrapper @@ -136,8 +139,7 @@ def _calculate(self, X, y, logger, categorical): return float(X.shape[0]) -@metafeatures.define("LogNumberOfInstances", - dependency="NumberOfInstances") +@metafeatures.define("LogNumberOfInstances", dependency="NumberOfInstances") class LogNumberOfInstances(MetaFeature): def _calculate(self, X, y, logger, categorical): return np.log(metafeatures.get_value("NumberOfInstances")) @@ -151,8 +153,9 @@ class NumberOfClasses(MetaFeature): Calls np.unique on the targets. If the dataset is a multilabel dataset, does this for each label seperately and returns the mean. """ + def _calculate(self, X, y, logger, categorical): - if type_of_target(y) == 'multilabel-indicator': + if type_of_target(y) == "multilabel-indicator": # We have a label binary indicator array: # each sample is one row of a 2d array of shape (n_samples, n_classes) return y.shape[1] @@ -168,8 +171,7 @@ def _calculate(self, X, y, logger, categorical): return float(X.shape[1]) -@metafeatures.define("LogNumberOfFeatures", - dependency="NumberOfFeatures") +@metafeatures.define("LogNumberOfFeatures", dependency="NumberOfFeatures") class LogNumberOfFeatures(MetaFeature): def _calculate(self, X, y, logger, categorical): return np.log(metafeatures.get_value("NumberOfFeatures")) @@ -183,13 +185,11 @@ def _calculate(self, X, y, logger, categorical): def _calculate_sparse(self, X, y, logger, categorical): data = [True if not np.isfinite(x) else False for x in X.data] - missing = X.__class__((data, X.indices, X.indptr), shape=X.shape, - dtype=bool) + missing = X.__class__((data, X.indices, X.indptr), shape=X.shape, dtype=bool) return missing -@metafeatures.define("NumberOfInstancesWithMissingValues", - dependency="MissingValues") +@metafeatures.define("NumberOfInstancesWithMissingValues", dependency="MissingValues") class NumberOfInstancesWithMissingValues(MetaFeature): def _calculate(self, X, y, logger, categorical): missing = helper_functions.get_value("MissingValues") @@ -199,14 +199,18 @@ def _calculate(self, X, y, logger, categorical): def _calculate_sparse(self, X, y, logger, categorical): missing = helper_functions.get_value("MissingValues") new_missing = missing.tocsr() - num_missing = [np.sum(new_missing.data[new_missing.indptr[i]:new_missing.indptr[i + 1]]) - for i in range(new_missing.shape[0])] + num_missing = [ + np.sum(new_missing.data[new_missing.indptr[i] : new_missing.indptr[i + 1]]) + for i in range(new_missing.shape[0]) + ] return float(np.sum([1 if num > 0 else 0 for num in num_missing])) -@metafeatures.define("PercentageOfInstancesWithMissingValues", - dependency="NumberOfInstancesWithMissingValues") +@metafeatures.define( + "PercentageOfInstancesWithMissingValues", + dependency="NumberOfInstancesWithMissingValues", +) class PercentageOfInstancesWithMissingValues(MetaFeature): def _calculate(self, X, y, logger, categorical): n_missing = metafeatures.get_value("NumberOfInstancesWithMissingValues") @@ -214,8 +218,7 @@ def _calculate(self, X, y, logger, categorical): return float(n_missing / n_total) -@metafeatures.define("NumberOfFeaturesWithMissingValues", - dependency="MissingValues") +@metafeatures.define("NumberOfFeaturesWithMissingValues", dependency="MissingValues") class NumberOfFeaturesWithMissingValues(MetaFeature): def _calculate(self, X, y, logger, categorical): missing = helper_functions.get_value("MissingValues") @@ -225,15 +228,18 @@ def _calculate(self, X, y, logger, categorical): def _calculate_sparse(self, X, y, logger, categorical): missing = helper_functions.get_value("MissingValues") new_missing = missing.tocsc() - num_missing = [np.sum( - new_missing.data[new_missing.indptr[i]:new_missing.indptr[i+1]]) - for i in range(missing.shape[1])] + num_missing = [ + np.sum(new_missing.data[new_missing.indptr[i] : new_missing.indptr[i + 1]]) + for i in range(missing.shape[1]) + ] return float(np.sum([1 if num > 0 else 0 for num in num_missing])) -@metafeatures.define("PercentageOfFeaturesWithMissingValues", - dependency="NumberOfFeaturesWithMissingValues") +@metafeatures.define( + "PercentageOfFeaturesWithMissingValues", + dependency="NumberOfFeaturesWithMissingValues", +) class PercentageOfFeaturesWithMissingValues(MetaFeature): def _calculate(self, X, y, logger, categorical): n_missing = metafeatures.get_value("NumberOfFeaturesWithMissingValues") @@ -250,12 +256,12 @@ def _calculate(self, X, y, logger, categorical): return float(np.count_nonzero(helper_functions.get_value("MissingValues"))) -@metafeatures.define("PercentageOfMissingValues", - dependency="NumberOfMissingValues") +@metafeatures.define("PercentageOfMissingValues", dependency="NumberOfMissingValues") class PercentageOfMissingValues(MetaFeature): def _calculate(self, X, y, logger, categorical): - return float(metafeatures.get_value("NumberOfMissingValues")) / \ - float(X.shape[0]*X.shape[1]) + return float(metafeatures.get_value("NumberOfMissingValues")) / float( + X.shape[0] * X.shape[1] + ) # TODO: generalize this! @@ -274,24 +280,28 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("RatioNumericalToNominal") class RatioNumericalToNominal(MetaFeature): def _calculate(self, X, y, logger, categorical): - num_categorical = float(metafeatures[ - "NumberOfCategoricalFeatures"](X, y, logger, categorical).value) - num_numerical = float(metafeatures[ - "NumberOfNumericFeatures"](X, y, logger, categorical).value) + num_categorical = float( + metafeatures["NumberOfCategoricalFeatures"](X, y, logger, categorical).value + ) + num_numerical = float( + metafeatures["NumberOfNumericFeatures"](X, y, logger, categorical).value + ) if num_categorical == 0.0: - return 0. + return 0.0 return num_numerical / num_categorical @metafeatures.define("RatioNominalToNumerical") class RatioNominalToNumerical(MetaFeature): def _calculate(self, X, y, logger, categorical): - num_categorical = float(metafeatures[ - "NumberOfCategoricalFeatures"](X, y, logger, categorical).value) - num_numerical = float(metafeatures[ - "NumberOfNumericFeatures"](X, y, logger, categorical).value) + num_categorical = float( + metafeatures["NumberOfCategoricalFeatures"](X, y, logger, categorical).value + ) + num_numerical = float( + metafeatures["NumberOfNumericFeatures"](X, y, logger, categorical).value + ) if num_numerical == 0.0: - return 0. + return 0.0 else: return num_categorical / num_numerical @@ -300,8 +310,9 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("DatasetRatio") class DatasetRatio(MetaFeature): def _calculate(self, X, y, logger, categorical): - return float(metafeatures["NumberOfFeatures"](X, y, logger).value) /\ - float(metafeatures["NumberOfInstances"](X, y, logger).value) + return float(metafeatures["NumberOfFeatures"](X, y, logger).value) / float( + metafeatures["NumberOfInstances"](X, y, logger).value + ) @metafeatures.define("LogDatasetRatio", dependency="DatasetRatio") @@ -313,12 +324,12 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("InverseDatasetRatio") class InverseDatasetRatio(MetaFeature): def _calculate(self, X, y, logger, categorical): - return float(metafeatures["NumberOfInstances"](X, y, logger).value) /\ - float(metafeatures["NumberOfFeatures"](X, y, logger).value) + return float(metafeatures["NumberOfInstances"](X, y, logger).value) / float( + metafeatures["NumberOfFeatures"](X, y, logger).value + ) -@metafeatures.define("LogInverseDatasetRatio", - dependency="InverseDatasetRatio") +@metafeatures.define("LogInverseDatasetRatio", dependency="InverseDatasetRatio") class LogInverseDatasetRatio(MetaFeature): def _calculate(self, X, y, logger, categorical): return np.log(metafeatures.get_value("InverseDatasetRatio")) @@ -385,12 +396,13 @@ def _calculate(self, X, y, logger, categorical): occurences = [] for i in range(y.shape[1]): occurences.extend( - [occurrence for occurrence in occurence_dict[ - i].values()]) + [occurrence for occurrence in occurence_dict[i].values()] + ) occurences = np.array(occurences) else: - occurences = np.array([occurrence for occurrence in occurence_dict.values()], - dtype=np.float64) + occurences = np.array( + [occurrence for occurrence in occurence_dict.values()], dtype=np.float64 + ) return (occurences / y.shape[0]).mean() @@ -403,15 +415,16 @@ def _calculate(self, X, y, logger, categorical): stds = [] for i in range(y.shape[1]): std = np.array( - [occurrence for occurrence in occurence_dict[ - i].values()], - dtype=np.float64) + [occurrence for occurrence in occurence_dict[i].values()], + dtype=np.float64, + ) std = (std / y.shape[0]).std() stds.append(std) return np.mean(stds) else: - occurences = np.array([occurrence for occurrence in occurence_dict.values()], - dtype=np.float64) + occurences = np.array( + [occurrence for occurrence in occurence_dict.values()], dtype=np.float64 + ) return (occurences / y.shape[0]).std() @@ -424,10 +437,11 @@ class NumSymbols(HelperFunction): def _calculate(self, X, y, logger, categorical): symbols_per_column = [] for i in range(X.shape[1]): - if categorical[X.columns[i] if hasattr(X, 'columns') else i]: - column = X.iloc[:, i] if hasattr(X, 'iloc') else X[:, i] - unique_values = column.unique() if hasattr( - column, 'unique') else np.unique(column) + if categorical[X.columns[i] if hasattr(X, "columns") else i]: + column = X.iloc[:, i] if hasattr(X, "iloc") else X[:, i] + unique_values = ( + column.unique() if hasattr(column, "unique") else np.unique(column) + ) num_unique = np.sum(pd.notna(unique_values)) symbols_per_column.append(num_unique) return symbols_per_column @@ -436,7 +450,7 @@ def _calculate_sparse(self, X, y, logger, categorical): symbols_per_column = [] new_X = X.tocsc() for i in range(new_X.shape[1]): - if categorical[X.columns[i] if hasattr(X, 'columns') else i]: + if categorical[X.columns[i] if hasattr(X, "columns") else i]: unique_values = np.unique(new_X.getcol(i).data) num_unique = np.sum(np.isfinite(unique_values)) symbols_per_column.append(num_unique) @@ -489,6 +503,7 @@ def _calculate(self, X, y, logger, categorical): sum = np.nansum(helper_functions.get_value("NumSymbols")) return sum if np.isfinite(sum) else 0 + ################################################################################ # Statistical meta features # Only use third and fourth statistical moment because it is common to @@ -502,19 +517,21 @@ class Kurtosisses(HelperFunction): def _calculate(self, X, y, logger, categorical): kurts = [] for i in range(X.shape[1]): - if not categorical[X.columns[i] if hasattr(X, 'columns') else i]: - kurts.append(scipy.stats.kurtosis( - X.iloc[:, i] if hasattr(X, 'iloc') else X[:, i] - )) + if not categorical[X.columns[i] if hasattr(X, "columns") else i]: + kurts.append( + scipy.stats.kurtosis( + X.iloc[:, i] if hasattr(X, "iloc") else X[:, i] + ) + ) return kurts def _calculate_sparse(self, X, y, logger, categorical): kurts = [] X_new = X.tocsc() for i in range(X_new.shape[1]): - if not categorical[X.columns[i] if hasattr(X, 'columns') else i]: + if not categorical[X.columns[i] if hasattr(X, "columns") else i]: start = X_new.indptr[i] - stop = X_new.indptr[i+1] + stop = X_new.indptr[i + 1] kurts.append(scipy.stats.kurtosis(X_new.data[start:stop])) return kurts @@ -556,17 +573,17 @@ class Skewnesses(HelperFunction): def _calculate(self, X, y, logger, categorical): skews = [] for i in range(X.shape[1]): - if not categorical[X.columns[i] if hasattr(X, 'columns') else i]: - skews.append(scipy.stats.skew( - X.iloc[:, i] if hasattr(X, 'iloc') else X[:, i] - )) + if not categorical[X.columns[i] if hasattr(X, "columns") else i]: + skews.append( + scipy.stats.skew(X.iloc[:, i] if hasattr(X, "iloc") else X[:, i]) + ) return skews def _calculate_sparse(self, X, y, logger, categorical): skews = [] X_new = X.tocsc() for i in range(X_new.shape[1]): - if not categorical[X.columns[i] if hasattr(X, 'columns') else i]: + if not categorical[X.columns[i] if hasattr(X, "columns") else i]: start = X_new.indptr[i] stop = X_new.indptr[i + 1] skews.append(scipy.stats.skew(X_new.data[start:stop])) @@ -628,8 +645,11 @@ def _calculate(self, X, y, logger, categorical): occurence_dict = defaultdict(float) for value in y if labels == 1 else y[:, i]: occurence_dict[value] += 1 - entropies.append(scipy.stats.entropy([occurence_dict[key] for key in - occurence_dict], base=2)) + entropies.append( + scipy.stats.entropy( + [occurence_dict[key] for key in occurence_dict], base=2 + ) + ) return np.mean(entropies) @@ -669,34 +689,35 @@ def _calculate(self, X, y, logger, categorical): class LandmarkLDA(MetaFeature): def _calculate(self, X, y, logger, categorical): import sklearn.discriminant_analysis - if type(y) in ('binary', 'multiclass'): + + if type(y) in ("binary", "multiclass"): kf = sklearn.model_selection.StratifiedKFold(n_splits=5) else: kf = sklearn.model_selection.KFold(n_splits=5) - accuracy = 0. + accuracy = 0.0 try: for train, test in kf.split(X, y): lda = sklearn.discriminant_analysis.LinearDiscriminantAnalysis() if len(y.shape) == 1 or y.shape[1] == 1: lda.fit( - X.iloc[train] if hasattr(X, 'iloc') else X[train], - y.iloc[train] if hasattr(y, 'iloc') else y[train], + X.iloc[train] if hasattr(X, "iloc") else X[train], + y.iloc[train] if hasattr(y, "iloc") else y[train], ) else: lda = OneVsRestClassifier(lda) lda.fit( - X.iloc[train] if hasattr(X, 'iloc') else X[train], - y.iloc[train] if hasattr(y, 'iloc') else y[train], + X.iloc[train] if hasattr(X, "iloc") else X[train], + y.iloc[train] if hasattr(y, "iloc") else y[train], ) predictions = lda.predict( - X.iloc[test] if hasattr(X, 'iloc') else X[test], + X.iloc[test] if hasattr(X, "iloc") else X[test], ) accuracy += sklearn.metrics.accuracy_score( predictions, - y.iloc[test] if hasattr(y, 'iloc') else y[test], + y.iloc[test] if hasattr(y, "iloc") else y[test], ) return accuracy / 5 except scipy.linalg.LinAlgError as e: @@ -716,33 +737,33 @@ class LandmarkNaiveBayes(MetaFeature): def _calculate(self, X, y, logger, categorical): import sklearn.naive_bayes - if type(y) in ('binary', 'multiclass'): + if type(y) in ("binary", "multiclass"): kf = sklearn.model_selection.StratifiedKFold(n_splits=5) else: kf = sklearn.model_selection.KFold(n_splits=5) - accuracy = 0. + accuracy = 0.0 for train, test in kf.split(X, y): nb = sklearn.naive_bayes.GaussianNB() if len(y.shape) == 1 or y.shape[1] == 1: nb.fit( - X.iloc[train] if hasattr(X, 'iloc') else X[train], - y.iloc[train] if hasattr(y, 'iloc') else y[train], + X.iloc[train] if hasattr(X, "iloc") else X[train], + y.iloc[train] if hasattr(y, "iloc") else y[train], ) else: nb = OneVsRestClassifier(nb) nb.fit( - X.iloc[train] if hasattr(X, 'iloc') else X[train], - y.iloc[train] if hasattr(y, 'iloc') else y[train], + X.iloc[train] if hasattr(X, "iloc") else X[train], + y.iloc[train] if hasattr(y, "iloc") else y[train], ) predictions = nb.predict( - X.iloc[test] if hasattr(X, 'iloc') else X[test], + X.iloc[test] if hasattr(X, "iloc") else X[test], ) accuracy += sklearn.metrics.accuracy_score( predictions, - y.iloc[test] if hasattr(y, 'iloc') else y[test], + y.iloc[test] if hasattr(y, "iloc") else y[test], ) return accuracy / 5 @@ -756,34 +777,34 @@ class LandmarkDecisionTree(MetaFeature): def _calculate(self, X, y, logger, categorical): import sklearn.tree - if type(y) in ('binary', 'multiclass'): + if type(y) in ("binary", "multiclass"): kf = sklearn.model_selection.StratifiedKFold(n_splits=5) else: kf = sklearn.model_selection.KFold(n_splits=5) - accuracy = 0. + accuracy = 0.0 for train, test in kf.split(X, y): random_state = sklearn.utils.check_random_state(42) tree = sklearn.tree.DecisionTreeClassifier(random_state=random_state) if len(y.shape) == 1 or y.shape[1] == 1: tree.fit( - X.iloc[train] if hasattr(X, 'iloc') else X[train], - y.iloc[train] if hasattr(y, 'iloc') else y[train], + X.iloc[train] if hasattr(X, "iloc") else X[train], + y.iloc[train] if hasattr(y, "iloc") else y[train], ) else: tree = OneVsRestClassifier(tree) tree.fit( - X.iloc[train] if hasattr(X, 'iloc') else X[train], - y.iloc[train] if hasattr(y, 'iloc') else y[train], + X.iloc[train] if hasattr(X, "iloc") else X[train], + y.iloc[train] if hasattr(y, "iloc") else y[train], ) predictions = tree.predict( - X.iloc[test] if hasattr(X, 'iloc') else X[test], + X.iloc[test] if hasattr(X, "iloc") else X[test], ) accuracy += sklearn.metrics.accuracy_score( predictions, - y.iloc[test] if hasattr(y, 'iloc') else y[test], + y.iloc[test] if hasattr(y, "iloc") else y[test], ) return accuracy / 5 @@ -803,34 +824,39 @@ class LandmarkDecisionNodeLearner(MetaFeature): def _calculate(self, X, y, logger, categorical): import sklearn.tree - if type(y) in ('binary', 'multiclass'): + if type(y) in ("binary", "multiclass"): kf = sklearn.model_selection.StratifiedKFold(n_splits=5) else: kf = sklearn.model_selection.KFold(n_splits=5) - accuracy = 0. + accuracy = 0.0 for train, test in kf.split(X, y): random_state = sklearn.utils.check_random_state(42) node = sklearn.tree.DecisionTreeClassifier( - criterion="entropy", max_depth=1, random_state=random_state, - min_samples_split=2, min_samples_leaf=1, max_features=None) + criterion="entropy", + max_depth=1, + random_state=random_state, + min_samples_split=2, + min_samples_leaf=1, + max_features=None, + ) if len(y.shape) == 1 or y.shape[1] == 1: node.fit( - X.iloc[train] if hasattr(X, 'iloc') else X[train], - y.iloc[train] if hasattr(y, 'iloc') else y[train], + X.iloc[train] if hasattr(X, "iloc") else X[train], + y.iloc[train] if hasattr(y, "iloc") else y[train], ) else: node = OneVsRestClassifier(node) node.fit( - X.iloc[train] if hasattr(X, 'iloc') else X[train], - y.iloc[train] if hasattr(y, 'iloc') else y[train], + X.iloc[train] if hasattr(X, "iloc") else X[train], + y.iloc[train] if hasattr(y, "iloc") else y[train], ) predictions = node.predict( - X.iloc[test] if hasattr(X, 'iloc') else X[test], + X.iloc[test] if hasattr(X, "iloc") else X[test], ) accuracy += sklearn.metrics.accuracy_score( predictions, - y.iloc[test] if hasattr(y, 'iloc') else y[test], + y.iloc[test] if hasattr(y, "iloc") else y[test], ) return accuracy / 5 @@ -843,27 +869,32 @@ class LandmarkRandomNodeLearner(MetaFeature): def _calculate(self, X, y, logger, categorical): import sklearn.tree - if type(y) in ('binary', 'multiclass'): + if type(y) in ("binary", "multiclass"): kf = sklearn.model_selection.StratifiedKFold(n_splits=5) else: kf = sklearn.model_selection.KFold(n_splits=5) - accuracy = 0. + accuracy = 0.0 for train, test in kf.split(X, y): random_state = sklearn.utils.check_random_state(42) node = sklearn.tree.DecisionTreeClassifier( - criterion="entropy", max_depth=1, random_state=random_state, - min_samples_split=2, min_samples_leaf=1, max_features=1) + criterion="entropy", + max_depth=1, + random_state=random_state, + min_samples_split=2, + min_samples_leaf=1, + max_features=1, + ) node.fit( - X.iloc[train] if hasattr(X, 'iloc') else X[train], - y.iloc[train] if hasattr(y, 'iloc') else y[train], + X.iloc[train] if hasattr(X, "iloc") else X[train], + y.iloc[train] if hasattr(y, "iloc") else y[train], ) predictions = node.predict( - X.iloc[test] if hasattr(X, 'iloc') else X[test], + X.iloc[test] if hasattr(X, "iloc") else X[test], ) accuracy += sklearn.metrics.accuracy_score( predictions, - y.iloc[test] if hasattr(y, 'iloc') else y[test], + y.iloc[test] if hasattr(y, "iloc") else y[test], ) return accuracy / 5 @@ -903,31 +934,31 @@ class Landmark1NN(MetaFeature): def _calculate(self, X, y, logger, categorical): import sklearn.neighbors - if type(y) in ('binary', 'multiclass'): + if type(y) in ("binary", "multiclass"): kf = sklearn.model_selection.StratifiedKFold(n_splits=5) else: kf = sklearn.model_selection.KFold(n_splits=5) - accuracy = 0. + accuracy = 0.0 for train, test in kf.split(X, y): kNN = sklearn.neighbors.KNeighborsClassifier(n_neighbors=1) if len(y.shape) == 1 or y.shape[1] == 1: kNN.fit( - X.iloc[train] if hasattr(X, 'iloc') else X[train], - y.iloc[train] if hasattr(y, 'iloc') else y[train], + X.iloc[train] if hasattr(X, "iloc") else X[train], + y.iloc[train] if hasattr(y, "iloc") else y[train], ) else: kNN = OneVsRestClassifier(kNN) kNN.fit( - X.iloc[train] if hasattr(X, 'iloc') else X[train], - y.iloc[train] if hasattr(y, 'iloc') else y[train], + X.iloc[train] if hasattr(X, "iloc") else X[train], + y.iloc[train] if hasattr(y, "iloc") else y[train], ) predictions = kNN.predict( - X.iloc[test] if hasattr(X, 'iloc') else X[test], + X.iloc[test] if hasattr(X, "iloc") else X[test], ) accuracy += sklearn.metrics.accuracy_score( predictions, - y.iloc[test] if hasattr(y, 'iloc') else y[test], + y.iloc[test] if hasattr(y, "iloc") else y[test], ) return accuracy / 5 @@ -945,6 +976,7 @@ def _calculate(self, X, y, logger, categorical): class PCA(HelperFunction): def _calculate(self, X, y, logger, categorical): import sklearn.decomposition + pca = sklearn.decomposition.PCA(copy=True) rs = np.random.RandomState(42) indices = np.arange(X.shape[0]) @@ -952,7 +984,7 @@ def _calculate(self, X, y, logger, categorical): try: rs.shuffle(indices) pca.fit( - X.iloc[indices] if hasattr(X, 'iloc') else X[indices], + X.iloc[indices] if hasattr(X, "iloc") else X[indices], ) return pca except LinAlgError: @@ -962,6 +994,7 @@ def _calculate(self, X, y, logger, categorical): def _calculate_sparse(self, X, y, logger, categorical): import sklearn.decomposition + rs = np.random.RandomState(42) indices = np.arange(X.shape[0]) # This is expensive, but necessary with scikit-learn 0.15 @@ -970,8 +1003,8 @@ def _calculate_sparse(self, X, y, logger, categorical): try: rs.shuffle(indices) truncated_svd = sklearn.decomposition.TruncatedSVD( - n_components=X.shape[1]-1, random_state=i, - algorithm="randomized") + n_components=X.shape[1] - 1, random_state=i, algorithm="randomized" + ) truncated_svd.fit(Xt[indices]) return truncated_svd except LinAlgError: @@ -987,12 +1020,12 @@ def _calculate(self, X, y, logger, categorical): pca_ = helper_functions.get_value("PCA") if pca_ is None: return np.NaN - sum_ = 0. + sum_ = 0.0 idx = 0 while sum_ < 0.95 and idx < len(pca_.explained_variance_ratio_): sum_ += pca_.explained_variance_ratio_[idx] idx += 1 - return float(idx)/float(X.shape[1]) + return float(idx) / float(X.shape[1]) # Kurtosis of first PC @@ -1027,8 +1060,9 @@ def _calculate(self, X, y, logger, categorical): return skewness[0] -def calculate_all_metafeatures_encoded_labels(X, y, categorical, dataset_name, logger, - calculate=None, dont_calculate=None): +def calculate_all_metafeatures_encoded_labels( + X, y, categorical, dataset_name, logger, calculate=None, dont_calculate=None +): """ Calculate only metafeatures for which a 1HotEncoded feature matrix is necessery. """ @@ -1036,25 +1070,46 @@ def calculate_all_metafeatures_encoded_labels(X, y, categorical, dataset_name, l calculate = set() calculate.update(npy_metafeatures) - return calculate_all_metafeatures(X, y, categorical, dataset_name, - calculate=calculate, - dont_calculate=dont_calculate, logger=logger) + return calculate_all_metafeatures( + X, + y, + categorical, + dataset_name, + calculate=calculate, + dont_calculate=dont_calculate, + logger=logger, + ) -def calculate_all_metafeatures_with_labels(X, y, categorical, dataset_name, logger, - calculate=None, dont_calculate=None): +def calculate_all_metafeatures_with_labels( + X, y, categorical, dataset_name, logger, calculate=None, dont_calculate=None +): if dont_calculate is None: dont_calculate = set() else: dont_calculate = copy.deepcopy(dont_calculate) dont_calculate.update(npy_metafeatures) - return calculate_all_metafeatures(X, y, categorical, dataset_name, - calculate=calculate, - dont_calculate=dont_calculate, logger=logger) - - -def calculate_all_metafeatures(X, y, categorical, dataset_name, logger, - calculate=None, dont_calculate=None, densify_threshold=1000): + return calculate_all_metafeatures( + X, + y, + categorical, + dataset_name, + calculate=calculate, + dont_calculate=dont_calculate, + logger=logger, + ) + + +def calculate_all_metafeatures( + X, + y, + categorical, + dataset_name, + logger, + calculate=None, + dont_calculate=None, + densify_threshold=1000, +): """Calculate all metafeatures.""" helper_functions.clear() @@ -1083,8 +1138,10 @@ def calculate_all_metafeatures(X, y, categorical, dataset_name, logger, # sparse matrices because of wrong sparse format) sparse = scipy.sparse.issparse(X) - feat_type = {key: 'categorical' if value else 'numerical' - for key, value in categorical.items()} + feat_type = { + key: "categorical" if value else "numerical" + for key, value in categorical.items() + } # TODO make this more cohesive to the overall structure (quick bug fix) if isinstance(X, pd.DataFrame): @@ -1095,9 +1152,12 @@ def calculate_all_metafeatures(X, y, categorical, dataset_name, logger, # The difference between feat_type and categorical, is that # categorical has True/False instead of categorical/numerical feat_type=feat_type, - force_sparse_output=True) + force_sparse_output=True, + ) X_transformed = DPP.fit_transform(X) - categorical_transformed = {i: False for i in range(X_transformed.shape[1])} + categorical_transformed = { + i: False for i in range(X_transformed.shape[1]) + } # Densify the transformed matrix if not sparse and scipy.sparse.issparse(X_transformed): @@ -1111,9 +1171,9 @@ def calculate_all_metafeatures(X, y, categorical, dataset_name, logger, # sorted in a strange way, but also prevents lda from failing in # some cases. # Because this is advanced indexing, a copy of the data is returned!!! - X_transformed = check_array(X_transformed, - force_all_finite=True, - accept_sparse='csr') + X_transformed = check_array( + X_transformed, force_all_finite=True, accept_sparse="csr" + ) indices = np.arange(X_transformed.shape[0]) rs = np.random.RandomState(42) @@ -1143,17 +1203,15 @@ def calculate_all_metafeatures(X, y, categorical, dataset_name, logger, elif is_metafeature and not metafeatures.is_calculated(dependency): to_visit.appendleft(name) continue - elif is_helper_function and not helper_functions.is_calculated( - dependency): - logger.info("%s: Going to calculate: %s", dataset_name, - dependency) + elif is_helper_function and not helper_functions.is_calculated(dependency): + logger.info("%s: Going to calculate: %s", dataset_name, dependency) value = helper_functions[dependency]( - X_, y_, categorical=categorical_, logger=logger) + X_, y_, categorical=categorical_, logger=logger + ) helper_functions.set_value(dependency, value) mf_[dependency] = value - logger.info("%s: Going to calculate: %s", dataset_name, - name) + logger.info("%s: Going to calculate: %s", dataset_name, name) value = metafeatures[name](X_, y_, logger, categorical_) metafeatures.set_value(name, value) @@ -1164,40 +1222,48 @@ def calculate_all_metafeatures(X, y, categorical, dataset_name, logger, return mf_ -npy_metafeatures = set(["LandmarkLDA", - "LandmarkNaiveBayes", - "LandmarkDecisionTree", - "LandmarkDecisionNodeLearner", - "LandmarkRandomNodeLearner", - "LandmarkWorstNodeLearner", - "Landmark1NN", - "PCAFractionOfComponentsFor95PercentVariance", - "PCAKurtosisFirstPC", - "PCASkewnessFirstPC", - "Skewnesses", - "SkewnessMin", - "SkewnessMax", - "SkewnessMean", - "SkewnessSTD", - "Kurtosisses", - "KurtosisMin", - "KurtosisMax", - "KurtosisMean", - "KurtosisSTD"]) +npy_metafeatures = set( + [ + "LandmarkLDA", + "LandmarkNaiveBayes", + "LandmarkDecisionTree", + "LandmarkDecisionNodeLearner", + "LandmarkRandomNodeLearner", + "LandmarkWorstNodeLearner", + "Landmark1NN", + "PCAFractionOfComponentsFor95PercentVariance", + "PCAKurtosisFirstPC", + "PCASkewnessFirstPC", + "Skewnesses", + "SkewnessMin", + "SkewnessMax", + "SkewnessMean", + "SkewnessSTD", + "Kurtosisses", + "KurtosisMin", + "KurtosisMax", + "KurtosisMean", + "KurtosisSTD", + ] +) subsets = dict() # All implemented metafeatures subsets["all"] = set(metafeatures.functions.keys()) # Metafeatures used by Pfahringer et al. (2000) in the first experiment -subsets["pfahringer_2000_experiment1"] = set(["number_of_features", - "number_of_numeric_features", - "number_of_categorical_features", - "number_of_classes", - "class_probability_max", - "landmark_lda", - "landmark_naive_bayes", - "landmark_decision_tree"]) +subsets["pfahringer_2000_experiment1"] = set( + [ + "number_of_features", + "number_of_numeric_features", + "number_of_categorical_features", + "number_of_classes", + "class_probability_max", + "landmark_lda", + "landmark_naive_bayes", + "landmark_decision_tree", + ] +) # Metafeatures used by Pfahringer et al. (2000) in the second experiment # worst node learner not implemented yet @@ -1209,19 +1275,27 @@ def calculate_all_metafeatures(X, y, categorical, dataset_name, logger, """ # Metafeatures used by Yogatama and Mann (2014) -subsets["yogotama_2014"] = set(["log_number_of_features", - "log_number_of_instances", - "number_of_classes"]) +subsets["yogotama_2014"] = set( + ["log_number_of_features", "log_number_of_instances", "number_of_classes"] +) # Metafeatures used by Bardenet et al. (2013) for the AdaBoost.MH experiment -subsets["bardenet_2013_boost"] = set(["number_of_classes", - "log_number_of_features", - "log_inverse_dataset_ratio", - "pca_95percent"]) +subsets["bardenet_2013_boost"] = set( + [ + "number_of_classes", + "log_number_of_features", + "log_inverse_dataset_ratio", + "pca_95percent", + ] +) # Metafeatures used by Bardenet et al. (2013) for the Neural Net experiment -subsets["bardenet_2013_nn"] = set(["number_of_classes", - "log_number_of_features", - "log_inverse_dataset_ratio", - "pca_kurtosis_first_pc", - "pca_skewness_first_pc"]) +subsets["bardenet_2013_nn"] = set( + [ + "number_of_classes", + "log_number_of_features", + "log_inverse_dataset_ratio", + "pca_kurtosis_first_pc", + "pca_skewness_first_pc", + ] +) diff --git a/autosklearn/metalearning/metalearning/__init__.py b/autosklearn/metalearning/metalearning/__init__.py index 8f0ce6cb7c..92bf78f389 100644 --- a/autosklearn/metalearning/metalearning/__init__.py +++ b/autosklearn/metalearning/metalearning/__init__.py @@ -1 +1 @@ -__author__ = 'feurerm' +__author__ = "feurerm" diff --git a/autosklearn/metalearning/metalearning/clustering/gmeans.py b/autosklearn/metalearning/metalearning/clustering/gmeans.py index 573537e446..5c5233f284 100644 --- a/autosklearn/metalearning/metalearning/clustering/gmeans.py +++ b/autosklearn/metalearning/metalearning/clustering/gmeans.py @@ -9,8 +9,14 @@ class GMeans(object): - def __init__(self, minimum_samples_per_cluster=2, n_init=10, significance=4, - restarts=10, random_state=None, ): + def __init__( + self, + minimum_samples_per_cluster=2, + n_init=10, + significance=4, + restarts=10, + random_state=None, + ): self.minimum_samples_per_cluster = minimum_samples_per_cluster self.n_init = n_init self.significance = significance @@ -21,8 +27,9 @@ def fit(self, X): self.inertia_ = np.inf for i in range(self.restarts): - KMeans = sklearn.cluster.KMeans(n_clusters=1, n_init=1, - random_state=self.random_state) + KMeans = sklearn.cluster.KMeans( + n_clusters=1, n_init=1, random_state=self.random_state + ) KMeans.fit(X) while True: @@ -34,14 +41,16 @@ def fit(self, X): indices = KMeans.labels_ == i X_ = X[indices] - if np.sum(indices) < self.minimum_samples_per_cluster*2: + if np.sum(indices) < self.minimum_samples_per_cluster * 2: cluster_centers.append(cluster_center) continue for i in range(10): - KMeans_ = sklearn.cluster.KMeans(n_clusters=2, - n_init=self.n_init, - random_state=self.random_state) + KMeans_ = sklearn.cluster.KMeans( + n_clusters=2, + n_init=self.n_init, + random_state=self.random_state, + ) predictions = KMeans_.fit_predict(X_) bins = np.bincount(predictions) minimum = np.min(bins) @@ -73,9 +82,12 @@ def fit(self, X): break # Refinement - KMeans = sklearn.cluster.KMeans(n_clusters=len(cluster_centers), n_init=1, - init=np.array(cluster_centers), - random_state=self.random_state) + KMeans = sklearn.cluster.KMeans( + n_clusters=len(cluster_centers), + n_init=1, + init=np.array(cluster_centers), + random_state=self.random_state, + ) KMeans.fit(X) if KMeans.inertia_ < self.inertia_: diff --git a/autosklearn/metalearning/metalearning/create_datasets.py b/autosklearn/metalearning/metalearning/create_datasets.py index a65b7840ed..1a60bebb7a 100644 --- a/autosklearn/metalearning/metalearning/create_datasets.py +++ b/autosklearn/metalearning/metalearning/create_datasets.py @@ -1,5 +1,6 @@ import itertools import logging + import numpy as np import pandas as pd import scipy.stats @@ -13,11 +14,13 @@ def create_regression_dataset(metafeatures, experiments): experiment = experiments[dataset_name] mf = metafeatures.loc[dataset_name] for i, run in enumerate(experiment): - x1 = pd.Series(data=[run.params[param] for param in run.params], - index=run.params.keys()) + x1 = pd.Series( + data=[run.params[param] for param in run.params], + index=run.params.keys(), + ) x2 = mf X.append(x1.append(x2)) - X_indices.append('%s_%d' % (dataset_name, i)) + X_indices.append("%s_%d" % (dataset_name, i)) Y.append(run.result) X = pd.DataFrame(X, index=X_indices) Y = pd.DataFrame(Y, index=X_indices) @@ -67,14 +70,19 @@ def create_predict_spearman_rank(metafeatures, experiments, iterator): responses_1 = np.zeros((len(experiments_1)), dtype=np.float64) responses_2 = np.zeros((len(experiments_1)), dtype=np.float64) - for idx, zipped in enumerate(zip( + for idx, zipped in enumerate( + zip( sorted(experiments_1, key=lambda t: str(t.configuration)), - sorted(experiments_2, key=lambda t: str(t.configuration)))): + sorted(experiments_2, key=lambda t: str(t.configuration)), + ) + ): # Test if the order of the params is the same exp_1, exp_2 = zipped print(exp_1.configuration, exp_2.configuration) - assert exp_1.configuration == exp_2.configuration,\ - (experiments_1, experiments_2) + assert exp_1.configuration == exp_2.configuration, ( + experiments_1, + experiments_2, + ) responses_1[idx] = exp_1.result if np.isfinite(exp_1.result) else 1 responses_2[idx] = exp_2.result if np.isfinite(exp_2.result) else 1 @@ -91,9 +99,11 @@ def create_predict_spearman_rank(metafeatures, experiments, iterator): logging.info("Metafeatures %s", metafeatures.shape) logging.info("X.shape %s", X.shape) logging.info("Y.shape %s", Y.shape) - assert X.shape == (len(cross_product), metafeatures.shape[1] * 2), \ - (X.shape, (len(cross), metafeatures.shape[1] * 2)) - assert Y.shape == (len(cross_product), ) + assert X.shape == (len(cross_product), metafeatures.shape[1] * 2), ( + X.shape, + (len(cross), metafeatures.shape[1] * 2), + ) + assert Y.shape == (len(cross_product),) # train sklearn regressor (tree) with 10fold CV indices = range(len(X)) np_rs = np.random.RandomState(42) @@ -103,8 +113,7 @@ def create_predict_spearman_rank(metafeatures, experiments, iterator): return X, Y -def create_predict_spearman_rank_with_cv(cv_metafeatures, cv_experiments, - iterator): +def create_predict_spearman_rank_with_cv(cv_metafeatures, cv_experiments, iterator): X = [] Y = [] Y_names = [] @@ -128,13 +137,18 @@ def create_predict_spearman_rank_with_cv(cv_metafeatures, cv_experiments, logging.info("Create spearman rank dataset with CV data %s", iterator) logging.info("Using %d datasets", len(dataset_names)) - logging.info("This will results in %d training points", len(cross_product) * len(folds_product)) + logging.info( + "This will results in %d training points", + len(cross_product) * len(folds_product), + ) logging.info("Length of dataset crossproduct %s", len(cross_product)) logging.info("Length of folds crossproduct %s", len(folds_product)) # Create inputs and targets for i, cross in enumerate(cross_product): - print("%d/%d: %s" % (i, len(cross_product), cross),) + print( + "%d/%d: %s" % (i, len(cross_product), cross), + ) for folds in folds_product: name = "%s-%d_%s-%d" % (cross[0], folds[0], cross[1], folds[1]) mf_1 = cv_metafeatures[cross[0]][folds[0]] @@ -266,7 +280,7 @@ def create_smac_files_file(cv_metafeatures, cv_experiments, dataset, train_instances_file.seek(0) for line in train_instances_file: fh.write(line) -""" +""" # noqa: E501 if __name__ == "__main__": diff --git a/autosklearn/metalearning/metalearning/kNearestDatasets/__init__.py b/autosklearn/metalearning/metalearning/kNearestDatasets/__init__.py index 8f0ce6cb7c..92bf78f389 100644 --- a/autosklearn/metalearning/metalearning/kNearestDatasets/__init__.py +++ b/autosklearn/metalearning/metalearning/kNearestDatasets/__init__.py @@ -1 +1 @@ -__author__ = 'feurerm' +__author__ = "feurerm" diff --git a/autosklearn/metalearning/metalearning/kNearestDatasets/kND.py b/autosklearn/metalearning/metalearning/kNearestDatasets/kND.py index 336d3b6bb3..f6c10c95d2 100644 --- a/autosklearn/metalearning/metalearning/kNearestDatasets/kND.py +++ b/autosklearn/metalearning/metalearning/kNearestDatasets/kND.py @@ -1,13 +1,12 @@ import numpy as np import pandas as pd - +import sklearn.utils from sklearn.neighbors import NearestNeighbors from sklearn.preprocessing import MinMaxScaler -import sklearn.utils class KNearestDatasets(object): - def __init__(self, logger, metric='l1', random_state=None, metric_params=None): + def __init__(self, logger, metric="l1", random_state=None, metric_params=None): self.logger = logger self.metric = metric @@ -37,8 +36,10 @@ def fit(self, metafeatures, runs): assert metafeatures.values.dtype in (np.float32, np.float64) assert np.isfinite(metafeatures.values).all() assert isinstance(runs, pd.DataFrame) - assert runs.shape[1] == metafeatures.shape[0], \ - (runs.shape[1], metafeatures.shape[0]) + assert runs.shape[1] == metafeatures.shape[0], ( + runs.shape[1], + metafeatures.shape[0], + ) self.metafeatures = metafeatures self.runs = runs @@ -54,7 +55,8 @@ def fit(self, metafeatures, runs): best_configuration_per_dataset[dataset_name] = None else: configuration_idx = runs[dataset_name].index[ - np.nanargmin(runs[dataset_name].values)] + np.nanargmin(runs[dataset_name].values) + ] best_configuration_per_dataset[dataset_name] = configuration_idx self.best_configuration_per_dataset = best_configuration_per_dataset @@ -72,9 +74,14 @@ def fit(self, metafeatures, runs): raise ValueError(self.metric) self._nearest_neighbors = NearestNeighbors( - n_neighbors=self.num_datasets, radius=None, algorithm="brute", - leaf_size=30, metric=self._metric, p=self._p, - metric_params=self.metric_params) + n_neighbors=self.num_datasets, + radius=None, + algorithm="brute", + leaf_size=30, + metric=self._metric, + p=self._p, + metric_params=self.metric_params, + ) def kNearestDatasets(self, x, k=1, return_distance=False): """Return the k most similar datasets with respect to self.metric @@ -101,7 +108,7 @@ def kNearestDatasets(self, x, k=1, return_distance=False): """ assert type(x) == pd.Series if k < -1 or k == 0: - raise ValueError('Number of neighbors k cannot be zero or negative.') + raise ValueError("Number of neighbors k cannot be zero or negative.") elif k == -1: k = self.num_datasets @@ -110,14 +117,17 @@ def kNearestDatasets(self, x, k=1, return_distance=False): x = self.scaler.transform(x) self._nearest_neighbors.fit(X_train) distances, neighbor_indices = self._nearest_neighbors.kneighbors( - x, n_neighbors=k, return_distance=True) + x, n_neighbors=k, return_distance=True + ) assert k == neighbor_indices.shape[1] - rval = [self.metafeatures.index[i] - # Neighbor indices is 2d, each row is the indices for one - # dataset in x. - for i in neighbor_indices[0]] + rval = [ + self.metafeatures.index[i] + # Neighbor indices is 2d, each row is the indices for one + # dataset in x. + for i in neighbor_indices[0] + ] if return_distance is False: return rval @@ -127,19 +137,19 @@ def kNearestDatasets(self, x, k=1, return_distance=False): def kBestSuggestions(self, x, k=1, exclude_double_configurations=True): assert type(x) == pd.Series if k < -1 or k == 0: - raise ValueError('Number of neighbors k cannot be zero or negative.') - nearest_datasets, distances = self.kNearestDatasets(x, -1, - return_distance=True) + raise ValueError("Number of neighbors k cannot be zero or negative.") + nearest_datasets, distances = self.kNearestDatasets(x, -1, return_distance=True) kbest = [] added_configurations = set() for dataset_name, distance in zip(nearest_datasets, distances): - best_configuration = self.best_configuration_per_dataset[ - dataset_name] + best_configuration = self.best_configuration_per_dataset[dataset_name] if best_configuration is None: - self.logger.info("Found no best configuration for instance %s" % dataset_name) + self.logger.info( + "Found no best configuration for instance %s" % dataset_name + ) continue if exclude_double_configurations: diff --git a/autosklearn/metalearning/metalearning/meta_base.py b/autosklearn/metalearning/metalearning/meta_base.py index 13653de528..f193a61fef 100644 --- a/autosklearn/metalearning/metalearning/meta_base.py +++ b/autosklearn/metalearning/metalearning/meta_base.py @@ -2,10 +2,10 @@ import numpy as np import pandas as pd +from ConfigSpace.configuration_space import Configuration from ..input import aslib_simple from ..metafeatures.metafeature import DatasetMetafeatures -from ConfigSpace.configuration_space import Configuration class Run(object): @@ -15,8 +15,11 @@ def __init__(self, configuration, result, runtime): self.runtime = runtime def __repr__(self): - return "Run:\nresult: %3.3f\nruntime: %3.3f\n%s" % \ - (self.result, self.runtime, str(self.configuration)) + return "Run:\nresult: %3.3f\nruntime: %3.3f\n%s" % ( + self.result, + self.runtime, + str(self.configuration), + ) class Instance(object): @@ -41,15 +44,18 @@ def __init__(self, configuration_space, aslib_directory, logger): aslib_reader = aslib_simple.AlgorithmSelectionProblem(self.aslib_directory) self.metafeatures = aslib_reader.metafeatures - self.algorithm_runs: OrderedDict[str, pd.DataFrame] = aslib_reader.algorithm_runs + self.algorithm_runs: OrderedDict[ + str, pd.DataFrame + ] = aslib_reader.algorithm_runs self.configurations = aslib_reader.configurations configurations = dict() for algorithm_id in self.configurations: configuration = self.configurations[algorithm_id] try: - configurations[str(algorithm_id)] = \ - (Configuration(configuration_space, values=configuration)) + configurations[str(algorithm_id)] = Configuration( + configuration_space, values=configuration + ) except (ValueError, KeyError) as e: self.logger.debug("Error reading configurations: %s", e) @@ -58,11 +64,13 @@ def __init__(self, configuration_space, aslib_directory, logger): def add_dataset(self, name, metafeatures): metafeatures.name = name if isinstance(metafeatures, DatasetMetafeatures): - data_ = {mf.name: mf.value for mf in metafeatures.metafeature_values.values()} + data_ = { + mf.name: mf.value for mf in metafeatures.metafeature_values.values() + } metafeatures = pd.Series(name=name, data=data_, dtype=np.float64) if name.lower() in self.metafeatures.index: self.logger.warning( - 'Dataset %s already in meta-data. Removing occurence.', name.lower() + "Dataset %s already in meta-data. Removing occurence.", name.lower() ) self.metafeatures.drop(name.lower(), inplace=True) self.metafeatures = self.metafeatures.append(metafeatures) @@ -97,8 +105,7 @@ def _get_metafeatures(self, features): """This is inside an extra function for testing purpose""" # Load the task - self.logger.info("Going to use the following metafeature subset: %s", - features) + self.logger.info("Going to use the following metafeature subset: %s", features) all_metafeatures = self.metafeatures all_metafeatures = all_metafeatures.loc[:, features] diff --git a/autosklearn/metalearning/mismbo.py b/autosklearn/metalearning/mismbo.py index 8a4f2e2bed..7b2956c489 100644 --- a/autosklearn/metalearning/mismbo.py +++ b/autosklearn/metalearning/mismbo.py @@ -2,15 +2,19 @@ import time -from autosklearn.metalearning.optimizers.metalearn_optimizer.metalearner \ - import MetaLearningOptimizer -from autosklearn.constants \ - import MULTILABEL_CLASSIFICATION, MULTICLASS_CLASSIFICATION, TASK_TYPES_TO_STRING +from autosklearn.constants import ( + MULTICLASS_CLASSIFICATION, + MULTILABEL_CLASSIFICATION, + TASK_TYPES_TO_STRING, +) +from autosklearn.metalearning.optimizers.metalearn_optimizer.metalearner import ( + MetaLearningOptimizer, +) def suggest_via_metalearning( - meta_base, dataset_name, metric, task, sparse, - num_initial_configurations, logger): + meta_base, dataset_name, metric, task, sparse, num_initial_configurations, logger +): if task == MULTILABEL_CLASSIFICATION: task = MULTICLASS_CLASSIFICATION @@ -24,10 +28,10 @@ def suggest_via_metalearning( dataset_name=dataset_name, configuration_space=meta_base.configuration_space, meta_base=meta_base, - distance='l1', + distance="l1", seed=1, - logger=logger,) - logger.info('Reading meta-data took %5.2f seconds', - time.time() - start) + logger=logger, + ) + logger.info("Reading meta-data took %5.2f seconds", time.time() - start) runs = ml.metalearning_suggest_all(exclude_double_configurations=True) return runs[:num_initial_configurations] diff --git a/autosklearn/metalearning/optimizers/__init__.py b/autosklearn/metalearning/optimizers/__init__.py index 8f0ce6cb7c..92bf78f389 100644 --- a/autosklearn/metalearning/optimizers/__init__.py +++ b/autosklearn/metalearning/optimizers/__init__.py @@ -1 +1 @@ -__author__ = 'feurerm' +__author__ = "feurerm" diff --git a/autosklearn/metalearning/optimizers/metalearn_optimizer/metalearner.py b/autosklearn/metalearning/optimizers/metalearn_optimizer/metalearner.py index 987f40b0f7..eff2956b9b 100644 --- a/autosklearn/metalearning/optimizers/metalearn_optimizer/metalearner.py +++ b/autosklearn/metalearning/optimizers/metalearn_optimizer/metalearner.py @@ -1,6 +1,7 @@ import ast -import pandas as pd + import numpy as np +import pandas as pd import sklearn.utils from autosklearn.metalearning.metalearning.kNearestDatasets.kND import KNearestDatasets @@ -11,9 +12,17 @@ def test_function(params): class MetaLearningOptimizer(object): - def __init__(self, dataset_name, configuration_space, - meta_base, logger, distance='l1', seed=None, use_features=None, - distance_kwargs=None): + def __init__( + self, + dataset_name, + configuration_space, + meta_base, + logger, + distance="l1", + seed=None, + use_features=None, + distance_kwargs=None, + ): self.dataset_name = dataset_name self.configuration_space = configuration_space self.meta_base = meta_base @@ -21,7 +30,7 @@ def __init__(self, dataset_name, configuration_space, self.seed = seed self.use_features = use_features self.distance_kwargs = distance_kwargs - self.kND = None # For caching, makes things faster... + self.kND = None # For caching, makes things faster... self.logger = logger @@ -32,7 +41,9 @@ def metalearning_suggest_all(self, exclude_double_configurations=True): hp_list = [] for neighbor in neighbors: try: - configuration = self.meta_base.get_configuration_from_algorithm_index(neighbor[2]) + configuration = self.meta_base.get_configuration_from_algorithm_index( + neighbor[2] + ) self.logger.info("%s %s %s" % (neighbor[0], neighbor[1], configuration)) except (KeyError): self.logger.warning("Configuration %s not found" % neighbor[2]) @@ -42,14 +53,16 @@ def metalearning_suggest_all(self, exclude_double_configurations=True): return hp_list def metalearning_suggest(self, history): - """Suggest the next most promising hyperparameters which were not yet evaluated""" + """Suggest the next promosing hyperparameters which were not yet evaluated""" # TODO test the object in the history! neighbors = self._learn() # Iterate over all datasets which are sorted ascending by distance history_with_indices = [] for run in history: - history_with_indices.append(self.meta_base.get_algorithm_index_from_configuration(run)) + history_with_indices.append( + self.meta_base.get_algorithm_index_from_configuration(run) + ) for idx, neighbor in enumerate(neighbors): already_evaluated = False @@ -62,16 +75,18 @@ def metalearning_suggest(self, history): break if not already_evaluated: - self.logger.info("Nearest dataset with hyperparameters of best value " - "not evaluated yet is %s with a distance of %f" % - (neighbor[0], neighbor[1])) + self.logger.info( + "Nearest dataset with hyperparameters of best value " + "not evaluated yet is %s with a distance of %f" + % (neighbor[0], neighbor[1]) + ) return self.meta_base.get_configuration_from_algorithm_index( - neighbor[2]) + neighbor[2] + ) raise StopIteration("No more values available.") def _learn(self, exclude_double_configurations=True): - dataset_metafeatures, all_other_metafeatures = \ - self._split_metafeature_array() + dataset_metafeatures, all_other_metafeatures = self._split_metafeature_array() # Remove metafeatures which could not be calculated for the target # dataset @@ -85,7 +100,8 @@ def _learn(self, exclude_double_configurations=True): # Do mean imputation of all other metafeatures all_other_metafeatures = all_other_metafeatures.fillna( - all_other_metafeatures.mean()) + all_other_metafeatures.mean() + ) if self.kND is None: # In case that we learn our distance function, get_value the parameters for @@ -98,10 +114,12 @@ def _learn(self, exclude_double_configurations=True): # To keep the distance the same in every iteration, we create a new # random state random_state = sklearn.utils.check_random_state(self.seed) - kND = KNearestDatasets(metric=self.distance, - random_state=random_state, - logger=self.logger, - metric_params=rf_params) + kND = KNearestDatasets( + metric=self.distance, + random_state=random_state, + logger=self.logger, + metric_params=rf_params, + ) runs = dict() # TODO move this code to the metabase @@ -121,13 +139,15 @@ def _learn(self, exclude_double_configurations=True): dataset_metafeatures, k=-1, exclude_double_configurations=exclude_double_configurations, - ) + ) def _split_metafeature_array(self): dataset_metafeatures = self.meta_base.get_metafeatures( - self.dataset_name, self.use_features) + self.dataset_name, self.use_features + ) all_other_datasets = self.meta_base.get_all_dataset_names() all_other_datasets.remove(self.dataset_name) all_other_metafeatures = self.meta_base.get_metafeatures( - all_other_datasets, self.use_features) + all_other_datasets, self.use_features + ) return dataset_metafeatures, all_other_metafeatures diff --git a/autosklearn/metalearning/optimizers/optimizer_base.py b/autosklearn/metalearning/optimizers/optimizer_base.py index dd336fa4a8..e437f1bb64 100644 --- a/autosklearn/metalearning/optimizers/optimizer_base.py +++ b/autosklearn/metalearning/optimizers/optimizer_base.py @@ -1,7 +1,7 @@ +import subprocess from collections import OrderedDict -from itertools import product from io import StringIO -import subprocess +from itertools import product def _parse_categorical(line): @@ -26,7 +26,7 @@ def _parse_categorical(line): first_bracket = line.find("{") second_bracket = line.find("}") - domain_values = line[first_bracket + 1:second_bracket] + domain_values = line[first_bracket + 1 : second_bracket] cat_values = domain_values.split(",") if len(cat_values) < 1: raise ValueError("Expected at least one value in %s" % line) diff --git a/autosklearn/metrics/__init__.py b/autosklearn/metrics/__init__.py index cb6920979f..3234329658 100644 --- a/autosklearn/metrics/__init__.py +++ b/autosklearn/metrics/__init__.py @@ -1,18 +1,22 @@ from abc import ABCMeta, abstractmethod +from typing import Any, Callable, Dict, List, Optional, Union, cast + from functools import partial from itertools import product -from typing import Any, Callable, Dict, List, Optional, Union, cast import numpy as np - import sklearn.metrics from sklearn.utils.multiclass import type_of_target - from smac.utils.constants import MAXINT from autosklearn.constants import ( - BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION, - MULTIOUTPUT_REGRESSION, REGRESSION, REGRESSION_TASKS, TASK_TYPES, + BINARY_CLASSIFICATION, + MULTICLASS_CLASSIFICATION, + MULTILABEL_CLASSIFICATION, + MULTIOUTPUT_REGRESSION, + REGRESSION, + REGRESSION_TASKS, + TASK_TYPES, ) from .util import sanitize_array @@ -26,7 +30,7 @@ def __init__( optimum: float, worst_possible_result: float, sign: float, - kwargs: Any + kwargs: Any, ) -> None: self.name = name self._kwargs = kwargs @@ -40,7 +44,7 @@ def __call__( self, y_true: np.ndarray, y_pred: np.ndarray, - sample_weight: Optional[List[float]] = None + sample_weight: Optional[List[float]] = None, ) -> float: pass @@ -53,7 +57,7 @@ def __call__( self, y_true: np.ndarray, y_pred: np.ndarray, - sample_weight: Optional[List[float]] = None + sample_weight: Optional[List[float]] = None, ) -> float: """Evaluate predicted target values for X relative to y_true. @@ -74,34 +78,37 @@ def __call__( Score function applied to prediction of estimator on X. """ type_true = type_of_target(y_true) - if type_true == 'binary' and type_of_target(y_pred) == 'continuous' and \ - len(y_pred.shape) == 1: + if ( + type_true == "binary" + and type_of_target(y_pred) == "continuous" + and len(y_pred.shape) == 1 + ): # For a pred scorer, no threshold, nor probability is required # If y_true is binary, and y_pred is continuous # it means that a rounding is necessary to obtain the binary class y_pred = np.around(y_pred, decimals=0) - elif len(y_pred.shape) == 1 or y_pred.shape[1] == 1 or \ - type_true == 'continuous': + elif ( + len(y_pred.shape) == 1 or y_pred.shape[1] == 1 or type_true == "continuous" + ): # must be regression, all other task types would return at least # two probabilities pass - elif type_true in ['binary', 'multiclass']: + elif type_true in ["binary", "multiclass"]: y_pred = np.argmax(y_pred, axis=1) - elif type_true == 'multilabel-indicator': + elif type_true == "multilabel-indicator": y_pred[y_pred > 0.5] = 1.0 y_pred[y_pred <= 0.5] = 0.0 - elif type_true == 'continuous-multioutput': + elif type_true == "continuous-multioutput": pass else: raise ValueError(type_true) if sample_weight is not None: - return self._sign * self._score_func(y_true, y_pred, - sample_weight=sample_weight, - **self._kwargs) + return self._sign * self._score_func( + y_true, y_pred, sample_weight=sample_weight, **self._kwargs + ) else: - return self._sign * self._score_func(y_true, y_pred, - **self._kwargs) + return self._sign * self._score_func(y_true, y_pred, **self._kwargs) class _ProbaScorer(Scorer): @@ -109,7 +116,7 @@ def __call__( self, y_true: np.ndarray, y_pred: np.ndarray, - sample_weight: Optional[List[float]] = None + sample_weight: Optional[List[float]] = None, ) -> float: """Evaluate predicted probabilities for X relative to y_true. Parameters @@ -136,21 +143,24 @@ def __call__( if n_labels_pred != n_labels_test: labels = list(range(n_labels_pred)) if sample_weight is not None: - return self._sign * self._score_func(y_true, y_pred, - sample_weight=sample_weight, - labels=labels, - **self._kwargs) + return self._sign * self._score_func( + y_true, + y_pred, + sample_weight=sample_weight, + labels=labels, + **self._kwargs, + ) else: - return self._sign * self._score_func(y_true, y_pred, - labels=labels, **self._kwargs) + return self._sign * self._score_func( + y_true, y_pred, labels=labels, **self._kwargs + ) if sample_weight is not None: - return self._sign * self._score_func(y_true, y_pred, - sample_weight=sample_weight, - **self._kwargs) + return self._sign * self._score_func( + y_true, y_pred, sample_weight=sample_weight, **self._kwargs + ) else: - return self._sign * self._score_func(y_true, y_pred, - **self._kwargs) + return self._sign * self._score_func(y_true, y_pred, **self._kwargs) class _ThresholdScorer(Scorer): @@ -158,7 +168,7 @@ def __call__( self, y_true: np.ndarray, y_pred: np.ndarray, - sample_weight: Optional[List[float]] = None + sample_weight: Optional[List[float]] = None, ) -> float: """Evaluate decision function output for X relative to y_true. Parameters @@ -189,9 +199,9 @@ def __call__( y_pred = np.vstack([p[:, -1] for p in y_pred]).T if sample_weight is not None: - return self._sign * self._score_func(y_true, y_pred, - sample_weight=sample_weight, - **self._kwargs) + return self._sign * self._score_func( + y_true, y_pred, sample_weight=sample_weight, **self._kwargs + ) else: return self._sign * self._score_func(y_true, y_pred, **self._kwargs) @@ -204,7 +214,7 @@ def make_scorer( greater_is_better: bool = True, needs_proba: bool = False, needs_threshold: bool = False, - **kwargs: Any + **kwargs: Any, ) -> Scorer: """Make a scorer from a performance metric or loss function. @@ -244,96 +254,114 @@ def make_scorer( """ sign = 1 if greater_is_better else -1 if needs_proba: - return _ProbaScorer(name, score_func, optimum, worst_possible_result, sign, kwargs) + return _ProbaScorer( + name, score_func, optimum, worst_possible_result, sign, kwargs + ) elif needs_threshold: - return _ThresholdScorer(name, score_func, optimum, worst_possible_result, sign, kwargs) + return _ThresholdScorer( + name, score_func, optimum, worst_possible_result, sign, kwargs + ) else: - return _PredictScorer(name, score_func, optimum, worst_possible_result, sign, kwargs) + return _PredictScorer( + name, score_func, optimum, worst_possible_result, sign, kwargs + ) # Standard regression scores -mean_absolute_error = make_scorer('mean_absolute_error', - sklearn.metrics.mean_absolute_error, - optimum=0, - worst_possible_result=MAXINT, - greater_is_better=False) -mean_squared_error = make_scorer('mean_squared_error', - sklearn.metrics.mean_squared_error, - optimum=0, - worst_possible_result=MAXINT, - greater_is_better=False, - squared=True) -root_mean_squared_error = make_scorer('root_mean_squared_error', - sklearn.metrics.mean_squared_error, - optimum=0, - worst_possible_result=MAXINT, - greater_is_better=False, - squared=False) -mean_squared_log_error = make_scorer('mean_squared_log_error', - sklearn.metrics.mean_squared_log_error, - optimum=0, - worst_possible_result=MAXINT, - greater_is_better=False,) -median_absolute_error = make_scorer('median_absolute_error', - sklearn.metrics.median_absolute_error, - optimum=0, - worst_possible_result=MAXINT, - greater_is_better=False) - -r2 = make_scorer('r2', sklearn.metrics.r2_score) +mean_absolute_error = make_scorer( + "mean_absolute_error", + sklearn.metrics.mean_absolute_error, + optimum=0, + worst_possible_result=MAXINT, + greater_is_better=False, +) +mean_squared_error = make_scorer( + "mean_squared_error", + sklearn.metrics.mean_squared_error, + optimum=0, + worst_possible_result=MAXINT, + greater_is_better=False, + squared=True, +) +root_mean_squared_error = make_scorer( + "root_mean_squared_error", + sklearn.metrics.mean_squared_error, + optimum=0, + worst_possible_result=MAXINT, + greater_is_better=False, + squared=False, +) +mean_squared_log_error = make_scorer( + "mean_squared_log_error", + sklearn.metrics.mean_squared_log_error, + optimum=0, + worst_possible_result=MAXINT, + greater_is_better=False, +) +median_absolute_error = make_scorer( + "median_absolute_error", + sklearn.metrics.median_absolute_error, + optimum=0, + worst_possible_result=MAXINT, + greater_is_better=False, +) + +r2 = make_scorer("r2", sklearn.metrics.r2_score) # Standard Classification Scores -accuracy = make_scorer('accuracy', - sklearn.metrics.accuracy_score) -balanced_accuracy = make_scorer('balanced_accuracy', - sklearn.metrics.balanced_accuracy_score) +accuracy = make_scorer("accuracy", sklearn.metrics.accuracy_score) +balanced_accuracy = make_scorer( + "balanced_accuracy", sklearn.metrics.balanced_accuracy_score +) # Score functions that need decision values -roc_auc = make_scorer('roc_auc', - sklearn.metrics.roc_auc_score, - greater_is_better=True, - needs_threshold=True) -average_precision = make_scorer('average_precision', - sklearn.metrics.average_precision_score, - needs_threshold=True) +roc_auc = make_scorer( + "roc_auc", + sklearn.metrics.roc_auc_score, + greater_is_better=True, + needs_threshold=True, +) +average_precision = make_scorer( + "average_precision", sklearn.metrics.average_precision_score, needs_threshold=True +) # NOTE: zero_division # # Specified as the explicit default, see sklearn docs: # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html#sklearn-metrics-precision-score precision = make_scorer( - 'precision', partial(sklearn.metrics.precision_score, zero_division=0) -) -recall = make_scorer( - 'recall', partial(sklearn.metrics.recall_score, zero_division=0) -) -f1 = make_scorer( - 'f1', partial(sklearn.metrics.f1_score, zero_division=0) + "precision", partial(sklearn.metrics.precision_score, zero_division=0) ) +recall = make_scorer("recall", partial(sklearn.metrics.recall_score, zero_division=0)) +f1 = make_scorer("f1", partial(sklearn.metrics.f1_score, zero_division=0)) # Score function for probabilistic classification -log_loss = make_scorer('log_loss', - sklearn.metrics.log_loss, - optimum=0, - worst_possible_result=MAXINT, - greater_is_better=False, - needs_proba=True) +log_loss = make_scorer( + "log_loss", + sklearn.metrics.log_loss, + optimum=0, + worst_possible_result=MAXINT, + greater_is_better=False, + needs_proba=True, +) # TODO what about mathews correlation coefficient etc? REGRESSION_METRICS = { scorer.name: scorer for scorer in [ - mean_absolute_error, mean_squared_error, root_mean_squared_error, - mean_squared_log_error, median_absolute_error, r2 + mean_absolute_error, + mean_squared_error, + root_mean_squared_error, + mean_squared_log_error, + median_absolute_error, + r2, ] } CLASSIFICATION_METRICS = { scorer.name: scorer - for scorer in [ - accuracy, balanced_accuracy, roc_auc, average_precision, log_loss - ] + for scorer in [accuracy, balanced_accuracy, roc_auc, average_precision, log_loss] } # NOTE: zero_division @@ -342,13 +370,13 @@ def make_scorer( # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html#sklearn-metrics-precision-score for (base_name, sklearn_metric), average in product( [ - ('precision', sklearn.metrics.precision_score), - ('recall', sklearn.metrics.recall_score), - ('f1', sklearn.metrics.f1_score), + ("precision", sklearn.metrics.precision_score), + ("recall", sklearn.metrics.recall_score), + ("f1", sklearn.metrics.f1_score), ], - ['macro', 'micro', 'samples', 'weighted'] + ["macro", "micro", "samples", "weighted"], ): - name = f'{base_name}_{average}' + name = f"{base_name}_{average}" scorer = make_scorer( name, partial(sklearn_metric, pos_label=None, average=average, zero_division=0) ) @@ -361,7 +389,7 @@ def calculate_score( prediction: np.ndarray, task_type: int, metric: Scorer, - scoring_functions: Optional[List[Scorer]] = None + scoring_functions: Optional[List[Scorer]] = None, ) -> Union[float, Dict[str, float]]: """ Returns a score (a magnitude that allows casting the @@ -396,11 +424,15 @@ def calculate_score( try: score_dict[metric_.name] = _compute_scorer( - metric_, prediction, solution, task_type) + metric_, prediction, solution, task_type + ) except ValueError as e: print(e, e.args[0]) - if e.args[0] == "Mean Squared Logarithmic Error cannot be used when " \ - "targets contain negative values.": + if ( + e.args[0] + == "Mean Squared Logarithmic Error cannot be used when " + "targets contain negative values." + ): continue else: raise e @@ -413,16 +445,21 @@ def calculate_score( try: score_dict[metric_.name] = _compute_scorer( - metric_, prediction, solution, task_type) + metric_, prediction, solution, task_type + ) except ValueError as e: - if e.args[0] == 'multiclass format is not supported': + if e.args[0] == "multiclass format is not supported": continue - elif e.args[0] == "Samplewise metrics are not available "\ - "outside of multilabel classification.": + elif ( + e.args[0] == "Samplewise metrics are not available " + "outside of multilabel classification." + ): continue - elif e.args[0] == "Target is multiclass but "\ - "average='binary'. Please choose another average "\ - "setting, one of [None, 'micro', 'macro', 'weighted'].": + elif ( + e.args[0] == "Target is multiclass but " + "average='binary'. Please choose another average " + "setting, one of [None, 'micro', 'macro', 'weighted']." + ): continue else: raise e @@ -438,7 +475,7 @@ def calculate_loss( prediction: np.ndarray, task_type: int, metric: Scorer, - scoring_functions: Optional[List[Scorer]] = None + scoring_functions: Optional[List[Scorer]] = None, ) -> Union[float, Dict[str, float]]: """ Returns a loss (a magnitude that allows casting the @@ -493,10 +530,7 @@ def calculate_loss( def calculate_metric( - metric: Scorer, - prediction: np.ndarray, - solution: np.ndarray, - task_type: int + metric: Scorer, prediction: np.ndarray, solution: np.ndarray, task_type: int ) -> float: """ Returns a metric for the given Auto-Sklearn Scorer object. @@ -529,10 +563,7 @@ def calculate_metric( def _compute_scorer( - metric: Scorer, - prediction: np.ndarray, - solution: np.ndarray, - task_type: int + metric: Scorer, prediction: np.ndarray, solution: np.ndarray, task_type: int ) -> float: """ Returns a score (a magnitude that allows casting the @@ -566,9 +597,9 @@ def _compute_scorer( # Must be at bottom so all metrics are defined default_metric_for_task: Dict[int, Scorer] = { - BINARY_CLASSIFICATION: CLASSIFICATION_METRICS['accuracy'], - MULTICLASS_CLASSIFICATION: CLASSIFICATION_METRICS['accuracy'], - MULTILABEL_CLASSIFICATION: CLASSIFICATION_METRICS['f1_macro'], - REGRESSION: REGRESSION_METRICS['r2'], - MULTIOUTPUT_REGRESSION: REGRESSION_METRICS['r2'], + BINARY_CLASSIFICATION: CLASSIFICATION_METRICS["accuracy"], + MULTICLASS_CLASSIFICATION: CLASSIFICATION_METRICS["accuracy"], + MULTILABEL_CLASSIFICATION: CLASSIFICATION_METRICS["f1_macro"], + REGRESSION: REGRESSION_METRICS["r2"], + MULTIOUTPUT_REGRESSION: REGRESSION_METRICS["r2"], } diff --git a/autosklearn/metrics/util.py b/autosklearn/metrics/util.py index b4537d13aa..c25a25b4dc 100644 --- a/autosklearn/metrics/util.py +++ b/autosklearn/metrics/util.py @@ -11,8 +11,8 @@ def sanitize_array(array: np.ndarray) -> np.ndarray: a = np.ravel(array) maxi = np.nanmax(a[np.isfinite(a)]) mini = np.nanmin(a[np.isfinite(a)]) - array[array == float('inf')] = maxi - array[array == float('-inf')] = mini + array[array == float("inf")] = maxi + array[array == float("-inf")] = mini mid = (maxi + mini) / 2 array[np.isnan(array)] = mid return array diff --git a/autosklearn/pipeline/base.py b/autosklearn/pipeline/base.py index 467e7b26c1..93c73b4716 100644 --- a/autosklearn/pipeline/base.py +++ b/autosklearn/pipeline/base.py @@ -1,17 +1,15 @@ from abc import ABCMeta from typing import Dict, Union -from ConfigSpace import Configuration - import numpy as np - import scipy.sparse - +from ConfigSpace import Configuration from sklearn.pipeline import Pipeline -from .components.base import AutoSklearnChoice, AutoSklearnComponent import autosklearn.pipeline.create_searchspace_util +from .components.base import AutoSklearnChoice, AutoSklearnComponent + DATASET_PROPERTIES_TYPE = Dict[str, Union[str, int, bool]] PIPELINE_DATA_DTYPE = Union[ np.ndarray, @@ -31,17 +29,26 @@ class BasePipeline(Pipeline): Notes ----- This class should not be instantiated, only subclassed.""" + __metaclass__ = ABCMeta - def __init__(self, config=None, steps=None, dataset_properties=None, - include=None, exclude=None, random_state=None, - init_params=None): + def __init__( + self, + config=None, + steps=None, + dataset_properties=None, + include=None, + exclude=None, + random_state=None, + init_params=None, + ): self.init_params = init_params if init_params is not None else {} self.include = include if include is not None else {} self.exclude = exclude if exclude is not None else {} - self.dataset_properties = dataset_properties if \ - dataset_properties is not None else {} + self.dataset_properties = ( + dataset_properties if dataset_properties is not None else {} + ) self.random_state = random_state if steps is None: @@ -62,13 +69,17 @@ def __init__(self, config=None, steps=None, dataset_properties=None, print(self.config_space._children) print(config.configuration_space._children) import difflib + diff = difflib.unified_diff( str(self.config_space).splitlines(), - str(config.configuration_space).splitlines()) - diff = '\n'.join(diff) - raise ValueError('Configuration passed does not come from the ' - 'same configuration space. Differences are: ' - '%s' % diff) + str(config.configuration_space).splitlines(), + ) + diff = "\n".join(diff) + raise ValueError( + "Configuration passed does not come from the " + "same configuration space. Differences are: " + "%s" % diff + ) self.config = config self.set_hyperparameters(self.config, init_params=init_params) @@ -111,21 +122,22 @@ def fit_transformer(self, X, y, fit_params=None): self.num_targets = 1 if len(y.shape) == 1 else y.shape[1] if fit_params is None: fit_params = {} - fit_params = {key.replace(":", "__"): value for key, value in - fit_params.items()} + fit_params = { + key.replace(":", "__"): value for key, value in fit_params.items() + } fit_params_steps = self._check_fit_params(**fit_params) Xt = self._fit(X, y, **fit_params_steps) return Xt, fit_params_steps[self.steps[-1][0]] def fit_estimator(self, X, y, **fit_params): - fit_params = {key.replace(":", "__"): value for key, value in - fit_params.items()} + fit_params = { + key.replace(":", "__"): value for key, value in fit_params.items() + } self._final_estimator.fit(X, y, **fit_params) return self def iterative_fit(self, X, y, n_iter=1, **fit_params): - self._final_estimator.iterative_fit(X, y, n_iter=n_iter, - **fit_params) + self._final_estimator.iterative_fit(X, y, n_iter=n_iter, **fit_params) def estimator_supports_iterative_fit(self): return self._final_estimator.estimator_supports_iterative_fit() @@ -163,26 +175,30 @@ def predict(self, X, batch_size=None): return super().predict(X).astype(self._output_dtype) else: if not isinstance(batch_size, int): - raise ValueError("Argument 'batch_size' must be of type int, " - "but is '%s'" % type(batch_size)) + raise ValueError( + "Argument 'batch_size' must be of type int, " + "but is '%s'" % type(batch_size) + ) if batch_size <= 0: - raise ValueError("Argument 'batch_size' must be positive, " - "but is %d" % batch_size) + raise ValueError( + "Argument 'batch_size' must be positive, " "but is %d" % batch_size + ) else: if self.num_targets == 1: y = np.zeros((X.shape[0],), dtype=self._output_dtype) else: - y = np.zeros((X.shape[0], self.num_targets), - dtype=self._output_dtype) + y = np.zeros( + (X.shape[0], self.num_targets), dtype=self._output_dtype + ) # Copied and adapted from the scikit-learn GP code - for k in range(max(1, int(np.ceil(float(X.shape[0]) / - batch_size)))): + for k in range(max(1, int(np.ceil(float(X.shape[0]) / batch_size)))): batch_from = k * batch_size batch_to = min([(k + 1) * batch_size, X.shape[0]]) - y[batch_from:batch_to] = \ - self.predict(X[batch_from:batch_to], batch_size=None) + y[batch_from:batch_to] = self.predict( + X[batch_from:batch_to], batch_size=None + ) return y @@ -197,29 +213,33 @@ def set_hyperparameters(self, configuration, init_params=None): ) sub_config_dict = {} for param in configuration: - if param.startswith('%s:' % node_name): + if param.startswith("%s:" % node_name): value = configuration[param] - new_name = param.replace('%s:' % node_name, '', 1) + new_name = param.replace("%s:" % node_name, "", 1) sub_config_dict[new_name] = value - sub_configuration = Configuration(sub_configuration_space, - values=sub_config_dict) + sub_configuration = Configuration( + sub_configuration_space, values=sub_config_dict + ) if init_params is not None: sub_init_params_dict = {} for param in init_params: - if param.startswith('%s:' % node_name): + if param.startswith("%s:" % node_name): value = init_params[param] - new_name = param.replace('%s:' % node_name, '', 1) + new_name = param.replace("%s:" % node_name, "", 1) sub_init_params_dict[new_name] = value else: sub_init_params_dict = None - if isinstance(node, (AutoSklearnChoice, AutoSklearnComponent, BasePipeline)): - node.set_hyperparameters(configuration=sub_configuration, - init_params=sub_init_params_dict) + if isinstance( + node, (AutoSklearnChoice, AutoSklearnComponent, BasePipeline) + ): + node.set_hyperparameters( + configuration=sub_configuration, init_params=sub_init_params_dict + ) else: - raise NotImplementedError('Not supported yet!') + raise NotImplementedError("Not supported yet!") # In-code check to make sure init params # is checked after pipeline creation @@ -236,14 +256,17 @@ def get_hyperparameter_search_space(self, dataset_properties=None): The configuration space describing the AutoSklearnClassifier. """ - if not hasattr(self, 'config_space') or self.config_space is None: + if not hasattr(self, "config_space") or self.config_space is None: self.config_space = self._get_hyperparameter_search_space( - include=self.include, exclude=self.exclude, - dataset_properties=self.dataset_properties) + include=self.include, + exclude=self.exclude, + dataset_properties=self.dataset_properties, + ) return self.config_space - def _get_hyperparameter_search_space(self, include=None, exclude=None, - dataset_properties=None): + def _get_hyperparameter_search_space( + self, include=None, exclude=None, dataset_properties=None + ): """Return the configuration space for the CASH problem. This method should be called by the method @@ -283,8 +306,9 @@ def _get_hyperparameter_search_space(self, include=None, exclude=None, """ raise NotImplementedError() - def _get_base_search_space(self, cs, dataset_properties, exclude, - include, pipeline): + def _get_base_search_space( + self, cs, dataset_properties, exclude, include, pipeline + ): if include is None: if self.include is None: include = {} @@ -294,8 +318,9 @@ def _get_base_search_space(self, cs, dataset_properties, exclude, keys = [pair[0] for pair in pipeline] for key in include: if key not in keys: - raise ValueError('Invalid key in include: %s; should be one ' - 'of %s' % (key, keys)) + raise ValueError( + "Invalid key in include: %s; should be one " "of %s" % (key, keys) + ) if exclude is None: if self.exclude is None: @@ -306,26 +331,32 @@ def _get_base_search_space(self, cs, dataset_properties, exclude, keys = [pair[0] for pair in pipeline] for key in exclude: if key not in keys: - raise ValueError('Invalid key in exclude: %s; should be one ' - 'of %s' % (key, keys)) + raise ValueError( + "Invalid key in exclude: %s; should be one " "of %s" % (key, keys) + ) - if 'sparse' not in dataset_properties: + if "sparse" not in dataset_properties: # This dataset is probably dense - dataset_properties['sparse'] = False - if 'signed' not in dataset_properties: + dataset_properties["sparse"] = False + if "signed" not in dataset_properties: # This dataset probably contains unsigned data - dataset_properties['signed'] = False + dataset_properties["signed"] = False matches = autosklearn.pipeline.create_searchspace_util.get_match_array( - pipeline, dataset_properties, include=include, exclude=exclude) + pipeline, dataset_properties, include=include, exclude=exclude + ) # Now we have only legal combinations at this step of the pipeline # Simple sanity checks assert np.sum(matches) != 0, "No valid pipeline found." - assert np.sum(matches) <= np.size(matches), \ - "'matches' is not binary; %s <= %d, %s" % \ - (str(np.sum(matches)), np.size(matches), str(matches.shape)) + assert np.sum(matches) <= np.size( + matches + ), "'matches' is not binary; %s <= %d, %s" % ( + str(np.sum(matches)), + np.size(matches), + str(matches.shape), + ) # Iterate each dimension of the matches array (each step of the # pipeline) to see if we can add a hyperparameter for that step @@ -340,26 +371,36 @@ def _get_base_search_space(self, cs, dataset_properties, exclude, cs.add_configuration_space( node_name, node.get_hyperparameter_search_space(dataset_properties), - ) + ) # If the node is a choice, we have to figure out which of its # choices are actually legal choices else: - choices_list = autosklearn.pipeline.create_searchspace_util.\ - find_active_choices(matches, node, node_idx, - dataset_properties, - include.get(node_name), - exclude.get(node_name)) + choices_list = ( + autosklearn.pipeline.create_searchspace_util.find_active_choices( + matches, + node, + node_idx, + dataset_properties, + include.get(node_name), + exclude.get(node_name), + ) + ) sub_config_space = node.get_hyperparameter_search_space( - dataset_properties, include=choices_list) + dataset_properties, include=choices_list + ) cs.add_configuration_space(node_name, sub_config_space) # And now add forbidden parameter configurations # According to matches if np.sum(matches) < np.size(matches): cs = autosklearn.pipeline.create_searchspace_util.add_forbidden( - conf_space=cs, pipeline=pipeline, matches=matches, - dataset_properties=dataset_properties, include=include, - exclude=exclude) + conf_space=cs, + pipeline=pipeline, + matches=matches, + dataset_properties=dataset_properties, + include=include, + exclude=exclude, + ) return cs @@ -371,36 +412,35 @@ def _check_init_params_honored(self, init_params): # None/empty dict, so no further check required return - # There is the scenario, where instance is passed as an argument to the init_params - # 'instance': '{"task_id": "73543c4a360aa24498c0967fbc2f926b"}'} + # There is the scenario, where instance is passed as an argument to the + # init_params 'instance': '{"task_id": "73543c4a360aa24498c0967fbc2f926b"}'} # coming from smac instance. Remove this key to make the testing stricter - init_params.pop('instance', None) + init_params.pop("instance", None) for key, value in init_params.items(): - if ':' not in key: - raise ValueError("Unsupported argument to init_params {}." - "When using init_params, a hierarchical format like " - "node_name:parameter must be provided.".format(key) - ) - node_name = key.split(':', 1)[0] + if ":" not in key: + raise ValueError( + "Unsupported argument to init_params {}." + "When using init_params, a hierarchical format like " + "node_name:parameter must be provided.".format(key) + ) + node_name = key.split(":", 1)[0] if node_name not in self.named_steps.keys(): - raise ValueError("The current node name specified via key={} of init_params " - "is not valid. Valid node names are {}".format( - key, - self.named_steps.keys() - ) - ) + raise ValueError( + "The current node name specified via key={} of init_params " + "is not valid. Valid node names are {}".format( + key, self.named_steps.keys() + ) + ) continue - variable_name = key.split(':')[-1] + variable_name = key.split(":")[-1] node = self.named_steps[node_name] if isinstance(node, BasePipeline): # If dealing with a sub pipe, # Call the child _check_init_params_honored with the updated config node._check_init_params_honored( - { - key.replace('%s:' % node_name, '', 1): value - } + {key.replace("%s:" % node_name, "", 1): value} ) continue @@ -412,8 +452,10 @@ def _check_init_params_honored(self, init_params): raise ValueError("Unsupported node type {}".format(type(node))) if variable_name not in node_dict or node_dict[variable_name] != value: - raise ValueError("Cannot properly set the pair {}->{} via init_params" - "".format(key, value)) + raise ValueError( + "Cannot properly set the pair {}->{} via init_params" + "".format(key, value) + ) def __repr__(self): class_name = self.__class__.__name__ @@ -424,34 +466,42 @@ def __repr__(self): if self.config[hp_name] is not None: configuration[hp_name] = self.config[hp_name] - configuration_string = ''.join( - ['configuration={\n ', - ',\n '.join(["'%s': %s" % (hp_name, repr(configuration[hp_name])) - for hp_name in sorted(configuration)]), - '}']) + configuration_string = "".join( + [ + "configuration={\n ", + ",\n ".join( + [ + "'%s': %s" % (hp_name, repr(configuration[hp_name])) + for hp_name in sorted(configuration) + ] + ), + "}", + ] + ) if len(self.dataset_properties) > 0: dataset_properties_string = [] - dataset_properties_string.append('dataset_properties={') + dataset_properties_string.append("dataset_properties={") for i, item in enumerate(self.dataset_properties.items()): if i != 0: - dataset_properties_string.append(',\n ') + dataset_properties_string.append(",\n ") else: - dataset_properties_string.append('\n ') + dataset_properties_string.append("\n ") if isinstance(item[1], str): - dataset_properties_string.append("'%s': '%s'" % (item[0], - item[1])) + dataset_properties_string.append("'%s': '%s'" % (item[0], item[1])) else: - dataset_properties_string.append("'%s': %s" % (item[0], - item[1])) - dataset_properties_string.append('}') - dataset_properties_string = ''.join(dataset_properties_string) - - rval = '%s(%s,\n%s)' % (class_name, configuration, - dataset_properties_string) + dataset_properties_string.append("'%s': %s" % (item[0], item[1])) + dataset_properties_string.append("}") + dataset_properties_string = "".join(dataset_properties_string) + + rval = "%s(%s,\n%s)" % ( + class_name, + configuration, + dataset_properties_string, + ) else: - rval = '%s(%s)' % (class_name, configuration_string) + rval = "%s(%s)" % (class_name, configuration_string) return rval @@ -473,32 +523,54 @@ def _validate_include_exclude_params(self): if self.include is not None and self.exclude is not None: for key in self.include.keys(): if key in self.exclude.keys(): - raise ValueError("Cannot specify include and exclude for same step '{}'." - .format(key)) + raise ValueError( + "Cannot specify include and exclude for same step '{}'.".format( + key + ) + ) - supported_steps = {step[0]: step[1] for step in self.steps - if isinstance(step[1], AutoSklearnChoice)} - for arg in ['include', 'exclude']: + supported_steps = { + step[0]: step[1] + for step in self.steps + if isinstance(step[1], AutoSklearnChoice) + } + for arg in ["include", "exclude"]: argument = getattr(self, arg) if not argument: continue for key in list(argument.keys()): if key not in supported_steps: - raise ValueError("The provided key '{}' in the '{}' argument is not valid. The" - " only supported keys for this task are {}" - .format(key, arg, list(supported_steps.keys()))) + raise ValueError( + "The provided key '{}' in the '{}' argument is not valid. The" + " only supported keys for this task are {}".format( + key, arg, list(supported_steps.keys()) + ) + ) candidate_components = argument[key] - if not (isinstance(candidate_components, list) and candidate_components): - raise ValueError("The provided value of the key '{}' in the '{}' argument is " - "not valid. The value must be a non-empty list." - .format(key, arg)) + if not ( + isinstance(candidate_components, list) and candidate_components + ): + raise ValueError( + "The provided value of the key '{}' in the '{}' argument is " + "not valid. The value must be a non-empty list.".format( + key, arg + ) + ) - available_components = list(supported_steps[key].get_available_components( - dataset_properties=self.dataset_properties).keys()) + available_components = list( + supported_steps[key] + .get_available_components( + dataset_properties=self.dataset_properties + ) + .keys() + ) for component in candidate_components: if component not in available_components: - raise ValueError("The provided component '{}' for the key '{}' in the '{}'" - " argument is not valid. The supported components for the" - " step '{}' for this task are {}" - .format(component, key, arg, key, available_components)) + raise ValueError( + "The provided component '{}' for the key '{}' in the '{}'" + " argument is not valid. The supported components for the" + " step '{}' for this task are {}".format( + component, key, arg, key, available_components + ) + ) diff --git a/autosklearn/pipeline/classification.py b/autosklearn/pipeline/classification.py index 44ae129f4d..1686e02809 100644 --- a/autosklearn/pipeline/classification.py +++ b/autosklearn/pipeline/classification.py @@ -1,21 +1,22 @@ +from typing import Optional, Union + import copy from itertools import product -from typing import Optional, Union import numpy as np - +from ConfigSpace.configuration_space import Configuration, ConfigurationSpace +from ConfigSpace.forbidden import ForbiddenAndConjunction, ForbiddenEqualsClause from sklearn.base import ClassifierMixin -from ConfigSpace.configuration_space import ConfigurationSpace, Configuration -from ConfigSpace.forbidden import ForbiddenEqualsClause, ForbiddenAndConjunction - -from autosklearn.pipeline.components.data_preprocessing import DataPreprocessorChoice - -from autosklearn.pipeline.components.classification import ClassifierChoice -from autosklearn.pipeline.components.data_preprocessing.balancing.balancing import \ - Balancing -from autosklearn.pipeline.components.feature_preprocessing import FeaturePreprocessorChoice from autosklearn.pipeline.base import BasePipeline +from autosklearn.pipeline.components.classification import ClassifierChoice +from autosklearn.pipeline.components.data_preprocessing import DataPreprocessorChoice +from autosklearn.pipeline.components.data_preprocessing.balancing.balancing import ( + Balancing, +) +from autosklearn.pipeline.components.feature_preprocessing import ( + FeaturePreprocessorChoice, +) from autosklearn.pipeline.constants import SPARSE @@ -75,13 +76,13 @@ def __init__( include=None, exclude=None, random_state: Optional[Union[int, np.random.RandomState]] = None, - init_params=None + init_params=None, ): self._output_dtype = np.int32 if dataset_properties is None: dataset_properties = dict() - if 'target_type' not in dataset_properties: - dataset_properties['target_type'] = 'classification' + if "target_type" not in dataset_properties: + dataset_properties["target_type"] = "classification" super().__init__( config=config, steps=steps, @@ -89,7 +90,7 @@ def __init__( include=include, exclude=exclude, random_state=random_state, - init_params=init_params + init_params=init_params, ) def fit_transformer(self, X, y, fit_params=None): @@ -97,21 +98,24 @@ def fit_transformer(self, X, y, fit_params=None): if fit_params is None: fit_params = {} - if self.config['balancing:strategy'] == 'weighting': - balancing = Balancing(strategy='weighting') + if self.config["balancing:strategy"] == "weighting": + balancing = Balancing(strategy="weighting") _init_params, _fit_params = balancing.get_weights( - y, self.config['classifier:__choice__'], - self.config['feature_preprocessor:__choice__'], - {}, {}) + y, + self.config["classifier:__choice__"], + self.config["feature_preprocessor:__choice__"], + {}, + {}, + ) _init_params.update(self.init_params) - self.set_hyperparameters(configuration=self.config, - init_params=_init_params) + self.set_hyperparameters( + configuration=self.config, init_params=_init_params + ) if _fit_params is not None: fit_params.update(_fit_params) - X, fit_params = super().fit_transformer( - X, y, fit_params=fit_params) + X, fit_params = super().fit_transformer(X, y, fit_params=fit_params) return X, fit_params @@ -136,29 +140,34 @@ def predict_proba(self, X, batch_size=None): else: if not isinstance(batch_size, int): - raise ValueError("Argument 'batch_size' must be of type int, " - "but is '%s'" % type(batch_size)) + raise ValueError( + "Argument 'batch_size' must be of type int, " + "but is '%s'" % type(batch_size) + ) if batch_size <= 0: - raise ValueError("Argument 'batch_size' must be positive, " - "but is %d" % batch_size) + raise ValueError( + "Argument 'batch_size' must be positive, " "but is %d" % batch_size + ) else: # Probe for the target array dimensions target = self.predict_proba(X[0:2].copy()) - y = np.zeros((X.shape[0], target.shape[1]), - dtype=np.float32) + y = np.zeros((X.shape[0], target.shape[1]), dtype=np.float32) for k in range(max(1, int(np.ceil(float(X.shape[0]) / batch_size)))): batch_from = k * batch_size batch_to = min([(k + 1) * batch_size, X.shape[0]]) - pred_prob = self.predict_proba(X[batch_from:batch_to], batch_size=None) + pred_prob = self.predict_proba( + X[batch_from:batch_to], batch_size=None + ) y[batch_from:batch_to] = pred_prob.astype(np.float32) return y - def _get_hyperparameter_search_space(self, include=None, exclude=None, - dataset_properties=None): + def _get_hyperparameter_search_space( + self, include=None, exclude=None, dataset_properties=None + ): """Create the hyperparameter configuration space. Parameters @@ -174,42 +183,52 @@ def _get_hyperparameter_search_space(self, include=None, exclude=None, if dataset_properties is None or not isinstance(dataset_properties, dict): dataset_properties = dict() - if 'target_type' not in dataset_properties: - dataset_properties['target_type'] = 'classification' - if dataset_properties['target_type'] != 'classification': - dataset_properties['target_type'] = 'classification' + if "target_type" not in dataset_properties: + dataset_properties["target_type"] = "classification" + if dataset_properties["target_type"] != "classification": + dataset_properties["target_type"] = "classification" - if 'sparse' not in dataset_properties: + if "sparse" not in dataset_properties: # This dataset is probably dense - dataset_properties['sparse'] = False + dataset_properties["sparse"] = False cs = self._get_base_search_space( - cs=cs, dataset_properties=dataset_properties, - exclude=exclude, include=include, pipeline=self.steps) + cs=cs, + dataset_properties=dataset_properties, + exclude=exclude, + include=include, + pipeline=self.steps, + ) - classifiers = cs.get_hyperparameter('classifier:__choice__').choices - preprocessors = cs.get_hyperparameter('feature_preprocessor:__choice__').choices + classifiers = cs.get_hyperparameter("classifier:__choice__").choices + preprocessors = cs.get_hyperparameter("feature_preprocessor:__choice__").choices available_classifiers = self._final_estimator.get_available_components( - dataset_properties) + dataset_properties + ) - possible_default_classifier = copy.copy(list( - available_classifiers.keys())) - default = cs.get_hyperparameter('classifier:__choice__').default_value + possible_default_classifier = copy.copy(list(available_classifiers.keys())) + default = cs.get_hyperparameter("classifier:__choice__").default_value del possible_default_classifier[possible_default_classifier.index(default)] # A classifier which can handle sparse data after the densifier is # forbidden for memory issues for key in classifiers: - if SPARSE in available_classifiers[key].get_properties()['input']: - if 'densifier' in preprocessors: + if SPARSE in available_classifiers[key].get_properties()["input"]: + if "densifier" in preprocessors: while True: try: forb_cls = ForbiddenEqualsClause( - cs.get_hyperparameter('classifier:__choice__'), key) - forb_fpp = ForbiddenEqualsClause(cs.get_hyperparameter( - 'feature_preprocessor:__choice__'), 'densifier') + cs.get_hyperparameter("classifier:__choice__"), key + ) + forb_fpp = ForbiddenEqualsClause( + cs.get_hyperparameter( + "feature_preprocessor:__choice__" + ), + "densifier", + ) cs.add_forbidden_clause( - ForbiddenAndConjunction(forb_cls, forb_fpp)) + ForbiddenAndConjunction(forb_cls, forb_fpp) + ) # Success break except ValueError: @@ -218,20 +237,29 @@ def _get_hyperparameter_search_space(self, include=None, exclude=None, default = possible_default_classifier.pop() except IndexError: raise ValueError( - "Cannot find a legal default configuration.") + "Cannot find a legal default configuration." + ) cs.get_hyperparameter( - 'classifier:__choice__').default_value = default + "classifier:__choice__" + ).default_value = default # which would take too long # Combinations of non-linear models with feature learning: classifiers_ = [ - "adaboost", "decision_tree", "extra_trees", - "gradient_boosting", "k_nearest_neighbors", - "libsvm_svc", "mlp", "random_forest", + "adaboost", + "decision_tree", + "extra_trees", + "gradient_boosting", + "k_nearest_neighbors", + "libsvm_svc", + "mlp", + "random_forest", "gaussian_nb", ] feature_learning = [ - "kernel_pca", "kitchen_sinks", "nystroem_sampler", + "kernel_pca", + "kitchen_sinks", + "nystroem_sampler", ] for c, f in product(classifiers_, feature_learning): @@ -241,11 +269,19 @@ def _get_hyperparameter_search_space(self, include=None, exclude=None, continue while True: try: - cs.add_forbidden_clause(ForbiddenAndConjunction( - ForbiddenEqualsClause(cs.get_hyperparameter( - "classifier:__choice__"), c), - ForbiddenEqualsClause(cs.get_hyperparameter( - "feature_preprocessor:__choice__"), f))) + cs.add_forbidden_clause( + ForbiddenAndConjunction( + ForbiddenEqualsClause( + cs.get_hyperparameter("classifier:__choice__"), c + ), + ForbiddenEqualsClause( + cs.get_hyperparameter( + "feature_preprocessor:__choice__" + ), + f, + ), + ) + ) break except KeyError: break @@ -254,16 +290,22 @@ def _get_hyperparameter_search_space(self, include=None, exclude=None, try: default = possible_default_classifier.pop() except IndexError: - raise ValueError( - "Cannot find a legal default configuration.") + raise ValueError("Cannot find a legal default configuration.") cs.get_hyperparameter( - 'classifier:__choice__').default_value = default + "classifier:__choice__" + ).default_value = default # Won't work # Multinomial NB etc don't use with features learning, pca etc classifiers_ = ["multinomial_nb"] - preproc_with_negative_X = ["kitchen_sinks", "pca", "truncatedSVD", - "fast_ica", "kernel_pca", "nystroem_sampler"] + preproc_with_negative_X = [ + "kitchen_sinks", + "pca", + "truncatedSVD", + "fast_ica", + "kernel_pca", + "nystroem_sampler", + ] for c, f in product(classifiers_, preproc_with_negative_X): if c not in classifiers: @@ -272,11 +314,19 @@ def _get_hyperparameter_search_space(self, include=None, exclude=None, continue while True: try: - cs.add_forbidden_clause(ForbiddenAndConjunction( - ForbiddenEqualsClause(cs.get_hyperparameter( - "feature_preprocessor:__choice__"), f), - ForbiddenEqualsClause(cs.get_hyperparameter( - "classifier:__choice__"), c))) + cs.add_forbidden_clause( + ForbiddenAndConjunction( + ForbiddenEqualsClause( + cs.get_hyperparameter( + "feature_preprocessor:__choice__" + ), + f, + ), + ForbiddenEqualsClause( + cs.get_hyperparameter("classifier:__choice__"), c + ), + ) + ) break except KeyError: break @@ -285,10 +335,10 @@ def _get_hyperparameter_search_space(self, include=None, exclude=None, try: default = possible_default_classifier.pop() except IndexError: - raise ValueError( - "Cannot find a legal default configuration.") + raise ValueError("Cannot find a legal default configuration.") cs.get_hyperparameter( - 'classifier:__choice__').default_value = default + "classifier:__choice__" + ).default_value = default self.configuration_space = cs self.dataset_properties = dataset_properties @@ -297,30 +347,36 @@ def _get_hyperparameter_search_space(self, include=None, exclude=None, def _get_pipeline_steps(self, dataset_properties): steps = [] - default_dataset_properties = {'target_type': 'classification'} + default_dataset_properties = {"target_type": "classification"} if dataset_properties is not None and isinstance(dataset_properties, dict): default_dataset_properties.update(dataset_properties) - steps.extend([ - [ - "data_preprocessor", DataPreprocessorChoice( - dataset_properties=default_dataset_properties, - random_state=self.random_state) - ], - [ - "balancing", Balancing(random_state=self.random_state) - ], + steps.extend( [ - "feature_preprocessor", FeaturePreprocessorChoice( - dataset_properties=default_dataset_properties, - random_state=self.random_state) - ], - [ - 'classifier', ClassifierChoice( - dataset_properties=default_dataset_properties, - random_state=self.random_state) + [ + "data_preprocessor", + DataPreprocessorChoice( + dataset_properties=default_dataset_properties, + random_state=self.random_state, + ), + ], + ["balancing", Balancing(random_state=self.random_state)], + [ + "feature_preprocessor", + FeaturePreprocessorChoice( + dataset_properties=default_dataset_properties, + random_state=self.random_state, + ), + ], + [ + "classifier", + ClassifierChoice( + dataset_properties=default_dataset_properties, + random_state=self.random_state, + ), + ], ] - ]) + ) return steps diff --git a/autosklearn/pipeline/components/base.py b/autosklearn/pipeline/components/base.py index 5864a2a5d6..c4a95df08c 100644 --- a/autosklearn/pipeline/components/base.py +++ b/autosklearn/pipeline/components/base.py @@ -1,9 +1,10 @@ -from collections import OrderedDict +from typing import Dict + import importlib import inspect import pkgutil import sys -from typing import Dict +from collections import OrderedDict from sklearn.base import BaseEstimator, TransformerMixin @@ -21,8 +22,11 @@ def find_components(package, directory, base_class): module = importlib.import_module(full_module_name) for member_name, obj in inspect.getmembers(module): - if inspect.isclass(obj) and issubclass(obj, base_class) and \ - obj != base_class: + if ( + inspect.isclass(obj) + and issubclass(obj, base_class) + and obj != base_class + ): # TODO test if the obj implements the interface # Keep in mind that this only instantiates the ensemble_wrapper, # but not the real target classifier @@ -42,24 +46,35 @@ def add_component(self, obj): name = obj.__name__ classifier = obj else: - raise TypeError('add_component works only with a subclass of %s' % - str(self.base_class)) + raise TypeError( + "add_component works only with a subclass of %s" % str(self.base_class) + ) properties = set(classifier.get_properties()) - should_be_there = {'shortname', 'name', 'handles_regression', - 'handles_classification', 'handles_multiclass', - 'handles_multilabel', 'handles_multioutput', - 'is_deterministic', 'input', 'output'} + should_be_there = { + "shortname", + "name", + "handles_regression", + "handles_classification", + "handles_multiclass", + "handles_multilabel", + "handles_multioutput", + "is_deterministic", + "input", + "output", + } for property in properties: if property not in should_be_there: - raise ValueError('Property %s must not be specified for ' - 'algorithm %s. Only the following properties ' - 'can be specified: %s' % - (property, name, str(should_be_there))) + raise ValueError( + "Property %s must not be specified for " + "algorithm %s. Only the following properties " + "can be specified: %s" % (property, name, str(should_be_there)) + ) for property in should_be_there: if property not in properties: - raise ValueError('Property %s not specified for algorithm %s' % - (property, name)) + raise ValueError( + "Property %s not specified for algorithm %s" % (property, name) + ) self.components[name] = classifier @@ -126,34 +141,35 @@ def set_hyperparameters(self, configuration, init_params=None): for param, value in params.items(): if not hasattr(self, param): - raise ValueError('Cannot set hyperparameter %s for %s because ' - 'the hyperparameter does not exist.' % - (param, str(self))) + raise ValueError( + "Cannot set hyperparameter %s for %s because " + "the hyperparameter does not exist." % (param, str(self)) + ) setattr(self, param, value) if init_params is not None: for param, value in init_params.items(): if not hasattr(self, param): - raise ValueError('Cannot set init param %s for %s because ' - 'the init param does not exist.' % - (param, str(self))) + raise ValueError( + "Cannot set init param %s for %s because " + "the init param does not exist." % (param, str(self)) + ) setattr(self, param, value) return self def __str__(self): - name = self.get_properties()['name'] + name = self.get_properties()["name"] return "autosklearn.pipeline %s" % name class IterativeComponent(AutoSklearnComponent): - def fit(self, X, y, sample_weight=None): self.iterative_fit(X, y, n_iter=2, refit=True) iteration = 2 while not self.configuration_fully_fitted(): - n_iter = int(2 ** iteration / 2) + n_iter = int(2**iteration / 2) self.iterative_fit(X, y, n_iter=n_iter, refit=False) iteration += 1 @@ -168,14 +184,15 @@ def get_current_iter(self): class IterativeComponentWithSampleWeight(AutoSklearnComponent): - def fit(self, X, y, sample_weight=None): self.iterative_fit(X, y, n_iter=2, refit=True, sample_weight=sample_weight) iteration = 2 while not self.configuration_fully_fitted(): - n_iter = int(2 ** iteration / 2) - self.iterative_fit(X, y, n_iter=n_iter, refit=False, sample_weight=sample_weight) + n_iter = int(2**iteration / 2) + self.iterative_fit( + X, y, n_iter=n_iter, refit=False, sample_weight=sample_weight + ) iteration += 1 return self @@ -356,23 +373,25 @@ def __init__(self, dataset_properties, random_state=None): def get_components(cls): raise NotImplementedError() - def get_available_components(self, dataset_properties=None, - include=None, - exclude=None): + def get_available_components( + self, dataset_properties=None, include=None, exclude=None + ): if dataset_properties is None: dataset_properties = {} if include is not None and exclude is not None: raise ValueError( - "The argument include and exclude cannot be used together.") + "The argument include and exclude cannot be used together." + ) available_comp = self.get_components() if include is not None: for incl in include: if incl not in available_comp: - raise ValueError("Trying to include unknown component: " - "%s" % incl) + raise ValueError( + "Trying to include unknown component: " "%s" % incl + ) components_dict = OrderedDict() for name in available_comp: @@ -381,14 +400,14 @@ def get_available_components(self, dataset_properties=None, elif exclude is not None and name in exclude: continue - if 'sparse' in dataset_properties and dataset_properties['sparse']: + if "sparse" in dataset_properties and dataset_properties["sparse"]: # In case the dataset is sparse, ignore # components that do not handle sparse data # Auto-sklearn uses SPARSE constant as a mechanism # to indicate whether a component can handle sparse data. # If SPARSE is not in the input properties of the component, it # means SPARSE is not a valid input to this component, so filter it out - if SPARSE not in available_comp[name].get_properties()['input']: + if SPARSE not in available_comp[name].get_properties()["input"]: continue components_dict[name] = available_comp[name] @@ -399,29 +418,28 @@ def set_hyperparameters(self, configuration, init_params=None): new_params = {} params = configuration.get_dictionary() - choice = params['__choice__'] - del params['__choice__'] + choice = params["__choice__"] + del params["__choice__"] for param, value in params.items(): - param = param.replace(choice, '').replace(':', '') + param = param.replace(choice, "").replace(":", "") new_params[param] = value if init_params is not None: for param, value in init_params.items(): - param = param.replace(choice, '').replace(':', '') + param = param.replace(choice, "").replace(":", "") new_params[param] = value - new_params['random_state'] = self.random_state + new_params["random_state"] = self.random_state self.new_params = new_params self.choice = self.get_components()[choice](**new_params) return self - def get_hyperparameter_search_space(self, dataset_properties=None, - default=None, - include=None, - exclude=None): + def get_hyperparameter_search_space( + self, dataset_properties=None, default=None, include=None, exclude=None + ): raise NotImplementedError() def fit(self, X, y, **kwargs): diff --git a/autosklearn/pipeline/components/classification/__init__.py b/autosklearn/pipeline/components/classification/__init__.py index 2dca6623ae..c95334273a 100644 --- a/autosklearn/pipeline/components/classification/__init__.py +++ b/autosklearn/pipeline/components/classification/__init__.py @@ -1,20 +1,27 @@ -__author__ = 'feurerm' +__author__ = "feurerm" -from collections import OrderedDict from typing import Type + import os +from collections import OrderedDict -from ..base import AutoSklearnClassificationAlgorithm, find_components, \ - ThirdPartyComponents, AutoSklearnChoice, _addons from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter +from ..base import ( + AutoSklearnChoice, + AutoSklearnClassificationAlgorithm, + ThirdPartyComponents, + _addons, + find_components, +) + classifier_directory = os.path.split(__file__)[0] -_classifiers = find_components(__package__, - classifier_directory, - AutoSklearnClassificationAlgorithm) +_classifiers = find_components( + __package__, classifier_directory, AutoSklearnClassificationAlgorithm +) additional_components = ThirdPartyComponents(AutoSklearnClassificationAlgorithm) -_addons['classification'] = additional_components +_addons["classification"] = additional_components def add_classifier(classifier: Type[AutoSklearnClassificationAlgorithm]) -> None: @@ -22,7 +29,6 @@ def add_classifier(classifier: Type[AutoSklearnClassificationAlgorithm]) -> None class ClassifierChoice(AutoSklearnChoice): - @classmethod def get_components(cls): components = OrderedDict() @@ -30,9 +36,9 @@ def get_components(cls): components.update(additional_components.components) return components - def get_available_components(cls, dataset_properties=None, - include=None, - exclude=None): + def get_available_components( + cls, dataset_properties=None, include=None, exclude=None + ): if dataset_properties is None: dataset_properties = {} @@ -40,13 +46,16 @@ def get_available_components(cls, dataset_properties=None, components_dict = OrderedDict() if include is not None and exclude is not None: - raise ValueError("The argument include and exclude cannot be used together.") + raise ValueError( + "The argument include and exclude cannot be used together." + ) if include is not None: for incl in include: if incl not in available_comp: - raise ValueError("Trying to include unknown component: " - "%s" % incl) + raise ValueError( + "Trying to include unknown component: " "%s" % incl + ) for name in available_comp: if include is not None and name not in include: @@ -60,43 +69,47 @@ def get_available_components(cls, dataset_properties=None, if entry == ClassifierChoice: continue - if entry.get_properties()['handles_classification'] is False: + if entry.get_properties()["handles_classification"] is False: continue - if dataset_properties.get('multiclass') is True and \ - entry.get_properties()['handles_multiclass'] is False: + if ( + dataset_properties.get("multiclass") is True + and entry.get_properties()["handles_multiclass"] is False + ): continue - if dataset_properties.get('multilabel') is True and \ - available_comp[name].get_properties()['handles_multilabel'] is False: + if ( + dataset_properties.get("multilabel") is True + and available_comp[name].get_properties()["handles_multilabel"] is False + ): continue components_dict[name] = entry return components_dict - def get_hyperparameter_search_space(self, dataset_properties=None, - default=None, - include=None, - exclude=None): + def get_hyperparameter_search_space( + self, dataset_properties=None, default=None, include=None, exclude=None + ): if dataset_properties is None: dataset_properties = {} if include is not None and exclude is not None: - raise ValueError("The arguments include and " - "exclude cannot be used together.") + raise ValueError( + "The arguments include and " "exclude cannot be used together." + ) cs = ConfigurationSpace() # Compile a list of all estimator objects for this problem available_estimators = self.get_available_components( - dataset_properties=dataset_properties, - include=include, - exclude=exclude) + dataset_properties=dataset_properties, include=include, exclude=exclude + ) if len(available_estimators) == 0: raise ValueError("No classifiers found") if default is None: - defaults = ['random_forest', 'liblinear_svc', 'sgd', - 'libsvm_svc'] + list(available_estimators.keys()) + defaults = ["random_forest", "liblinear_svc", "sgd", "libsvm_svc"] + list( + available_estimators.keys() + ) for default_ in defaults: if default_ in available_estimators: if include is not None and default_ not in include: @@ -106,18 +119,20 @@ def get_hyperparameter_search_space(self, dataset_properties=None, default = default_ break - estimator = CategoricalHyperparameter('__choice__', - list(available_estimators.keys()), - default_value=default) + estimator = CategoricalHyperparameter( + "__choice__", list(available_estimators.keys()), default_value=default + ) cs.add_hyperparameter(estimator) for estimator_name in available_estimators.keys(): - estimator_configuration_space = available_estimators[estimator_name].\ - get_hyperparameter_search_space(dataset_properties) - parent_hyperparameter = {'parent': estimator, - 'value': estimator_name} - cs.add_configuration_space(estimator_name, - estimator_configuration_space, - parent_hyperparameter=parent_hyperparameter) + estimator_configuration_space = available_estimators[ + estimator_name + ].get_hyperparameter_search_space(dataset_properties) + parent_hyperparameter = {"parent": estimator, "value": estimator_name} + cs.add_configuration_space( + estimator_name, + estimator_configuration_space, + parent_hyperparameter=parent_hyperparameter, + ) self.configuration_space = cs self.dataset_properties = dataset_properties @@ -127,7 +142,7 @@ def predict_proba(self, X): return self.choice.predict_proba(X) def estimator_supports_iterative_fit(self): - return hasattr(self.choice, 'iterative_fit') + return hasattr(self.choice, "iterative_fit") def get_max_iter(self): if self.estimator_supports_iterative_fit(): diff --git a/autosklearn/pipeline/components/classification/adaboost.py b/autosklearn/pipeline/components/classification/adaboost.py index 31567aaeae..3634f53956 100644 --- a/autosklearn/pipeline/components/classification/adaboost.py +++ b/autosklearn/pipeline/components/classification/adaboost.py @@ -1,15 +1,18 @@ from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - UniformIntegerHyperparameter, CategoricalHyperparameter +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformFloatHyperparameter, + UniformIntegerHyperparameter, +) from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA class AdaboostClassifier(AutoSklearnClassificationAlgorithm): - - def __init__(self, n_estimators, learning_rate, algorithm, max_depth, - random_state=None): + def __init__( + self, n_estimators, learning_rate, algorithm, max_depth, random_state=None + ): self.n_estimators = n_estimators self.learning_rate = learning_rate self.algorithm = algorithm @@ -31,7 +34,7 @@ def fit(self, X, Y, sample_weight=None): n_estimators=self.n_estimators, learning_rate=self.learning_rate, algorithm=self.algorithm, - random_state=self.random_state + random_state=self.random_state, ) estimator.fit(X, Y, sample_weight=sample_weight) @@ -51,29 +54,35 @@ def predict_proba(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'AB', - 'name': 'AdaBoost Classifier', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': False, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "AB", + "name": "AdaBoost Classifier", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": False, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() n_estimators = UniformIntegerHyperparameter( - name="n_estimators", lower=50, upper=500, default_value=50, log=False) + name="n_estimators", lower=50, upper=500, default_value=50, log=False + ) learning_rate = UniformFloatHyperparameter( - name="learning_rate", lower=0.01, upper=2, default_value=0.1, log=True) + name="learning_rate", lower=0.01, upper=2, default_value=0.1, log=True + ) algorithm = CategoricalHyperparameter( - name="algorithm", choices=["SAMME.R", "SAMME"], default_value="SAMME.R") + name="algorithm", choices=["SAMME.R", "SAMME"], default_value="SAMME.R" + ) max_depth = UniformIntegerHyperparameter( - name="max_depth", lower=1, upper=10, default_value=1, log=False) + name="max_depth", lower=1, upper=10, default_value=1, log=False + ) cs.add_hyperparameters([n_estimators, learning_rate, algorithm, max_depth]) return cs diff --git a/autosklearn/pipeline/components/classification/bernoulli_nb.py b/autosklearn/pipeline/components/classification/bernoulli_nb.py index 9bb2f8c590..8271c5f602 100644 --- a/autosklearn/pipeline/components/classification/bernoulli_nb.py +++ b/autosklearn/pipeline/components/classification/bernoulli_nb.py @@ -1,13 +1,12 @@ import numpy as np - from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - CategoricalHyperparameter - -from autosklearn.pipeline.components.base import ( - AutoSklearnClassificationAlgorithm, +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformFloatHyperparameter, ) -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE + +from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA from autosklearn.util.common import check_for_bool @@ -23,14 +22,18 @@ def fit(self, X, y): import sklearn.naive_bayes self.fit_prior = check_for_bool(self.fit_prior) - self.estimator = sklearn.naive_bayes.BernoulliNB(alpha=self.alpha, fit_prior=self.fit_prior) + self.estimator = sklearn.naive_bayes.BernoulliNB( + alpha=self.alpha, fit_prior=self.fit_prior + ) self.classes_ = np.unique(y.astype(int)) # Fallback for multilabel classification if len(y.shape) > 1 and y.shape[1] > 1: import sklearn.multiclass + self.estimator = sklearn.multiclass.OneVsRestClassifier( - self.estimator, n_jobs=1) + self.estimator, n_jobs=1 + ) self.estimator.fit(X, y) return self @@ -47,16 +50,18 @@ def predict_proba(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'BernoulliNB', - 'name': 'Bernoulli Naive Bayes classifier', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "BernoulliNB", + "name": "Bernoulli Naive Bayes classifier", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): @@ -65,12 +70,13 @@ def get_hyperparameter_search_space(dataset_properties=None): # the smoothing parameter is a non-negative float # I will limit it to 1000 and put it on a logarithmic scale. (SF) # Please adjust that, if you know a proper range, this is just a guess. - alpha = UniformFloatHyperparameter(name="alpha", lower=1e-2, upper=100, - default_value=1, log=True) + alpha = UniformFloatHyperparameter( + name="alpha", lower=1e-2, upper=100, default_value=1, log=True + ) - fit_prior = CategoricalHyperparameter(name="fit_prior", - choices=["True", "False"], - default_value="True") + fit_prior = CategoricalHyperparameter( + name="fit_prior", choices=["True", "False"], default_value="True" + ) cs.add_hyperparameters([alpha, fit_prior]) diff --git a/autosklearn/pipeline/components/classification/decision_tree.py b/autosklearn/pipeline/components/classification/decision_tree.py index 045e5c3e44..fbfc6b7c6a 100644 --- a/autosklearn/pipeline/components/classification/decision_tree.py +++ b/autosklearn/pipeline/components/classification/decision_tree.py @@ -1,22 +1,35 @@ import numpy as np - from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - UniformIntegerHyperparameter, CategoricalHyperparameter, \ - UnParametrizedHyperparameter, Constant +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + Constant, + UniformFloatHyperparameter, + UniformIntegerHyperparameter, + UnParametrizedHyperparameter, +) -from autosklearn.pipeline.components.base import \ - AutoSklearnClassificationAlgorithm -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE -from autosklearn.pipeline.implementations.util import convert_multioutput_multiclass_to_multilabel +from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA +from autosklearn.pipeline.implementations.util import ( + convert_multioutput_multiclass_to_multilabel, +) from autosklearn.util.common import check_none class DecisionTree(AutoSklearnClassificationAlgorithm): - def __init__(self, criterion, max_features, max_depth_factor, - min_samples_split, min_samples_leaf, min_weight_fraction_leaf, - max_leaf_nodes, min_impurity_decrease, class_weight=None, - random_state=None): + def __init__( + self, + criterion, + max_features, + max_depth_factor, + min_samples_split, + min_samples_leaf, + min_weight_fraction_leaf, + max_leaf_nodes, + min_impurity_decrease, + class_weight=None, + random_state=None, + ): self.criterion = criterion self.max_features = max_features self.max_depth_factor = max_depth_factor @@ -40,8 +53,8 @@ def fit(self, X, y, sample_weight=None): num_features = X.shape[1] self.max_depth_factor = int(self.max_depth_factor) max_depth_factor = max( - 1, - int(np.round(self.max_depth_factor * num_features, 0))) + 1, int(np.round(self.max_depth_factor * num_features, 0)) + ) self.min_samples_split = int(self.min_samples_split) self.min_samples_leaf = int(self.min_samples_leaf) if check_none(self.max_leaf_nodes): @@ -60,7 +73,8 @@ def fit(self, X, y, sample_weight=None): min_weight_fraction_leaf=self.min_weight_fraction_leaf, min_impurity_decrease=self.min_impurity_decrease, class_weight=self.class_weight, - random_state=self.random_state) + random_state=self.random_state, + ) self.estimator.fit(X, y, sample_weight=sample_weight) return self @@ -78,37 +92,53 @@ def predict_proba(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'DT', - 'name': 'Decision Tree Classifier', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "DT", + "name": "Decision Tree Classifier", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() criterion = CategoricalHyperparameter( - "criterion", ["gini", "entropy"], default_value="gini") + "criterion", ["gini", "entropy"], default_value="gini" + ) max_depth_factor = UniformFloatHyperparameter( - 'max_depth_factor', 0., 2., default_value=0.5) + "max_depth_factor", 0.0, 2.0, default_value=0.5 + ) min_samples_split = UniformIntegerHyperparameter( - "min_samples_split", 2, 20, default_value=2) + "min_samples_split", 2, 20, default_value=2 + ) min_samples_leaf = UniformIntegerHyperparameter( - "min_samples_leaf", 1, 20, default_value=1) + "min_samples_leaf", 1, 20, default_value=1 + ) min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.0) - max_features = UnParametrizedHyperparameter('max_features', 1.0) + max_features = UnParametrizedHyperparameter("max_features", 1.0) max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") - min_impurity_decrease = UnParametrizedHyperparameter('min_impurity_decrease', 0.0) + min_impurity_decrease = UnParametrizedHyperparameter( + "min_impurity_decrease", 0.0 + ) - cs.add_hyperparameters([criterion, max_features, max_depth_factor, - min_samples_split, min_samples_leaf, - min_weight_fraction_leaf, max_leaf_nodes, - min_impurity_decrease]) + cs.add_hyperparameters( + [ + criterion, + max_features, + max_depth_factor, + min_samples_split, + min_samples_leaf, + min_weight_fraction_leaf, + max_leaf_nodes, + min_impurity_decrease, + ] + ) return cs diff --git a/autosklearn/pipeline/components/classification/extra_trees.py b/autosklearn/pipeline/components/classification/extra_trees.py index bc3eb5e4d7..5c7ce1879a 100644 --- a/autosklearn/pipeline/components/classification/extra_trees.py +++ b/autosklearn/pipeline/components/classification/extra_trees.py @@ -1,13 +1,19 @@ from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - UniformIntegerHyperparameter, CategoricalHyperparameter, UnParametrizedHyperparameter +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformFloatHyperparameter, + UniformIntegerHyperparameter, + UnParametrizedHyperparameter, +) from autosklearn.pipeline.components.base import ( AutoSklearnClassificationAlgorithm, IterativeComponentWithSampleWeight, ) -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE -from autosklearn.pipeline.implementations.util import convert_multioutput_multiclass_to_multilabel +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA +from autosklearn.pipeline.implementations.util import ( + convert_multioutput_multiclass_to_multilabel, +) from autosklearn.util.common import check_for_bool, check_none @@ -15,12 +21,23 @@ class ExtraTreesClassifier( IterativeComponentWithSampleWeight, AutoSklearnClassificationAlgorithm, ): - - def __init__(self, criterion, min_samples_leaf, - min_samples_split, max_features, bootstrap, max_leaf_nodes, - max_depth, min_weight_fraction_leaf, min_impurity_decrease, - oob_score=False, n_jobs=1, random_state=None, verbose=0, - class_weight=None): + def __init__( + self, + criterion, + min_samples_leaf, + min_samples_split, + max_features, + bootstrap, + max_leaf_nodes, + max_depth, + min_weight_fraction_leaf, + min_impurity_decrease, + oob_score=False, + n_jobs=1, + random_state=None, + verbose=0, + class_weight=None, + ): self.n_estimators = self.get_max_iter() self.criterion = criterion @@ -55,8 +72,9 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): if self.estimator is None: max_features = int(X.shape[1] ** float(self.max_features)) if self.criterion not in ("gini", "entropy"): - raise ValueError("'criterion' is not in ('gini', 'entropy'): " - "%s" % self.criterion) + raise ValueError( + "'criterion' is not in ('gini', 'entropy'): " "%s" % self.criterion + ) if check_none(self.max_depth): self.max_depth = None @@ -77,27 +95,30 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): self.n_jobs = int(self.n_jobs) self.verbose = int(self.verbose) - self.estimator = ETC(n_estimators=n_iter, - criterion=self.criterion, - max_depth=self.max_depth, - min_samples_split=self.min_samples_split, - min_samples_leaf=self.min_samples_leaf, - bootstrap=self.bootstrap, - max_features=max_features, - max_leaf_nodes=self.max_leaf_nodes, - min_weight_fraction_leaf=self.min_weight_fraction_leaf, - min_impurity_decrease=self.min_impurity_decrease, - oob_score=self.oob_score, - n_jobs=self.n_jobs, - verbose=self.verbose, - random_state=self.random_state, - class_weight=self.class_weight, - warm_start=True) + self.estimator = ETC( + n_estimators=n_iter, + criterion=self.criterion, + max_depth=self.max_depth, + min_samples_split=self.min_samples_split, + min_samples_leaf=self.min_samples_leaf, + bootstrap=self.bootstrap, + max_features=max_features, + max_leaf_nodes=self.max_leaf_nodes, + min_weight_fraction_leaf=self.min_weight_fraction_leaf, + min_impurity_decrease=self.min_impurity_decrease, + oob_score=self.oob_score, + n_jobs=self.n_jobs, + verbose=self.verbose, + random_state=self.random_state, + class_weight=self.class_weight, + warm_start=True, + ) else: self.estimator.n_estimators += n_iter - self.estimator.n_estimators = min(self.estimator.n_estimators, - self.n_estimators) + self.estimator.n_estimators = min( + self.estimator.n_estimators, self.n_estimators + ) self.estimator.fit(X, y, sample_weight=sample_weight) return self @@ -121,46 +142,67 @@ def predict_proba(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'ET', - 'name': 'Extra Trees Classifier', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "ET", + "name": "Extra Trees Classifier", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() criterion = CategoricalHyperparameter( - "criterion", ["gini", "entropy"], default_value="gini") - - # The maximum number of features used in the forest is calculated as m^max_features, where - # m is the total number of features, and max_features is the hyperparameter specified below. - # The default is 0.5, which yields sqrt(m) features as max_features in the estimator. This - # corresponds with Geurts' heuristic. + "criterion", ["gini", "entropy"], default_value="gini" + ) + + # The maximum number of features used in the forest is calculated as + # m^max_features, where m is the total number of features, + # and max_features is the hyperparameter specified below. + # The default is 0.5, which yields sqrt(m) features as max_features + # in the estimator. This corresponds with Geurts' heuristic. max_features = UniformFloatHyperparameter( - "max_features", 0., 1., default_value=0.5) + "max_features", 0.0, 1.0, default_value=0.5 + ) max_depth = UnParametrizedHyperparameter(name="max_depth", value="None") min_samples_split = UniformIntegerHyperparameter( - "min_samples_split", 2, 20, default_value=2) + "min_samples_split", 2, 20, default_value=2 + ) min_samples_leaf = UniformIntegerHyperparameter( - "min_samples_leaf", 1, 20, default_value=1) - min_weight_fraction_leaf = UnParametrizedHyperparameter('min_weight_fraction_leaf', 0.) + "min_samples_leaf", 1, 20, default_value=1 + ) + min_weight_fraction_leaf = UnParametrizedHyperparameter( + "min_weight_fraction_leaf", 0.0 + ) max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") - min_impurity_decrease = UnParametrizedHyperparameter('min_impurity_decrease', 0.0) + min_impurity_decrease = UnParametrizedHyperparameter( + "min_impurity_decrease", 0.0 + ) bootstrap = CategoricalHyperparameter( - "bootstrap", ["True", "False"], default_value="False") - cs.add_hyperparameters([criterion, max_features, - max_depth, min_samples_split, min_samples_leaf, - min_weight_fraction_leaf, max_leaf_nodes, - min_impurity_decrease, bootstrap]) + "bootstrap", ["True", "False"], default_value="False" + ) + cs.add_hyperparameters( + [ + criterion, + max_features, + max_depth, + min_samples_split, + min_samples_leaf, + min_weight_fraction_leaf, + max_leaf_nodes, + min_impurity_decrease, + bootstrap, + ] + ) return cs diff --git a/autosklearn/pipeline/components/classification/gaussian_nb.py b/autosklearn/pipeline/components/classification/gaussian_nb.py index cae1733baf..8e978e9631 100644 --- a/autosklearn/pipeline/components/classification/gaussian_nb.py +++ b/autosklearn/pipeline/components/classification/gaussian_nb.py @@ -1,15 +1,11 @@ import numpy as np - from ConfigSpace.configuration_space import ConfigurationSpace -from autosklearn.pipeline.components.base import ( - AutoSklearnClassificationAlgorithm, -) -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS +from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, UNSIGNED_DATA class GaussianNB(AutoSklearnClassificationAlgorithm): - def __init__(self, random_state=None, verbose=0): self.random_state = random_state @@ -25,8 +21,10 @@ def fit(self, X, y): # Fallback for multilabel classification if len(y.shape) > 1 and y.shape[1] > 1: import sklearn.multiclass + self.estimator = sklearn.multiclass.OneVsRestClassifier( - self.estimator, n_jobs=1) + self.estimator, n_jobs=1 + ) self.estimator.fit(X, y) return self @@ -43,16 +41,18 @@ def predict_proba(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'GaussianNB', - 'name': 'Gaussian Naive Bayes classifier', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "GaussianNB", + "name": "Gaussian Naive Bayes classifier", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): diff --git a/autosklearn/pipeline/components/classification/gradient_boosting.py b/autosklearn/pipeline/components/classification/gradient_boosting.py index 0faca0faa2..50b0b284bd 100644 --- a/autosklearn/pipeline/components/classification/gradient_boosting.py +++ b/autosklearn/pipeline/components/classification/gradient_boosting.py @@ -1,26 +1,42 @@ import numpy as np - -from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - UniformIntegerHyperparameter, UnParametrizedHyperparameter, Constant, \ - CategoricalHyperparameter from ConfigSpace.conditions import EqualsCondition, InCondition +from ConfigSpace.configuration_space import ConfigurationSpace +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + Constant, + UniformFloatHyperparameter, + UniformIntegerHyperparameter, + UnParametrizedHyperparameter, +) from autosklearn.pipeline.components.base import ( AutoSklearnClassificationAlgorithm, - IterativeComponentWithSampleWeight) -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS + IterativeComponentWithSampleWeight, +) +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, UNSIGNED_DATA from autosklearn.util.common import check_none class GradientBoostingClassifier( - IterativeComponentWithSampleWeight, - AutoSklearnClassificationAlgorithm + IterativeComponentWithSampleWeight, AutoSklearnClassificationAlgorithm ): - def __init__(self, loss, learning_rate, min_samples_leaf, max_depth, - max_leaf_nodes, max_bins, l2_regularization, early_stop, tol, scoring, - n_iter_no_change=0, validation_fraction=None, random_state=None, - verbose=0): + def __init__( + self, + loss, + learning_rate, + min_samples_leaf, + max_depth, + max_leaf_nodes, + max_bins, + l2_regularization, + early_stop, + tol, + scoring, + n_iter_no_change=0, + validation_fraction=None, + random_state=None, + verbose=0, + ): self.loss = loss self.learning_rate = learning_rate self.max_iter = self.get_max_iter() @@ -119,13 +135,14 @@ def iterative_fit(self, X, y, n_iter=2, refit=False, sample_weight=None): ) else: self.estimator.max_iter += n_iter - self.estimator.max_iter = min(self.estimator.max_iter, - self.max_iter) + self.estimator.max_iter = min(self.estimator.max_iter, self.max_iter) self.estimator.fit(X, y, sample_weight=sample_weight) - if self.estimator.max_iter >= self.max_iter \ - or self.estimator.max_iter > self.estimator.n_iter_: + if ( + self.estimator.max_iter >= self.max_iter + or self.estimator.max_iter > self.estimator.n_iter_ + ): self.fully_fit_ = True @@ -134,7 +151,7 @@ def iterative_fit(self, X, y, n_iter=2, refit=False, sample_weight=None): def configuration_fully_fitted(self): if self.estimator is None: return False - elif not hasattr(self, 'fully_fit_'): + elif not hasattr(self, "fully_fit_"): return False else: return self.fully_fit_ @@ -151,53 +168,77 @@ def predict_proba(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'GB', - 'name': 'Gradient Boosting Classifier', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': False, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "GB", + "name": "Gradient Boosting Classifier", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": False, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() loss = Constant("loss", "auto") learning_rate = UniformFloatHyperparameter( - name="learning_rate", lower=0.01, upper=1, default_value=0.1, log=True) + name="learning_rate", lower=0.01, upper=1, default_value=0.1, log=True + ) min_samples_leaf = UniformIntegerHyperparameter( - name="min_samples_leaf", lower=1, upper=200, default_value=20, log=True) - max_depth = UnParametrizedHyperparameter( - name="max_depth", value="None") + name="min_samples_leaf", lower=1, upper=200, default_value=20, log=True + ) + max_depth = UnParametrizedHyperparameter(name="max_depth", value="None") max_leaf_nodes = UniformIntegerHyperparameter( - name="max_leaf_nodes", lower=3, upper=2047, default_value=31, log=True) + name="max_leaf_nodes", lower=3, upper=2047, default_value=31, log=True + ) max_bins = Constant("max_bins", 255) l2_regularization = UniformFloatHyperparameter( - name="l2_regularization", lower=1E-10, upper=1, default_value=1E-10, log=True) + name="l2_regularization", + lower=1e-10, + upper=1, + default_value=1e-10, + log=True, + ) early_stop = CategoricalHyperparameter( - name="early_stop", choices=["off", "valid", "train"], default_value="off") - tol = UnParametrizedHyperparameter( - name="tol", value=1e-7) - scoring = UnParametrizedHyperparameter( - name="scoring", value="loss") + name="early_stop", choices=["off", "valid", "train"], default_value="off" + ) + tol = UnParametrizedHyperparameter(name="tol", value=1e-7) + scoring = UnParametrizedHyperparameter(name="scoring", value="loss") n_iter_no_change = UniformIntegerHyperparameter( - name="n_iter_no_change", lower=1, upper=20, default_value=10) + name="n_iter_no_change", lower=1, upper=20, default_value=10 + ) validation_fraction = UniformFloatHyperparameter( - name="validation_fraction", lower=0.01, upper=0.4, default_value=0.1) - - cs.add_hyperparameters([loss, learning_rate, min_samples_leaf, - max_depth, max_leaf_nodes, max_bins, l2_regularization, - early_stop, tol, scoring, n_iter_no_change, - validation_fraction]) + name="validation_fraction", lower=0.01, upper=0.4, default_value=0.1 + ) + + cs.add_hyperparameters( + [ + loss, + learning_rate, + min_samples_leaf, + max_depth, + max_leaf_nodes, + max_bins, + l2_regularization, + early_stop, + tol, + scoring, + n_iter_no_change, + validation_fraction, + ] + ) n_iter_no_change_cond = InCondition( - n_iter_no_change, early_stop, ["valid", "train"]) + n_iter_no_change, early_stop, ["valid", "train"] + ) validation_fraction_cond = EqualsCondition( - validation_fraction, early_stop, "valid") + validation_fraction, early_stop, "valid" + ) cs.add_conditions([n_iter_no_change_cond, validation_fraction_cond]) diff --git a/autosklearn/pipeline/components/classification/k_nearest_neighbors.py b/autosklearn/pipeline/components/classification/k_nearest_neighbors.py index 6901451f11..fe55e0783d 100644 --- a/autosklearn/pipeline/components/classification/k_nearest_neighbors.py +++ b/autosklearn/pipeline/components/classification/k_nearest_neighbors.py @@ -1,12 +1,14 @@ from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformIntegerHyperparameter +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformIntegerHyperparameter, +) from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA class KNearestNeighborsClassifier(AutoSklearnClassificationAlgorithm): - def __init__(self, n_neighbors, weights, p, random_state=None): self.n_neighbors = n_neighbors self.weights = weights @@ -14,13 +16,12 @@ def __init__(self, n_neighbors, weights, p, random_state=None): self.random_state = random_state def fit(self, X, Y): - import sklearn.neighbors import sklearn.multiclass + import sklearn.neighbors - estimator = \ - sklearn.neighbors.KNeighborsClassifier(n_neighbors=self.n_neighbors, - weights=self.weights, - p=self.p) + estimator = sklearn.neighbors.KNeighborsClassifier( + n_neighbors=self.n_neighbors, weights=self.weights, p=self.p + ) if len(Y.shape) == 2 and Y.shape[1] > 1: self.estimator = sklearn.multiclass.OneVsRestClassifier(estimator, n_jobs=1) @@ -42,25 +43,29 @@ def predict_proba(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'KNN', - 'name': 'K-Nearest Neighbor Classification', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "KNN", + "name": "K-Nearest Neighbor Classification", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() n_neighbors = UniformIntegerHyperparameter( - name="n_neighbors", lower=1, upper=100, log=True, default_value=1) + name="n_neighbors", lower=1, upper=100, log=True, default_value=1 + ) weights = CategoricalHyperparameter( - name="weights", choices=["uniform", "distance"], default_value="uniform") + name="weights", choices=["uniform", "distance"], default_value="uniform" + ) p = CategoricalHyperparameter(name="p", choices=[1, 2], default_value=2) cs.add_hyperparameters([n_neighbors, weights, p]) diff --git a/autosklearn/pipeline/components/classification/lda.py b/autosklearn/pipeline/components/classification/lda.py index 1897db78ca..29a08f80b5 100644 --- a/autosklearn/pipeline/components/classification/lda.py +++ b/autosklearn/pipeline/components/classification/lda.py @@ -1,17 +1,18 @@ -from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, CategoricalHyperparameter from ConfigSpace.conditions import EqualsCondition +from ConfigSpace.configuration_space import ConfigurationSpace +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformFloatHyperparameter, +) -from autosklearn.pipeline.components.base import \ - AutoSklearnClassificationAlgorithm -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS +from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, UNSIGNED_DATA from autosklearn.pipeline.implementations.util import softmax from autosklearn.util.common import check_none class LDA(AutoSklearnClassificationAlgorithm): - def __init__(self, shrinkage, tol, shrinkage_factor=0.5, - random_state=None): + def __init__(self, shrinkage, tol, shrinkage_factor=0.5, random_state=None): self.shrinkage = shrinkage self.tol = tol self.shrinkage_factor = shrinkage_factor @@ -23,20 +24,21 @@ def fit(self, X, Y): if check_none(self.shrinkage): self.shrinkage_ = None - solver = 'svd' + solver = "svd" elif self.shrinkage == "auto": - self.shrinkage_ = 'auto' - solver = 'lsqr' + self.shrinkage_ = "auto" + solver = "lsqr" elif self.shrinkage == "manual": self.shrinkage_ = float(self.shrinkage_factor) - solver = 'lsqr' + solver = "lsqr" else: raise ValueError(self.shrinkage) self.tol = float(self.tol) estimator = sklearn.discriminant_analysis.LinearDiscriminantAnalysis( - shrinkage=self.shrinkage_, tol=self.tol, solver=solver) + shrinkage=self.shrinkage_, tol=self.tol, solver=solver + ) if len(Y.shape) == 2 and Y.shape[1] > 1: self.estimator = sklearn.multiclass.OneVsRestClassifier(estimator, n_jobs=1) @@ -60,25 +62,29 @@ def predict_proba(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'LDA', - 'name': 'Linear Discriminant Analysis', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "LDA", + "name": "Linear Discriminant Analysis", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() shrinkage = CategoricalHyperparameter( - "shrinkage", ["None", "auto", "manual"], default_value="None") - shrinkage_factor = UniformFloatHyperparameter( - "shrinkage_factor", 0., 1., 0.5) - tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, default_value=1e-4, log=True) + "shrinkage", ["None", "auto", "manual"], default_value="None" + ) + shrinkage_factor = UniformFloatHyperparameter("shrinkage_factor", 0.0, 1.0, 0.5) + tol = UniformFloatHyperparameter( + "tol", 1e-5, 1e-1, default_value=1e-4, log=True + ) cs.add_hyperparameters([shrinkage, shrinkage_factor, tol]) cs.add_condition(EqualsCondition(shrinkage_factor, shrinkage, "manual")) diff --git a/autosklearn/pipeline/components/classification/liblinear_svc.py b/autosklearn/pipeline/components/classification/liblinear_svc.py index 9c625139f5..3f57ef8f94 100644 --- a/autosklearn/pipeline/components/classification/liblinear_svc.py +++ b/autosklearn/pipeline/components/classification/liblinear_svc.py @@ -1,20 +1,32 @@ from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - CategoricalHyperparameter, Constant -from ConfigSpace.forbidden import ForbiddenEqualsClause, \ - ForbiddenAndConjunction +from ConfigSpace.forbidden import ForbiddenAndConjunction, ForbiddenEqualsClause +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + Constant, + UniformFloatHyperparameter, +) from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA from autosklearn.pipeline.implementations.util import softmax -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE from autosklearn.util.common import check_for_bool, check_none class LibLinear_SVC(AutoSklearnClassificationAlgorithm): # Liblinear is not deterministic as it uses a RNG inside - def __init__(self, penalty, loss, dual, tol, C, multi_class, - fit_intercept, intercept_scaling, class_weight=None, - random_state=None): + def __init__( + self, + penalty, + loss, + dual, + tol, + C, + multi_class, + fit_intercept, + intercept_scaling, + class_weight=None, + random_state=None, + ): self.penalty = penalty self.loss = loss self.dual = dual @@ -28,8 +40,8 @@ def __init__(self, penalty, loss, dual, tol, C, multi_class, self.estimator = None def fit(self, X, Y): - import sklearn.svm import sklearn.multiclass + import sklearn.svm self.C = float(self.C) self.tol = float(self.tol) @@ -43,16 +55,18 @@ def fit(self, X, Y): if check_none(self.class_weight): self.class_weight = None - estimator = sklearn.svm.LinearSVC(penalty=self.penalty, - loss=self.loss, - dual=self.dual, - tol=self.tol, - C=self.C, - class_weight=self.class_weight, - fit_intercept=self.fit_intercept, - intercept_scaling=self.intercept_scaling, - multi_class=self.multi_class, - random_state=self.random_state) + estimator = sklearn.svm.LinearSVC( + penalty=self.penalty, + loss=self.loss, + dual=self.dual, + tol=self.tol, + C=self.C, + class_weight=self.class_weight, + fit_intercept=self.fit_intercept, + intercept_scaling=self.intercept_scaling, + multi_class=self.multi_class, + random_state=self.random_state, + ) if len(Y.shape) == 2 and Y.shape[1] > 1: self.estimator = sklearn.multiclass.OneVsRestClassifier(estimator, n_jobs=1) @@ -76,50 +90,51 @@ def predict_proba(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'Liblinear-SVC', - 'name': 'Liblinear Support Vector Classification', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': False, - 'is_deterministic': False, - 'input': (SPARSE, DENSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "Liblinear-SVC", + "name": "Liblinear Support Vector Classification", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": False, + "is_deterministic": False, + "input": (SPARSE, DENSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() - penalty = CategoricalHyperparameter( - "penalty", ["l1", "l2"], default_value="l2") + penalty = CategoricalHyperparameter("penalty", ["l1", "l2"], default_value="l2") loss = CategoricalHyperparameter( - "loss", ["hinge", "squared_hinge"], default_value="squared_hinge") + "loss", ["hinge", "squared_hinge"], default_value="squared_hinge" + ) dual = Constant("dual", "False") # This is set ad-hoc tol = UniformFloatHyperparameter( - "tol", 1e-5, 1e-1, default_value=1e-4, log=True) - C = UniformFloatHyperparameter( - "C", 0.03125, 32768, log=True, default_value=1.0) + "tol", 1e-5, 1e-1, default_value=1e-4, log=True + ) + C = UniformFloatHyperparameter("C", 0.03125, 32768, log=True, default_value=1.0) multi_class = Constant("multi_class", "ovr") # These are set ad-hoc fit_intercept = Constant("fit_intercept", "True") intercept_scaling = Constant("intercept_scaling", 1) - cs.add_hyperparameters([penalty, loss, dual, tol, C, multi_class, - fit_intercept, intercept_scaling]) + cs.add_hyperparameters( + [penalty, loss, dual, tol, C, multi_class, fit_intercept, intercept_scaling] + ) penalty_and_loss = ForbiddenAndConjunction( - ForbiddenEqualsClause(penalty, "l1"), - ForbiddenEqualsClause(loss, "hinge") + ForbiddenEqualsClause(penalty, "l1"), ForbiddenEqualsClause(loss, "hinge") ) constant_penalty_and_loss = ForbiddenAndConjunction( ForbiddenEqualsClause(dual, "False"), ForbiddenEqualsClause(penalty, "l2"), - ForbiddenEqualsClause(loss, "hinge") + ForbiddenEqualsClause(loss, "hinge"), ) penalty_and_dual = ForbiddenAndConjunction( - ForbiddenEqualsClause(dual, "False"), - ForbiddenEqualsClause(penalty, "l1") + ForbiddenEqualsClause(dual, "False"), ForbiddenEqualsClause(penalty, "l1") ) cs.add_forbidden_clause(penalty_and_loss) cs.add_forbidden_clause(constant_penalty_and_loss) diff --git a/autosklearn/pipeline/components/classification/libsvm_svc.py b/autosklearn/pipeline/components/classification/libsvm_svc.py index 97c55be49d..ba423161c1 100644 --- a/autosklearn/pipeline/components/classification/libsvm_svc.py +++ b/autosklearn/pipeline/components/classification/libsvm_svc.py @@ -1,21 +1,35 @@ import resource import sys -from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.conditions import EqualsCondition, InCondition -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - UniformIntegerHyperparameter, CategoricalHyperparameter, \ - UnParametrizedHyperparameter +from ConfigSpace.configuration_space import ConfigurationSpace +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformFloatHyperparameter, + UniformIntegerHyperparameter, + UnParametrizedHyperparameter, +) from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA from autosklearn.pipeline.implementations.util import softmax from autosklearn.util.common import check_for_bool, check_none class LibSVM_SVC(AutoSklearnClassificationAlgorithm): - def __init__(self, C, kernel, gamma, shrinking, tol, max_iter, - class_weight=None, degree=3, coef0=0, random_state=None): + def __init__( + self, + C, + kernel, + gamma, + shrinking, + tol, + max_iter, + class_weight=None, + degree=3, + coef0=0, + random_state=None, + ): self.C = C self.kernel = kernel self.degree = degree @@ -31,9 +45,9 @@ def __init__(self, C, kernel, gamma, shrinking, tol, max_iter, def fit(self, X, Y): import sklearn.svm - # Calculate the size of the kernel cache (in MB) for sklearn's LibSVM. The cache size is - # calculated as 2/3 of the available memory (which is calculated as the memory limit minus - # the used memory) + # Calculate the size of the kernel cache (in MB) for sklearn's LibSVM. + # The cache size is calculated as 2/3 of the available memory + # (which is calculated as the memory limit minus the used memory) try: # Retrieve memory limits imposed on the process soft, hard = resource.getrlimit(resource.RLIMIT_AS) @@ -45,9 +59,9 @@ def fit(self, X, Y): # Retrieve memory used by this process maxrss = resource.getrusage(resource.RUSAGE_SELF)[2] / 1024 - # In MacOS, the MaxRSS output of resource.getrusage in bytes; on other platforms, - # it's in kilobytes - if sys.platform == 'darwin': + # In MacOS, the MaxRSS output of resource.getrusage in bytes; + # on other platforms, it's in kilobytes + if sys.platform == "darwin": maxrss = maxrss / 1024 cache_size = (soft - maxrss) / 1.5 @@ -80,18 +94,20 @@ def fit(self, X, Y): if check_none(self.class_weight): self.class_weight = None - self.estimator = sklearn.svm.SVC(C=self.C, - kernel=self.kernel, - degree=self.degree, - gamma=self.gamma, - coef0=self.coef0, - shrinking=self.shrinking, - tol=self.tol, - class_weight=self.class_weight, - max_iter=self.max_iter, - random_state=self.random_state, - cache_size=cache_size, - decision_function_shape='ovr') + self.estimator = sklearn.svm.SVC( + C=self.C, + kernel=self.kernel, + degree=self.degree, + gamma=self.gamma, + coef0=self.coef0, + shrinking=self.shrinking, + tol=self.tol, + class_weight=self.class_weight, + max_iter=self.max_iter, + random_state=self.random_state, + cache_size=cache_size, + decision_function_shape="ovr", + ) self.estimator.fit(X, Y) return self @@ -109,41 +125,45 @@ def predict_proba(self, X): @staticmethod def get_properties(dataset_properties=None): return { - 'shortname': 'LibSVM-SVC', - 'name': 'LibSVM Support Vector Classification', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': False, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + "shortname": "LibSVM-SVC", + "name": "LibSVM Support Vector Classification", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": False, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): - C = UniformFloatHyperparameter("C", 0.03125, 32768, log=True, - default_value=1.0) + C = UniformFloatHyperparameter("C", 0.03125, 32768, log=True, default_value=1.0) # No linear kernel here, because we have liblinear - kernel = CategoricalHyperparameter(name="kernel", - choices=["rbf", "poly", "sigmoid"], - default_value="rbf") + kernel = CategoricalHyperparameter( + name="kernel", choices=["rbf", "poly", "sigmoid"], default_value="rbf" + ) degree = UniformIntegerHyperparameter("degree", 2, 5, default_value=3) - gamma = UniformFloatHyperparameter("gamma", 3.0517578125e-05, 8, - log=True, default_value=0.1) + gamma = UniformFloatHyperparameter( + "gamma", 3.0517578125e-05, 8, log=True, default_value=0.1 + ) # TODO this is totally ad-hoc coef0 = UniformFloatHyperparameter("coef0", -1, 1, default_value=0) # probability is no hyperparameter, but an argument to the SVM algo - shrinking = CategoricalHyperparameter("shrinking", ["True", "False"], - default_value="True") - tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, default_value=1e-3, - log=True) + shrinking = CategoricalHyperparameter( + "shrinking", ["True", "False"], default_value="True" + ) + tol = UniformFloatHyperparameter( + "tol", 1e-5, 1e-1, default_value=1e-3, log=True + ) # cache size is not a hyperparameter, but an argument to the program! max_iter = UnParametrizedHyperparameter("max_iter", -1) cs = ConfigurationSpace() - cs.add_hyperparameters([C, kernel, degree, gamma, coef0, shrinking, - tol, max_iter]) + cs.add_hyperparameters( + [C, kernel, degree, gamma, coef0, shrinking, tol, max_iter] + ) degree_depends_on_poly = EqualsCondition(degree, kernel, "poly") coef0_condition = InCondition(coef0, kernel, ["poly", "sigmoid"]) diff --git a/autosklearn/pipeline/components/classification/mlp.py b/autosklearn/pipeline/components/classification/mlp.py index e26f2318cf..f7001d7bc1 100644 --- a/autosklearn/pipeline/components/classification/mlp.py +++ b/autosklearn/pipeline/components/classification/mlp.py @@ -1,30 +1,45 @@ import copy -import numpy as np -from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - UniformIntegerHyperparameter, UnParametrizedHyperparameter, Constant, \ - CategoricalHyperparameter +import numpy as np from ConfigSpace.conditions import InCondition +from ConfigSpace.configuration_space import ConfigurationSpace +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + Constant, + UniformFloatHyperparameter, + UniformIntegerHyperparameter, + UnParametrizedHyperparameter, +) from autosklearn.pipeline.components.base import ( AutoSklearnClassificationAlgorithm, IterativeComponent, ) -from autosklearn.pipeline.constants import SPARSE, DENSE, UNSIGNED_DATA, PREDICTIONS +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA from autosklearn.util.common import check_for_bool -class MLPClassifier( - IterativeComponent, - AutoSklearnClassificationAlgorithm -): - def __init__(self, hidden_layer_depth, num_nodes_per_layer, activation, alpha, - learning_rate_init, early_stopping, solver, batch_size, - n_iter_no_change, tol, - shuffle, beta_1, beta_2, epsilon, - validation_fraction=None, - random_state=None, verbose=0): +class MLPClassifier(IterativeComponent, AutoSklearnClassificationAlgorithm): + def __init__( + self, + hidden_layer_depth, + num_nodes_per_layer, + activation, + alpha, + learning_rate_init, + early_stopping, + solver, + batch_size, + n_iter_no_change, + tol, + shuffle, + beta_1, + beta_2, + epsilon, + validation_fraction=None, + random_state=None, + verbose=0, + ): self.hidden_layer_depth = hidden_layer_depth self.num_nodes_per_layer = num_nodes_per_layer self.max_iter = self.get_max_iter() @@ -60,6 +75,7 @@ def iterative_fit(self, X, y, n_iter=2, refit=False): Set n_iter=2 for the same reason as for SGD """ from sklearn.neural_network import MLPClassifier + n_iter = max(n_iter, 2) if refit: @@ -71,8 +87,9 @@ def iterative_fit(self, X, y, n_iter=2, refit=False): self.max_iter = int(self.max_iter) self.hidden_layer_depth = int(self.hidden_layer_depth) self.num_nodes_per_layer = int(self.num_nodes_per_layer) - self.hidden_layer_sizes = tuple(self.num_nodes_per_layer - for i in range(self.hidden_layer_depth)) + self.hidden_layer_sizes = tuple( + self.num_nodes_per_layer for i in range(self.hidden_layer_depth) + ) self.activation = str(self.activation) self.alpha = float(self.alpha) self.learning_rate_init = float(self.learning_rate_init) @@ -88,7 +105,9 @@ def iterative_fit(self, X, y, n_iter=2, refit=False): self.n_iter_no_change = int(self.n_iter_no_change) self.early_stopping_val = True else: - raise ValueError("Set early stopping to unknown value %s" % self.early_stopping) + raise ValueError( + "Set early stopping to unknown value %s" % self.early_stopping + ) # elif self.early_stopping == "off": # self.validation_fraction = 0 # self.tol = 10000 @@ -142,8 +161,10 @@ def iterative_fit(self, X, y, n_iter=2, refit=False): new_max_iter = min(self.max_iter - self.estimator.n_iter_, n_iter) self.estimator.max_iter = new_max_iter self.estimator.fit(X, y) - if self.estimator.n_iter_ >= self.max_iter or \ - self.estimator._no_improvement_count > self.n_iter_no_change: + if ( + self.estimator.n_iter_ >= self.max_iter + or self.estimator._no_improvement_count > self.n_iter_no_change + ): self._fully_fit = True return self @@ -151,7 +172,7 @@ def iterative_fit(self, X, y, n_iter=2, refit=False): def configuration_fully_fitted(self): if self.estimator is None: return False - elif not hasattr(self, '_fully_fit'): + elif not hasattr(self, "_fully_fit"): return False else: return self._fully_fit @@ -168,42 +189,55 @@ def predict_proba(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'MLP', - 'name': 'Multilayer Percepton', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "MLP", + "name": "Multilayer Percepton", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() - hidden_layer_depth = UniformIntegerHyperparameter(name="hidden_layer_depth", - lower=1, upper=3, default_value=1) - num_nodes_per_layer = UniformIntegerHyperparameter(name="num_nodes_per_layer", - lower=16, upper=264, default_value=32, - log=True) - activation = CategoricalHyperparameter(name="activation", choices=['tanh', 'relu'], - default_value='relu') - alpha = UniformFloatHyperparameter(name="alpha", lower=1e-7, upper=1e-1, default_value=1e-4, - log=True) - - learning_rate_init = UniformFloatHyperparameter(name="learning_rate_init", - lower=1e-4, upper=0.5, default_value=1e-3, - log=True) + hidden_layer_depth = UniformIntegerHyperparameter( + name="hidden_layer_depth", lower=1, upper=3, default_value=1 + ) + num_nodes_per_layer = UniformIntegerHyperparameter( + name="num_nodes_per_layer", lower=16, upper=264, default_value=32, log=True + ) + activation = CategoricalHyperparameter( + name="activation", choices=["tanh", "relu"], default_value="relu" + ) + alpha = UniformFloatHyperparameter( + name="alpha", lower=1e-7, upper=1e-1, default_value=1e-4, log=True + ) + + learning_rate_init = UniformFloatHyperparameter( + name="learning_rate_init", + lower=1e-4, + upper=0.5, + default_value=1e-3, + log=True, + ) # Not allowing to turn off early stopping - early_stopping = CategoricalHyperparameter(name="early_stopping", - choices=["valid", "train"], # , "off"], - default_value="valid") + early_stopping = CategoricalHyperparameter( + name="early_stopping", + choices=["valid", "train"], # , "off"], + default_value="valid", + ) # Constants - n_iter_no_change = Constant(name="n_iter_no_change", value=32) # default=10 is too low + n_iter_no_change = Constant( + name="n_iter_no_change", value=32 + ) # default=10 is too low validation_fraction = Constant(name="validation_fraction", value=0.1) tol = UnParametrizedHyperparameter(name="tol", value=1e-4) - solver = Constant(name="solver", value='adam') + solver = Constant(name="solver", value="adam") # Relying on sklearn defaults for now batch_size = UnParametrizedHyperparameter(name="batch_size", value="auto") @@ -221,17 +255,33 @@ def get_hyperparameter_search_space(dataset_properties=None): # max_fun --> only used when solver=lbfgs # activation=["identity", "logistic"] --> not useful for classification - cs.add_hyperparameters([hidden_layer_depth, num_nodes_per_layer, - activation, alpha, - learning_rate_init, early_stopping, - n_iter_no_change, validation_fraction, tol, - solver, batch_size, shuffle, - beta_1, beta_2, epsilon]) + cs.add_hyperparameters( + [ + hidden_layer_depth, + num_nodes_per_layer, + activation, + alpha, + learning_rate_init, + early_stopping, + n_iter_no_change, + validation_fraction, + tol, + solver, + batch_size, + shuffle, + beta_1, + beta_2, + epsilon, + ] + ) - validation_fraction_cond = InCondition(validation_fraction, early_stopping, ["valid"]) + validation_fraction_cond = InCondition( + validation_fraction, early_stopping, ["valid"] + ) cs.add_conditions([validation_fraction_cond]) # We always use early stopping - # n_iter_no_change_cond = InCondition(n_iter_no_change, early_stopping, ["valid", "train"]) + # n_iter_no_change_cond = \ + # InCondition(n_iter_no_change, early_stopping, ["valid", "train"]) # tol_cond = InCondition(n_iter_no_change, early_stopping, ["valid", "train"]) # cs.add_conditions([n_iter_no_change_cond, tol_cond]) return cs diff --git a/autosklearn/pipeline/components/classification/multinomial_nb.py b/autosklearn/pipeline/components/classification/multinomial_nb.py index e678bd4c77..7b65be8a5c 100644 --- a/autosklearn/pipeline/components/classification/multinomial_nb.py +++ b/autosklearn/pipeline/components/classification/multinomial_nb.py @@ -1,18 +1,16 @@ import numpy as np - from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - CategoricalHyperparameter - -from autosklearn.pipeline.components.base import ( - AutoSklearnClassificationAlgorithm, +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformFloatHyperparameter, ) -from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, SIGNED_DATA + +from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SIGNED_DATA, SPARSE from autosklearn.util.common import check_for_bool class MultinomialNB(AutoSklearnClassificationAlgorithm): - def __init__(self, alpha, fit_prior, random_state=None, verbose=0): self.alpha = alpha self.fit_prior = fit_prior @@ -21,8 +19,8 @@ def __init__(self, alpha, fit_prior, random_state=None, verbose=0): self.estimator = None def fit(self, X, y): - import sklearn.naive_bayes import scipy.sparse + import sklearn.naive_bayes self.fit_prior = check_for_bool(self.fit_prior) self.alpha = float(self.alpha) @@ -31,7 +29,7 @@ def fit(self, X, y): self.estimator = sklearn.naive_bayes.MultinomialNB( alpha=self.alpha, fit_prior=self.fit_prior, - ) + ) self.classes_ = np.unique(y.astype(int)) # Because the pipeline guarantees that each feature is positive, @@ -44,8 +42,10 @@ def fit(self, X, y): # Fallback for multilabel classification if len(y.shape) > 1 and y.shape[1] > 1: import sklearn.multiclass + self.estimator = sklearn.multiclass.OneVsRestClassifier( - self.estimator, n_jobs=1) + self.estimator, n_jobs=1 + ) self.estimator.fit(X, y) return self @@ -62,16 +62,18 @@ def predict_proba(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'MultinomialNB', - 'name': 'Multinomial Naive Bayes classifier', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, SIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "MultinomialNB", + "name": "Multinomial Naive Bayes classifier", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, SPARSE, SIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): @@ -80,12 +82,13 @@ def get_hyperparameter_search_space(dataset_properties=None): # the smoothing parameter is a non-negative float # I will limit it to 100 and put it on a logarithmic scale. (SF) # Please adjust that, if you know a proper range, this is just a guess. - alpha = UniformFloatHyperparameter(name="alpha", lower=1e-2, upper=100, - default_value=1, log=True) + alpha = UniformFloatHyperparameter( + name="alpha", lower=1e-2, upper=100, default_value=1, log=True + ) - fit_prior = CategoricalHyperparameter(name="fit_prior", - choices=["True", "False"], - default_value="True") + fit_prior = CategoricalHyperparameter( + name="fit_prior", choices=["True", "False"], default_value="True" + ) cs.add_hyperparameters([alpha, fit_prior]) diff --git a/autosklearn/pipeline/components/classification/passive_aggressive.py b/autosklearn/pipeline/components/classification/passive_aggressive.py index 5fb1f1bbf7..494ea7db06 100644 --- a/autosklearn/pipeline/components/classification/passive_aggressive.py +++ b/autosklearn/pipeline/components/classification/passive_aggressive.py @@ -1,14 +1,16 @@ import numpy as np - from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - CategoricalHyperparameter, UnParametrizedHyperparameter +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformFloatHyperparameter, + UnParametrizedHyperparameter, +) from autosklearn.pipeline.components.base import ( AutoSklearnClassificationAlgorithm, IterativeComponentWithSampleWeight, ) -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, PREDICTIONS +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA from autosklearn.pipeline.implementations.util import softmax from autosklearn.util.common import check_for_bool @@ -76,9 +78,11 @@ def iterative_fit(self, X, y, n_iter=2, refit=False, sample_weight=None): # Fallback for multilabel classification if len(y.shape) > 1 and y.shape[1] > 1: import sklearn.multiclass + self.estimator.max_iter = self.get_max_iter() self.estimator = sklearn.multiclass.OneVsRestClassifier( - self.estimator, n_jobs=1) + self.estimator, n_jobs=1 + ) self.estimator.fit(X, y) self.fully_fit_ = True else: @@ -91,7 +95,8 @@ def iterative_fit(self, X, y, n_iter=2, refit=False, sample_weight=None): self.estimator._validate_params() lr = "pa1" if self.estimator.loss == "hinge" else "pa2" self.estimator._partial_fit( - X, y, + X, + y, alpha=1.0, C=self.estimator.C, loss="hinge", @@ -100,12 +105,12 @@ def iterative_fit(self, X, y, n_iter=2, refit=False, sample_weight=None): classes=None, sample_weight=sample_weight, coef_init=None, - intercept_init=None + intercept_init=None, ) self.n_iter_ += self.estimator.n_iter_ if ( self.estimator.max_iter >= self.max_iter - or self.estimator.max_iter > self.n_iter_ + or self.estimator.max_iter > self.n_iter_ ): self.fully_fit_ = True @@ -114,7 +119,7 @@ def iterative_fit(self, X, y, n_iter=2, refit=False, sample_weight=None): def configuration_fully_fitted(self): if self.estimator is None: return False - elif not hasattr(self, 'fully_fit_'): + elif not hasattr(self, "fully_fit_"): return False else: return self.fully_fit_ @@ -133,16 +138,18 @@ def predict_proba(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'PassiveAggressive Classifier', - 'name': 'Passive Aggressive Classifier', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "PassiveAggressive Classifier", + "name": "Passive Aggressive Classifier", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): @@ -152,11 +159,13 @@ def get_hyperparameter_search_space(dataset_properties=None): "loss", ["hinge", "squared_hinge"], default_value="hinge" ) - tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, default_value=1e-4, - log=True) + tol = UniformFloatHyperparameter( + "tol", 1e-5, 1e-1, default_value=1e-4, log=True + ) # Note: Average could also be an Integer if > 1 - average = CategoricalHyperparameter('average', ['False', 'True'], - default_value='False') + average = CategoricalHyperparameter( + "average", ["False", "True"], default_value="False" + ) cs = ConfigurationSpace() cs.add_hyperparameters([loss, fit_intercept, tol, C, average]) diff --git a/autosklearn/pipeline/components/classification/qda.py b/autosklearn/pipeline/components/classification/qda.py index 7405b21fae..7b25858392 100644 --- a/autosklearn/pipeline/components/classification/qda.py +++ b/autosklearn/pipeline/components/classification/qda.py @@ -1,16 +1,13 @@ +import numpy as np from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformFloatHyperparameter -from autosklearn.pipeline.components.base import \ - AutoSklearnClassificationAlgorithm -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS +from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, UNSIGNED_DATA from autosklearn.pipeline.implementations.util import softmax -import numpy as np - class QDA(AutoSklearnClassificationAlgorithm): - def __init__(self, reg_param, random_state=None): self.reg_param = float(reg_param) self.estimator = None @@ -18,11 +15,13 @@ def __init__(self, reg_param, random_state=None): def fit(self, X, Y): import sklearn.discriminant_analysis - estimator = sklearn.discriminant_analysis.\ - QuadraticDiscriminantAnalysis(reg_param=self.reg_param) + estimator = sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis( + reg_param=self.reg_param + ) if len(Y.shape) == 2 and Y.shape[1] > 1: import sklearn.multiclass + self.estimator = sklearn.multiclass.OneVsRestClassifier(estimator, n_jobs=1) else: self.estimator = estimator @@ -32,16 +31,17 @@ def fit(self, X, Y): if len(Y.shape) == 2 and Y.shape[1] > 1: problems = [] for est in self.estimator.estimators_: - problem = np.any(np.any([np.any(s <= 0.0) for s in - est.scalings_])) + problem = np.any(np.any([np.any(s <= 0.0) for s in est.scalings_])) problems.append(problem) problem = np.any(problems) else: - problem = np.any(np.any([np.any(s <= 0.0) for s in - self.estimator.scalings_])) + problem = np.any( + np.any([np.any(s <= 0.0) for s in self.estimator.scalings_]) + ) if problem: - raise ValueError('Numerical problems in QDA. QDA.scalings_ ' - 'contains values <= 0.0') + raise ValueError( + "Numerical problems in QDA. QDA.scalings_ " "contains values <= 0.0" + ) return self def predict(self, X): @@ -58,21 +58,22 @@ def predict_proba(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'QDA', - 'name': 'Quadratic Discriminant Analysis', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "QDA", + "name": "Quadratic Discriminant Analysis", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): - reg_param = UniformFloatHyperparameter('reg_param', 0.0, 1.0, - default_value=0.0) + reg_param = UniformFloatHyperparameter("reg_param", 0.0, 1.0, default_value=0.0) cs = ConfigurationSpace() cs.add_hyperparameter(reg_param) return cs diff --git a/autosklearn/pipeline/components/classification/random_forest.py b/autosklearn/pipeline/components/classification/random_forest.py index c2f4e9779a..6ccd720b3a 100644 --- a/autosklearn/pipeline/components/classification/random_forest.py +++ b/autosklearn/pipeline/components/classification/random_forest.py @@ -1,13 +1,19 @@ from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - UniformIntegerHyperparameter, CategoricalHyperparameter, UnParametrizedHyperparameter +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformFloatHyperparameter, + UniformIntegerHyperparameter, + UnParametrizedHyperparameter, +) from autosklearn.pipeline.components.base import ( AutoSklearnClassificationAlgorithm, IterativeComponentWithSampleWeight, ) -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE -from autosklearn.pipeline.implementations.util import convert_multioutput_multiclass_to_multilabel +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA +from autosklearn.pipeline.implementations.util import ( + convert_multioutput_multiclass_to_multilabel, +) from autosklearn.util.common import check_for_bool, check_none @@ -15,11 +21,21 @@ class RandomForest( IterativeComponentWithSampleWeight, AutoSklearnClassificationAlgorithm, ): - def __init__(self, criterion, max_features, - max_depth, min_samples_split, min_samples_leaf, - min_weight_fraction_leaf, bootstrap, max_leaf_nodes, - min_impurity_decrease, random_state=None, n_jobs=1, - class_weight=None): + def __init__( + self, + criterion, + max_features, + max_depth, + min_samples_split, + min_samples_leaf, + min_weight_fraction_leaf, + bootstrap, + max_leaf_nodes, + min_impurity_decrease, + random_state=None, + n_jobs=1, + class_weight=None, + ): self.n_estimators = self.get_max_iter() self.criterion = criterion self.max_features = max_features @@ -88,11 +104,13 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): random_state=self.random_state, n_jobs=self.n_jobs, class_weight=self.class_weight, - warm_start=True) + warm_start=True, + ) else: self.estimator.n_estimators += n_iter - self.estimator.n_estimators = min(self.estimator.n_estimators, - self.n_estimators) + self.estimator.n_estimators = min( + self.estimator.n_estimators, self.n_estimators + ) self.estimator.fit(X, y, sample_weight=sample_weight) return self @@ -117,42 +135,63 @@ def predict_proba(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'RF', - 'name': 'Random Forest Classifier', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "RF", + "name": "Random Forest Classifier", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() criterion = CategoricalHyperparameter( - "criterion", ["gini", "entropy"], default_value="gini") - - # The maximum number of features used in the forest is calculated as m^max_features, where - # m is the total number of features, and max_features is the hyperparameter specified below. - # The default is 0.5, which yields sqrt(m) features as max_features in the estimator. This - # corresponds with Geurts' heuristic. + "criterion", ["gini", "entropy"], default_value="gini" + ) + + # The maximum number of features used in the forest is calculated as + # m^max_features, where m is the total number of features, and max_features + # is the hyperparameter specified below. The default is 0.5, which yields + # sqrt(m) features as max_features in the estimator. + # This corresponds with Geurts' heuristic. max_features = UniformFloatHyperparameter( - "max_features", 0., 1., default_value=0.5) + "max_features", 0.0, 1.0, default_value=0.5 + ) max_depth = UnParametrizedHyperparameter("max_depth", "None") min_samples_split = UniformIntegerHyperparameter( - "min_samples_split", 2, 20, default_value=2) + "min_samples_split", 2, 20, default_value=2 + ) min_samples_leaf = UniformIntegerHyperparameter( - "min_samples_leaf", 1, 20, default_value=1) - min_weight_fraction_leaf = UnParametrizedHyperparameter("min_weight_fraction_leaf", 0.) + "min_samples_leaf", 1, 20, default_value=1 + ) + min_weight_fraction_leaf = UnParametrizedHyperparameter( + "min_weight_fraction_leaf", 0.0 + ) max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") - min_impurity_decrease = UnParametrizedHyperparameter('min_impurity_decrease', 0.0) + min_impurity_decrease = UnParametrizedHyperparameter( + "min_impurity_decrease", 0.0 + ) bootstrap = CategoricalHyperparameter( - "bootstrap", ["True", "False"], default_value="True") - cs.add_hyperparameters([criterion, max_features, - max_depth, min_samples_split, min_samples_leaf, - min_weight_fraction_leaf, max_leaf_nodes, - bootstrap, min_impurity_decrease]) + "bootstrap", ["True", "False"], default_value="True" + ) + cs.add_hyperparameters( + [ + criterion, + max_features, + max_depth, + min_samples_split, + min_samples_leaf, + min_weight_fraction_leaf, + max_leaf_nodes, + bootstrap, + min_impurity_decrease, + ] + ) return cs diff --git a/autosklearn/pipeline/components/classification/sgd.py b/autosklearn/pipeline/components/classification/sgd.py index 6875541824..469c2605dd 100644 --- a/autosklearn/pipeline/components/classification/sgd.py +++ b/autosklearn/pipeline/components/classification/sgd.py @@ -1,13 +1,16 @@ -from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - CategoricalHyperparameter, UnParametrizedHyperparameter from ConfigSpace.conditions import EqualsCondition, InCondition +from ConfigSpace.configuration_space import ConfigurationSpace +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformFloatHyperparameter, + UnParametrizedHyperparameter, +) from autosklearn.pipeline.components.base import ( AutoSklearnClassificationAlgorithm, IterativeComponentWithSampleWeight, ) -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA from autosklearn.pipeline.implementations.util import softmax from autosklearn.util.common import check_for_bool @@ -16,9 +19,21 @@ class SGD( IterativeComponentWithSampleWeight, AutoSklearnClassificationAlgorithm, ): - def __init__(self, loss, penalty, alpha, fit_intercept, tol, - learning_rate, l1_ratio=0.15, epsilon=0.1, - eta0=0.01, power_t=0.5, average=False, random_state=None): + def __init__( + self, + loss, + penalty, + alpha, + fit_intercept, + tol, + learning_rate, + l1_ratio=0.15, + epsilon=0.1, + eta0=0.01, + power_t=0.5, + average=False, + random_state=None, + ): self.max_iter = self.get_max_iter() self.loss = loss self.penalty = penalty @@ -61,32 +76,31 @@ def iterative_fit(self, X, y, n_iter=2, refit=False, sample_weight=None): self.fully_fit_ = False self.alpha = float(self.alpha) - self.l1_ratio = float(self.l1_ratio) if self.l1_ratio is not None \ - else 0.15 - self.epsilon = float(self.epsilon) if self.epsilon is not None \ - else 0.1 + self.l1_ratio = float(self.l1_ratio) if self.l1_ratio is not None else 0.15 + self.epsilon = float(self.epsilon) if self.epsilon is not None else 0.1 self.eta0 = float(self.eta0) - self.power_t = float(self.power_t) if self.power_t is not None \ - else 0.5 + self.power_t = float(self.power_t) if self.power_t is not None else 0.5 self.average = check_for_bool(self.average) self.fit_intercept = check_for_bool(self.fit_intercept) self.tol = float(self.tol) - self.estimator = SGDClassifier(loss=self.loss, - penalty=self.penalty, - alpha=self.alpha, - fit_intercept=self.fit_intercept, - max_iter=n_iter, - tol=self.tol, - learning_rate=self.learning_rate, - l1_ratio=self.l1_ratio, - epsilon=self.epsilon, - eta0=self.eta0, - power_t=self.power_t, - shuffle=True, - average=self.average, - random_state=self.random_state, - warm_start=True) + self.estimator = SGDClassifier( + loss=self.loss, + penalty=self.penalty, + alpha=self.alpha, + fit_intercept=self.fit_intercept, + max_iter=n_iter, + tol=self.tol, + learning_rate=self.learning_rate, + l1_ratio=self.l1_ratio, + epsilon=self.epsilon, + eta0=self.eta0, + power_t=self.power_t, + shuffle=True, + average=self.average, + random_state=self.random_state, + warm_start=True, + ) self.estimator.fit(X, y, sample_weight=sample_weight) self.n_iter_ = self.estimator.n_iter_ else: @@ -94,7 +108,8 @@ def iterative_fit(self, X, y, n_iter=2, refit=False, sample_weight=None): self.estimator.max_iter = min(self.estimator.max_iter, self.max_iter) self.estimator._validate_params() self.estimator._partial_fit( - X, y, + X, + y, alpha=self.estimator.alpha, C=1.0, loss=self.estimator.loss, @@ -103,11 +118,14 @@ def iterative_fit(self, X, y, n_iter=2, refit=False, sample_weight=None): sample_weight=sample_weight, classes=None, coef_init=None, - intercept_init=None + intercept_init=None, ) self.n_iter_ += self.estimator.n_iter_ - if self.estimator.max_iter >= self.max_iter or self.estimator.max_iter > self.n_iter_: + if ( + self.estimator.max_iter >= self.max_iter + or self.estimator.max_iter > self.n_iter_ + ): self.fully_fit_ = True return self @@ -115,7 +133,7 @@ def iterative_fit(self, X, y, n_iter=2, refit=False, sample_weight=None): def configuration_fully_fitted(self): if self.estimator is None: return False - elif not hasattr(self, 'fully_fit_'): + elif not hasattr(self, "fully_fit_"): return False else: return self.fully_fit_ @@ -137,16 +155,18 @@ def predict_proba(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'SGD Classifier', - 'name': 'Stochastic Gradient Descent Classifier', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': False, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "SGD Classifier", + "name": "Stochastic Gradient Descent Classifier", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": False, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): @@ -156,44 +176,63 @@ def get_hyperparameter_search_space(dataset_properties=None): "loss", ["hinge", "log", "modified_huber", "squared_hinge", "perceptron"], default_value="log", - ) + ) penalty = CategoricalHyperparameter( - "penalty", ["l1", "l2", "elasticnet"], default_value="l2") + "penalty", ["l1", "l2", "elasticnet"], default_value="l2" + ) alpha = UniformFloatHyperparameter( - "alpha", 1e-7, 1e-1, log=True, default_value=0.0001) + "alpha", 1e-7, 1e-1, log=True, default_value=0.0001 + ) l1_ratio = UniformFloatHyperparameter( - "l1_ratio", 1e-9, 1, log=True, default_value=0.15) + "l1_ratio", 1e-9, 1, log=True, default_value=0.15 + ) fit_intercept = UnParametrizedHyperparameter("fit_intercept", "True") - tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, log=True, - default_value=1e-4) + tol = UniformFloatHyperparameter( + "tol", 1e-5, 1e-1, log=True, default_value=1e-4 + ) epsilon = UniformFloatHyperparameter( - "epsilon", 1e-5, 1e-1, default_value=1e-4, log=True) + "epsilon", 1e-5, 1e-1, default_value=1e-4, log=True + ) learning_rate = CategoricalHyperparameter( - "learning_rate", ["optimal", "invscaling", "constant"], - default_value="invscaling") + "learning_rate", + ["optimal", "invscaling", "constant"], + default_value="invscaling", + ) eta0 = UniformFloatHyperparameter( - "eta0", 1e-7, 1e-1, default_value=0.01, log=True) - power_t = UniformFloatHyperparameter("power_t", 1e-5, 1, - default_value=0.5) + "eta0", 1e-7, 1e-1, default_value=0.01, log=True + ) + power_t = UniformFloatHyperparameter("power_t", 1e-5, 1, default_value=0.5) average = CategoricalHyperparameter( - "average", ["False", "True"], default_value="False") - cs.add_hyperparameters([loss, penalty, alpha, l1_ratio, fit_intercept, - tol, epsilon, learning_rate, eta0, power_t, - average]) + "average", ["False", "True"], default_value="False" + ) + cs.add_hyperparameters( + [ + loss, + penalty, + alpha, + l1_ratio, + fit_intercept, + tol, + epsilon, + learning_rate, + eta0, + power_t, + average, + ] + ) # TODO add passive/aggressive here, although not properly documented? elasticnet = EqualsCondition(l1_ratio, penalty, "elasticnet") epsilon_condition = EqualsCondition(epsilon, loss, "modified_huber") - power_t_condition = EqualsCondition(power_t, learning_rate, - "invscaling") + power_t_condition = EqualsCondition(power_t, learning_rate, "invscaling") # eta0 is only relevant if learning_rate!='optimal' according to code # https://github.com/scikit-learn/scikit-learn/blob/0.19.X/sklearn/ # linear_model/sgd_fast.pyx#L603 - eta0_in_inv_con = InCondition(eta0, learning_rate, ["invscaling", - "constant"]) - cs.add_conditions([elasticnet, epsilon_condition, power_t_condition, - eta0_in_inv_con]) + eta0_in_inv_con = InCondition(eta0, learning_rate, ["invscaling", "constant"]) + cs.add_conditions( + [elasticnet, epsilon_condition, power_t_condition, eta0_in_inv_con] + ) return cs diff --git a/autosklearn/pipeline/components/data_preprocessing/__init__.py b/autosklearn/pipeline/components/data_preprocessing/__init__.py index 3ba5981965..5693efd441 100644 --- a/autosklearn/pipeline/components/data_preprocessing/__init__.py +++ b/autosklearn/pipeline/components/data_preprocessing/__init__.py @@ -1,18 +1,24 @@ +from typing import Dict, Optional, Type + import os from collections import OrderedDict -from typing import Dict, Optional, Type from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter + from autosklearn.pipeline.base import PIPELINE_DATA_DTYPE -from ..base import find_components, \ - ThirdPartyComponents, AutoSklearnChoice, AutoSklearnPreprocessingAlgorithm +from ..base import ( + AutoSklearnChoice, + AutoSklearnPreprocessingAlgorithm, + ThirdPartyComponents, + find_components, +) classifier_directory = os.path.split(__file__)[0] -_preprocessors = find_components(__package__, - classifier_directory, - AutoSklearnPreprocessingAlgorithm) +_preprocessors = find_components( + __package__, classifier_directory, AutoSklearnPreprocessingAlgorithm +) _addons = ThirdPartyComponents(AutoSklearnPreprocessingAlgorithm) @@ -21,7 +27,6 @@ def add_preprocessor(preprocessor: Type[AutoSklearnPreprocessingAlgorithm]) -> N class DataPreprocessorChoice(AutoSklearnChoice): - @classmethod def get_components(cls) -> OrderedDict: components: OrderedDict = OrderedDict() @@ -29,23 +34,28 @@ def get_components(cls) -> OrderedDict: components.update(_addons.components) return components - def get_available_components(self, dataset_properties: Optional[Dict] = None, - include: Optional[Dict] = None, - exclude: Optional[Dict] = None) -> OrderedDict: + def get_available_components( + self, + dataset_properties: Optional[Dict] = None, + include: Optional[Dict] = None, + exclude: Optional[Dict] = None, + ) -> OrderedDict: if dataset_properties is None: dataset_properties = {} if include is not None and exclude is not None: raise ValueError( - "The argument include and exclude cannot be used together.") + "The argument include and exclude cannot be used together." + ) available_comp = self.get_components() if include is not None: for incl in include: if incl not in available_comp: - raise ValueError("Trying to include unknown component: " - "%s" % incl) + raise ValueError( + "Trying to include unknown component: " "%s" % incl + ) # TODO check for task type classification and/or regression! @@ -59,38 +69,47 @@ def get_available_components(self, dataset_properties: Optional[Dict] = None, entry = available_comp[name] # Exclude itself to avoid infinite loop - if entry == DataPreprocessorChoice or hasattr(entry, 'get_components'): + if entry == DataPreprocessorChoice or hasattr(entry, "get_components"): continue - target_type = dataset_properties['target_type'] - if target_type == 'classification': - if entry.get_properties()['handles_classification'] is False: + target_type = dataset_properties["target_type"] + if target_type == "classification": + if entry.get_properties()["handles_classification"] is False: continue - if dataset_properties.get('multiclass') is True and \ - entry.get_properties()['handles_multiclass'] is False: + if ( + dataset_properties.get("multiclass") is True + and entry.get_properties()["handles_multiclass"] is False + ): continue - if dataset_properties.get('multilabel') is True and \ - entry.get_properties()['handles_multilabel'] is False: + if ( + dataset_properties.get("multilabel") is True + and entry.get_properties()["handles_multilabel"] is False + ): continue - elif target_type == 'regression': - if entry.get_properties()['handles_regression'] is False: + elif target_type == "regression": + if entry.get_properties()["handles_regression"] is False: continue - if dataset_properties.get('multioutput') is True and \ - entry.get_properties()['handles_multioutput'] is False: + if ( + dataset_properties.get("multioutput") is True + and entry.get_properties()["handles_multioutput"] is False + ): continue else: - raise ValueError('Unknown target type %s' % target_type) + raise ValueError("Unknown target type %s" % target_type) components_dict[name] = entry return components_dict - def get_hyperparameter_search_space(self, dataset_properties: Optional[Dict] = None, - default: str = None, - include: Optional[Dict] = None, - exclude: Optional[Dict] = None) -> ConfigurationSpace: + def get_hyperparameter_search_space( + self, + dataset_properties: Optional[Dict] = None, + default: str = None, + include: Optional[Dict] = None, + exclude: Optional[Dict] = None, + ) -> ConfigurationSpace: cs = ConfigurationSpace() if dataset_properties is None: @@ -98,12 +117,11 @@ def get_hyperparameter_search_space(self, dataset_properties: Optional[Dict] = N # Compile a list of legal preprocessors for this problem available_preprocessors = self.get_available_components( - dataset_properties=dataset_properties, - include=include, exclude=exclude) + dataset_properties=dataset_properties, include=include, exclude=exclude + ) if len(available_preprocessors) == 0: - raise ValueError( - "No preprocessors found, please add NoPreprocessing") + raise ValueError("No preprocessors found, please add NoPreprocessing") if default is None: defaults = ["feature_type"] @@ -112,43 +130,48 @@ def get_hyperparameter_search_space(self, dataset_properties: Optional[Dict] = N default = default_ break - preprocessor = CategoricalHyperparameter('__choice__', - list(available_preprocessors.keys()), - default_value=default) + preprocessor = CategoricalHyperparameter( + "__choice__", list(available_preprocessors.keys()), default_value=default + ) cs.add_hyperparameter(preprocessor) for name in available_preprocessors: preprocessor_configuration_space = available_preprocessors[name]( - dataset_properties=dataset_properties). \ - get_hyperparameter_search_space(dataset_properties) - parent_hyperparameter = {'parent': preprocessor, 'value': name} - cs.add_configuration_space(name, preprocessor_configuration_space, - parent_hyperparameter=parent_hyperparameter) + dataset_properties=dataset_properties + ).get_hyperparameter_search_space(dataset_properties) + parent_hyperparameter = {"parent": preprocessor, "value": name} + cs.add_configuration_space( + name, + preprocessor_configuration_space, + parent_hyperparameter=parent_hyperparameter, + ) return cs def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: return self.choice.transform(X) - def set_hyperparameters(self, configuration: ConfigurationSpace, - init_params: Optional[Dict] = None) -> 'DataPreprocessorChoice': + def set_hyperparameters( + self, configuration: ConfigurationSpace, init_params: Optional[Dict] = None + ) -> "DataPreprocessorChoice": config = {} params = configuration.get_dictionary() - choice = params['__choice__'] - del params['__choice__'] + choice = params["__choice__"] + del params["__choice__"] for param, value in params.items(): - param = param.replace(choice, '').split(':', 1)[1] + param = param.replace(choice, "").split(":", 1)[1] config[param] = value new_params = {} feat_type = None if init_params is not None: for param, value in init_params.items(): - param = param.replace(choice, '').split(':', 1)[-1] + param = param.replace(choice, "").split(":", 1)[-1] if "feat_type" in param: feat_type = value else: new_params[param] = value - self.choice = self.get_components()[choice](config=config, init_params=new_params, - feat_type=feat_type) + self.choice = self.get_components()[choice]( + config=config, init_params=new_params, feat_type=feat_type + ) return self diff --git a/autosklearn/pipeline/components/data_preprocessing/balancing/balancing.py b/autosklearn/pipeline/components/data_preprocessing/balancing/balancing.py index 7850a1665b..721fe63fc5 100644 --- a/autosklearn/pipeline/components/data_preprocessing/balancing/balancing.py +++ b/autosklearn/pipeline/components/data_preprocessing/balancing/balancing.py @@ -1,38 +1,47 @@ -from typing import Any, List, Dict, Optional, Tuple, Union +from typing import Any, Dict, List, Optional, Tuple, Union import numpy as np - from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter - from sklearn.base import BaseEstimator from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE -from autosklearn.pipeline.components.base import \ - AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, SIGNED_DATA, INPUT +from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.constants import ( + DENSE, + INPUT, + SIGNED_DATA, + SPARSE, + UNSIGNED_DATA, +) class Balancing(AutoSklearnPreprocessingAlgorithm): def __init__( self, - strategy: str = 'none', + strategy: str = "none", random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> None: self.strategy = strategy self.random_state = random_state - def fit(self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None) -> 'Balancing': + def fit( + self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None + ) -> "Balancing": self.fitted_ = True return self def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: return X - def get_weights(self, Y: PIPELINE_DATA_DTYPE, - classifier: BaseEstimator, preprocessor: BaseEstimator, - init_params: Optional[Dict[str, Any]], fit_params: Optional[Dict[str, Any]], - ) -> Tuple[Optional[Dict[str, Any]], Optional[Dict[str, Any]]]: + def get_weights( + self, + Y: PIPELINE_DATA_DTYPE, + classifier: BaseEstimator, + preprocessor: BaseEstimator, + init_params: Optional[Dict[str, Any]], + fit_params: Optional[Dict[str, Any]], + ) -> Tuple[Optional[Dict[str, Any]], Optional[Dict[str, Any]]]: if init_params is None: init_params = {} @@ -45,12 +54,18 @@ def get_weights(self, Y: PIPELINE_DATA_DTYPE, # https://github.com/scikit-learn/scikit-learn/blob/0.15.X/sklearn/ensemble/weight_boosting.py#L121 # Have RF and ET in here because they emit a warning if class_weights # are used together with warmstarts - clf_ = ['adaboost', 'random_forest', 'extra_trees', 'sgd', 'passive_aggressive', - 'gradient_boosting'] + clf_ = [ + "adaboost", + "random_forest", + "extra_trees", + "sgd", + "passive_aggressive", + "gradient_boosting", + ] pre_: List[str] = [] if classifier in clf_ or preprocessor in pre_: if len(Y.shape) > 1: - offsets = [2 ** i for i in range(Y.shape[1])] + offsets = [2**i for i in range(Y.shape[1])] Y_ = np.sum(Y * offsets, axis=1) else: Y_ = Y @@ -68,65 +83,68 @@ def get_weights(self, Y: PIPELINE_DATA_DTYPE, sample_weights[mask] *= cw[i] if classifier in clf_: - fit_params['classifier:sample_weight'] = sample_weights + fit_params["classifier:sample_weight"] = sample_weights if preprocessor in pre_: - fit_params['feature_preprocessor:sample_weight'] = sample_weights + fit_params["feature_preprocessor:sample_weight"] = sample_weights # Classifiers which can adjust sample weights themselves via the # argument `class_weight` - clf_ = ['decision_tree', 'liblinear_svc', - 'libsvm_svc'] - pre_ = ['liblinear_svc_preprocessor', - 'extra_trees_preproc_for_classification'] + clf_ = ["decision_tree", "liblinear_svc", "libsvm_svc"] + pre_ = ["liblinear_svc_preprocessor", "extra_trees_preproc_for_classification"] if classifier in clf_: - init_params['classifier:class_weight'] = 'balanced' + init_params["classifier:class_weight"] = "balanced" if preprocessor in pre_: - init_params['feature_preprocessor:class_weight'] = 'balanced' + init_params["feature_preprocessor:class_weight"] = "balanced" - clf_ = ['ridge'] + clf_ = ["ridge"] if classifier in clf_: class_weights = {} unique, counts = np.unique(Y, return_counts=True) - cw = 1. / counts + cw = 1.0 / counts cw = cw / np.mean(cw) for i, ue in enumerate(unique): class_weights[ue] = cw[i] if classifier in clf_: - init_params['classifier:class_weight'] = class_weights + init_params["classifier:class_weight"] = class_weights return init_params, fit_params @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'Balancing', - 'name': 'Balancing Imbalanced Class Distributions', - 'handles_missing_values': True, - 'handles_nominal_values': True, - 'handles_numerical_features': True, - 'prefers_data_scaled': False, - 'prefers_data_normalized': False, - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': False, - 'is_deterministic': True, - 'handles_sparse': True, - 'handles_dense': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA, SIGNED_DATA), - 'output': (INPUT,), - 'preferred_dtype': None} + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "Balancing", + "name": "Balancing Imbalanced Class Distributions", + "handles_missing_values": True, + "handles_nominal_values": True, + "handles_numerical_features": True, + "prefers_data_scaled": False, + "prefers_data_normalized": False, + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": False, + "is_deterministic": True, + "handles_sparse": True, + "handles_dense": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA, SIGNED_DATA), + "output": (INPUT,), + "preferred_dtype": None, + } @staticmethod - def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> ConfigurationSpace: + def get_hyperparameter_search_space( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> ConfigurationSpace: # TODO add replace by zero! strategy = CategoricalHyperparameter( - "strategy", ["none", "weighting"], default_value="none") + "strategy", ["none", "weighting"], default_value="none" + ) cs = ConfigurationSpace() cs.add_hyperparameter(strategy) return cs diff --git a/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/__init__.py b/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/__init__.py index c4d34ab306..5d1647b24a 100644 --- a/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/__init__.py +++ b/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/__init__.py @@ -1,33 +1,34 @@ -from collections import OrderedDict -import os - from typing import Any, Dict, Optional +import os +from collections import OrderedDict + from ConfigSpace import Configuration from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter - from sklearn.base import BaseEstimator -from ...base import AutoSklearnPreprocessingAlgorithm, find_components, \ - ThirdPartyComponents, AutoSklearnChoice, _addons - from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE +from ...base import ( + AutoSklearnChoice, + AutoSklearnPreprocessingAlgorithm, + ThirdPartyComponents, + _addons, + find_components, +) + ohe_directory = os.path.split(__file__)[0] -_ohes = find_components(__package__, - ohe_directory, - AutoSklearnPreprocessingAlgorithm) +_ohes = find_components(__package__, ohe_directory, AutoSklearnPreprocessingAlgorithm) additional_components = ThirdPartyComponents(AutoSklearnPreprocessingAlgorithm) -_addons['data_preprocessing.categorical_encoding'] = additional_components +_addons["data_preprocessing.categorical_encoding"] = additional_components -def add_ohe(ohe: 'OHEChoice') -> None: +def add_ohe(ohe: "OHEChoice") -> None: additional_components.add_component(ohe) class OHEChoice(AutoSklearnChoice): - @classmethod def get_components(cls: BaseEstimator) -> Dict[str, BaseEstimator]: components: Dict[str, BaseEstimator] = OrderedDict() @@ -49,48 +50,52 @@ def get_hyperparameter_search_space( # Compile a list of legal preprocessors for this problem available_preprocessors = self.get_available_components( - dataset_properties=dataset_properties, - include=include, exclude=exclude) + dataset_properties=dataset_properties, include=include, exclude=exclude + ) if len(available_preprocessors) == 0: raise ValueError( "No ohe hot encoders found, please add any one hot encoder " - "component.") + "component." + ) if default is None: - defaults = ['one_hot_encoding', 'no_encoding'] + defaults = ["one_hot_encoding", "no_encoding"] for default_ in defaults: if default_ in available_preprocessors: default = default_ break - preprocessor = CategoricalHyperparameter('__choice__', - list( - available_preprocessors.keys()), - default_value=default) + preprocessor = CategoricalHyperparameter( + "__choice__", list(available_preprocessors.keys()), default_value=default + ) cs.add_hyperparameter(preprocessor) for name in available_preprocessors: - preprocessor_configuration_space = available_preprocessors[name]. \ - get_hyperparameter_search_space(dataset_properties) - parent_hyperparameter = {'parent': preprocessor, 'value': name} - cs.add_configuration_space(name, preprocessor_configuration_space, - parent_hyperparameter=parent_hyperparameter) + preprocessor_configuration_space = available_preprocessors[ + name + ].get_hyperparameter_search_space(dataset_properties) + parent_hyperparameter = {"parent": preprocessor, "value": name} + cs.add_configuration_space( + name, + preprocessor_configuration_space, + parent_hyperparameter=parent_hyperparameter, + ) self.configuration_space = cs self.dataset_properties = dataset_properties return cs - def set_hyperparameters(self, configuration: Configuration, - init_params: Optional[Dict[str, Any]] = None - ) -> 'OHEChoice': + def set_hyperparameters( + self, configuration: Configuration, init_params: Optional[Dict[str, Any]] = None + ) -> "OHEChoice": new_params = {} params = configuration.get_dictionary() - choice = params['__choice__'] - del params['__choice__'] + choice = params["__choice__"] + del params["__choice__"] for param, value in params.items(): - param = param.replace(choice, '').replace(':', '') + param = param.replace(choice, "").replace(":", "") new_params[param] = value if init_params is not None: @@ -100,10 +105,10 @@ def set_hyperparameters(self, configuration: Configuration, # in order to not pass it to the no encoding if choice not in param: continue - param = param.replace(choice, '').replace(':', '') + param = param.replace(choice, "").replace(":", "") new_params[param] = value - new_params['random_state'] = self.random_state + new_params["random_state"] = self.random_state self.new_params = new_params self.choice = self.get_components()[choice](**new_params) diff --git a/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/encoding.py b/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/encoding.py index 3ebb411457..43d578219f 100644 --- a/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/encoding.py +++ b/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/encoding.py @@ -1,30 +1,29 @@ from typing import Dict, Optional, Tuple, Union import numpy as np - -from ConfigSpace.configuration_space import ConfigurationSpace - import scipy.sparse - +from ConfigSpace.configuration_space import ConfigurationSpace from sklearn.preprocessing import OrdinalEncoder from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class OrdinalEncoding(AutoSklearnPreprocessingAlgorithm): def __init__( - self, - random_state: Optional[Union[int, np.random.RandomState]] = None + self, random_state: Optional[Union[int, np.random.RandomState]] = None ) -> None: self.random_state = random_state - def fit(self, X: PIPELINE_DATA_DTYPE, - y: Optional[PIPELINE_DATA_DTYPE] = None) -> 'OrdinalEncoding': + def fit( + self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None + ) -> "OrdinalEncoding": if not scipy.sparse.issparse(X): self.preprocessor = OrdinalEncoder( - categories='auto', handle_unknown='use_encoded_value', unknown_value=-1, + categories="auto", + handle_unknown="use_encoded_value", + unknown_value=-1, ) self.preprocessor.fit(X, y) return self @@ -50,20 +49,23 @@ def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: return self.preprocessor.transform(X) + 1 @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'OrdinalEncoder', - 'name': 'Ordinal Encoder', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - # TODO find out of this is right! - 'handles_sparse': True, - 'handles_dense': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,), } + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "OrdinalEncoder", + "name": "Ordinal Encoder", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + # TODO find out of this is right! + "handles_sparse": True, + "handles_dense": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), + } @staticmethod def get_hyperparameter_search_space( diff --git a/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/no_encoding.py b/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/no_encoding.py index ab196396ed..028a4fb9c1 100644 --- a/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/no_encoding.py +++ b/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/no_encoding.py @@ -1,24 +1,23 @@ from typing import Dict, Optional, Tuple, Union -import numpy as np +import numpy as np from ConfigSpace.configuration_space import ConfigurationSpace from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE -from autosklearn.pipeline.components.base import \ - AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT +from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class NoEncoding(AutoSklearnPreprocessingAlgorithm): def __init__( - self, - random_state: Optional[Union[int, np.random.RandomState]] = None + self, random_state: Optional[Union[int, np.random.RandomState]] = None ) -> None: pass - def fit(self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None - ) -> 'NoEncoding': - self.preprocessor = 'passthrough' + def fit( + self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None + ) -> "NoEncoding": + self.preprocessor = "passthrough" self.fitted_ = True return self @@ -26,22 +25,26 @@ def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: return X @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'no encoding', - 'name': 'No categorical variable encoding', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'handles_sparse': True, - 'handles_dense': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,)} + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "no encoding", + "name": "No categorical variable encoding", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "handles_sparse": True, + "handles_dense": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), + } @staticmethod - def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> ConfigurationSpace: + def get_hyperparameter_search_space( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> ConfigurationSpace: cs = ConfigurationSpace() return cs diff --git a/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/one_hot_encoding.py b/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/one_hot_encoding.py index 8973d1979f..9b9ee87c81 100644 --- a/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/one_hot_encoding.py +++ b/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/one_hot_encoding.py @@ -1,33 +1,31 @@ from typing import Dict, Optional, Tuple, Union -from ConfigSpace.configuration_space import ConfigurationSpace - +import numpy as np import scipy.sparse - +from ConfigSpace.configuration_space import ConfigurationSpace from sklearn.preprocessing import OneHotEncoder as DenseOneHotEncoder -import numpy as np - from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE -from autosklearn.pipeline.implementations.SparseOneHotEncoder import SparseOneHotEncoder from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA +from autosklearn.pipeline.implementations.SparseOneHotEncoder import SparseOneHotEncoder class OneHotEncoder(AutoSklearnPreprocessingAlgorithm): def __init__( - self, - random_state: Optional[Union[int, np.random.RandomState]] = None + self, random_state: Optional[Union[int, np.random.RandomState]] = None ) -> None: self.random_state = random_state - def fit(self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None - ) -> 'OneHotEncoder': + def fit( + self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None + ) -> "OneHotEncoder": if scipy.sparse.issparse(X): self.preprocessor = SparseOneHotEncoder() else: self.preprocessor = DenseOneHotEncoder( - sparse=False, categories='auto', handle_unknown='ignore') + sparse=False, categories="auto", handle_unknown="ignore" + ) self.preprocessor.fit(X, y) return self @@ -37,22 +35,26 @@ def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: return self.preprocessor.transform(X) @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': '1Hot', - 'name': 'One Hot Encoder', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - # TODO find out of this is right! - 'handles_sparse': True, - 'handles_dense': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,), } + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "1Hot", + "name": "One Hot Encoder", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + # TODO find out of this is right! + "handles_sparse": True, + "handles_dense": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), + } @staticmethod - def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> ConfigurationSpace: + def get_hyperparameter_search_space( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> ConfigurationSpace: return ConfigurationSpace() diff --git a/autosklearn/pipeline/components/data_preprocessing/category_shift/category_shift.py b/autosklearn/pipeline/components/data_preprocessing/category_shift/category_shift.py index 318a84ee2d..f2dc2bf304 100644 --- a/autosklearn/pipeline/components/data_preprocessing/category_shift/category_shift.py +++ b/autosklearn/pipeline/components/data_preprocessing/category_shift/category_shift.py @@ -1,32 +1,32 @@ from typing import Dict, Optional, Tuple, Union -from ConfigSpace.configuration_space import ConfigurationSpace - import numpy as np +from ConfigSpace.configuration_space import ConfigurationSpace -from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE import autosklearn.pipeline.implementations.CategoryShift +from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class CategoryShift(AutoSklearnPreprocessingAlgorithm): - """ Add 3 to every category. + """Add 3 to every category. Down in the pipeline, category 2 will be attribute to missing values, category 1 will be assigned to low occurence categories, and category 0 is not used, so to provide compatibility with sparse matrices. """ def __init__( - self, - random_state: Optional[Union[int, np.random.RandomState]] = None + self, random_state: Optional[Union[int, np.random.RandomState]] = None ) -> None: self.random_state = random_state - def fit(self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None - ) -> 'CategoryShift': - self.preprocessor = autosklearn.pipeline.implementations.CategoryShift\ - .CategoryShift() + def fit( + self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None + ) -> "CategoryShift": + self.preprocessor = ( + autosklearn.pipeline.implementations.CategoryShift.CategoryShift() + ) self.preprocessor.fit(X, y) return self @@ -36,29 +36,33 @@ def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: return self.preprocessor.transform(X) @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'CategShift', - 'name': 'Category Shift', - 'handles_missing_values': True, - 'handles_nominal_values': True, - 'handles_numerical_features': True, - 'prefers_data_scaled': False, - 'prefers_data_normalized': False, - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': True, - # TODO find out of this is right! - 'handles_sparse': True, - 'handles_dense': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,), - 'preferred_dtype': None} + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "CategShift", + "name": "Category Shift", + "handles_missing_values": True, + "handles_nominal_values": True, + "handles_numerical_features": True, + "prefers_data_scaled": False, + "prefers_data_normalized": False, + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": True, + # TODO find out of this is right! + "handles_sparse": True, + "handles_dense": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), + "preferred_dtype": None, + } @staticmethod - def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> ConfigurationSpace: + def get_hyperparameter_search_space( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> ConfigurationSpace: return ConfigurationSpace() diff --git a/autosklearn/pipeline/components/data_preprocessing/feature_reduction/truncated_svd.py b/autosklearn/pipeline/components/data_preprocessing/feature_reduction/truncated_svd.py index 3bc4e7c002..c104a18fd7 100644 --- a/autosklearn/pipeline/components/data_preprocessing/feature_reduction/truncated_svd.py +++ b/autosklearn/pipeline/components/data_preprocessing/feature_reduction/truncated_svd.py @@ -1,15 +1,13 @@ from typing import Dict, Optional, Tuple, Union -from ConfigSpace.configuration_space import ConfigurationSpace import ConfigSpace.hyperparameters as CSH - import numpy as np +from ConfigSpace.configuration_space import ConfigurationSpace +from sklearn.decomposition import TruncatedSVD from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT - -from sklearn.decomposition import TruncatedSVD +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class FeatureReduction(AutoSklearnPreprocessingAlgorithm): @@ -20,22 +18,27 @@ class FeatureReduction(AutoSklearnPreprocessingAlgorithm): def __init__( self, n_components: Optional[int] = None, - random_state: Optional[Union[int, np.random.RandomState]] = None + random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> None: self.n_components = n_components self.random_state = random_state - def fit(self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None - ) -> 'FeatureReduction': + def fit( + self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None + ) -> "FeatureReduction": if X.shape[1] > self.n_components: - self.preprocessor = TruncatedSVD(n_components=self.n_components, - random_state=self.random_state) + self.preprocessor = TruncatedSVD( + n_components=self.n_components, random_state=self.random_state + ) elif X.shape[1] <= self.n_components and X.shape[1] != 1: - self.preprocessor = TruncatedSVD(n_components=X.shape[1] - 1, - random_state=self.random_state) + self.preprocessor = TruncatedSVD( + n_components=X.shape[1] - 1, random_state=self.random_state + ) else: - raise ValueError("The text embedding consists only of a single dimension.\n" - "Are you sure that your text data is necessary?") + raise ValueError( + "The text embedding consists only of a single dimension.\n" + "Are you sure that your text data is necessary?" + ) self.preprocessor.fit(X) return self @@ -45,32 +48,38 @@ def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: return self.preprocessor.transform(X) @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'TextFeatureReduction', - 'name': 'TextFeatureReduction', - 'handles_missing_values': True, - 'handles_nominal_values': True, - 'handles_numerical_features': True, - 'prefers_data_scaled': False, - 'prefers_data_normalized': False, - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': True, - 'handles_sparse': True, - 'handles_dense': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,), - 'preferred_dtype': None} + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "TextFeatureReduction", + "name": "TextFeatureReduction", + "handles_missing_values": True, + "handles_nominal_values": True, + "handles_numerical_features": True, + "prefers_data_scaled": False, + "prefers_data_normalized": False, + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": True, + "handles_sparse": True, + "handles_dense": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), + "preferred_dtype": None, + } @staticmethod - def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> ConfigurationSpace: + def get_hyperparameter_search_space( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> ConfigurationSpace: cs = ConfigurationSpace() cs.add_hyperparameter( - CSH.UniformIntegerHyperparameter("n_components", lower=1, upper=10000, - default_value=100, log=True)) + CSH.UniformIntegerHyperparameter( + "n_components", lower=1, upper=10000, default_value=100, log=True + ) + ) return cs diff --git a/autosklearn/pipeline/components/data_preprocessing/feature_type.py b/autosklearn/pipeline/components/data_preprocessing/feature_type.py index cfd31e8c3a..5c37e4cb98 100644 --- a/autosklearn/pipeline/components/data_preprocessing/feature_type.py +++ b/autosklearn/pipeline/components/data_preprocessing/feature_type.py @@ -1,38 +1,40 @@ -from typing import Any, List, Dict, Optional, Tuple, Union +from typing import Any, Dict, List, Optional, Tuple, Union +import numpy as np import sklearn.compose -from scipy import sparse - from ConfigSpace import Configuration from ConfigSpace.configuration_space import ConfigurationSpace - -import numpy as np - +from scipy import sparse from sklearn.base import BaseEstimator +from autosklearn.data.validation import SUPPORTED_FEAT_TYPES, SUPPORTED_TARGET_TYPES from autosklearn.pipeline.base import ( - BasePipeline, - DATASET_PROPERTIES_TYPE, - PIPELINE_DATA_DTYPE, - ) -from autosklearn.pipeline.components.data_preprocessing.feature_type_categorical \ - import CategoricalPreprocessingPipeline -from autosklearn.pipeline.components.data_preprocessing.feature_type_numerical \ - import NumericalPreprocessingPipeline -from autosklearn.pipeline.components.data_preprocessing.feature_type_text \ - import TextPreprocessingPipeline -from autosklearn.pipeline.components.base import AutoSklearnComponent, AutoSklearnChoice, \ - AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT -from autosklearn.data.validation import ( - SUPPORTED_FEAT_TYPES, - SUPPORTED_TARGET_TYPES, + DATASET_PROPERTIES_TYPE, + PIPELINE_DATA_DTYPE, + BasePipeline, +) +from autosklearn.pipeline.components.base import ( + AutoSklearnChoice, + AutoSklearnComponent, + AutoSklearnPreprocessingAlgorithm, +) +from autosklearn.pipeline.components.data_preprocessing.feature_type_categorical import ( # noqa : E501 + CategoricalPreprocessingPipeline, ) +from autosklearn.pipeline.components.data_preprocessing.feature_type_numerical import ( + NumericalPreprocessingPipeline, +) +from autosklearn.pipeline.components.data_preprocessing.feature_type_text import ( + TextPreprocessingPipeline, +) +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class FeatTypeSplit(AutoSklearnPreprocessingAlgorithm): - """ This component is used to apply distinct transformations to categorical, - numerical and text features of a dataset. It is built on top of sklearn's ColumnTransformer. + """ + This component is used to apply distinct transformations to categorical, + numerical and text features of a dataset. It is built on top of sklearn's + ColumnTransformer. """ def __init__( @@ -70,9 +72,14 @@ def __init__( # TODO: Extract the child configuration space from the FeatTypeSplit to the # pipeline if needed self.categ_ppl = CategoricalPreprocessingPipeline( - config=None, steps=pipeline, dataset_properties=dataset_properties, - include=include, exclude=exclude, random_state=random_state, - init_params=init_params) + config=None, + steps=pipeline, + dataset_properties=dataset_properties, + include=include, + exclude=exclude, + random_state=random_state, + init_params=init_params, + ) # The pipeline that will be applied to the numerical features (i.e. columns) # of the dataset # Configuration of the data-preprocessor is different from the configuration of @@ -81,9 +88,14 @@ def __init__( # TODO: Extract the child configuration space from the FeatTypeSplit to the # pipeline if needed self.numer_ppl = NumericalPreprocessingPipeline( - config=None, steps=pipeline, dataset_properties=dataset_properties, - include=include, exclude=exclude, random_state=random_state, - init_params=init_params) + config=None, + steps=pipeline, + dataset_properties=dataset_properties, + include=include, + exclude=exclude, + random_state=random_state, + init_params=init_params, + ) # The pipeline that will be applied to the text features (i.e. columns) # of the dataset @@ -93,9 +105,14 @@ def __init__( # TODO: Extract the child configuration space from the FeatTypeSplit to the # pipeline if needed self.txt_ppl = TextPreprocessingPipeline( - config=None, steps=pipeline, dataset_properties=dataset_properties, - include=include, exclude=exclude, random_state=random_state, - init_params=init_params) + config=None, + steps=pipeline, + dataset_properties=dataset_properties, + include=include, + exclude=exclude, + random_state=random_state, + init_params=init_params, + ) self._transformers: List[Tuple[str, AutoSklearnComponent]] = [ ("categorical_transformer", self.categ_ppl), @@ -106,8 +123,9 @@ def __init__( self.set_hyperparameters(self.config, init_params=init_params) self.column_transformer = column_transformer - def fit(self, X: SUPPORTED_FEAT_TYPES, y: Optional[SUPPORTED_TARGET_TYPES] = None - ) -> 'FeatTypeSplit': + def fit( + self, X: SUPPORTED_FEAT_TYPES, y: Optional[SUPPORTED_TARGET_TYPES] = None + ) -> "FeatTypeSplit": n_feats = X.shape[1] categorical_features = [] @@ -116,78 +134,99 @@ def fit(self, X: SUPPORTED_FEAT_TYPES, y: Optional[SUPPORTED_TARGET_TYPES] = Non if self.feat_type is not None: # Make sure that we are not missing any column! expected = set(self.feat_type.keys()) - if hasattr(X, 'columns'): + if hasattr(X, "columns"): columns = set(X.columns) else: columns = set(range(n_feats)) if expected != columns: - raise ValueError(f"Train data has columns={expected} yet the" - f" feat_types are feat={columns}") - categorical_features = [key for key, value in self.feat_type.items() - if value.lower() == 'categorical'] - numerical_features = [key for key, value in self.feat_type.items() - if value.lower() == 'numerical'] - text_features = [key for key, value in self.feat_type.items() - if value.lower() == "string"] + raise ValueError( + f"Train data has columns={expected} yet the" + f" feat_types are feat={columns}" + ) + categorical_features = [ + key + for key, value in self.feat_type.items() + if value.lower() == "categorical" + ] + numerical_features = [ + key + for key, value in self.feat_type.items() + if value.lower() == "numerical" + ] + text_features = [ + key + for key, value in self.feat_type.items() + if value.lower() == "string" + ] sklearn_transf_spec = [ (name, transformer, feature_columns) - for name, transformer, feature_columns - in [ + for name, transformer, feature_columns in [ ("text_transformer", self.txt_ppl, text_features), ("categorical_transformer", self.categ_ppl, categorical_features), - ("numerical_transformer", self.numer_ppl, numerical_features) + ("numerical_transformer", self.numer_ppl, numerical_features), ] if len(feature_columns) > 0 ] else: # self.feature_type == None assumes numerical case - sklearn_transf_spec = [("numerical_transformer", self.numer_ppl, [True]*n_feats)] + sklearn_transf_spec = [ + ("numerical_transformer", self.numer_ppl, [True] * n_feats) + ] # And one last check in case feat type is None # And to make sure the final specification has all the columns # considered in the column transformer - total_columns = sum([len(features) for name, ppl, features in sklearn_transf_spec]) + total_columns = sum( + [len(features) for name, ppl, features in sklearn_transf_spec] + ) if total_columns != n_feats: - raise ValueError("Missing columns in the specification of the data validator" - f" for train data={np.shape(X)} and spec={sklearn_transf_spec}") + raise ValueError( + "Missing columns in the specification of the data validator" + f" for train data={np.shape(X)} and spec={sklearn_transf_spec}" + ) self.sparse_ = sparse.issparse(X) or self.force_sparse_output self.column_transformer = sklearn.compose.ColumnTransformer( transformers=sklearn_transf_spec, sparse_threshold=float(self.sparse_), - ) + ) self.column_transformer.fit(X, y) return self def transform(self, X: SUPPORTED_FEAT_TYPES) -> PIPELINE_DATA_DTYPE: if self.column_transformer is None: - raise ValueError("Cannot call transform on a Datapreprocessor that has not" - "yet been fit. Please check the log files for errors " - "while trying to fit the model." - ) + raise ValueError( + "Cannot call transform on a Datapreprocessor that has not" + "yet been fit. Please check the log files for errors " + "while trying to fit the model." + ) return self.column_transformer.transform(X) @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'FeatTypeSplit', - 'name': 'Feature Type Splitter', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - # TODO find out of this is right! - 'handles_sparse': True, - 'handles_dense': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,), } - - def set_hyperparameters(self, configuration: Configuration, - init_params: Optional[Dict[str, Any]] = None) -> 'FeatTypeSplit': - if init_params is not None and 'feat_type' in init_params.keys(): - self.feat_type = init_params['feat_type'] + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "FeatTypeSplit", + "name": "Feature Type Splitter", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + # TODO find out of this is right! + "handles_sparse": True, + "handles_dense": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), + } + + def set_hyperparameters( + self, configuration: Configuration, init_params: Optional[Dict[str, Any]] = None + ) -> "FeatTypeSplit": + if init_params is not None and "feat_type" in init_params.keys(): + self.feat_type = init_params["feat_type"] self.config = configuration @@ -197,29 +236,32 @@ def set_hyperparameters(self, configuration: Configuration, ) sub_config_dict = {} for param in configuration: - if param.startswith('%s:' % transf_name): + if param.startswith("%s:" % transf_name): value = configuration[param] - new_name = param.replace('%s:' % transf_name, '', 1) + new_name = param.replace("%s:" % transf_name, "", 1) sub_config_dict[new_name] = value - sub_configuration = Configuration(sub_configuration_space, - values=sub_config_dict) + sub_configuration = Configuration( + sub_configuration_space, values=sub_config_dict + ) sub_init_params_dict: Optional[Dict[str, Any]] = None if init_params is not None: sub_init_params_dict = {} for param in init_params: - if param.startswith('%s:' % transf_name): + if param.startswith("%s:" % transf_name): value = init_params[param] - new_name = param.replace('%s:' % transf_name, '', 1) + new_name = param.replace("%s:" % transf_name, "", 1) sub_init_params_dict[new_name] = value - if isinstance(transf_op, ( - AutoSklearnChoice, AutoSklearnComponent, BasePipeline)): + if isinstance( + transf_op, (AutoSklearnChoice, AutoSklearnComponent, BasePipeline) + ): transf_op.set_hyperparameters( - configuration=sub_configuration, init_params=sub_init_params_dict) + configuration=sub_configuration, init_params=sub_init_params_dict + ) else: - raise NotImplementedError('Not supported yet!') + raise NotImplementedError("Not supported yet!") return self @@ -230,7 +272,8 @@ def get_hyperparameter_search_space( self.dataset_properties = dataset_properties cs = ConfigurationSpace() cs = FeatTypeSplit._get_hyperparameter_search_space_recursevely( - dataset_properties, cs, self._transformers) + dataset_properties, cs, self._transformers + ) return cs @staticmethod @@ -243,8 +286,10 @@ def _get_hyperparameter_search_space_recursevely( if hasattr(st_operation, "get_hyperparameter_search_space"): cs.add_configuration_space( st_name, - st_operation.get_hyperparameter_search_space(dataset_properties)) + st_operation.get_hyperparameter_search_space(dataset_properties), + ) else: return FeatTypeSplit._get_hyperparameter_search_space_recursevely( - dataset_properties, cs, st_operation) + dataset_properties, cs, st_operation + ) return cs diff --git a/autosklearn/pipeline/components/data_preprocessing/feature_type_categorical.py b/autosklearn/pipeline/components/data_preprocessing/feature_type_categorical.py index f8430aa978..0a46887799 100644 --- a/autosklearn/pipeline/components/data_preprocessing/feature_type_categorical.py +++ b/autosklearn/pipeline/components/data_preprocessing/feature_type_categorical.py @@ -1,27 +1,26 @@ -from typing import Any, List, Dict, Optional, Tuple, Union - -from ConfigSpace.configuration_space import Configuration, ConfigurationSpace +from typing import Any, Dict, List, Optional, Tuple, Union import numpy as np - +from ConfigSpace.configuration_space import Configuration, ConfigurationSpace from sklearn.base import BaseEstimator -from autosklearn.pipeline.components.data_preprocessing.category_shift.\ - category_shift import CategoryShift -from autosklearn.pipeline.components.data_preprocessing.imputation.\ - categorical_imputation import CategoricalImputation -from autosklearn.pipeline.components.data_preprocessing.minority_coalescense \ - import CoalescenseChoice -from autosklearn.pipeline.components.data_preprocessing.categorical_encoding \ - import OHEChoice -from autosklearn.pipeline.components.data_preprocessing.categorical_encoding.encoding import ( - OrdinalEncoding +from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, BasePipeline +from autosklearn.pipeline.components.data_preprocessing.categorical_encoding import ( # noqa: E501 + OHEChoice, +) +from autosklearn.pipeline.components.data_preprocessing.categorical_encoding.encoding import ( # noqa: E501 + OrdinalEncoding, +) +from autosklearn.pipeline.components.data_preprocessing.category_shift.category_shift import ( # noqa: E501 + CategoryShift, ) -from autosklearn.pipeline.base import ( - BasePipeline, - DATASET_PROPERTIES_TYPE, +from autosklearn.pipeline.components.data_preprocessing.imputation.categorical_imputation import ( # noqa: E501 + CategoricalImputation, ) -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT +from autosklearn.pipeline.components.data_preprocessing.minority_coalescense import ( + CoalescenseChoice, +) +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class CategoricalPreprocessingPipeline(BasePipeline): @@ -53,35 +52,43 @@ def __init__( include: Optional[Dict[str, str]] = None, exclude: Optional[Dict[str, str]] = None, random_state: Optional[Union[int, np.random.RandomState]] = None, - init_params: Optional[Dict[str, Any]] = None + init_params: Optional[Dict[str, Any]] = None, ) -> None: self._output_dtype = np.int32 super().__init__( - config, steps, dataset_properties, include, exclude, - random_state, init_params + config, + steps, + dataset_properties, + include, + exclude, + random_state, + init_params, ) @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'cat_datapreproc', - 'name': 'categorical data preprocessing', - 'handles_missing_values': True, - 'handles_nominal_values': True, - 'handles_numerical_features': True, - 'prefers_data_scaled': False, - 'prefers_data_normalized': False, - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'is_deterministic': True, - # TODO find out if this is right! - 'handles_sparse': True, - 'handles_dense': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,), - 'preferred_dtype': None} + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "cat_datapreproc", + "name": "categorical data preprocessing", + "handles_missing_values": True, + "handles_nominal_values": True, + "handles_numerical_features": True, + "prefers_data_scaled": False, + "prefers_data_normalized": False, + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "is_deterministic": True, + # TODO find out if this is right! + "handles_sparse": True, + "handles_dense": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), + "preferred_dtype": None, + } def _get_hyperparameter_search_space( self, @@ -102,27 +109,34 @@ def _get_hyperparameter_search_space( dataset_properties = dict() cs = self._get_base_search_space( - cs=cs, dataset_properties=dataset_properties, - exclude=exclude, include=include, pipeline=self.steps) + cs=cs, + dataset_properties=dataset_properties, + exclude=exclude, + include=include, + pipeline=self.steps, + ) return cs - def _get_pipeline_steps(self, - dataset_properties: Optional[Dict[str, str]] = None, - ) -> List[Tuple[str, BaseEstimator]]: + def _get_pipeline_steps( + self, + dataset_properties: Optional[Dict[str, str]] = None, + ) -> List[Tuple[str, BaseEstimator]]: steps = [] default_dataset_properties = {} if dataset_properties is not None and isinstance(dataset_properties, dict): default_dataset_properties.update(dataset_properties) - steps.extend([ - ("imputation", CategoricalImputation()), - ("encoding", OrdinalEncoding()), - ("category_shift", CategoryShift()), - ("category_coalescence", CoalescenseChoice(default_dataset_properties)), - ("categorical_encoding", OHEChoice(default_dataset_properties)), - ]) + steps.extend( + [ + ("imputation", CategoricalImputation()), + ("encoding", OrdinalEncoding()), + ("category_shift", CategoryShift()), + ("category_coalescence", CoalescenseChoice(default_dataset_properties)), + ("categorical_encoding", OHEChoice(default_dataset_properties)), + ] + ) return steps diff --git a/autosklearn/pipeline/components/data_preprocessing/feature_type_numerical.py b/autosklearn/pipeline/components/data_preprocessing/feature_type_numerical.py index 5ef47e2699..18008378ab 100644 --- a/autosklearn/pipeline/components/data_preprocessing/feature_type_numerical.py +++ b/autosklearn/pipeline/components/data_preprocessing/feature_type_numerical.py @@ -1,23 +1,20 @@ -from typing import Any, List, Dict, Optional, Tuple, Union - -from ConfigSpace.configuration_space import Configuration, ConfigurationSpace +from typing import Any, Dict, List, Optional, Tuple, Union import numpy as np - +from ConfigSpace.configuration_space import Configuration, ConfigurationSpace from sklearn.base import BaseEstimator -from autosklearn.pipeline.components.data_preprocessing import rescaling as \ - rescaling_components -from autosklearn.pipeline.components.data_preprocessing.imputation.numerical_imputation \ - import NumericalImputation -from autosklearn.pipeline.components.data_preprocessing.variance_threshold\ - .variance_threshold import VarianceThreshold - -from autosklearn.pipeline.base import ( - BasePipeline, - DATASET_PROPERTIES_TYPE, +from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, BasePipeline +from autosklearn.pipeline.components.data_preprocessing import ( + rescaling as rescaling_components, +) +from autosklearn.pipeline.components.data_preprocessing.imputation.numerical_imputation import ( # noqa: E501 + NumericalImputation, ) -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT +from autosklearn.pipeline.components.data_preprocessing.variance_threshold.variance_threshold import ( # noqa: E501 + VarianceThreshold, +) +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class NumericalPreprocessingPipeline(BasePipeline): @@ -48,35 +45,43 @@ def __init__( include: Optional[Dict[str, str]] = None, exclude: Optional[Dict[str, str]] = None, random_state: Optional[Union[int, np.random.RandomState]] = None, - init_params: Optional[Dict[str, Any]] = None + init_params: Optional[Dict[str, Any]] = None, ) -> None: self._output_dtype = np.int32 super().__init__( - config, steps, dataset_properties, include, exclude, - random_state, init_params + config, + steps, + dataset_properties, + include, + exclude, + random_state, + init_params, ) @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'num_datapreproc', - 'name': 'numeric data preprocessing', - 'handles_missing_values': True, - 'handles_nominal_values': True, - 'handles_numerical_features': True, - 'prefers_data_scaled': False, - 'prefers_data_normalized': False, - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'is_deterministic': True, - # TODO find out if this is right! - 'handles_sparse': True, - 'handles_dense': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,), - 'preferred_dtype': None} + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "num_datapreproc", + "name": "numeric data preprocessing", + "handles_missing_values": True, + "handles_nominal_values": True, + "handles_numerical_features": True, + "prefers_data_scaled": False, + "prefers_data_normalized": False, + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "is_deterministic": True, + # TODO find out if this is right! + "handles_sparse": True, + "handles_dense": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), + "preferred_dtype": None, + } def _get_hyperparameter_search_space( self, @@ -100,25 +105,35 @@ def _get_hyperparameter_search_space( dataset_properties = dict() cs = self._get_base_search_space( - cs=cs, dataset_properties=dataset_properties, - exclude=exclude, include=include, pipeline=self.steps) + cs=cs, + dataset_properties=dataset_properties, + exclude=exclude, + include=include, + pipeline=self.steps, + ) return cs - def _get_pipeline_steps(self, - dataset_properties: Optional[Dict[str, str]] = None, - ) -> List[Tuple[str, BaseEstimator]]: + def _get_pipeline_steps( + self, + dataset_properties: Optional[Dict[str, str]] = None, + ) -> List[Tuple[str, BaseEstimator]]: steps = [] default_dataset_properties = {} if dataset_properties is not None and isinstance(dataset_properties, dict): default_dataset_properties.update(dataset_properties) - steps.extend([ - ("imputation", NumericalImputation()), - ("variance_threshold", VarianceThreshold()), - ("rescaling", rescaling_components.RescalingChoice(default_dataset_properties)), - ]) + steps.extend( + [ + ("imputation", NumericalImputation()), + ("variance_threshold", VarianceThreshold()), + ( + "rescaling", + rescaling_components.RescalingChoice(default_dataset_properties), + ), + ] + ) return steps diff --git a/autosklearn/pipeline/components/data_preprocessing/feature_type_text.py b/autosklearn/pipeline/components/data_preprocessing/feature_type_text.py index a21980f000..6030460ee1 100644 --- a/autosklearn/pipeline/components/data_preprocessing/feature_type_text.py +++ b/autosklearn/pipeline/components/data_preprocessing/feature_type_text.py @@ -1,20 +1,17 @@ -from typing import Any, List, Dict, Optional, Tuple, Union - -from ConfigSpace.configuration_space import Configuration, ConfigurationSpace +from typing import Any, Dict, List, Optional, Tuple, Union import numpy as np - +from ConfigSpace.configuration_space import Configuration, ConfigurationSpace from sklearn.base import BaseEstimator -from autosklearn.pipeline.components.data_preprocessing.text_encoding \ - import BagOfWordChoice -from autosklearn.pipeline.components.data_preprocessing.feature_reduction.truncated_svd import \ - FeatureReduction -from autosklearn.pipeline.base import ( - BasePipeline, - DATASET_PROPERTIES_TYPE, +from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, BasePipeline +from autosklearn.pipeline.components.data_preprocessing.feature_reduction.truncated_svd import ( # noqa: 501 + FeatureReduction, +) +from autosklearn.pipeline.components.data_preprocessing.text_encoding import ( + BagOfWordChoice, ) -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class TextPreprocessingPipeline(BasePipeline): @@ -43,34 +40,42 @@ def __init__( include: Optional[Dict[str, str]] = None, exclude: Optional[Dict[str, str]] = None, random_state: Optional[Union[int, np.random.RandomState]] = None, - init_params: Optional[Dict[str, Any]] = None + init_params: Optional[Dict[str, Any]] = None, ) -> None: self._output_dtype = np.int32 super().__init__( - config, steps, dataset_properties, include, exclude, - random_state, init_params + config, + steps, + dataset_properties, + include, + exclude, + random_state, + init_params, ) @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'txt_datapreproc', - 'name': 'text data preprocessing', - 'handles_missing_values': True, - 'handles_nominal_values': False, - 'handles_numerical_features': False, - 'prefers_data_scaled': False, - 'prefers_data_normalized': False, - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'is_deterministic': True, - 'handles_sparse': True, - 'handles_dense': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,), - 'preferred_dtype': None} + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "txt_datapreproc", + "name": "text data preprocessing", + "handles_missing_values": True, + "handles_nominal_values": False, + "handles_numerical_features": False, + "prefers_data_scaled": False, + "prefers_data_normalized": False, + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "is_deterministic": True, + "handles_sparse": True, + "handles_dense": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), + "preferred_dtype": None, + } def _get_hyperparameter_search_space( self, @@ -94,25 +99,36 @@ def _get_hyperparameter_search_space( dataset_properties = dict() cs = self._get_base_search_space( - cs=cs, dataset_properties=dataset_properties, - exclude=exclude, include=include, pipeline=self.steps) + cs=cs, + dataset_properties=dataset_properties, + exclude=exclude, + include=include, + pipeline=self.steps, + ) return cs - def _get_pipeline_steps(self, - dataset_properties: Optional[Dict[str, str]] = None, - ) -> List[Tuple[str, BaseEstimator]]: + def _get_pipeline_steps( + self, + dataset_properties: Optional[Dict[str, str]] = None, + ) -> List[Tuple[str, BaseEstimator]]: steps = [] default_dataset_properties = {} if dataset_properties is not None and isinstance(dataset_properties, dict): default_dataset_properties.update(dataset_properties) - steps.extend([ - ("text_encoding", BagOfWordChoice(default_dataset_properties, - random_state=self.random_state)), - ("feature_reduction", FeatureReduction(random_state=self.random_state)) - ]) + steps.extend( + [ + ( + "text_encoding", + BagOfWordChoice( + default_dataset_properties, random_state=self.random_state + ), + ), + ("feature_reduction", FeatureReduction(random_state=self.random_state)), + ] + ) return steps def _get_estimator_hyperparameter_name(self) -> str: diff --git a/autosklearn/pipeline/components/data_preprocessing/imputation/categorical_imputation.py b/autosklearn/pipeline/components/data_preprocessing/imputation/categorical_imputation.py index 519155ea20..00b627daed 100644 --- a/autosklearn/pipeline/components/data_preprocessing/imputation/categorical_imputation.py +++ b/autosklearn/pipeline/components/data_preprocessing/imputation/categorical_imputation.py @@ -1,13 +1,12 @@ from typing import Dict, Optional, Tuple, Union -from ConfigSpace.configuration_space import ConfigurationSpace - import numpy as np +from ConfigSpace.configuration_space import ConfigurationSpace from scipy.sparse import spmatrix from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class CategoricalImputation(AutoSklearnPreprocessingAlgorithm): @@ -20,16 +19,16 @@ class CategoricalImputation(AutoSklearnPreprocessingAlgorithm): """ def __init__( - self, - random_state: Optional[Union[int, np.random.RandomState]] = None + self, random_state: Optional[Union[int, np.random.RandomState]] = None ) -> None: self.random_state = random_state - def fit(self, X: PIPELINE_DATA_DTYPE, - y: Optional[PIPELINE_DATA_DTYPE] = None) -> 'CategoricalImputation': + def fit( + self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None + ) -> "CategoricalImputation": import sklearn.impute - if hasattr(X, 'columns'): + if hasattr(X, "columns"): kind = X[X.columns[-1]].dtype.kind else: # Series, sparse and numpy have dtype @@ -53,7 +52,7 @@ def fit(self, X: PIPELINE_DATA_DTYPE, fill_value = min(np.unique(X)) - 1 self.preprocessor = sklearn.impute.SimpleImputer( - strategy='constant', copy=False, fill_value=fill_value + strategy="constant", copy=False, fill_value=fill_value ) self.preprocessor.fit(X) return self @@ -65,29 +64,33 @@ def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: return X @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'CategoricalImputation', - 'name': 'Categorical Imputation', - 'handles_missing_values': True, - 'handles_nominal_values': True, - 'handles_numerical_features': True, - 'prefers_data_scaled': False, - 'prefers_data_normalized': False, - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': True, - # TODO find out of this is right! - 'handles_sparse': True, - 'handles_dense': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,), - 'preferred_dtype': None} + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "CategoricalImputation", + "name": "Categorical Imputation", + "handles_missing_values": True, + "handles_nominal_values": True, + "handles_numerical_features": True, + "prefers_data_scaled": False, + "prefers_data_normalized": False, + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": True, + # TODO find out of this is right! + "handles_sparse": True, + "handles_dense": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), + "preferred_dtype": None, + } @staticmethod - def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> ConfigurationSpace: + def get_hyperparameter_search_space( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> ConfigurationSpace: return ConfigurationSpace() diff --git a/autosklearn/pipeline/components/data_preprocessing/imputation/numerical_imputation.py b/autosklearn/pipeline/components/data_preprocessing/imputation/numerical_imputation.py index e730718032..d7d6a645ab 100644 --- a/autosklearn/pipeline/components/data_preprocessing/imputation/numerical_imputation.py +++ b/autosklearn/pipeline/components/data_preprocessing/imputation/numerical_imputation.py @@ -1,31 +1,31 @@ from typing import Dict, Optional, Tuple, Union +import numpy as np from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter -import numpy as np - from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class NumericalImputation(AutoSklearnPreprocessingAlgorithm): - def __init__( self, - strategy: str = 'mean', - random_state: Optional[Union[int, np.random.RandomState]] = None + strategy: str = "mean", + random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> None: self.strategy = strategy self.random_state = random_state - def fit(self, X: PIPELINE_DATA_DTYPE, - y: Optional[PIPELINE_DATA_DTYPE] = None) -> 'NumericalImputation': + def fit( + self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None + ) -> "NumericalImputation": import sklearn.impute self.preprocessor = sklearn.impute.SimpleImputer( - strategy=self.strategy, copy=False) + strategy=self.strategy, copy=False + ) self.preprocessor.fit(X) return self @@ -35,34 +35,39 @@ def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: return self.preprocessor.transform(X) @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'NumericalImputation', - 'name': 'Numerical Imputation', - 'handles_missing_values': True, - 'handles_nominal_values': True, - 'handles_numerical_features': True, - 'prefers_data_scaled': False, - 'prefers_data_normalized': False, - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': True, - # TODO find out if this is right! - 'handles_sparse': True, - 'handles_dense': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,), - 'preferred_dtype': None} + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "NumericalImputation", + "name": "Numerical Imputation", + "handles_missing_values": True, + "handles_nominal_values": True, + "handles_numerical_features": True, + "prefers_data_scaled": False, + "prefers_data_normalized": False, + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": True, + # TODO find out if this is right! + "handles_sparse": True, + "handles_dense": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), + "preferred_dtype": None, + } @staticmethod - def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> ConfigurationSpace: + def get_hyperparameter_search_space( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> ConfigurationSpace: # TODO add replace by zero! strategy = CategoricalHyperparameter( - "strategy", ["mean", "median", "most_frequent"], default_value="mean") + "strategy", ["mean", "median", "most_frequent"], default_value="mean" + ) cs = ConfigurationSpace() cs.add_hyperparameter(strategy) return cs diff --git a/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/__init__.py b/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/__init__.py index 0db0955cb5..fbf999761c 100644 --- a/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/__init__.py +++ b/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/__init__.py @@ -1,24 +1,27 @@ -from collections import OrderedDict -import os - from typing import Any, Dict, Optional +import os +from collections import OrderedDict + from ConfigSpace import Configuration from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter - -from ...base import AutoSklearnPreprocessingAlgorithm, find_components, \ - ThirdPartyComponents, AutoSklearnChoice, _addons - from sklearn.base import BaseEstimator from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE +from ...base import ( + AutoSklearnChoice, + AutoSklearnPreprocessingAlgorithm, + ThirdPartyComponents, + _addons, + find_components, +) + mc_directory = os.path.split(__file__)[0] -_mcs = find_components( - __package__, mc_directory, AutoSklearnPreprocessingAlgorithm) +_mcs = find_components(__package__, mc_directory, AutoSklearnPreprocessingAlgorithm) additional_components = ThirdPartyComponents(AutoSklearnPreprocessingAlgorithm) -_addons['data_preprocessing.minority_coalescense'] = additional_components +_addons["data_preprocessing.minority_coalescense"] = additional_components def add_mc(mc: BaseEstimator) -> None: @@ -26,7 +29,6 @@ def add_mc(mc: BaseEstimator) -> None: class CoalescenseChoice(AutoSklearnChoice): - @classmethod def get_components(cls: BaseEstimator) -> Dict[str, BaseEstimator]: components: Dict[str, BaseEstimator] = OrderedDict() @@ -48,46 +50,52 @@ def get_hyperparameter_search_space( # Compile a list of legal preprocessors for this problem available_preprocessors = self.get_available_components( - dataset_properties=dataset_properties, - include=include, exclude=exclude) + dataset_properties=dataset_properties, include=include, exclude=exclude + ) if len(available_preprocessors) == 0: raise ValueError( "No minority coalescers found, please add any one minority coalescer" - "component.") + "component." + ) if default is None: - defaults = ['minority_coalescer', 'no_coalescense'] + defaults = ["minority_coalescer", "no_coalescense"] for default_ in defaults: if default_ in available_preprocessors: default = default_ break preprocessor = CategoricalHyperparameter( - '__choice__', list(available_preprocessors.keys()), default_value=default) + "__choice__", list(available_preprocessors.keys()), default_value=default + ) cs.add_hyperparameter(preprocessor) for name in available_preprocessors: - preprocessor_configuration_space = available_preprocessors[name]. \ - get_hyperparameter_search_space(dataset_properties) - parent_hyperparameter = {'parent': preprocessor, 'value': name} - cs.add_configuration_space(name, preprocessor_configuration_space, - parent_hyperparameter=parent_hyperparameter) + preprocessor_configuration_space = available_preprocessors[ + name + ].get_hyperparameter_search_space(dataset_properties) + parent_hyperparameter = {"parent": preprocessor, "value": name} + cs.add_configuration_space( + name, + preprocessor_configuration_space, + parent_hyperparameter=parent_hyperparameter, + ) self.configuration_space = cs self.dataset_properties = dataset_properties return cs - def set_hyperparameters(self, configuration: Configuration, - init_params: Optional[Dict[str, Any]] = None - ) -> 'CoalescenseChoice': + def set_hyperparameters( + self, configuration: Configuration, init_params: Optional[Dict[str, Any]] = None + ) -> "CoalescenseChoice": new_params = {} params = configuration.get_dictionary() - choice = params['__choice__'] - del params['__choice__'] + choice = params["__choice__"] + del params["__choice__"] for param, value in params.items(): - param = param.replace(choice, '').replace(':', '') + param = param.replace(choice, "").replace(":", "") new_params[param] = value if init_params is not None: @@ -97,10 +105,10 @@ def set_hyperparameters(self, configuration: Configuration, # in order to not pass it to the no encoding if choice not in param: continue - param = param.replace(choice, '').replace(':', '') + param = param.replace(choice, "").replace(":", "") new_params[param] = value - new_params['random_state'] = self.random_state + new_params["random_state"] = self.random_state self.new_params = new_params self.choice = self.get_components()[choice](**new_params) diff --git a/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/minority_coalescer.py b/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/minority_coalescer.py index 5b3b66caa7..278cf0bfb9 100644 --- a/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/minority_coalescer.py +++ b/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/minority_coalescer.py @@ -1,34 +1,35 @@ from typing import Dict, Optional, Tuple, Union - +import numpy as np from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformFloatHyperparameter -import numpy as np - import autosklearn.pipeline.implementations.MinorityCoalescer from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class MinorityCoalescer(AutoSklearnPreprocessingAlgorithm): - """ Group together categories which occurence is less than a specified minimum fraction. - """ + """Group categories whose occurence is less than a specified minimum fraction.""" def __init__( self, minimum_fraction: float = 0.01, - random_state: Optional[Union[int, np.random.RandomState]] = None + random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> None: self.minimum_fraction = minimum_fraction - def fit(self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None - ) -> 'MinorityCoalescer': + def fit( + self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None + ) -> "MinorityCoalescer": self.minimum_fraction = float(self.minimum_fraction) - self.preprocessor = autosklearn.pipeline.implementations.MinorityCoalescer\ - .MinorityCoalescer(minimum_fraction=self.minimum_fraction) + self.preprocessor = ( + autosklearn.pipeline.implementations.MinorityCoalescer.MinorityCoalescer( + minimum_fraction=self.minimum_fraction + ) + ) self.preprocessor.fit(X, y) return self @@ -38,26 +39,31 @@ def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: return self.preprocessor.transform(X) @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'coalescer', - 'name': 'Categorical minority coalescer', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - # TODO find out of this is right! - 'handles_sparse': True, - 'handles_dense': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,), } + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "coalescer", + "name": "Categorical minority coalescer", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + # TODO find out of this is right! + "handles_sparse": True, + "handles_dense": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), + } @staticmethod - def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> ConfigurationSpace: + def get_hyperparameter_search_space( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> ConfigurationSpace: cs = ConfigurationSpace() minimum_fraction = UniformFloatHyperparameter( - "minimum_fraction", lower=.0001, upper=0.5, default_value=0.01, log=True) + "minimum_fraction", lower=0.0001, upper=0.5, default_value=0.01, log=True + ) cs.add_hyperparameter(minimum_fraction) return cs diff --git a/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/no_coalescense.py b/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/no_coalescense.py index d252821ccc..d05c146d98 100644 --- a/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/no_coalescense.py +++ b/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/no_coalescense.py @@ -1,47 +1,49 @@ from typing import Dict, Optional, Tuple, Union - +import numpy as np from ConfigSpace.configuration_space import ConfigurationSpace -import numpy as np from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE -from autosklearn.pipeline.components.base import \ - AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT +from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class NoCoalescence(AutoSklearnPreprocessingAlgorithm): def __init__( - self, - random_state: Optional[Union[int, np.random.RandomState]] = None + self, random_state: Optional[Union[int, np.random.RandomState]] = None ) -> None: pass - def fit(self, X: np.array, y: Optional[PIPELINE_DATA_DTYPE] = None - ) -> PIPELINE_DATA_DTYPE: - self.preprocessor = 'passthrough' + def fit( + self, X: np.array, y: Optional[PIPELINE_DATA_DTYPE] = None + ) -> PIPELINE_DATA_DTYPE: + self.preprocessor = "passthrough" return self def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: return X @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'no coalescence', - 'name': 'No categorical variable coalescence', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'handles_sparse': True, - 'handles_dense': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,)} + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "no coalescence", + "name": "No categorical variable coalescence", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "handles_sparse": True, + "handles_dense": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), + } @staticmethod - def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> ConfigurationSpace: + def get_hyperparameter_search_space( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> ConfigurationSpace: cs = ConfigurationSpace() return cs diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/__init__.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/__init__.py index b37ad3ce24..2a9fbdb842 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/__init__.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/__init__.py @@ -1,26 +1,31 @@ -from collections import OrderedDict -import os - from typing import Dict, Optional +import os +from collections import OrderedDict + from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter - from sklearn.base import BaseEstimator -from ...base import AutoSklearnPreprocessingAlgorithm, find_components, \ - ThirdPartyComponents, AutoSklearnChoice, _addons from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE -from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling import ( - Rescaling +from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling import ( # noqa: E501 + Rescaling, +) + +from ...base import ( + AutoSklearnChoice, + AutoSklearnPreprocessingAlgorithm, + ThirdPartyComponents, + _addons, + find_components, ) rescaling_directory = os.path.split(__file__)[0] -_rescalers = find_components(__package__, - rescaling_directory, - AutoSklearnPreprocessingAlgorithm) +_rescalers = find_components( + __package__, rescaling_directory, AutoSklearnPreprocessingAlgorithm +) additional_components = ThirdPartyComponents(AutoSklearnPreprocessingAlgorithm) -_addons['data_preprocessing.rescaling'] = additional_components +_addons["data_preprocessing.rescaling"] = additional_components def add_rescaler(rescaler: Rescaling) -> None: @@ -28,7 +33,6 @@ def add_rescaler(rescaler: Rescaling) -> None: class RescalingChoice(AutoSklearnChoice): - @classmethod def get_components(cls: BaseEstimator) -> Dict[str, BaseEstimator]: components: Dict[str, BaseEstimator] = OrderedDict() @@ -50,31 +54,33 @@ def get_hyperparameter_search_space( # Compile a list of legal preprocessors for this problem available_preprocessors = self.get_available_components( - dataset_properties=dataset_properties, - include=include, exclude=exclude) + dataset_properties=dataset_properties, include=include, exclude=exclude + ) if len(available_preprocessors) == 0: - raise ValueError( - "No rescalers found, please add any rescaling component.") + raise ValueError("No rescalers found, please add any rescaling component.") if default is None: - defaults = ['standardize', 'none', 'minmax', 'normalize'] + defaults = ["standardize", "none", "minmax", "normalize"] for default_ in defaults: if default_ in available_preprocessors: default = default_ break - preprocessor = CategoricalHyperparameter('__choice__', - list( - available_preprocessors.keys()), - default_value=default) + preprocessor = CategoricalHyperparameter( + "__choice__", list(available_preprocessors.keys()), default_value=default + ) cs.add_hyperparameter(preprocessor) for name in available_preprocessors: - preprocessor_configuration_space = available_preprocessors[name]. \ - get_hyperparameter_search_space(dataset_properties) - parent_hyperparameter = {'parent': preprocessor, 'value': name} - cs.add_configuration_space(name, preprocessor_configuration_space, - parent_hyperparameter=parent_hyperparameter) + preprocessor_configuration_space = available_preprocessors[ + name + ].get_hyperparameter_search_space(dataset_properties) + parent_hyperparameter = {"parent": preprocessor, "value": name} + cs.add_configuration_space( + name, + preprocessor_configuration_space, + parent_hyperparameter=parent_hyperparameter, + ) self.configuration_space = cs self.dataset_properties = dataset_properties diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py index dc9c9c60ac..05e1a4e898 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py @@ -1,9 +1,7 @@ from typing import Optional, Union -from ConfigSpace.configuration_space import ConfigurationSpace - import numpy as np - +from ConfigSpace.configuration_space import ConfigurationSpace from sklearn.base import BaseEstimator from sklearn.exceptions import NotFittedError @@ -14,16 +12,13 @@ class Rescaling(object): # Rescaling does not support fit_transform (as of 0.19.1)! def __init__( - self, - random_state: Optional[Union[int, np.random.RandomState]] = None + self, random_state: Optional[Union[int, np.random.RandomState]] = None ) -> None: self.preprocessor: Optional[BaseEstimator] = None def fit( - self, - X: PIPELINE_DATA_DTYPE, - y: Optional[PIPELINE_DATA_DTYPE] = None - ) -> 'AutoSklearnPreprocessingAlgorithm': + self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None + ) -> "AutoSklearnPreprocessingAlgorithm": if self.preprocessor is None: raise NotFittedError() @@ -42,7 +37,8 @@ def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: return transformed_X @staticmethod - def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> ConfigurationSpace: + def get_hyperparameter_search_space( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> ConfigurationSpace: cs = ConfigurationSpace() return cs diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/minmax.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/minmax.py index 67650376e1..3663a23d35 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/minmax.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/minmax.py @@ -3,36 +3,43 @@ import numpy as np from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, SIGNED_DATA, INPUT -from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling \ - import Rescaling from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling import ( # noqa: E501 + Rescaling, +) +from autosklearn.pipeline.constants import DENSE, INPUT, SIGNED_DATA, UNSIGNED_DATA class MinMaxScalerComponent(Rescaling, AutoSklearnPreprocessingAlgorithm): - def __init__(self, random_state: Optional[Union[int, np.random.RandomState]] = None): + def __init__( + self, random_state: Optional[Union[int, np.random.RandomState]] = None + ): from sklearn.preprocessing import MinMaxScaler + self.preprocessor = MinMaxScaler(copy=False) @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'MinMaxScaler', - 'name': 'MinMaxScaler', - 'handles_missing_values': False, - 'handles_nominal_values': False, - 'handles_numerical_features': True, - 'prefers_data_scaled': False, - 'prefers_data_normalized': False, - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': True, - # TODO find out if this is right! - 'handles_sparse': False, - 'handles_dense': True, - 'input': (DENSE, UNSIGNED_DATA), - 'output': (INPUT, SIGNED_DATA), - 'preferred_dtype': None} + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "MinMaxScaler", + "name": "MinMaxScaler", + "handles_missing_values": False, + "handles_nominal_values": False, + "handles_numerical_features": True, + "prefers_data_scaled": False, + "prefers_data_normalized": False, + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": True, + # TODO find out if this is right! + "handles_sparse": False, + "handles_dense": True, + "input": (DENSE, UNSIGNED_DATA), + "output": (INPUT, SIGNED_DATA), + "preferred_dtype": None, + } diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/none.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/none.py index 83377e2544..ee94213f57 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/none.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/none.py @@ -1,41 +1,45 @@ from typing import Dict, Optional, Tuple, Union from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, INPUT, SPARSE -from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling \ - import Rescaling from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling import ( # noqa: E501 + Rescaling, +) +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class NoRescalingComponent(Rescaling, AutoSklearnPreprocessingAlgorithm): - - def fit(self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None - ) -> 'AutoSklearnPreprocessingAlgorithm': - self.preprocessor = 'passthrough' + def fit( + self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None + ) -> "AutoSklearnPreprocessingAlgorithm": + self.preprocessor = "passthrough" return self def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: return X @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'NoRescaling', - 'name': 'NoRescaling', - 'handles_missing_values': False, - 'handles_nominal_values': False, - 'handles_numerical_features': True, - 'prefers_data_scaled': False, - 'prefers_data_normalized': False, - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': True, - # TODO find out if this is right! - 'handles_sparse': True, - 'handles_dense': True, - 'input': (SPARSE, DENSE, UNSIGNED_DATA), - 'output': (INPUT,), - 'preferred_dtype': None} + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "NoRescaling", + "name": "NoRescaling", + "handles_missing_values": False, + "handles_nominal_values": False, + "handles_numerical_features": True, + "prefers_data_scaled": False, + "prefers_data_normalized": False, + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": True, + # TODO find out if this is right! + "handles_sparse": True, + "handles_dense": True, + "input": (SPARSE, DENSE, UNSIGNED_DATA), + "output": (INPUT,), + "preferred_dtype": None, + } diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/normalize.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/normalize.py index 036c75a4ef..00395833e9 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/normalize.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/normalize.py @@ -3,41 +3,45 @@ import numpy as np from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, INPUT, SPARSE -from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling \ - import Rescaling from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling import ( # noqa: E501 + Rescaling, +) +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class NormalizerComponent(Rescaling, AutoSklearnPreprocessingAlgorithm): def __init__( - self, - random_state: Optional[Union[int, np.random.RandomState]] = None + self, random_state: Optional[Union[int, np.random.RandomState]] = None ) -> None: # Use custom implementation because sklearn implementation cannot # handle float32 input matrix from sklearn.preprocessing import Normalizer + self.preprocessor = Normalizer(copy=False) @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'Normalizer', - 'name': 'Normalizer', - 'handles_missing_values': False, - 'handles_nominal_values': False, - 'handles_numerical_features': True, - 'prefers_data_scaled': False, - 'prefers_data_normalized': False, - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': True, - # TODO find out if this is right! - 'handles_sparse': True, - 'handles_dense': True, - 'input': (SPARSE, DENSE, UNSIGNED_DATA), - 'output': (INPUT,), - 'preferred_dtype': None} + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "Normalizer", + "name": "Normalizer", + "handles_missing_values": False, + "handles_nominal_values": False, + "handles_numerical_features": True, + "prefers_data_scaled": False, + "prefers_data_normalized": False, + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": True, + # TODO find out if this is right! + "handles_sparse": True, + "handles_dense": True, + "input": (SPARSE, DENSE, UNSIGNED_DATA), + "output": (INPUT,), + "preferred_dtype": None, + } diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/power_transformer.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/power_transformer.py index 759c921caa..dd9ab616ae 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/power_transformer.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/power_transformer.py @@ -3,10 +3,11 @@ import numpy as np from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, INPUT -from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling \ - import Rescaling from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling import ( # noqa: E501 + Rescaling, +) +from autosklearn.pipeline.constants import DENSE, INPUT, UNSIGNED_DATA class PowerTransformerComponent(Rescaling, AutoSklearnPreprocessingAlgorithm): @@ -15,27 +16,31 @@ def __init__( random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> None: from sklearn.preprocessing import PowerTransformer + self.preprocessor = PowerTransformer(copy=False) @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'PowerTransformer', - 'name': 'PowerTransformer', - 'handles_missing_values': False, - 'handles_nominal_values': False, - 'handles_numerical_features': True, - 'prefers_data_scaled': False, - 'prefers_data_normalized': False, - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': True, - # TODO find out of this is right! - 'handles_sparse': False, - 'handles_dense': True, - 'input': (DENSE, UNSIGNED_DATA), - 'output': (INPUT,), - 'preferred_dtype': None} + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "PowerTransformer", + "name": "PowerTransformer", + "handles_missing_values": False, + "handles_nominal_values": False, + "handles_numerical_features": True, + "prefers_data_scaled": False, + "prefers_data_normalized": False, + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": True, + # TODO find out of this is right! + "handles_sparse": False, + "handles_dense": True, + "input": (DENSE, UNSIGNED_DATA), + "output": (INPUT,), + "preferred_dtype": None, + } diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py index b7206fbaaf..2611c0650d 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py @@ -1,17 +1,24 @@ from typing import Dict, Optional, Tuple, Union import numpy as np - -from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformIntegerHyperparameter, \ - CategoricalHyperparameter +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformIntegerHyperparameter, +) -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, SIGNED_DATA, SPARSE, INPUT -from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling \ - import Rescaling -from autosklearn.pipeline.components.base import \ - AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE +from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling import ( # noqa: E501 + Rescaling, +) +from autosklearn.pipeline.constants import ( + DENSE, + INPUT, + SIGNED_DATA, + SPARSE, + UNSIGNED_DATA, +) class QuantileTransformerComponent(Rescaling, AutoSklearnPreprocessingAlgorithm): @@ -19,46 +26,51 @@ def __init__( self, n_quantiles: int, output_distribution: str, - random_state: Optional[Union[int, np.random.RandomState]] = None + random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> None: from sklearn.preprocessing import QuantileTransformer + self.n_quantiles = n_quantiles self.output_distribution = output_distribution self.preprocessor = QuantileTransformer( n_quantiles=n_quantiles, output_distribution=output_distribution, copy=False, - random_state=random_state + random_state=random_state, ) @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'QuantileTransformer', - 'name': 'QuantileTransformer', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': True, - # TODO find out if this is right! - 'handles_sparse': True, - 'handles_dense': True, - 'input': (SPARSE, DENSE, UNSIGNED_DATA), - 'output': (INPUT, SIGNED_DATA), - 'preferred_dtype': None} + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "QuantileTransformer", + "name": "QuantileTransformer", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": True, + # TODO find out if this is right! + "handles_sparse": True, + "handles_dense": True, + "input": (SPARSE, DENSE, UNSIGNED_DATA), + "output": (INPUT, SIGNED_DATA), + "preferred_dtype": None, + } @staticmethod - def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> ConfigurationSpace: + def get_hyperparameter_search_space( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> ConfigurationSpace: cs = ConfigurationSpace() # TODO parametrize like the Random Forest as n_quantiles = n_features^param n_quantiles = UniformIntegerHyperparameter( - 'n_quantiles', lower=10, upper=2000, default_value=1000 + "n_quantiles", lower=10, upper=2000, default_value=1000 ) output_distribution = CategoricalHyperparameter( - 'output_distribution', ['normal', 'uniform'] + "output_distribution", ["normal", "uniform"] ) cs.add_hyperparameters((n_quantiles, output_distribution)) return cs diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/robust_scaler.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/robust_scaler.py index 614b79ee40..af3b4c0558 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/robust_scaler.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/robust_scaler.py @@ -1,19 +1,23 @@ from typing import Dict, Optional, Tuple, Union import numpy as np - - -from scipy import sparse -from sklearn.exceptions import NotFittedError from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformFloatHyperparameter +from scipy import sparse +from sklearn.exceptions import NotFittedError from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, SIGNED_DATA, INPUT, SPARSE -from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling \ - import Rescaling -from autosklearn.pipeline.components.base import \ - AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling import ( # noqa: E501 + Rescaling, +) +from autosklearn.pipeline.constants import ( + DENSE, + INPUT, + SIGNED_DATA, + SPARSE, + UNSIGNED_DATA, +) class RobustScalerComponent(Rescaling, AutoSklearnPreprocessingAlgorithm): @@ -21,48 +25,51 @@ def __init__( self, q_min: float, q_max: float, - random_state: Optional[Union[int, np.random.RandomState]] = None + random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> None: from sklearn.preprocessing import RobustScaler + self.q_min = q_min self.q_max = q_max self.preprocessor = RobustScaler( - quantile_range=(self.q_min, self.q_max), copy=False, + quantile_range=(self.q_min, self.q_max), + copy=False, ) @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'RobustScaler', - 'name': 'RobustScaler', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': True, - # TODO find out if this is right! - 'handles_sparse': True, - 'handles_dense': True, - 'input': (SPARSE, DENSE, UNSIGNED_DATA), - 'output': (INPUT, SIGNED_DATA), - 'preferred_dtype': None} + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "RobustScaler", + "name": "RobustScaler", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": True, + # TODO find out if this is right! + "handles_sparse": True, + "handles_dense": True, + "input": (SPARSE, DENSE, UNSIGNED_DATA), + "output": (INPUT, SIGNED_DATA), + "preferred_dtype": None, + } @staticmethod - def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> ConfigurationSpace: + def get_hyperparameter_search_space( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> ConfigurationSpace: cs = ConfigurationSpace() - q_min = UniformFloatHyperparameter( - 'q_min', 0.001, 0.3, default_value=0.25 - ) - q_max = UniformFloatHyperparameter( - 'q_max', 0.7, 0.999, default_value=0.75 - ) + q_min = UniformFloatHyperparameter("q_min", 0.001, 0.3, default_value=0.25) + q_max = UniformFloatHyperparameter("q_max", 0.7, 0.999, default_value=0.75) cs.add_hyperparameters((q_min, q_max)) return cs - def fit(self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None - ) -> 'AutoSklearnPreprocessingAlgorithm': + def fit( + self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None + ) -> "AutoSklearnPreprocessingAlgorithm": if self.preprocessor is None: raise NotFittedError() if sparse.isspmatrix(X): diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/standardize.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/standardize.py index adb156ab93..a1da729907 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/standardize.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/standardize.py @@ -1,52 +1,54 @@ from typing import Dict, Optional, Tuple, Union import numpy as np - from scipy import sparse - from sklearn.exceptions import NotFittedError from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT -from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling \ - import Rescaling -from autosklearn.pipeline.components.base import \ - AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling import ( # noqa: E501 + Rescaling, +) +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class StandardScalerComponent(Rescaling, AutoSklearnPreprocessingAlgorithm): def __init__( - self, - random_state: Optional[Union[int, np.random.RandomState]] = None + self, random_state: Optional[Union[int, np.random.RandomState]] = None ) -> None: from sklearn.preprocessing import StandardScaler + self.preprocessor = StandardScaler(copy=False) @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'StandardScaler', - 'name': 'StandardScaler', - 'handles_missing_values': False, - 'handles_nominal_values': False, - 'handles_numerical_features': True, - 'prefers_data_scaled': False, - 'prefers_data_normalized': False, - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': True, - # TODO find out if this is right! - 'handles_sparse': True, - 'handles_dense': True, - 'input': (SPARSE, DENSE, UNSIGNED_DATA), - 'output': (INPUT,), - 'preferred_dtype': None} - - def fit(self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None - ) -> 'AutoSklearnPreprocessingAlgorithm': + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "StandardScaler", + "name": "StandardScaler", + "handles_missing_values": False, + "handles_nominal_values": False, + "handles_numerical_features": True, + "prefers_data_scaled": False, + "prefers_data_normalized": False, + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": True, + # TODO find out if this is right! + "handles_sparse": True, + "handles_dense": True, + "input": (SPARSE, DENSE, UNSIGNED_DATA), + "output": (INPUT,), + "preferred_dtype": None, + } + + def fit( + self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None + ) -> "AutoSklearnPreprocessingAlgorithm": if self.preprocessor is None: raise NotFittedError() if sparse.isspmatrix(X): diff --git a/autosklearn/pipeline/components/data_preprocessing/text_encoding/__init__.py b/autosklearn/pipeline/components/data_preprocessing/text_encoding/__init__.py index 949ce83298..990ad579ca 100644 --- a/autosklearn/pipeline/components/data_preprocessing/text_encoding/__init__.py +++ b/autosklearn/pipeline/components/data_preprocessing/text_encoding/__init__.py @@ -1,34 +1,35 @@ -from collections import OrderedDict -import os - from typing import Any, Dict, Optional +import os +from collections import OrderedDict + from ConfigSpace import Configuration from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter - from sklearn.base import BaseEstimator -from ...base import AutoSklearnPreprocessingAlgorithm, find_components,\ - ThirdPartyComponents, AutoSklearnChoice, _addons - from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE +from ...base import ( + AutoSklearnChoice, + AutoSklearnPreprocessingAlgorithm, + ThirdPartyComponents, + _addons, + find_components, +) + bow_directory = os.path.split(__file__)[0] -_bows = find_components(__package__, - bow_directory, - AutoSklearnPreprocessingAlgorithm) +_bows = find_components(__package__, bow_directory, AutoSklearnPreprocessingAlgorithm) additional_components = ThirdPartyComponents(AutoSklearnPreprocessingAlgorithm) -_addons['data_preprocessing.text_encoding'] = additional_components +_addons["data_preprocessing.text_encoding"] = additional_components -def add_bow(classifier: 'BagOfWordChoice') -> None: +def add_bow(classifier: "BagOfWordChoice") -> None: additional_components.add_component(classifier) class BagOfWordChoice(AutoSklearnChoice): - @classmethod def get_components(cls: BaseEstimator) -> Dict[str, BaseEstimator]: components: Dict[str, BaseEstimator] = OrderedDict() @@ -50,49 +51,53 @@ def get_hyperparameter_search_space( # Compile a list of legal preprocessors for this problem available_preprocessors = self.get_available_components( - dataset_properties=dataset_properties, - include=include, exclude=exclude) + dataset_properties=dataset_properties, include=include, exclude=exclude + ) if len(available_preprocessors) == 0: raise ValueError( "No bag of word encoders found, please add any bag of word encoder" - "component.") + "component." + ) if default is None: - defaults = ['bag_of_words_encoding'] + defaults = ["bag_of_words_encoding"] for default_ in defaults: if default_ in available_preprocessors: default = default_ break - preprocessor = CategoricalHyperparameter('__choice__', - list( - available_preprocessors.keys()), - default_value=default) + preprocessor = CategoricalHyperparameter( + "__choice__", list(available_preprocessors.keys()), default_value=default + ) cs.add_hyperparameter(preprocessor) for name in available_preprocessors: - preprocessor_configuration_space = available_preprocessors[name]. \ - get_hyperparameter_search_space(dataset_properties) - parent_hyperparameter = {'parent': preprocessor, 'value': name} - cs.add_configuration_space(name, preprocessor_configuration_space, - parent_hyperparameter=parent_hyperparameter) + preprocessor_configuration_space = available_preprocessors[ + name + ].get_hyperparameter_search_space(dataset_properties) + parent_hyperparameter = {"parent": preprocessor, "value": name} + cs.add_configuration_space( + name, + preprocessor_configuration_space, + parent_hyperparameter=parent_hyperparameter, + ) self.configuration_space = cs self.dataset_properties = dataset_properties return cs - def set_hyperparameters(self, configuration: Configuration, - init_params: Optional[Dict[str, Any]] = None - ) -> 'BagOfWordChoice': + def set_hyperparameters( + self, configuration: Configuration, init_params: Optional[Dict[str, Any]] = None + ) -> "BagOfWordChoice": new_params = {} params = configuration.get_dictionary() - choice = params['__choice__'] - del params['__choice__'] + choice = params["__choice__"] + del params["__choice__"] for param, value in params.items(): - param = param.replace(choice, '').replace(':', '') + param = param.replace(choice, "").replace(":", "") new_params[param] = value if init_params is not None: @@ -102,10 +107,10 @@ def set_hyperparameters(self, configuration: Configuration, # in order to not pass it to the no encoding if choice not in param: continue - param = param.replace(choice, '').replace(':', '') + param = param.replace(choice, "").replace(":", "") new_params[param] = value - new_params['random_state'] = self.random_state + new_params["random_state"] = self.random_state self.new_params = new_params self.choice = self.get_components()[choice](**new_params) diff --git a/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding.py b/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding.py index c66c67046b..47a80684f7 100644 --- a/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding.py +++ b/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding.py @@ -1,19 +1,17 @@ from typing import Dict, Optional, Tuple, Union -from ConfigSpace.configuration_space import ConfigurationSpace -import ConfigSpace.hyperparameters as CSH -from ConfigSpace import EqualsCondition +import itertools +import ConfigSpace.hyperparameters as CSH import numpy as np import pandas as pd -import itertools - +from ConfigSpace import EqualsCondition +from ConfigSpace.configuration_space import ConfigurationSpace +from sklearn.feature_extraction.text import CountVectorizer from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT - -from sklearn.feature_extraction.text import CountVectorizer +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class BagOfWordEncoder(AutoSklearnPreprocessingAlgorithm): @@ -23,7 +21,7 @@ def __init__( min_df_choice: str = "min_df_absolute", min_df_absolute: int = 0, min_df_relative: float = 0.01, - random_state: Optional[Union[int, np.random.RandomState]] = None + random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> None: self.ngram_range = ngram_range self.random_state = random_state @@ -31,30 +29,38 @@ def __init__( self.min_df_absolute = min_df_absolute self.min_df_relative = min_df_relative - def fit(self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None - ) -> 'BagOfWordEncoder': - - if isinstance(X, pd.DataFrame): - X.fillna("", inplace=True) - # define a CountVectorizer for every feature (implicitly defined by order of columns, - # maybe change the list - # to a dictionary with features as keys) - if self.min_df_choice == "min_df_absolute": - self.preprocessor = CountVectorizer(min_df=self.min_df_absolute, - ngram_range=(1, self.ngram_range)) - elif self.min_df_choice == "min_df_relative": - self.preprocessor = CountVectorizer(min_df=self.min_df_relative, - ngram_range=(1, self.ngram_range)) - else: - raise KeyError() + def fit( + self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None + ) -> "BagOfWordEncoder": + + if not isinstance(X, pd.DataFrame): + raise ValueError( + "Your text data is not encoded in a pandas.DataFrame\n" + "Please make sure to use a pandas.DataFrame and ensure" + "that the text features are encoded as strings." + ) + + X.fillna("", inplace=True) + + # define a CountVectorizer for used on every feature + if self.min_df_choice == "min_df_absolute": + self.preprocessor = CountVectorizer( + min_df=self.min_df_absolute, + ngram_range=(1, self.ngram_range), + ) - all_text = itertools.chain.from_iterable(X[col] for col in X.columns) - self.preprocessor = self.preprocessor.fit(all_text) + elif self.min_df_choice == "min_df_relative": + self.preprocessor = CountVectorizer( + min_df=self.min_df_relative, + ngram_range=(1, self.ngram_range), + ) else: - raise ValueError("Your text data is not encoded in a pandas.DataFrame\n" - "Please make sure to use a pandas.DataFrame and ensure" - "that the text features are encoded as strings.") + raise KeyError() + + all_text = itertools.chain.from_iterable(X[col] for col in X.columns) + self.preprocessor = self.preprocessor.fit(all_text) + return self def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: @@ -70,42 +76,55 @@ def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: return X_transformed @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'BOW', - 'name': 'Bag Of Word Encoder', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'handles_sparse': True, - 'handles_dense': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,), } + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "BOW", + "name": "Bag Of Word Encoder", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "handles_sparse": True, + "handles_dense": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), + } @staticmethod - def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> ConfigurationSpace: + def get_hyperparameter_search_space( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> ConfigurationSpace: cs = ConfigurationSpace() - hp_ngram_range = CSH.UniformIntegerHyperparameter(name="ngram_range", lower=1, upper=3, - default_value=1) - hp_min_df_choice_bow = CSH.CategoricalHyperparameter("min_df_choice", - choices=["min_df_absolute", - "min_df_relative"]) - hp_min_df_absolute_bow = CSH.UniformIntegerHyperparameter(name="min_df_absolute", lower=0, - upper=10, - default_value=0) - hp_min_df_relative_bow = CSH.UniformFloatHyperparameter(name="min_df_relative", lower=0.01, - upper=1.0, - default_value=0.01, log=True) + hp_ngram_range = CSH.UniformIntegerHyperparameter( + name="ngram_range", lower=1, upper=3, default_value=1 + ) + hp_min_df_choice_bow = CSH.CategoricalHyperparameter( + "min_df_choice", choices=["min_df_absolute", "min_df_relative"] + ) + hp_min_df_absolute_bow = CSH.UniformIntegerHyperparameter( + name="min_df_absolute", lower=0, upper=10, default_value=0 + ) + hp_min_df_relative_bow = CSH.UniformFloatHyperparameter( + name="min_df_relative", lower=0.01, upper=1.0, default_value=0.01, log=True + ) cs.add_hyperparameters( - [hp_ngram_range, hp_min_df_choice_bow, hp_min_df_absolute_bow, hp_min_df_relative_bow]) - - cond_min_df_absolute_bow = EqualsCondition(hp_min_df_absolute_bow, hp_min_df_choice_bow, - "min_df_absolute") - cond_min_df_relative_bow = EqualsCondition(hp_min_df_relative_bow, hp_min_df_choice_bow, - "min_df_relative") + [ + hp_ngram_range, + hp_min_df_choice_bow, + hp_min_df_absolute_bow, + hp_min_df_relative_bow, + ] + ) + + cond_min_df_absolute_bow = EqualsCondition( + hp_min_df_absolute_bow, hp_min_df_choice_bow, "min_df_absolute" + ) + cond_min_df_relative_bow = EqualsCondition( + hp_min_df_relative_bow, hp_min_df_choice_bow, "min_df_relative" + ) cs.add_conditions([cond_min_df_absolute_bow, cond_min_df_relative_bow]) # maybe add bigrams ... diff --git a/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding_distinct.py b/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding_distinct.py index 85851ca72e..22a0be1088 100644 --- a/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding_distinct.py +++ b/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding_distinct.py @@ -1,18 +1,16 @@ from typing import Dict, Optional, Tuple, Union -from ConfigSpace.configuration_space import ConfigurationSpace import ConfigSpace.hyperparameters as CSH -from ConfigSpace import EqualsCondition - import numpy as np import pandas as pd +from ConfigSpace import EqualsCondition +from ConfigSpace.configuration_space import ConfigurationSpace from scipy.sparse import hstack +from sklearn.feature_extraction.text import CountVectorizer from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT - -from sklearn.feature_extraction.text import CountVectorizer +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class BagOfWordEncoder(AutoSklearnPreprocessingAlgorithm): @@ -22,7 +20,7 @@ def __init__( min_df_choice: str = "min_df_absolute", min_df_absolute: int = 0, min_df_relative: float = 0.01, - random_state: Optional[Union[int, np.random.RandomState]] = None + random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> None: self.ngram_range = ngram_range self.random_state = random_state @@ -30,8 +28,9 @@ def __init__( self.min_df_absolute = min_df_absolute self.min_df_relative = min_df_relative - def fit(self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None - ) -> 'BagOfWordEncoder': + def fit( + self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None + ) -> "BagOfWordEncoder": if isinstance(X, pd.DataFrame): X.fillna("", inplace=True) @@ -40,8 +39,9 @@ def fit(self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None self.preprocessor = {} for feature in X.columns: - vectorizer = CountVectorizer(min_df=self.min_df_absolute, - ngram_range=(1, self.ngram_range)).fit(X[feature]) + vectorizer = CountVectorizer( + min_df=self.min_df_absolute, ngram_range=(1, self.ngram_range) + ).fit(X[feature]) self.preprocessor[feature] = vectorizer elif self.min_df_choice == "min_df_relative": @@ -49,15 +49,18 @@ def fit(self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None self.preprocessor = {} for feature in X.columns: - vectorizer = CountVectorizer(min_df=self.min_df_relative, - ngram_range=(1, self.ngram_range)).fit(X[feature]) + vectorizer = CountVectorizer( + min_df=self.min_df_relative, ngram_range=(1, self.ngram_range) + ).fit(X[feature]) self.preprocessor[feature] = vectorizer else: raise KeyError() else: - raise ValueError("Your text data is not encoded in a pandas.DataFrame\n" - "Please make sure to use a pandas.DataFrame and ensure" - "that the text features are encoded as strings.") + raise ValueError( + "Your text data is not encoded in a pandas.DataFrame\n" + "Please make sure to use a pandas.DataFrame and ensure" + "that the text features are encoded as strings." + ) return self def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: @@ -77,42 +80,55 @@ def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: return X_new @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'BOW', - 'name': 'Bag Of Word Encoder', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'handles_sparse': True, - 'handles_dense': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,), } + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "BOW", + "name": "Bag Of Word Encoder", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "handles_sparse": True, + "handles_dense": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), + } @staticmethod - def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> ConfigurationSpace: + def get_hyperparameter_search_space( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> ConfigurationSpace: cs = ConfigurationSpace() - hp_ngram_range = CSH.UniformIntegerHyperparameter(name="ngram_range", lower=1, upper=3, - default_value=1) - hp_min_df_choice_bow = CSH.CategoricalHyperparameter("min_df_choice", - choices=["min_df_absolute", - "min_df_relative"]) - hp_min_df_absolute_bow = CSH.UniformIntegerHyperparameter(name="min_df_absolute", lower=0, - upper=10, - default_value=0) - hp_min_df_relative_bow = CSH.UniformFloatHyperparameter(name="min_df_relative", lower=0.01, - upper=1.0, - default_value=0.01, log=True) + hp_ngram_range = CSH.UniformIntegerHyperparameter( + name="ngram_range", lower=1, upper=3, default_value=1 + ) + hp_min_df_choice_bow = CSH.CategoricalHyperparameter( + "min_df_choice", choices=["min_df_absolute", "min_df_relative"] + ) + hp_min_df_absolute_bow = CSH.UniformIntegerHyperparameter( + name="min_df_absolute", lower=0, upper=10, default_value=0 + ) + hp_min_df_relative_bow = CSH.UniformFloatHyperparameter( + name="min_df_relative", lower=0.01, upper=1.0, default_value=0.01, log=True + ) cs.add_hyperparameters( - [hp_ngram_range, hp_min_df_choice_bow, hp_min_df_absolute_bow, hp_min_df_relative_bow]) - - cond_min_df_absolute_bow = EqualsCondition(hp_min_df_absolute_bow, hp_min_df_choice_bow, - "min_df_absolute") - cond_min_df_relative_bow = EqualsCondition(hp_min_df_relative_bow, hp_min_df_choice_bow, - "min_df_relative") + [ + hp_ngram_range, + hp_min_df_choice_bow, + hp_min_df_absolute_bow, + hp_min_df_relative_bow, + ] + ) + + cond_min_df_absolute_bow = EqualsCondition( + hp_min_df_absolute_bow, hp_min_df_choice_bow, "min_df_absolute" + ) + cond_min_df_relative_bow = EqualsCondition( + hp_min_df_relative_bow, hp_min_df_choice_bow, "min_df_relative" + ) cs.add_conditions([cond_min_df_absolute_bow, cond_min_df_relative_bow]) # maybe add bigrams ... diff --git a/autosklearn/pipeline/components/data_preprocessing/text_encoding/tfidf_encoding.py b/autosklearn/pipeline/components/data_preprocessing/text_encoding/tfidf_encoding.py index 0b7ef239f1..aea4a05906 100644 --- a/autosklearn/pipeline/components/data_preprocessing/text_encoding/tfidf_encoding.py +++ b/autosklearn/pipeline/components/data_preprocessing/text_encoding/tfidf_encoding.py @@ -1,18 +1,17 @@ from typing import Dict, Optional, Tuple, Union -from ConfigSpace.configuration_space import ConfigurationSpace -import ConfigSpace.hyperparameters as CSH -from ConfigSpace import EqualsCondition +import itertools +import ConfigSpace.hyperparameters as CSH import numpy as np import pandas as pd -import itertools +from ConfigSpace import EqualsCondition +from ConfigSpace.configuration_space import ConfigurationSpace +from sklearn.feature_extraction.text import TfidfVectorizer from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT - -from sklearn.feature_extraction.text import TfidfVectorizer +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class TfidfEncoder(AutoSklearnPreprocessingAlgorithm): @@ -23,7 +22,7 @@ def __init__( min_df_choice: str = "min_df_absolute", min_df_absolute: int = 0, min_df_relative: float = 0.01, - random_state: Optional[Union[int, np.random.RandomState]] = None + random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> None: self.ngram_range = ngram_range self.random_state = random_state @@ -32,32 +31,41 @@ def __init__( self.min_df_absolute = min_df_absolute self.min_df_relative = min_df_relative - def fit(self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None - ) -> 'TfidfEncoder': - - if isinstance(X, pd.DataFrame): - X.fillna("", inplace=True) - # define a CountVectorizer for every feature (implicitly defined by order of columns, - # maybe change the list - # to a dictionary with features as keys) - if self.min_df_choice == "min_df_absolute": - self.preprocessor = TfidfVectorizer(min_df=self.min_df_absolute, - use_idf=self.use_idf, - ngram_range=(1, self.ngram_range)) - elif self.min_df_choice == "min_df_relative": - self.preprocessor = TfidfVectorizer(min_df=self.min_df_relative, - use_idf=self.use_idf, - ngram_range=(1, self.ngram_range)) - else: - raise KeyError() + def fit( + self, + X: PIPELINE_DATA_DTYPE, + y: Optional[PIPELINE_DATA_DTYPE] = None, + ) -> "TfidfEncoder": + + if not isinstance(X, pd.DataFrame): + raise ValueError( + "Your text data is not encoded in a pandas.DataFrame\n" + "Please make sure to use a pandas.DataFrame and ensure" + " that the text features are encoded as strings." + ) + + X.fillna("", inplace=True) + + if self.min_df_choice == "min_df_absolute": + self.preprocessor = TfidfVectorizer( + min_df=self.min_df_absolute, + use_idf=self.use_idf, + ngram_range=(1, self.ngram_range), + ) - all_text = itertools.chain.from_iterable(X[col] for col in X.columns) - self.preprocessor = self.preprocessor.fit(all_text) + elif self.min_df_choice == "min_df_relative": + self.preprocessor = TfidfVectorizer( + min_df=self.min_df_relative, + use_idf=self.use_idf, + ngram_range=(1, self.ngram_range), + ) else: - raise ValueError("Your text data is not encoded in a pandas.DataFrame\n" - "Please make sure to use a pandas.DataFrame and ensure" - " that the text features are encoded as strings.") + raise KeyError() + + all_text = itertools.chain.from_iterable(X[col] for col in X.columns) + self.preprocessor = self.preprocessor.fit(all_text) + return self def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: @@ -73,41 +81,57 @@ def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: return X_transformed @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return {'shortname': 'RBOW', - 'name': 'Relative Bag Of Word Encoder', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'handles_sparse': True, - 'handles_dense': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,), } + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + return { + "shortname": "RBOW", + "name": "Relative Bag Of Word Encoder", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "handles_sparse": True, + "handles_dense": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), + } @staticmethod - def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> ConfigurationSpace: + def get_hyperparameter_search_space( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> ConfigurationSpace: cs = ConfigurationSpace() - hp_ngram_range = CSH.UniformIntegerHyperparameter(name="ngram_range", lower=1, upper=3, - default_value=1) + hp_ngram_range = CSH.UniformIntegerHyperparameter( + name="ngram_range", lower=1, upper=3, default_value=1 + ) hp_use_idf = CSH.CategoricalHyperparameter("use_idf", choices=[False, True]) - hp_min_df_choice = CSH.CategoricalHyperparameter("min_df_choice", - choices=["min_df_absolute", - "min_df_relative"]) - hp_min_df_absolute = CSH.UniformIntegerHyperparameter(name="min_df_absolute", lower=0, - upper=10, default_value=0) - hp_min_df_relative = CSH.UniformFloatHyperparameter(name="min_df_relative", lower=0.01, - upper=1.0, default_value=0.01, log=True) + hp_min_df_choice = CSH.CategoricalHyperparameter( + "min_df_choice", choices=["min_df_absolute", "min_df_relative"] + ) + hp_min_df_absolute = CSH.UniformIntegerHyperparameter( + name="min_df_absolute", lower=0, upper=10, default_value=0 + ) + hp_min_df_relative = CSH.UniformFloatHyperparameter( + name="min_df_relative", lower=0.01, upper=1.0, default_value=0.01, log=True + ) cs.add_hyperparameters( - [hp_ngram_range, hp_use_idf, hp_min_df_choice, hp_min_df_absolute, hp_min_df_relative]) - - cond_min_df_absolute = EqualsCondition(hp_min_df_absolute, hp_min_df_choice, - "min_df_absolute") - cond_min_df_relative = EqualsCondition(hp_min_df_relative, hp_min_df_choice, - "min_df_relative") + [ + hp_ngram_range, + hp_use_idf, + hp_min_df_choice, + hp_min_df_absolute, + hp_min_df_relative, + ] + ) + + cond_min_df_absolute = EqualsCondition( + hp_min_df_absolute, hp_min_df_choice, "min_df_absolute" + ) + cond_min_df_relative = EqualsCondition( + hp_min_df_relative, hp_min_df_choice, "min_df_relative" + ) cs.add_conditions([cond_min_df_absolute, cond_min_df_relative]) # maybe add bigrams ... diff --git a/autosklearn/pipeline/components/data_preprocessing/variance_threshold/variance_threshold.py b/autosklearn/pipeline/components/data_preprocessing/variance_threshold/variance_threshold.py index c6dc42a4df..365ae405a0 100644 --- a/autosklearn/pipeline/components/data_preprocessing/variance_threshold/variance_threshold.py +++ b/autosklearn/pipeline/components/data_preprocessing/variance_threshold/variance_threshold.py @@ -1,29 +1,25 @@ from typing import Dict, Optional, Tuple, Union -from ConfigSpace.configuration_space import ConfigurationSpace - import numpy as np +import sklearn.feature_selection +from ConfigSpace.configuration_space import ConfigurationSpace from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT - -import sklearn.feature_selection +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class VarianceThreshold(AutoSklearnPreprocessingAlgorithm): def __init__( - self, - random_state: Optional[Union[int, np.random.RandomState]] = None + self, random_state: Optional[Union[int, np.random.RandomState]] = None ) -> None: # VarianceThreshold does not support fit_transform (as of 0.19.1)! self.random_state = random_state - def fit(self, X: PIPELINE_DATA_DTYPE, - y: Optional[PIPELINE_DATA_DTYPE] = None) -> 'VarianceThreshold': - self.preprocessor = sklearn.feature_selection.VarianceThreshold( - threshold=0.0 - ) + def fit( + self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None + ) -> "VarianceThreshold": + self.preprocessor = sklearn.feature_selection.VarianceThreshold(threshold=0.0) self.preprocessor = self.preprocessor.fit(X) return self @@ -33,25 +29,27 @@ def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: return self.preprocessor.transform(X) @staticmethod - def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: + def get_properties( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: return { - 'shortname': 'Variance Threshold', - 'name': 'Variance Threshold (constant feature removal)', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': True, - 'handles_sparse': True, - 'handles_dense': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,), + "shortname": "Variance Threshold", + "name": "Variance Threshold (constant feature removal)", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": True, + "handles_sparse": True, + "handles_dense": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), } @staticmethod - def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None - ) -> ConfigurationSpace: + def get_hyperparameter_search_space( + dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, + ) -> ConfigurationSpace: cs = ConfigurationSpace() return cs diff --git a/autosklearn/pipeline/components/feature_preprocessing/__init__.py b/autosklearn/pipeline/components/feature_preprocessing/__init__.py index e124d135d0..cd52d6ad34 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/__init__.py +++ b/autosklearn/pipeline/components/feature_preprocessing/__init__.py @@ -1,18 +1,25 @@ +from typing import Type + import os from collections import OrderedDict -from typing import Type -from ..base import AutoSklearnPreprocessingAlgorithm, find_components, \ - ThirdPartyComponents, AutoSklearnChoice, _addons from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter +from ..base import ( + AutoSklearnChoice, + AutoSklearnPreprocessingAlgorithm, + ThirdPartyComponents, + _addons, + find_components, +) + classifier_directory = os.path.split(__file__)[0] -_preprocessors = find_components(__package__, - classifier_directory, - AutoSklearnPreprocessingAlgorithm) +_preprocessors = find_components( + __package__, classifier_directory, AutoSklearnPreprocessingAlgorithm +) additional_components = ThirdPartyComponents(AutoSklearnPreprocessingAlgorithm) -_addons['feature_preprocessing'] = additional_components +_addons["feature_preprocessing"] = additional_components def add_preprocessor(preprocessor: Type[AutoSklearnPreprocessingAlgorithm]) -> None: @@ -20,7 +27,6 @@ def add_preprocessor(preprocessor: Type[AutoSklearnPreprocessingAlgorithm]) -> N class FeaturePreprocessorChoice(AutoSklearnChoice): - @classmethod def get_components(cls): components = OrderedDict() @@ -28,23 +34,25 @@ def get_components(cls): components.update(additional_components.components) return components - def get_available_components(self, dataset_properties=None, - include=None, - exclude=None): + def get_available_components( + self, dataset_properties=None, include=None, exclude=None + ): if dataset_properties is None: dataset_properties = {} if include is not None and exclude is not None: raise ValueError( - "The argument include and exclude cannot be used together.") + "The argument include and exclude cannot be used together." + ) available_comp = self.get_components() if include is not None: for incl in include: if incl not in available_comp: - raise ValueError("Trying to include unknown component: " - "%s" % incl) + raise ValueError( + "Trying to include unknown component: " "%s" % incl + ) # TODO check for task type classification and/or regression! @@ -58,38 +66,43 @@ def get_available_components(self, dataset_properties=None, entry = available_comp[name] # Exclude itself to avoid infinite loop - if entry == FeaturePreprocessorChoice or hasattr(entry, 'get_components'): + if entry == FeaturePreprocessorChoice or hasattr(entry, "get_components"): continue - target_type = dataset_properties['target_type'] - if target_type == 'classification': - if entry.get_properties()['handles_classification'] is False: + target_type = dataset_properties["target_type"] + if target_type == "classification": + if entry.get_properties()["handles_classification"] is False: continue - if dataset_properties.get('multiclass') is True and \ - entry.get_properties()['handles_multiclass'] is False: + if ( + dataset_properties.get("multiclass") is True + and entry.get_properties()["handles_multiclass"] is False + ): continue - if dataset_properties.get('multilabel') is True and \ - entry.get_properties()['handles_multilabel'] is False: + if ( + dataset_properties.get("multilabel") is True + and entry.get_properties()["handles_multilabel"] is False + ): continue - elif target_type == 'regression': - if entry.get_properties()['handles_regression'] is False: + elif target_type == "regression": + if entry.get_properties()["handles_regression"] is False: continue - if dataset_properties.get('multioutput') is True and \ - entry.get_properties()['handles_multioutput'] is False: + if ( + dataset_properties.get("multioutput") is True + and entry.get_properties()["handles_multioutput"] is False + ): continue else: - raise ValueError('Unknown target type %s' % target_type) + raise ValueError("Unknown target type %s" % target_type) components_dict[name] = entry return components_dict - def get_hyperparameter_search_space(self, dataset_properties=None, - default=None, - include=None, - exclude=None): + def get_hyperparameter_search_space( + self, dataset_properties=None, default=None, include=None, exclude=None + ): cs = ConfigurationSpace() if dataset_properties is None: @@ -97,32 +110,33 @@ def get_hyperparameter_search_space(self, dataset_properties=None, # Compile a list of legal preprocessors for this problem available_preprocessors = self.get_available_components( - dataset_properties=dataset_properties, - include=include, exclude=exclude) + dataset_properties=dataset_properties, include=include, exclude=exclude + ) if len(available_preprocessors) == 0: - raise ValueError( - "No preprocessors found, please add NoPreprocessing") + raise ValueError("No preprocessors found, please add NoPreprocessing") if default is None: - defaults = ['no_preprocessing', 'select_percentile', 'pca', - 'truncatedSVD'] + defaults = ["no_preprocessing", "select_percentile", "pca", "truncatedSVD"] for default_ in defaults: if default_ in available_preprocessors: default = default_ break - preprocessor = CategoricalHyperparameter('__choice__', - list( - available_preprocessors.keys()), - default_value=default) + preprocessor = CategoricalHyperparameter( + "__choice__", list(available_preprocessors.keys()), default_value=default + ) cs.add_hyperparameter(preprocessor) for name in available_preprocessors: - preprocessor_configuration_space = available_preprocessors[name]. \ - get_hyperparameter_search_space(dataset_properties) - parent_hyperparameter = {'parent': preprocessor, 'value': name} - cs.add_configuration_space(name, preprocessor_configuration_space, - parent_hyperparameter=parent_hyperparameter) + preprocessor_configuration_space = available_preprocessors[ + name + ].get_hyperparameter_search_space(dataset_properties) + parent_hyperparameter = {"parent": preprocessor, "value": name} + cs.add_configuration_space( + name, + preprocessor_configuration_space, + parent_hyperparameter=parent_hyperparameter, + ) return cs diff --git a/autosklearn/pipeline/components/feature_preprocessing/densifier.py b/autosklearn/pipeline/components/feature_preprocessing/densifier.py index 0f0732f298..f5c88ecadf 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/densifier.py +++ b/autosklearn/pipeline/components/feature_preprocessing/densifier.py @@ -1,7 +1,7 @@ from ConfigSpace.configuration_space import ConfigurationSpace from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import SPARSE, UNSIGNED_DATA, DENSE, INPUT +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class Densifier(AutoSklearnPreprocessingAlgorithm): @@ -14,6 +14,7 @@ def fit(self, X, y=None): def transform(self, X): from scipy import sparse + if sparse.issparse(X): return X.todense().getA() else: @@ -21,16 +22,18 @@ def transform(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'RandomTreesEmbedding', - 'name': 'Random Trees Embedding', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': True, - 'input': (SPARSE, UNSIGNED_DATA), - 'output': (DENSE, INPUT)} + return { + "shortname": "RandomTreesEmbedding", + "name": "Random Trees Embedding", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": True, + "input": (SPARSE, UNSIGNED_DATA), + "output": (DENSE, INPUT), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): diff --git a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py index 622180af8f..dad45795b8 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py +++ b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py @@ -1,27 +1,43 @@ from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - UniformIntegerHyperparameter, CategoricalHyperparameter, \ - UnParametrizedHyperparameter, Constant - -from autosklearn.pipeline.components.base import \ - AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + Constant, + UniformFloatHyperparameter, + UniformIntegerHyperparameter, + UnParametrizedHyperparameter, +) + +from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA from autosklearn.util.common import check_for_bool, check_none class ExtraTreesPreprocessorClassification(AutoSklearnPreprocessingAlgorithm): - - def __init__(self, n_estimators, criterion, min_samples_leaf, - min_samples_split, max_features, bootstrap, max_leaf_nodes, - max_depth, min_weight_fraction_leaf, min_impurity_decrease, - oob_score=False, n_jobs=1, random_state=None, verbose=0, - class_weight=None): + def __init__( + self, + n_estimators, + criterion, + min_samples_leaf, + min_samples_split, + max_features, + bootstrap, + max_leaf_nodes, + max_depth, + min_weight_fraction_leaf, + min_impurity_decrease, + oob_score=False, + n_jobs=1, + random_state=None, + verbose=0, + class_weight=None, + ): self.n_estimators = n_estimators self.estimator_increment = 10 if criterion not in ("gini", "entropy"): - raise ValueError("'criterion' is not in ('gini', 'entropy'): " - "%s" % criterion) + raise ValueError( + "'criterion' is not in ('gini', 'entropy'): " "%s" % criterion + ) self.criterion = criterion self.min_samples_leaf = min_samples_leaf self.min_samples_split = min_samples_split @@ -78,11 +94,12 @@ def fit(self, X, Y, sample_weight=None): n_jobs=self.n_jobs, verbose=self.verbose, random_state=self.random_state, - class_weight=self.class_weight) + class_weight=self.class_weight, + ) estimator.fit(X, Y, sample_weight=sample_weight) - self.preprocessor = SelectFromModel(estimator=estimator, - threshold='mean', - prefit=True) + self.preprocessor = SelectFromModel( + estimator=estimator, threshold="mean", prefit=True + ) return self def transform(self, X): @@ -92,16 +109,18 @@ def transform(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'ETC', - 'name': 'Extra Trees Classifier Preprocessing', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,)} + return { + "shortname": "ETC", + "name": "Extra Trees Classifier Preprocessing", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): @@ -109,28 +128,45 @@ def get_hyperparameter_search_space(dataset_properties=None): n_estimators = Constant("n_estimators", 100) criterion = CategoricalHyperparameter( - "criterion", ["gini", "entropy"], default_value="gini") - max_features = UniformFloatHyperparameter("max_features", 0, 1, - default_value=0.5) + "criterion", ["gini", "entropy"], default_value="gini" + ) + max_features = UniformFloatHyperparameter( + "max_features", 0, 1, default_value=0.5 + ) max_depth = UnParametrizedHyperparameter(name="max_depth", value="None") max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") min_samples_split = UniformIntegerHyperparameter( - "min_samples_split", 2, 20, default_value=2) + "min_samples_split", 2, 20, default_value=2 + ) min_samples_leaf = UniformIntegerHyperparameter( - "min_samples_leaf", 1, 20, default_value=1) + "min_samples_leaf", 1, 20, default_value=1 + ) min_weight_fraction_leaf = UnParametrizedHyperparameter( - 'min_weight_fraction_leaf', 0.) + "min_weight_fraction_leaf", 0.0 + ) min_impurity_decrease = UnParametrizedHyperparameter( - 'min_impurity_decrease', 0.) + "min_impurity_decrease", 0.0 + ) bootstrap = CategoricalHyperparameter( - "bootstrap", ["True", "False"], default_value="False") - - cs.add_hyperparameters([n_estimators, criterion, max_features, - max_depth, max_leaf_nodes, min_samples_split, - min_samples_leaf, min_weight_fraction_leaf, - min_impurity_decrease, bootstrap]) + "bootstrap", ["True", "False"], default_value="False" + ) + + cs.add_hyperparameters( + [ + n_estimators, + criterion, + max_features, + max_depth, + max_leaf_nodes, + min_samples_split, + min_samples_leaf, + min_weight_fraction_leaf, + min_impurity_decrease, + bootstrap, + ] + ) return cs diff --git a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py index e8e28a2736..3287b837c5 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py +++ b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py @@ -1,29 +1,43 @@ import numpy as np - from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - UniformIntegerHyperparameter, CategoricalHyperparameter, \ - UnParametrizedHyperparameter, Constant - -from autosklearn.pipeline.components.base import \ - AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + Constant, + UniformFloatHyperparameter, + UniformIntegerHyperparameter, + UnParametrizedHyperparameter, +) + +from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA from autosklearn.util.common import check_for_bool, check_none class ExtraTreesPreprocessorRegression(AutoSklearnPreprocessingAlgorithm): - - def __init__(self, n_estimators, criterion, min_samples_leaf, - min_samples_split, max_features, - bootstrap=False, max_leaf_nodes=None, max_depth="None", - min_weight_fraction_leaf=0.0, - oob_score=False, n_jobs=1, random_state=None, verbose=0): + def __init__( + self, + n_estimators, + criterion, + min_samples_leaf, + min_samples_split, + max_features, + bootstrap=False, + max_leaf_nodes=None, + max_depth="None", + min_weight_fraction_leaf=0.0, + oob_score=False, + n_jobs=1, + random_state=None, + verbose=0, + ): self.n_estimators = n_estimators self.estimator_increment = 10 if criterion not in ("mse", "friedman_mse", "mae"): - raise ValueError("'criterion' is not in ('mse', 'friedman_mse', " - "'mae'): %s" % criterion) + raise ValueError( + "'criterion' is not in ('mse', 'friedman_mse', " + "'mae'): %s" % criterion + ) self.criterion = criterion self.min_samples_leaf = min_samples_leaf self.min_samples_split = min_samples_split @@ -64,23 +78,29 @@ def fit(self, X, Y): self.min_weight_fraction_leaf = float(self.min_weight_fraction_leaf) num_features = X.shape[1] - max_features = int( - float(self.max_features) * (np.log(num_features) + 1)) + max_features = int(float(self.max_features) * (np.log(num_features) + 1)) # Use at most half of the features max_features = max(1, min(int(X.shape[1] / 2), max_features)) estimator = ExtraTreesRegressor( - n_estimators=self.n_estimators, criterion=self.criterion, - max_depth=self.max_depth, min_samples_split=self.min_samples_split, - min_samples_leaf=self.min_samples_leaf, bootstrap=self.bootstrap, - max_features=max_features, max_leaf_nodes=self.max_leaf_nodes, - oob_score=self.oob_score, n_jobs=self.n_jobs, verbose=self.verbose, + n_estimators=self.n_estimators, + criterion=self.criterion, + max_depth=self.max_depth, + min_samples_split=self.min_samples_split, + min_samples_leaf=self.min_samples_leaf, + bootstrap=self.bootstrap, + max_features=max_features, + max_leaf_nodes=self.max_leaf_nodes, + oob_score=self.oob_score, + n_jobs=self.n_jobs, + verbose=self.verbose, min_weight_fraction_leaf=self.min_weight_fraction_leaf, - random_state=self.random_state) + random_state=self.random_state, + ) estimator.fit(X, Y) - self.preprocessor = SelectFromModel(estimator=estimator, - threshold='mean', - prefit=True) + self.preprocessor = SelectFromModel( + estimator=estimator, threshold="mean", prefit=True + ) return self @@ -91,42 +111,58 @@ def transform(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'ETR', - 'name': 'Extra Trees Regressor Preprocessing', - 'handles_regression': True, - 'handles_classification': False, - 'handles_multiclass': False, - 'handles_multilabel': False, - 'handles_multioutput': True, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,)} + return { + "shortname": "ETR", + "name": "Extra Trees Regressor Preprocessing", + "handles_regression": True, + "handles_classification": False, + "handles_multiclass": False, + "handles_multilabel": False, + "handles_multioutput": True, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() n_estimators = Constant("n_estimators", 100) - criterion = CategoricalHyperparameter("criterion", - ["mse", 'friedman_mse', 'mae']) + criterion = CategoricalHyperparameter( + "criterion", ["mse", "friedman_mse", "mae"] + ) max_features = UniformFloatHyperparameter( - "max_features", 0.1, 1.0, default_value=1.0) + "max_features", 0.1, 1.0, default_value=1.0 + ) max_depth = UnParametrizedHyperparameter(name="max_depth", value="None") max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") min_samples_split = UniformIntegerHyperparameter( - "min_samples_split", 2, 20, default_value=2) + "min_samples_split", 2, 20, default_value=2 + ) min_samples_leaf = UniformIntegerHyperparameter( - "min_samples_leaf", 1, 20, default_value=1) - min_weight_fraction_leaf = Constant('min_weight_fraction_leaf', 0.) + "min_samples_leaf", 1, 20, default_value=1 + ) + min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.0) bootstrap = CategoricalHyperparameter( - "bootstrap", ["True", "False"], default_value="False") - - cs.add_hyperparameters([n_estimators, criterion, max_features, max_depth, - max_leaf_nodes, min_samples_split, - min_samples_leaf, min_weight_fraction_leaf, - bootstrap]) + "bootstrap", ["True", "False"], default_value="False" + ) + + cs.add_hyperparameters( + [ + n_estimators, + criterion, + max_features, + max_depth, + max_leaf_nodes, + min_samples_split, + min_samples_leaf, + min_weight_fraction_leaf, + bootstrap, + ] + ) return cs diff --git a/autosklearn/pipeline/components/feature_preprocessing/fast_ica.py b/autosklearn/pipeline/components/feature_preprocessing/fast_ica.py index 549d708506..695ff3c2cc 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/fast_ica.py +++ b/autosklearn/pipeline/components/feature_preprocessing/fast_ica.py @@ -1,19 +1,19 @@ import warnings -from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import CategoricalHyperparameter, \ - UniformIntegerHyperparameter from ConfigSpace.conditions import EqualsCondition +from ConfigSpace.configuration_space import ConfigurationSpace +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformIntegerHyperparameter, +) -from autosklearn.pipeline.components.base import \ - AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import INPUT, UNSIGNED_DATA, DENSE +from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.constants import DENSE, INPUT, UNSIGNED_DATA from autosklearn.util.common import check_for_bool, check_none class FastICA(AutoSklearnPreprocessingAlgorithm): - def __init__(self, algorithm, whiten, fun, n_components=None, - random_state=None): + def __init__(self, algorithm, whiten, fun, n_components=None, random_state=None): self.algorithm = algorithm self.whiten = whiten self.fun = fun @@ -31,18 +31,25 @@ def fit(self, X, Y=None): self.n_components = int(self.n_components) self.preprocessor = sklearn.decomposition.FastICA( - n_components=self.n_components, algorithm=self.algorithm, - fun=self.fun, whiten=self.whiten, random_state=self.random_state + n_components=self.n_components, + algorithm=self.algorithm, + fun=self.fun, + whiten=self.whiten, + random_state=self.random_state, ) # Make the RuntimeWarning an Exception! with warnings.catch_warnings(): - warnings.filterwarnings("error", message='array must not contain infs or NaNs') + warnings.filterwarnings( + "error", message="array must not contain infs or NaNs" + ) try: self.preprocessor.fit(X) except ValueError as e: - if 'array must not contain infs or NaNs' in e.args[0]: - raise ValueError("Bug in scikit-learn: " - "https://github.com/scikit-learn/scikit-learn/pull/2738") + if "array must not contain infs or NaNs" in e.args[0]: + raise ValueError( + "Bug in scikit-learn: " + "https://github.com/scikit-learn/scikit-learn/pull/2738" + ) return self @@ -53,25 +60,31 @@ def transform(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'FastICA', - 'name': 'Fast Independent Component Analysis', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': False, - 'input': (DENSE, UNSIGNED_DATA), - 'output': (INPUT, UNSIGNED_DATA)} + return { + "shortname": "FastICA", + "name": "Fast Independent Component Analysis", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": False, + "input": (DENSE, UNSIGNED_DATA), + "output": (INPUT, UNSIGNED_DATA), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() - n_components = UniformIntegerHyperparameter("n_components", 10, 2000, default_value=100) - algorithm = CategoricalHyperparameter('algorithm', ['parallel', 'deflation'], 'parallel') - whiten = CategoricalHyperparameter('whiten', ['False', 'True'], 'False') - fun = CategoricalHyperparameter('fun', ['logcosh', 'exp', 'cube'], 'logcosh') + n_components = UniformIntegerHyperparameter( + "n_components", 10, 2000, default_value=100 + ) + algorithm = CategoricalHyperparameter( + "algorithm", ["parallel", "deflation"], "parallel" + ) + whiten = CategoricalHyperparameter("whiten", ["False", "True"], "False") + fun = CategoricalHyperparameter("fun", ["logcosh", "exp", "cube"], "logcosh") cs.add_hyperparameters([n_components, algorithm, whiten, fun]) cs.add_condition(EqualsCondition(n_components, whiten, "True")) diff --git a/autosklearn/pipeline/components/feature_preprocessing/feature_agglomeration.py b/autosklearn/pipeline/components/feature_preprocessing/feature_agglomeration.py index e23ff1b865..d51242de21 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/feature_agglomeration.py +++ b/autosklearn/pipeline/components/feature_preprocessing/feature_agglomeration.py @@ -1,28 +1,28 @@ import numpy as np - from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import CategoricalHyperparameter, \ - UniformIntegerHyperparameter -from ConfigSpace.forbidden import ForbiddenInClause, \ - ForbiddenAndConjunction, ForbiddenEqualsClause +from ConfigSpace.forbidden import ( + ForbiddenAndConjunction, + ForbiddenEqualsClause, + ForbiddenInClause, +) +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformIntegerHyperparameter, +) -from autosklearn.pipeline.components.base import \ - AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, INPUT +from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.constants import DENSE, INPUT, UNSIGNED_DATA class FeatureAgglomeration(AutoSklearnPreprocessingAlgorithm): - def __init__(self, n_clusters, affinity, linkage, pooling_func, - random_state=None): + def __init__(self, n_clusters, affinity, linkage, pooling_func, random_state=None): self.n_clusters = n_clusters self.affinity = affinity self.linkage = linkage self.pooling_func = pooling_func self.random_state = random_state - self.pooling_func_mapping = dict(mean=np.mean, - median=np.median, - max=np.max) + self.pooling_func_mapping = dict(mean=np.mean, median=np.median, max=np.max) def fit(self, X, Y=None): import sklearn.cluster @@ -34,8 +34,11 @@ def fit(self, X, Y=None): self.pooling_func = self.pooling_func_mapping[self.pooling_func] self.preprocessor = sklearn.cluster.FeatureAgglomeration( - n_clusters=n_clusters, affinity=self.affinity, - linkage=self.linkage, pooling_func=self.pooling_func) + n_clusters=n_clusters, + affinity=self.affinity, + linkage=self.linkage, + pooling_func=self.pooling_func, + ) self.preprocessor.fit(X) return self @@ -46,32 +49,38 @@ def transform(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'Feature Agglomeration', - 'name': 'Feature Agglomeration', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': True, - 'input': (DENSE, UNSIGNED_DATA), - 'output': (INPUT,)} + return { + "shortname": "Feature Agglomeration", + "name": "Feature Agglomeration", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": True, + "input": (DENSE, UNSIGNED_DATA), + "output": (INPUT,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() n_clusters = UniformIntegerHyperparameter("n_clusters", 2, 400, 25) affinity = CategoricalHyperparameter( - "affinity", ["euclidean", "manhattan", "cosine"], "euclidean") + "affinity", ["euclidean", "manhattan", "cosine"], "euclidean" + ) linkage = CategoricalHyperparameter( - "linkage", ["ward", "complete", "average"], "ward") + "linkage", ["ward", "complete", "average"], "ward" + ) pooling_func = CategoricalHyperparameter( - "pooling_func", ["mean", "median", "max"]) + "pooling_func", ["mean", "median", "max"] + ) cs.add_hyperparameters([n_clusters, affinity, linkage, pooling_func]) affinity_and_linkage = ForbiddenAndConjunction( ForbiddenInClause(affinity, ["manhattan", "cosine"]), - ForbiddenEqualsClause(linkage, "ward")) + ForbiddenEqualsClause(linkage, "ward"), + ) cs.add_forbidden_clause(affinity_and_linkage) return cs diff --git a/autosklearn/pipeline/components/feature_preprocessing/kernel_pca.py b/autosklearn/pipeline/components/feature_preprocessing/kernel_pca.py index 7ed0086248..4e96bfb1c2 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/kernel_pca.py +++ b/autosklearn/pipeline/components/feature_preprocessing/kernel_pca.py @@ -1,20 +1,22 @@ import warnings import numpy as np - -from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import CategoricalHyperparameter, \ - UniformIntegerHyperparameter, UniformFloatHyperparameter from ConfigSpace.conditions import EqualsCondition, InCondition +from ConfigSpace.configuration_space import ConfigurationSpace +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformFloatHyperparameter, + UniformIntegerHyperparameter, +) -from autosklearn.pipeline.components.base import \ - AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import SPARSE, DENSE, UNSIGNED_DATA +from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA class KernelPCA(AutoSklearnPreprocessingAlgorithm): - def __init__(self, n_components, kernel, degree=3, gamma=0.25, coef0=0.0, - random_state=None): + def __init__( + self, n_components, kernel, degree=3, gamma=0.25, coef0=0.0, random_state=None + ): self.n_components = n_components self.kernel = kernel self.degree = degree @@ -32,9 +34,14 @@ def fit(self, X, Y=None): self.coef0 = float(self.coef0) self.preprocessor = sklearn.decomposition.KernelPCA( - n_components=self.n_components, kernel=self.kernel, - degree=self.degree, gamma=self.gamma, coef0=self.coef0, - remove_zero_eig=True, random_state=self.random_state) + n_components=self.n_components, + kernel=self.kernel, + degree=self.degree, + gamma=self.gamma, + coef0=self.coef0, + remove_zero_eig=True, + random_state=self.random_state, + ) if scipy.sparse.issparse(X): X = X.astype(np.float64) with warnings.catch_warnings(): @@ -43,7 +50,7 @@ def fit(self, X, Y=None): # Raise an informative error message, equation is based ~line 249 in # kernel_pca.py in scikit-learn if len(self.preprocessor.alphas_ / self.preprocessor.lambdas_) == 0: - raise ValueError('KernelPCA removed all features!') + raise ValueError("KernelPCA removed all features!") return self def transform(self, X): @@ -61,29 +68,35 @@ def transform(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'KernelPCA', - 'name': 'Kernel Principal Component Analysis', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': False, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (DENSE, UNSIGNED_DATA)} + return { + "shortname": "KernelPCA", + "name": "Kernel Principal Component Analysis", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": False, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (DENSE, UNSIGNED_DATA), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): n_components = UniformIntegerHyperparameter( - "n_components", 10, 2000, default_value=100) - kernel = CategoricalHyperparameter('kernel', ['poly', 'rbf', 'sigmoid', 'cosine'], 'rbf') + "n_components", 10, 2000, default_value=100 + ) + kernel = CategoricalHyperparameter( + "kernel", ["poly", "rbf", "sigmoid", "cosine"], "rbf" + ) gamma = UniformFloatHyperparameter( "gamma", - 3.0517578125e-05, 8, + 3.0517578125e-05, + 8, log=True, default_value=0.01, ) - degree = UniformIntegerHyperparameter('degree', 2, 5, 3) + degree = UniformIntegerHyperparameter("degree", 2, 5, 3) coef0 = UniformFloatHyperparameter("coef0", -1, 1, default_value=0) cs = ConfigurationSpace() cs.add_hyperparameters([n_components, kernel, degree, gamma, coef0]) diff --git a/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py b/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py index 12ff57c21d..a81e9ddd78 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py +++ b/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py @@ -1,21 +1,22 @@ from typing import Optional, Union -from numpy.random import RandomState from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - UniformIntegerHyperparameter +from ConfigSpace.hyperparameters import ( + UniformFloatHyperparameter, + UniformIntegerHyperparameter, +) +from numpy.random import RandomState from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import SPARSE, DENSE, UNSIGNED_DATA, INPUT +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class RandomKitchenSinks(AutoSklearnPreprocessingAlgorithm): - def __init__( self, gamma: float, n_components: int, - random_state: Optional[Union[int, RandomState]] = None + random_state: Optional[Union[int, RandomState]] = None, ) -> None: """ Parameters @@ -42,7 +43,7 @@ def fit(self, X, Y=None): self.preprocessor = sklearn.kernel_approximation.RBFSampler( gamma=self.gamma, n_components=self.n_components, - random_state=self.random_state + random_state=self.random_state, ) self.preprocessor.fit(X) return self @@ -54,23 +55,27 @@ def transform(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'KitchenSink', - 'name': 'Random Kitchen Sinks', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': True, - 'input': (SPARSE, DENSE, UNSIGNED_DATA), - 'output': (INPUT, UNSIGNED_DATA)} + return { + "shortname": "KitchenSink", + "name": "Random Kitchen Sinks", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": True, + "input": (SPARSE, DENSE, UNSIGNED_DATA), + "output": (INPUT, UNSIGNED_DATA), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): gamma = UniformFloatHyperparameter( - "gamma", 3.0517578125e-05, 8, default_value=1.0, log=True) + "gamma", 3.0517578125e-05, 8, default_value=1.0, log=True + ) n_components = UniformIntegerHyperparameter( - "n_components", 50, 10000, default_value=100, log=True) + "n_components", 50, 10000, default_value=100, log=True + ) cs = ConfigurationSpace() cs.add_hyperparameters([gamma, n_components]) return cs diff --git a/autosklearn/pipeline/components/feature_preprocessing/liblinear_svc_preprocessor.py b/autosklearn/pipeline/components/feature_preprocessing/liblinear_svc_preprocessor.py index 6e6de1a998..546c8742ad 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/liblinear_svc_preprocessor.py +++ b/autosklearn/pipeline/components/feature_preprocessing/liblinear_svc_preprocessor.py @@ -1,19 +1,31 @@ from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - CategoricalHyperparameter, Constant -from ConfigSpace.forbidden import ForbiddenEqualsClause, \ - ForbiddenAndConjunction +from ConfigSpace.forbidden import ForbiddenAndConjunction, ForbiddenEqualsClause +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + Constant, + UniformFloatHyperparameter, +) from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import SPARSE, DENSE, UNSIGNED_DATA, INPUT +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA from autosklearn.util.common import check_for_bool, check_none class LibLinear_Preprocessor(AutoSklearnPreprocessingAlgorithm): # Liblinear is not deterministic as it uses a RNG inside - def __init__(self, penalty, loss, dual, tol, C, multi_class, - fit_intercept, intercept_scaling, class_weight=None, - random_state=None): + def __init__( + self, + penalty, + loss, + dual, + tol, + C, + multi_class, + fit_intercept, + intercept_scaling, + class_weight=None, + random_state=None, + ): self.penalty = penalty self.loss = loss self.dual = dual @@ -39,21 +51,23 @@ def fit(self, X, Y): if check_none(self.class_weight): self.class_weight = None - estimator = sklearn.svm.LinearSVC(penalty=self.penalty, - loss=self.loss, - dual=self.dual, - tol=self.tol, - C=self.C, - class_weight=self.class_weight, - fit_intercept=self.fit_intercept, - intercept_scaling=self.intercept_scaling, - multi_class=self.multi_class, - random_state=self.random_state) + estimator = sklearn.svm.LinearSVC( + penalty=self.penalty, + loss=self.loss, + dual=self.dual, + tol=self.tol, + C=self.C, + class_weight=self.class_weight, + fit_intercept=self.fit_intercept, + intercept_scaling=self.intercept_scaling, + multi_class=self.multi_class, + random_state=self.random_state, + ) estimator.fit(X, Y) - self.preprocessor = SelectFromModel(estimator=estimator, - threshold='mean', - prefit=True) + self.preprocessor = SelectFromModel( + estimator=estimator, threshold="mean", prefit=True + ) return self @@ -64,15 +78,17 @@ def transform(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'LinearSVC Preprocessor', - 'name': 'Liblinear Support Vector Classification Preprocessing', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': False, - 'handles_multioutput': False, - 'input': (SPARSE, DENSE, UNSIGNED_DATA), - 'output': (INPUT,)} + return { + "shortname": "LinearSVC Preprocessor", + "name": "Liblinear Support Vector Classification Preprocessing", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": False, + "handles_multioutput": False, + "input": (SPARSE, DENSE, UNSIGNED_DATA), + "output": (INPUT,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): @@ -80,22 +96,25 @@ def get_hyperparameter_search_space(dataset_properties=None): penalty = Constant("penalty", "l1") loss = CategoricalHyperparameter( - "loss", ["hinge", "squared_hinge"], default_value="squared_hinge") + "loss", ["hinge", "squared_hinge"], default_value="squared_hinge" + ) dual = Constant("dual", "False") # This is set ad-hoc - tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, default_value=1e-4, log=True) + tol = UniformFloatHyperparameter( + "tol", 1e-5, 1e-1, default_value=1e-4, log=True + ) C = UniformFloatHyperparameter("C", 0.03125, 32768, log=True, default_value=1.0) multi_class = Constant("multi_class", "ovr") # These are set ad-hoc fit_intercept = Constant("fit_intercept", "True") intercept_scaling = Constant("intercept_scaling", 1) - cs.add_hyperparameters([penalty, loss, dual, tol, C, multi_class, - fit_intercept, intercept_scaling]) + cs.add_hyperparameters( + [penalty, loss, dual, tol, C, multi_class, fit_intercept, intercept_scaling] + ) penalty_and_loss = ForbiddenAndConjunction( - ForbiddenEqualsClause(penalty, "l1"), - ForbiddenEqualsClause(loss, "hinge") + ForbiddenEqualsClause(penalty, "l1"), ForbiddenEqualsClause(loss, "hinge") ) cs.add_forbidden_clause(penalty_and_loss) return cs diff --git a/autosklearn/pipeline/components/feature_preprocessing/no_preprocessing.py b/autosklearn/pipeline/components/feature_preprocessing/no_preprocessing.py index 92e949b46d..550872d551 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/no_preprocessing.py +++ b/autosklearn/pipeline/components/feature_preprocessing/no_preprocessing.py @@ -1,16 +1,15 @@ from ConfigSpace.configuration_space import ConfigurationSpace from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import SPARSE, DENSE, UNSIGNED_DATA, INPUT +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class NoPreprocessing(AutoSklearnPreprocessingAlgorithm): - def __init__(self, random_state): - """ This preprocessors does not change the data """ + """This preprocessors does not change the data""" def fit(self, X, Y=None): - self.preprocessor = 'passthrough' + self.preprocessor = "passthrough" self.fitted_ = True return self @@ -21,16 +20,18 @@ def transform(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'no', - 'name': 'NoPreprocessing', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': True, - 'input': (SPARSE, DENSE, UNSIGNED_DATA), - 'output': (INPUT,)} + return { + "shortname": "no", + "name": "NoPreprocessing", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": True, + "input": (SPARSE, DENSE, UNSIGNED_DATA), + "output": (INPUT,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): diff --git a/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py b/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py index d450d8f09f..097f59e0f1 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py +++ b/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py @@ -1,17 +1,26 @@ import numpy as np - +from ConfigSpace.conditions import EqualsCondition, InCondition from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - UniformIntegerHyperparameter, CategoricalHyperparameter -from ConfigSpace.conditions import InCondition, EqualsCondition +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformFloatHyperparameter, + UniformIntegerHyperparameter, +) from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import SPARSE, DENSE, UNSIGNED_DATA, INPUT, SIGNED_DATA +from autosklearn.pipeline.constants import ( + DENSE, + INPUT, + SIGNED_DATA, + SPARSE, + UNSIGNED_DATA, +) class Nystroem(AutoSklearnPreprocessingAlgorithm): - def __init__(self, kernel, n_components, gamma=1.0, degree=3, - coef0=1, random_state=None): + def __init__( + self, kernel, n_components, gamma=1.0, degree=3, coef0=1, random_state=None + ): self.kernel = kernel self.n_components = n_components self.gamma = gamma @@ -29,13 +38,17 @@ def fit(self, X, Y=None): self.coef0 = float(self.coef0) self.preprocessor = sklearn.kernel_approximation.Nystroem( - kernel=self.kernel, n_components=self.n_components, - gamma=self.gamma, degree=self.degree, coef0=self.coef0, - random_state=self.random_state) + kernel=self.kernel, + n_components=self.n_components, + gamma=self.gamma, + degree=self.degree, + coef0=self.coef0, + random_state=self.random_state, + ) # Because the pipeline guarantees that each feature is positive, # clip all values below zero to zero - if self.kernel == 'chi2': + if self.kernel == "chi2": if scipy.sparse.issparse(X): X.data[X.data < 0] = 0.0 else: @@ -49,7 +62,7 @@ def transform(self, X): # Because the pipeline guarantees that each feature is positive, # clip all values below zero to zero - if self.kernel == 'chi2': + if self.kernel == "chi2": if scipy.sparse.issparse(X): X.data[X.data < 0] = 0.0 else: @@ -64,38 +77,43 @@ def get_properties(dataset_properties=None): data_type = UNSIGNED_DATA if dataset_properties is not None: - signed = dataset_properties.get('signed') + signed = dataset_properties.get("signed") if signed is not None: data_type = SIGNED_DATA if signed is True else UNSIGNED_DATA - return {'shortname': 'Nystroem', - 'name': 'Nystroem kernel approximation', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': True, - 'input': (SPARSE, DENSE, data_type), - 'output': (INPUT, UNSIGNED_DATA)} + return { + "shortname": "Nystroem", + "name": "Nystroem kernel approximation", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": True, + "input": (SPARSE, DENSE, data_type), + "output": (INPUT, UNSIGNED_DATA), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): - if dataset_properties is not None and \ - (dataset_properties.get("sparse") is True or - dataset_properties.get("signed") is False): + if dataset_properties is not None and ( + dataset_properties.get("sparse") is True + or dataset_properties.get("signed") is False + ): allow_chi2 = False else: allow_chi2 = True - possible_kernels = ['poly', 'rbf', 'sigmoid', 'cosine'] + possible_kernels = ["poly", "rbf", "sigmoid", "cosine"] if allow_chi2: possible_kernels.append("chi2") - kernel = CategoricalHyperparameter('kernel', possible_kernels, 'rbf') + kernel = CategoricalHyperparameter("kernel", possible_kernels, "rbf") n_components = UniformIntegerHyperparameter( - "n_components", 50, 10000, default_value=100, log=True) - gamma = UniformFloatHyperparameter("gamma", 3.0517578125e-05, 8, - log=True, default_value=0.1) - degree = UniformIntegerHyperparameter('degree', 2, 5, 3) + "n_components", 50, 10000, default_value=100, log=True + ) + gamma = UniformFloatHyperparameter( + "gamma", 3.0517578125e-05, 8, log=True, default_value=0.1 + ) + degree = UniformIntegerHyperparameter("degree", 2, 5, 3) coef0 = UniformFloatHyperparameter("coef0", -1, 1, default_value=0) cs = ConfigurationSpace() diff --git a/autosklearn/pipeline/components/feature_preprocessing/pca.py b/autosklearn/pipeline/components/feature_preprocessing/pca.py index ae992520fa..a1ad9f3981 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/pca.py +++ b/autosklearn/pipeline/components/feature_preprocessing/pca.py @@ -1,8 +1,9 @@ import numpy as np - from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - CategoricalHyperparameter +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformFloatHyperparameter, +) from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA @@ -18,12 +19,13 @@ def __init__(self, keep_variance, whiten, random_state=None): def fit(self, X, Y=None): import sklearn.decomposition + n_components = float(self.keep_variance) self.whiten = check_for_bool(self.whiten) - self.preprocessor = sklearn.decomposition.PCA(n_components=n_components, - whiten=self.whiten, - copy=True) + self.preprocessor = sklearn.decomposition.PCA( + n_components=n_components, whiten=self.whiten, copy=True + ) self.preprocessor.fit(X) if not np.isfinite(self.preprocessor.components_).all(): @@ -38,24 +40,28 @@ def transform(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'PCA', - 'name': 'Principle Component Analysis', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - # TODO document that we have to be very careful - 'is_deterministic': False, - 'input': (DENSE, UNSIGNED_DATA), - 'output': (DENSE, UNSIGNED_DATA)} + return { + "shortname": "PCA", + "name": "Principle Component Analysis", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + # TODO document that we have to be very careful + "is_deterministic": False, + "input": (DENSE, UNSIGNED_DATA), + "output": (DENSE, UNSIGNED_DATA), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): keep_variance = UniformFloatHyperparameter( - "keep_variance", 0.5, 0.9999, default_value=0.9999) + "keep_variance", 0.5, 0.9999, default_value=0.9999 + ) whiten = CategoricalHyperparameter( - "whiten", ["False", "True"], default_value="False") + "whiten", ["False", "True"], default_value="False" + ) cs = ConfigurationSpace() cs.add_hyperparameters([keep_variance, whiten]) return cs diff --git a/autosklearn/pipeline/components/feature_preprocessing/polynomial.py b/autosklearn/pipeline/components/feature_preprocessing/polynomial.py index 478040c497..bd5312bba0 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/polynomial.py +++ b/autosklearn/pipeline/components/feature_preprocessing/polynomial.py @@ -1,10 +1,11 @@ from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import CategoricalHyperparameter, \ - UniformIntegerHyperparameter +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformIntegerHyperparameter, +) -from autosklearn.pipeline.components.base import \ - AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT +from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA from autosklearn.util.common import check_for_bool @@ -25,8 +26,10 @@ def fit(self, X, Y): self.include_bias = check_for_bool(self.include_bias) self.preprocessor = sklearn.preprocessing.PolynomialFeatures( - degree=self.degree, interaction_only=self.interaction_only, - include_bias=self.include_bias) + degree=self.degree, + interaction_only=self.interaction_only, + include_bias=self.include_bias, + ) self.preprocessor.fit(X, Y) return self @@ -37,25 +40,29 @@ def transform(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'PolynomialFeatures', - 'name': 'PolynomialFeatures', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,)} + return { + "shortname": "PolynomialFeatures", + "name": "PolynomialFeatures", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): # More than degree 3 is too expensive! degree = UniformIntegerHyperparameter("degree", 2, 3, 2) - interaction_only = CategoricalHyperparameter("interaction_only", - ["False", "True"], "False") - include_bias = CategoricalHyperparameter("include_bias", - ["True", "False"], "True") + interaction_only = CategoricalHyperparameter( + "interaction_only", ["False", "True"], "False" + ) + include_bias = CategoricalHyperparameter( + "include_bias", ["True", "False"], "True" + ) cs = ConfigurationSpace() cs.add_hyperparameters([degree, interaction_only, include_bias]) diff --git a/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py b/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py index a5e9ff1b8c..9daed1ae97 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py +++ b/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py @@ -1,17 +1,30 @@ from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformIntegerHyperparameter, \ - UnParametrizedHyperparameter, Constant, CategoricalHyperparameter +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + Constant, + UniformIntegerHyperparameter, + UnParametrizedHyperparameter, +) from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, SIGNED_DATA -from autosklearn.util.common import check_none, check_for_bool +from autosklearn.pipeline.constants import DENSE, SIGNED_DATA, SPARSE, UNSIGNED_DATA +from autosklearn.util.common import check_for_bool, check_none class RandomTreesEmbedding(AutoSklearnPreprocessingAlgorithm): - - def __init__(self, n_estimators, max_depth, min_samples_split, - min_samples_leaf, min_weight_fraction_leaf, max_leaf_nodes, - bootstrap, sparse_output=True, n_jobs=1, random_state=None): + def __init__( + self, + n_estimators, + max_depth, + min_samples_split, + min_samples_leaf, + min_weight_fraction_leaf, + max_leaf_nodes, + bootstrap, + sparse_output=True, + n_jobs=1, + random_state=None, + ): self.n_estimators = n_estimators self.max_depth = max_depth self.min_samples_split = min_samples_split @@ -48,7 +61,7 @@ def _fit(self, X, Y=None): max_leaf_nodes=self.max_leaf_nodes, sparse_output=self.sparse_output, n_jobs=self.n_jobs, - random_state=self.random_state + random_state=self.random_state, ) self.preprocessor.fit(X, Y) return self @@ -67,37 +80,48 @@ def transform(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'RandomTreesEmbedding', - 'name': 'Random Trees Embedding', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (SPARSE, SIGNED_DATA)} + return { + "shortname": "RandomTreesEmbedding", + "name": "Random Trees Embedding", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (SPARSE, SIGNED_DATA), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): - n_estimators = UniformIntegerHyperparameter(name="n_estimators", - lower=10, upper=100, - default_value=10) - max_depth = UniformIntegerHyperparameter(name="max_depth", - lower=2, upper=10, - default_value=5) - min_samples_split = UniformIntegerHyperparameter(name="min_samples_split", - lower=2, upper=20, - default_value=2) - min_samples_leaf = UniformIntegerHyperparameter(name="min_samples_leaf", - lower=1, upper=20, - default_value=1) - min_weight_fraction_leaf = Constant('min_weight_fraction_leaf', 1.0) - max_leaf_nodes = UnParametrizedHyperparameter(name="max_leaf_nodes", - value="None") - bootstrap = CategoricalHyperparameter('bootstrap', ['True', 'False']) + n_estimators = UniformIntegerHyperparameter( + name="n_estimators", lower=10, upper=100, default_value=10 + ) + max_depth = UniformIntegerHyperparameter( + name="max_depth", lower=2, upper=10, default_value=5 + ) + min_samples_split = UniformIntegerHyperparameter( + name="min_samples_split", lower=2, upper=20, default_value=2 + ) + min_samples_leaf = UniformIntegerHyperparameter( + name="min_samples_leaf", lower=1, upper=20, default_value=1 + ) + min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 1.0) + max_leaf_nodes = UnParametrizedHyperparameter( + name="max_leaf_nodes", value="None" + ) + bootstrap = CategoricalHyperparameter("bootstrap", ["True", "False"]) cs = ConfigurationSpace() - cs.add_hyperparameters([n_estimators, max_depth, min_samples_split, - min_samples_leaf, min_weight_fraction_leaf, - max_leaf_nodes, bootstrap]) + cs.add_hyperparameters( + [ + n_estimators, + max_depth, + min_samples_split, + min_samples_leaf, + min_weight_fraction_leaf, + max_leaf_nodes, + bootstrap, + ] + ) return cs diff --git a/autosklearn/pipeline/components/feature_preprocessing/select_percentile.py b/autosklearn/pipeline/components/feature_preprocessing/select_percentile.py index c928e2f471..66f760bfb0 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/select_percentile.py +++ b/autosklearn/pipeline/components/feature_preprocessing/select_percentile.py @@ -1,11 +1,10 @@ class SelectPercentileBase(object): - def fit(self, X, y): import sklearn.feature_selection self.preprocessor = sklearn.feature_selection.SelectPercentile( - score_func=self.score_func, - percentile=self.percentile) + score_func=self.score_func, percentile=self.percentile + ) self.preprocessor.fit(X, y) return self diff --git a/autosklearn/pipeline/components/feature_preprocessing/select_percentile_classification.py b/autosklearn/pipeline/components/feature_preprocessing/select_percentile_classification.py index f6a3a1152c..3caa50b46d 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/select_percentile_classification.py +++ b/autosklearn/pipeline/components/feature_preprocessing/select_percentile_classification.py @@ -1,19 +1,30 @@ -from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import \ - UniformFloatHyperparameter, CategoricalHyperparameter, Constant from functools import partial -from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.components.feature_preprocessing.select_percentile import \ - SelectPercentileBase -from autosklearn.pipeline.constants import SPARSE, DENSE, INPUT, UNSIGNED_DATA, SIGNED_DATA - - -class SelectPercentileClassification(SelectPercentileBase, - AutoSklearnPreprocessingAlgorithm): +from ConfigSpace.configuration_space import ConfigurationSpace +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + Constant, + UniformFloatHyperparameter, +) +from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.components.feature_preprocessing.select_percentile import ( + SelectPercentileBase, +) +from autosklearn.pipeline.constants import ( + DENSE, + INPUT, + SIGNED_DATA, + SPARSE, + UNSIGNED_DATA, +) + + +class SelectPercentileClassification( + SelectPercentileBase, AutoSklearnPreprocessingAlgorithm +): def __init__(self, percentile, score_func="chi2", random_state=None): - """ Parameters: + """Parameters: random state : ignored score_func : callable, Function taking two arrays X and y, and @@ -28,11 +39,15 @@ def __init__(self, percentile, score_func="chi2", random_state=None): elif score_func == "f_classif": self.score_func = sklearn.feature_selection.f_classif elif score_func == "mutual_info": - self.score_func = partial(sklearn.feature_selection.mutual_info_classif, - random_state=self.random_state) + self.score_func = partial( + sklearn.feature_selection.mutual_info_classif, + random_state=self.random_state, + ) else: - raise ValueError("score_func must be in ('chi2, 'f_classif', 'mutual_info'), " - "but is: %s" % score_func) + raise ValueError( + "score_func must be in ('chi2, 'f_classif', 'mutual_info'), " + "but is: %s" % score_func + ) def fit(self, X, y): import scipy.sparse @@ -41,7 +56,7 @@ def fit(self, X, y): self.preprocessor = sklearn.feature_selection.SelectPercentile( score_func=self.score_func, percentile=self.percentile, - ) + ) # Because the pipeline guarantees that each feature is positive, # clip all values below zero to zero @@ -70,44 +85,45 @@ def transform(self, X): raise NotImplementedError() Xt = self.preprocessor.transform(X) if Xt.shape[1] == 0: - raise ValueError( - "%s removed all features." % self.__class__.__name__) + raise ValueError("%s removed all features." % self.__class__.__name__) return Xt @staticmethod def get_properties(dataset_properties=None): data_type = UNSIGNED_DATA if dataset_properties is not None: - signed = dataset_properties.get('signed') + signed = dataset_properties.get("signed") if signed is not None: data_type = SIGNED_DATA if signed is True else UNSIGNED_DATA - return {'shortname': 'SPC', - 'name': 'Select Percentile Classification', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': False, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (SPARSE, DENSE, data_type), - 'output': (INPUT,)} + return { + "shortname": "SPC", + "name": "Select Percentile Classification", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": False, + "handles_multioutput": False, + "is_deterministic": True, + "input": (SPARSE, DENSE, data_type), + "output": (INPUT,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): percentile = UniformFloatHyperparameter( - name="percentile", lower=1, upper=99, default_value=50) + name="percentile", lower=1, upper=99, default_value=50 + ) score_func = CategoricalHyperparameter( name="score_func", choices=["chi2", "f_classif", "mutual_info"], - default_value="chi2" + default_value="chi2", ) if dataset_properties is not None: # Chi2 can handle sparse data, so we respect this - if 'sparse' in dataset_properties and dataset_properties['sparse']: - score_func = Constant( - name="score_func", value="chi2") + if "sparse" in dataset_properties and dataset_properties["sparse"]: + score_func = Constant(name="score_func", value="chi2") cs = ConfigurationSpace() cs.add_hyperparameters([percentile, score_func]) diff --git a/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py b/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py index 79b528c095..e9343fead4 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py +++ b/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py @@ -1,18 +1,23 @@ -from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, CategoricalHyperparameter from functools import partial -from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.components.feature_preprocessing.select_percentile import \ - SelectPercentileBase -from autosklearn.pipeline.constants import SPARSE, DENSE, UNSIGNED_DATA, INPUT +from ConfigSpace.configuration_space import ConfigurationSpace +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformFloatHyperparameter, +) +from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.components.feature_preprocessing.select_percentile import ( + SelectPercentileBase, +) +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA -class SelectPercentileRegression(SelectPercentileBase, - AutoSklearnPreprocessingAlgorithm): +class SelectPercentileRegression( + SelectPercentileBase, AutoSklearnPreprocessingAlgorithm +): def __init__(self, percentile, score_func="f_regression", random_state=None): - """ Parameters: + """Parameters: random state : ignored score_func : callable, Function taking two arrays X and y, and @@ -25,31 +30,37 @@ def __init__(self, percentile, score_func="f_regression", random_state=None): if score_func == "f_regression": self.score_func = sklearn.feature_selection.f_regression elif score_func == "mutual_info": - self.score_func = partial(sklearn.feature_selection.mutual_info_regression, - random_state=self.random_state) + self.score_func = partial( + sklearn.feature_selection.mutual_info_regression, + random_state=self.random_state, + ) else: raise ValueError("Don't know this scoring function: %s" % score_func) @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'SPR', - 'name': 'Select Percentile Regression', - 'handles_regression': True, - 'handles_classification': False, - 'handles_multiclass': False, - 'handles_multilabel': False, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,)} + return { + "shortname": "SPR", + "name": "Select Percentile Regression", + "handles_regression": True, + "handles_classification": False, + "handles_multiclass": False, + "handles_multilabel": False, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): percentile = UniformFloatHyperparameter( - "percentile", lower=1, upper=99, default_value=50) + "percentile", lower=1, upper=99, default_value=50 + ) score_func = CategoricalHyperparameter( - name="score_func", choices=["f_regression", "mutual_info"]) + name="score_func", choices=["f_regression", "mutual_info"] + ) cs = ConfigurationSpace() cs.add_hyperparameters([percentile, score_func]) diff --git a/autosklearn/pipeline/components/feature_preprocessing/select_rates_classification.py b/autosklearn/pipeline/components/feature_preprocessing/select_rates_classification.py index de6e950f0b..0c4768d000 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/select_rates_classification.py +++ b/autosklearn/pipeline/components/feature_preprocessing/select_rates_classification.py @@ -1,17 +1,24 @@ -from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - CategoricalHyperparameter -from ConfigSpace import NotEqualsCondition from functools import partial -from autosklearn.pipeline.components.base import \ - AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import SIGNED_DATA, UNSIGNED_DATA, SPARSE, DENSE, INPUT +from ConfigSpace import NotEqualsCondition +from ConfigSpace.configuration_space import ConfigurationSpace +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformFloatHyperparameter, +) + +from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.constants import ( + DENSE, + INPUT, + SIGNED_DATA, + SPARSE, + UNSIGNED_DATA, +) class SelectClassificationRates(AutoSklearnPreprocessingAlgorithm): - def __init__(self, alpha, mode='fpr', - score_func="chi2", random_state=None): + def __init__(self, alpha, mode="fpr", score_func="chi2", random_state=None): import sklearn.feature_selection self.random_state = random_state # We don't use this @@ -23,14 +30,18 @@ def __init__(self, alpha, mode='fpr', elif score_func == "f_classif": self.score_func = sklearn.feature_selection.f_classif elif score_func == "mutual_info_classif": - self.score_func = partial(sklearn.feature_selection.mutual_info_classif, - random_state=self.random_state) + self.score_func = partial( + sklearn.feature_selection.mutual_info_classif, + random_state=self.random_state, + ) # mutual info classif constantly crashes without mode percentile - self.mode = 'percentile' + self.mode = "percentile" else: - raise ValueError("score_func must be in ('chi2, 'f_classif', 'mutual_info_classif') " - "for classification " - "but is: %s " % (score_func)) + raise ValueError( + "score_func must be in ('chi2, 'f_classif', 'mutual_info_classif') " + "for classification " + "but is: %s " % (score_func) + ) def fit(self, X, y): import scipy.sparse @@ -39,7 +50,8 @@ def fit(self, X, y): self.alpha = float(self.alpha) self.preprocessor = sklearn.feature_selection.GenericUnivariateSelect( - score_func=self.score_func, param=self.alpha, mode=self.mode) + score_func=self.score_func, param=self.alpha, mode=self.mode + ) # Because the pipeline guarantees that each feature is positive, # clip all values below zero to zero @@ -69,16 +81,16 @@ def transform(self, X): try: Xt = self.preprocessor.transform(X) except ValueError as e: - if "zero-size array to reduction operation maximum which has no " \ - "identity" in e.message: - raise ValueError( - "%s removed all features." % self.__class__.__name__) + if ( + "zero-size array to reduction operation maximum which has no " + "identity" in e.message + ): + raise ValueError("%s removed all features." % self.__class__.__name__) else: raise e if Xt.shape[1] == 0: - raise ValueError( - "%s removed all features." % self.__class__.__name__) + raise ValueError("%s removed all features." % self.__class__.__name__) return Xt @staticmethod @@ -86,37 +98,39 @@ def get_properties(dataset_properties=None): data_type = UNSIGNED_DATA if dataset_properties is not None: - signed = dataset_properties.get('signed') + signed = dataset_properties.get("signed") if signed is not None: data_type = SIGNED_DATA if signed is True else UNSIGNED_DATA - return {'shortname': 'SR', - 'name': 'Univariate Feature Selection based on rates', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': False, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (SPARSE, DENSE, data_type), - 'output': (INPUT,)} + return { + "shortname": "SR", + "name": "Univariate Feature Selection based on rates", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": False, + "handles_multioutput": False, + "is_deterministic": True, + "input": (SPARSE, DENSE, data_type), + "output": (INPUT,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): alpha = UniformFloatHyperparameter( - name="alpha", lower=0.01, upper=0.5, default_value=0.1) + name="alpha", lower=0.01, upper=0.5, default_value=0.1 + ) - if dataset_properties is not None and dataset_properties.get('sparse'): - choices = ['chi2', 'mutual_info_classif'] + if dataset_properties is not None and dataset_properties.get("sparse"): + choices = ["chi2", "mutual_info_classif"] else: - choices = ['chi2', 'f_classif', 'mutual_info_classif'] + choices = ["chi2", "f_classif", "mutual_info_classif"] score_func = CategoricalHyperparameter( - name="score_func", - choices=choices, - default_value="chi2") + name="score_func", choices=choices, default_value="chi2" + ) - mode = CategoricalHyperparameter('mode', ['fpr', 'fdr', 'fwe'], 'fpr') + mode = CategoricalHyperparameter("mode", ["fpr", "fdr", "fwe"], "fpr") cs = ConfigurationSpace() cs.add_hyperparameter(alpha) @@ -125,7 +139,7 @@ def get_hyperparameter_search_space(dataset_properties=None): # mutual_info_classif constantly crashes if mode is not percentile # as a WA, fix the mode for this score - cond = NotEqualsCondition(mode, score_func, 'mutual_info_classif') + cond = NotEqualsCondition(mode, score_func, "mutual_info_classif") cs.add_condition(cond) return cs diff --git a/autosklearn/pipeline/components/feature_preprocessing/select_rates_regression.py b/autosklearn/pipeline/components/feature_preprocessing/select_rates_regression.py index b5bfd2a103..ffec19e6ec 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/select_rates_regression.py +++ b/autosklearn/pipeline/components/feature_preprocessing/select_rates_regression.py @@ -1,17 +1,20 @@ -from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - CategoricalHyperparameter -from ConfigSpace import NotEqualsCondition from functools import partial -from autosklearn.pipeline.components.base import \ - AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import UNSIGNED_DATA, SPARSE, DENSE, INPUT +from ConfigSpace import NotEqualsCondition +from ConfigSpace.configuration_space import ConfigurationSpace +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformFloatHyperparameter, +) + +from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class SelectRegressionRates(AutoSklearnPreprocessingAlgorithm): - def __init__(self, alpha, mode='percentile', - score_func="f_regression", random_state=None): + def __init__( + self, alpha, mode="percentile", score_func="f_regression", random_state=None + ): import sklearn.feature_selection self.random_state = random_state # We don't use this @@ -21,14 +24,18 @@ def __init__(self, alpha, mode='percentile', if score_func == "f_regression": self.score_func = sklearn.feature_selection.f_regression elif score_func == "mutual_info_regression": - self.score_func = partial(sklearn.feature_selection.mutual_info_regression, - random_state=self.random_state) + self.score_func = partial( + sklearn.feature_selection.mutual_info_regression, + random_state=self.random_state, + ) # Mutual info consistently crashes if percentile is not the mode - self.mode = 'percentile' + self.mode = "percentile" else: - raise ValueError("score_func must be in ('f_regression, 'mutual_info_regression') " - "for task=regression " - "but is: %s " % (score_func)) + raise ValueError( + "score_func must be in ('f_regression, 'mutual_info_regression') " + "for task=regression " + "but is: %s " % (score_func) + ) def fit(self, X, y): import sklearn.feature_selection @@ -36,7 +43,8 @@ def fit(self, X, y): self.alpha = float(self.alpha) self.preprocessor = sklearn.feature_selection.GenericUnivariateSelect( - score_func=self.score_func, param=self.alpha, mode=self.mode) + score_func=self.score_func, param=self.alpha, mode=self.mode + ) self.preprocessor.fit(X, y) return self @@ -48,47 +56,49 @@ def transform(self, X): try: Xt = self.preprocessor.transform(X) except ValueError as e: - if "zero-size array to reduction operation maximum which has no " \ - "identity" in e.message: - raise ValueError( - "%s removed all features." % self.__class__.__name__) + if ( + "zero-size array to reduction operation maximum which has no " + "identity" in e.message + ): + raise ValueError("%s removed all features." % self.__class__.__name__) else: raise e if Xt.shape[1] == 0: - raise ValueError( - "%s removed all features." % self.__class__.__name__) + raise ValueError("%s removed all features." % self.__class__.__name__) return Xt @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'SR', - 'name': 'Univariate Feature Selection based on rates', - 'handles_regression': True, - 'handles_classification': False, - 'handles_multiclass': True, - 'handles_multilabel': False, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (SPARSE, DENSE, UNSIGNED_DATA), - 'output': (INPUT,)} + return { + "shortname": "SR", + "name": "Univariate Feature Selection based on rates", + "handles_regression": True, + "handles_classification": False, + "handles_multiclass": True, + "handles_multilabel": False, + "handles_multioutput": False, + "is_deterministic": True, + "input": (SPARSE, DENSE, UNSIGNED_DATA), + "output": (INPUT,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): alpha = UniformFloatHyperparameter( - name="alpha", lower=0.01, upper=0.5, default_value=0.1) + name="alpha", lower=0.01, upper=0.5, default_value=0.1 + ) - if dataset_properties is not None and dataset_properties.get('sparse'): - choices = ['mutual_info_regression', 'f_regression'] + if dataset_properties is not None and dataset_properties.get("sparse"): + choices = ["mutual_info_regression", "f_regression"] else: - choices = ['f_regression'] + choices = ["f_regression"] score_func = CategoricalHyperparameter( - name="score_func", - choices=choices, - default_value="f_regression") + name="score_func", choices=choices, default_value="f_regression" + ) - mode = CategoricalHyperparameter('mode', ['fpr', 'fdr', 'fwe'], 'fpr') + mode = CategoricalHyperparameter("mode", ["fpr", "fdr", "fwe"], "fpr") cs = ConfigurationSpace() cs.add_hyperparameter(alpha) @@ -96,8 +106,8 @@ def get_hyperparameter_search_space(dataset_properties=None): cs.add_hyperparameter(mode) # Mutual info consistently crashes if percentile is not the mode - if 'mutual_info_regression' in choices: - cond = NotEqualsCondition(mode, score_func, 'mutual_info_regression') + if "mutual_info_regression" in choices: + cond = NotEqualsCondition(mode, score_func, "mutual_info_regression") cs.add_condition(cond) return cs diff --git a/autosklearn/pipeline/components/feature_preprocessing/truncatedSVD.py b/autosklearn/pipeline/components/feature_preprocessing/truncatedSVD.py index 078b2b4a2d..4d6f6b7ca9 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/truncatedSVD.py +++ b/autosklearn/pipeline/components/feature_preprocessing/truncatedSVD.py @@ -2,7 +2,7 @@ from ConfigSpace.hyperparameters import UniformIntegerHyperparameter from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import SPARSE, UNSIGNED_DATA, DENSE, INPUT +from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA class TruncatedSVD(AutoSklearnPreprocessingAlgorithm): @@ -17,7 +17,8 @@ def fit(self, X, Y): self.target_dim = int(self.target_dim) target_dim = min(self.target_dim, X.shape[1] - 1) self.preprocessor = sklearn.decomposition.TruncatedSVD( - target_dim, algorithm='randomized', random_state=self.random_state) + target_dim, algorithm="randomized", random_state=self.random_state + ) # TODO: remove when migrating to sklearn 0.16 # Circumvents a bug in sklearn # https://github.com/scikit-learn/scikit-learn/commit/f08b8c8e52663167819f242f605db39f3b5a6d0c @@ -33,21 +34,24 @@ def transform(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'TSVD', - 'name': 'Truncated Singular Value Decomposition', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': True, - 'input': (SPARSE, UNSIGNED_DATA), - 'output': (DENSE, INPUT)} + return { + "shortname": "TSVD", + "name": "Truncated Singular Value Decomposition", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": True, + "input": (SPARSE, UNSIGNED_DATA), + "output": (DENSE, INPUT), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): target_dim = UniformIntegerHyperparameter( - "target_dim", 10, 256, default_value=128) + "target_dim", 10, 256, default_value=128 + ) cs = ConfigurationSpace() cs.add_hyperparameter(target_dim) return cs diff --git a/autosklearn/pipeline/components/regression/__init__.py b/autosklearn/pipeline/components/regression/__init__.py index 651b49b602..73033467a7 100644 --- a/autosklearn/pipeline/components/regression/__init__.py +++ b/autosklearn/pipeline/components/regression/__init__.py @@ -1,18 +1,25 @@ -from collections import OrderedDict from typing import Type + import os +from collections import OrderedDict -from ..base import AutoSklearnRegressionAlgorithm, find_components, \ - ThirdPartyComponents, AutoSklearnChoice, _addons from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter +from ..base import ( + AutoSklearnChoice, + AutoSklearnRegressionAlgorithm, + ThirdPartyComponents, + _addons, + find_components, +) + regressor_directory = os.path.split(__file__)[0] -_regressors = find_components(__package__, - regressor_directory, - AutoSklearnRegressionAlgorithm) +_regressors = find_components( + __package__, regressor_directory, AutoSklearnRegressionAlgorithm +) additional_components = ThirdPartyComponents(AutoSklearnRegressionAlgorithm) -_addons['regression'] = additional_components +_addons["regression"] = additional_components def add_regressor(regressor: Type[AutoSklearnRegressionAlgorithm]) -> None: @@ -20,7 +27,6 @@ def add_regressor(regressor: Type[AutoSklearnRegressionAlgorithm]) -> None: class RegressorChoice(AutoSklearnChoice): - @classmethod def get_components(cls): components = OrderedDict() @@ -29,10 +35,9 @@ def get_components(cls): return components @classmethod - def get_available_components(cls, - dataset_properties=None, - include=None, - exclude=None): + def get_available_components( + cls, dataset_properties=None, include=None, exclude=None + ): available_comp = cls.get_components() components_dict = OrderedDict() if dataset_properties is None: @@ -40,13 +45,15 @@ def get_available_components(cls, if include is not None and exclude is not None: raise ValueError( - "The argument include and exclude cannot be used together.") + "The argument include and exclude cannot be used together." + ) if include is not None: for incl in include: if incl not in available_comp: - raise ValueError("Trying to include unknown component: " - "%s" % incl) + raise ValueError( + "Trying to include unknown component: " "%s" % incl + ) for name in available_comp: if include is not None and name not in include: @@ -60,36 +67,39 @@ def get_available_components(cls, if entry == RegressorChoice: continue - if entry.get_properties()['handles_regression'] is False: + if entry.get_properties()["handles_regression"] is False: continue - if dataset_properties.get('multioutput') is True and \ - entry.get_properties()['handles_multioutput'] is False: + if ( + dataset_properties.get("multioutput") is True + and entry.get_properties()["handles_multioutput"] is False + ): continue components_dict[name] = entry return components_dict - def get_hyperparameter_search_space(self, dataset_properties=None, - default=None, - include=None, - exclude=None): + def get_hyperparameter_search_space( + self, dataset_properties=None, default=None, include=None, exclude=None + ): if include is not None and exclude is not None: - raise ValueError("The argument include and exclude cannot be used together.") + raise ValueError( + "The argument include and exclude cannot be used together." + ) cs = ConfigurationSpace() # Compile a list of all estimator objects for this problem available_estimators = self.get_available_components( - dataset_properties=dataset_properties, - include=include, - exclude=exclude) + dataset_properties=dataset_properties, include=include, exclude=exclude + ) if len(available_estimators) == 0: raise ValueError("No regressors found") if default is None: - defaults = ['random_forest', 'support_vector_regression'] + \ - list(available_estimators.keys()) + defaults = ["random_forest", "support_vector_regression"] + list( + available_estimators.keys() + ) for default_ in defaults: if default_ in available_estimators: if include is not None and default_ not in include: @@ -99,21 +109,25 @@ def get_hyperparameter_search_space(self, dataset_properties=None, default = default_ break - estimator = CategoricalHyperparameter('__choice__', - list(available_estimators.keys()), - default_value=default) + estimator = CategoricalHyperparameter( + "__choice__", list(available_estimators.keys()), default_value=default + ) cs.add_hyperparameter(estimator) for estimator_name in available_estimators.keys(): - estimator_configuration_space = available_estimators[estimator_name].\ - get_hyperparameter_search_space(dataset_properties) - parent_hyperparameter = {'parent': estimator, 'value': estimator_name} - cs.add_configuration_space(estimator_name, estimator_configuration_space, - parent_hyperparameter=parent_hyperparameter) + estimator_configuration_space = available_estimators[ + estimator_name + ].get_hyperparameter_search_space(dataset_properties) + parent_hyperparameter = {"parent": estimator, "value": estimator_name} + cs.add_configuration_space( + estimator_name, + estimator_configuration_space, + parent_hyperparameter=parent_hyperparameter, + ) return cs def estimator_supports_iterative_fit(self): - return hasattr(self.choice, 'iterative_fit') + return hasattr(self.choice, "iterative_fit") def get_max_iter(self): if self.estimator_supports_iterative_fit(): diff --git a/autosklearn/pipeline/components/regression/adaboost.py b/autosklearn/pipeline/components/regression/adaboost.py index 2eb58ae2ea..e78a57e6a2 100644 --- a/autosklearn/pipeline/components/regression/adaboost.py +++ b/autosklearn/pipeline/components/regression/adaboost.py @@ -1,9 +1,12 @@ from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - UniformIntegerHyperparameter, CategoricalHyperparameter +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformFloatHyperparameter, + UniformIntegerHyperparameter, +) from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA class AdaboostRegressor(AutoSklearnRegressionAlgorithm): @@ -22,15 +25,14 @@ def fit(self, X, y): self.n_estimators = int(self.n_estimators) self.learning_rate = float(self.learning_rate) self.max_depth = int(self.max_depth) - base_estimator = sklearn.tree.DecisionTreeRegressor( - max_depth=self.max_depth) + base_estimator = sklearn.tree.DecisionTreeRegressor(max_depth=self.max_depth) self.estimator = sklearn.ensemble.AdaBoostRegressor( base_estimator=base_estimator, n_estimators=self.n_estimators, learning_rate=self.learning_rate, loss=self.loss, - random_state=self.random_state + random_state=self.random_state, ) if y.ndim == 2 and y.shape[1] == 1: @@ -46,16 +48,18 @@ def predict(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'AB', - 'name': 'AdaBoost Regressor', - 'handles_regression': True, - 'handles_classification': False, - 'handles_multiclass': False, - 'handles_multilabel': False, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (PREDICTIONS, )} + return { + "shortname": "AB", + "name": "AdaBoost Regressor", + "handles_regression": True, + "handles_classification": False, + "handles_multiclass": False, + "handles_multilabel": False, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): @@ -63,16 +67,19 @@ def get_hyperparameter_search_space(dataset_properties=None): # base_estimator = Constant(name="base_estimator", value="None") n_estimators = UniformIntegerHyperparameter( - name="n_estimators", lower=50, upper=500, default_value=50, - log=False) + name="n_estimators", lower=50, upper=500, default_value=50, log=False + ) learning_rate = UniformFloatHyperparameter( - name="learning_rate", lower=0.01, upper=2, default_value=0.1, - log=True) + name="learning_rate", lower=0.01, upper=2, default_value=0.1, log=True + ) loss = CategoricalHyperparameter( - name="loss", choices=["linear", "square", "exponential"], - default_value="linear") + name="loss", + choices=["linear", "square", "exponential"], + default_value="linear", + ) max_depth = UniformIntegerHyperparameter( - name="max_depth", lower=1, upper=10, default_value=1, log=False) + name="max_depth", lower=1, upper=10, default_value=1, log=False + ) cs.add_hyperparameters([n_estimators, learning_rate, loss, max_depth]) return cs diff --git a/autosklearn/pipeline/components/regression/ard_regression.py b/autosklearn/pipeline/components/regression/ard_regression.py index 46dcac5d93..219cb775af 100644 --- a/autosklearn/pipeline/components/regression/ard_regression.py +++ b/autosklearn/pipeline/components/regression/ard_regression.py @@ -1,15 +1,27 @@ from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - UnParametrizedHyperparameter +from ConfigSpace.hyperparameters import ( + UniformFloatHyperparameter, + UnParametrizedHyperparameter, +) from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, UNSIGNED_DATA from autosklearn.util.common import check_for_bool class ARDRegression(AutoSklearnRegressionAlgorithm): - def __init__(self, n_iter, tol, alpha_1, alpha_2, lambda_1, lambda_2, - threshold_lambda, fit_intercept, random_state=None): + def __init__( + self, + n_iter, + tol, + alpha_1, + alpha_2, + lambda_1, + lambda_2, + threshold_lambda, + fit_intercept, + random_state=None, + ): self.random_state = random_state self.estimator = None @@ -46,7 +58,7 @@ def fit(self, X, y): fit_intercept=True, normalize=False, copy_X=False, - verbose=False + verbose=False, ) if y.ndim == 2 and y.shape[1] == 1: @@ -62,43 +74,71 @@ def predict(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'ARD', - 'name': 'ARD Regression', - 'handles_regression': True, - 'handles_classification': False, - 'handles_multiclass': False, - 'handles_multilabel': False, - 'handles_multioutput': False, - 'prefers_data_normalized': True, - 'is_deterministic': True, - 'input': (DENSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "ARD", + "name": "ARD Regression", + "handles_regression": True, + "handles_classification": False, + "handles_multiclass": False, + "handles_multilabel": False, + "handles_multioutput": False, + "prefers_data_normalized": True, + "is_deterministic": True, + "input": (DENSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() n_iter = UnParametrizedHyperparameter("n_iter", value=300) - tol = UniformFloatHyperparameter("tol", 10 ** -5, 10 ** -1, - default_value=10 ** -3, log=True) - alpha_1 = UniformFloatHyperparameter(name="alpha_1", lower=10 ** -10, - upper=10 ** -3, default_value=10 ** -6) - alpha_2 = UniformFloatHyperparameter(name="alpha_2", log=True, - lower=10 ** -10, upper=10 ** -3, - default_value=10 ** -6) - lambda_1 = UniformFloatHyperparameter(name="lambda_1", log=True, - lower=10 ** -10, upper=10 ** -3, - default_value=10 ** -6) - lambda_2 = UniformFloatHyperparameter(name="lambda_2", log=True, - lower=10 ** -10, upper=10 ** -3, - default_value=10 ** -6) - threshold_lambda = UniformFloatHyperparameter(name="threshold_lambda", - log=True, - lower=10 ** 3, - upper=10 ** 5, - default_value=10 ** 4) + tol = UniformFloatHyperparameter( + "tol", 10**-5, 10**-1, default_value=10**-3, log=True + ) + alpha_1 = UniformFloatHyperparameter( + name="alpha_1", lower=10**-10, upper=10**-3, default_value=10**-6 + ) + alpha_2 = UniformFloatHyperparameter( + name="alpha_2", + log=True, + lower=10**-10, + upper=10**-3, + default_value=10**-6, + ) + lambda_1 = UniformFloatHyperparameter( + name="lambda_1", + log=True, + lower=10**-10, + upper=10**-3, + default_value=10**-6, + ) + lambda_2 = UniformFloatHyperparameter( + name="lambda_2", + log=True, + lower=10**-10, + upper=10**-3, + default_value=10**-6, + ) + threshold_lambda = UniformFloatHyperparameter( + name="threshold_lambda", + log=True, + lower=10**3, + upper=10**5, + default_value=10**4, + ) fit_intercept = UnParametrizedHyperparameter("fit_intercept", "True") - cs.add_hyperparameters([n_iter, tol, alpha_1, alpha_2, lambda_1, - lambda_2, threshold_lambda, fit_intercept]) + cs.add_hyperparameters( + [ + n_iter, + tol, + alpha_1, + alpha_2, + lambda_1, + lambda_2, + threshold_lambda, + fit_intercept, + ] + ) return cs diff --git a/autosklearn/pipeline/components/regression/decision_tree.py b/autosklearn/pipeline/components/regression/decision_tree.py index 5ecbd254be..db59767587 100644 --- a/autosklearn/pipeline/components/regression/decision_tree.py +++ b/autosklearn/pipeline/components/regression/decision_tree.py @@ -1,20 +1,31 @@ import numpy as np - from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - UniformIntegerHyperparameter, CategoricalHyperparameter, \ - UnParametrizedHyperparameter, Constant +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + Constant, + UniformFloatHyperparameter, + UniformIntegerHyperparameter, + UnParametrizedHyperparameter, +) -from autosklearn.pipeline.components.base import \ - AutoSklearnRegressionAlgorithm -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE +from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA from autosklearn.util.common import check_none class DecisionTree(AutoSklearnRegressionAlgorithm): - def __init__(self, criterion, max_features, max_depth_factor, - min_samples_split, min_samples_leaf, min_weight_fraction_leaf, - max_leaf_nodes, min_impurity_decrease, random_state=None): + def __init__( + self, + criterion, + max_features, + max_depth_factor, + min_samples_split, + min_samples_leaf, + min_weight_fraction_leaf, + max_leaf_nodes, + min_impurity_decrease, + random_state=None, + ): self.criterion = criterion self.max_features = max_features self.max_depth_factor = max_depth_factor @@ -36,8 +47,8 @@ def fit(self, X, y, sample_weight=None): num_features = X.shape[1] self.max_depth_factor = int(self.max_depth_factor) max_depth_factor = max( - 1, - int(np.round(self.max_depth_factor * num_features, 0))) + 1, int(np.round(self.max_depth_factor * num_features, 0)) + ) self.min_samples_split = int(self.min_samples_split) self.min_samples_leaf = int(self.min_samples_leaf) if check_none(self.max_leaf_nodes): @@ -55,7 +66,8 @@ def fit(self, X, y, sample_weight=None): max_leaf_nodes=self.max_leaf_nodes, min_weight_fraction_leaf=self.min_weight_fraction_leaf, min_impurity_decrease=self.min_impurity_decrease, - random_state=self.random_state) + random_state=self.random_state, + ) if y.ndim == 2 and y.shape[1] == 1: y = y.flatten() @@ -70,38 +82,53 @@ def predict(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'DT', - 'name': 'Decision Tree Classifier', - 'handles_regression': True, - 'handles_classification': False, - 'handles_multiclass': False, - 'handles_multilabel': False, - 'handles_multioutput': True, - 'is_deterministic': False, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "DT", + "name": "Decision Tree Classifier", + "handles_regression": True, + "handles_classification": False, + "handles_multiclass": False, + "handles_multilabel": False, + "handles_multioutput": True, + "is_deterministic": False, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() - criterion = CategoricalHyperparameter('criterion', - ['mse', 'friedman_mse', 'mae']) - max_features = Constant('max_features', 1.0) + criterion = CategoricalHyperparameter( + "criterion", ["mse", "friedman_mse", "mae"] + ) + max_features = Constant("max_features", 1.0) max_depth_factor = UniformFloatHyperparameter( - 'max_depth_factor', 0., 2., default_value=0.5) + "max_depth_factor", 0.0, 2.0, default_value=0.5 + ) min_samples_split = UniformIntegerHyperparameter( - "min_samples_split", 2, 20, default_value=2) + "min_samples_split", 2, 20, default_value=2 + ) min_samples_leaf = UniformIntegerHyperparameter( - "min_samples_leaf", 1, 20, default_value=1) + "min_samples_leaf", 1, 20, default_value=1 + ) min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.0) max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") min_impurity_decrease = UnParametrizedHyperparameter( - 'min_impurity_decrease', 0.0) + "min_impurity_decrease", 0.0 + ) - cs.add_hyperparameters([criterion, max_features, max_depth_factor, - min_samples_split, min_samples_leaf, - min_weight_fraction_leaf, max_leaf_nodes, - min_impurity_decrease]) + cs.add_hyperparameters( + [ + criterion, + max_features, + max_depth_factor, + min_samples_split, + min_samples_leaf, + min_weight_fraction_leaf, + max_leaf_nodes, + min_impurity_decrease, + ] + ) return cs diff --git a/autosklearn/pipeline/components/regression/extra_trees.py b/autosklearn/pipeline/components/regression/extra_trees.py index a676f0483d..c4646a2709 100644 --- a/autosklearn/pipeline/components/regression/extra_trees.py +++ b/autosklearn/pipeline/components/regression/extra_trees.py @@ -1,12 +1,16 @@ from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - UniformIntegerHyperparameter, CategoricalHyperparameter, UnParametrizedHyperparameter +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformFloatHyperparameter, + UniformIntegerHyperparameter, + UnParametrizedHyperparameter, +) from autosklearn.pipeline.components.base import ( AutoSklearnRegressionAlgorithm, IterativeComponent, ) -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA from autosklearn.util.common import check_for_bool, check_none @@ -14,10 +18,22 @@ class ExtraTreesRegressor( IterativeComponent, AutoSklearnRegressionAlgorithm, ): - def __init__(self, criterion, min_samples_leaf, - min_samples_split, max_features, bootstrap, max_leaf_nodes, - max_depth, min_weight_fraction_leaf, min_impurity_decrease, - oob_score=False, n_jobs=1, random_state=None, verbose=0): + def __init__( + self, + criterion, + min_samples_leaf, + min_samples_split, + max_features, + bootstrap, + max_leaf_nodes, + max_depth, + min_weight_fraction_leaf, + min_impurity_decrease, + oob_score=False, + n_jobs=1, + random_state=None, + verbose=0, + ): self.n_estimators = self.get_max_iter() self.criterion = criterion @@ -53,7 +69,8 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): if self.criterion not in ("mse", "friedman_mse", "mae"): raise ValueError( "'criterion' is not in ('mse', 'friedman_mse', " - "'mae): %s" % self.criterion) + "'mae): %s" % self.criterion + ) if check_none(self.max_depth): self.max_depth = None @@ -75,25 +92,28 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): self.n_jobs = int(self.n_jobs) self.verbose = int(self.verbose) - self.estimator = ETR(n_estimators=n_iter, - criterion=self.criterion, - max_depth=self.max_depth, - min_samples_split=self.min_samples_split, - min_samples_leaf=self.min_samples_leaf, - bootstrap=self.bootstrap, - max_features=self.max_features, - max_leaf_nodes=self.max_leaf_nodes, - min_weight_fraction_leaf=self.min_weight_fraction_leaf, - min_impurity_decrease=self.min_impurity_decrease, - oob_score=self.oob_score, - n_jobs=self.n_jobs, - verbose=self.verbose, - random_state=self.random_state, - warm_start=True) + self.estimator = ETR( + n_estimators=n_iter, + criterion=self.criterion, + max_depth=self.max_depth, + min_samples_split=self.min_samples_split, + min_samples_leaf=self.min_samples_leaf, + bootstrap=self.bootstrap, + max_features=self.max_features, + max_leaf_nodes=self.max_leaf_nodes, + min_weight_fraction_leaf=self.min_weight_fraction_leaf, + min_impurity_decrease=self.min_impurity_decrease, + oob_score=self.oob_score, + n_jobs=self.n_jobs, + verbose=self.verbose, + random_state=self.random_state, + warm_start=True, + ) else: self.estimator.n_estimators += n_iter - self.estimator.n_estimators = min(self.estimator.n_estimators, - self.n_estimators) + self.estimator.n_estimators = min( + self.estimator.n_estimators, self.n_estimators + ) if y.ndim == 2 and y.shape[1] == 1: y = y.flatten() @@ -114,44 +134,62 @@ def predict(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'ET', - 'name': 'Extra Trees Regressor', - 'handles_regression': True, - 'handles_classification': False, - 'handles_multiclass': False, - 'handles_multilabel': False, - 'handles_multioutput': True, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "ET", + "name": "Extra Trees Regressor", + "handles_regression": True, + "handles_classification": False, + "handles_multiclass": False, + "handles_multilabel": False, + "handles_multioutput": True, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() - criterion = CategoricalHyperparameter("criterion", - ['mse', 'friedman_mse', 'mae']) + criterion = CategoricalHyperparameter( + "criterion", ["mse", "friedman_mse", "mae"] + ) max_features = UniformFloatHyperparameter( - "max_features", 0.1, 1.0, default_value=1) + "max_features", 0.1, 1.0, default_value=1 + ) max_depth = UnParametrizedHyperparameter(name="max_depth", value="None") - min_weight_fraction_leaf = UnParametrizedHyperparameter('min_weight_fraction_leaf', 0.) + min_weight_fraction_leaf = UnParametrizedHyperparameter( + "min_weight_fraction_leaf", 0.0 + ) max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") min_samples_split = UniformIntegerHyperparameter( - "min_samples_split", 2, 20, default_value=2) + "min_samples_split", 2, 20, default_value=2 + ) min_samples_leaf = UniformIntegerHyperparameter( - "min_samples_leaf", 1, 20, default_value=1) + "min_samples_leaf", 1, 20, default_value=1 + ) min_impurity_decrease = UnParametrizedHyperparameter( - 'min_impurity_decrease', 0.0 + "min_impurity_decrease", 0.0 ) bootstrap = CategoricalHyperparameter( - "bootstrap", ["True", "False"], default_value="False") + "bootstrap", ["True", "False"], default_value="False" + ) - cs.add_hyperparameters([criterion, max_features, - max_depth, max_leaf_nodes, min_samples_split, - min_samples_leaf, min_impurity_decrease, min_weight_fraction_leaf, - bootstrap]) + cs.add_hyperparameters( + [ + criterion, + max_features, + max_depth, + max_leaf_nodes, + min_samples_split, + min_samples_leaf, + min_impurity_decrease, + min_weight_fraction_leaf, + bootstrap, + ] + ) return cs diff --git a/autosklearn/pipeline/components/regression/gaussian_process.py b/autosklearn/pipeline/components/regression/gaussian_process.py index c587b13b0e..1acf238cd1 100644 --- a/autosklearn/pipeline/components/regression/gaussian_process.py +++ b/autosklearn/pipeline/components/regression/gaussian_process.py @@ -2,7 +2,7 @@ from ConfigSpace.hyperparameters import UniformFloatHyperparameter from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, UNSIGNED_DATA class GaussianProcess(AutoSklearnRegressionAlgorithm): @@ -22,19 +22,19 @@ def fit(self, X, y): n_features = X.shape[1] kernel = sklearn.gaussian_process.kernels.RBF( - length_scale=[1.0]*n_features, - length_scale_bounds=[(self.thetaL, self.thetaU)]*n_features + length_scale=[1.0] * n_features, + length_scale_bounds=[(self.thetaL, self.thetaU)] * n_features, ) # Instanciate a Gaussian Process model self.estimator = sklearn.gaussian_process.GaussianProcessRegressor( kernel=kernel, n_restarts_optimizer=10, - optimizer='fmin_l_bfgs_b', + optimizer="fmin_l_bfgs_b", alpha=self.alpha, copy_X_train=True, random_state=self.random_state, - normalize_y=True + normalize_y=True, ) if y.ndim == 2 and y.shape[1] == 1: @@ -51,25 +51,30 @@ def predict(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'GP', - 'name': 'Gaussian Process', - 'handles_regression': True, - 'handles_classification': False, - 'handles_multiclass': False, - 'handles_multilabel': False, - 'handles_multioutput': True, - 'is_deterministic': True, - 'input': (DENSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "GP", + "name": "Gaussian Process", + "handles_regression": True, + "handles_classification": False, + "handles_multiclass": False, + "handles_multilabel": False, + "handles_multioutput": True, + "is_deterministic": True, + "input": (DENSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): alpha = UniformFloatHyperparameter( - name="alpha", lower=1e-14, upper=1.0, default_value=1e-8, log=True) + name="alpha", lower=1e-14, upper=1.0, default_value=1e-8, log=True + ) thetaL = UniformFloatHyperparameter( - name="thetaL", lower=1e-10, upper=1e-3, default_value=1e-6, log=True) + name="thetaL", lower=1e-10, upper=1e-3, default_value=1e-6, log=True + ) thetaU = UniformFloatHyperparameter( - name="thetaU", lower=1.0, upper=100000, default_value=100000.0, log=True) + name="thetaU", lower=1.0, upper=100000, default_value=100000.0, log=True + ) cs = ConfigurationSpace() cs.add_hyperparameters([alpha, thetaL, thetaU]) diff --git a/autosklearn/pipeline/components/regression/gradient_boosting.py b/autosklearn/pipeline/components/regression/gradient_boosting.py index ad57596b9a..b7503f5fd0 100644 --- a/autosklearn/pipeline/components/regression/gradient_boosting.py +++ b/autosklearn/pipeline/components/regression/gradient_boosting.py @@ -1,16 +1,19 @@ import numpy as np - -from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - UniformIntegerHyperparameter, CategoricalHyperparameter, Constant, \ - UnParametrizedHyperparameter from ConfigSpace.conditions import EqualsCondition, InCondition +from ConfigSpace.configuration_space import ConfigurationSpace +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + Constant, + UniformFloatHyperparameter, + UniformIntegerHyperparameter, + UnParametrizedHyperparameter, +) from autosklearn.pipeline.components.base import ( AutoSklearnRegressionAlgorithm, IterativeComponent, ) -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, UNSIGNED_DATA from autosklearn.util.common import check_none @@ -18,10 +21,23 @@ class GradientBoosting( IterativeComponent, AutoSklearnRegressionAlgorithm, ): - def __init__(self, loss, learning_rate, min_samples_leaf, max_depth, - max_leaf_nodes, max_bins, l2_regularization, early_stop, tol, scoring, - n_iter_no_change=0, validation_fraction=None, random_state=None, - verbose=0): + def __init__( + self, + loss, + learning_rate, + min_samples_leaf, + max_depth, + max_leaf_nodes, + max_bins, + l2_regularization, + early_stop, + tol, + scoring, + n_iter_no_change=0, + validation_fraction=None, + random_state=None, + verbose=0, + ): self.loss = loss self.learning_rate = learning_rate self.max_iter = self.get_max_iter() @@ -48,7 +64,7 @@ def get_current_iter(self): return self.estimator.n_iter_ def iterative_fit(self, X, y, n_iter=2, refit=False): - """ Set n_iter=2 for the same reason as for SGD """ + """Set n_iter=2 for the same reason as for SGD""" import sklearn.ensemble from sklearn.experimental import enable_hist_gradient_boosting # noqa @@ -106,8 +122,7 @@ def iterative_fit(self, X, y, n_iter=2, refit=False): ) else: self.estimator.max_iter += n_iter - self.estimator.max_iter = min(self.estimator.max_iter, - self.max_iter) + self.estimator.max_iter = min(self.estimator.max_iter, self.max_iter) if y.ndim == 2 and y.shape[1] == 1: y = y.flatten() @@ -125,7 +140,7 @@ def iterative_fit(self, X, y, n_iter=2, refit=False): def configuration_fully_fitted(self): if self.estimator is None: return False - elif not hasattr(self, 'fully_fit_'): + elif not hasattr(self, "fully_fit_"): return False else: return self.fully_fit_ @@ -137,54 +152,79 @@ def predict(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'GB', - 'name': 'Gradient Boosting Regressor', - 'handles_regression': True, - 'handles_classification': False, - 'handles_multiclass': False, - 'handles_multilabel': False, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "GB", + "name": "Gradient Boosting Regressor", + "handles_regression": True, + "handles_classification": False, + "handles_multiclass": False, + "handles_multilabel": False, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() loss = CategoricalHyperparameter( - "loss", ["least_squares"], default_value="least_squares") + "loss", ["least_squares"], default_value="least_squares" + ) learning_rate = UniformFloatHyperparameter( - name="learning_rate", lower=0.01, upper=1, default_value=0.1, log=True) + name="learning_rate", lower=0.01, upper=1, default_value=0.1, log=True + ) min_samples_leaf = UniformIntegerHyperparameter( - name="min_samples_leaf", lower=1, upper=200, default_value=20, log=True) - max_depth = UnParametrizedHyperparameter( - name="max_depth", value="None") + name="min_samples_leaf", lower=1, upper=200, default_value=20, log=True + ) + max_depth = UnParametrizedHyperparameter(name="max_depth", value="None") max_leaf_nodes = UniformIntegerHyperparameter( - name="max_leaf_nodes", lower=3, upper=2047, default_value=31, log=True) + name="max_leaf_nodes", lower=3, upper=2047, default_value=31, log=True + ) max_bins = Constant("max_bins", 255) l2_regularization = UniformFloatHyperparameter( - name="l2_regularization", lower=1E-10, upper=1, default_value=1E-10, log=True) + name="l2_regularization", + lower=1e-10, + upper=1, + default_value=1e-10, + log=True, + ) early_stop = CategoricalHyperparameter( - name="early_stop", choices=["off", "valid", "train"], default_value="off") - tol = UnParametrizedHyperparameter( - name="tol", value=1e-7) - scoring = UnParametrizedHyperparameter( - name="scoring", value="loss") + name="early_stop", choices=["off", "valid", "train"], default_value="off" + ) + tol = UnParametrizedHyperparameter(name="tol", value=1e-7) + scoring = UnParametrizedHyperparameter(name="scoring", value="loss") n_iter_no_change = UniformIntegerHyperparameter( - name="n_iter_no_change", lower=1, upper=20, default_value=10) + name="n_iter_no_change", lower=1, upper=20, default_value=10 + ) validation_fraction = UniformFloatHyperparameter( - name="validation_fraction", lower=0.01, upper=0.4, default_value=0.1) - - cs.add_hyperparameters([loss, learning_rate, min_samples_leaf, - max_depth, max_leaf_nodes, max_bins, l2_regularization, - early_stop, tol, scoring, n_iter_no_change, - validation_fraction]) + name="validation_fraction", lower=0.01, upper=0.4, default_value=0.1 + ) + + cs.add_hyperparameters( + [ + loss, + learning_rate, + min_samples_leaf, + max_depth, + max_leaf_nodes, + max_bins, + l2_regularization, + early_stop, + tol, + scoring, + n_iter_no_change, + validation_fraction, + ] + ) n_iter_no_change_cond = InCondition( - n_iter_no_change, early_stop, ["valid", "train"]) + n_iter_no_change, early_stop, ["valid", "train"] + ) validation_fraction_cond = EqualsCondition( - validation_fraction, early_stop, "valid") + validation_fraction, early_stop, "valid" + ) cs.add_conditions([n_iter_no_change_cond, validation_fraction_cond]) diff --git a/autosklearn/pipeline/components/regression/k_nearest_neighbors.py b/autosklearn/pipeline/components/regression/k_nearest_neighbors.py index e4943e2ca5..83c13cd191 100644 --- a/autosklearn/pipeline/components/regression/k_nearest_neighbors.py +++ b/autosklearn/pipeline/components/regression/k_nearest_neighbors.py @@ -1,9 +1,11 @@ from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import CategoricalHyperparameter, \ - UniformIntegerHyperparameter +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformIntegerHyperparameter, +) from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA class KNearestNeighborsRegressor(AutoSklearnRegressionAlgorithm): @@ -19,11 +21,9 @@ def fit(self, X, y): self.n_neighbors = int(self.n_neighbors) self.p = int(self.p) - self.estimator = \ - sklearn.neighbors.KNeighborsRegressor( - n_neighbors=self.n_neighbors, - weights=self.weights, - p=self.p) + self.estimator = sklearn.neighbors.KNeighborsRegressor( + n_neighbors=self.n_neighbors, weights=self.weights, p=self.p + ) if y.ndim == 2 and y.shape[1] == 1: y = y.flatten() @@ -38,25 +38,29 @@ def predict(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'KNN', - 'name': 'K-Nearest Neighbor Classification', - 'handles_regression': True, - 'handles_classification': False, - 'handles_multiclass': False, - 'handles_multilabel': False, - 'handles_multioutput': True, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "KNN", + "name": "K-Nearest Neighbor Classification", + "handles_regression": True, + "handles_classification": False, + "handles_multiclass": False, + "handles_multilabel": False, + "handles_multioutput": True, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() n_neighbors = UniformIntegerHyperparameter( - name="n_neighbors", lower=1, upper=100, log=True, default_value=1) + name="n_neighbors", lower=1, upper=100, log=True, default_value=1 + ) weights = CategoricalHyperparameter( - name="weights", choices=["uniform", "distance"], default_value="uniform") + name="weights", choices=["uniform", "distance"], default_value="uniform" + ) p = CategoricalHyperparameter(name="p", choices=[1, 2], default_value=2) cs.add_hyperparameters([n_neighbors, weights, p]) diff --git a/autosklearn/pipeline/components/regression/liblinear_svr.py b/autosklearn/pipeline/components/regression/liblinear_svr.py index 73c1550ff3..e129331298 100644 --- a/autosklearn/pipeline/components/regression/liblinear_svr.py +++ b/autosklearn/pipeline/components/regression/liblinear_svr.py @@ -1,18 +1,29 @@ from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - CategoricalHyperparameter, Constant -from ConfigSpace.forbidden import ForbiddenEqualsClause, \ - ForbiddenAndConjunction +from ConfigSpace.forbidden import ForbiddenAndConjunction, ForbiddenEqualsClause +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + Constant, + UniformFloatHyperparameter, +) from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA from autosklearn.util.common import check_for_bool class LibLinear_SVR(AutoSklearnRegressionAlgorithm): # Liblinear is not deterministic as it uses a RNG inside - def __init__(self, loss, epsilon, dual, tol, C, fit_intercept, - intercept_scaling, random_state=None): + def __init__( + self, + loss, + epsilon, + dual, + tol, + C, + fit_intercept, + intercept_scaling, + random_state=None, + ): self.epsilon = epsilon self.loss = loss self.dual = dual @@ -34,14 +45,16 @@ def fit(self, X, y): self.fit_intercept = check_for_bool(self.fit_intercept) self.intercept_scaling = float(self.intercept_scaling) - self.estimator = sklearn.svm.LinearSVR(epsilon=self.epsilon, - loss=self.loss, - dual=self.dual, - tol=self.tol, - C=self.C, - fit_intercept=self.fit_intercept, - intercept_scaling=self.intercept_scaling, - random_state=self.random_state) + self.estimator = sklearn.svm.LinearSVR( + epsilon=self.epsilon, + loss=self.loss, + dual=self.dual, + tol=self.tol, + C=self.C, + fit_intercept=self.fit_intercept, + intercept_scaling=self.intercept_scaling, + random_state=self.random_state, + ) if y.ndim == 2 and y.shape[1] == 1: y = y.flatten() @@ -56,41 +69,47 @@ def predict(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'Liblinear-SVR', - 'name': 'Liblinear Support Vector Regression', - 'handles_regression': True, - 'handles_classification': False, - 'handles_multiclass': False, - 'handles_multilabel': False, - 'handles_multioutput': False, - 'is_deterministic': False, - 'input': (SPARSE, DENSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "Liblinear-SVR", + "name": "Liblinear Support Vector Regression", + "handles_regression": True, + "handles_classification": False, + "handles_multiclass": False, + "handles_multilabel": False, + "handles_multioutput": False, + "is_deterministic": False, + "input": (SPARSE, DENSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() - C = UniformFloatHyperparameter( - "C", 0.03125, 32768, log=True, default_value=1.0) + C = UniformFloatHyperparameter("C", 0.03125, 32768, log=True, default_value=1.0) loss = CategoricalHyperparameter( - "loss", ["epsilon_insensitive", "squared_epsilon_insensitive"], - default_value="squared_epsilon_insensitive") + "loss", + ["epsilon_insensitive", "squared_epsilon_insensitive"], + default_value="squared_epsilon_insensitive", + ) # Random Guess epsilon = UniformFloatHyperparameter( - name="epsilon", lower=0.001, upper=1, default_value=0.1, log=True) + name="epsilon", lower=0.001, upper=1, default_value=0.1, log=True + ) dual = Constant("dual", "False") # These are set ad-hoc tol = UniformFloatHyperparameter( - "tol", 1e-5, 1e-1, default_value=1e-4, log=True) + "tol", 1e-5, 1e-1, default_value=1e-4, log=True + ) fit_intercept = Constant("fit_intercept", "True") intercept_scaling = Constant("intercept_scaling", 1) - cs.add_hyperparameters([C, loss, epsilon, dual, tol, fit_intercept, - intercept_scaling]) + cs.add_hyperparameters( + [C, loss, epsilon, dual, tol, fit_intercept, intercept_scaling] + ) dual_and_loss = ForbiddenAndConjunction( ForbiddenEqualsClause(dual, "False"), - ForbiddenEqualsClause(loss, "epsilon_insensitive") + ForbiddenEqualsClause(loss, "epsilon_insensitive"), ) cs.add_forbidden_clause(dual_and_loss) diff --git a/autosklearn/pipeline/components/regression/libsvm_svr.py b/autosklearn/pipeline/components/regression/libsvm_svr.py index 6b6c70415c..d4173d7f01 100644 --- a/autosklearn/pipeline/components/regression/libsvm_svr.py +++ b/autosklearn/pipeline/components/regression/libsvm_svr.py @@ -1,20 +1,35 @@ import resource import sys +from ConfigSpace.conditions import EqualsCondition, InCondition from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.conditions import InCondition, EqualsCondition -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - UniformIntegerHyperparameter, CategoricalHyperparameter, \ - UnParametrizedHyperparameter +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformFloatHyperparameter, + UniformIntegerHyperparameter, + UnParametrizedHyperparameter, +) + from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA from autosklearn.util.common import check_for_bool, check_none class LibSVM_SVR(AutoSklearnRegressionAlgorithm): - def __init__(self, kernel, C, epsilon, tol, shrinking, gamma=0.1, - degree=3, coef0=0.0, verbose=False, - max_iter=-1, random_state=None): + def __init__( + self, + kernel, + C, + epsilon, + tol, + shrinking, + gamma=0.1, + degree=3, + coef0=0.0, + verbose=False, + max_iter=-1, + random_state=None, + ): self.kernel = kernel self.C = C self.epsilon = epsilon @@ -31,9 +46,9 @@ def __init__(self, kernel, C, epsilon, tol, shrinking, gamma=0.1, def fit(self, X, y): import sklearn.svm - # Calculate the size of the kernel cache (in MB) for sklearn's LibSVM. The cache size is - # calculated as 2/3 of the available memory (which is calculated as the memory limit minus - # the used memory) + # Calculate the size of the kernel cache (in MB) for sklearn's LibSVM. + # The cache size is calculated as 2/3 of the available memory + # (which is calculated as the memory limit minus the used memory) try: # Retrieve memory limits imposed on the process soft, hard = resource.getrlimit(resource.RLIMIT_AS) @@ -45,9 +60,9 @@ def fit(self, X, y): # Retrieve memory used by this process maxrss = resource.getrusage(resource.RUSAGE_SELF)[2] / 1024 - # In MacOS, the MaxRSS output of resource.getrusage in bytes; on other platforms, - # it's in kilobytes - if sys.platform == 'darwin': + # In MacOS, the MaxRSS output of resource.getrusage in bytes; + # on other platforms, it's in kilobytes + if sys.platform == "darwin": maxrss = maxrss / 1024 cache_size = (soft - maxrss) / 1.5 @@ -83,7 +98,7 @@ def fit(self, X, y): coef0=self.coef0, cache_size=cache_size, verbose=self.verbose, - max_iter=self.max_iter + max_iter=self.max_iter, ) self.scaler = sklearn.preprocessing.StandardScaler(copy=True) @@ -119,56 +134,70 @@ def predict(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'SVR', - 'name': 'Support Vector Regression', - 'handles_regression': True, - 'handles_classification': False, - 'handles_multiclass': False, - 'handles_multilabel': False, - 'handles_multioutput': False, - 'prefers_data_normalized': True, - 'is_deterministic': True, - 'input': (SPARSE, DENSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "SVR", + "name": "Support Vector Regression", + "handles_regression": True, + "handles_classification": False, + "handles_multiclass": False, + "handles_multilabel": False, + "handles_multioutput": False, + "prefers_data_normalized": True, + "is_deterministic": True, + "input": (SPARSE, DENSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): C = UniformFloatHyperparameter( - name="C", lower=0.03125, upper=32768, log=True, default_value=1.0) + name="C", lower=0.03125, upper=32768, log=True, default_value=1.0 + ) # Random Guess - epsilon = UniformFloatHyperparameter(name="epsilon", lower=0.001, - upper=1, default_value=0.1, - log=True) + epsilon = UniformFloatHyperparameter( + name="epsilon", lower=0.001, upper=1, default_value=0.1, log=True + ) kernel = CategoricalHyperparameter( - name="kernel", choices=['linear', 'poly', 'rbf', 'sigmoid'], - default_value="rbf") + name="kernel", + choices=["linear", "poly", "rbf", "sigmoid"], + default_value="rbf", + ) degree = UniformIntegerHyperparameter( - name="degree", lower=2, upper=5, default_value=3) + name="degree", lower=2, upper=5, default_value=3 + ) gamma = UniformFloatHyperparameter( - name="gamma", lower=3.0517578125e-05, upper=8, log=True, default_value=0.1) + name="gamma", lower=3.0517578125e-05, upper=8, log=True, default_value=0.1 + ) # TODO this is totally ad-hoc coef0 = UniformFloatHyperparameter( - name="coef0", lower=-1, upper=1, default_value=0) + name="coef0", lower=-1, upper=1, default_value=0 + ) # probability is no hyperparameter, but an argument to the SVM algo shrinking = CategoricalHyperparameter( - name="shrinking", choices=["True", "False"], default_value="True") + name="shrinking", choices=["True", "False"], default_value="True" + ) tol = UniformFloatHyperparameter( - name="tol", lower=1e-5, upper=1e-1, default_value=1e-3, log=True) + name="tol", lower=1e-5, upper=1e-1, default_value=1e-3, log=True + ) max_iter = UnParametrizedHyperparameter("max_iter", -1) cs = ConfigurationSpace() - cs.add_hyperparameters([C, kernel, degree, gamma, coef0, shrinking, - tol, max_iter, epsilon]) + cs.add_hyperparameters( + [C, kernel, degree, gamma, coef0, shrinking, tol, max_iter, epsilon] + ) degree_depends_on_poly = EqualsCondition(degree, kernel, "poly") - gamma_depends_on_kernel = InCondition(child=gamma, parent=kernel, - values=('poly', 'rbf')) - coef0_depends_on_kernel = InCondition(child=coef0, parent=kernel, - values=('poly', 'sigmoid')) - cs.add_conditions([degree_depends_on_poly, gamma_depends_on_kernel, - coef0_depends_on_kernel]) + gamma_depends_on_kernel = InCondition( + child=gamma, parent=kernel, values=("poly", "rbf") + ) + coef0_depends_on_kernel = InCondition( + child=coef0, parent=kernel, values=("poly", "sigmoid") + ) + cs.add_conditions( + [degree_depends_on_poly, gamma_depends_on_kernel, coef0_depends_on_kernel] + ) return cs diff --git a/autosklearn/pipeline/components/regression/mlp.py b/autosklearn/pipeline/components/regression/mlp.py index 8eec40a2cc..645c29403a 100644 --- a/autosklearn/pipeline/components/regression/mlp.py +++ b/autosklearn/pipeline/components/regression/mlp.py @@ -1,27 +1,43 @@ import numpy as np - -from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - UniformIntegerHyperparameter, UnParametrizedHyperparameter, Constant, \ - CategoricalHyperparameter from ConfigSpace.conditions import InCondition +from ConfigSpace.configuration_space import ConfigurationSpace +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + Constant, + UniformFloatHyperparameter, + UniformIntegerHyperparameter, + UnParametrizedHyperparameter, +) from autosklearn.pipeline.components.base import ( AutoSklearnRegressionAlgorithm, IterativeComponent, ) -from autosklearn.pipeline.constants import SPARSE, DENSE, UNSIGNED_DATA, PREDICTIONS +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA from autosklearn.util.common import check_for_bool -class MLPRegressor( - IterativeComponent, - AutoSklearnRegressionAlgorithm -): - def __init__(self, hidden_layer_depth, num_nodes_per_layer, activation, alpha, - learning_rate_init, early_stopping, solver, batch_size, - n_iter_no_change, tol, shuffle, beta_1, beta_2, epsilon, - validation_fraction=None, random_state=None, verbose=0): +class MLPRegressor(IterativeComponent, AutoSklearnRegressionAlgorithm): + def __init__( + self, + hidden_layer_depth, + num_nodes_per_layer, + activation, + alpha, + learning_rate_init, + early_stopping, + solver, + batch_size, + n_iter_no_change, + tol, + shuffle, + beta_1, + beta_2, + epsilon, + validation_fraction=None, + random_state=None, + verbose=0, + ): self.hidden_layer_depth = hidden_layer_depth self.num_nodes_per_layer = num_nodes_per_layer self.max_iter = self.get_max_iter() @@ -52,11 +68,10 @@ def get_current_iter(self): return self.estimator.n_iter_ def iterative_fit(self, X, y, n_iter=2, refit=False): - """ - Set n_iter=2 for the same reason as for SGD - """ - from sklearn.neural_network import MLPRegressor + """Set n_iter=2 for the same reason as for SGD""" import sklearn.preprocessing + from sklearn.neural_network import MLPRegressor + n_iter = max(n_iter, 2) if refit: @@ -69,8 +84,9 @@ def iterative_fit(self, X, y, n_iter=2, refit=False): self.max_iter = int(self.max_iter) self.hidden_layer_depth = int(self.hidden_layer_depth) self.num_nodes_per_layer = int(self.num_nodes_per_layer) - self.hidden_layer_sizes = tuple(self.num_nodes_per_layer - for i in range(self.hidden_layer_depth)) + self.hidden_layer_sizes = tuple( + self.num_nodes_per_layer for i in range(self.hidden_layer_depth) + ) self.activation = str(self.activation) self.alpha = float(self.alpha) self.learning_rate_init = float(self.learning_rate_init) @@ -86,7 +102,9 @@ def iterative_fit(self, X, y, n_iter=2, refit=False): self.n_iter_no_change = int(self.n_iter_no_change) self.early_stopping_val = True else: - raise ValueError("Set early stopping to unknown value %s" % self.early_stopping) + raise ValueError( + "Set early stopping to unknown value %s" % self.early_stopping + ) # elif self.early_stopping == "off": # self.validation_fraction = 0 # self.tol = 10000 @@ -172,7 +190,7 @@ def iterative_fit(self, X, y, n_iter=2, refit=False): def configuration_fully_fitted(self): if self.estimator is None: return False - elif not hasattr(self, '_fully_fit'): + elif not hasattr(self, "_fully_fit"): return False else: return self._fully_fit @@ -193,43 +211,56 @@ def predict(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'MLP', - 'name': 'Multilayer Percepton', - 'handles_regression': True, - 'handles_classification': False, - 'handles_multiclass': False, - 'handles_multilabel': False, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "MLP", + "name": "Multilayer Percepton", + "handles_regression": True, + "handles_classification": False, + "handles_multiclass": False, + "handles_multilabel": False, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() - hidden_layer_depth = UniformIntegerHyperparameter(name="hidden_layer_depth", - lower=1, upper=3, default_value=1) - num_nodes_per_layer = UniformIntegerHyperparameter(name="num_nodes_per_layer", - lower=16, upper=264, default_value=32, - log=True) - activation = CategoricalHyperparameter(name="activation", choices=['tanh', 'relu'], - default_value='tanh') - alpha = UniformFloatHyperparameter(name="alpha", lower=1e-7, upper=1e-1, default_value=1e-4, - log=True) - - learning_rate_init = UniformFloatHyperparameter(name="learning_rate_init", - lower=1e-4, upper=0.5, default_value=1e-3, - log=True) + hidden_layer_depth = UniformIntegerHyperparameter( + name="hidden_layer_depth", lower=1, upper=3, default_value=1 + ) + num_nodes_per_layer = UniformIntegerHyperparameter( + name="num_nodes_per_layer", lower=16, upper=264, default_value=32, log=True + ) + activation = CategoricalHyperparameter( + name="activation", choices=["tanh", "relu"], default_value="tanh" + ) + alpha = UniformFloatHyperparameter( + name="alpha", lower=1e-7, upper=1e-1, default_value=1e-4, log=True + ) + + learning_rate_init = UniformFloatHyperparameter( + name="learning_rate_init", + lower=1e-4, + upper=0.5, + default_value=1e-3, + log=True, + ) # Not allowing to turn off early stopping - early_stopping = CategoricalHyperparameter(name="early_stopping", - choices=["valid", "train"], # , "off"], - default_value="valid") + early_stopping = CategoricalHyperparameter( + name="early_stopping", + choices=["valid", "train"], # , "off"], + default_value="valid", + ) # Constants - n_iter_no_change = Constant(name="n_iter_no_change", value=32) # default=10 is too low + n_iter_no_change = Constant( + name="n_iter_no_change", value=32 + ) # default=10 is too low validation_fraction = Constant(name="validation_fraction", value=0.1) tol = UnParametrizedHyperparameter(name="tol", value=1e-4) - solver = Constant(name="solver", value='adam') + solver = Constant(name="solver", value="adam") # Relying on sklearn defaults for now batch_size = UnParametrizedHyperparameter(name="batch_size", value="auto") @@ -247,17 +278,33 @@ def get_hyperparameter_search_space(dataset_properties=None): # max_fun --> only used when solver=lbfgs # activation=["identity", "logistic"] --> not useful for classification - cs.add_hyperparameters([hidden_layer_depth, num_nodes_per_layer, - activation, alpha, - learning_rate_init, early_stopping, - n_iter_no_change, validation_fraction, tol, - solver, batch_size, shuffle, - beta_1, beta_2, epsilon]) - - validation_fraction_cond = InCondition(validation_fraction, early_stopping, ["valid"]) + cs.add_hyperparameters( + [ + hidden_layer_depth, + num_nodes_per_layer, + activation, + alpha, + learning_rate_init, + early_stopping, + n_iter_no_change, + validation_fraction, + tol, + solver, + batch_size, + shuffle, + beta_1, + beta_2, + epsilon, + ] + ) + + validation_fraction_cond = InCondition( + validation_fraction, early_stopping, ["valid"] + ) cs.add_conditions([validation_fraction_cond]) # We always use early stopping - # n_iter_no_change_cond = InCondition(n_iter_no_change, early_stopping, ["valid", "train"]) + # n_iter_no_change_cond = \ + # InCondition(n_iter_no_change, early_stopping, ["valid", "train"]) # tol_cond = InCondition(n_iter_no_change, early_stopping, ["valid", "train"]) # cs.add_conditions([n_iter_no_change_cond, tol_cond]) diff --git a/autosklearn/pipeline/components/regression/random_forest.py b/autosklearn/pipeline/components/regression/random_forest.py index eeaddb9e1a..128113fc43 100644 --- a/autosklearn/pipeline/components/regression/random_forest.py +++ b/autosklearn/pipeline/components/regression/random_forest.py @@ -1,12 +1,16 @@ from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - UniformIntegerHyperparameter, CategoricalHyperparameter, UnParametrizedHyperparameter +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformFloatHyperparameter, + UniformIntegerHyperparameter, + UnParametrizedHyperparameter, +) from autosklearn.pipeline.components.base import ( AutoSklearnRegressionAlgorithm, IterativeComponent, ) -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA from autosklearn.util.common import check_for_bool, check_none @@ -14,10 +18,20 @@ class RandomForest( IterativeComponent, AutoSklearnRegressionAlgorithm, ): - def __init__(self, criterion, max_features, - max_depth, min_samples_split, min_samples_leaf, - min_weight_fraction_leaf, bootstrap, max_leaf_nodes, - min_impurity_decrease, random_state=None, n_jobs=1): + def __init__( + self, + criterion, + max_features, + max_depth, + min_samples_split, + min_samples_leaf, + min_weight_fraction_leaf, + bootstrap, + max_leaf_nodes, + min_impurity_decrease, + random_state=None, + n_jobs=1, + ): self.n_estimators = self.get_max_iter() self.criterion = criterion self.max_features = max_features @@ -79,11 +93,13 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): min_impurity_decrease=self.min_impurity_decrease, random_state=self.random_state, n_jobs=self.n_jobs, - warm_start=True) + warm_start=True, + ) else: self.estimator.n_estimators += n_iter - self.estimator.n_estimators = min(self.estimator.n_estimators, - self.n_estimators) + self.estimator.n_estimators = min( + self.estimator.n_estimators, self.n_estimators + ) if y.ndim == 2 and y.shape[1] == 1: y = y.flatten() @@ -104,45 +120,63 @@ def predict(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'RF', - 'name': 'Random Forest Regressor', - 'handles_regression': True, - 'handles_classification': False, - 'handles_multiclass': False, - 'handles_multilabel': False, - 'handles_multioutput': True, - 'prefers_data_normalized': False, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "RF", + "name": "Random Forest Regressor", + "handles_regression": True, + "handles_classification": False, + "handles_multiclass": False, + "handles_multilabel": False, + "handles_multioutput": True, + "prefers_data_normalized": False, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() - criterion = CategoricalHyperparameter("criterion", - ['mse', 'friedman_mse', 'mae']) + criterion = CategoricalHyperparameter( + "criterion", ["mse", "friedman_mse", "mae"] + ) # In contrast to the random forest classifier we want to use more max_features # and therefore have this not on a sqrt scale max_features = UniformFloatHyperparameter( - "max_features", 0.1, 1.0, default_value=1.0) + "max_features", 0.1, 1.0, default_value=1.0 + ) max_depth = UnParametrizedHyperparameter("max_depth", "None") min_samples_split = UniformIntegerHyperparameter( - "min_samples_split", 2, 20, default_value=2) + "min_samples_split", 2, 20, default_value=2 + ) min_samples_leaf = UniformIntegerHyperparameter( - "min_samples_leaf", 1, 20, default_value=1) - min_weight_fraction_leaf = \ - UnParametrizedHyperparameter("min_weight_fraction_leaf", 0.) + "min_samples_leaf", 1, 20, default_value=1 + ) + min_weight_fraction_leaf = UnParametrizedHyperparameter( + "min_weight_fraction_leaf", 0.0 + ) max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") min_impurity_decrease = UnParametrizedHyperparameter( - 'min_impurity_decrease', 0.0) + "min_impurity_decrease", 0.0 + ) bootstrap = CategoricalHyperparameter( - "bootstrap", ["True", "False"], default_value="True") - - cs.add_hyperparameters([criterion, max_features, - max_depth, min_samples_split, min_samples_leaf, - min_weight_fraction_leaf, max_leaf_nodes, - min_impurity_decrease, bootstrap]) + "bootstrap", ["True", "False"], default_value="True" + ) + + cs.add_hyperparameters( + [ + criterion, + max_features, + max_depth, + min_samples_split, + min_samples_leaf, + min_weight_fraction_leaf, + max_leaf_nodes, + min_impurity_decrease, + bootstrap, + ] + ) return cs diff --git a/autosklearn/pipeline/components/regression/sgd.py b/autosklearn/pipeline/components/regression/sgd.py index 8b3e7dbd34..3b3f939fa8 100644 --- a/autosklearn/pipeline/components/regression/sgd.py +++ b/autosklearn/pipeline/components/regression/sgd.py @@ -1,13 +1,16 @@ +from ConfigSpace.conditions import EqualsCondition, InCondition from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - CategoricalHyperparameter, UnParametrizedHyperparameter -from ConfigSpace.conditions import InCondition, EqualsCondition +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformFloatHyperparameter, + UnParametrizedHyperparameter, +) from autosklearn.pipeline.components.base import ( AutoSklearnRegressionAlgorithm, IterativeComponent, ) -from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE +from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA from autosklearn.util.common import check_for_bool @@ -15,9 +18,21 @@ class SGD( IterativeComponent, AutoSklearnRegressionAlgorithm, ): - def __init__(self, loss, penalty, alpha, fit_intercept, tol, - learning_rate, l1_ratio=0.15, epsilon=0.1, - eta0=0.01, power_t=0.5, average=False, random_state=None): + def __init__( + self, + loss, + penalty, + alpha, + fit_intercept, + tol, + learning_rate, + l1_ratio=0.15, + epsilon=0.1, + eta0=0.01, + power_t=0.5, + average=False, + random_state=None, + ): self.max_iter = self.get_max_iter() self.loss = loss self.penalty = penalty @@ -43,8 +58,8 @@ def get_current_iter(self): return self.n_iter_ def iterative_fit(self, X, y, n_iter=2, refit=False): - from sklearn.linear_model import SGDRegressor import sklearn.preprocessing + from sklearn.linear_model import SGDRegressor # Need to fit at least two iterations, otherwise early stopping will not # work because we cannot determine whether the algorithm actually @@ -62,32 +77,31 @@ def iterative_fit(self, X, y, n_iter=2, refit=False): self.fully_fit_ = False self.alpha = float(self.alpha) - self.l1_ratio = float( - self.l1_ratio) if self.l1_ratio is not None else 0.15 - self.epsilon = float( - self.epsilon) if self.epsilon is not None else 0.1 + self.l1_ratio = float(self.l1_ratio) if self.l1_ratio is not None else 0.15 + self.epsilon = float(self.epsilon) if self.epsilon is not None else 0.1 self.eta0 = float(self.eta0) - self.power_t = float( - self.power_t) if self.power_t is not None else 0.25 + self.power_t = float(self.power_t) if self.power_t is not None else 0.25 self.average = check_for_bool(self.average) self.fit_intercept = check_for_bool(self.fit_intercept) self.tol = float(self.tol) - self.estimator = SGDRegressor(loss=self.loss, - penalty=self.penalty, - alpha=self.alpha, - fit_intercept=self.fit_intercept, - max_iter=n_iter, - tol=self.tol, - learning_rate=self.learning_rate, - l1_ratio=self.l1_ratio, - epsilon=self.epsilon, - eta0=self.eta0, - power_t=self.power_t, - shuffle=True, - average=self.average, - random_state=self.random_state, - warm_start=True) + self.estimator = SGDRegressor( + loss=self.loss, + penalty=self.penalty, + alpha=self.alpha, + fit_intercept=self.fit_intercept, + max_iter=n_iter, + tol=self.tol, + learning_rate=self.learning_rate, + l1_ratio=self.l1_ratio, + epsilon=self.epsilon, + eta0=self.eta0, + power_t=self.power_t, + shuffle=True, + average=self.average, + random_state=self.random_state, + warm_start=True, + ) self.scaler = sklearn.preprocessing.StandardScaler(copy=True) @@ -119,7 +133,8 @@ def iterative_fit(self, X, y, n_iter=2, refit=False): self.estimator._validate_params() self.estimator._partial_fit( - X, y_scaled, + X, + y_scaled, alpha=self.estimator.alpha, C=1.0, loss=self.estimator.loss, @@ -127,11 +142,14 @@ def iterative_fit(self, X, y, n_iter=2, refit=False): max_iter=n_iter, sample_weight=None, coef_init=None, - intercept_init=None + intercept_init=None, ) self.n_iter_ += self.estimator.n_iter_ - if self.estimator.max_iter >= self.max_iter or self.estimator.max_iter > self.n_iter_: + if ( + self.estimator.max_iter >= self.max_iter + or self.estimator.max_iter > self.n_iter_ + ): self.fully_fit_ = True return self @@ -139,7 +157,7 @@ def iterative_fit(self, X, y, n_iter=2, refit=False): def configuration_fully_fitted(self): if self.estimator is None: return False - elif not hasattr(self, 'fully_fit_'): + elif not hasattr(self, "fully_fit_"): return False else: return self.fully_fit_ @@ -152,18 +170,19 @@ def predict(self, X): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'SGD Regressor', - 'name': 'Stochastic Gradient Descent Regressor', - 'handles_regression': True, - 'handles_classification': False, - 'handles_multiclass': False, - 'handles_multilabel': False, - 'handles_multioutput': False, - 'is_deterministic': True, - 'handles_sparse': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,), - } + return { + "shortname": "SGD Regressor", + "name": "Stochastic Gradient Descent Regressor", + "handles_regression": True, + "handles_classification": False, + "handles_multiclass": False, + "handles_multilabel": False, + "handles_multioutput": False, + "is_deterministic": True, + "handles_sparse": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): @@ -171,34 +190,58 @@ def get_hyperparameter_search_space(dataset_properties=None): loss = CategoricalHyperparameter( "loss", - ["squared_loss", "huber", "epsilon_insensitive", "squared_epsilon_insensitive"], + [ + "squared_loss", + "huber", + "epsilon_insensitive", + "squared_epsilon_insensitive", + ], default_value="squared_loss", - ) + ) penalty = CategoricalHyperparameter( - "penalty", ["l1", "l2", "elasticnet"], default_value="l2") + "penalty", ["l1", "l2", "elasticnet"], default_value="l2" + ) alpha = UniformFloatHyperparameter( - "alpha", 1e-7, 1e-1, log=True, default_value=0.0001) + "alpha", 1e-7, 1e-1, log=True, default_value=0.0001 + ) l1_ratio = UniformFloatHyperparameter( - "l1_ratio", 1e-9, 1., log=True, default_value=0.15) - fit_intercept = UnParametrizedHyperparameter( - "fit_intercept", "True") + "l1_ratio", 1e-9, 1.0, log=True, default_value=0.15 + ) + fit_intercept = UnParametrizedHyperparameter("fit_intercept", "True") tol = UniformFloatHyperparameter( - "tol", 1e-5, 1e-1, default_value=1e-4, log=True) + "tol", 1e-5, 1e-1, default_value=1e-4, log=True + ) epsilon = UniformFloatHyperparameter( - "epsilon", 1e-5, 1e-1, default_value=0.1, log=True) + "epsilon", 1e-5, 1e-1, default_value=0.1, log=True + ) learning_rate = CategoricalHyperparameter( - "learning_rate", ["optimal", "invscaling", "constant"], - default_value="invscaling") + "learning_rate", + ["optimal", "invscaling", "constant"], + default_value="invscaling", + ) eta0 = UniformFloatHyperparameter( - "eta0", 1e-7, 1e-1, default_value=0.01, log=True) - power_t = UniformFloatHyperparameter( - "power_t", 1e-5, 1, default_value=0.25) + "eta0", 1e-7, 1e-1, default_value=0.01, log=True + ) + power_t = UniformFloatHyperparameter("power_t", 1e-5, 1, default_value=0.25) average = CategoricalHyperparameter( - "average", ["False", "True"], default_value="False") - - cs.add_hyperparameters([loss, penalty, alpha, l1_ratio, fit_intercept, - tol, epsilon, learning_rate, eta0, - power_t, average]) + "average", ["False", "True"], default_value="False" + ) + + cs.add_hyperparameters( + [ + loss, + penalty, + alpha, + l1_ratio, + fit_intercept, + tol, + epsilon, + learning_rate, + eta0, + power_t, + average, + ] + ) # TODO add passive/aggressive here, although not properly documented? elasticnet = EqualsCondition(l1_ratio, penalty, "elasticnet") @@ -206,17 +249,16 @@ def get_hyperparameter_search_space(dataset_properties=None): epsilon, loss, ["huber", "epsilon_insensitive", "squared_epsilon_insensitive"], - ) + ) # eta0 is only relevant if learning_rate!='optimal' according to code # https://github.com/scikit-learn/scikit-learn/blob/0.19.X/sklearn/ # linear_model/sgd_fast.pyx#L603 - eta0_in_inv_con = InCondition(eta0, learning_rate, ["invscaling", - "constant"]) - power_t_condition = EqualsCondition(power_t, learning_rate, - "invscaling") + eta0_in_inv_con = InCondition(eta0, learning_rate, ["invscaling", "constant"]) + power_t_condition = EqualsCondition(power_t, learning_rate, "invscaling") - cs.add_conditions([elasticnet, epsilon_condition, power_t_condition, - eta0_in_inv_con]) + cs.add_conditions( + [elasticnet, epsilon_condition, power_t_condition, eta0_in_inv_con] + ) return cs diff --git a/autosklearn/pipeline/constants.py b/autosklearn/pipeline/constants.py index 924baa185a..9dea960847 100644 --- a/autosklearn/pipeline/constants.py +++ b/autosklearn/pipeline/constants.py @@ -7,24 +7,29 @@ MULTIOUTPUT_REGRESSION = 5 REGRESSION_TASKS = [REGRESSION, MULTIOUTPUT_REGRESSION] -CLASSIFICATION_TASKS = [BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, - MULTILABEL_CLASSIFICATION] +CLASSIFICATION_TASKS = [ + BINARY_CLASSIFICATION, + MULTICLASS_CLASSIFICATION, + MULTILABEL_CLASSIFICATION, +] TASK_TYPES = REGRESSION_TASKS + CLASSIFICATION_TASKS -TASK_TYPES_TO_STRING = \ - {BINARY_CLASSIFICATION: "binary.classification", - MULTICLASS_CLASSIFICATION: "multiclass.classification", - MULTILABEL_CLASSIFICATION: "multilabel.classification", - REGRESSION: "regression", - MULTIOUTPUT_REGRESSION: "multioutput.regression"} +TASK_TYPES_TO_STRING = { + BINARY_CLASSIFICATION: "binary.classification", + MULTICLASS_CLASSIFICATION: "multiclass.classification", + MULTILABEL_CLASSIFICATION: "multilabel.classification", + REGRESSION: "regression", + MULTIOUTPUT_REGRESSION: "multioutput.regression", +} -STRING_TO_TASK_TYPES = \ - {"binary.classification": BINARY_CLASSIFICATION, - "multiclass.classification": MULTICLASS_CLASSIFICATION, - "multilabel.classification": MULTILABEL_CLASSIFICATION, - "regression": REGRESSION, - "multioutput.regression": MULTIOUTPUT_REGRESSION} +STRING_TO_TASK_TYPES = { + "binary.classification": BINARY_CLASSIFICATION, + "multiclass.classification": MULTICLASS_CLASSIFICATION, + "multilabel.classification": MULTILABEL_CLASSIFICATION, + "regression": REGRESSION, + "multioutput.regression": MULTIOUTPUT_REGRESSION, +} DENSE = 6 SPARSE = 7 @@ -34,10 +39,11 @@ SIGNED_DATA = 10 UNSIGNED_DATA = 11 -DATASET_PROPERTIES_TO_STRING = \ - {DENSE: 'dense', - SPARSE: 'sparse', - PREDICTIONS: 'predictions', - INPUT: 'input', - SIGNED_DATA: 'signed data', - UNSIGNED_DATA: 'unsigned data'} +DATASET_PROPERTIES_TO_STRING = { + DENSE: "dense", + SPARSE: "sparse", + PREDICTIONS: "predictions", + INPUT: "input", + SIGNED_DATA: "signed data", + UNSIGNED_DATA: "unsigned data", +} diff --git a/autosklearn/pipeline/create_searchspace_util.py b/autosklearn/pipeline/create_searchspace_util.py index 8c2ac33f0f..dff69acc6e 100644 --- a/autosklearn/pipeline/create_searchspace_util.py +++ b/autosklearn/pipeline/create_searchspace_util.py @@ -1,18 +1,21 @@ import itertools import numpy as np +from ConfigSpace.forbidden import ForbiddenAndConjunction, ForbiddenEqualsClause -from ConfigSpace.forbidden import ForbiddenAndConjunction -from ConfigSpace.forbidden import ForbiddenEqualsClause +from autosklearn.pipeline.constants import ( + DENSE, + INPUT, + PREDICTIONS, + SIGNED_DATA, + SPARSE, + UNSIGNED_DATA, +) -from autosklearn.pipeline.constants import \ - SIGNED_DATA, UNSIGNED_DATA, PREDICTIONS, INPUT, DENSE, SPARSE - -def get_match_array(pipeline, dataset_properties, - include=None, exclude=None): - sparse = dataset_properties.get('sparse') - signed = dataset_properties.get('signed') +def get_match_array(pipeline, dataset_properties, include=None, exclude=None): + sparse = dataset_properties.get("sparse") + signed = dataset_properties.get("signed") # Duck typing, not sure if it's good... node_i_is_choice = [] @@ -24,18 +27,24 @@ def get_match_array(pipeline, dataset_properties, is_choice = hasattr(node, "get_available_components") node_i_is_choice.append(is_choice) - node_include = include.get( - node_name) if include is not None else None - node_exclude = exclude.get( - node_name) if exclude is not None else None + node_include = include.get(node_name) if include is not None else None + node_exclude = exclude.get(node_name) if exclude is not None else None if is_choice: - node_i_choices_names.append(list(node.get_available_components( - dataset_properties, include=node_include, - exclude=node_exclude).keys())) - node_i_choices.append(list(node.get_available_components( - dataset_properties, include=node_include, - exclude=node_exclude).values())) + node_i_choices_names.append( + list( + node.get_available_components( + dataset_properties, include=node_include, exclude=node_exclude + ).keys() + ) + ) + node_i_choices.append( + list( + node.get_available_components( + dataset_properties, include=node_include, exclude=node_exclude + ).values() + ) + ) else: node_i_choices.append([node]) @@ -47,20 +56,24 @@ def get_match_array(pipeline, dataset_properties, pipeline_idxs = [range(dim) for dim in matches_dimensions] for pipeline_instantiation_idxs in itertools.product(*pipeline_idxs): - pipeline_instantiation = [node_i_choices[i][idx] for i, idx in - enumerate(pipeline_instantiation_idxs)] + pipeline_instantiation = [ + node_i_choices[i][idx] for i, idx in enumerate(pipeline_instantiation_idxs) + ] data_is_sparse = sparse dataset_is_signed = signed for node in pipeline_instantiation: - node_input = node.get_properties()['input'] - node_output = node.get_properties()['output'] + node_input = node.get_properties()["input"] + node_output = node.get_properties()["output"] # First check if these two instantiations of this node can work # together. Do this in multiple if statements to maintain # readability - if (data_is_sparse and SPARSE not in node_input) or \ - not data_is_sparse and DENSE not in node_input: + if ( + (data_is_sparse and SPARSE not in node_input) + or not data_is_sparse + and DENSE not in node_input + ): matches[pipeline_instantiation_idxs] = 0 break # No need to check if the node can handle SIGNED_DATA; this is @@ -69,10 +82,16 @@ def get_match_array(pipeline, dataset_properties, matches[pipeline_instantiation_idxs] = 0 break - if (INPUT in node_output and DENSE not in node_output and SPARSE not in node_output) \ - or PREDICTIONS in node_output \ - or (not data_is_sparse and DENSE in node_input and DENSE in node_output) \ - or (data_is_sparse and SPARSE in node_input and SPARSE in node_output): + if ( + ( + INPUT in node_output + and DENSE not in node_output + and SPARSE not in node_output + ) + or PREDICTIONS in node_output + or (not data_is_sparse and DENSE in node_input and DENSE in node_output) + or (data_is_sparse and SPARSE in node_input and SPARSE in node_output) + ): # Don't change the data_is_sparse flag pass elif data_is_sparse and DENSE in node_output: @@ -87,8 +106,11 @@ def get_match_array(pipeline, dataset_properties, if PREDICTIONS in node_output: pass - elif (INPUT in node_output and SIGNED_DATA not in node_output and - UNSIGNED_DATA not in node_output): + elif ( + INPUT in node_output + and SIGNED_DATA not in node_output + and UNSIGNED_DATA not in node_output + ): pass elif SIGNED_DATA in node_output: dataset_is_signed = True @@ -103,27 +125,32 @@ def get_match_array(pipeline, dataset_properties, return matches -def find_active_choices(matches, node, node_idx, dataset_properties, include=None, exclude=None): +def find_active_choices( + matches, node, node_idx, dataset_properties, include=None, exclude=None +): if not hasattr(node, "get_available_components"): raise ValueError() - available_components = node.get_available_components(dataset_properties, - include=include, - exclude=exclude) - assert matches.shape[node_idx] == len(available_components), \ - (matches.shape[node_idx], len(available_components)) + available_components = node.get_available_components( + dataset_properties, include=include, exclude=exclude + ) + assert matches.shape[node_idx] == len(available_components), ( + matches.shape[node_idx], + len(available_components), + ) choices = [] for c_idx, component in enumerate(available_components): - slices = tuple(slice(None) if idx != node_idx else slice(c_idx, c_idx+1) - for idx in range(len(matches.shape))) + slices = tuple( + slice(None) if idx != node_idx else slice(c_idx, c_idx + 1) + for idx in range(len(matches.shape)) + ) if np.sum(matches[slices]) > 0: choices.append(component) return choices -def add_forbidden(conf_space, pipeline, matches, dataset_properties, - include, exclude): +def add_forbidden(conf_space, pipeline, matches, dataset_properties, include, exclude): # Not sure if this works for 3D node_i_is_choice = [] node_i_choices_names = [] @@ -134,18 +161,20 @@ def add_forbidden(conf_space, pipeline, matches, dataset_properties, is_choice = hasattr(node, "get_available_components") node_i_is_choice.append(is_choice) - node_include = include.get( - node_name) if include is not None else None - node_exclude = exclude.get( - node_name) if exclude is not None else None + node_include = include.get(node_name) if include is not None else None + node_exclude = exclude.get(node_name) if exclude is not None else None if is_choice: - node_i_choices_names.append(node.get_available_components( - dataset_properties, include=node_include, - exclude=node_exclude).keys()) - node_i_choices.append(node.get_available_components( - dataset_properties, include=node_include, - exclude=node_exclude).values()) + node_i_choices_names.append( + node.get_available_components( + dataset_properties, include=node_include, exclude=node_exclude + ).keys() + ) + node_i_choices.append( + node.get_available_components( + dataset_properties, include=node_include, exclude=node_exclude + ).values() + ) else: node_i_choices_names.append([node_name]) @@ -185,8 +214,8 @@ def add_forbidden(conf_space, pipeline, matches, dataset_properties, for idx in indices: node = all_nodes[idx] available_components = node.get_available_components( - dataset_properties, - include=node_i_choices_names[idx]) + dataset_properties, include=node_i_choices_names[idx] + ) assert len(available_components) > 0, len(available_components) skip_array_shape.append(len(available_components)) num_node_choices.append(range(len(available_components))) @@ -198,9 +227,11 @@ def add_forbidden(conf_space, pipeline, matches, dataset_properties, for node_idx, choice_idx in enumerate(product): node_idx += start_idx slices_ = tuple( - slice(None) if idx != node_idx else - slice(choice_idx, choice_idx + 1) for idx in - range(len(matches.shape))) + slice(None) + if idx != node_idx + else slice(choice_idx, choice_idx + 1) + for idx in range(len(matches.shape)) + ) if np.sum(matches[slices_]) == 0: skip_array[product] = 1 @@ -210,10 +241,13 @@ def add_forbidden(conf_space, pipeline, matches, dataset_properties, continue slices = tuple( - slice(None) if idx not in indices else - slice(product[idx - start_idx], - product[idx - start_idx] + 1) for idx in - range(len(matches.shape))) + slice(None) + if idx not in indices + else slice( + product[idx - start_idx], product[idx - start_idx] + 1 + ) + for idx in range(len(matches.shape)) + ) # This prints the affected nodes # print [node_choice_names[i][product[i]] @@ -221,9 +255,12 @@ def add_forbidden(conf_space, pipeline, matches, dataset_properties, # np.sum(matches[slices]) if np.sum(matches[slices]) == 0: - constraint = tuple([(node_names[i], - node_choice_names[i][product[i]]) - for i in range(len(product))]) + constraint = tuple( + [ + (node_names[i], node_choice_names[i][product[i]]) + for i in range(len(product)) + ] + ) # Check if a more general constraint/forbidden clause # was already added @@ -231,8 +268,12 @@ def add_forbidden(conf_space, pipeline, matches, dataset_properties, for constraint_length in range(2, len(constraint)): constr_starts = len(constraint) - constraint_length + 1 for constraint_start_idx in range(constr_starts): - constraint_end_idx = constraint_start_idx + constraint_length - sub_constraint = constraint[constraint_start_idx:constraint_end_idx] + constraint_end_idx = ( + constraint_start_idx + constraint_length + ) + sub_constraint = constraint[ + constraint_start_idx:constraint_end_idx + ] if sub_constraint in constraints: continue_ = True break @@ -246,9 +287,13 @@ def add_forbidden(conf_space, pipeline, matches, dataset_properties, forbiddens = [] for i in range(len(product)): forbiddens.append( - ForbiddenEqualsClause(conf_space.get_hyperparameter( - node_names[i] + ":__choice__"), - node_choice_names[i][product[i]])) + ForbiddenEqualsClause( + conf_space.get_hyperparameter( + node_names[i] + ":__choice__" + ), + node_choice_names[i][product[i]], + ) + ) forbidden = ForbiddenAndConjunction(*forbiddens) conf_space.add_forbidden_clause(forbidden) diff --git a/autosklearn/pipeline/implementations/CategoryShift.py b/autosklearn/pipeline/implementations/CategoryShift.py index 3eee659d3f..4c504cf666 100644 --- a/autosklearn/pipeline/implementations/CategoryShift.py +++ b/autosklearn/pipeline/implementations/CategoryShift.py @@ -1,12 +1,11 @@ import numpy as np from scipy import sparse -from sklearn.utils import check_array from sklearn.base import BaseEstimator, TransformerMixin +from sklearn.utils import check_array class CategoryShift(BaseEstimator, TransformerMixin): - """ Add 3 to every category. - """ + """Add 3 to every category.""" def __init__(self, random_state=None): self.random_state = random_state @@ -15,14 +14,16 @@ def _convert_and_check_X(self, X): X_data = X.data if sparse.issparse(X) else X # Check if data is numeric and positive - if X_data.dtype.kind not in set('buif') or np.nanmin(X_data) < 0: - raise ValueError('Categories should be non-negative numbers. ' - 'NOTE: floats will be casted to integers.') + if X_data.dtype.kind not in set("buif") or np.nanmin(X_data) < 0: + raise ValueError( + "Categories should be non-negative numbers. " + "NOTE: floats will be casted to integers." + ) # Use check_array to make sure we are using the right kind of sparse array # Notice that we cannot convert the array to integer right now. That would get # rid of the np.nans and we need them later on for the imputation. - X = check_array(X, accept_sparse='csc', force_all_finite=False, copy=True) + X = check_array(X, accept_sparse="csc", force_all_finite=False, copy=True) return X def fit(self, X, y=None): diff --git a/autosklearn/pipeline/implementations/MinorityCoalescer.py b/autosklearn/pipeline/implementations/MinorityCoalescer.py index 989cb4a9c9..e9bed0349e 100644 --- a/autosklearn/pipeline/implementations/MinorityCoalescer.py +++ b/autosklearn/pipeline/implementations/MinorityCoalescer.py @@ -4,7 +4,7 @@ class MinorityCoalescer(BaseEstimator, TransformerMixin): - """ Group together categories which occurence is less than a specified + """Group together categories which occurence is less than a specified minimum fraction. Coalesced categories get index of one. """ @@ -31,7 +31,8 @@ def fit(self, X, y=None): indptr_start = X.indptr[column] indptr_end = X.indptr[column + 1] unique, counts = np.unique( - X.data[indptr_start:indptr_end], return_counts=True) + X.data[indptr_start:indptr_end], return_counts=True + ) colsize = indptr_end - indptr_start else: unique, counts = np.unique(X[:, column], return_counts=True) @@ -61,11 +62,15 @@ def transform(self, X): indptr_start = X.indptr[column] indptr_end = X.indptr[column + 1] X.data[indptr_start:indptr_end][ - X.data[indptr_start:indptr_end] == unique_value] = 1 + X.data[indptr_start:indptr_end] == unique_value + ] = 1 else: unique = np.unique(X[:, column]) - unique_values = [unique_value for unique_value in unique - if unique_value not in self.do_not_coalesce_[column]] + unique_values = [ + unique_value + for unique_value in unique + if unique_value not in self.do_not_coalesce_[column] + ] mask = np.isin(X[:, column], unique_values) X[mask, column] = 1 return X diff --git a/autosklearn/pipeline/implementations/SparseOneHotEncoder.py b/autosklearn/pipeline/implementations/SparseOneHotEncoder.py index beee99261b..18d491596c 100644 --- a/autosklearn/pipeline/implementations/SparseOneHotEncoder.py +++ b/autosklearn/pipeline/implementations/SparseOneHotEncoder.py @@ -43,8 +43,7 @@ def fit(self, X, y=None): def _check_X(self, X): if not sparse.issparse(X): raise TypeError("SparseOneHotEncoder requires X to be sparse") - X = check_array(X, accept_sparse='csc', force_all_finite=False, - dtype=np.int32) + X = check_array(X, accept_sparse="csc", force_all_finite=False, dtype=np.int32) if X.min() < 0: raise ValueError("X needs to contain only non-negative integers.") @@ -63,15 +62,17 @@ def fit_transform(self, X, y=None): row_indices = X.indices column_indices = [] for i in range(len(X.indptr) - 1): - nbr = X.indptr[i+1] - X.indptr[i] + nbr = X.indptr[i + 1] - X.indptr[i] column_indices_ = [indices[i]] * nbr - column_indices_ += X.data[X.indptr[i]:X.indptr[i+1]] + column_indices_ += X.data[X.indptr[i] : X.indptr[i + 1]] column_indices.extend(column_indices_) data = np.ones(X.data.size) - out = sparse.coo_matrix((data, (row_indices, column_indices)), - shape=(n_samples, indices[-1]), - dtype=np.int32).tocsc() + out = sparse.coo_matrix( + (data, (row_indices, column_indices)), + shape=(n_samples, indices[-1]), + dtype=np.int32, + ).tocsc() mask = np.array(out.sum(axis=0)).ravel() != 0 active_features = np.where(mask)[0] @@ -85,9 +86,10 @@ def transform(self, X): n_samples, n_features = X.shape indices = self.feature_indices_ if n_features != indices.shape[0] - 1: - raise ValueError("X has different shape than during fitting." - " Expected %d, got %d." - % (indices.shape[0] - 1, n_features)) + raise ValueError( + "X has different shape than during fitting." + " Expected %d, got %d." % (indices.shape[0] - 1, n_features) + ) n_values_check = X.max(axis=0).toarray().flatten() + 1 @@ -99,7 +101,7 @@ def transform(self, X): for i, n_value_check in enumerate(n_values_check): if (n_value_check - 1) >= self.n_values_[i]: indptr_start = X.indptr[i] - indptr_end = X.indptr[i+1] + indptr_end = X.indptr[i + 1] zeros_mask = X.data[indptr_start:indptr_end] >= self.n_values_[i] X.data[indptr_start:indptr_end][zeros_mask] = 0 @@ -108,13 +110,15 @@ def transform(self, X): for i in range(len(X.indptr) - 1): nbr = X.indptr[i + 1] - X.indptr[i] column_indices_ = [indices[i]] * nbr - column_indices_ += X.data[X.indptr[i]:X.indptr[i + 1]] + column_indices_ += X.data[X.indptr[i] : X.indptr[i + 1]] column_indices.extend(column_indices_) data = np.ones(X.data.size) - out = sparse.coo_matrix((data, (row_indices, column_indices)), - shape=(n_samples, indices[-1]), - dtype=np.int32).tocsc() + out = sparse.coo_matrix( + (data, (row_indices, column_indices)), + shape=(n_samples, indices[-1]), + dtype=np.int32, + ).tocsc() out = out[:, self.active_features_] return out.tocsr() diff --git a/autosklearn/pipeline/implementations/__init__.py b/autosklearn/pipeline/implementations/__init__.py index 8f0ce6cb7c..92bf78f389 100644 --- a/autosklearn/pipeline/implementations/__init__.py +++ b/autosklearn/pipeline/implementations/__init__.py @@ -1 +1 @@ -__author__ = 'feurerm' +__author__ = "feurerm" diff --git a/autosklearn/pipeline/implementations/util.py b/autosklearn/pipeline/implementations/util.py index cacf3a9b5d..4ebc01ad39 100644 --- a/autosklearn/pipeline/implementations/util.py +++ b/autosklearn/pipeline/implementations/util.py @@ -18,7 +18,7 @@ def softmax(df): def convert_multioutput_multiclass_to_multilabel(probas): - """ Converts the model predicted probabilities to useable format. + """Converts the model predicted probabilities to useable format. In some cases, models predicted_proba can output an array of shape (2, n_samples, n_labels) where the 2 stands for the probability of positive @@ -55,8 +55,10 @@ def convert_multioutput_multiclass_to_multilabel(probas): # In case multioutput-multiclass input was used, where we have # a probability for each class elif n_probabilities > 2: - raise ValueError('Multioutput-Multiclass supported by ' - 'scikit-learn, but not by auto-sklearn!') + raise ValueError( + "Multioutput-Multiclass supported by " + "scikit-learn, but not by auto-sklearn!" + ) else: RuntimeError(f"Unkown predict_proba output={probas}") @@ -64,7 +66,7 @@ def convert_multioutput_multiclass_to_multilabel(probas): elif isinstance(probas, np.ndarray): if len(probas.shape) > 2: - raise ValueError('New unsupported sklearn output!') + raise ValueError("New unsupported sklearn output!") else: return probas diff --git a/autosklearn/pipeline/regression.py b/autosklearn/pipeline/regression.py index af2885be4d..638f8ae3cb 100644 --- a/autosklearn/pipeline/regression.py +++ b/autosklearn/pipeline/regression.py @@ -1,21 +1,19 @@ +from typing import Optional, Union + import copy from itertools import product -from typing import Optional, Union import numpy as np +from ConfigSpace.configuration_space import Configuration, ConfigurationSpace +from ConfigSpace.forbidden import ForbiddenAndConjunction, ForbiddenEqualsClause from sklearn.base import RegressorMixin -from ConfigSpace.forbidden import ForbiddenEqualsClause, ForbiddenAndConjunction - -from autosklearn.pipeline.components.data_preprocessing import DataPreprocessorChoice - - -from ConfigSpace.configuration_space import ConfigurationSpace, Configuration -from autosklearn.pipeline.components import regression as \ - regression_components -from autosklearn.pipeline.components import feature_preprocessing as \ - feature_preprocessing_components from autosklearn.pipeline.base import BasePipeline +from autosklearn.pipeline.components import ( + feature_preprocessing as feature_preprocessing_components, +) +from autosklearn.pipeline.components import regression as regression_components +from autosklearn.pipeline.components.data_preprocessing import DataPreprocessorChoice from autosklearn.pipeline.constants import SPARSE @@ -66,6 +64,7 @@ class SimpleRegressionPipeline(RegressorMixin, BasePipeline): -------- """ + def __init__( self, config: Optional[Configuration] = None, @@ -74,30 +73,34 @@ def __init__( include=None, exclude=None, random_state: Optional[Union[int, np.random.RandomState]] = None, - init_params=None + init_params=None, ): self._output_dtype = np.float32 if dataset_properties is None: dataset_properties = dict() - if 'target_type' not in dataset_properties: - dataset_properties['target_type'] = 'regression' + if "target_type" not in dataset_properties: + dataset_properties["target_type"] = "regression" super().__init__( - config=config, steps=steps, + config=config, + steps=steps, dataset_properties=dataset_properties, - include=include, exclude=exclude, random_state=random_state, - init_params=init_params) + include=include, + exclude=exclude, + random_state=random_state, + init_params=init_params, + ) def fit_estimator(self, X, y, **fit_params): self.y_max_ = np.nanmax(y) self.y_min_ = np.nanmin(y) - return super(SimpleRegressionPipeline, self).fit_estimator( - X, y, **fit_params) + return super(SimpleRegressionPipeline, self).fit_estimator(X, y, **fit_params) def iterative_fit(self, X, y, n_iter=1, **fit_params): self.y_max_ = np.nanmax(y) self.y_min_ = np.nanmin(y) return super(SimpleRegressionPipeline, self).iterative_fit( - X, y, n_iter=n_iter, **fit_params) + X, y, n_iter=n_iter, **fit_params + ) def predict(self, X, batch_size=None): y = super().predict(X, batch_size=batch_size) @@ -108,8 +111,9 @@ def predict(self, X, batch_size=None): y[y < (0.5 * self.y_min_)] = 0.5 * self.y_min_ return y - def _get_hyperparameter_search_space(self, include=None, exclude=None, - dataset_properties=None): + def _get_hyperparameter_search_space( + self, include=None, exclude=None, dataset_properties=None + ): """Return the configuration space for the CASH problem. Parameters @@ -134,43 +138,57 @@ def _get_hyperparameter_search_space(self, include=None, exclude=None, if dataset_properties is None or not isinstance(dataset_properties, dict): dataset_properties = dict() - if 'target_type' not in dataset_properties: - dataset_properties['target_type'] = 'regression' - if dataset_properties['target_type'] != 'regression': - dataset_properties['target_type'] = 'regression' + if "target_type" not in dataset_properties: + dataset_properties["target_type"] = "regression" + if dataset_properties["target_type"] != "regression": + dataset_properties["target_type"] = "regression" - if 'sparse' not in dataset_properties: + if "sparse" not in dataset_properties: # This dataset is probably dense - dataset_properties['sparse'] = False + dataset_properties["sparse"] = False cs = self._get_base_search_space( - cs=cs, dataset_properties=dataset_properties, - exclude=exclude, include=include, pipeline=self.steps) + cs=cs, + dataset_properties=dataset_properties, + exclude=exclude, + include=include, + pipeline=self.steps, + ) - regressors = cs.get_hyperparameter('regressor:__choice__').choices - preprocessors = cs.get_hyperparameter('feature_preprocessor:__choice__').choices + regressors = cs.get_hyperparameter("regressor:__choice__").choices + preprocessors = cs.get_hyperparameter("feature_preprocessor:__choice__").choices available_regressors = self._final_estimator.get_available_components( - dataset_properties) + dataset_properties + ) - possible_default_regressor = copy.copy(list( - available_regressors.keys())) - default = cs.get_hyperparameter('regressor:__choice__').default_value - del possible_default_regressor[ - possible_default_regressor.index(default)] + possible_default_regressor = copy.copy(list(available_regressors.keys())) + default = cs.get_hyperparameter("regressor:__choice__").default_value + del possible_default_regressor[possible_default_regressor.index(default)] # A regressor which can handle sparse data after the densifier is # forbidden for memory issues for key in regressors: - if SPARSE in available_regressors[key].get_properties(dataset_properties=None)['input']: - if 'densifier' in preprocessors: + if ( + SPARSE + in available_regressors[key].get_properties(dataset_properties=None)[ + "input" + ] + ): + if "densifier" in preprocessors: while True: try: forb_reg = ForbiddenEqualsClause( - cs.get_hyperparameter('regressor:__choice__'), key) - forb_fpp = ForbiddenEqualsClause(cs.get_hyperparameter( - 'feature_preprocessor:__choice__'), 'densifier') + cs.get_hyperparameter("regressor:__choice__"), key + ) + forb_fpp = ForbiddenEqualsClause( + cs.get_hyperparameter( + "feature_preprocessor:__choice__" + ), + "densifier", + ) cs.add_forbidden_clause( - ForbiddenAndConjunction(forb_reg, forb_fpp)) + ForbiddenAndConjunction(forb_reg, forb_fpp) + ) # Success break except ValueError: @@ -179,16 +197,25 @@ def _get_hyperparameter_search_space(self, include=None, exclude=None, default = possible_default_regressor.pop() except IndexError: raise ValueError( - "Cannot find a legal default configuration.") + "Cannot find a legal default configuration." + ) cs.get_hyperparameter( - 'regressor:__choice__').default_value = default + "regressor:__choice__" + ).default_value = default # which would take too long # Combinations of tree-based models with feature learning: regressors_ = [ - "adaboost", "ard_regression", "decision_tree", - "extra_trees", "gaussian_process", "gradient_boosting", - "k_nearest_neighbors", "libsvm_svr", "mlp", "random_forest" + "adaboost", + "ard_regression", + "decision_tree", + "extra_trees", + "gaussian_process", + "gradient_boosting", + "k_nearest_neighbors", + "libsvm_svr", + "mlp", + "random_forest", ] feature_learning_ = ["kitchen_sinks", "kernel_pca", "nystroem_sampler"] @@ -199,11 +226,19 @@ def _get_hyperparameter_search_space(self, include=None, exclude=None, continue while True: try: - cs.add_forbidden_clause(ForbiddenAndConjunction( - ForbiddenEqualsClause(cs.get_hyperparameter( - "regressor:__choice__"), r), - ForbiddenEqualsClause(cs.get_hyperparameter( - "feature_preprocessor:__choice__"), f))) + cs.add_forbidden_clause( + ForbiddenAndConjunction( + ForbiddenEqualsClause( + cs.get_hyperparameter("regressor:__choice__"), r + ), + ForbiddenEqualsClause( + cs.get_hyperparameter( + "feature_preprocessor:__choice__" + ), + f, + ), + ) + ) break except KeyError: break @@ -212,10 +247,10 @@ def _get_hyperparameter_search_space(self, include=None, exclude=None, try: default = possible_default_regressor.pop() except IndexError: - raise ValueError( - "Cannot find a legal default configuration.") + raise ValueError("Cannot find a legal default configuration.") cs.get_hyperparameter( - 'regressor:__choice__').default_value = default + "regressor:__choice__" + ).default_value = default self.configuration_space = cs self.dataset_properties = dataset_properties @@ -227,33 +262,34 @@ def _get_estimator_components(self): def _get_pipeline_steps(self, dataset_properties, init_params=None): steps = [] - default_dataset_properties = {'target_type': 'regression'} + default_dataset_properties = {"target_type": "regression"} if dataset_properties is not None and isinstance(dataset_properties, dict): default_dataset_properties.update(dataset_properties) - steps.extend([ - [ - 'data_preprocessor', - DataPreprocessorChoice( - dataset_properties=default_dataset_properties, - random_state=self.random_state - ) - ], - [ - 'feature_preprocessor', - feature_preprocessing_components.FeaturePreprocessorChoice( - dataset_properties=default_dataset_properties, - random_state=self.random_state - ) - ], + steps.extend( [ - 'regressor', - regression_components.RegressorChoice( - default_dataset_properties, - random_state=self.random_state - ) + [ + "data_preprocessor", + DataPreprocessorChoice( + dataset_properties=default_dataset_properties, + random_state=self.random_state, + ), + ], + [ + "feature_preprocessor", + feature_preprocessing_components.FeaturePreprocessorChoice( + dataset_properties=default_dataset_properties, + random_state=self.random_state, + ), + ], + [ + "regressor", + regression_components.RegressorChoice( + default_dataset_properties, random_state=self.random_state + ), + ], ] - ]) + ) return steps diff --git a/autosklearn/pipeline/util.py b/autosklearn/pipeline/util.py index 1a70deb30b..228c31357d 100644 --- a/autosklearn/pipeline/util.py +++ b/autosklearn/pipeline/util.py @@ -18,8 +18,7 @@ def find_sklearn_classes(class_): for root, dirs, files in os.walk(sklearn_path): all_subdirectories.append(root) - for module_loader, module_name, ispkg in \ - pkgutil.iter_modules(all_subdirectories): + for module_loader, module_name, ispkg in pkgutil.iter_modules(all_subdirectories): # Work around some issues... if module_name in ["hmm", "mixture"]: @@ -33,8 +32,7 @@ def find_sklearn_classes(class_): pkg = importlib.import_module(full_module_name) for member_name, obj in inspect.getmembers(pkg): - if inspect.isclass(obj) and \ - issubclass(obj, class_): + if inspect.isclass(obj) and issubclass(obj, class_): classifier = obj # print member_name, obj classifiers.add(classifier) @@ -44,15 +42,20 @@ def find_sklearn_classes(class_): print(classifier) -def get_dataset(dataset='iris', make_sparse=False, add_NaNs=False, - train_size_maximum=150, make_multilabel=False, - make_binary=False): +def get_dataset( + dataset="iris", + make_sparse=False, + add_NaNs=False, + train_size_maximum=150, + make_multilabel=False, + make_binary=False, +): iris = getattr(sklearn.datasets, "load_%s" % dataset)() X = iris.data.astype(np.float32) Y = iris.target rs = np.random.RandomState(42) indices = np.arange(X.shape[0]) - train_size = min(int(len(indices) / 3. * 2.), train_size_maximum) + train_size = min(int(len(indices) / 3.0 * 2.0), train_size_maximum) rs.shuffle(indices) X = X[indices] Y = Y[indices] @@ -76,8 +79,10 @@ def get_dataset(dataset='iris', make_sparse=False, add_NaNs=False, X_test.eliminate_zeros() if make_binary and make_multilabel: - raise ValueError('Can convert dataset only to one of the two ' - 'options binary or multilabel!') + raise ValueError( + "Can convert dataset only to one of the two " + "options binary or multilabel!" + ) if make_binary: Y_train[Y_train > 1] = 1 @@ -97,23 +102,31 @@ def get_dataset(dataset='iris', make_sparse=False, add_NaNs=False, return X_train, Y_train, X_test, Y_test -def _test_classifier(classifier, dataset='iris', sparse=False, - train_size_maximum=150, make_multilabel=False, - make_binary=False): - X_train, Y_train, X_test, Y_test = get_dataset(dataset=dataset, - make_sparse=sparse, - train_size_maximum=train_size_maximum, - make_multilabel=make_multilabel, - make_binary=make_binary) +def _test_classifier( + classifier, + dataset="iris", + sparse=False, + train_size_maximum=150, + make_multilabel=False, + make_binary=False, +): + X_train, Y_train, X_test, Y_test = get_dataset( + dataset=dataset, + make_sparse=sparse, + train_size_maximum=train_size_maximum, + make_multilabel=make_multilabel, + make_binary=make_binary, + ) configuration_space = classifier.get_hyperparameter_search_space( - dataset_properties={'sparse': sparse} + dataset_properties={"sparse": sparse} ) default_config = configuration_space.get_default_configuration() classifier = classifier(random_state=0, **default_config) - if hasattr(classifier, 'iterative_fit'): + if hasattr(classifier, "iterative_fit"): + class counter(object): def __init__(self, func): self.n_calls = 0 @@ -122,11 +135,12 @@ def __init__(self, func): def __call__(self, *args, **kwargs): self.n_calls += 1 return self.func(*args, **kwargs) + classifier.iterative_fit = counter(classifier.iterative_fit) predictor = classifier.fit(X_train, Y_train) - if hasattr(classifier, 'iterative_fit'): + if hasattr(classifier, "iterative_fit"): n_calls = classifier.iterative_fit.n_calls else: n_calls = None @@ -135,11 +149,10 @@ def __call__(self, *args, **kwargs): return predictions, Y_test, n_calls -def _test_classifier_iterative_fit(classifier, dataset='iris', sparse=False): - X_train, Y_train, X_test, Y_test = get_dataset(dataset=dataset, - make_sparse=sparse) +def _test_classifier_iterative_fit(classifier, dataset="iris", sparse=False): + X_train, Y_train, X_test, Y_test = get_dataset(dataset=dataset, make_sparse=sparse) configuration_space = classifier.get_hyperparameter_search_space( - dataset_properties={'sparse': sparse} + dataset_properties={"sparse": sparse} ) default_config = configuration_space.get_default_configuration() @@ -148,7 +161,7 @@ def _test_classifier_iterative_fit(classifier, dataset='iris', sparse=False): iteration = 2 while not classifier.configuration_fully_fitted(): - n_iter = int(2 ** iteration / 2) + n_iter = int(2**iteration / 2) classifier.iterative_fit(X_train, Y_train, n_iter=n_iter) iteration += 1 @@ -156,15 +169,21 @@ def _test_classifier_iterative_fit(classifier, dataset='iris', sparse=False): return predictions, Y_test, classifier -def _test_classifier_predict_proba(classifier, dataset='iris', sparse=False, - train_size_maximum=150, - make_multilabel=False, - make_binary=False): - X_train, Y_train, X_test, Y_test = get_dataset(dataset=dataset, - make_sparse=sparse, - train_size_maximum=train_size_maximum, - make_multilabel=make_multilabel, - make_binary=make_binary) +def _test_classifier_predict_proba( + classifier, + dataset="iris", + sparse=False, + train_size_maximum=150, + make_multilabel=False, + make_binary=False, +): + X_train, Y_train, X_test, Y_test = get_dataset( + dataset=dataset, + make_sparse=sparse, + train_size_maximum=train_size_maximum, + make_multilabel=make_multilabel, + make_binary=make_binary, + ) configuration_space = classifier.get_hyperparameter_search_space() default_config = configuration_space.get_default_configuration() @@ -175,11 +194,12 @@ def _test_classifier_predict_proba(classifier, dataset='iris', sparse=False, return predictions, Y_test -def _test_preprocessing(Preprocessor, dataset='iris', make_sparse=False, - train_size_maximum=150): - X_train, Y_train, X_test, Y_test = get_dataset(dataset=dataset, - make_sparse=make_sparse, - train_size_maximum=train_size_maximum) +def _test_preprocessing( + Preprocessor, dataset="iris", make_sparse=False, train_size_maximum=150 +): + X_train, Y_train, X_test, Y_test = get_dataset( + dataset=dataset, make_sparse=make_sparse, train_size_maximum=train_size_maximum + ) original_X_train = X_train.copy() configuration_space = Preprocessor.get_hyperparameter_search_space() default_config = configuration_space.get_default_configuration() @@ -191,8 +211,9 @@ def _test_preprocessing(Preprocessor, dataset='iris', make_sparse=False, class PreprocessingTestCase(unittest.TestCase): - def _test_preprocessing_dtype(self, Preprocessor, add_NaNs=False, - test_sparse=True, dataset='iris'): + def _test_preprocessing_dtype( + self, Preprocessor, add_NaNs=False, test_sparse=True, dataset="iris" + ): # Dense # np.float32 X_train, Y_train, X_test, Y_test = get_dataset(dataset, add_NaNs=add_NaNs) @@ -220,8 +241,9 @@ def _test_preprocessing_dtype(self, Preprocessor, add_NaNs=False, if test_sparse is True: # Sparse # np.float32 - X_train, Y_train, X_test, Y_test = get_dataset(dataset, make_sparse=True, - add_NaNs=add_NaNs) + X_train, Y_train, X_test, Y_test = get_dataset( + dataset, make_sparse=True, add_NaNs=add_NaNs + ) self.assertEqual(X_train.dtype, np.float32) configuration_space = Preprocessor.get_hyperparameter_search_space() default_config = configuration_space.get_default_configuration() @@ -232,9 +254,9 @@ def _test_preprocessing_dtype(self, Preprocessor, add_NaNs=False, # self.assertEqual(Xt.dtype, np.float32) # np.float64 - X_train, Y_train, X_test, Y_test = get_dataset(dataset, - make_sparse=True, - add_NaNs=add_NaNs) + X_train, Y_train, X_test, Y_test = get_dataset( + dataset, make_sparse=True, add_NaNs=add_NaNs + ) X_train = X_train.astype(np.float64) configuration_space = Preprocessor.get_hyperparameter_search_space() default_config = configuration_space.get_default_configuration() @@ -245,9 +267,8 @@ def _test_preprocessing_dtype(self, Preprocessor, add_NaNs=False, # self.assertEqual(Xt.dtype, np.float64) -def _test_regressor(Regressor, dataset='diabetes', sparse=False): - X_train, Y_train, X_test, Y_test = get_dataset(dataset=dataset, - make_sparse=sparse) +def _test_regressor(Regressor, dataset="diabetes", sparse=False): + X_train, Y_train, X_test, Y_test = get_dataset(dataset=dataset, make_sparse=sparse) configuration_space = Regressor.get_hyperparameter_search_space() default_config = configuration_space.get_default_configuration() @@ -258,7 +279,8 @@ def _test_regressor(Regressor, dataset='diabetes', sparse=False): X_test_hash = hash(str(X_test)) Y_train_hash = hash(str(Y_train)) - if hasattr(regressor, 'iterative_fit'): + if hasattr(regressor, "iterative_fit"): + class counter(object): def __init__(self, func): self.n_calls = 0 @@ -272,24 +294,25 @@ def __call__(self, *args, **kwargs): predictor = regressor.fit(X_train, Y_train) - if hasattr(regressor, 'iterative_fit'): + if hasattr(regressor, "iterative_fit"): n_calls = regressor.iterative_fit.n_calls else: n_calls = None predictions = predictor.predict(X_test) - if X_train_hash != hash(str(X_train)) or \ - X_test_hash != hash(str(X_test)) or \ - Y_train_hash != hash(str(Y_train)): + if ( + X_train_hash != hash(str(X_train)) + or X_test_hash != hash(str(X_test)) + or Y_train_hash != hash(str(Y_train)) + ): raise ValueError("Model modified data") return predictions, Y_test, n_calls -def _test_regressor_iterative_fit(Regressor, dataset='diabetes', sparse=False): - X_train, Y_train, X_test, Y_test = get_dataset(dataset=dataset, - make_sparse=sparse) +def _test_regressor_iterative_fit(Regressor, dataset="diabetes", sparse=False): + X_train, Y_train, X_test, Y_test = get_dataset(dataset=dataset, make_sparse=sparse) configuration_space = Regressor.get_hyperparameter_search_space( - dataset_properties={'sparse': sparse} + dataset_properties={"sparse": sparse} ) default_config = configuration_space.get_default_configuration() regressor = Regressor(random_state=0, **default_config) @@ -297,7 +320,7 @@ def _test_regressor_iterative_fit(Regressor, dataset='diabetes', sparse=False): regressor.iterative_fit(X_train, Y_train, n_iter=2, refit=True) iteration = 2 while not regressor.configuration_fully_fitted(): - n_iter = int(2 ** iteration / 2) + n_iter = int(2**iteration / 2) regressor.iterative_fit(X_train, Y_train, n_iter=n_iter) iteration += 1 diff --git a/autosklearn/smbo.py b/autosklearn/smbo.py index 3cb823f2ff..b179efa8d0 100644 --- a/autosklearn/smbo.py +++ b/autosklearn/smbo.py @@ -1,4 +1,6 @@ +import typing from typing import Dict, List, Optional + import copy import json import logging @@ -6,151 +8,191 @@ import os import time import traceback -import typing import warnings import dask.distributed import pynisher - +from smac.callbacks import IncorporateRunResultCallback from smac.facade.smac_ac_facade import SMAC4AC -from smac.intensification.simple_intensifier import SimpleIntensifier from smac.intensification.intensification import Intensifier +from smac.intensification.simple_intensifier import SimpleIntensifier from smac.runhistory.runhistory2epm import RunHistory2EPM4LogCost from smac.scenario.scenario import Scenario -from smac.tae.serial_runner import SerialRunner from smac.tae.dask_runner import DaskParallelRunner -from smac.callbacks import IncorporateRunResultCallback - +from smac.tae.serial_runner import SerialRunner import autosklearn.metalearning -from autosklearn.constants import MULTILABEL_CLASSIFICATION, \ - BINARY_CLASSIFICATION, TASK_TYPES_TO_STRING, CLASSIFICATION_TASKS, \ - MULTICLASS_CLASSIFICATION, REGRESSION, MULTIOUTPUT_REGRESSION -from autosklearn.ensemble_builder import EnsembleBuilderManager -from autosklearn.metalearning.mismbo import suggest_via_metalearning +from autosklearn.constants import ( + BINARY_CLASSIFICATION, + CLASSIFICATION_TASKS, + MULTICLASS_CLASSIFICATION, + MULTILABEL_CLASSIFICATION, + MULTIOUTPUT_REGRESSION, + REGRESSION, + TASK_TYPES_TO_STRING, +) from autosklearn.data.abstract_data_manager import AbstractDataManager +from autosklearn.ensemble_builder import EnsembleBuilderManager from autosklearn.evaluation import ExecuteTaFuncWithQueue, get_cost_of_crash +from autosklearn.metalearning.metafeatures.metafeatures import ( + calculate_all_metafeatures_encoded_labels, + calculate_all_metafeatures_with_labels, +) +from autosklearn.metalearning.metalearning.meta_base import MetaBase +from autosklearn.metalearning.mismbo import suggest_via_metalearning from autosklearn.util.logging_ import get_named_client_logger from autosklearn.util.parallel import preload_modules -from autosklearn.metalearning.metalearning.meta_base import MetaBase -from autosklearn.metalearning.metafeatures.metafeatures import \ - calculate_all_metafeatures_with_labels, calculate_all_metafeatures_encoded_labels EXCLUDE_META_FEATURES_CLASSIFICATION = { - 'Landmark1NN', - 'LandmarkDecisionNodeLearner', - 'LandmarkDecisionTree', - 'LandmarkLDA', - 'LandmarkNaiveBayes', - 'LandmarkRandomNodeLearner', - 'PCAFractionOfComponentsFor95PercentVariance', - 'PCAKurtosisFirstPC', - 'PCASkewnessFirstPC', - 'PCA', + "Landmark1NN", + "LandmarkDecisionNodeLearner", + "LandmarkDecisionTree", + "LandmarkLDA", + "LandmarkNaiveBayes", + "LandmarkRandomNodeLearner", + "PCAFractionOfComponentsFor95PercentVariance", + "PCAKurtosisFirstPC", + "PCASkewnessFirstPC", + "PCA", } EXCLUDE_META_FEATURES_REGRESSION = { - 'Landmark1NN', - 'LandmarkDecisionNodeLearner', - 'LandmarkDecisionTree', - 'LandmarkLDA', - 'LandmarkNaiveBayes', - 'PCAFractionOfComponentsFor95PercentVariance', - 'PCAKurtosisFirstPC', - 'PCASkewnessFirstPC', - 'NumberOfClasses', - 'ClassOccurences', - 'ClassProbabilityMin', - 'ClassProbabilityMax', - 'ClassProbabilityMean', - 'ClassProbabilitySTD', - 'ClassEntropy', - 'LandmarkRandomNodeLearner', - 'PCA', + "Landmark1NN", + "LandmarkDecisionNodeLearner", + "LandmarkDecisionTree", + "LandmarkLDA", + "LandmarkNaiveBayes", + "PCAFractionOfComponentsFor95PercentVariance", + "PCAKurtosisFirstPC", + "PCASkewnessFirstPC", + "NumberOfClasses", + "ClassOccurences", + "ClassProbabilityMin", + "ClassProbabilityMax", + "ClassProbabilityMean", + "ClassProbabilitySTD", + "ClassEntropy", + "LandmarkRandomNodeLearner", + "PCA", } def get_send_warnings_to_logger(logger): def _send_warnings_to_log(message, category, filename, lineno, file, line): - logger.debug('%s:%s: %s:%s', filename, lineno, category.__name__, message) + logger.debug("%s:%s: %s:%s", filename, lineno, category.__name__, message) + return _send_warnings_to_log # metalearning helpers -def _calculate_metafeatures(data_feat_type, data_info_task, basename, - x_train, y_train, watcher, logger_): +def _calculate_metafeatures( + data_feat_type, data_info_task, basename, x_train, y_train, watcher, logger_ +): with warnings.catch_warnings(): warnings.showwarning = get_send_warnings_to_logger(logger_) # == Calculate metafeatures - task_name = 'CalculateMetafeatures' + task_name = "CalculateMetafeatures" watcher.start_task(task_name) - categorical = {col: True if feat_type.lower() == 'categorical' else False - for col, feat_type in data_feat_type.items()} + categorical = { + col: True if feat_type.lower() == "categorical" else False + for col, feat_type in data_feat_type.items() + } - EXCLUDE_META_FEATURES = EXCLUDE_META_FEATURES_CLASSIFICATION \ - if data_info_task in CLASSIFICATION_TASKS else EXCLUDE_META_FEATURES_REGRESSION + EXCLUDE_META_FEATURES = ( + EXCLUDE_META_FEATURES_CLASSIFICATION + if data_info_task in CLASSIFICATION_TASKS + else EXCLUDE_META_FEATURES_REGRESSION + ) - if data_info_task in [MULTICLASS_CLASSIFICATION, BINARY_CLASSIFICATION, - MULTILABEL_CLASSIFICATION, REGRESSION, - MULTIOUTPUT_REGRESSION]: - logger_.info('Start calculating metafeatures for %s', basename) + if data_info_task in [ + MULTICLASS_CLASSIFICATION, + BINARY_CLASSIFICATION, + MULTILABEL_CLASSIFICATION, + REGRESSION, + MULTIOUTPUT_REGRESSION, + ]: + logger_.info("Start calculating metafeatures for %s", basename) result = calculate_all_metafeatures_with_labels( - x_train, y_train, categorical=categorical, + x_train, + y_train, + categorical=categorical, dataset_name=basename, - dont_calculate=EXCLUDE_META_FEATURES, logger=logger_) + dont_calculate=EXCLUDE_META_FEATURES, + logger=logger_, + ) for key in list(result.metafeature_values.keys()): - if result.metafeature_values[key].type_ != 'METAFEATURE': + if result.metafeature_values[key].type_ != "METAFEATURE": del result.metafeature_values[key] else: result = None - logger_.info('Metafeatures not calculated') + logger_.info("Metafeatures not calculated") watcher.stop_task(task_name) logger_.info( - 'Calculating Metafeatures (categorical attributes) took %5.2f', - watcher.wall_elapsed(task_name)) + "Calculating Metafeatures (categorical attributes) took %5.2f", + watcher.wall_elapsed(task_name), + ) return result -def _calculate_metafeatures_encoded(data_feat_type, basename, x_train, y_train, watcher, - task, logger_): +def _calculate_metafeatures_encoded( + data_feat_type, basename, x_train, y_train, watcher, task, logger_ +): with warnings.catch_warnings(): warnings.showwarning = get_send_warnings_to_logger(logger_) - EXCLUDE_META_FEATURES = EXCLUDE_META_FEATURES_CLASSIFICATION \ - if task in CLASSIFICATION_TASKS else EXCLUDE_META_FEATURES_REGRESSION + EXCLUDE_META_FEATURES = ( + EXCLUDE_META_FEATURES_CLASSIFICATION + if task in CLASSIFICATION_TASKS + else EXCLUDE_META_FEATURES_REGRESSION + ) - task_name = 'CalculateMetafeaturesEncoded' + task_name = "CalculateMetafeaturesEncoded" watcher.start_task(task_name) - categorical = {col: True if feat_type.lower() == 'categorical' else False - for col, feat_type in data_feat_type.items()} + categorical = { + col: True if feat_type.lower() == "categorical" else False + for col, feat_type in data_feat_type.items() + } result = calculate_all_metafeatures_encoded_labels( - x_train, y_train, categorical=categorical, - dataset_name=basename, dont_calculate=EXCLUDE_META_FEATURES, logger=logger_) + x_train, + y_train, + categorical=categorical, + dataset_name=basename, + dont_calculate=EXCLUDE_META_FEATURES, + logger=logger_, + ) for key in list(result.metafeature_values.keys()): - if result.metafeature_values[key].type_ != 'METAFEATURE': + if result.metafeature_values[key].type_ != "METAFEATURE": del result.metafeature_values[key] watcher.stop_task(task_name) logger_.info( - 'Calculating Metafeatures (encoded attributes) took %5.2fsec', - watcher.wall_elapsed(task_name)) + "Calculating Metafeatures (encoded attributes) took %5.2fsec", + watcher.wall_elapsed(task_name), + ) return result -def _get_metalearning_configurations(meta_base, basename, metric, - configuration_space, - task, - initial_configurations_via_metalearning, - is_sparse, - watcher, logger): - task_name = 'InitialConfigurations' +def _get_metalearning_configurations( + meta_base, + basename, + metric, + configuration_space, + task, + initial_configurations_via_metalearning, + is_sparse, + watcher, + logger, +): + task_name = "InitialConfigurations" watcher.start_task(task_name) try: metalearning_configurations = suggest_via_metalearning( - meta_base, basename, metric, + meta_base, + basename, + metric, task, is_sparse == 1, initial_configurations_via_metalearning, @@ -165,16 +207,21 @@ def _get_metalearning_configurations(meta_base, basename, metric, return metalearning_configurations -def _print_debug_info_of_init_configuration(initial_configurations, basename, - time_for_task, logger, watcher): - logger.debug('Initial Configurations: (%d)' % len(initial_configurations)) +def _print_debug_info_of_init_configuration( + initial_configurations, basename, time_for_task, logger, watcher +): + logger.debug("Initial Configurations: (%d)" % len(initial_configurations)) for initial_configuration in initial_configurations: logger.debug(initial_configuration) - logger.debug('Looking for initial configurations took %5.2fsec', - watcher.wall_elapsed('InitialConfigurations')) + logger.debug( + "Looking for initial configurations took %5.2fsec", + watcher.wall_elapsed("InitialConfigurations"), + ) logger.info( - 'Time left for %s after finding initial configurations: %5.2fsec', - basename, time_for_task - watcher.wall_elapsed(basename)) + "Time left for %s after finding initial configurations: %5.2fsec", + basename, + time_for_task - watcher.wall_elapsed(basename), + ) def get_smac_object( @@ -186,7 +233,7 @@ def get_smac_object( n_jobs, dask_client, ): - if len(scenario_dict['instances']) > 1: + if len(scenario_dict["instances"]) > 1: intensifier = Intensifier else: intensifier = SimpleIntensifier @@ -213,35 +260,37 @@ def get_smac_object( class AutoMLSMBO(object): - - def __init__(self, config_space, dataset_name, - backend, - total_walltime_limit, - func_eval_time_limit, - memory_limit, - metric, - watcher, - n_jobs, - dask_client: dask.distributed.Client, - port: int, - start_num_run=1, - data_memory_limit=None, - num_metalearning_cfgs=25, - config_file=None, - seed=1, - metadata_directory=None, - resampling_strategy='holdout', - resampling_strategy_args=None, - include: Optional[Dict[str, List[str]]] = None, - exclude: Optional[Dict[str, List[str]]] = None, - disable_file_output=False, - smac_scenario_args=None, - get_smac_object_callback=None, - scoring_functions=None, - pynisher_context='spawn', - ensemble_callback: typing.Optional[EnsembleBuilderManager] = None, - trials_callback: typing.Optional[IncorporateRunResultCallback] = None - ): + def __init__( + self, + config_space, + dataset_name, + backend, + total_walltime_limit, + func_eval_time_limit, + memory_limit, + metric, + watcher, + n_jobs, + dask_client: dask.distributed.Client, + port: int, + start_num_run=1, + data_memory_limit=None, + num_metalearning_cfgs=25, + config_file=None, + seed=1, + metadata_directory=None, + resampling_strategy="holdout", + resampling_strategy_args=None, + include: Optional[Dict[str, List[str]]] = None, + exclude: Optional[Dict[str, List[str]]] = None, + disable_file_output=False, + smac_scenario_args=None, + get_smac_object_callback=None, + scoring_functions=None, + pynisher_context="spawn", + ensemble_callback: typing.Optional[EnsembleBuilderManager] = None, + trials_callback: typing.Optional[IncorporateRunResultCallback] = None, + ): super(AutoMLSMBO, self).__init__() # data related self.dataset_name = dataset_name @@ -289,7 +338,11 @@ def __init__(self, config_space, dataset_name, self.trials_callback = trials_callback dataset_name_ = "" if dataset_name is None else dataset_name - logger_name = '%s(%d):%s' % (self.__class__.__name__, self.seed, ":" + dataset_name_) + logger_name = "%s(%d):%s" % ( + self.__class__.__name__, + self.seed, + ":" + dataset_name_, + ) if port is None: self.logger = logging.getLogger(__name__) else: @@ -308,7 +361,7 @@ def reset_data_manager(self, max_mem=None): else: self.datamanager = self.backend.load_datamanager() - self.task = self.datamanager.info['task'] + self.task = self.datamanager.info["task"] def collect_metalearning_suggestions(self, meta_base): metalearning_configurations = _get_metalearning_configurations( @@ -317,16 +370,18 @@ def collect_metalearning_suggestions(self, meta_base): metric=self.metric, configuration_space=self.config_space, task=self.task, - is_sparse=self.datamanager.info['is_sparse'], + is_sparse=self.datamanager.info["is_sparse"], initial_configurations_via_metalearning=self.num_metalearning_cfgs, watcher=self.watcher, - logger=self.logger) + logger=self.logger, + ) _print_debug_info_of_init_configuration( metalearning_configurations, self.dataset_name, self.total_walltime_limit, self.logger, - self.watcher) + self.watcher, + ) return metalearning_configurations @@ -336,23 +391,24 @@ def _calculate_metafeatures_with_limits(self, time_limit): try: context = multiprocessing.get_context(self.pynisher_context) preload_modules(context) - safe_mf = pynisher.enforce_limits(mem_in_mb=self.memory_limit, - wall_time_in_s=int(time_limit), - grace_period_in_s=30, - context=context, - logger=self.logger)( - _calculate_metafeatures) + safe_mf = pynisher.enforce_limits( + mem_in_mb=self.memory_limit, + wall_time_in_s=int(time_limit), + grace_period_in_s=30, + context=context, + logger=self.logger, + )(_calculate_metafeatures) res = safe_mf( data_feat_type=self.datamanager.feat_type, - data_info_task=self.datamanager.info['task'], - x_train=self.datamanager.data['X_train'], - y_train=self.datamanager.data['Y_train'], + data_info_task=self.datamanager.info["task"], + x_train=self.datamanager.data["X_train"], + y_train=self.datamanager.data["Y_train"], basename=self.dataset_name, watcher=self.watcher, - logger_=self.logger + logger_=self.logger, ) except Exception as e: - self.logger.error('Error getting metafeatures: %s', str(e)) + self.logger.error("Error getting metafeatures: %s", str(e)) return res @@ -362,30 +418,30 @@ def _calculate_metafeatures_encoded_with_limits(self, time_limit): try: context = multiprocessing.get_context(self.pynisher_context) preload_modules(context) - safe_mf = pynisher.enforce_limits(mem_in_mb=self.memory_limit, - wall_time_in_s=int(time_limit), - grace_period_in_s=30, - context=context, - logger=self.logger)( - _calculate_metafeatures_encoded) + safe_mf = pynisher.enforce_limits( + mem_in_mb=self.memory_limit, + wall_time_in_s=int(time_limit), + grace_period_in_s=30, + context=context, + logger=self.logger, + )(_calculate_metafeatures_encoded) res = safe_mf( data_feat_type=self.datamanager.feat_type, - task=self.datamanager.info['task'], - x_train=self.datamanager.data['X_train'], - y_train=self.datamanager.data['Y_train'], + task=self.datamanager.info["task"], + x_train=self.datamanager.data["X_train"], + y_train=self.datamanager.data["Y_train"], basename=self.dataset_name, watcher=self.watcher, - logger_=self.logger + logger_=self.logger, ) except Exception as e: - self.logger.error('Error getting metafeatures (encoded) : %s', - str(e)) + self.logger.error("Error getting metafeatures (encoded) : %s", str(e)) return res def run_smbo(self): - self.watcher.start_task('SMBO') + self.watcher.start_task("SMBO") # == first things first: load the datamanager self.reset_data_manager() @@ -401,14 +457,14 @@ def run_smbo(self): metalearning_configurations = self.get_metalearning_suggestions() - if self.resampling_strategy in ['partial-cv', - 'partial-cv-iterative-fit']: - num_folds = self.resampling_strategy_args['folds'] - instances = [[json.dumps({'task_id': self.dataset_name, - 'fold': fold_number})] - for fold_number in range(num_folds)] + if self.resampling_strategy in ["partial-cv", "partial-cv-iterative-fit"]: + num_folds = self.resampling_strategy_args["folds"] + instances = [ + [json.dumps({"task_id": self.dataset_name, "fold": fold_number})] + for fold_number in range(num_folds) + ] else: - instances = [[json.dumps({'task_id': self.dataset_name})]] + instances = [[json.dumps({"task_id": self.dataset_name})]] # TODO rebuild target algorithm to be it's own target algorithm # evaluator, which takes into account that a run can be killed prior @@ -428,62 +484,64 @@ def run_smbo(self): scoring_functions=self.scoring_functions, port=self.port, pynisher_context=self.pynisher_context, - **self.resampling_strategy_args + **self.resampling_strategy_args, ) ta = ExecuteTaFuncWithQueue startup_time = self.watcher.wall_elapsed(self.dataset_name) total_walltime_limit = self.total_walltime_limit - startup_time - 5 scenario_dict = { - 'abort_on_first_run_crash': False, - 'save-results-instantly': True, - 'cs': self.config_space, - 'cutoff_time': self.func_eval_time_limit, - 'deterministic': 'true', - 'instances': instances, - 'memory_limit': self.memory_limit, - 'output-dir': self.backend.get_smac_output_directory(), - 'run_obj': 'quality', - 'wallclock_limit': total_walltime_limit, - 'cost_for_crash': self.worst_possible_result, + "abort_on_first_run_crash": False, + "save-results-instantly": True, + "cs": self.config_space, + "cutoff_time": self.func_eval_time_limit, + "deterministic": "true", + "instances": instances, + "memory_limit": self.memory_limit, + "output-dir": self.backend.get_smac_output_directory(), + "run_obj": "quality", + "wallclock_limit": total_walltime_limit, + "cost_for_crash": self.worst_possible_result, } if self.smac_scenario_args is not None: for arg in [ - 'abort_on_first_run_crash', - 'cs', - 'deterministic', - 'instances', - 'output-dir', - 'run_obj', - 'shared-model', - 'cost_for_crash', + "abort_on_first_run_crash", + "cs", + "deterministic", + "instances", + "output-dir", + "run_obj", + "shared-model", + "cost_for_crash", ]: if arg in self.smac_scenario_args: - self.logger.warning('Cannot override scenario argument %s, ' - 'will ignore this.', arg) + self.logger.warning( + "Cannot override scenario argument %s, " "will ignore this.", + arg, + ) del self.smac_scenario_args[arg] for arg in [ - 'cutoff_time', - 'memory_limit', - 'wallclock_limit', + "cutoff_time", + "memory_limit", + "wallclock_limit", ]: if arg in self.smac_scenario_args: self.logger.warning( - 'Overriding scenario argument %s: %s with value %s', + "Overriding scenario argument %s: %s with value %s", arg, scenario_dict[arg], - self.smac_scenario_args[arg] + self.smac_scenario_args[arg], ) scenario_dict.update(self.smac_scenario_args) smac_args = { - 'scenario_dict': scenario_dict, - 'seed': seed, - 'ta': ta, - 'ta_kwargs': ta_kwargs, - 'metalearning_configurations': metalearning_configurations, - 'n_jobs': self.n_jobs, - 'dask_client': self.dask_client, + "scenario_dict": scenario_dict, + "seed": seed, + "ta": ta, + "ta_kwargs": ta_kwargs, + "metalearning_configurations": metalearning_configurations, + "n_jobs": self.n_jobs, + "dask_client": self.dask_client, } if self.get_smac_object_callback is not None: smac = self.get_smac_object_callback(**smac_args) @@ -516,25 +574,33 @@ def get_metalearning_suggestions(self): # If metadata directory is None, use default if self.metadata_directory is None: metalearning_directory = os.path.dirname( - autosklearn.metalearning.__file__) + autosklearn.metalearning.__file__ + ) # There is no multilabel data in OpenML if self.task == MULTILABEL_CLASSIFICATION: meta_task = BINARY_CLASSIFICATION else: meta_task = self.task metadata_directory = os.path.join( - metalearning_directory, 'files', - '%s_%s_%s' % (self.metric, TASK_TYPES_TO_STRING[meta_task], - 'sparse' if self.datamanager.info['is_sparse'] - else 'dense')) + metalearning_directory, + "files", + "%s_%s_%s" + % ( + self.metric, + TASK_TYPES_TO_STRING[meta_task], + "sparse" if self.datamanager.info["is_sparse"] else "dense", + ), + ) self.metadata_directory = metadata_directory # If metadata directory is specified by user, # then verify that it exists. else: if not os.path.exists(self.metadata_directory): - raise ValueError('The specified metadata directory \'%s\' ' - 'does not exist!' % self.metadata_directory) + raise ValueError( + "The specified metadata directory '%s' " + "does not exist!" % self.metadata_directory + ) else: # There is no multilabel data in OpenML @@ -545,51 +611,66 @@ def get_metalearning_suggestions(self): metadata_directory = os.path.join( self.metadata_directory, - '%s_%s_%s' % (self.metric, TASK_TYPES_TO_STRING[meta_task], - 'sparse' if self.datamanager.info['is_sparse'] - else 'dense')) + "%s_%s_%s" + % ( + self.metric, + TASK_TYPES_TO_STRING[meta_task], + "sparse" if self.datamanager.info["is_sparse"] else "dense", + ), + ) # Check that the metadata directory has the correct # subdirectory needed for this dataset. - if os.path.basename(metadata_directory) not in \ - os.listdir(self.metadata_directory): - raise ValueError('The specified metadata directory ' - '\'%s\' does not have the correct ' - 'subdirectory \'%s\'' % - (self.metadata_directory, - os.path.basename(metadata_directory)) - ) + if os.path.basename(metadata_directory) not in os.listdir( + self.metadata_directory + ): + raise ValueError( + "The specified metadata directory " + "'%s' does not have the correct " + "subdirectory '%s'" + % ( + self.metadata_directory, + os.path.basename(metadata_directory), + ) + ) self.metadata_directory = metadata_directory if os.path.exists(self.metadata_directory): - self.logger.info('Metadata directory: %s', - self.metadata_directory) - meta_base = MetaBase(self.config_space, self.metadata_directory, self.logger) + self.logger.info("Metadata directory: %s", self.metadata_directory) + meta_base = MetaBase( + self.config_space, self.metadata_directory, self.logger + ) - metafeature_calculation_time_limit = int( - self.total_walltime_limit / 4) + metafeature_calculation_time_limit = int(self.total_walltime_limit / 4) metafeature_calculation_start_time = time.time() meta_features = self._calculate_metafeatures_with_limits( - metafeature_calculation_time_limit) + metafeature_calculation_time_limit + ) metafeature_calculation_end_time = time.time() - metafeature_calculation_time_limit = \ - metafeature_calculation_time_limit - ( - metafeature_calculation_end_time - - metafeature_calculation_start_time) + metafeature_calculation_time_limit = ( + metafeature_calculation_time_limit + - ( + metafeature_calculation_end_time + - metafeature_calculation_start_time + ) + ) if metafeature_calculation_time_limit < 1: self.logger.warning( - 'Time limit for metafeature calculation less ' - 'than 1 seconds (%f). Skipping calculation ' - 'of metafeatures for encoded dataset.', - metafeature_calculation_time_limit) + "Time limit for metafeature calculation less " + "than 1 seconds (%f). Skipping calculation " + "of metafeatures for encoded dataset.", + metafeature_calculation_time_limit, + ) meta_features_encoded = None else: with warnings.catch_warnings(): warnings.showwarning = get_send_warnings_to_logger(self.logger) - meta_features_encoded = \ + meta_features_encoded = ( self._calculate_metafeatures_encoded_with_limits( - metafeature_calculation_time_limit) + metafeature_calculation_time_limit + ) + ) # In case there is a problem calculating the encoded meta-features if meta_features is None: @@ -598,26 +679,28 @@ def get_metalearning_suggestions(self): else: if meta_features_encoded is not None: meta_features.metafeature_values.update( - meta_features_encoded.metafeature_values) + meta_features_encoded.metafeature_values + ) if meta_features is not None: meta_base.add_dataset(self.dataset_name, meta_features) # Do mean imputation of the meta-features - should be done specific # for each prediction model! all_metafeatures = meta_base.get_metafeatures( - features=list(meta_features.keys())) - all_metafeatures.fillna(all_metafeatures.mean(), - inplace=True) + features=list(meta_features.keys()) + ) + all_metafeatures.fillna(all_metafeatures.mean(), inplace=True) with warnings.catch_warnings(): warnings.showwarning = get_send_warnings_to_logger(self.logger) - metalearning_configurations = self.collect_metalearning_suggestions( - meta_base) + metalearning_configurations = ( + self.collect_metalearning_suggestions(meta_base) + ) if metalearning_configurations is None: metalearning_configurations = [] self.reset_data_manager() - self.logger.info('%s', meta_features) + self.logger.info("%s", meta_features) # Convert meta-features into a dictionary because the scenario # expects a dictionary @@ -627,13 +710,15 @@ def get_metalearning_suggestions(self): meta_features_list = [] for meta_feature_name in all_metafeatures.columns: meta_features_list.append( - meta_features[meta_feature_name].value) + meta_features[meta_feature_name].value + ) self.logger.info(list(meta_features_dict.keys())) else: meta_features = None - self.logger.warning('Could not find meta-data directory %s' % - metadata_directory) + self.logger.warning( + "Could not find meta-data directory %s" % metadata_directory + ) else: meta_features = None diff --git a/autosklearn/util/__init__.py b/autosklearn/util/__init__.py index 6eeff57a7d..9f2d05ccd5 100644 --- a/autosklearn/util/__init__.py +++ b/autosklearn/util/__init__.py @@ -1,7 +1,7 @@ # -*- encoding: utf-8 -*- import re - -SUBPATTERN = r'((?P==|>=|>|<)(?P(\d+)?(\.[a-zA-Z0-9]+)?(\.[a-zA-Z0-9]+)?))' +SUBPATTERN = r"((?P==|>=|>|<)(?P(\d+)?(\.[a-zA-Z0-9]+)?(\.[a-zA-Z0-9]+)?))" # noqa: E501 RE_PATTERN = re.compile( - r'^(?P[\w\-]+)%s?(,%s)?$' % (SUBPATTERN % (1, 1), SUBPATTERN % (2, 2))) + r"^(?P[\w\-]+)%s?(,%s)?$" % (SUBPATTERN % (1, 1), SUBPATTERN % (2, 2)) +) diff --git a/autosklearn/util/common.py b/autosklearn/util/common.py index 4905d0eaa8..ddee4dc1ab 100644 --- a/autosklearn/util/common.py +++ b/autosklearn/util/common.py @@ -5,22 +5,21 @@ import numpy as np -__all__ = [ - 'check_pid', - 'warn_if_not_float' -] +__all__ = ["check_pid", "warn_if_not_float"] -def warn_if_not_float(X: np.ndarray, estimator: str = 'This algorithm') -> bool: +def warn_if_not_float(X: np.ndarray, estimator: str = "This algorithm") -> bool: """Warning utility function to check that data type is floating point. Returns True if a warning was raised (i.e. the input is not float) and False otherwise, for easier input validation. """ if not isinstance(estimator, str): estimator = estimator.__class__.__name__ - if X.dtype.kind != 'f': - warnings.warn("%s assumes floating point values as input, " - "got %s" % (estimator, X.dtype)) + if X.dtype.kind != "f": + warnings.warn( + "%s assumes floating point values as input, " + "got %s" % (estimator, X.dtype) + ) return True return False diff --git a/autosklearn/util/data.py b/autosklearn/util/data.py index ff6eb3c337..bdd4cf31b2 100644 --- a/autosklearn/util/data.py +++ b/autosklearn/util/data.py @@ -1,4 +1,3 @@ -import warnings from typing import ( Any, Dict, @@ -10,21 +9,18 @@ Tuple, Type, Union, - cast + cast, ) -import numpy as np +import warnings +import numpy as np import pandas as pd - from scipy.sparse import spmatrix - from sklearn.model_selection import train_test_split -from autosklearn.data.validation import SUPPORTED_FEAT_TYPES from autosklearn.evaluation.splitter import CustomStratifiedShuffleSplit - # TODO: TypedDict with python 3.8 # # When upgrading to python 3.8 as minimum version, this should be a TypedDict @@ -32,15 +28,14 @@ DatasetCompressionSpec = Dict[str, Union[float, List[str]]] # Default specification for arg `dataset_compression` -default_dataset_compression_arg: DatasetCompressionSpec = { +default_dataset_compression_arg: DatasetCompressionSpec = { "memory_allocation": 0.1, - "methods": ["precision", "subsample"] + "methods": ["precision", "subsample"], } def validate_dataset_compression_arg( - dataset_compression: Mapping[str, Any], - memory_limit: int + dataset_compression: Mapping[str, Any], memory_limit: int ) -> DatasetCompressionSpec: """Validates and return a correct dataset_compression argument @@ -58,22 +53,24 @@ def validate_dataset_compression_arg( """ if isinstance(dataset_compression, Mapping): # Fill with defaults if they don't exist - dataset_compression = { - **default_dataset_compression_arg, - **dataset_compression - } + dataset_compression = {**default_dataset_compression_arg, **dataset_compression} + + parsed_keys = set(dataset_compression.keys()) + default_keys = set(default_dataset_compression_arg.keys()) # Must contain known keys - if set(dataset_compression.keys()) != set(default_dataset_compression_arg.keys()): + if parsed_keys != default_keys: raise ValueError( - f"Unknown key in dataset_compression, {list(dataset_compression.keys())}." - f"\nPossible keys are {list(default_dataset_compression_arg.keys())}" + f"Unknown key(s) in ``dataset_compression``, {parsed_keys}." + f"\nPossible keys are {default_keys}" ) memory_allocation = dataset_compression["memory_allocation"] # "memory_allocation" must be float or int - if not (isinstance(memory_allocation, float) or isinstance(memory_allocation, int)): + if not ( + isinstance(memory_allocation, float) or isinstance(memory_allocation, int) + ): raise ValueError( "key 'memory_allocation' must be an `int` or `float`" f"\ntype = {memory_allocation}" @@ -89,9 +86,11 @@ def validate_dataset_compression_arg( ) # "memory_allocation" if absolute, should be > 0 and < memory_limit - if isinstance(memory_allocation, int) and not (0 < memory_allocation < memory_limit): + if isinstance(memory_allocation, int) and not ( + 0 < memory_allocation < memory_limit + ): raise ValueError( - f"key 'memory_allocation' if int must be in (0, memory_limit={memory_limit})" + f"key 'memory_allocation' if int must be in (0, {memory_limit})" f"\nmemory_allocation = {memory_allocation}" f"\ndataset_compression = {dataset_compression}" ) @@ -109,11 +108,13 @@ def validate_dataset_compression_arg( # "methods" must contain known methods if any( - method not in cast(Sequence, default_dataset_compression_arg["methods"]) # mypy + method + not in cast(Sequence, default_dataset_compression_arg["methods"]) # mypy for method in dataset_compression["methods"] ): + valid_methods = default_dataset_compression_arg["methods"] raise ValueError( - f"key 'methods' can only contain {default_dataset_compression_arg['methods']}" + f"key 'methods' can only contain {valid_methods}" f"\nmethods = {dataset_compression['methods']}" f"\ndataset_compression = {dataset_compression}" ) @@ -160,10 +161,10 @@ class _DtypeReductionMapping(Mapping): # provide only as much precision as np.longdouble, # that is, 80 bits on most x86 machines and 64 bits # in standard Windows builds. - if hasattr(np, 'float96'): + if hasattr(np, "float96"): _mapping[np.float96] = np.float64 - if hasattr(np, 'float128'): + if hasattr(np, "float128"): _mapping[np.float128] = np.float64 @classmethod @@ -191,8 +192,10 @@ def binarization(array: Union[List, np.ndarray]) -> np.ndarray: # into 1 and the min into 0 array = np.array(array, dtype=float) # conversion needed to use np.inf if len(np.unique(array)) > 2: - raise ValueError('The argument must be a binary-class datafile. ' - '{} classes detected'.format(len(np.unique(array)))) + raise ValueError( + "The argument must be a binary-class datafile. " + "{} classes detected".format(len(np.unique(array))) + ) # manipulation which aims at avoid error in data # with for example classes '1' and '2'. @@ -252,8 +255,8 @@ def subsample( is_classification: bool, sample_size: Union[float, int], random_state: Optional[Union[int, np.random.RandomState]] = None, -) -> Tuple[SUPPORTED_FEAT_TYPES, Union[List, np.ndarray, pd.DataFrame, pd.Series]]: - """ Subsamples data returning the same type as it recieved. +) -> Tuple[Union[np.ndarray, spmatrix], np.ndarray]: + """Subsamples data returning the same type as it recieved. If `is_classification`, we split using a stratified shuffle split which preserves unique labels in the training set. @@ -298,8 +301,7 @@ def subsample( if is_classification: splitter = CustomStratifiedShuffleSplit( - train_size=sample_size, - random_state=random_state + train_size=sample_size, random_state=random_state ) left_idxs, _ = next(splitter.split(X=X, y=y)) @@ -319,7 +321,8 @@ def subsample( else: X, _, y, _ = train_test_split( # type: ignore - X, y, + X, + y, train_size=sample_size, random_state=random_state, ) @@ -330,7 +333,7 @@ def subsample( def reduce_precision( X: Union[np.ndarray, spmatrix] ) -> Tuple[Union[np.ndarray, spmatrix], Type]: - """ Reduces the precision of a np.ndarray or spmatrix containing floats + """Reduces the precision of a np.ndarray or spmatrix containing floats Parameters ---------- @@ -343,8 +346,10 @@ def reduce_precision( Returns the reduced data X along with the dtype it was reduced to. """ if X.dtype not in supported_precision_reductions: - raise ValueError(f"X.dtype = {X.dtype} not equal to any supported" - f" {supported_precision_reductions}") + raise ValueError( + f"X.dtype = {X.dtype} not equal to any supported" + f" {supported_precision_reductions}" + ) precision = reduction_mapping[X.dtype] return X.astype(precision), precision @@ -356,10 +361,10 @@ def reduce_dataset_size_if_too_large( memory_limit: int, is_classification: bool, random_state: Union[int, np.random.RandomState] = None, - operations: List[str] = ['precision', 'subsample'], + operations: List[str] = ["precision", "subsample"], memory_allocation: Union[int, float] = 0.1, ) -> Tuple[Union[np.ndarray, spmatrix], np.ndarray]: - f""" Reduces the size of the dataset if it's too close to the memory limit. + f"""Reduces the size of the dataset if it's too close to the memory limit. Follows the order of the operations passed in and retains the type of its input. @@ -408,8 +413,8 @@ def reduce_dataset_size_if_too_large( **subsample** - Reduce the amount of samples of the dataset such that it fits into the allocated memory. - Ensures stratification and that unique labels are present + Reduce the amount of samples of the dataset such that it fits into the allocated + memory. Ensures stratification and that unique labels are present memory_allocation: Union[int, float] = 0.1 The amount of memory to allocate to the dataset. A float specifys that the @@ -437,27 +442,31 @@ def reduce_dataset_size_if_too_large( allocated_memory = memory_allocation else: - raise ValueError(f"Unknown type for `memory_allocation` {type(memory_allocation)}") + raise ValueError( + f"Unknown type for `memory_allocation` {type(memory_allocation)}" + ) - if 'precision' in operations and X.dtype not in supported_precision_reductions: + if "precision" in operations and X.dtype not in supported_precision_reductions: raise ValueError(f"Unsupported type `{X.dtype}` for precision reduction") def megabytes(arr: Union[np.ndarray, spmatrix]) -> float: - return (arr.nbytes if isinstance(X, np.ndarray) else arr.data.nbytes) / (2**20) + return (arr.nbytes if isinstance(X, np.ndarray) else arr.data.nbytes) / ( + 2**20 + ) for operation in operations: - if operation == 'precision': + if operation == "precision": # If the dataset is too big for the allocated memory, # we then try to reduce the precision if it's a high precision dataset if megabytes(X) > allocated_memory: X, precision = reduce_precision(X) warnings.warn( - f'Dataset too large for allocated memory {allocated_memory}MB, ' - f'reduced the precision from {X.dtype} to {precision}', + f"Dataset too large for allocated memory {allocated_memory}MB, " + f"reduced the precision from {X.dtype} to {precision}", ) - elif operation == 'subsample': + elif operation == "subsample": # If the dataset is still too big such that we couldn't fit # into the allocated memory, we subsample it so that it does if megabytes(X) > allocated_memory: @@ -470,16 +479,18 @@ def megabytes(arr: Union[np.ndarray, spmatrix]) -> float: # Tried the generic `def subsample(X: T) -> T` approach but it was # failing elsewhere, keeping it simple for now X, y = subsample( # type: ignore - X, y, + X, + y, sample_size=sample_percentage, is_classification=is_classification, - random_state=random_state + random_state=random_state, ) n_samples_after = X.shape[0] warnings.warn( - f"Dataset too large for allocated memory {allocated_memory}MB, reduced" - f" number of samples from {n_samples_before} to {n_samples_after}." + f"Dataset too large for allocated memory {allocated_memory}MB," + f" reduced number of samples from {n_samples_before} to" + f" {n_samples_after}." ) else: diff --git a/autosklearn/util/dependencies.py b/autosklearn/util/dependencies.py index 08dfdac47e..390ac351bb 100644 --- a/autosklearn/util/dependencies.py +++ b/autosklearn/util/dependencies.py @@ -1,6 +1,7 @@ +from typing import List, Optional, Union, no_type_check + import importlib from distutils.version import LooseVersion -from typing import List, Optional, Union, no_type_check import pkg_resources @@ -23,12 +24,12 @@ def verify_packages(packages: Optional[Union[str, List[str]]]) -> None: match = RE_PATTERN.match(package) if match: - name = match.group('name') - operation = match.group('operation1') - version = match.group('version1') + name = match.group("name") + operation = match.group("operation1") + version = match.group("version1") _verify_package(name, operation, version) else: - raise ValueError('Unable to read requirement: %s' % package) + raise ValueError("Unable to read requirement: %s" % package) # Module has no attribute __version__ wa @@ -49,48 +50,56 @@ def _verify_package(name: str, operation: Optional[str], version: str) -> None: required_version = LooseVersion(version) - if operation == '==': + if operation == "==": check = required_version == installed_version - elif operation == '>': + elif operation == ">": check = installed_version > required_version - elif operation == '<': + elif operation == "<": check = installed_version < required_version - elif operation == '>=': - check = installed_version > required_version or \ - installed_version == required_version + elif operation == ">=": + check = ( + installed_version > required_version + or installed_version == required_version + ) else: - raise NotImplementedError( - 'operation \'%s\' is not supported' % operation) + raise NotImplementedError("operation '%s' is not supported" % operation) if not check: - raise IncorrectPackageVersionError(name, installed_version, operation, - required_version) + raise IncorrectPackageVersionError( + name, installed_version, operation, required_version + ) class MissingPackageError(Exception): - error_message = 'Mandatory package \'{name}\' not found!' + error_message = "Mandatory package '{name}' not found!" def __init__(self, package_name: str): self.package_name = package_name super(MissingPackageError, self).__init__( - self.error_message.format(name=package_name)) + self.error_message.format(name=package_name) + ) class IncorrectPackageVersionError(Exception): - error_message = "found '{name}' version {installed_version} but requires {name} version " \ - "{operation}{required_version}" - - def __init__(self, - package_name: str, - installed_version: Union[str, LooseVersion], - operation: Optional[str], - required_version: Union[str, LooseVersion] - ): + error_message = ( + "found '{name}' version {installed_version} but requires {name} version " + "{operation}{required_version}" + ) + + def __init__( + self, + package_name: str, + installed_version: Union[str, LooseVersion], + operation: Optional[str], + required_version: Union[str, LooseVersion], + ): self.package_name = package_name self.installed_version = installed_version self.operation = operation self.required_version = required_version - message = self.error_message.format(name=package_name, - installed_version=installed_version, - operation=operation, - required_version=required_version) + message = self.error_message.format( + name=package_name, + installed_version=installed_version, + operation=operation, + required_version=required_version, + ) super(IncorrectPackageVersionError, self).__init__(message) diff --git a/autosklearn/util/logging_.py b/autosklearn/util/logging_.py index fc298cd053..a85e4a80d6 100644 --- a/autosklearn/util/logging_.py +++ b/autosklearn/util/logging_.py @@ -1,4 +1,6 @@ # -*- encoding: utf-8 -*- +from typing import Any, Dict, Iterator, Optional, TextIO, Type, cast + import logging import logging.config import logging.handlers @@ -12,7 +14,6 @@ import threading import warnings from contextlib import contextmanager -from typing import Any, Dict, Iterator, Optional, TextIO, Type, cast import yaml @@ -26,41 +27,43 @@ def setup_logger( # logging_config must be a dictionary object specifying the configuration # for the loggers to be used in auto-sklearn. if logging_config is None: - with open(os.path.join(os.path.dirname(__file__), 'logging.yaml'), 'r') as fh: + with open(os.path.join(os.path.dirname(__file__), "logging.yaml"), "r") as fh: logging_config = yaml.safe_load(fh) # Make sure we have a filename handler - if 'handlers' not in logging_config: - logging_config['handlers'] = {} - if 'file_handler' not in logging_config['handlers']: - logging_config['handlers']['file_handler'] = { - 'class': 'logging.FileHandler', - 'level': 'DEBUG', - 'filename': 'autosklearn.log' + if "handlers" not in logging_config: + logging_config["handlers"] = {} + if "file_handler" not in logging_config["handlers"]: + logging_config["handlers"]["file_handler"] = { + "class": "logging.FileHandler", + "level": "DEBUG", + "filename": "autosklearn.log", } - if 'distributed_logfile' not in logging_config['handlers']: + if "distributed_logfile" not in logging_config["handlers"]: # We have to create a file handler - logging_config['handlers']['distributed_logfile'] = { - 'class': 'logging.FileHandler', - 'level': 'DEBUG', - 'filename': 'distributed.log' + logging_config["handlers"]["distributed_logfile"] = { + "class": "logging.FileHandler", + "level": "DEBUG", + "filename": "distributed.log", } if filename is None: - filename = logging_config['handlers']['file_handler']['filename'] + filename = logging_config["handlers"]["file_handler"]["filename"] if distributedlog_filename is None: - distributedlog_filename = logging_config['handlers']['distributed_logfile']['filename'] + distributedlog_filename = logging_config["handlers"]["distributed_logfile"][ + "filename" + ] # Make path absolute only if required # This is needed because this function might be called multiple times with the same # dict, and we don't want /path/path/.log but rather just /path/.log - if os.path.sep not in logging_config['handlers']['file_handler']['filename']: - logging_config['handlers']['file_handler']['filename'] = os.path.join( + if os.path.sep not in logging_config["handlers"]["file_handler"]["filename"]: + logging_config["handlers"]["file_handler"]["filename"] = os.path.join( output_dir, filename ) - if os.path.sep not in logging_config['handlers']['distributed_logfile']['filename']: - logging_config['handlers']['distributed_logfile']['filename'] = os.path.join( + if os.path.sep not in logging_config["handlers"]["distributed_logfile"]["filename"]: + logging_config["handlers"]["distributed_logfile"]["filename"] = os.path.join( output_dir, distributedlog_filename ) logging.config.dictConfig(logging_config) @@ -70,13 +73,12 @@ def _create_logger(name: str) -> logging.Logger: return logging.getLogger(name) -def get_logger(name: str) -> 'PickableLoggerAdapter': +def get_logger(name: str) -> "PickableLoggerAdapter": logger = PickableLoggerAdapter(name) return logger class PickableLoggerAdapter(object): - def __init__(self, name: str): self.name = name self.logger = _create_logger(name) @@ -90,7 +92,7 @@ def __getstate__(self) -> Dict[str, Any]: Dictionary, representing the object state to be pickled. Ignores the self.logger field and only returns the logger name. """ - return {'name': self.name} + return {"name": self.name} def __setstate__(self, state: Dict[str, Any]) -> None: """ @@ -102,7 +104,7 @@ def __setstate__(self, state: Dict[str, Any]) -> None: state - dictionary, containing the logger name. """ - self.name = state['name'] + self.name = state["name"] self.logger = _create_logger(self.name) def debug(self, msg: str, *args: Any, **kwargs: Any) -> None: @@ -132,20 +134,16 @@ def isEnabledFor(self, level: int) -> bool: def get_named_client_logger( name: str, - host: str = 'localhost', + host: str = "localhost", port: int = logging.handlers.DEFAULT_TCP_LOGGING_PORT, -) -> 'PicklableClientLogger': - logger = PicklableClientLogger( - name=name, - host=host, - port=port - ) +) -> "PicklableClientLogger": + logger = PicklableClientLogger(name=name, host=host, port=port) return logger def _get_named_client_logger( name: str, - host: str = 'localhost', + host: str = "localhost", port: int = logging.handlers.DEFAULT_TCP_LOGGING_PORT, ) -> logging.Logger: """ @@ -176,7 +174,7 @@ def _get_named_client_logger( # We add client not only to identify that this is the client # communication part of the logger, but to make sure we have # a new singleton with the desired socket handlers - local_logger = _create_logger('Client-' + name) + local_logger = _create_logger("Client-" + name) local_logger.propagate = False local_logger.setLevel(logging.DEBUG) @@ -184,8 +182,9 @@ def _get_named_client_logger( # Ignore mypy logging.handlers.SocketHandler has no attribute port # This is not the case clearly, yet MyPy assumes this is not the case # Even when using direct casting or getattr - ports = [getattr(handler, 'port', None - ) for handler in local_logger.handlers] # type: ignore[attr-defined] + ports = [ + getattr(handler, "port", None) for handler in local_logger.handlers + ] # type: ignore[attr-defined] except AttributeError: # We do not want to log twice but adding multiple times the same # handler. So we check to what ports we communicate to @@ -201,16 +200,11 @@ def _get_named_client_logger( class PicklableClientLogger(PickableLoggerAdapter): - def __init__(self, name: str, host: str, port: int): self.name = name self.host = host self.port = port - self.logger = _get_named_client_logger( - name=name, - host=host, - port=port - ) + self.logger = _get_named_client_logger(name=name, host=host, port=port) def __getstate__(self) -> Dict[str, Any]: """ @@ -222,9 +216,9 @@ def __getstate__(self) -> Dict[str, Any]: the self.logger field and only returns the logger name. """ return { - 'name': self.name, - 'host': self.host, - 'port': self.port, + "name": self.name, + "host": self.host, + "port": self.port, } def __setstate__(self, state: Dict[str, Any]) -> None: @@ -237,9 +231,9 @@ def __setstate__(self, state: Dict[str, Any]) -> None: state - dictionary, containing the logger name. """ - self.name = state['name'] - self.host = state['host'] - self.port = state['port'] + self.name = state["name"] + self.host = state["host"] + self.port = state["port"] self.logger = _get_named_client_logger( name=self.name, host=self.host, @@ -264,7 +258,7 @@ def handle(self) -> None: chunk = self.connection.recv(4) # type: ignore[attr-defined] if len(chunk) < 4: break - slen = struct.unpack('>L', chunk)[0] + slen = struct.unpack(">L", chunk)[0] chunk = self.connection.recv(slen) # type: ignore[attr-defined] while len(chunk) < slen: chunk = chunk + self.connection.recv(slen - len(chunk)) # type: ignore[attr-defined] # noqa: E501 @@ -301,9 +295,9 @@ def start_log_server( logging_config: Dict, output_dir: str, ) -> None: - setup_logger(filename=filename, - logging_config=logging_config, - output_dir=output_dir) + setup_logger( + filename=filename, logging_config=logging_config, output_dir=output_dir + ) while True: # Loop until we find a valid port @@ -334,7 +328,7 @@ class LogRecordSocketReceiver(socketserver.ThreadingTCPServer): def __init__( self, - host: str = 'localhost', + host: str = "localhost", port: int = logging.handlers.DEFAULT_TCP_LOGGING_PORT, handler: Type[LogRecordStreamHandler] = LogRecordStreamHandler, logname: Optional[str] = None, @@ -347,9 +341,7 @@ def __init__( def serve_until_stopped(self) -> None: while True: - rd, wr, ex = select.select([self.socket.fileno()], - [], [], - self.timeout) + rd, wr, ex = select.select([self.socket.fileno()], [], [], self.timeout) if rd: self.handle_request() if self.event is not None and self.event.is_set(): @@ -358,7 +350,7 @@ def serve_until_stopped(self) -> None: @contextmanager def warnings_to(logger: Optional[PicklableClientLogger] = None) -> Iterator[None]: - """ A context manager to catch warnings and send them to the logger + """A context manager to catch warnings and send them to the logger If no logger is passed, warnings propogate as they normally would. @@ -371,6 +363,7 @@ def warnings_to(logger: Optional[PicklableClientLogger] = None) -> Iterator[None if logger: with warnings.catch_warnings(): + def to_log( logger: PicklableClientLogger, message: str, @@ -378,15 +371,16 @@ def to_log( filename: str, lineno: int, file: Optional[TextIO] = None, - line: Optional[str] = None + line: Optional[str] = None, ) -> None: logger.warning(f"{filename}:{lineno} {category.__name__}:{message}") # Mypy was complaining that logger didn't exist in `to_log` see here: # https://mypy.readthedocs.io/en/stable/common_issues.html#narrowing-and-inner-functions # we explicitly pass it in and have to force it's type with `cast` - warnings.showwarning = lambda *args: \ - to_log(cast(PicklableClientLogger, logger), *args) + warnings.showwarning = lambda *args: to_log( + cast(PicklableClientLogger, logger), *args + ) yield # Else do nothing, warnings go to wherever they would without this context diff --git a/autosklearn/util/parallel.py b/autosklearn/util/parallel.py index 2f0ea6b016..0804588a61 100644 --- a/autosklearn/util/parallel.py +++ b/autosklearn/util/parallel.py @@ -5,16 +5,19 @@ def preload_modules(context: multiprocessing.context.BaseContext) -> None: all_loaded_modules = sys.modules.keys() preload = [ - loaded_module for loaded_module in all_loaded_modules - if loaded_module.split('.')[0] in ( - 'smac', - 'autosklearn', - 'numpy', - 'scipy', - 'pandas', - 'pynisher', - 'sklearn', - 'ConfigSpace', - ) and 'logging' not in loaded_module + loaded_module + for loaded_module in all_loaded_modules + if loaded_module.split(".")[0] + in ( + "smac", + "autosklearn", + "numpy", + "scipy", + "pandas", + "pynisher", + "sklearn", + "ConfigSpace", + ) + and "logging" not in loaded_module ] context.set_forkserver_preload(preload) diff --git a/autosklearn/util/pipeline.py b/autosklearn/util/pipeline.py index c1f5a2ca23..d3291069f5 100755 --- a/autosklearn/util/pipeline.py +++ b/autosklearn/util/pipeline.py @@ -1,29 +1,27 @@ # -*- encoding: utf-8 -*- from typing import Any, Dict, List, Optional, Union -from ConfigSpace.configuration_space import ConfigurationSpace - import numpy as np +from ConfigSpace.configuration_space import ConfigurationSpace from autosklearn.constants import ( BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION, MULTIOUTPUT_REGRESSION, - REGRESSION_TASKS + REGRESSION_TASKS, ) from autosklearn.pipeline.classification import SimpleClassificationPipeline from autosklearn.pipeline.regression import SimpleRegressionPipeline - -__all__ = ['get_configuration_space'] +__all__ = ["get_configuration_space"] def get_configuration_space( info: Dict[str, Any], include: Optional[Dict[str, List[str]]] = None, exclude: Optional[Dict[str, List[str]]] = None, - random_state: Optional[Union[int, np.random.RandomState]] = None + random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> ConfigurationSpace: """Get the configuration of a pipeline given some dataset info @@ -46,17 +44,19 @@ def get_configuration_space( ConfigurationSpace The configuration space for the pipeline """ - if info['task'] in REGRESSION_TASKS: + if info["task"] in REGRESSION_TASKS: return _get_regression_configuration_space(info, include, exclude, random_state) else: - return _get_classification_configuration_space(info, include, exclude, random_state) + return _get_classification_configuration_space( + info, include, exclude, random_state + ) def _get_regression_configuration_space( info: Dict[str, Any], include: Optional[Dict[str, List[str]]], exclude: Optional[Dict[str, List[str]]], - random_state: Optional[Union[int, np.random.RandomState]] = None + random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> ConfigurationSpace: """Get the configuration of a regression pipeline given some dataset info @@ -79,25 +79,22 @@ def _get_regression_configuration_space( ConfigurationSpace The configuration space for the regression pipeline """ - task_type = info['task'] + task_type = info["task"] sparse = False multioutput = False if task_type == MULTIOUTPUT_REGRESSION: multioutput = True - if info['is_sparse'] == 1: + if info["is_sparse"] == 1: sparse = True - dataset_properties = { - 'multioutput': multioutput, - 'sparse': sparse - } + dataset_properties = {"multioutput": multioutput, "sparse": sparse} configuration_space = SimpleRegressionPipeline( dataset_properties=dataset_properties, include=include, exclude=exclude, - random_state=random_state + random_state=random_state, ).get_hyperparameter_search_space() return configuration_space @@ -106,7 +103,7 @@ def _get_classification_configuration_space( info: Dict[str, Any], include: Optional[Dict[str, List[str]]], exclude: Optional[Dict[str, List[str]]], - random_state: Optional[Union[int, np.random.RandomState]] = None + random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> ConfigurationSpace: """Get the configuration of a classification pipeline given some dataset info @@ -129,7 +126,7 @@ def _get_classification_configuration_space( ConfigurationSpace The configuration space for the classification pipeline """ - task_type = info['task'] + task_type = info["task"] multilabel = False multiclass = False @@ -142,18 +139,18 @@ def _get_classification_configuration_space( if task_type == BINARY_CLASSIFICATION: pass - if info['is_sparse'] == 1: + if info["is_sparse"] == 1: sparse = True dataset_properties = { - 'multilabel': multilabel, - 'multiclass': multiclass, - 'sparse': sparse + "multilabel": multilabel, + "multiclass": multiclass, + "sparse": sparse, } return SimpleClassificationPipeline( dataset_properties=dataset_properties, include=include, exclude=exclude, - random_state=random_state + random_state=random_state, ).get_hyperparameter_search_space() diff --git a/autosklearn/util/single_thread_client.py b/autosklearn/util/single_thread_client.py index b6e92f5ec9..c27794c328 100644 --- a/autosklearn/util/single_thread_client.py +++ b/autosklearn/util/single_thread_client.py @@ -1,4 +1,5 @@ import typing + from pathlib import Path from typing import Any diff --git a/autosklearn/util/stopwatch.py b/autosklearn/util/stopwatch.py index aff17a1acf..9ced028cd0 100644 --- a/autosklearn/util/stopwatch.py +++ b/autosklearn/util/stopwatch.py @@ -5,10 +5,11 @@ @project: AutoML2015 """ +from typing import Tuple + import sys import time from collections import OrderedDict -from typing import Tuple class TimingTask(object): @@ -31,7 +32,7 @@ def stop(self) -> None: self._cpu_dur = self._cpu_tac - self._cpu_tic self._wall_dur = self._wall_tac - self._wall_tic else: - sys.stdout.write('Task has already stopped\n') + sys.stdout.write("Task has already stopped\n") @property def name(self) -> str: @@ -72,7 +73,7 @@ class StopWatch: def __init__(self) -> None: self._tasks = OrderedDict() - self._tasks['stopwatch_time'] = TimingTask('stopwatch_time') + self._tasks["stopwatch_time"] = TimingTask("stopwatch_time") def insert_task(self, name: str, cpu_dur: float, wall_dur: float) -> None: if name not in self._tasks: @@ -109,20 +110,20 @@ def stop_task(self, name: str) -> None: try: self._tasks[name].stop() except KeyError: - sys.stderr.write('There is no such task: %s\n' % name) + sys.stderr.write("There is no such task: %s\n" % name) def get_cpu_dur(self, name: str) -> float: try: return self._tasks[name].cpu_dur except KeyError: - sys.stderr.write('There is no such task: %s\n' % name) + sys.stderr.write("There is no such task: %s\n" % name) return 0.0 def get_wall_dur(self, name: str) -> float: try: return self._tasks[name].wall_dur except KeyError: - sys.stderr.write('There is no such task: %s\n' % name) + sys.stderr.write("There is no such task: %s\n" % name) return 0.0 def cpu_sum(self) -> float: @@ -134,19 +135,27 @@ def wall_sum(self) -> float: return sum([max(0, self._tasks[tsk].wall_dur) for tsk in self._tasks]) def __repr__(self) -> str: - ret_str = '| %10s | %10s | %10s | %10s | %10s | %10s | %10s |\n' % \ - ('Name', 'CPUStart', 'CPUEnd', 'CPUDur', 'WallStart', - 'WallEnd', - 'WallDur') - ret_str += '+' + '------------+' * 7 + '\n' - offset = self._tasks['stopwatch_time'].wall_tic + ret_str = "| %10s | %10s | %10s | %10s | %10s | %10s | %10s |\n" % ( + "Name", + "CPUStart", + "CPUEnd", + "CPUDur", + "WallStart", + "WallEnd", + "WallDur", + ) + ret_str += "+" + "------------+" * 7 + "\n" + offset = self._tasks["stopwatch_time"].wall_tic for tsk in self._tasks: if self._tasks[tsk].wall_tac: wall_tac = self._tasks[tsk].wall_tac - offset - ret_str += '| %10s | %10.5f | %10.5f | %10.5f | %10s | %10s | %10s |\n' % \ - (tsk, self._tasks[tsk].cpu_tic, self._tasks[tsk].cpu_tac, - self.cpu_elapsed(tsk), - self._tasks[tsk].wall_tic - offset, - wall_tac if self._tasks[tsk].wall_tac else False, - self.wall_elapsed(tsk)) + ret_str += "| %10s | %10.5f | %10.5f | %10.5f | %10s | %10s | %10s |\n" % ( + tsk, + self._tasks[tsk].cpu_tic, + self._tasks[tsk].cpu_tac, + self.cpu_elapsed(tsk), + self._tasks[tsk].wall_tic - offset, + wall_tac if self._tasks[tsk].wall_tac else False, + self.wall_elapsed(tsk), + ) return ret_str diff --git a/doc/conf.py b/doc/conf.py index 5d114b3550..6efcd5c736 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -21,11 +21,12 @@ import sys import sphinx_bootstrap_theme import autosklearn + # Add the parent directory of this file to the PYTHONPATH import os current_directory = os.path.dirname(__file__) -parent_directory = os.path.join(current_directory, '..') +parent_directory = os.path.join(current_directory, "..") parent_directory = os.path.abspath(parent_directory) sys.path.append(parent_directory) @@ -39,15 +40,21 @@ # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. -extensions = ['sphinx.ext.autodoc', 'sphinx.ext.autosummary', - 'sphinx.ext.doctest', 'sphinx.ext.coverage', - 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', - 'sphinx_gallery.gen_gallery', 'sphinx.ext.autosectionlabel', - 'sphinx_toolbox.collapse', - # sphinx.ext.autosexctionlabel raises duplicate label warnings - # because same section headers are used multiple times throughout - # the documentation. - 'numpydoc'] +extensions = [ + "sphinx.ext.autodoc", + "sphinx.ext.autosummary", + "sphinx.ext.doctest", + "sphinx.ext.coverage", + "sphinx.ext.mathjax", + "sphinx.ext.viewcode", + "sphinx_gallery.gen_gallery", + "sphinx.ext.autosectionlabel", + "sphinx_toolbox.collapse", + # sphinx.ext.autosexctionlabel raises duplicate label warnings + # because same section headers are used multiple times throughout + # the documentation. + "numpydoc", +] from sphinx_gallery.sorting import ExplicitOrder, FileNameSortKey @@ -63,8 +70,9 @@ # Sphinx-gallery configuration. # get current branch -binder_branch = 'master' +binder_branch = "master" import autosklearn + if "dev" in autosklearn.__version__: binder_branch = "development" @@ -72,7 +80,7 @@ # We do this by setting an evironment variable we check and modifying the python config # object. # We have this extra processing as it enters as a raw string and we need a boolean value -gallery_env_var ="SPHINX_GALLERY_PLOT" +gallery_env_var = "SPHINX_GALLERY_PLOT" sphinx_plot_gallery_flag = True if gallery_env_var in os.environ: @@ -82,52 +90,55 @@ elif value in ["True", "true", "1"]: sphinx_plot_gallery_flag = True else: - raise ValueError(f'Env variable {gallery_env_var} must be set to "false" or "true"') + raise ValueError( + f'Env variable {gallery_env_var} must be set to "false" or "true"' + ) sphinx_gallery_conf = { # path to the examples - 'examples_dirs': '../examples', + "examples_dirs": "../examples", # path where to save gallery generated examples - 'gallery_dirs': 'examples', - #TODO: fix back/forward references for the examples. + "gallery_dirs": "examples", + # TODO: fix back/forward references for the examples. #'doc_module': ('autosklearn'), #'reference_url': { # 'autosklearn': None - #}, - 'plot_gallery': sphinx_plot_gallery_flag, - 'backreferences_dir': None, - 'filename_pattern': 'example.*.py$', - 'ignore_pattern': r'custom_metrics\.py|__init__\.py|example_parallel_manual_spawning_python.py', - 'binder': { - # Required keys - 'org': 'automl', - 'repo': 'auto-sklearn', - 'branch': binder_branch, - 'binderhub_url': 'https://mybinder.org', - 'dependencies': ['../.binder/apt.txt', '../.binder/requirements.txt'], - #'filepath_prefix': '' # A prefix to prepend to any filepaths in Binder links. - # Jupyter notebooks for Binder will be copied to this directory (relative to built documentation root). - 'notebooks_dir': 'notebooks/', - 'use_jupyter_lab': True, # Whether Binder links should start Jupyter Lab instead of the Jupyter Notebook interface. - }, + # }, + "plot_gallery": sphinx_plot_gallery_flag, + "backreferences_dir": None, + "filename_pattern": "example.*.py$", + "ignore_pattern": r"custom_metrics\.py|__init__\.py|example_parallel_manual_spawning_python.py", + "binder": { + # Required keys + "org": "automl", + "repo": "auto-sklearn", + "branch": binder_branch, + "binderhub_url": "https://mybinder.org", + "dependencies": ["../.binder/apt.txt", "../.binder/requirements.txt"], + #'filepath_prefix': '' # A prefix to prepend to any filepaths in Binder links. + # Jupyter notebooks for Binder will be copied to this directory (relative to built documentation root). + "notebooks_dir": "notebooks/", + "use_jupyter_lab": True, # Whether Binder links should start Jupyter Lab instead of the Jupyter Notebook interface. + }, } # Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] +templates_path = ["_templates"] # The suffix of source filenames. -source_suffix = '.rst' +source_suffix = ".rst" # The encoding of source files. # source_encoding = 'utf-8-sig' # The master toctree document. -master_doc = 'index' +master_doc = "index" # General information about the project. -project = u'AutoSklearn' -copyright = u"2014-{}, Machine Learning Professorship Freiburg".format( - datetime.datetime.now().year) +project = "AutoSklearn" +copyright = "2014-{}, Machine Learning Professorship Freiburg".format( + datetime.datetime.now().year +) # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the @@ -150,7 +161,7 @@ # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. -exclude_patterns = ['_build', '_templates', '_static'] +exclude_patterns = ["_build", "_templates", "_static"] # The reST default role (used for this markup: `text`) to use for all # documents. @@ -168,7 +179,7 @@ # show_authors = False # The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' +pygments_style = "sphinx" # A list of ignored prefixes for module index sorting. # modindex_common_prefix = [] @@ -180,44 +191,37 @@ # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. -html_theme = 'bootstrap' +html_theme = "bootstrap" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. html_theme_options = { # Navigation bar title. (Default: ``project`` value) - 'navbar_title': "auto-sklearn", - + "navbar_title": "auto-sklearn", # Tab name for entire site. (Default: "Site") # 'navbar_site_name': "Site", - # A list of tuples containting pages to link to. The value should # be in the form [(name, page), ..] - 'navbar_links': [ - ('Start', 'index'), - ('Releases', 'releases'), - ('Installation', 'installation'), - ('Manual', 'manual'), - ('Examples', 'examples/index'), - ('API', 'api'), - ('Extending', 'extending'), - ('FAQ', 'faq'), + "navbar_links": [ + ("Start", "index"), + ("Releases", "releases"), + ("Installation", "installation"), + ("Manual", "manual"), + ("Examples", "examples/index"), + ("API", "api"), + ("Extending", "extending"), + ("FAQ", "faq"), ], - # Render the next and previous page links in navbar. (Default: true) - 'navbar_sidebarrel': False, - + "navbar_sidebarrel": False, # Render the current pages TOC in the navbar. (Default: true) - 'navbar_pagenav': False, - + "navbar_pagenav": False, # Tab name for the current pages TOC. (Default: "Page") - 'navbar_pagenav_name': "On this page", - + "navbar_pagenav_name": "On this page", # Global TOC depth for "site" navbar tab. (Default: 1) # Switching to -1 shows all levels. - 'globaltoc_depth': 1, - + "globaltoc_depth": 1, # Include hidden TOCs in Site navbar? # # Note: If this is "false", you cannot have mixed ``:hidden:`` and @@ -225,29 +229,24 @@ # will break. # # Values: "true" (default) or "false" - 'globaltoc_includehidden': "false", - + "globaltoc_includehidden": "false", # HTML navbar class (Default: "navbar") to attach to
element. # For black navbar, do "navbar navbar-inverse" - 'navbar_class': "navbar", - + "navbar_class": "navbar", # Fix navigation bar to top of page? # Values: "true" (default) or "false" - 'navbar_fixed_top': "true", - + "navbar_fixed_top": "true", # Location of link to source. # Options are "nav" (default), "footer" or anything else to exclude. - 'source_link_position': "footer", - + "source_link_position": "footer", # Bootswatch (http://bootswatch.com/) theme. # # Options are nothing with "" (default) or the name of a valid theme # such as "amelia" or "cosmo". - 'bootswatch_theme': "cosmo", - + "bootswatch_theme": "cosmo", # Choose Bootstrap version. # Values: "3" (default) or "2" (in quotes) - 'bootstrap_version': "3", + "bootstrap_version": "3", } # Add any paths that contain custom themes here, relative to this directory. @@ -288,7 +287,7 @@ # html_use_smartypants = True # Custom sidebar templates, maps document names to template names. -html_sidebars = {'**': ['localtoc.html']} +html_sidebars = {"**": ["localtoc.html"]} # Additional templates that should be rendered to pages, maps page names to # template names. @@ -321,7 +320,7 @@ # html_file_suffix = None # Output file base name for HTML help builder. -htmlhelp_basename = 'AutoSklearndoc' +htmlhelp_basename = "AutoSklearndoc" # -- Options for LaTeX output --------------------------------------------- @@ -337,9 +336,15 @@ # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). -latex_documents = [('index', 'AutoSklearn.tex', u'AutoSklearn Documentation', - u'Matthias Feurer, Aaron Klein, Katharina Eggensperger', - 'manual'), ] +latex_documents = [ + ( + "index", + "AutoSklearn.tex", + "AutoSklearn Documentation", + "Matthias Feurer, Aaron Klein, Katharina Eggensperger", + "manual", + ), +] # The name of an image file (relative to this directory) to place at the top of # the title page. @@ -365,8 +370,15 @@ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). -man_pages = [('index', 'autosklearn', u'AutoSklearn Documentation', - [u'Matthias Feurer, Aaron Klein, Katharina Eggensperger'], 1)] +man_pages = [ + ( + "index", + "autosklearn", + "AutoSklearn Documentation", + ["Matthias Feurer, Aaron Klein, Katharina Eggensperger"], + 1, + ) +] # If true, show URL addresses after external links. # man_show_urls = False @@ -376,10 +388,17 @@ # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) -texinfo_documents = [('index', 'AutoSklearn', u'AutoSklearn Documentation', - u'Matthias Feurer, Aaron Klein, Katharina Eggensperger', - 'AutoSklearn', 'One line description of project.', - 'Miscellaneous'), ] +texinfo_documents = [ + ( + "index", + "AutoSklearn", + "AutoSklearn Documentation", + "Matthias Feurer, Aaron Klein, Katharina Eggensperger", + "AutoSklearn", + "One line description of project.", + "Miscellaneous", + ), +] # Documents to append as an appendix to all manuals. # texinfo_appendices = [] @@ -396,12 +415,12 @@ # Only the class’ docstring is inserted. This is the default. # You can still document __init__ as a separate method using automethod or # the members option to autoclass. -#"both" +# "both" # Both the class’ and the __init__ method’s docstring are concatenated and # inserted. # "init" # Only the __init__ method’s docstring is inserted. -autoclass_content = 'both' +autoclass_content = "both" def setup(app): diff --git a/examples/20_basic/example_classification.py b/examples/20_basic/example_classification.py index fcb99b65ef..621dcf4f86 100644 --- a/examples/20_basic/example_classification.py +++ b/examples/20_basic/example_classification.py @@ -20,8 +20,9 @@ # ============ X, y = sklearn.datasets.load_breast_cancer(return_X_y=True) -X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=1) +X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1 +) ############################################################################ # Build and fit a classifier @@ -30,9 +31,9 @@ automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=120, per_run_time_limit=30, - tmp_folder='/tmp/autosklearn_classification_example_tmp', + tmp_folder="/tmp/autosklearn_classification_example_tmp", ) -automl.fit(X_train, y_train, dataset_name='breast_cancer') +automl.fit(X_train, y_train, dataset_name="breast_cancer") ############################################################################ # View the models found by auto-sklearn @@ -52,4 +53,3 @@ predictions = automl.predict(X_test) print("Accuracy score:", sklearn.metrics.accuracy_score(y_test, predictions)) - diff --git a/examples/20_basic/example_multilabel_classification.py b/examples/20_basic/example_multilabel_classification.py index 835b110ea6..bedf974868 100644 --- a/examples/20_basic/example_multilabel_classification.py +++ b/examples/20_basic/example_multilabel_classification.py @@ -29,8 +29,8 @@ # This is to comply with Scikit-learn requirement: # "Positive classes are indicated with 1 and negative classes with 0 or -1." # More information on: https://scikit-learn.org/stable/modules/multiclass.html -y[y == 'TRUE'] = 1 -y[y == 'FALSE'] = 0 +y[y == "TRUE"] = 1 +y[y == "FALSE"] = 0 y = y.astype(int) # Using type of target is a good way to make sure your data @@ -51,9 +51,9 @@ # Bellow two flags are provided to speed up calculations # Not recommended for a real implementation initial_configurations_via_metalearning=0, - smac_scenario_args={'runcount_limit': 1}, + smac_scenario_args={"runcount_limit": 1}, ) -automl.fit(X_train, y_train, dataset_name='reuters') +automl.fit(X_train, y_train, dataset_name="reuters") ############################################################################ # View the models found by auto-sklearn diff --git a/examples/20_basic/example_multioutput_regression.py b/examples/20_basic/example_multioutput_regression.py index a2e345fcac..cb12643adb 100644 --- a/examples/20_basic/example_multioutput_regression.py +++ b/examples/20_basic/example_multioutput_regression.py @@ -32,9 +32,9 @@ automl = AutoSklearnRegressor( time_left_for_this_task=120, per_run_time_limit=30, - tmp_folder='/tmp/autosklearn_multioutput_regression_example_tmp', + tmp_folder="/tmp/autosklearn_multioutput_regression_example_tmp", ) -automl.fit(X_train, y_train, dataset_name='synthetic') +automl.fit(X_train, y_train, dataset_name="synthetic") ############################################################################ # View the models found by auto-sklearn diff --git a/examples/20_basic/example_regression.py b/examples/20_basic/example_regression.py index 6b47607db0..5ade1c2866 100644 --- a/examples/20_basic/example_regression.py +++ b/examples/20_basic/example_regression.py @@ -21,8 +21,9 @@ X, y = sklearn.datasets.load_diabetes(return_X_y=True) -X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=1) +X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1 +) ########################### # Build and fit a regressor @@ -31,9 +32,9 @@ automl = autosklearn.regression.AutoSklearnRegressor( time_left_for_this_task=120, per_run_time_limit=30, - tmp_folder='/tmp/autosklearn_regression_example_tmp', + tmp_folder="/tmp/autosklearn_regression_example_tmp", ) -automl.fit(X_train, y_train, dataset_name='diabetes') +automl.fit(X_train, y_train, dataset_name="diabetes") ############################################################################ # View the models found by auto-sklearn @@ -69,12 +70,12 @@ # than the true value), points above the diagonal were underestimated (predicted value is lower than # the true value). -plt.scatter(train_predictions, y_train, label="Train samples", c='#d95f02') -plt.scatter(test_predictions, y_test, label="Test samples", c='#7570b3') +plt.scatter(train_predictions, y_train, label="Train samples", c="#d95f02") +plt.scatter(test_predictions, y_test, label="Test samples", c="#7570b3") plt.xlabel("Predicted value") plt.ylabel("True value") plt.legend() -plt.plot([30, 400], [30, 400], c='k', zorder=0) +plt.plot([30, 400], [30, 400], c="k", zorder=0) plt.xlim([30, 400]) plt.ylim([30, 400]) plt.tight_layout() diff --git a/examples/40_advanced/custom_metrics.py b/examples/40_advanced/custom_metrics.py index 6b548e5718..c6ad14efdd 100644 --- a/examples/40_advanced/custom_metrics.py +++ b/examples/40_advanced/custom_metrics.py @@ -9,6 +9,7 @@ # Custom metrics definition # ========================= + def accuracy(solution, prediction): # custom function defining accuracy return np.mean(solution == prediction) diff --git a/examples/40_advanced/example_calc_multiple_metrics.py b/examples/40_advanced/example_calc_multiple_metrics.py index c7a4e78503..fa4d17cc1e 100644 --- a/examples/40_advanced/example_calc_multiple_metrics.py +++ b/examples/40_advanced/example_calc_multiple_metrics.py @@ -25,9 +25,9 @@ def error(solution, prediction): def get_metric_result(cv_results): results = pd.DataFrame.from_dict(cv_results) - results = results[results['status'] == "Success"] - cols = ['rank_test_scores', 'param_classifier:__choice__', 'mean_test_score'] - cols.extend([key for key in cv_results.keys() if key.startswith('metric_')]) + results = results[results["status"] == "Success"] + cols = ["rank_test_scores", "param_classifier:__choice__", "mean_test_score"] + cols.extend([key for key in cv_results.keys() if key.startswith("metric_")]) return results[cols] @@ -36,25 +36,26 @@ def get_metric_result(cv_results): # ============ X, y = sklearn.datasets.load_breast_cancer(return_X_y=True) -X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=1) +X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1 +) ############################################################################ # Build and fit a classifier # ========================== error_rate = autosklearn.metrics.make_scorer( - name='custom_error', + name="custom_error", score_func=error, optimum=0, greater_is_better=False, needs_proba=False, - needs_threshold=False + needs_threshold=False, ) cls = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=120, per_run_time_limit=30, - scoring_functions=[balanced_accuracy, precision, recall, f1, error_rate] + scoring_functions=[balanced_accuracy, precision, recall, f1, error_rate], ) cls.fit(X_train, y_train, X_test, y_test) diff --git a/examples/40_advanced/example_debug_logging.py b/examples/40_advanced/example_debug_logging.py index 07e2e3ed99..664ce0b461 100644 --- a/examples/40_advanced/example_debug_logging.py +++ b/examples/40_advanced/example_debug_logging.py @@ -28,8 +28,9 @@ # Load kr-vs-kp dataset from https://www.openml.org/d/3 X, y = data = sklearn.datasets.fetch_openml(data_id=3, return_X_y=True, as_frame=True) -X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=1) +X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1 +) ############################################################################ @@ -40,33 +41,31 @@ # We will instead create a custom one as follows: logging_config = { - 'version': 1, - 'disable_existing_loggers': True, - 'formatters': { - 'custom': { + "version": 1, + "disable_existing_loggers": True, + "formatters": { + "custom": { # More format options are available in the official # `documentation `_ - 'format': '%(asctime)s - %(name)s - %(levelname)s - %(message)s' + "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s" } }, - # Any INFO level msg will be printed to the console - 'handlers': { - 'console': { - 'level': 'INFO', - 'formatter': 'custom', - 'class': 'logging.StreamHandler', - 'stream': 'ext://sys.stdout', + "handlers": { + "console": { + "level": "INFO", + "formatter": "custom", + "class": "logging.StreamHandler", + "stream": "ext://sys.stdout", }, }, - - 'loggers': { - '': { # root logger - 'level': 'DEBUG', + "loggers": { + "": { # root logger + "level": "DEBUG", }, - 'Client-EnsembleBuilder': { - 'level': 'DEBUG', - 'handlers': ['console'], + "Client-EnsembleBuilder": { + "level": "DEBUG", + "handlers": ["console"], }, }, } @@ -80,11 +79,11 @@ # Bellow two flags are provided to speed up calculations # Not recommended for a real implementation initial_configurations_via_metalearning=0, - smac_scenario_args={'runcount_limit': 2}, + smac_scenario_args={"runcount_limit": 2}, # Pass the config file we created logging_config=logging_config, # *auto-sklearn* generates temporal files under tmp_folder - tmp_folder='./tmp_folder', + tmp_folder="./tmp_folder", # By default tmp_folder is deleted. We will preserve it # for debug purposes delete_tmp_folder_after_terminate=False, @@ -101,5 +100,5 @@ # * tmp_folder/smac3-output # Auto-sklearn always outputs to this log file # tmp_folder/AutoML*.log -for filename in pathlib.Path('./tmp_folder').glob('*'): +for filename in pathlib.Path("./tmp_folder").glob("*"): print(filename) diff --git a/examples/40_advanced/example_feature_types.py b/examples/40_advanced/example_feature_types.py index 6317eb5a46..7d22edd715 100644 --- a/examples/40_advanced/example_feature_types.py +++ b/examples/40_advanced/example_feature_types.py @@ -4,9 +4,10 @@ Feature Types ============= -In *auto-sklearn* it is possible to specify the feature types of a dataset when calling the method -:meth:`fit() ` by specifying the argument -``feat_type``. The following example demonstrates a way it can be done. +In *auto-sklearn* it is possible to specify the feature types of a dataset when calling +the method :meth:`fit() ` by +specifying the argument ``feat_type``. +The following example demonstrates a way it can be done. Additionally, you can provide a properly formatted pandas DataFrame, and the feature types will be automatically inferred, as demonstrated in @@ -26,11 +27,12 @@ # ============ # Load Australian dataset from https://www.openml.org/d/40981 bunch = data = sklearn.datasets.fetch_openml(data_id=40981, as_frame=True) -y = bunch['target'].to_numpy() -X = bunch['data'].to_numpy(np.float) +y = bunch["target"].to_numpy() +X = bunch["data"].to_numpy(np.float) -X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=1) +X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1 +) # Auto-sklearn can automatically recognize categorical/numerical data from a pandas # DataFrame. This example highlights how the user can provide the feature types, @@ -38,8 +40,7 @@ # feat_type is a list that tags each column from a DataFrame/ numpy array / list # with the case-insensitive string categorical or numerical, accordingly. feat_type = [ - 'Categorical' if x.name == 'category' else 'Numerical' - for x in bunch['data'].dtypes + "Categorical" if x.name == "category" else "Numerical" for x in bunch["data"].dtypes ] ############################################################################ @@ -51,7 +52,7 @@ # Bellow two flags are provided to speed up calculations # Not recommended for a real implementation initial_configurations_via_metalearning=0, - smac_scenario_args={'runcount_limit': 1}, + smac_scenario_args={"runcount_limit": 1}, ) cls.fit(X_train, y_train, X_test, y_test, feat_type=feat_type) diff --git a/examples/40_advanced/example_get_pipeline_components.py b/examples/40_advanced/example_get_pipeline_components.py index f7a97ead27..80686889ac 100644 --- a/examples/40_advanced/example_get_pipeline_components.py +++ b/examples/40_advanced/example_get_pipeline_components.py @@ -27,8 +27,9 @@ # ============ X, y = sklearn.datasets.load_breast_cancer(return_X_y=True) -X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=1) +X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1 +) ############################################################################ # Build and fit the classifier @@ -40,20 +41,16 @@ disable_evaluator_output=False, # To simplify querying the models in the final ensemble, we # restrict auto-sklearn to use only pca as a preprocessor - include={ - 'feature_preprocessor': ['pca'] - }, + include={"feature_preprocessor": ["pca"]}, ) -automl.fit(X_train, y_train, dataset_name='breast_cancer') +automl.fit(X_train, y_train, dataset_name="breast_cancer") ############################################################################ # Predict using the model # ======================= predictions = automl.predict(X_test) -print("Accuracy score:{}".format( - sklearn.metrics.accuracy_score(y_test, predictions)) -) +print("Accuracy score:{}".format(sklearn.metrics.accuracy_score(y_test, predictions))) ############################################################################ @@ -104,7 +101,7 @@ # Let's iterative over all entries for run_key in automl.automl_.runhistory_.data: - print('#########') + print("#########") print(run_key) print(automl.automl_.runhistory_.data[run_key]) @@ -166,7 +163,7 @@ print("Lowest loss:", losses_and_configurations[0][0]) print( "Best configuration:", - automl.automl_.runhistory_.ids_config[losses_and_configurations[0][1]] + automl.automl_.runhistory_.ids_config[losses_and_configurations[0][1]], ) ############################################################################ @@ -188,7 +185,7 @@ # The explained variance ratio per stage for i, (weight, pipeline) in enumerate(automl.get_models_with_weights()): for stage_name, component in pipeline.named_steps.items(): - if 'feature_preprocessor' in stage_name: + if "feature_preprocessor" in stage_name: print( "The {}th pipeline has a explained variance of {}".format( i, @@ -196,6 +193,6 @@ # Access the sklearn object via the choice attribute # We want the explained variance attributed of # each principal component - component.choice.preprocessor.explained_variance_ratio_ + component.choice.preprocessor.explained_variance_ratio_, ) ) diff --git a/examples/40_advanced/example_inspect_predictions.py b/examples/40_advanced/example_inspect_predictions.py index 24e149a37b..cf6de2476f 100644 --- a/examples/40_advanced/example_inspect_predictions.py +++ b/examples/40_advanced/example_inspect_predictions.py @@ -36,9 +36,9 @@ automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=120, per_run_time_limit=30, - tmp_folder='/tmp/autosklearn_inspect_predictions_example_tmp', + tmp_folder="/tmp/autosklearn_inspect_predictions_example_tmp", ) -automl.fit(X_train, y_train, dataset_name='Run_or_walk_information') +automl.fit(X_train, y_train, dataset_name="Run_or_walk_information") s = automl.score(X_train, y_train) print(f"Train score {s}") @@ -61,16 +61,19 @@ r = permutation_importance(automl, X_test, y_test, n_repeats=10, random_state=0) sort_idx = r.importances_mean.argsort()[::-1] -plt.boxplot(r.importances[sort_idx].T, - labels=[dataset.feature_names[i] for i in sort_idx]) +plt.boxplot( + r.importances[sort_idx].T, labels=[dataset.feature_names[i] for i in sort_idx] +) plt.xticks(rotation=90) plt.tight_layout() plt.show() for i in sort_idx[::-1]: - print(f"{dataset.feature_names[i]:10s}: {r.importances_mean[i]:.3f} +/- " - f"{r.importances_std[i]:.3f}") + print( + f"{dataset.feature_names[i]:10s}: {r.importances_mean[i]:.3f} +/- " + f"{r.importances_std[i]:.3f}" + ) ############################################################################################ # Create partial dependence (PD) and individual conditional expectation (ICE) plots - part 2 @@ -90,11 +93,14 @@ # combining ICE (thin lines) and PD (thick line) features = [1, 2] -plot_partial_dependence(automl, dataset.data, - features=features, - grid_resolution=5, - kind="both", - feature_names=dataset.feature_names) +plot_partial_dependence( + automl, + dataset.data, + features=features, + grid_resolution=5, + kind="both", + feature_names=dataset.feature_names, +) plt.tight_layout() plt.show() @@ -106,9 +112,12 @@ # these features. Again, we'll look at acceleration_y and acceleration_z. features = [[1, 2]] -plot_partial_dependence(automl, dataset.data, - features=features, - grid_resolution=5, - feature_names=dataset.feature_names) +plot_partial_dependence( + automl, + dataset.data, + features=features, + grid_resolution=5, + feature_names=dataset.feature_names, +) plt.tight_layout() plt.show() diff --git a/examples/40_advanced/example_interpretable_models.py b/examples/40_advanced/example_interpretable_models.py index a78695082c..7b551de7b8 100644 --- a/examples/40_advanced/example_interpretable_models.py +++ b/examples/40_advanced/example_interpretable_models.py @@ -29,7 +29,9 @@ # Show available preprocessors # ============================ -from autosklearn.pipeline.components.feature_preprocessing import FeaturePreprocessorChoice +from autosklearn.pipeline.components.feature_preprocessing import ( + FeaturePreprocessorChoice, +) for name in FeaturePreprocessorChoice.get_components(): print(name) @@ -39,8 +41,9 @@ # ============ X, y = sklearn.datasets.load_breast_cancer(return_X_y=True) -X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=1) +X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1 +) ############################################################################ # Build and fit a classifier @@ -55,18 +58,18 @@ automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=120, per_run_time_limit=30, - tmp_folder='/tmp/autosklearn_interpretable_models_example_tmp', + tmp_folder="/tmp/autosklearn_interpretable_models_example_tmp", include={ - 'classifier': [ - 'decision_tree', 'lda', 'sgd' + "classifier": ["decision_tree", "lda", "sgd"], + "feature_preprocessor": [ + "no_preprocessing", + "polynomial", + "select_percentile_classification", ], - 'feature_preprocessor': [ - 'no_preprocessing', 'polynomial', 'select_percentile_classification' - ] }, ensemble_size=1, ) -automl.fit(X_train, y_train, dataset_name='breast_cancer') +automl.fit(X_train, y_train, dataset_name="breast_cancer") ############################################################################ # Print the final ensemble constructed by auto-sklearn diff --git a/examples/40_advanced/example_metrics.py b/examples/40_advanced/example_metrics.py index 2cf39f1553..33d0f678fd 100644 --- a/examples/40_advanced/example_metrics.py +++ b/examples/40_advanced/example_metrics.py @@ -51,8 +51,9 @@ def error_wk(solution, prediction, extra_argument): # ============ X, y = sklearn.datasets.load_breast_cancer(return_X_y=True) -X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=1) +X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1 +) ############################################################################ # Print a list of available metrics @@ -68,7 +69,7 @@ def error_wk(solution, prediction, extra_argument): # First example: Use predefined accuracy metric # ============================================= -print("#"*80) +print("#" * 80) print("Use predefined accuracy metric") cls = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=60, @@ -87,7 +88,7 @@ def error_wk(solution, prediction, extra_argument): # Second example: Use own accuracy metric # ======================================= -print("#"*80) +print("#" * 80) print("Use self defined accuracy metric") accuracy_scorer = autosklearn.metrics.make_scorer( name="accu", @@ -114,15 +115,15 @@ def error_wk(solution, prediction, extra_argument): # Third example: Use own error metric # =================================== -print("#"*80) +print("#" * 80) print("Use self defined error metric") error_rate = autosklearn.metrics.make_scorer( - name='error', + name="error", score_func=error, optimum=0, greater_is_better=False, needs_proba=False, - needs_threshold=False + needs_threshold=False, ) cls = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=60, @@ -141,7 +142,7 @@ def error_wk(solution, prediction, extra_argument): # Fourth example: Use own accuracy metric with additional argument # ================================================================ -print("#"*80) +print("#" * 80) print("Use self defined accuracy with additional argument") accuracy_scorer = autosklearn.metrics.make_scorer( name="accu_add", @@ -153,10 +154,7 @@ def error_wk(solution, prediction, extra_argument): extra_argument=None, ) cls = autosklearn.classification.AutoSklearnClassifier( - time_left_for_this_task=60, - per_run_time_limit=30, - seed=1, - metric=accuracy_scorer + time_left_for_this_task=60, per_run_time_limit=30, seed=1, metric=accuracy_scorer ) cls.fit(X_train, y_train) @@ -169,7 +167,7 @@ def error_wk(solution, prediction, extra_argument): # Fifth example: Use own accuracy metric with additional argument # =============================================================== -print("#"*80) +print("#" * 80) print("Use self defined error with additional argument") error_rate = autosklearn.metrics.make_scorer( name="error_add", diff --git a/examples/40_advanced/example_pandas_train_test.py b/examples/40_advanced/example_pandas_train_test.py index 910cac4c31..7e584fd8aa 100644 --- a/examples/40_advanced/example_pandas_train_test.py +++ b/examples/40_advanced/example_pandas_train_test.py @@ -58,22 +58,19 @@ # Targets for classification are also automatically encoded # If using fetch_openml, data is already properly encoded, below # is an example for user reference -X = pd.DataFrame( - data=X, - columns=['A' + str(i) for i in range(1, 15)] -) -desired_boolean_columns = ['A1'] -desired_categorical_columns = ['A4', 'A5', 'A6', 'A8', 'A9', 'A11', 'A12'] -desired_numerical_columns = ['A2', 'A3', 'A7', 'A10', 'A13', 'A14'] +X = pd.DataFrame(data=X, columns=["A" + str(i) for i in range(1, 15)]) +desired_boolean_columns = ["A1"] +desired_categorical_columns = ["A4", "A5", "A6", "A8", "A9", "A11", "A12"] +desired_numerical_columns = ["A2", "A3", "A7", "A10", "A13", "A14"] for column in X.columns: if column in desired_boolean_columns: - X[column] = X[column].astype('bool') + X[column] = X[column].astype("bool") elif column in desired_categorical_columns: - X[column] = X[column].astype('category') + X[column] = X[column].astype("category") else: X[column] = pd.to_numeric(X[column]) -y = pd.DataFrame(y, dtype='category') +y = pd.DataFrame(y, dtype="category") X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( X, y, test_size=0.5, random_state=3 @@ -100,15 +97,15 @@ ############################################################################ # Plot the ensemble performance # =================================== -# The *performance_over_time_* attribute returns a pandas dataframe, which can +# The *performance_over_time_* attribute returns a pandas dataframe, which can # be directly used for plotting poT = cls.performance_over_time_ poT.plot( - x='Timestamp', - kind='line', + x="Timestamp", + kind="line", legend=True, - title='Auto-sklearn accuracy over time', + title="Auto-sklearn accuracy over time", grid=True, ) plt.show() diff --git a/examples/40_advanced/example_resampling.py b/examples/40_advanced/example_resampling.py index 124316a60a..aa6a272373 100644 --- a/examples/40_advanced/example_resampling.py +++ b/examples/40_advanced/example_resampling.py @@ -22,8 +22,9 @@ # ============ X, y = sklearn.datasets.load_breast_cancer(return_X_y=True) -X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=1) +X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1 +) ############################################################################ # Holdout @@ -32,15 +33,15 @@ automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=120, per_run_time_limit=30, - tmp_folder='/tmp/autosklearn_resampling_example_tmp', + tmp_folder="/tmp/autosklearn_resampling_example_tmp", disable_evaluator_output=False, # 'holdout' with 'train_size'=0.67 is the default argument setting # for AutoSklearnClassifier. It is explicitly specified in this example # for demonstrational purpose. - resampling_strategy='holdout', - resampling_strategy_arguments={'train_size': 0.67}, + resampling_strategy="holdout", + resampling_strategy_arguments={"train_size": 0.67}, ) -automl.fit(X_train, y_train, dataset_name='breast_cancer') +automl.fit(X_train, y_train, dataset_name="breast_cancer") ############################################################################ # Get the Score of the final ensemble @@ -57,18 +58,18 @@ automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=120, per_run_time_limit=30, - tmp_folder='/tmp/autosklearn_resampling_example_tmp', + tmp_folder="/tmp/autosklearn_resampling_example_tmp", disable_evaluator_output=False, - resampling_strategy='cv', - resampling_strategy_arguments={'folds': 5}, + resampling_strategy="cv", + resampling_strategy_arguments={"folds": 5}, ) -automl.fit(X_train, y_train, dataset_name='breast_cancer') +automl.fit(X_train, y_train, dataset_name="breast_cancer") # One can use models trained during cross-validation directly to predict # for unseen data. For this, all k models trained during k-fold # cross-validation are considered as a single soft-voting ensemble inside # the ensemble constructed with ensemble selection. -print('Before re-fit') +print("Before re-fit") predictions = automl.predict(X_test) print("Accuracy score CV", sklearn.metrics.accuracy_score(y_test, predictions)) @@ -78,7 +79,7 @@ # During fit(), models are fit on individual cross-validation folds. To use # all available data, we call refit() which trains all models in the # final ensemble on the whole dataset. -print('After re-fit') +print("After re-fit") automl.refit(X_train.copy(), y_train.copy()) predictions = automl.predict(X_test) print("Accuracy score CV", sklearn.metrics.accuracy_score(y_test, predictions)) @@ -106,11 +107,11 @@ automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=120, per_run_time_limit=30, - tmp_folder='/tmp/autosklearn_resampling_example_tmp', + tmp_folder="/tmp/autosklearn_resampling_example_tmp", disable_evaluator_output=False, resampling_strategy=resampling_strategy, ) -automl.fit(X_train, y_train, dataset_name='breast_cancer') +automl.fit(X_train, y_train, dataset_name="breast_cancer") print(automl.sprint_statistics()) @@ -126,4 +127,6 @@ # Obviously, this score is pretty bad as we "destroyed" the dataset by # splitting it on the first feature. predictions = automl.predict(X_test) -print("Accuracy score custom split", sklearn.metrics.accuracy_score(y_test, predictions)) +print( + "Accuracy score custom split", sklearn.metrics.accuracy_score(y_test, predictions) +) diff --git a/examples/40_advanced/example_single_configuration.py b/examples/40_advanced/example_single_configuration.py index 3d230f4ab0..d216caef7c 100644 --- a/examples/40_advanced/example_single_configuration.py +++ b/examples/40_advanced/example_single_configuration.py @@ -46,9 +46,7 @@ # We will limit the configuration space only to # have RandomForest as a valid model. We recommend enabling all # possible models to get a better performance. - include={ - 'classifier': ['random_forest'] - }, + include={"classifier": ["random_forest"]}, delete_tmp_folder_after_terminate=False, ) @@ -60,17 +58,21 @@ # min_samples_split in the Random Forest. We recommend you to look into # how the ConfigSpace package works here: # https://automl.github.io/ConfigSpace/master/ -cs = cls.get_configuration_space(X, y, dataset_name='kr-vs-kp') +cs = cls.get_configuration_space(X, y, dataset_name="kr-vs-kp") config = cs.sample_configuration() -config._values['classifier:random_forest:min_samples_split'] = 11 +config._values["classifier:random_forest:min_samples_split"] = 11 # Make sure that your changed configuration complies with the configuration space config.is_valid_configuration() -pipeline, run_info, run_value = cls.fit_pipeline(X=X_train, y=y_train, - dataset_name='kr-vs-kp', - config=config, - X_test=X_test, y_test=y_test) +pipeline, run_info, run_value = cls.fit_pipeline( + X=X_train, + y=y_train, + dataset_name="kr-vs-kp", + config=config, + X_test=X_test, + y_test=y_test, +) # This object complies with Scikit-Learn Pipeline API. # https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html @@ -84,9 +86,9 @@ # We can make sure that our pipeline configuration was honored as follows print("Passed Configuration:", pipeline.config) -print("Random Forest:", pipeline.named_steps['classifier'].choice.estimator) +print("Random Forest:", pipeline.named_steps["classifier"].choice.estimator) # We can also search for new configurations using the fit() method # Any configurations found by Auto-Sklearn -- even the ones created using # fit_pipeline() are stored to disk and can be used for Ensemble Selection -cs = cls.fit(X, y, dataset_name='kr-vs-kp') +cs = cls.fit(X, y, dataset_name="kr-vs-kp") diff --git a/examples/40_advanced/example_text_preprocessing.py b/examples/40_advanced/example_text_preprocessing.py index f60188781b..76c2d91cfc 100644 --- a/examples/40_advanced/example_text_preprocessing.py +++ b/examples/40_advanced/example_text_preprocessing.py @@ -25,20 +25,28 @@ print(f"{X.info()}\n") # manually convert these to string columns -X = X.astype({'name': 'string', 'ticket': 'string', 'cabin': 'string', 'boat': 'string', - 'home.dest': 'string'}) +X = X.astype( + { + "name": "string", + "ticket": "string", + "cabin": "string", + "boat": "string", + "home.dest": "string", + } +) # now *auto-sklearn* handles the string columns with its text feature preprocessing pipeline -X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=1) +X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1 +) cls = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=30, # Bellow two flags are provided to speed up calculations # Not recommended for a real implementation initial_configurations_via_metalearning=0, - smac_scenario_args={'runcount_limit': 1}, + smac_scenario_args={"runcount_limit": 1}, ) cls.fit(X_train, y_train, X_test, y_test) @@ -48,20 +56,24 @@ X, y = sklearn.datasets.fetch_openml(data_id=40945, return_X_y=True, as_frame=True) -X = X.select_dtypes(exclude=['object']) +X = X.select_dtypes(exclude=["object"]) -X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=1) +X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1 +) cls = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=30, # Bellow two flags are provided to speed up calculations # Not recommended for a real implementation initial_configurations_via_metalearning=0, - smac_scenario_args={'runcount_limit': 1}, + smac_scenario_args={"runcount_limit": 1}, ) cls.fit(X_train, y_train, X_test, y_test) predictions = cls.predict(X_test) -print("Accuracy score without text preprocessing", sklearn.metrics.accuracy_score(y_test, predictions)) +print( + "Accuracy score without text preprocessing", + sklearn.metrics.accuracy_score(y_test, predictions), +) diff --git a/examples/60_search/example_parallel_manual_spawning_cli.py b/examples/60_search/example_parallel_manual_spawning_cli.py index 41200cd78c..fa2bff375b 100644 --- a/examples/60_search/example_parallel_manual_spawning_cli.py +++ b/examples/60_search/example_parallel_manual_spawning_cli.py @@ -68,7 +68,7 @@ from autosklearn.classification import AutoSklearnClassifier from autosklearn.constants import MULTICLASS_CLASSIFICATION -tmp_folder = '/tmp/autosklearn_parallel_3_example_tmp' +tmp_folder = "/tmp/autosklearn_parallel_3_example_tmp" worker_processes = [] @@ -83,7 +83,7 @@ # location. This filename is also given to the worker so they can find all # relevant information to connect to the scheduler. -scheduler_file_name = 'scheduler-file.json' +scheduler_file_name = "scheduler-file.json" ############################################################################ @@ -99,12 +99,16 @@ # We will now execute this bash command from within Python to have a # self-contained example: + def cli_start_scheduler(scheduler_file_name): - command = ( - f"dask-scheduler --scheduler-file {scheduler_file_name} --idle-timeout 10" + command = f"dask-scheduler --scheduler-file {scheduler_file_name} --idle-timeout 10" + proc = subprocess.run( + command, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + shell=True, + check=True, ) - proc = subprocess.run(command, stdout=subprocess.PIPE, - stderr=subprocess.STDOUT, shell=True, check=True) while proc.returncode is None: time.sleep(1) @@ -112,7 +116,7 @@ def cli_start_scheduler(scheduler_file_name): if __name__ == "__main__": process_python_worker = multiprocessing.Process( target=cli_start_scheduler, - args=(scheduler_file_name, ), + args=(scheduler_file_name,), ) process_python_worker.start() worker_processes.append(process_python_worker) @@ -141,22 +145,25 @@ def cli_start_scheduler(scheduler_file_name): # We disable dask's memory management by passing ``--memory-limit`` as # Auto-sklearn does the memory management itself. + def cli_start_worker(scheduler_file_name): command = ( "DASK_DISTRIBUTED__WORKER__DAEMON=False " "dask-worker --nthreads 1 --lifetime 35 --memory-limit 0 " f"--scheduler-file {scheduler_file_name}" ) - proc = subprocess.run(command, stdout=subprocess.PIPE, - stderr=subprocess.STDOUT, shell=True) + proc = subprocess.run( + command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True + ) while proc.returncode is None: time.sleep(1) -if __name__ == '__main__': + +if __name__ == "__main__": for _ in range(2): process_cli_worker = multiprocessing.Process( target=cli_start_worker, - args=(scheduler_file_name, ), + args=(scheduler_file_name,), ) process_cli_worker.start() worker_processes.append(process_cli_worker) @@ -178,8 +185,9 @@ def cli_start_worker(scheduler_file_name): # ~~~~~~~~~~~~~~~~~~ if __name__ == "__main__": X, y = sklearn.datasets.load_breast_cancer(return_X_y=True) - X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=1) + X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1 + ) automl = AutoSklearnClassifier( delete_tmp_folder_after_terminate=False, @@ -198,7 +206,7 @@ def cli_start_worker(scheduler_file_name): automl.fit_ensemble( y_train, task=MULTICLASS_CLASSIFICATION, - dataset_name='digits', + dataset_name="digits", ensemble_size=20, ensemble_nbest=50, ) @@ -215,7 +223,7 @@ def cli_start_worker(scheduler_file_name): # This is only necessary if the workers are started from within this python # script. In a real application one would start them directly from the command # line. -if __name__ == '__main__': +if __name__ == "__main__": process_python_worker.join() for process in worker_processes: process.join() diff --git a/examples/60_search/example_parallel_manual_spawning_python.py b/examples/60_search/example_parallel_manual_spawning_python.py index ed723598a9..75c5bcee30 100644 --- a/examples/60_search/example_parallel_manual_spawning_python.py +++ b/examples/60_search/example_parallel_manual_spawning_python.py @@ -58,7 +58,7 @@ from autosklearn.classification import AutoSklearnClassifier from autosklearn.constants import MULTICLASS_CLASSIFICATION -tmp_folder = '/tmp/autosklearn_parallel_2_example_tmp' +tmp_folder = "/tmp/autosklearn_parallel_2_example_tmp" ############################################################################ @@ -73,8 +73,9 @@ # https://docs.dask.org/en/latest/setup/python-advanced.html for further # information. + def start_python_worker(scheduler_address): - dask.config.set({'distributed.worker.daemon': False}) + dask.config.set({"distributed.worker.daemon": False}) async def do_work(): async with dask.distributed.Nanny( @@ -97,14 +98,17 @@ async def do_work(): # To use auto-sklearn in parallel we must guard the code with # ``if __name__ == '__main__'``. We then start a dask cluster as a context, # which means that it is automatically stopped once all computation is done. -if __name__ == '__main__': +if __name__ == "__main__": X, y = sklearn.datasets.load_breast_cancer(return_X_y=True) - X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=1) + X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1 + ) # 1. Create a dask scheduler (LocalCluster) with dask.distributed.LocalCluster( - n_workers=0, processes=True, threads_per_worker=1, + n_workers=0, + processes=True, + threads_per_worker=1, ) as cluster: # 2. Start the workers @@ -114,7 +118,7 @@ async def do_work(): for _ in range(2): process_python_worker = multiprocessing.Process( target=start_python_worker, - args=(cluster.scheduler_address, ), + args=(cluster.scheduler_address,), ) process_python_worker.start() worker_processes.append(process_python_worker) @@ -141,7 +145,7 @@ async def do_work(): automl.fit_ensemble( y_train, task=MULTICLASS_CLASSIFICATION, - dataset_name='digits', + dataset_name="digits", ensemble_size=20, ensemble_nbest=50, ) diff --git a/examples/60_search/example_parallel_n_jobs.py b/examples/60_search/example_parallel_n_jobs.py index b7265ce3fa..1cb5014ca8 100644 --- a/examples/60_search/example_parallel_n_jobs.py +++ b/examples/60_search/example_parallel_n_jobs.py @@ -27,26 +27,27 @@ # Data Loading # ============ X, y = sklearn.datasets.load_breast_cancer(return_X_y=True) -X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=1) +X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1 +) ############################################################################ # Build and fit a classifier # ========================== # # To use ``n_jobs_`` we must guard the code -if __name__ == '__main__': +if __name__ == "__main__": automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=120, per_run_time_limit=30, - tmp_folder='/tmp/autosklearn_parallel_1_example_tmp', + tmp_folder="/tmp/autosklearn_parallel_1_example_tmp", n_jobs=4, # Each one of the 4 jobs is allocated 3GB memory_limit=3072, seed=5, ) - automl.fit(X_train, y_train, dataset_name='breast_cancer') + automl.fit(X_train, y_train, dataset_name="breast_cancer") # Print statistics about the auto-sklearn run such as number of # iterations, number of models failed with a time out. diff --git a/examples/60_search/example_random_search.py b/examples/60_search/example_random_search.py index 2c9cc76695..520c8c18b0 100644 --- a/examples/60_search/example_random_search.py +++ b/examples/60_search/example_random_search.py @@ -29,8 +29,9 @@ # ============ X, y = sklearn.datasets.load_breast_cancer(return_X_y=True) -X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=1) +X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1 +) ############################################################################ @@ -48,8 +49,10 @@ def get_roar_object_callback( """Random online adaptive racing.""" if n_jobs > 1 or (dask_client and len(dask_client.nthreads()) > 1): - raise ValueError("Please make sure to guard the code invoking Auto-sklearn by " - "`if __name__ == '__main__'` and remove this exception.") + raise ValueError( + "Please make sure to guard the code invoking Auto-sklearn by " + "`if __name__ == '__main__'` and remove this exception." + ) scenario = Scenario(scenario_dict) return ROAR( @@ -66,15 +69,15 @@ def get_roar_object_callback( automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=60, per_run_time_limit=15, - tmp_folder='/tmp/autosklearn_random_search_example_tmp', + tmp_folder="/tmp/autosklearn_random_search_example_tmp", initial_configurations_via_metalearning=0, # The callback to get the SMAC object get_smac_object_callback=get_roar_object_callback, ) -automl.fit(X_train, y_train, dataset_name='breast_cancer') +automl.fit(X_train, y_train, dataset_name="breast_cancer") -print('#' * 80) -print('Results for ROAR.') +print("#" * 80) +print("Results for ROAR.") # Print the final ensemble constructed by auto-sklearn via ROAR. pprint(automl.show_models(), indent=4) predictions = automl.predict(X_test) @@ -88,22 +91,18 @@ def get_roar_object_callback( # Fit a classifier using Random Search # ==================================== def get_random_search_object_callback( - scenario_dict, - seed, - ta, - ta_kwargs, - metalearning_configurations, - n_jobs, - dask_client + scenario_dict, seed, ta, ta_kwargs, metalearning_configurations, n_jobs, dask_client ): - """ Random search """ + """Random search""" if n_jobs > 1 or (dask_client and len(dask_client.nthreads()) > 1): - raise ValueError("Please make sure to guard the code invoking Auto-sklearn by " - "`if __name__ == '__main__'` and remove this exception.") + raise ValueError( + "Please make sure to guard the code invoking Auto-sklearn by " + "`if __name__ == '__main__'` and remove this exception." + ) - scenario_dict['minR'] = len(scenario_dict['instances']) - scenario_dict['initial_incumbent'] = 'RANDOM' + scenario_dict["minR"] = len(scenario_dict["instances"]) + scenario_dict["initial_incumbent"] = "RANDOM" scenario = Scenario(scenario_dict) return ROAR( scenario=scenario, @@ -119,15 +118,15 @@ def get_random_search_object_callback( automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=60, per_run_time_limit=15, - tmp_folder='/tmp/autosklearn_random_search_example_tmp', + tmp_folder="/tmp/autosklearn_random_search_example_tmp", initial_configurations_via_metalearning=0, # Passing the callback to get the SMAC object get_smac_object_callback=get_random_search_object_callback, ) -automl.fit(X_train, y_train, dataset_name='breast_cancer') +automl.fit(X_train, y_train, dataset_name="breast_cancer") -print('#' * 80) -print('Results for random search.') +print("#" * 80) +print("Results for random search.") # Print the final ensemble constructed by auto-sklearn via random search. pprint(automl.show_models(), indent=4) diff --git a/examples/60_search/example_sequential.py b/examples/60_search/example_sequential.py index fad088396d..1ff63649da 100644 --- a/examples/60_search/example_sequential.py +++ b/examples/60_search/example_sequential.py @@ -22,8 +22,9 @@ # ====================================== X, y = sklearn.datasets.load_breast_cancer(return_X_y=True) -X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=1) +X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1 +) ############################################################################ # Build and fit the classifier @@ -32,14 +33,14 @@ automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=120, per_run_time_limit=30, - tmp_folder='/tmp/autosklearn_sequential_example_tmp', + tmp_folder="/tmp/autosklearn_sequential_example_tmp", # Do not construct ensembles in parallel to avoid using more than one # core at a time. The ensemble will be constructed after auto-sklearn # finished fitting all machine learning models. ensemble_size=0, delete_tmp_folder_after_terminate=False, ) -automl.fit(X_train, y_train, dataset_name='breast_cancer') +automl.fit(X_train, y_train, dataset_name="breast_cancer") # This call to fit_ensemble uses all models trained in the previous call # to fit to build an ensemble which can be used with automl.predict() diff --git a/examples/60_search/example_successive_halving.py b/examples/60_search/example_successive_halving.py index fdb29da6e0..e57be7f157 100644 --- a/examples/60_search/example_successive_halving.py +++ b/examples/60_search/example_successive_halving.py @@ -27,6 +27,7 @@ # Define a callback that instantiates SuccessiveHalving # ===================================================== + def get_smac_object_callback(budget_type): def get_smac_object( scenario_dict, @@ -43,8 +44,10 @@ def get_smac_object( from smac.scenario.scenario import Scenario if n_jobs > 1 or (dask_client and len(dask_client.nthreads()) > 1): - raise ValueError("Please make sure to guard the code invoking Auto-sklearn by " - "`if __name__ == '__main__'` and remove this exception.") + raise ValueError( + "Please make sure to guard the code invoking Auto-sklearn by " + "`if __name__ == '__main__'` and remove this exception." + ) scenario = Scenario(scenario_dict) if len(metalearning_configurations) > 0: @@ -54,7 +57,7 @@ def get_smac_object( initial_configurations = None rh2EPM = RunHistory2EPM4LogCost - ta_kwargs['budget_type'] = budget_type + ta_kwargs["budget_type"] = budget_type return SMAC4AC( scenario=scenario, @@ -66,14 +69,15 @@ def get_smac_object( run_id=seed, intensifier=SuccessiveHalving, intensifier_kwargs={ - 'initial_budget': 10.0, - 'max_budget': 100, - 'eta': 2, - 'min_chall': 1 + "initial_budget": 10.0, + "max_budget": 100, + "eta": 2, + "min_chall": 1, }, n_jobs=n_jobs, dask_client=dask_client, ) + return get_smac_object @@ -82,8 +86,9 @@ def get_smac_object( # ============ X, y = sklearn.datasets.load_breast_cancer(return_X_y=True) -X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=1, shuffle=True) +X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1, shuffle=True +) ############################################################################ # Build and fit a classifier @@ -92,23 +97,26 @@ def get_smac_object( automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=40, per_run_time_limit=10, - tmp_folder='/tmp/autosklearn_sh_example_tmp', + tmp_folder="/tmp/autosklearn_sh_example_tmp", disable_evaluator_output=False, # 'holdout' with 'train_size'=0.67 is the default argument setting # for AutoSklearnClassifier. It is explicitly specified in this example # for demonstrational purpose. - resampling_strategy='holdout', - resampling_strategy_arguments={'train_size': 0.67}, + resampling_strategy="holdout", + resampling_strategy_arguments={"train_size": 0.67}, include={ - 'classifier': [ - 'extra_trees', 'gradient_boosting', 'random_forest', - 'sgd', 'passive_aggressive' + "classifier": [ + "extra_trees", + "gradient_boosting", + "random_forest", + "sgd", + "passive_aggressive", ], - 'feature_preprocessor': ['no_preprocessing'] + "feature_preprocessor": ["no_preprocessing"], }, - get_smac_object_callback=get_smac_object_callback('iterations'), + get_smac_object_callback=get_smac_object_callback("iterations"), ) -automl.fit(X_train, y_train, dataset_name='breast_cancer') +automl.fit(X_train, y_train, dataset_name="breast_cancer") pprint(automl.show_models(), indent=4) predictions = automl.predict(X_test) @@ -122,25 +130,29 @@ def get_smac_object( # ======================================================== X, y = sklearn.datasets.load_breast_cancer(return_X_y=True) -X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=1, shuffle=True) +X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1, shuffle=True +) automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=40, per_run_time_limit=10, - tmp_folder='/tmp/autosklearn_sh_example_tmp_01', + tmp_folder="/tmp/autosklearn_sh_example_tmp_01", disable_evaluator_output=False, - resampling_strategy='cv', + resampling_strategy="cv", include={ - 'classifier': [ - 'extra_trees', 'gradient_boosting', 'random_forest', - 'sgd', 'passive_aggressive' + "classifier": [ + "extra_trees", + "gradient_boosting", + "random_forest", + "sgd", + "passive_aggressive", ], - 'feature_preprocessor': ['no_preprocessing'] + "feature_preprocessor": ["no_preprocessing"], }, - get_smac_object_callback=get_smac_object_callback('iterations'), + get_smac_object_callback=get_smac_object_callback("iterations"), ) -automl.fit(X_train, y_train, dataset_name='breast_cancer') +automl.fit(X_train, y_train, dataset_name="breast_cancer") # Print the final ensemble constructed by auto-sklearn. pprint(automl.show_models(), indent=4) @@ -156,25 +168,29 @@ def get_smac_object( # ============================================================= X, y = sklearn.datasets.load_breast_cancer(return_X_y=True) -X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=1, shuffle=True) +X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1, shuffle=True +) automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=40, per_run_time_limit=10, - tmp_folder='/tmp/autosklearn_sh_example_tmp_cv_02', + tmp_folder="/tmp/autosklearn_sh_example_tmp_cv_02", disable_evaluator_output=False, - resampling_strategy='cv-iterative-fit', + resampling_strategy="cv-iterative-fit", include={ - 'classifier': [ - 'extra_trees', 'gradient_boosting', 'random_forest', - 'sgd', 'passive_aggressive' + "classifier": [ + "extra_trees", + "gradient_boosting", + "random_forest", + "sgd", + "passive_aggressive", ], - 'feature_preprocessor': ['no_preprocessing'] + "feature_preprocessor": ["no_preprocessing"], }, - get_smac_object_callback=get_smac_object_callback('iterations'), + get_smac_object_callback=get_smac_object_callback("iterations"), ) -automl.fit(X_train, y_train, dataset_name='breast_cancer') +automl.fit(X_train, y_train, dataset_name="breast_cancer") # Print the final ensemble constructed by auto-sklearn. pprint(automl.show_models(), indent=4) @@ -190,22 +206,23 @@ def get_smac_object( # =============================================================== X, y = sklearn.datasets.load_breast_cancer(return_X_y=True) -X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=1, shuffle=True) +X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1, shuffle=True +) automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=40, per_run_time_limit=10, - tmp_folder='/tmp/autosklearn_sh_example_tmp_03', + tmp_folder="/tmp/autosklearn_sh_example_tmp_03", disable_evaluator_output=False, # 'holdout' with 'train_size'=0.67 is the default argument setting # for AutoSklearnClassifier. It is explicitly specified in this example # for demonstrational purpose. - resampling_strategy='holdout', - resampling_strategy_arguments={'train_size': 0.67}, - get_smac_object_callback=get_smac_object_callback('subsample'), + resampling_strategy="holdout", + resampling_strategy_arguments={"train_size": 0.67}, + get_smac_object_callback=get_smac_object_callback("subsample"), ) -automl.fit(X_train, y_train, dataset_name='breast_cancer') +automl.fit(X_train, y_train, dataset_name="breast_cancer") # Print the final ensemble constructed by auto-sklearn. pprint(automl.show_models(), indent=4) @@ -222,27 +239,26 @@ def get_smac_object( # subsamples otherwise X, y = sklearn.datasets.load_breast_cancer(return_X_y=True) -X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=1, shuffle=True) +X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1, shuffle=True +) automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=40, per_run_time_limit=10, - tmp_folder='/tmp/autosklearn_sh_example_tmp_04', + tmp_folder="/tmp/autosklearn_sh_example_tmp_04", disable_evaluator_output=False, # 'holdout' with 'train_size'=0.67 is the default argument setting # for AutoSklearnClassifier. It is explicitly specified in this example # for demonstrational purpose. - resampling_strategy='holdout', - resampling_strategy_arguments={'train_size': 0.67}, + resampling_strategy="holdout", + resampling_strategy_arguments={"train_size": 0.67}, include={ - 'classifier': [ - 'extra_trees', 'gradient_boosting', 'random_forest', 'sgd' - ] + "classifier": ["extra_trees", "gradient_boosting", "random_forest", "sgd"] }, - get_smac_object_callback=get_smac_object_callback('mixed'), + get_smac_object_callback=get_smac_object_callback("mixed"), ) -automl.fit(X_train, y_train, dataset_name='breast_cancer') +automl.fit(X_train, y_train, dataset_name="breast_cancer") # Print the final ensemble constructed by auto-sklearn. pprint(automl.show_models(), indent=4) diff --git a/examples/80_extending/example_extending_classification.py b/examples/80_extending/example_extending_classification.py index b6132f4c18..b5112c022b 100644 --- a/examples/80_extending/example_extending_classification.py +++ b/examples/80_extending/example_extending_classification.py @@ -9,16 +9,22 @@ from pprint import pprint from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import CategoricalHyperparameter, \ - UniformIntegerHyperparameter, UniformFloatHyperparameter +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + UniformIntegerHyperparameter, + UniformFloatHyperparameter, +) import sklearn.metrics import autosklearn.classification import autosklearn.pipeline.components.classification -from autosklearn.pipeline.components.base \ - import AutoSklearnClassificationAlgorithm -from autosklearn.pipeline.constants import DENSE, SIGNED_DATA, UNSIGNED_DATA, \ - PREDICTIONS +from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm +from autosklearn.pipeline.constants import ( + DENSE, + SIGNED_DATA, + UNSIGNED_DATA, + PREDICTIONS, +) from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split @@ -28,8 +34,8 @@ # Create MLP classifier component for auto-sklearn # ================================================ -class MLPClassifier(AutoSklearnClassificationAlgorithm): +class MLPClassifier(AutoSklearnClassificationAlgorithm): def __init__( self, hidden_layer_depth, @@ -52,15 +58,18 @@ def fit(self, X, y): self.alpha = float(self.alpha) from sklearn.neural_network import MLPClassifier + hidden_layer_sizes = tuple( self.num_nodes_per_layer for i in range(self.hidden_layer_depth) ) - self.estimator = MLPClassifier(hidden_layer_sizes=hidden_layer_sizes, - activation=self.activation, - alpha=self.alpha, - solver=self.solver, - random_state=self.random_state) + self.estimator = MLPClassifier( + hidden_layer_sizes=hidden_layer_sizes, + activation=self.activation, + alpha=self.alpha, + solver=self.solver, + random_state=self.random_state, + ) self.estimator.fit(X, y) return self @@ -77,17 +86,17 @@ def predict_proba(self, X): @staticmethod def get_properties(dataset_properties=None): return { - 'shortname': 'MLP Classifier', - 'name': 'MLP CLassifier', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': False, - 'handles_multioutput': False, - 'is_deterministic': False, + "shortname": "MLP Classifier", + "name": "MLP CLassifier", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": False, + "handles_multioutput": False, + "is_deterministic": False, # Both input and output must be tuple(iterable) - 'input': [DENSE, SIGNED_DATA, UNSIGNED_DATA], - 'output': [PREDICTIONS] + "input": [DENSE, SIGNED_DATA, UNSIGNED_DATA], + "output": [PREDICTIONS], } @staticmethod @@ -100,18 +109,25 @@ def get_hyperparameter_search_space(dataset_properties=None): name="num_nodes_per_layer", lower=16, upper=216, default_value=32 ) activation = CategoricalHyperparameter( - name="activation", choices=['identity', 'logistic', 'tanh', 'relu'], - default_value='relu' + name="activation", + choices=["identity", "logistic", "tanh", "relu"], + default_value="relu", ) alpha = UniformFloatHyperparameter( name="alpha", lower=0.0001, upper=1.0, default_value=0.0001 ) solver = CategoricalHyperparameter( - name="solver", choices=['lbfgs', 'sgd', 'adam'], default_value='adam' + name="solver", choices=["lbfgs", "sgd", "adam"], default_value="adam" + ) + cs.add_hyperparameters( + [ + hidden_layer_depth, + num_nodes_per_layer, + activation, + alpha, + solver, + ] ) - cs.add_hyperparameters([ - hidden_layer_depth, num_nodes_per_layer, activation, alpha, solver, - ]) return cs @@ -134,13 +150,11 @@ def get_hyperparameter_search_space(dataset_properties=None): clf = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=30, per_run_time_limit=10, - include={ - 'classifier': ['MLPClassifier'] - }, + include={"classifier": ["MLPClassifier"]}, # Bellow two flags are provided to speed up calculations # Not recommended for a real implementation initial_configurations_via_metalearning=0, - smac_scenario_args={'runcount_limit': 5}, + smac_scenario_args={"runcount_limit": 5}, ) clf.fit(X_train, y_train) diff --git a/examples/80_extending/example_extending_data_preprocessor.py b/examples/80_extending/example_extending_data_preprocessor.py index 7fdd72e971..aa5c443255 100644 --- a/examples/80_extending/example_extending_data_preprocessor.py +++ b/examples/80_extending/example_extending_data_preprocessor.py @@ -21,9 +21,8 @@ # Create NoPreprocessing component for auto-sklearn # ================================================= class NoPreprocessing(AutoSklearnPreprocessingAlgorithm): - def __init__(self, **kwargs): - """ This preprocessors does not change the data """ + """This preprocessors does not change the data""" # Some internal checks makes sure parameters are set for key, val in kwargs.items(): setattr(self, key, val) @@ -37,16 +36,16 @@ def transform(self, X): @staticmethod def get_properties(dataset_properties=None): return { - 'shortname': 'NoPreprocessing', - 'name': 'NoPreprocessing', - 'handles_regression': True, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': True, - 'is_deterministic': True, - 'input': (SPARSE, DENSE, UNSIGNED_DATA), - 'output': (INPUT,) + "shortname": "NoPreprocessing", + "name": "NoPreprocessing", + "handles_regression": True, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": True, + "is_deterministic": True, + "input": (SPARSE, DENSE, UNSIGNED_DATA), + "output": (INPUT,), } @staticmethod @@ -70,13 +69,11 @@ def get_hyperparameter_search_space(dataset_properties=None): clf = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=120, - include={ - 'data_preprocessor': ['NoPreprocessing'] - }, + include={"data_preprocessor": ["NoPreprocessing"]}, # Bellow two flags are provided to speed up calculations # Not recommended for a real implementation initial_configurations_via_metalearning=0, - smac_scenario_args={'runcount_limit': 5}, + smac_scenario_args={"runcount_limit": 5}, ) clf.fit(X_train, y_train) diff --git a/examples/80_extending/example_extending_preprocessor.py b/examples/80_extending/example_extending_preprocessor.py index 9ac93a45b3..1eb3fc1daf 100644 --- a/examples/80_extending/example_extending_preprocessor.py +++ b/examples/80_extending/example_extending_preprocessor.py @@ -10,16 +10,17 @@ from pprint import pprint from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, CategoricalHyperparameter +from ConfigSpace.hyperparameters import ( + UniformFloatHyperparameter, + CategoricalHyperparameter, +) from ConfigSpace.conditions import InCondition import sklearn.metrics import autosklearn.classification import autosklearn.pipeline.components.feature_preprocessing -from autosklearn.pipeline.components.base \ - import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, SIGNED_DATA, \ - UNSIGNED_DATA +from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.constants import DENSE, SIGNED_DATA, UNSIGNED_DATA from autosklearn.util.common import check_none from sklearn.datasets import load_breast_cancer @@ -30,7 +31,6 @@ # Create LDA component for auto-sklearn # ===================================== class LDA(AutoSklearnPreprocessingAlgorithm): - def __init__(self, solver, tol, shrinkage=None, random_state=None): self.solver = solver self.shrinkage = shrinkage @@ -46,6 +46,7 @@ def fit(self, X, y=None): self.tol = float(self.tol) import sklearn.discriminant_analysis + self.preprocessor = sklearn.discriminant_analysis.LinearDiscriminantAnalysis( shrinkage=self.shrinkage, solver=self.solver, @@ -62,23 +63,23 @@ def transform(self, X): @staticmethod def get_properties(dataset_properties=None): return { - 'shortname': 'LDA', - 'name': 'Linear Discriminant Analysis', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': False, - 'handles_multilabel': False, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, UNSIGNED_DATA, SIGNED_DATA), - 'output': (DENSE, UNSIGNED_DATA, SIGNED_DATA) + "shortname": "LDA", + "name": "Linear Discriminant Analysis", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": False, + "handles_multilabel": False, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, UNSIGNED_DATA, SIGNED_DATA), + "output": (DENSE, UNSIGNED_DATA, SIGNED_DATA), } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() solver = CategoricalHyperparameter( - name="solver", choices=['svd', 'lsqr', 'eigen'], default_value='svd' + name="solver", choices=["svd", "lsqr", "eigen"], default_value="svd" ) shrinkage = UniformFloatHyperparameter( name="shrinkage", lower=0.0, upper=1.0, default_value=0.5 @@ -87,7 +88,7 @@ def get_hyperparameter_search_space(dataset_properties=None): name="tol", lower=0.0001, upper=1, default_value=0.0001 ) cs.add_hyperparameters([solver, shrinkage, tol]) - shrinkage_condition = InCondition(shrinkage, solver, ['lsqr', 'eigen']) + shrinkage_condition = InCondition(shrinkage, solver, ["lsqr", "eigen"]) cs.add_condition(shrinkage_condition) return cs @@ -115,13 +116,11 @@ def get_hyperparameter_search_space(dataset_properties=None): clf = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=30, - include={ - 'feature_preprocessor': ['LDA'] - }, + include={"feature_preprocessor": ["LDA"]}, # Bellow two flags are provided to speed up calculations # Not recommended for a real implementation initial_configurations_via_metalearning=0, - smac_scenario_args={'runcount_limit': 5}, + smac_scenario_args={"runcount_limit": 5}, ) clf.fit(X_train, y_train) diff --git a/examples/80_extending/example_extending_regression.py b/examples/80_extending/example_extending_regression.py index 3bdc008d4e..4d6987a9db 100644 --- a/examples/80_extending/example_extending_regression.py +++ b/examples/80_extending/example_extending_regression.py @@ -9,16 +9,24 @@ from pprint import pprint from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ - UniformIntegerHyperparameter, CategoricalHyperparameter +from ConfigSpace.hyperparameters import ( + UniformFloatHyperparameter, + UniformIntegerHyperparameter, + CategoricalHyperparameter, +) from ConfigSpace.conditions import EqualsCondition import sklearn.metrics import autosklearn.regression import autosklearn.pipeline.components.regression from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm -from autosklearn.pipeline.constants import SPARSE, DENSE, \ - SIGNED_DATA, UNSIGNED_DATA, PREDICTIONS +from autosklearn.pipeline.constants import ( + SPARSE, + DENSE, + SIGNED_DATA, + UNSIGNED_DATA, + PREDICTIONS, +) from sklearn.datasets import load_diabetes from sklearn.model_selection import train_test_split @@ -28,8 +36,8 @@ # Implement kernel ridge regression component for auto-sklearn # ============================================================ -class KernelRidgeRegression(AutoSklearnRegressionAlgorithm): +class KernelRidgeRegression(AutoSklearnRegressionAlgorithm): def __init__(self, alpha, kernel, gamma, degree, coef0, random_state=None): self.alpha = alpha self.kernel = kernel @@ -46,12 +54,13 @@ def fit(self, X, y): self.coef0 = float(self.coef0) import sklearn.kernel_ridge + self.estimator = sklearn.kernel_ridge.KernelRidge( alpha=self.alpha, kernel=self.kernel, gamma=self.gamma, degree=self.degree, - coef0=self.coef0 + coef0=self.coef0, ) self.estimator.fit(X, y) return self @@ -64,42 +73,46 @@ def predict(self, X): @staticmethod def get_properties(dataset_properties=None): return { - 'shortname': 'KRR', - 'name': 'Kernel Ridge Regression', - 'handles_regression': True, - 'handles_classification': False, - 'handles_multiclass': False, - 'handles_multilabel': False, - 'handles_multioutput': True, - 'is_deterministic': True, - 'input': (SPARSE, DENSE, UNSIGNED_DATA, SIGNED_DATA), - 'output': (PREDICTIONS,) + "shortname": "KRR", + "name": "Kernel Ridge Regression", + "handles_regression": True, + "handles_classification": False, + "handles_multiclass": False, + "handles_multilabel": False, + "handles_multioutput": True, + "is_deterministic": True, + "input": (SPARSE, DENSE, UNSIGNED_DATA, SIGNED_DATA), + "output": (PREDICTIONS,), } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() alpha = UniformFloatHyperparameter( - name='alpha', lower=10 ** -5, upper=1, log=True, default_value=1.0 + name="alpha", lower=10**-5, upper=1, log=True, default_value=1.0 ) kernel = CategoricalHyperparameter( - name='kernel', + name="kernel", # We restrict ourselves to two possible kernels for this example - choices=['polynomial', 'rbf'], - default_value='polynomial' + choices=["polynomial", "rbf"], + default_value="polynomial", ) gamma = UniformFloatHyperparameter( - name='gamma', lower=0.00001, upper=1, default_value=0.1, log=True + name="gamma", lower=0.00001, upper=1, default_value=0.1, log=True ) degree = UniformIntegerHyperparameter( - name='degree', lower=2, upper=5, default_value=3 + name="degree", lower=2, upper=5, default_value=3 ) coef0 = UniformFloatHyperparameter( - name='coef0', lower=1e-2, upper=1e2, log=True, default_value=1, + name="coef0", + lower=1e-2, + upper=1e2, + log=True, + default_value=1, ) cs.add_hyperparameters([alpha, kernel, gamma, degree, coef0]) - degree_condition = EqualsCondition(degree, kernel, 'polynomial') - coef0_condition = EqualsCondition(coef0, kernel, 'polynomial') + degree_condition = EqualsCondition(degree, kernel, "polynomial") + coef0_condition = EqualsCondition(coef0, kernel, "polynomial") cs.add_conditions([degree_condition, coef0_condition]) return cs @@ -123,13 +136,11 @@ def get_hyperparameter_search_space(dataset_properties=None): reg = autosklearn.regression.AutoSklearnRegressor( time_left_for_this_task=30, per_run_time_limit=10, - include={ - 'regressor': ['KernelRidgeRegression'] - }, + include={"regressor": ["KernelRidgeRegression"]}, # Bellow two flags are provided to speed up calculations # Not recommended for a real implementation initial_configurations_via_metalearning=0, - smac_scenario_args={'runcount_limit': 5}, + smac_scenario_args={"runcount_limit": 5}, ) reg.fit(X_train, y_train) diff --git a/examples/80_extending/example_restrict_number_of_hyperparameters.py b/examples/80_extending/example_restrict_number_of_hyperparameters.py index 9c6ec2501f..d8bd2f4a98 100644 --- a/examples/80_extending/example_restrict_number_of_hyperparameters.py +++ b/examples/80_extending/example_restrict_number_of_hyperparameters.py @@ -9,15 +9,19 @@ """ from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformIntegerHyperparameter, UniformFloatHyperparameter +from ConfigSpace.hyperparameters import ( + UniformIntegerHyperparameter, + UniformFloatHyperparameter, +) from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split import autosklearn.classification import autosklearn.pipeline.components.classification -from autosklearn.pipeline.components.classification \ - import AutoSklearnClassificationAlgorithm +from autosklearn.pipeline.components.classification import ( + AutoSklearnClassificationAlgorithm, +) from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE @@ -29,8 +33,8 @@ # default parametrization (``max_features``). Instead, it also # tunes the number of estimators (``n_estimators``). -class CustomRandomForest(AutoSklearnClassificationAlgorithm): +class CustomRandomForest(AutoSklearnClassificationAlgorithm): def __init__(self, n_estimators, max_features, random_state=None): self.n_estimators = n_estimators self.max_features = max_features @@ -67,16 +71,16 @@ def predict_proba(self, X): @staticmethod def get_properties(dataset_properties=None): return { - 'shortname': 'RF', - 'name': 'Random Forest Classifier', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,) + "shortname": "RF", + "name": "Random Forest Classifier", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), } @staticmethod @@ -87,8 +91,12 @@ def get_hyperparameter_search_space(dataset_properties=None): # m is the total number of features, and max_features is the hyperparameter specified below. # The default is 0.5, which yields sqrt(m) features as max_features in the estimator. This # corresponds with Geurts' heuristic. - max_features = UniformFloatHyperparameter("max_features", 0., 1., default_value=0.5) - n_estimators = UniformIntegerHyperparameter("n_estimators", 10, 1000, default_value=100) + max_features = UniformFloatHyperparameter( + "max_features", 0.0, 1.0, default_value=0.5 + ) + n_estimators = UniformIntegerHyperparameter( + "n_estimators", 10, 1000, default_value=100 + ) cs.add_hyperparameters([max_features, n_estimators]) return cs @@ -114,13 +122,11 @@ def get_hyperparameter_search_space(dataset_properties=None): time_left_for_this_task=30, per_run_time_limit=10, # Here we exclude auto-sklearn's default random forest component - exclude={ - 'classifier': ['random_forest'] - }, + exclude={"classifier": ["random_forest"]}, # Bellow two flags are provided to speed up calculations # Not recommended for a real implementation initial_configurations_via_metalearning=0, - smac_scenario_args={'runcount_limit': 1}, + smac_scenario_args={"runcount_limit": 1}, ) clf.fit(X_train, y_train) @@ -131,5 +137,5 @@ def get_hyperparameter_search_space(dataset_properties=None): # Observe that this configuration space only contains our custom random # forest, but not auto-sklearn's ``random_forest`` cs = clf.get_configuration_space(X_train, y_train) -assert 'random_forest' not in str(cs) +assert "random_forest" not in str(cs) print(cs) diff --git a/misc/create_hyperparameter_table.py b/misc/create_hyperparameter_table.py index dd23f8ac29..7495ee686a 100644 --- a/misc/create_hyperparameter_table.py +++ b/misc/create_hyperparameter_table.py @@ -15,43 +15,43 @@ CONST = "constant" UN = "unparameterized" -template_string = \ -""" +template_string = r""" \documentclass{article} %% For LaTeX2 \usepackage[a4paper, left=5mm, right=5mm, top=5mm, bottom=5mm]{geometry} -%%\\usepackage[landscape]{geometry} -\\usepackage{multirow} %% import command \multicolmun -\\usepackage{tabularx} %% Convenient table formatting -\\usepackage{booktabs} %% provides \\toprule, \midrule and \\bottomrule +%%\usepackage[landscape]{geometry} +\usepackage{multirow} %% import command \multicolmun +\usepackage{tabularx} %% Convenient table formatting +\usepackage{booktabs} %% provides \\toprule, \midrule and \\bottomrule -\\begin{document} +\begin{document} %s -\\end{document} +\end{document} """ -caption_str = "Number of Hyperparameters for each possible %s " \ - "for a dataset with these properties: %s" - -table_str = \ -""" -\\begin{table}[t!] -\\centering -\\scriptsize -\\caption{ %s } -\\begin{tabularx}{\\textwidth}{ X X X X X X } -\\toprule -name & \#$\lambda$ & cat (cond) & cont (cond) & const & un \\\\ -\\toprule -\\\\ +caption_str = ( + "Number of Hyperparameters for each possible %s " + "for a dataset with these properties: %s" +) + +table_str = r""" +\begin{table}[t!] +\centering +\scriptsize +\caption{ %s } +\begin{tabularx}{\textwidth}{ X X X X X X } +\toprule +name & \#$\lambda$ & cat (cond) & cont (cond) & const & un \\ +\toprule +\\ %s -\\\\ -\\toprule -\\bottomrule -\\end{tabularx} -\\end{table} +\\ +\toprule +\bottomrule +\end{tabularx} +\end{table} """ @@ -59,11 +59,13 @@ def get_dict(task_type="classifier", **kwargs): assert task_type in ("classifier", "regressor") if task_type == "classifier": - cs = autosklearn.pipeline.classification.SimpleClassificationPipeline\ - .get_hyperparameter_search_space(dataset_properties=kwargs) + cs = autosklearn.pipeline.classification.SimpleClassificationPipeline.get_hyperparameter_search_space( + dataset_properties=kwargs + ) elif task_type == "regressor": - cs = autosklearn.pipeline.regression.SimpleRegressionPipeline\ - .get_hyperparameter_search_space(dataset_properties=kwargs) + cs = autosklearn.pipeline.regression.SimpleRegressionPipeline.get_hyperparameter_search_space( + dataset_properties=kwargs + ) else: raise ValueError("'task_type' is not in ('classifier', 'regressor')") @@ -73,7 +75,7 @@ def get_dict(task_type="classifier", **kwargs): for h in cs.get_hyperparameters(): if h.name == "feature_preprocessor:__choice__": preprocessor = h - elif h.name == (task_type + ':__choice__'): + elif h.name == (task_type + ":__choice__"): estimator = h if estimator is None: @@ -100,8 +102,9 @@ def get_dict(task_type="classifier", **kwargs): preprocessor_dict[i][UN] = 0 for h in cs.get_hyperparameters(): - if h.name == "feature_preprocessor:__choice__" or \ - h.name == (task_type + ':__choice__'): + if h.name == "feature_preprocessor:__choice__" or h.name == ( + task_type + ":__choice__" + ): continue # walk over both dicts for d in (estimator_dict, preprocessor_dict): @@ -116,14 +119,18 @@ def get_dict(task_type="classifier", **kwargs): d[est][CAT] += 1 elif isinstance(h, ConfigSpace.hyperparameters.Constant): d[est][CONST] += 1 - elif isinstance(h, ConfigSpace.hyperparameters.UnParametrizedHyperparameter): + elif isinstance( + h, ConfigSpace.hyperparameters.UnParametrizedHyperparameter + ): d[est][UN] += 1 else: raise ValueError("Don't know that type: %s" % type(h)) for h in cs.get_conditions(): - if h.parent.name == (task_type + ':__choice__') or h.parent.name == \ - "feature_preprocessor:__choice__": + if ( + h.parent.name == (task_type + ":__choice__") + or h.parent.name == "feature_preprocessor:__choice__" + ): # ignore this condition # print "IGNORE", h continue @@ -132,22 +139,30 @@ def get_dict(task_type="classifier", **kwargs): for d in (estimator_dict, preprocessor_dict): est = h.child.name.split(":")[1] if est not in d: - #print "Could not find %s" % est + # print "Could not find %s" % est continue - #print "####" - #print vars(h) - #print h.parent - #print type(h) - if isinstance(h.child, ConfigSpace.hyperparameters.UniformIntegerHyperparameter): + # print "####" + # print vars(h) + # print h.parent + # print type(h) + if isinstance( + h.child, ConfigSpace.hyperparameters.UniformIntegerHyperparameter + ): d[est][COND][CONT] += 1 - elif isinstance(h.child, ConfigSpace.hyperparameters.UniformFloatHyperparameter): + elif isinstance( + h.child, ConfigSpace.hyperparameters.UniformFloatHyperparameter + ): d[est][COND][CONT] += 1 - elif isinstance(h.child, ConfigSpace.hyperparameters.CategoricalHyperparameter): + elif isinstance( + h.child, ConfigSpace.hyperparameters.CategoricalHyperparameter + ): d[est][COND][CAT] += 1 elif isinstance(h.child, ConfigSpace.hyperparameters.Constant): d[est][COND][CONST] += 1 - elif isinstance(h.child, ConfigSpace.hyperparameters.UnParametrizedHyperparameter): + elif isinstance( + h.child, ConfigSpace.hyperparameters.UnParametrizedHyperparameter + ): d[est][COND][UN] += 1 else: raise ValueError("Don't know that type: %s" % type(h)) @@ -159,7 +174,11 @@ def build_table(d): lines = list() for est in d.keys(): sum_ = 0 - t_list = list([est.replace("_", " "), ]) + t_list = list( + [ + est.replace("_", " "), + ] + ) for t in (CAT, CONT): sum_ += d[est][t] t_list.append("%d (%d)" % (d[est][t], d[est][COND][t])) @@ -175,33 +194,68 @@ def main(): parser = ArgumentParser() # General Options - parser.add_argument("-s", "--save", dest="save", default=None, - help="Where to save plot instead of showing it?") - parser.add_argument("-t", "--type", dest="task_type", default="classifier", - choices=("classifier", ), help="Type of dataset") - parser.add_argument("--sparse", dest="sparse", default=False, - action="store_true", help="dataset property") + parser.add_argument( + "-s", + "--save", + dest="save", + default=None, + help="Where to save plot instead of showing it?", + ) + parser.add_argument( + "-t", + "--type", + dest="task_type", + default="classifier", + choices=("classifier",), + help="Type of dataset", + ) + parser.add_argument( + "--sparse", + dest="sparse", + default=False, + action="store_true", + help="dataset property", + ) prop = parser.add_mutually_exclusive_group(required=True) - prop.add_argument("--multilabel", dest="multilabel", default=False, - action="store_true", help="dataset property") - prop.add_argument("--multiclass", dest="multiclass", default=False, - action="store_true", help="dataset property") - prop.add_argument("--binary", dest="binary", default=False, - action="store_true", help="dataset property") + prop.add_argument( + "--multilabel", + dest="multilabel", + default=False, + action="store_true", + help="dataset property", + ) + prop.add_argument( + "--multiclass", + dest="multiclass", + default=False, + action="store_true", + help="dataset property", + ) + prop.add_argument( + "--binary", + dest="binary", + default=False, + action="store_true", + help="dataset property", + ) args, unknown = parser.parse_known_args() - props = {"sparse": args.sparse, - "multilabel": args.multilabel, - "multiclass": args.multiclass} + props = { + "sparse": args.sparse, + "multilabel": args.multilabel, + "multiclass": args.multiclass, + } est_dict, preproc_dict = get_dict(task_type=args.task_type, **props) est_table = build_table(est_dict) preproc_table = build_table(preproc_dict) est_table = table_str % (caption_str % (args.task_type, str(props)), est_table) - preproc_table = table_str % (caption_str % ( - "feature_preprocessor", str(props)), preproc_table) + preproc_table = table_str % ( + caption_str % ("feature_preprocessor", str(props)), + preproc_table, + ) tex_doc = template_string % "\n".join([est_table, preproc_table]) if args.save is None: @@ -210,7 +264,7 @@ def main(): fh = open(args.save, "w") fh.write(tex_doc) fh.close() - proc = subprocess.Popen(shlex.split('pdflatex %s' % args.save)) + proc = subprocess.Popen(shlex.split("pdflatex %s" % args.save)) proc.communicate() try: os.remove(args.save.replace(".tex", ".aux")) @@ -221,4 +275,4 @@ def main(): if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/misc/create_list_of_potential_models.py b/misc/create_list_of_potential_models.py index 8153c639e7..cec7959ab1 100644 --- a/misc/create_list_of_potential_models.py +++ b/misc/create_list_of_potential_models.py @@ -5,30 +5,32 @@ import sklearn.base -files = glob.glob(os.path.join(os.path.dirname(sklearn.__file__), "**/*.py"), - recursive=True) +files = glob.glob( + os.path.join(os.path.dirname(sklearn.__file__), "**/*.py"), recursive=True +) + def find_all(cls): found = set() for file in files: - parts = file.split('/') - parts[-1] = parts[-1].replace('.py', '') - sklearn_dir = parts.index('sklearn') - name = '.'.join(parts[sklearn_dir:]) + parts = file.split("/") + parts[-1] = parts[-1].replace(".py", "") + sklearn_dir = parts.index("sklearn") + name = ".".join(parts[sklearn_dir:]) module = importlib.import_module(name) for member in module.__dict__.values(): if not inspect.isclass(member): continue if issubclass(member, cls): found.add(member) - print('#####') + print("#####") found = list(found) found.sort(key=lambda t: str(t)) for f in found: print(f) return found -#classifiers = find_all(sklearn.base.ClassifierMixin) -#regressors = find_all(sklearn.base.RegressorMixin) -preprocs = find_all(sklearn.base.TransformerMixin) +# classifiers = find_all(sklearn.base.ClassifierMixin) +# regressors = find_all(sklearn.base.RegressorMixin) +preprocs = find_all(sklearn.base.TransformerMixin) diff --git a/mypy.ini b/mypy.ini deleted file mode 100644 index 8c8b6589af..0000000000 --- a/mypy.ini +++ /dev/null @@ -1,8 +0,0 @@ -[mypy] -# Reports any config lines that are not recognized -warn_unused_configs=True -ignore_missing_imports=True -follow_imports=skip -disallow_untyped_defs=True -disallow_incomplete_defs=True -disallow_untyped_decorators=True diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000..0e48e3fc5f --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,152 @@ +# For TOML reference +# https://learnxinyminutes.com/docs/toml/ + +[tool.pytest.ini_options] +testpaths = ["test"] +minversion = "3.7" +#addopts = "--cov=autosklearn" + +[tool.coverage.run] +branch = true +context = "autosklearn" + +[tool.coverage.report] +show_missing = true +skip_covered = true +exclude_lines = [ + "pragma: no cover", + '\.\.\.', + "raise NotImplementedError", + "if TYPE_CHECKING" +] + +[tool.black] +target-version = ['py37'] + +[tool.isort] +py_version = "37" +profile = "black" # Play nicely with black +src_paths = ["autosklearn", "test"] +known_types = ["typing", "abc"] # We put these in their own section TYPES +known_first_party = ["autosklearn"] # Say that autosklearn is FIRSTPARTY +known_test = ["test"] # Say that test.* is TEST +sections = ["FUTURE", "TYPES", "STDLIB", "THIRDPARTY", "FIRSTPARTY", "TEST", "LOCALFOLDER"] # section ordering +multi_line_output = 3 # https://pycqa.github.io/isort/docs/configuration/multi_line_output_modes.html + +[tool.pydocstyle] +convention = "numpy" +add-ignore = [ # http://www.pydocstyle.org/en/stable/error_codes.html + "D100", # Missing docstring in public module + "D101", # Missing docstring in public class + "D104", # Missing docstring in public package + "D105", # Missing docstring in magic method + + "D203", # 1 blank line required before class docstring + "D205", # 1 blank line required between summary and description + "D210", # No whitespaces allowed surrounding docstring text + "D212", # Multi-line docstring summary should start at the first line + "D213", # Multi-line docstring summary should start at the second line + + "D400", # First line should end with a period + "D401", # First line should be in imperative mood + "D404", # First word of the docstring should not be "This" + "D413", # Missing blank line after last section + "D415" # First line should end with a period, question mark, or exclamation point +] + +[tool.mypy] +python_version = "3.7" + +show_error_codes = true + +warn_unused_configs = true # warn about unused [tool.mypy] lines + +follow_imports = "normal" # Type check top level api code we use from imports +ignore_missing_imports = false # prefer explicit ignores + +disallow_untyped_defs = true # All functions must have types +disallow_untyped_decorators = true # ... even decorators +disallow_incomplete_defs = true # ...all types + +# This is a problem with the tests of `automl_common` being distributed as a submodule +# probably indicative that is should be a package. +exclude = "autosklearn/automl_common/test" + +# This is handled by automl_common itself in its own CI +[[tool.mypy.overrides]] +module = ["autosklearn.automl_common.common.*"] +ignore_errors = true + +# Submodules that need to be updated with mypy +[[tool.mypy.overrides]] +module = [ + "autosklearn", #__init__ + "autosklearn.estimators", + "autosklearn.automl", + "autosklearn.smbo", + "autosklearn.experimental.askl2", + "autosklearn.ensemble_builder", + "autosklearn.ensembles.singlebest_ensemble", + "autosklearn.ensembles.ensemble_selection", + "autosklearn.evaluation", #__init__ + "autosklearn.evaluation.abstract_evaluator", + "autosklearn.evaluation.test_evaluator", + "autosklearn.evaluation.train_evaluator", + "autosklearn.metalearning.input.aslib_simple", + "autosklearn.metalearning.mismbo", + "autosklearn.metalearning.metafeatures.metafeature", + "autosklearn.metalearning.metafeatures.metafeatures", + "autosklearn.metalearning.metalearning.meta_base", + "autosklearn.metalearning.metalearning.metrics.misc", + "autosklearn.metalearning.metalearning.create_datasets", + "autosklearn.metalearning.metalearning.kNearestDatasets.kND", + "autosklearn.metalearning.metalearning.clustering.gmeans", + "autosklearn.metalearning.optimizers.optimizer_base", + "autosklearn.metalearning.optimizers.metalearn_optimizer.metalearn_optimizer_parser", + "autosklearn.metalearning.optimizers.metalearn_optimizer.metalearner", + "autosklearn.pipeline.base", + "autosklearn.pipeline.classification", + "autosklearn.pipeline.regression", + "autosklearn.pipeline.components.base", + "autosklearn.pipeline.components.data_preprocessing.*", + "autosklearn.pipeline.components.regression.*", + "autosklearn.pipeline.components.classification.*", + "autosklearn.pipeline.components.feature_preprocessing.*", + "autosklearn.pipeline.util", + "autosklearn.pipeline.logging_", + "autosklearn.pipeline.create_searchspace_util", + "autosklearn.pipeline.implementations.util", + "autosklearn.pipeline.implementations.SparseOneHotEncoder", + "autosklearn.pipeline.implementations.MinorityCoalescer", + "autosklearn.pipeline.implementations.CategoryShift", + "autosklearn.experimental.selector", + "autosklearn.data.validation", + "autosklearn.data.abstract_data_manager", + "autosklearn.data.xy_data_manager", + "autosklearn.data.target_validator", + "autosklearn.data.feature_validator", + "autosklearn.util.single_threaded_client", + "autosklearn.util.logging_", +] +ignore_errors = true + +# Packages without exported types +[[tool.mypy.overrides]] +module = [ + "sklearn.*", + "dask.*", + "ConfigSpace.*", + "arff.*", + "scipy.*", + "smac.*", + "pandas.*", + "pynisher.*", + "distro.*", + "joblib.*", + "threadpoolctl.*", + "setuptools.*", + "pkg_resources.*", + "yaml.*", +] +ignore_missing_imports = true + diff --git a/pytest.ini b/pytest.ini deleted file mode 100644 index 049e247a21..0000000000 --- a/pytest.ini +++ /dev/null @@ -1,3 +0,0 @@ -[pytest] -testpaths = - test diff --git a/scripts/01_create_commands.py b/scripts/01_create_commands.py index c6e28c606b..72e406d3d7 100644 --- a/scripts/01_create_commands.py +++ b/scripts/01_create_commands.py @@ -5,45 +5,49 @@ import openml -sys.path.append('.') +sys.path.append(".") from update_metadata_util import classification_tasks, regression_tasks parser = argparse.ArgumentParser() -parser.add_argument('--working-directory', type=str, required=True) -parser.add_argument('--test', action='store_true') +parser.add_argument("--working-directory", type=str, required=True) +parser.add_argument("--test", action="store_true") args = parser.parse_args() working_directory = args.working_directory test = args.test -command_file_name = os.path.join(working_directory, 'metadata_commands.txt') +command_file_name = os.path.join(working_directory, "metadata_commands.txt") this_directory = os.path.dirname(os.path.abspath(__file__)) -script_name = 'run_auto-sklearn_for_metadata_generation.py' +script_name = "run_auto-sklearn_for_metadata_generation.py" absolute_script_name = os.path.join(this_directory, script_name) commands = [] -for task_id in (classification_tasks if not test else (233, 245, 258)): - for metric in ('accuracy', 'balanced_accuracy', 'roc_auc', 'logloss'): +for task_id in classification_tasks if not test else (233, 245, 258): + for metric in ("accuracy", "balanced_accuracy", "roc_auc", "logloss"): if ( len(openml.tasks.get_task(task_id, download_data=False).class_labels) > 2 - and metric == 'roc_auc' + and metric == "roc_auc" ): continue - command = ('python3 %s --working-directory %s --time-limit 86400 ' - '--per-run-time-limit 1800 --task-id %d -s 1 --metric %s' % - (absolute_script_name, working_directory, task_id, metric)) + command = ( + "python3 %s --working-directory %s --time-limit 86400 " + "--per-run-time-limit 1800 --task-id %d -s 1 --metric %s" + % (absolute_script_name, working_directory, task_id, metric) + ) commands.append(command) -for task_id in (regression_tasks if not test else (360029, 360033)): - for metric in ('r2', 'root_mean_squared_error', 'mean_absolute_error'): - command = ('python3 %s --working-directory %s --time-limit 86400 ' - '--per-run-time-limit 1800 --task-id %d -s 1 --metric %s' % - (absolute_script_name, working_directory, task_id, metric)) +for task_id in regression_tasks if not test else (360029, 360033): + for metric in ("r2", "root_mean_squared_error", "mean_absolute_error"): + command = ( + "python3 %s --working-directory %s --time-limit 86400 " + "--per-run-time-limit 1800 --task-id %d -s 1 --metric %s" + % (absolute_script_name, working_directory, task_id, metric) + ) commands.append(command) -with open(command_file_name, 'w') as fh: +with open(command_file_name, "w") as fh: for command in commands: fh.writelines(command) - fh.write('\n') + fh.write("\n") diff --git a/scripts/02_retrieve_metadata.py b/scripts/02_retrieve_metadata.py index 611b190dfa..f87f65ecc4 100644 --- a/scripts/02_retrieve_metadata.py +++ b/scripts/02_retrieve_metadata.py @@ -16,8 +16,9 @@ from autosklearn.util import pipeline -def retrieve_matadata(validation_directory, metric, configuration_space, - cutoff=0, only_best=True): +def retrieve_matadata( + validation_directory, metric, configuration_space, cutoff=0, only_best=True +): if not only_best: raise NotImplementedError() if cutoff > 0: @@ -29,9 +30,9 @@ def retrieve_matadata(validation_directory, metric, configuration_space, configurations_to_ids = dict() try: - validation_trajectory_files = glob.glob(os.path.join( - validation_directory, '*', '*', 'validation_trajectory_*.json' - )) + validation_trajectory_files = glob.glob( + os.path.join(validation_directory, "*", "*", "validation_trajectory_*.json") + ) except FileNotFoundError: return {}, {} @@ -66,7 +67,8 @@ def retrieve_matadata(validation_directory, metric, configuration_space, try: best_configuration = Configuration( - configuration_space=configuration_space, values=config) + configuration_space=configuration_space, values=config + ) best_value = score best_configuration_dir = validation_trajectory_file except Exception as e: @@ -74,18 +76,22 @@ def retrieve_matadata(validation_directory, metric, configuration_space, n_broken += 1 if task_name is None: - print('Could not find any configuration better than the default configuration!') + print( + "Could not find any configuration better than the default configuration!" + ) continue if best_configuration is None: - print('Could not find a valid configuration; total %d, better %d, broken %d' - % (n_configs, n_better, n_broken)) + print( + "Could not find a valid configuration; total %d, better %d, broken %d" + % (n_configs, n_better, n_broken) + ) continue elif best_configuration in configurations_to_ids: - print('Found configuration in', best_configuration_dir) + print("Found configuration in", best_configuration_dir) config_id = configurations_to_ids[best_configuration] else: - print('Found configuration in', best_configuration_dir) + print("Found configuration in", best_configuration_dir) config_id = len(configurations_to_ids) configurations_to_ids[config_id] = best_configuration configurations[config_id] = best_configuration @@ -102,34 +108,33 @@ def retrieve_matadata(validation_directory, metric, configuration_space, return outputs, configurations -def write_output(outputs, configurations, output_dir, configuration_space, - metric): +def write_output(outputs, configurations, output_dir, configuration_space, metric): arff_object = dict() - arff_object['attributes'] = [('instance_id', 'STRING'), - ('repetition', 'NUMERIC'), - ('algorithm', 'STRING'), - (metric, 'NUMERIC'), - ('runstatus', - ['ok', 'timeout', 'memout', 'not_applicable', - 'crash', 'other'])] - arff_object['relation'] = "ALGORITHM_RUNS" - arff_object['description'] = "" + arff_object["attributes"] = [ + ("instance_id", "STRING"), + ("repetition", "NUMERIC"), + ("algorithm", "STRING"), + (metric, "NUMERIC"), + ("runstatus", ["ok", "timeout", "memout", "not_applicable", "crash", "other"]), + ] + arff_object["relation"] = "ALGORITHM_RUNS" + arff_object["description"] = "" data = [] keep_configurations = set() for dataset, (configuration_id, value) in outputs.items(): if not np.isfinite(value): - runstatus = 'not_applicable' + runstatus = "not_applicable" value = None else: - runstatus = 'ok' + runstatus = "ok" line = [dataset, 1, configuration_id + 1, value, runstatus] data.append(line) keep_configurations.add(configuration_id) - arff_object['data'] = data + arff_object["data"] = data with open(os.path.join(output_dir, "algorithm_runs.arff"), "w") as fh: arff.dump(arff_object, fh) @@ -139,7 +144,7 @@ def write_output(outputs, configurations, output_dir, configuration_space, if idx not in keep_configurations: continue configuration = configurations[idx] - line = {'idx': idx + 1} + line = {"idx": idx + 1} for hp_name in configuration: value = configuration[hp_name] if value is not None: @@ -147,7 +152,7 @@ def write_output(outputs, configurations, output_dir, configuration_space, hyperparameters.append(line) - fieldnames = ['idx'] + fieldnames = ["idx"] for hyperparameter in configuration_space.get_hyperparameters(): fieldnames.append(hyperparameter.name) fieldnames = [fieldnames[0]] + sorted(fieldnames[1:]) @@ -158,16 +163,17 @@ def write_output(outputs, configurations, output_dir, configuration_space, csv_writer.writerow(line) description = dict() - description['algorithms_deterministic'] = \ - ",".join([str(configuration_id + 1) - for configuration_id in sorted(configurations.keys())]) - description['algorithms_stochastic'] = \ - ",".join([]) - description['performance_measures'] = metric - description['performance_type'] = 'solution_quality' - - with open(os.path.join(output_dir, "description.results.txt"), - "w") as fh: + description["algorithms_deterministic"] = ",".join( + [ + str(configuration_id + 1) + for configuration_id in sorted(configurations.keys()) + ] + ) + description["algorithms_stochastic"] = ",".join([]) + description["performance_measures"] = metric + description["performance_type"] = "solution_quality" + + with open(os.path.join(output_dir, "description.results.txt"), "w") as fh: for key in description: fh.write("%s: %s\n" % (key, description[key])) @@ -184,44 +190,56 @@ def main(): cutoff = args.cutoff only_best = args.only_best - for task_type in ('classification', 'regression'): - if task_type == 'classification': + for task_type in ("classification", "regression"): + if task_type == "classification": metadata_sets = itertools.product( - [0, 1], [BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION], - CLASSIFICATION_METRICS) - input_directory = os.path.join(working_directory, 'configuration', - 'classification') - elif task_type == 'regression': - metadata_sets = itertools.product( - [0, 1], [REGRESSION], REGRESSION_METRICS) - input_directory = os.path.join(working_directory, 'configuration', - 'regression') + [0, 1], + [BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION], + CLASSIFICATION_METRICS, + ) + input_directory = os.path.join( + working_directory, "configuration", "classification" + ) + elif task_type == "regression": + metadata_sets = itertools.product([0, 1], [REGRESSION], REGRESSION_METRICS) + input_directory = os.path.join( + working_directory, "configuration", "regression" + ) else: raise ValueError(task_type) - output_dir = os.path.join(working_directory, 'configuration_results') + output_dir = os.path.join(working_directory, "configuration_results") for sparse, task, metric in metadata_sets: print(TASK_TYPES_TO_STRING[task], metric, sparse) - output_dir_ = os.path.join(output_dir, '%s_%s_%s' % ( - metric, TASK_TYPES_TO_STRING[task], - 'sparse' if sparse else 'dense')) + output_dir_ = os.path.join( + output_dir, + "%s_%s_%s" + % (metric, TASK_TYPES_TO_STRING[task], "sparse" if sparse else "dense"), + ) configuration_space = pipeline.get_configuration_space( - {'is_sparse': sparse, 'task': task}) + {"is_sparse": sparse, "task": task} + ) outputs, configurations = retrieve_matadata( validation_directory=input_directory, metric=metric, cutoff=cutoff, configuration_space=configuration_space, - only_best=only_best) + only_best=only_best, + ) if len(outputs) == 0: - print("No output found for %s, %s, %s" % - (metric, TASK_TYPES_TO_STRING[task], - 'sparse' if sparse else 'dense')) + print( + "No output found for %s, %s, %s" + % ( + metric, + TASK_TYPES_TO_STRING[task], + "sparse" if sparse else "dense", + ) + ) continue try: @@ -229,8 +247,9 @@ def main(): except: pass - write_output(outputs, configurations, output_dir_, - configuration_space, metric) + write_output( + outputs, configurations, output_dir_, configuration_space, metric + ) if __name__ == "__main__": diff --git a/scripts/03_calculate_metafeatures.py b/scripts/03_calculate_metafeatures.py index 1d058c5dae..3b32dde8e3 100644 --- a/scripts/03_calculate_metafeatures.py +++ b/scripts/03_calculate_metafeatures.py @@ -11,15 +11,22 @@ import numpy as np import pandas as pd -from autosklearn.constants import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION +from autosklearn.constants import ( + BINARY_CLASSIFICATION, + MULTICLASS_CLASSIFICATION, + REGRESSION, +) from autosklearn.metalearning.metafeatures import metafeatures -from autosklearn.smbo import _calculate_metafeatures, _calculate_metafeatures_encoded, \ - EXCLUDE_META_FEATURES_REGRESSION, EXCLUDE_META_FEATURES_CLASSIFICATION +from autosklearn.smbo import ( + _calculate_metafeatures, + _calculate_metafeatures_encoded, + EXCLUDE_META_FEATURES_REGRESSION, + EXCLUDE_META_FEATURES_CLASSIFICATION, +) from autosklearn.util.stopwatch import StopWatch -sys.path.append('.') -from update_metadata_util import load_task, classification_tasks, \ - regression_tasks +sys.path.append(".") +from update_metadata_util import load_task, classification_tasks, regression_tasks logger = logging.getLogger("03_calculate_metafeatures") @@ -28,7 +35,7 @@ def calculate_metafeatures(task_id): X_train, y_train, X_test, y_test, cat, task_type, dataset_name = load_task(task_id) watch = StopWatch() - if task_type == 'classification': + if task_type == "classification": if len(np.unique(y_train)) == 2: task_type = BINARY_CLASSIFICATION else: @@ -37,20 +44,27 @@ def calculate_metafeatures(task_id): task_type = REGRESSION _metafeatures_labels = _calculate_metafeatures( - x_train=X_train, y_train=y_train, data_feat_type=cat, - data_info_task=task_type, basename=dataset_name, logger_=logger, + x_train=X_train, + y_train=y_train, + data_feat_type=cat, + data_info_task=task_type, + basename=dataset_name, + logger_=logger, watcher=watch, ) _metafeatures_encoded_labels = _calculate_metafeatures_encoded( - x_train=X_train, y_train=y_train, data_feat_type=cat, - task=task_type, basename=dataset_name, logger_=logger, + x_train=X_train, + y_train=y_train, + data_feat_type=cat, + task=task_type, + basename=dataset_name, + logger_=logger, watcher=watch, ) mf = _metafeatures_labels - mf.metafeature_values.update( - _metafeatures_encoded_labels.metafeature_values) + mf.metafeature_values.update(_metafeatures_encoded_labels.metafeature_values) return mf @@ -59,15 +73,15 @@ def calculate_metafeatures(task_id): parser = ArgumentParser() parser.add_argument("--working-directory", type=str, required=True) parser.add_argument("--memory-limit", type=int, default=3072) - parser.add_argument("--test-mode", action='store_true') + parser.add_argument("--test-mode", action="store_true") args = parser.parse_args() working_directory = args.working_directory memory_limit = args.memory_limit test_mode = args.test_mode - for task_type in ('classification', 'regression'): - output_directory = os.path.join(working_directory, 'metafeatures', task_type) + for task_type in ("classification", "regression"): + output_directory = os.path.join(working_directory, "metafeatures", task_type) try: os.makedirs(output_directory) except: @@ -75,7 +89,7 @@ def calculate_metafeatures(task_id): all_metafeatures = {} - if task_type == 'classification': + if task_type == "classification": tasks = classification_tasks else: tasks = regression_tasks @@ -90,12 +104,9 @@ def producer(): for task_id in tasks: yield task_id - memory = joblib.Memory(location='/tmp/joblib', verbose=10) + memory = joblib.Memory(location="/tmp/joblib", verbose=10) cached_calculate_metafeatures = memory.cache(calculate_metafeatures) - mfs = [ - cached_calculate_metafeatures(task_id) - for task_id in producer() - ] + mfs = [cached_calculate_metafeatures(task_id) for task_id in producer()] for mf in mfs: if mf is not None: @@ -110,45 +121,50 @@ def producer(): for i, task_id in enumerate(all_metafeatures): calculation_times[task_id] = dict() for metafeature_name in sorted( - all_metafeatures[task_id].metafeature_values): + all_metafeatures[task_id].metafeature_values + ): metafeature_value = all_metafeatures[task_id].metafeature_values[ - metafeature_name] - calculation_times[task_id][metafeature_name] = \ - metafeature_value.time + metafeature_name + ] + calculation_times[task_id][metafeature_name] = metafeature_value.time if metafeature_value.type_ == "HELPERFUNCTION": - helperfunction_values[task_id][metafeature_name] = \ - metafeature_value.value + helperfunction_values[task_id][ + metafeature_name + ] = metafeature_value.value else: - metafeature_values[task_id][metafeature_name] = \ - metafeature_value.value + metafeature_values[task_id][ + metafeature_name + ] = metafeature_value.value calculation_times = pd.DataFrame(calculation_times).transpose() calculation_times = calculation_times.sort_index() - with open(os.path.join(output_directory, "calculation_times.csv"), - "w") as fh: + with open(os.path.join(output_directory, "calculation_times.csv"), "w") as fh: fh.write(calculation_times.to_csv()) # Write all metafeatures in the aslib1.0 format - metafeature_values = metafeature_values = pd.DataFrame(metafeature_values).transpose() + metafeature_values = metafeature_values = pd.DataFrame( + metafeature_values + ).transpose() metafeature_values = metafeature_values.sort_index() arff_object = dict() - arff_object['attributes'] = [('instance_id', 'STRING'), - ('repetition', 'NUMERIC')] + \ - [('%s' % name, 'NUMERIC') for name in - metafeature_values.columns] - arff_object['relation'] = "FEATURE_VALUES" - arff_object['description'] = "" + arff_object["attributes"] = [ + ("instance_id", "STRING"), + ("repetition", "NUMERIC"), + ] + [("%s" % name, "NUMERIC") for name in metafeature_values.columns] + arff_object["relation"] = "FEATURE_VALUES" + arff_object["description"] = "" data = [] for idx in metafeature_values.index: line = [idx, 1] - line += [value if np.isfinite(value) else None - for value in metafeature_values.loc[idx, :].values] + line += [ + value if np.isfinite(value) else None + for value in metafeature_values.loc[idx, :].values + ] data.append(line) - arff_object['data'] = data + arff_object["data"] = data - with open(os.path.join(output_directory, "feature_values.arff"), - "w") as fh: + with open(os.path.join(output_directory, "feature_values.arff"), "w") as fh: arff.dump(arff_object, fh) # Feature steps and runtimes according to the aslib1.0 format @@ -157,7 +173,8 @@ def producer(): exclude_metafeatures = ( EXCLUDE_META_FEATURES_CLASSIFICATION - if task_type == 'classification' else EXCLUDE_META_FEATURES_REGRESSION + if task_type == "classification" + else EXCLUDE_META_FEATURES_REGRESSION ) for metafeature_name in metafeatures.metafeatures.functions: @@ -174,42 +191,48 @@ def producer(): # Write the feature runstatus in the aslib1.0 format arff_object = dict() - arff_object['attributes'] = [('instance_id', 'STRING'), - ('repetition', 'NUMERIC')] + \ - [('%s' % name, - ['ok', 'timeout', 'memout', 'presolved', - 'crash', 'other']) - for name in feature_steps] - arff_object['relation'] = "FEATURE_RUNSTATUS" - arff_object['description'] = "" + arff_object["attributes"] = [ + ("instance_id", "STRING"), + ("repetition", "NUMERIC"), + ] + [ + ("%s" % name, ["ok", "timeout", "memout", "presolved", "crash", "other"]) + for name in feature_steps + ] + arff_object["relation"] = "FEATURE_RUNSTATUS" + arff_object["description"] = "" data = [] for idx in metafeature_values.index: line = [idx, 1] for feature_step in feature_steps: if feature_step in helperfunction_values[idx]: - line.append('ok' if helperfunction_values[feature_step] is not \ - None else 'other') + line.append( + "ok" + if helperfunction_values[feature_step] is not None + else "other" + ) elif feature_step in metafeature_values.loc[idx]: - line.append('ok' if np.isfinite(metafeature_values.loc[idx][ - feature_step]) else 'other') + line.append( + "ok" + if np.isfinite(metafeature_values.loc[idx][feature_step]) + else "other" + ) else: - line.append('other') + line.append("other") data.append(line) - arff_object['data'] = data + arff_object["data"] = data - with open(os.path.join(output_directory, "feature_runstatus.arff"), - "w") as fh: + with open(os.path.join(output_directory, "feature_runstatus.arff"), "w") as fh: arff.dump(arff_object, fh) arff_object = dict() - arff_object['attributes'] = [('instance_id', 'STRING'), - ('repetition', 'NUMERIC')] + \ - [('%s' % feature_step, 'NUMERIC') for - feature_step in feature_steps] - arff_object['relation'] = "FEATURE_COSTS" - arff_object['description'] = "" + arff_object["attributes"] = [ + ("instance_id", "STRING"), + ("repetition", "NUMERIC"), + ] + [("%s" % feature_step, "NUMERIC") for feature_step in feature_steps] + arff_object["relation"] = "FEATURE_COSTS" + arff_object["description"] = "" data = [] for instance_id in calculation_times.index: @@ -220,33 +243,35 @@ def producer(): for feature in feature_steps[feature_step]: time_ += calculation_times[feature][instance_id] if not np.isfinite(time_): - raise ValueError("Feature cost %s for instance %s and feature " - "step %s not finite" % (time_, instance_id, feature)) + raise ValueError( + "Feature cost %s for instance %s and feature " + "step %s not finite" % (time_, instance_id, feature) + ) line.append(time_) data.append(line) - arff_object['data'] = data + arff_object["data"] = data - with open(os.path.join(output_directory, "feature_costs.arff"), - "w") as fh: + with open(os.path.join(output_directory, "feature_costs.arff"), "w") as fh: arff.dump(arff_object, fh) # Write the features part of the description.txt to a file description = OrderedDict() - description['features_cutoff_time'] = '3600' - description['features_cutoff_memory'] = args.memory_limit - description['number_of_feature_steps'] = str(len(feature_steps)) + description["features_cutoff_time"] = "3600" + description["features_cutoff_memory"] = args.memory_limit + description["number_of_feature_steps"] = str(len(feature_steps)) for feature_step in feature_steps: - description['feature_step %s' % feature_step] = \ - ", ".join(feature_steps[feature_step]) - description['features_deterministic'] = ", ".join([ - metafeature_name for - metafeature_name in - metafeature_names]) - description['features_stochastic'] = '' - description['default_steps'] = ", ".join(feature_steps) - - with open(os.path.join(output_directory, - "description.features.txt"), "w") as fh: + description["feature_step %s" % feature_step] = ", ".join( + feature_steps[feature_step] + ) + description["features_deterministic"] = ", ".join( + [metafeature_name for metafeature_name in metafeature_names] + ) + description["features_stochastic"] = "" + description["default_steps"] = ", ".join(feature_steps) + + with open( + os.path.join(output_directory, "description.features.txt"), "w" + ) as fh: for entry in description: fh.write("%s: %s\n" % (entry, description[entry])) diff --git a/scripts/04_create_aslib_files.py b/scripts/04_create_aslib_files.py index d5e10a9c15..8c83dc1648 100644 --- a/scripts/04_create_aslib_files.py +++ b/scripts/04_create_aslib_files.py @@ -10,16 +10,16 @@ if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--working-directory", type=str, required=True) - parser.add_argument("--scenario_id", type=str, default='auto-sklearn') + parser.add_argument("--scenario_id", type=str, default="auto-sklearn") parser.add_argument("--algorithm_cutoff_time", type=int, default=1800) parser.add_argument("--algorithm_cutoff_memory", type=int, default=3072) args = parser.parse_args() working_directory = args.working_directory - output_dir = os.path.join(working_directory, 'metadata') - results_dir = os.path.join(working_directory, 'configuration_results') - metafeatures_dir = os.path.join(working_directory, 'metafeatures') + output_dir = os.path.join(working_directory, "metadata") + results_dir = os.path.join(working_directory, "configuration_results") + metafeatures_dir = os.path.join(working_directory, "metafeatures") scenario_id = args.scenario_id algorithm_cutoff_time = args.algorithm_cutoff_time @@ -31,25 +31,29 @@ except (OSError, IOError): pass - for task_type in ('classification', 'regression'): - if task_type == 'classification': + for task_type in ("classification", "regression"): + if task_type == "classification": metadata_sets = itertools.product( - [0, 1], [BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION], - CLASSIFICATION_METRICS) - elif task_type == 'regression': - metadata_sets = itertools.product( - [0, 1], [REGRESSION], REGRESSION_METRICS) + [0, 1], + [BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION], + CLASSIFICATION_METRICS, + ) + elif task_type == "regression": + metadata_sets = itertools.product([0, 1], [REGRESSION], REGRESSION_METRICS) else: raise ValueError(task_type) - input_directory = os.path.join(working_directory, 'configuration', task_type) + input_directory = os.path.join(working_directory, "configuration", task_type) metafeatures_dir_for_task = os.path.join(metafeatures_dir, task_type) for sparse, task, metric in metadata_sets: print(TASK_TYPES_TO_STRING[task], metric, sparse) - dir_name = '%s_%s_%s' % (metric, TASK_TYPES_TO_STRING[task], - 'sparse' if sparse else 'dense') + dir_name = "%s_%s_%s" % ( + metric, + TASK_TYPES_TO_STRING[task], + "sparse" if sparse else "dense", + ) output_dir_ = os.path.join(output_dir, dir_name) results_dir_ = os.path.join(results_dir, dir_name) @@ -67,21 +71,19 @@ pass # Create description.txt - with open(os.path.join(metafeatures_dir_for_task, - "description.features.txt")) as fh: + with open( + os.path.join(metafeatures_dir_for_task, "description.features.txt") + ) as fh: description_metafeatures = fh.read() - with open(os.path.join(results_dir_, - "description.results.txt")) as fh: + with open(os.path.join(results_dir_, "description.results.txt")) as fh: description_results = fh.read() description = [description_metafeatures, description_results] description.append("scenario_id: %s" % scenario_id) description.append("maximize: false") - description.append( - "algorithm_cutoff_time: %d" % algorithm_cutoff_time) - description.append( - "algorithm_cutoff_memory: %d" % algorithm_cutoff_memory) + description.append("algorithm_cutoff_time: %d" % algorithm_cutoff_time) + description.append("algorithm_cutoff_memory: %d" % algorithm_cutoff_memory) with open(os.path.join(output_dir_, "description.txt"), "w") as fh: for line in description: @@ -89,59 +91,54 @@ fh.write("\n") # Copy feature values and add instance id - with open(os.path.join(metafeatures_dir_for_task, - "feature_values.arff")) as fh: + with open( + os.path.join(metafeatures_dir_for_task, "feature_values.arff") + ) as fh: feature_values = arff.load(fh) - feature_values['relation'] = scenario_id + "_" + feature_values[ - 'relation'] + feature_values["relation"] = scenario_id + "_" + feature_values["relation"] - with open(os.path.join(output_dir_, "feature_values.arff"), - "w") as fh: + with open(os.path.join(output_dir_, "feature_values.arff"), "w") as fh: arff.dump(feature_values, fh) # Copy feature runstatus and add instance id - with open(os.path.join(metafeatures_dir_for_task, - "feature_runstatus.arff")) as fh: + with open( + os.path.join(metafeatures_dir_for_task, "feature_runstatus.arff") + ) as fh: feature_runstatus = arff.load(fh) - feature_runstatus['relation'] = scenario_id + "_" + \ - feature_runstatus['relation'] + feature_runstatus["relation"] = ( + scenario_id + "_" + feature_runstatus["relation"] + ) - with open(os.path.join(output_dir_, "feature_runstatus.arff"), "w") \ - as fh: + with open(os.path.join(output_dir_, "feature_runstatus.arff"), "w") as fh: arff.dump(feature_runstatus, fh) # Copy feature runstatus and add instance id with open( - os.path.join(metafeatures_dir_for_task, "feature_costs.arff")) as fh: + os.path.join(metafeatures_dir_for_task, "feature_costs.arff") + ) as fh: feature_costs = arff.load(fh) - feature_costs['relation'] = scenario_id + "_" + feature_costs[ - 'relation'] - for i in range(len(feature_costs['data'])): - for j in range(2, len(feature_costs['data'][i])): - feature_costs['data'][i][j] = \ - round(feature_costs['data'][i][j], 5) + feature_costs["relation"] = scenario_id + "_" + feature_costs["relation"] + for i in range(len(feature_costs["data"])): + for j in range(2, len(feature_costs["data"][i])): + feature_costs["data"][i][j] = round(feature_costs["data"][i][j], 5) - with open(os.path.join(output_dir_, "feature_costs.arff"), "w") \ - as fh: + with open(os.path.join(output_dir_, "feature_costs.arff"), "w") as fh: arff.dump(feature_costs, fh) # Copy algorithm runs and add instance id with open(os.path.join(results_dir_, "algorithm_runs.arff")) as fh: algorithm_runs = arff.load(fh) - algorithm_runs['relation'] = scenario_id + "_" + algorithm_runs[ - 'relation'] + algorithm_runs["relation"] = scenario_id + "_" + algorithm_runs["relation"] - with open(os.path.join(output_dir_, "algorithm_runs.arff"), "w") \ - as fh: + with open(os.path.join(output_dir_, "algorithm_runs.arff"), "w") as fh: arff.dump(algorithm_runs, fh) # Copy configurations file with open(os.path.join(results_dir_, "configurations.csv")) as fh: algorithm_runs = fh.read() - with open(os.path.join(output_dir_, "configurations.csv"), "w") \ - as fh: + with open(os.path.join(output_dir_, "configurations.csv"), "w") as fh: fh.write(algorithm_runs) diff --git a/scripts/2015_nips_paper/plot/plot_ranks.py b/scripts/2015_nips_paper/plot/plot_ranks.py index 5be095389c..b2e85248b7 100644 --- a/scripts/2015_nips_paper/plot/plot_ranks.py +++ b/scripts/2015_nips_paper/plot/plot_ranks.py @@ -17,8 +17,8 @@ def read_csv(fn, has_header=True, data_type=str): """ data = list() header = None - with open(fn, 'r') as csvfile: - csv_reader = csv.reader(csvfile, delimiter=',', quotechar='|') + with open(fn, "r") as csvfile: + csv_reader = csv.reader(csvfile, delimiter=",", quotechar="|") for row in csv_reader: if header is None and has_header: header = row @@ -37,7 +37,7 @@ def fill_trajectory(performance_list, time_list): series = pd.concat(series_list, axis=1) # Fill missing performance values (NaNs) with last non-NaN value. - series = series.fillna(method='ffill') + series = series.fillna(method="ffill") # return the trajectories over seeds (series object) return series @@ -52,10 +52,10 @@ def main(): working_directory = "../log_output" # list of models - model_list = ['vanilla', 'ensemble', 'metalearning', 'meta_ensemble'] + model_list = ["vanilla", "ensemble", "metalearning", "meta_ensemble"] # list of seeds - seed_dir = os.path.join(working_directory, 'vanilla') + seed_dir = os.path.join(working_directory, "vanilla") seed_list = [seed for seed in os.listdir(seed_dir)] # list of tasks @@ -74,21 +74,23 @@ def main(): for seed in seed_list: # collect all csv files of different seeds for current model and # current task. - if model in ['vanilla', 'ensemble']: - csv_file = os.path.join(working_directory, - 'vanilla', - seed, - task_id, - "score_{}.csv".format(model) - ) - - elif model in ['metalearning', 'meta_ensemble']: - csv_file = os.path.join(working_directory, - 'metalearning', - seed, - task_id, - "score_{}.csv".format(model), - ) + if model in ["vanilla", "ensemble"]: + csv_file = os.path.join( + working_directory, + "vanilla", + seed, + task_id, + "score_{}.csv".format(model), + ) + + elif model in ["metalearning", "meta_ensemble"]: + csv_file = os.path.join( + working_directory, + "metalearning", + seed, + task_id, + "score_{}.csv".format(model), + ) csv_files.append(csv_file) performance_list = [] @@ -99,8 +101,9 @@ def main(): _, csv_data = read_csv(fl, has_header=True) csv_data = np.array(csv_data) # Replace too high values with args.maxsize - data = [min([sys.maxsize, float(i.strip())]) for i in - csv_data[:, 2]] # test trajectories are stored in third column + data = [ + min([sys.maxsize, float(i.strip())]) for i in csv_data[:, 2] + ] # test trajectories are stored in third column time_steps = [float(i.strip()) for i in csv_data[:, 0]] assert time_steps[0] == 0 @@ -123,15 +126,16 @@ def main(): n_tasks = len(task_list) for i in range(n_iter): - pick = np.random.choice(all_trajectories[0][0].shape[1], - size=(len(model_list))) + pick = np.random.choice(all_trajectories[0][0].shape[1], size=(len(model_list))) for j in range(n_tasks): all_trajectories_tmp = pd.DataFrame( - {model_list[k]: at[j].iloc[:, pick[k]] for - k, at in enumerate(all_trajectories)} + { + model_list[k]: at[j].iloc[:, pick[k]] + for k, at in enumerate(all_trajectories) + } ) - all_trajectories_tmp = all_trajectories_tmp.fillna(method='ffill', axis=0) + all_trajectories_tmp = all_trajectories_tmp.fillna(method="ffill", axis=0) r_tmp = all_trajectories_tmp.rank(axis=1) all_rankings.append(r_tmp) @@ -141,7 +145,7 @@ def main(): for ranking in all_rankings: ranks_for_model.append(ranking.loc[:, model]) ranks_for_model = pd.DataFrame(ranks_for_model) - ranks_for_model = ranks_for_model.fillna(method='ffill', axis=1) + ranks_for_model = ranks_for_model.fillna(method="ffill", axis=1) final_ranks.append(ranks_for_model.mean(skipna=True)) # Step 3. Plot the average ranks over time. @@ -155,8 +159,8 @@ def main(): X_data.append(max_runtime) y_data.append(y) plt.plot(X_data, y_data, label=model) - plt.xlabel('time [sec]') - plt.ylabel('average rank') + plt.xlabel("time [sec]") + plt.ylabel("average rank") plt.legend() plt.savefig(saveto) diff --git a/scripts/2015_nips_paper/run/remove_dataset_from_metadata.py b/scripts/2015_nips_paper/run/remove_dataset_from_metadata.py index f31e16e65f..d16e67e23c 100644 --- a/scripts/2015_nips_paper/run/remove_dataset_from_metadata.py +++ b/scripts/2015_nips_paper/run/remove_dataset_from_metadata.py @@ -4,25 +4,27 @@ from shutil import copyfile -def remove_dataset_from_aslib_arff(input_file, - output_file, - id, - ): +def remove_dataset_from_aslib_arff( + input_file, + output_file, + id, +): with open(input_file) as fh: arff_object = arff.load(fh) - for i in range(len(arff_object['data']) - 1, -1, -1): - if str(arff_object['data'][i][0]) == str(id): - del arff_object['data'][i] + for i in range(len(arff_object["data"]) - 1, -1, -1): + if str(arff_object["data"][i][0]) == str(id): + del arff_object["data"][i] with open(output_file, "w") as fh: arff.dump(arff_object, fh) del arff_object -def remove_dataset(metadata_directory, - output_directory, - id, - ): +def remove_dataset( + metadata_directory, + output_directory, + id, +): metadata_sub_directories = os.listdir(metadata_directory) for metadata_sub_directory in metadata_sub_directories: diff --git a/scripts/2015_nips_paper/run/run_auto_sklearn.py b/scripts/2015_nips_paper/run/run_auto_sklearn.py index 366280692e..960ab7be80 100644 --- a/scripts/2015_nips_paper/run/run_auto_sklearn.py +++ b/scripts/2015_nips_paper/run/run_auto_sklearn.py @@ -21,11 +21,12 @@ def load_task(task_id): X_test = X[test_indices] y_test = y[test_indices] dataset = openml.datasets.get_dataset(task.dataset_id) - _, _, cat = dataset.get_data(return_categorical_indicator=True, - target=task.target_name) + _, _, cat = dataset.get_data( + return_categorical_indicator=True, target=task.target_name + ) del _ del dataset - cat = ['categorical' if c else 'numerical' for c in cat] + cat = ["categorical" if c else "numerical" for c in cat] unique = np.unique(y_train) mapping = {unique_value: i for i, unique_value in enumerate(unique)} @@ -35,13 +36,14 @@ def load_task(task_id): return X_train, y_train, X_test, y_test, cat -def run_experiment(working_directory, - time_limit, - per_run_time_limit, - task_id, - seed, - use_metalearning, - ): +def run_experiment( + working_directory, + time_limit, + per_run_time_limit, + task_id, + seed, + use_metalearning, +): # set this to local dataset cache # openml.config.cache_directory = os.path.join(working_directory, "../cache") @@ -57,12 +59,14 @@ def run_experiment(working_directory, if use_metalearning is True: # path to the original metadata directory. metadata_directory = os.path.abspath(os.path.dirname(__file__)) - metadata_directory = os.path.join(metadata_directory, - "../../../autosklearn/metalearning/files/") + metadata_directory = os.path.join( + metadata_directory, "../../../autosklearn/metalearning/files/" + ) # Create new metadata directory not containing task_id. - new_metadata_directory = os.path.abspath(os.path.join(working_directory, - "metadata_%i" % task_id)) + new_metadata_directory = os.path.abspath( + os.path.join(working_directory, "metadata_%i" % task_id) + ) try: os.makedirs(new_metadata_directory) @@ -73,100 +77,105 @@ def run_experiment(working_directory, remove_dataset(metadata_directory, new_metadata_directory, task_id) automl_arguments = { - 'time_left_for_this_task': time_limit, - 'per_run_time_limit': per_run_time_limit, - 'initial_configurations_via_metalearning': 25, - 'ensemble_size': 0, - 'seed': seed, - 'memory_limit': 3072, - 'resampling_strategy': 'holdout', - 'resampling_strategy_arguments': {'train_size': 0.67}, - 'tmp_folder': tmp_dir, - 'delete_tmp_folder_after_terminate': False, - 'disable_evaluator_output': False, - 'metadata_directory': new_metadata_directory + "time_left_for_this_task": time_limit, + "per_run_time_limit": per_run_time_limit, + "initial_configurations_via_metalearning": 25, + "ensemble_size": 0, + "seed": seed, + "memory_limit": 3072, + "resampling_strategy": "holdout", + "resampling_strategy_arguments": {"train_size": 0.67}, + "tmp_folder": tmp_dir, + "delete_tmp_folder_after_terminate": False, + "disable_evaluator_output": False, + "metadata_directory": new_metadata_directory, } # Without metalearning else: automl_arguments = { - 'time_left_for_this_task': time_limit, - 'per_run_time_limit': per_run_time_limit, - 'initial_configurations_via_metalearning': 0, - 'ensemble_size': 0, - 'seed': seed, - 'memory_limit': 3072, - 'resampling_strategy': 'holdout', - 'resampling_strategy_arguments': {'train_size': 0.67}, - 'tmp_folder': tmp_dir, - 'delete_tmp_folder_after_terminate': False, - 'disable_evaluator_output': False, + "time_left_for_this_task": time_limit, + "per_run_time_limit": per_run_time_limit, + "initial_configurations_via_metalearning": 0, + "ensemble_size": 0, + "seed": seed, + "memory_limit": 3072, + "resampling_strategy": "holdout", + "resampling_strategy_arguments": {"train_size": 0.67}, + "tmp_folder": tmp_dir, + "delete_tmp_folder_after_terminate": False, + "disable_evaluator_output": False, } automl = AutoSklearnClassifier(**automl_arguments) X_train, y_train, X_test, y_test, cat = load_task(task_id) - automl.fit(X_train, y_train, - dataset_name=str(task_id), - X_test=X_test, y_test=y_test, - metric=balanced_accuracy) + automl.fit( + X_train, + y_train, + dataset_name=str(task_id), + X_test=X_test, + y_test=y_test, + metric=balanced_accuracy, + ) -def main(working_directory, - output_file, - task_id, - seed, - model, - time_limit, - per_run_time_limit): +def main( + working_directory, output_file, task_id, seed, model, time_limit, per_run_time_limit +): # vanilla and metalearning must be called first before ensemble and # meta_ensemble can be called, respectively. if model == "vanilla": - run_experiment(working_directory, - time_limit, - per_run_time_limit, - task_id, - seed, - use_metalearning=False, - ) - score_ensemble.main(working_directory, - output_file, - task_id, - seed, - ensemble_size=1, - ) + run_experiment( + working_directory, + time_limit, + per_run_time_limit, + task_id, + seed, + use_metalearning=False, + ) + score_ensemble.main( + working_directory, + output_file, + task_id, + seed, + ensemble_size=1, + ) elif model == "metalearning": - run_experiment(working_directory, - time_limit, - per_run_time_limit, - task_id, - seed, - use_metalearning=True, - ) - score_ensemble.main(working_directory, - output_file, - task_id, - seed, - ensemble_size=1, - ) + run_experiment( + working_directory, + time_limit, + per_run_time_limit, + task_id, + seed, + use_metalearning=True, + ) + score_ensemble.main( + working_directory, + output_file, + task_id, + seed, + ensemble_size=1, + ) else: - score_ensemble.main(working_directory, - output_file, - task_id, - seed, - ensemble_size=50, - ) + score_ensemble.main( + working_directory, + output_file, + task_id, + seed, + ensemble_size=50, + ) if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--working-directory', type=str, required=True) + parser.add_argument("--working-directory", type=str, required=True) parser.add_argument("--output-file", type=str, required=True) parser.add_argument("--time-limit", type=int, required=True) parser.add_argument("--per-runtime-limit", type=int, required=True) - parser.add_argument('--task-id', type=int, required=True) - parser.add_argument('-s', '--seed', type=int) + parser.add_argument("--task-id", type=int, required=True) + parser.add_argument("-s", "--seed", type=int) parser.add_argument("--model", type=str, required=True) args = parser.parse_args() @@ -178,11 +187,12 @@ def main(working_directory, time_limit = args.time_limit per_run_time_limit = args.per_runtime_limit - main(working_directory, - output_file, - task_id, - seed, - model, - time_limit, - per_run_time_limit, - ) + main( + working_directory, + output_file, + task_id, + seed, + model, + time_limit, + per_run_time_limit, + ) diff --git a/scripts/2015_nips_paper/run/score_ensemble.py b/scripts/2015_nips_paper/run/score_ensemble.py index 3d10954d94..1e873f01fd 100644 --- a/scripts/2015_nips_paper/run/score_ensemble.py +++ b/scripts/2015_nips_paper/run/score_ensemble.py @@ -14,21 +14,21 @@ def _load_file(f): - split = f.split('_') + split = f.split("_") as_seed = int(split[-2]) - ta_seed = int(split[-1].split('.')[0]) + ta_seed = int(split[-1].split(".")[0]) np_array = np.load(f) return np_array, (as_seed, ta_seed), os.path.getmtime(f) def read_files(directory, seed=None, n_jobs=1): - seed_pattern = '*' if seed is None else str(seed) - glob_pattern = os.path.join(directory, "predictions_*_%s_*.npy" % - seed_pattern) + seed_pattern = "*" if seed is None else str(seed) + glob_pattern = os.path.join(directory, "predictions_*_%s_*.npy" % seed_pattern) files = sorted(glob.glob(glob_pattern)) files = joblib.Parallel(n_jobs=n_jobs, verbose=10)( - joblib.delayed(_load_file)(f=f) for f in files) + joblib.delayed(_load_file)(f=f) for f in files + ) return files @@ -38,13 +38,13 @@ def main(input_directories, output_file, task_id, seed, ensemble_size, n_jobs=1) if isinstance(input_directories, str): # add seed and task id directories - input_directories += '/%i/%i' % (seed, task_id) + input_directories += "/%i/%i" % (seed, task_id) input_directories = [input_directories] else: new_directories = [] for dir in input_directories: - dir += '/%i/%i' % (seed, task_id) + dir += "/%i/%i" % (seed, task_id) new_directories.append(dir) input_directories = new_directories @@ -54,28 +54,28 @@ def main(input_directories, output_file, task_id, seed, ensemble_size, n_jobs=1) # Get the prediction files. for input_directory in input_directories: - print('Loading files from input directory:', input_directory) + print("Loading files from input directory:", input_directory) validation_files_ = read_files( - os.path.join(input_directory, - '.auto-sklearn/predictions_ensemble'), - n_jobs=n_jobs) + os.path.join(input_directory, ".auto-sklearn/predictions_ensemble"), + n_jobs=n_jobs, + ) validation_files.extend(validation_files_) test_files_ = read_files( - os.path.join(input_directory, - '.auto-sklearn/predictions_test'), - n_jobs=n_jobs) + os.path.join(input_directory, ".auto-sklearn/predictions_test"), + n_jobs=n_jobs, + ) test_files.extend(test_files_) assert len(validation_files_) > 0 assert len(validation_files_) == len(test_files_) - print('Loaded %d files!' % len(validation_files_)) + print("Loaded %d files!" % len(validation_files_)) # if not specified, we get all files. - seed_pattern = '*' if seed is None else str(seed) - glob_pattern = os.path.join(input_directory, - ".auto-sklearn", - "start_time_%s" % seed_pattern) + seed_pattern = "*" if seed is None else str(seed) + glob_pattern = os.path.join( + input_directory, ".auto-sklearn", "start_time_%s" % seed_pattern + ) start_time_files = glob.glob(glob_pattern) # find the earliest startime. @@ -90,14 +90,15 @@ def main(input_directories, output_file, task_id, seed, ensemble_size, n_jobs=1) validation_files.sort(key=lambda t: t[-1]) - keys_to_test_files = {test_file[1]: test_file - for test_file in test_files} + keys_to_test_files = {test_file[1]: test_file for test_file in test_files} # Resort such that both files have the same order - test_files = [keys_to_test_files[validation_file[1]] - for validation_file in validation_files] + test_files = [ + keys_to_test_files[validation_file[1]] for validation_file in validation_files + ] assert [validation_file[1] for validation_file in validation_files] == [ - test_file[1] for test_file in test_files] + test_file[1] for test_file in test_files + ] losses = [] top_models_at_step = dict() @@ -106,7 +107,7 @@ def main(input_directories, output_file, task_id, seed, ensemble_size, n_jobs=1) temporary_directory=input_directory, output_directory=input_directory + "_output", delete_tmp_folder_after_terminate=False, - prefix="auto-sklearn" + prefix="auto-sklearn", ) valid_labels = backend.load_targets_ensemble() score = balanced_accuracy @@ -124,46 +125,63 @@ def main(input_directories, output_file, task_id, seed, ensemble_size, n_jobs=1) if top_model in models_to_remove: models_to_remove.remove(top_model) - print("Removing the following %d models from the library: %s" - % (len(models_to_remove), models_to_remove)) + print( + "Removing the following %d models from the library: %s" + % (len(models_to_remove), models_to_remove) + ) for model_id in models_to_remove: validation_files[model_id] = None test_files[model_id] = None - print('Starting ensemble building!') + print("Starting ensemble building!") output = joblib.Parallel(n_jobs=n_jobs, verbose=20)( - joblib.delayed( - evaluate)(input_directory=input_directories[0], - validation_files=[validation_files[j] for - j in range(len(validation_files)) - if j in top_models_at_step[i]], - test_files=[test_files[j] for - j in range(len(test_files)) - if j in top_models_at_step[i]], - ensemble_size=ensemble_size) - for i in range(len(test_files))) + joblib.delayed(evaluate)( + input_directory=input_directories[0], + validation_files=[ + validation_files[j] + for j in range(len(validation_files)) + if j in top_models_at_step[i] + ], + test_files=[ + test_files[j] + for j in range(len(test_files)) + if j in top_models_at_step[i] + ], + ensemble_size=ensemble_size, + ) + for i in range(len(test_files)) + ) # Create output csv file file_path = os.path.abspath("%s/%s" % (input_directory, output_file)) with open(file_path, "w") as csv_file: - fieldnames = ['Time', 'Training (Empirical) Performance', - 'Test Set Performance'] + fieldnames = [ + "Time", + "Training (Empirical) Performance", + "Test Set Performance", + ] csv_writer = csv.DictWriter(csv_file, fieldnames=fieldnames) csv_writer.writeheader() # First time step - csv_writer.writerow({'Time': 0, - 'Training (Empirical) Performance': 1.0, - 'Test Set Performance': 1.0}) + csv_writer.writerow( + { + "Time": 0, + "Training (Empirical) Performance": 1.0, + "Test Set Performance": 1.0, + } + ) for i, o in enumerate(output): - csv_writer.writerow({'Time': o['ensemble_time'] - + o['time_function_evaluation'] - - starttime, - 'Training (Empirical) Performance': - o['ensemble_error'], - 'Test Set Performance': - o['ensemble_test_error']}) + csv_writer.writerow( + { + "Time": o["ensemble_time"] + + o["time_function_evaluation"] + - starttime, + "Training (Empirical) Performance": o["ensemble_error"], + "Test Set Performance": o["ensemble_test_error"], + } + ) def evaluate(input_directory, validation_files, test_files, ensemble_size=50): @@ -187,18 +205,18 @@ def evaluate(input_directory, validation_files, test_files, ensemble_size=50): # Build the ensemble start = time.time() - ensemble_selection = EnsembleSelection(ensemble_size=ensemble_size, - task_type=D.info['task'], - metric=score, - random_state=np.random.RandomState()) + ensemble_selection = EnsembleSelection( + ensemble_size=ensemble_size, + task_type=D.info["task"], + metric=score, + random_state=np.random.RandomState(), + ) validation_predictions = np.array([v[0] for v in validation_files]) test_predictions = np.array([t[0] for t in test_files]) - ensemble_selection.fit(validation_predictions, valid_labels, - identifiers=None) - y_hat_ensemble = ensemble_selection.predict(np.array( - validation_predictions)) + ensemble_selection.fit(validation_predictions, valid_labels, identifiers=None) + y_hat_ensemble = ensemble_selection.predict(np.array(validation_predictions)) y_hat_test = ensemble_selection.predict(np.array(test_predictions)) # Compute validation error @@ -209,21 +227,22 @@ def evaluate(input_directory, validation_files, test_files, ensemble_size=50): ensemble_time = time.time() - start - rval = {'ensemble_time': ensemble_time, - 'time_function_evaluation': time_function_evaluation, - 'ensemble_error': ensemble_error, - 'ensemble_test_error': ensemble_test_error} + rval = { + "ensemble_time": ensemble_time, + "time_function_evaluation": time_function_evaluation, + "ensemble_error": ensemble_error, + "ensemble_test_error": ensemble_test_error, + } return rval -if __name__ == '__main__': +if __name__ == "__main__": parser = ArgumentParser() - parser.add_argument('--input-directory', type=str, - required=True, nargs='+') - parser.add_argument('--task-id', type=int, required=True) - parser.add_argument('-s', '--seed', type=int) - parser.add_argument("--output-file", type=str, default='score_ensemble.csv') + parser.add_argument("--input-directory", type=str, required=True, nargs="+") + parser.add_argument("--task-id", type=int, required=True) + parser.add_argument("-s", "--seed", type=int) + parser.add_argument("--output-file", type=str, default="score_ensemble.csv") parser.add_argument("--ensemble-size", type=int, default=50) parser.add_argument("--n-jobs", type=int, default=1) args = parser.parse_args() diff --git a/scripts/2015_nips_paper/setup/get_tasks.py b/scripts/2015_nips_paper/setup/get_tasks.py index 09f06a0a64..98c4ee085e 100644 --- a/scripts/2015_nips_paper/setup/get_tasks.py +++ b/scripts/2015_nips_paper/setup/get_tasks.py @@ -4,30 +4,162 @@ # List of dataset IDs used for the NIPS experiment. -dataset_ids = [1000, 1002, 1018, 1019, 1020, 1021, 1036, 1040, 1041, 1049, 1050, 1053, - 1056, 1067, 1068, 1069, 1111, 1112, 1114, 1116, 1119, 1120, 1128, 1130, - 1134, 1138, 1139, 1142, 1146, 1161, 1166, 12, 14, 16, 179, 180, 181, 182, - 184, 185, 18, 21, 22, 23, 24, 26, 273, 28, 293, 300, 30, 31, 32, 351, 354, - 357, 36, 389, 38, 390, 391, 392, 393, 395, 396, 398, 399, 3, 401, 44, 46, - 554, 57, 60, 679, 6, 715, 718, 720, 722, 723, 727, 728, 734, 735, 737, - 740, 741, 743, 751, 752, 761, 772, 797, 799, 803, 806, 807, 813, 816, 819, - 821, 822, 823, 833, 837, 843, 845, 846, 847, 849, 866, 871, 881, 897, 901, - 903, 904, 910, 912, 913, 914, 917, 923, 930, 934, 953, 958, 959, 962, 966, - 971, 976, 977, 978, 979, 980, 991, 993, 995] +dataset_ids = [ + 1000, + 1002, + 1018, + 1019, + 1020, + 1021, + 1036, + 1040, + 1041, + 1049, + 1050, + 1053, + 1056, + 1067, + 1068, + 1069, + 1111, + 1112, + 1114, + 1116, + 1119, + 1120, + 1128, + 1130, + 1134, + 1138, + 1139, + 1142, + 1146, + 1161, + 1166, + 12, + 14, + 16, + 179, + 180, + 181, + 182, + 184, + 185, + 18, + 21, + 22, + 23, + 24, + 26, + 273, + 28, + 293, + 300, + 30, + 31, + 32, + 351, + 354, + 357, + 36, + 389, + 38, + 390, + 391, + 392, + 393, + 395, + 396, + 398, + 399, + 3, + 401, + 44, + 46, + 554, + 57, + 60, + 679, + 6, + 715, + 718, + 720, + 722, + 723, + 727, + 728, + 734, + 735, + 737, + 740, + 741, + 743, + 751, + 752, + 761, + 772, + 797, + 799, + 803, + 806, + 807, + 813, + 816, + 819, + 821, + 822, + 823, + 833, + 837, + 843, + 845, + 846, + 847, + 849, + 866, + 871, + 881, + 897, + 901, + 903, + 904, + 910, + 912, + 913, + 914, + 917, + 923, + 930, + 934, + 953, + 958, + 959, + 962, + 966, + 971, + 976, + 977, + 978, + 979, + 980, + 991, + 993, + 995, +] def get_task_ids(dataset_ids): # return task ids of corresponding datset ids. # active tasks - tasks_a = openml.tasks.list_tasks(task_type_id=1, status='active') + tasks_a = openml.tasks.list_tasks(task_type_id=1, status="active") tasks_a = pd.DataFrame.from_dict(tasks_a, orient="index") # query only those with holdout as the resampling startegy. tasks_a = tasks_a[(tasks_a.estimation_procedure == "33% Holdout set")] # deactivated tasks - tasks_d = openml.tasks.list_tasks(task_type_id=1, status='deactivated') + tasks_d = openml.tasks.list_tasks(task_type_id=1, status="deactivated") tasks_d = pd.DataFrame.from_dict(tasks_d, orient="index") tasks_d = tasks_d[(tasks_d.estimation_procedure == "33% Holdout set")] @@ -47,9 +179,9 @@ def get_task_ids(dataset_ids): def main(): task_ids = sorted(get_task_ids(dataset_ids)) - string_to_print = '' + string_to_print = "" for tid in task_ids: - string_to_print += str(tid) + ' ' + string_to_print += str(tid) + " " print(string_to_print) # print the task ids for bash script. diff --git a/scripts/run_auto-sklearn_for_metadata_generation.py b/scripts/run_auto-sklearn_for_metadata_generation.py index e1fc71a135..6b82b233c7 100644 --- a/scripts/run_auto-sklearn_for_metadata_generation.py +++ b/scripts/run_auto-sklearn_for_metadata_generation.py @@ -1,4 +1,4 @@ -if __name__ == '__main__': +if __name__ == "__main__": import argparse import json @@ -11,27 +11,35 @@ from autosklearn.classification import AutoSklearnClassifier from autosklearn.regression import AutoSklearnRegressor from autosklearn.evaluation import ExecuteTaFuncWithQueue, get_cost_of_crash - from autosklearn.metrics import accuracy, balanced_accuracy, roc_auc, log_loss, r2, \ - mean_squared_error, mean_absolute_error, root_mean_squared_error, CLASSIFICATION_METRICS, \ - REGRESSION_METRICS + from autosklearn.metrics import ( + accuracy, + balanced_accuracy, + roc_auc, + log_loss, + r2, + mean_squared_error, + mean_absolute_error, + root_mean_squared_error, + CLASSIFICATION_METRICS, + REGRESSION_METRICS, + ) from smac.runhistory.runhistory import RunInfo from smac.scenario.scenario import Scenario from smac.stats.stats import Stats from smac.tae import StatusType - sys.path.append('.') + sys.path.append(".") from update_metadata_util import load_task - parser = argparse.ArgumentParser() - parser.add_argument('--working-directory', type=str, required=True) - parser.add_argument('--time-limit', type=int, required=True) - parser.add_argument('--per-run-time-limit', type=int, required=True) - parser.add_argument('--task-id', type=int, required=True) - parser.add_argument('--metric', type=str, required=True) - parser.add_argument('-s', '--seed', type=int, required=True) - parser.add_argument('--unittest', action='store_true') + parser.add_argument("--working-directory", type=str, required=True) + parser.add_argument("--time-limit", type=int, required=True) + parser.add_argument("--per-run-time-limit", type=int, required=True) + parser.add_argument("--task-id", type=int, required=True) + parser.add_argument("--metric", type=str, required=True) + parser.add_argument("-s", "--seed", type=int, required=True) + parser.add_argument("--unittest", action="store_true") args = parser.parse_args() working_directory = args.working_directory @@ -44,8 +52,9 @@ X_train, y_train, X_test, y_test, cat, task_type, dataset_name = load_task(task_id) - configuration_output_dir = os.path.join(working_directory, 'configuration', - task_type) + configuration_output_dir = os.path.join( + working_directory, "configuration", task_type + ) os.makedirs(configuration_output_dir, exist_ok=True) tmp_dir = os.path.join(configuration_output_dir, str(task_id), metric) os.makedirs(tmp_dir, exist_ok=True) @@ -54,49 +63,55 @@ autosklearn_directory = os.path.join(tempdir, "dir") automl_arguments = { - 'time_left_for_this_task': time_limit, - 'per_run_time_limit': per_run_time_limit, - 'initial_configurations_via_metalearning': 0, - 'ensemble_size': 0, - 'ensemble_nbest': 0, - 'seed': seed, - 'memory_limit': 3072, - 'resampling_strategy': 'partial-cv', - 'delete_tmp_folder_after_terminate': False, - 'tmp_folder': autosklearn_directory, - 'disable_evaluator_output': True, + "time_left_for_this_task": time_limit, + "per_run_time_limit": per_run_time_limit, + "initial_configurations_via_metalearning": 0, + "ensemble_size": 0, + "ensemble_nbest": 0, + "seed": seed, + "memory_limit": 3072, + "resampling_strategy": "partial-cv", + "delete_tmp_folder_after_terminate": False, + "tmp_folder": autosklearn_directory, + "disable_evaluator_output": True, } if is_test: - automl_arguments['resampling_strategy_arguments'] = {'folds': 2} - if task_type == 'classification': - include = {'classifier': ['libsvm_svc'], 'feature_preprocessor': ['no_preprocessing']} - automl_arguments['include'] = include - elif task_type == 'regression': - include = {'regressor': ['extra_trees'], 'feature_preprocessor': ['no_preprocessing']} - automl_arguments['include'] = include + automl_arguments["resampling_strategy_arguments"] = {"folds": 2} + if task_type == "classification": + include = { + "classifier": ["libsvm_svc"], + "feature_preprocessor": ["no_preprocessing"], + } + automl_arguments["include"] = include + elif task_type == "regression": + include = { + "regressor": ["extra_trees"], + "feature_preprocessor": ["no_preprocessing"], + } + automl_arguments["include"] = include else: - raise ValueError('Unsupported task type: %s' % str(task_type)) + raise ValueError("Unsupported task type: %s" % str(task_type)) else: - automl_arguments['resampling_strategy_arguments'] = {'folds': 10} + automl_arguments["resampling_strategy_arguments"] = {"folds": 10} include = None metric = { - 'accuracy': accuracy, - 'balanced_accuracy': balanced_accuracy, - 'roc_auc': roc_auc, - 'logloss': log_loss, - 'r2': r2, - 'mean_squared_error': mean_squared_error, - 'root_mean_squared_error': root_mean_squared_error, - 'mean_absolute_error': mean_absolute_error, + "accuracy": accuracy, + "balanced_accuracy": balanced_accuracy, + "roc_auc": roc_auc, + "logloss": log_loss, + "r2": r2, + "mean_squared_error": mean_squared_error, + "root_mean_squared_error": root_mean_squared_error, + "mean_absolute_error": mean_absolute_error, }[metric] - automl_arguments['metric'] = metric + automl_arguments["metric"] = metric - if task_type == 'classification': + if task_type == "classification": automl = AutoSklearnClassifier(**automl_arguments) scorer_list = CLASSIFICATION_METRICS - elif task_type == 'regression': + elif task_type == "regression": automl = AutoSklearnRegressor(**automl_arguments) scorer_list = REGRESSION_METRICS else: @@ -104,8 +119,14 @@ scoring_functions = [scorer for name, scorer in scorer_list.items()] - automl.fit(X_train, y_train, dataset_name=dataset_name, - feat_type=cat, X_test=X_test, y_test=y_test) + automl.fit( + X_train, + y_train, + dataset_name=dataset_name, + feat_type=cat, + X_test=X_test, + y_test=y_test, + ) trajectory = automl.trajectory_ incumbent_id_to_model = {} @@ -117,40 +138,44 @@ else: memory_limit_factor = 2 - print('Starting to validate configurations') + print("Starting to validate configurations") for i, entry in enumerate(trajectory): - print('Starting to validate configuration %d/%d' % (i + 1, len(trajectory))) + print("Starting to validate configuration %d/%d" % (i + 1, len(trajectory))) incumbent_id = entry.incumbent_id train_performance = entry.train_perf if incumbent_id not in incumbent_id_to_model: config = entry.incumbent - logger = logging.getLogger('Testing:)') + logger = logging.getLogger("Testing:)") stats = Stats( - Scenario({ - 'cutoff_time': per_run_time_limit * 2, - 'run_obj': 'quality', - }) + Scenario( + { + "cutoff_time": per_run_time_limit * 2, + "run_obj": "quality", + } + ) ) stats.start_timing() # To avoid the output "first run crashed"... stats.submitted_ta_runs += 1 stats.finished_ta_runs += 1 - memory_lim = memory_limit_factor * automl_arguments['memory_limit'] + memory_lim = memory_limit_factor * automl_arguments["memory_limit"] pipeline, run_info, run_value = automl.fit_pipeline( - X=X_train, y=y_train, - X_test=X_test, y_test=y_test, - resampling_strategy='test', + X=X_train, + y=y_train, + X_test=X_test, + y_test=y_test, + resampling_strategy="test", memory_limit=memory_lim, disable_file_output=True, logger=logger, stats=stats, scoring_functions=scoring_functions, include=include, - metric=automl_arguments['metric'], - pynisher_context='spawn', - cutoff=per_run_time_limit*3, + metric=automl_arguments["metric"], + pynisher_context="spawn", + cutoff=per_run_time_limit * 3, config=config, ) @@ -159,58 +184,65 @@ # print(additional_run_info) - validated_trajectory.append(list(entry) + [task_id] + - [run_value.additional_info]) - print('Finished validating configuration %d/%d' % (i + 1, len(trajectory))) - print('Finished to validate configurations') - - print('Starting to copy data to configuration directory', flush=True) - validated_trajectory = [entry[:2] + [entry[2].get_dictionary()] + entry[3:] - for entry in validated_trajectory] - validated_trajectory_file = os.path.join(tmp_dir, 'validation_trajectory_%d.json' % seed) - with open(validated_trajectory_file, 'w') as fh: + validated_trajectory.append( + list(entry) + [task_id] + [run_value.additional_info] + ) + print("Finished validating configuration %d/%d" % (i + 1, len(trajectory))) + print("Finished to validate configurations") + + print("Starting to copy data to configuration directory", flush=True) + validated_trajectory = [ + entry[:2] + [entry[2].get_dictionary()] + entry[3:] + for entry in validated_trajectory + ] + validated_trajectory_file = os.path.join( + tmp_dir, "validation_trajectory_%d.json" % seed + ) + with open(validated_trajectory_file, "w") as fh: json.dump(validated_trajectory, fh, indent=4) - for dirpath, dirnames, filenames in os.walk(autosklearn_directory, topdown=False): print(dirpath, dirnames, filenames) for filename in filenames: - if filename == 'datamanager.pkl': + if filename == "datamanager.pkl": os.remove(os.path.join(dirpath, filename)) - elif filename == 'configspace.pcs': + elif filename == "configspace.pcs": os.remove(os.path.join(dirpath, filename)) for dirname in dirnames: - if dirname in ('models', 'cv_models'): + if dirname in ("models", "cv_models"): os.rmdir(os.path.join(dirpath, dirname)) - print('*' * 80) - print('Going to copy the configuration directory') - script = 'cp -r %s %s' % (autosklearn_directory, os.path.join(tmp_dir, 'auto-sklearn-output')) + print("*" * 80) + print("Going to copy the configuration directory") + script = "cp -r %s %s" % ( + autosklearn_directory, + os.path.join(tmp_dir, "auto-sklearn-output"), + ) proc = subprocess.run( script, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, - executable='/bin/bash', + executable="/bin/bash", ) - print('*' * 80) + print("*" * 80) print(script) print(proc.stdout) print(proc.stderr) - print('Finished copying the configuration directory') + print("Finished copying the configuration directory") - if not tempdir.startswith('/tmp'): - raise ValueError('%s must not start with /tmp' % tempdir) - script = 'rm -rf %s' % tempdir - print('*' * 80) + if not tempdir.startswith("/tmp"): + raise ValueError("%s must not start with /tmp" % tempdir) + script = "rm -rf %s" % tempdir + print("*" * 80) print(script) proc = subprocess.run( script, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, - executable='/bin/bash', + executable="/bin/bash", ) print(proc.stdout) print(proc.stderr) - print('Finished configuring') + print("Finished configuring") diff --git a/scripts/update_metadata_util.py b/scripts/update_metadata_util.py index 153e63c6cf..8ed99d9bd0 100644 --- a/scripts/update_metadata_util.py +++ b/scripts/update_metadata_util.py @@ -3,37 +3,327 @@ classification_tasks = [ - 232, 236, 241, 245, 253, 254, 256, 258, 260, 262, 267, 271, 273, 275, 279, 288, 336, - 340, 2119, 2120, 2121, 2122, 2123, 2125, 2356, 3044, 3047, 3048, 3049, 3053, 3054, - 3055, 75089, 75092, 75093, 75098, 75100, 75108, 75109, 75112, 75114, 75115, 75116, - 75118, 75120, 75121, 75125, 75126, 75129, 75131, 75133, 75134, 75136, 75139, 75141, - 75142, 75143, 75146, 75147, 75148, 75149, 75153, 75154, 75156, 75157, 75159, 75161, - 75163, 75166, 75169, 75171, 75173, 75174, 75176, 75178, 75179, 75180, 75184, 75185, - 75187, 75192, 75195, 75196, 75199, 75210, 75212, 75213, 75215, 75217, 75219, 75221, - 75223, 75225, 75232, 75233, 75234, 75235, 75236, 75237, 75239, 75250, 126021, 126024, - 126028, 126030, 126031, 146574, 146575, 146576, 146577, 146578, 146583, 146586, - 146592, 146593, 146594, 146596, 146597, 146600, 146601, 146602, 146603, 146679, - 166859, 166866, 166872, 166875, 166882, 166897, 166905, 166906, 166913, 166915, - 166931, 166932, 166944, 166950, 166951, 166953, 166956, 166957, 166958, 166959, - 166970, 166996, 167085, 167086, 167087, 167088, 167089, 167090, 167094, 167096, - 167097, 167099, 167100, 167101, 167103, 167105, 167106, 167202, 167203, 167204, - 167205, 168785, 168791, 189779, 189786, 189828, 189829, 189836, 189840, 189841, - 189843, 189844, 189845, 189846, 189857, 189858, 189859, 189863, 189864, 189869, - 189870, 189875, 189878, 189880, 189881, 189882, 189883, 189884, 189887, 189890, - 189893, 189894, 189899, 189900, 189902, 190154, 190155, 190156, 190157, 190158, - 190159, 211720, 211721, 211722, 211723, 211724 + 232, + 236, + 241, + 245, + 253, + 254, + 256, + 258, + 260, + 262, + 267, + 271, + 273, + 275, + 279, + 288, + 336, + 340, + 2119, + 2120, + 2121, + 2122, + 2123, + 2125, + 2356, + 3044, + 3047, + 3048, + 3049, + 3053, + 3054, + 3055, + 75089, + 75092, + 75093, + 75098, + 75100, + 75108, + 75109, + 75112, + 75114, + 75115, + 75116, + 75118, + 75120, + 75121, + 75125, + 75126, + 75129, + 75131, + 75133, + 75134, + 75136, + 75139, + 75141, + 75142, + 75143, + 75146, + 75147, + 75148, + 75149, + 75153, + 75154, + 75156, + 75157, + 75159, + 75161, + 75163, + 75166, + 75169, + 75171, + 75173, + 75174, + 75176, + 75178, + 75179, + 75180, + 75184, + 75185, + 75187, + 75192, + 75195, + 75196, + 75199, + 75210, + 75212, + 75213, + 75215, + 75217, + 75219, + 75221, + 75223, + 75225, + 75232, + 75233, + 75234, + 75235, + 75236, + 75237, + 75239, + 75250, + 126021, + 126024, + 126028, + 126030, + 126031, + 146574, + 146575, + 146576, + 146577, + 146578, + 146583, + 146586, + 146592, + 146593, + 146594, + 146596, + 146597, + 146600, + 146601, + 146602, + 146603, + 146679, + 166859, + 166866, + 166872, + 166875, + 166882, + 166897, + 166905, + 166906, + 166913, + 166915, + 166931, + 166932, + 166944, + 166950, + 166951, + 166953, + 166956, + 166957, + 166958, + 166959, + 166970, + 166996, + 167085, + 167086, + 167087, + 167088, + 167089, + 167090, + 167094, + 167096, + 167097, + 167099, + 167100, + 167101, + 167103, + 167105, + 167106, + 167202, + 167203, + 167204, + 167205, + 168785, + 168791, + 189779, + 189786, + 189828, + 189829, + 189836, + 189840, + 189841, + 189843, + 189844, + 189845, + 189846, + 189857, + 189858, + 189859, + 189863, + 189864, + 189869, + 189870, + 189875, + 189878, + 189880, + 189881, + 189882, + 189883, + 189884, + 189887, + 189890, + 189893, + 189894, + 189899, + 189900, + 189902, + 190154, + 190155, + 190156, + 190157, + 190158, + 190159, + 211720, + 211721, + 211722, + 211723, + 211724, ] regression_tasks = [ - 359997, 359998, 359999, 360000, 360001, 360002, 360003, 167146, 360004, 360005, 360006, - 360007, 211696, 360009, 360010, 360011, 360012, 360013, 360014, 360015, 360016, 360017, - 360018, 360019, 360020, 360021, 360022, 360023, 360024, 360025, 360026, 360027, 360028, - 360029, 360030, 360031, 360032, 360033, 360034, 360035, 360036, 360037, 360038, 360039, - 360040, 360041, 360042, 360043, 360044, 360045, 360046, 360047, 360048, 360049, 360050, - 360051, 360052, 360053, 360054, 360055, 360056, 360057, 360058, 360059, 360060, 360061, - 360062, 360063, 360064, 360066, 360067, 360068, 360069, 360070, 360071, 360072, 360073, - 360074, 360075, 360076, 360077, 360078, 360079, 360080, 360081, 360082, 360083, 360084, - 360085, 360086, 360087, 360088, 360089, 360090, 360091, 360092, 360093, 360094, 360095, - 360096, 360097, 360098, 360100, 360101, 360102, 360103, 360104, 360105, 360106, 360107, + 359997, + 359998, + 359999, + 360000, + 360001, + 360002, + 360003, + 167146, + 360004, + 360005, + 360006, + 360007, + 211696, + 360009, + 360010, + 360011, + 360012, + 360013, + 360014, + 360015, + 360016, + 360017, + 360018, + 360019, + 360020, + 360021, + 360022, + 360023, + 360024, + 360025, + 360026, + 360027, + 360028, + 360029, + 360030, + 360031, + 360032, + 360033, + 360034, + 360035, + 360036, + 360037, + 360038, + 360039, + 360040, + 360041, + 360042, + 360043, + 360044, + 360045, + 360046, + 360047, + 360048, + 360049, + 360050, + 360051, + 360052, + 360053, + 360054, + 360055, + 360056, + 360057, + 360058, + 360059, + 360060, + 360061, + 360062, + 360063, + 360064, + 360066, + 360067, + 360068, + 360069, + 360070, + 360071, + 360072, + 360073, + 360074, + 360075, + 360076, + 360077, + 360078, + 360079, + 360080, + 360081, + 360082, + 360083, + 360084, + 360085, + 360086, + 360087, + 360088, + 360089, + 360090, + 360091, + 360092, + 360093, + 360094, + 360095, + 360096, + 360097, + 360098, + 360100, + 360101, + 360102, + 360103, + 360104, + 360105, + 360106, + 360107, 360108, ] @@ -51,13 +341,13 @@ def load_task(task_id): name = dataset.name.lower() del _ del dataset - cat = {i: 'categorical' if c else 'numerical' for i, c in enumerate(cat)} + cat = {i: "categorical" if c else "numerical" for i, c in enumerate(cat)} if isinstance(task, openml.tasks.OpenMLClassificationTask): - task_type = 'classification' + task_type = "classification" elif isinstance(task, openml.tasks.OpenMLRegressionTask): - task_type = 'regression' + task_type = "regression" else: - raise ValueError('Unknown task type') + raise ValueError("Unknown task type") return X_train, y_train, X_test, y_test, cat, task_type, name diff --git a/setup.py b/setup.py index 6107e60321..003b573bd4 100644 --- a/setup.py +++ b/setup.py @@ -1,38 +1,46 @@ # -*- encoding: utf-8 -*- import os import sys -from setuptools import setup, find_packages +from setuptools import find_packages, setup + +HERE = os.path.abspath(os.path.dirname(__file__)) # Check if Auto-sklearn *could* run on the given system -if os.name != 'posix': +if os.name != "posix": raise ValueError( - 'Detected unsupported operating system: %s. Please check ' - 'the compability information of auto-sklearn: https://automl.github.io' - '/auto-sklearn/master/installation.html#windows-osx-compatibility' % - sys.platform + "Detected unsupported operating system: %s. Please check " + "the compability information of auto-sklearn: https://automl.github.io" + "/auto-sklearn/master/installation.html#windows-osx-compatibility" + % sys.platform ) if sys.version_info < (3, 7): raise ValueError( - 'Unsupported Python version %d.%d.%d found. Auto-sklearn requires Python ' - '3.7 or higher.' % (sys.version_info.major, sys.version_info.minor, sys.version_info.micro) + "Unsupported Python version %d.%d.%d found. Auto-sklearn requires Python " + "3.7 or higher." + % (sys.version_info.major, sys.version_info.minor, sys.version_info.micro) ) -HERE = os.path.abspath(os.path.dirname(__file__)) -with open(os.path.join(HERE, 'requirements.txt')) as fp: - install_reqs = [r.rstrip() for r in fp.readlines() - if not r.startswith('#') and not r.startswith('git+')] +with open(os.path.join(HERE, "requirements.txt")) as fp: + install_reqs = [ + r.rstrip() + for r in fp.readlines() + if not r.startswith("#") and not r.startswith("git+") + ] -extras_reqs={ +extras_reqs = { "test": [ "pytest>=4.6", - "mypy", + "pytest-cov", "pytest-xdist", "pytest-timeout", + "mypy", + "isort", + "black", + "pydocstyle", "openml", "pre-commit", - "pytest-cov", ], "examples": [ "matplotlib", @@ -46,32 +54,32 @@ "sphinx_bootstrap_theme", "numpydoc", "sphinx_toolbox", - "docutils==0.16" + "docutils==0.16", ], } -with open(os.path.join(HERE, 'autosklearn', '__version__.py')) as fh: +with open(os.path.join(HERE, "autosklearn", "__version__.py")) as fh: version = fh.readlines()[-1].split()[-1].strip("\"'") -with open(os.path.join(HERE, 'README.md')) as fh: +with open(os.path.join(HERE, "README.md")) as fh: long_description = fh.read() setup( - name='auto-sklearn', - author='Matthias Feurer', - author_email='feurerm@informatik.uni-freiburg.de', - description='Automated machine learning.', + name="auto-sklearn", + author="Matthias Feurer", + author_email="feurerm@informatik.uni-freiburg.de", + description="Automated machine learning.", long_description=long_description, - long_description_content_type='text/markdown', + long_description_content_type="text/markdown", version=version, - packages=find_packages(exclude=['test', 'scripts', 'examples']), + packages=find_packages(exclude=["test", "scripts", "examples"]), extras_require=extras_reqs, install_requires=install_reqs, include_package_data=True, - license='BSD3', - platforms=['Linux'], + license="BSD3", + platforms=["Linux"], classifiers=[ "Environment :: Console", "Intended Audience :: Developers", @@ -83,10 +91,10 @@ "Operating System :: OS Independent", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Information Analysis", - 'Programming Language :: Python :: 3.7', - 'Programming Language :: Python :: 3.8', - 'Programming Language :: Python :: 3.9', + "Programming Language :: Python :: 3.7", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", ], - python_requires='>=3.7', - url='https://automl.github.io/auto-sklearn', + python_requires=">=3.7", + url="https://automl.github.io/auto-sklearn", ) diff --git a/test/conftest.py b/test/conftest.py index d3df7508cd..16a285b9df 100644 --- a/test/conftest.py +++ b/test/conftest.py @@ -3,12 +3,12 @@ import time import unittest.mock -from dask.distributed import Client, get_client import psutil import pytest +from dask.distributed import Client, get_client -from autosklearn.automl_common.common.utils.backend import create, Backend from autosklearn.automl import AutoML +from autosklearn.automl_common.common.utils.backend import Backend, create class AutoMLStub(AutoML): @@ -36,9 +36,11 @@ def automl_stub(request): def backend(request): test_dir = os.path.dirname(__file__) - tmp = os.path.join(test_dir, '.tmp__%s__%s' % (request.module.__name__, request.node.name)) + tmp = os.path.join( + test_dir, ".tmp__%s__%s" % (request.module.__name__, request.node.name) + ) - for dir in (tmp, ): + for dir in (tmp,): for i in range(10): if os.path.exists(dir): try: @@ -49,14 +51,12 @@ def backend(request): # Make sure the folders we wanna create do not already exist. backend = create( - temporary_directory=tmp, - output_directory=None, - prefix="auto-sklearn" + temporary_directory=tmp, output_directory=None, prefix="auto-sklearn" ) def get_finalizer(tmp_dir): def session_run_at_end(): - for dir in (tmp_dir, ): + for dir in (tmp_dir,): for i in range(10): if os.path.exists(dir): try: @@ -64,7 +64,9 @@ def session_run_at_end(): break except OSError: time.sleep(1) + return session_run_at_end + request.addfinalizer(get_finalizer(tmp)) return backend @@ -72,7 +74,7 @@ def session_run_at_end(): @pytest.fixture(scope="function") def tmp_dir(request): - return _dir_fixture('tmp', request) + return _dir_fixture("tmp", request) def _dir_fixture(dir_type, request): @@ -124,8 +126,10 @@ def session_run_at_end(): client.shutdown() client.close() del client + return session_run_at_end - request.addfinalizer(get_finalizer(client.scheduler_info()['address'])) + + request.addfinalizer(get_finalizer(client.scheduler_info()["address"])) return client @@ -149,8 +153,10 @@ def session_run_at_end(): client.shutdown() client.close() del client + return session_run_at_end - request.addfinalizer(get_finalizer(client.scheduler_info()['address'])) + + request.addfinalizer(get_finalizer(client.scheduler_info()["address"])) return client diff --git a/test/test_automl/automl_utils.py b/test/test_automl/automl_utils.py index 768f94ff8d..577ea97359 100644 --- a/test/test_automl/automl_utils.py +++ b/test/test_automl/automl_utils.py @@ -1,17 +1,17 @@ # -*- encoding: utf-8 -*- -import re -import os -import glob import typing -import numpy as np +import glob +import os +import re +import numpy as np scores_dict = { - 'train_single': ["single_best_train_score", "single_best_optimization_score"], - 'test_single': ["single_best_test_score"], - 'train_ensamble': ["ensemble_optimization_score"], - 'test_ensamble': ["ensemble_test_score"] + "train_single": ["single_best_train_score", "single_best_optimization_score"], + "test_single": ["single_best_test_score"], + "train_ensamble": ["ensemble_optimization_score"], + "test_ensamble": ["ensemble_test_score"], } @@ -19,15 +19,15 @@ def print_debug_information(automl): # In case it is called with estimator, # Get the automl object - if hasattr(automl, 'automl_'): + if hasattr(automl, "automl_"): automl = automl.automl_ # Log file path - log_file = glob.glob(os.path.join( - automl._backend.temporary_directory, 'AutoML*.log'))[0] + log_file = glob.glob( + os.path.join(automl._backend.temporary_directory, "AutoML*.log") + )[0] - include_messages = ['INFO', 'DEBUG', 'WARN', - 'CRITICAL', 'ERROR', 'FATAL'] + include_messages = ["INFO", "DEBUG", "WARN", "CRITICAL", "ERROR", "FATAL"] # There is a lot of content in the log files. Only # parsing the main message and ignore the metalearning @@ -37,53 +37,69 @@ def print_debug_information(automl): content = logfile.readlines() # Get the messages to debug easier! - content = [line for line in content if any( - msg in line for msg in include_messages - ) and 'metalearning' not in line] + content = [ + line + for line in content + if any(msg in line for msg in include_messages) + and "metalearning" not in line + ] except Exception as e: return str(e) # Also add the run history if any - if hasattr(automl, 'runhistory_') and hasattr(automl.runhistory_, 'data'): + if hasattr(automl, "runhistory_") and hasattr(automl.runhistory_, "data"): for k, v in automl.runhistory_.data.items(): content += ["{}->{}".format(k, v)] else: - content += ['No RunHistory'] + content += ["No RunHistory"] # Also add the ensemble history if any if len(automl.ensemble_performance_history) > 0: content += [str(h) for h in automl.ensemble_performance_history] else: - content += ['No Ensemble History'] + content += ["No Ensemble History"] return os.linesep.join(content) def _includes(scores, all_scores): - return all(score in all_scores for score in scores) and len(scores) == len(all_scores) + return all(score in all_scores for score in scores) and len(scores) == len( + all_scores + ) def count_succeses(cv_results): return np.sum( - [status in ['Success', 'Success (but do not advance to higher budget)'] - for status in cv_results['status']] + [ + status in ["Success", "Success (but do not advance to higher budget)"] + for status in cv_results["status"] + ] ) def includes_all_scores(scores): - all_scores = scores_dict["train_single"] + scores_dict["test_single"] + \ - scores_dict["train_ensamble"] + scores_dict["test_ensamble"] + ["Timestamp"] + all_scores = ( + scores_dict["train_single"] + + scores_dict["test_single"] + + scores_dict["train_ensamble"] + + scores_dict["test_ensamble"] + + ["Timestamp"] + ) return _includes(scores, all_scores) def include_single_scores(scores): - all_scores = scores_dict["train_single"] + scores_dict["test_single"] + ["Timestamp"] + all_scores = ( + scores_dict["train_single"] + scores_dict["test_single"] + ["Timestamp"] + ) return _includes(scores, all_scores) def includes_train_scores(scores): - all_scores = scores_dict["train_single"] + scores_dict["train_ensamble"] + ["Timestamp"] + all_scores = ( + scores_dict["train_single"] + scores_dict["train_ensamble"] + ["Timestamp"] + ) return _includes(scores, all_scores) @@ -113,7 +129,7 @@ def parse_logfile(self) -> typing.List[str]: assert os.path.exists(self.logfile), "{} not found".format(self.logfile) with open(self.logfile) as fh: - content = [line.strip() for line in fh if re.search(r'[\w+]', line)] + content = [line.strip() for line in fh if re.search(r"[\w+]", line)] return content def count_ensembler_iterations(self) -> int: @@ -129,11 +145,12 @@ def count_ensembler_iterations(self) -> int: # We expect the start msg to be something like: # [DEBUG] [2020-11-26 19:22:42,160:EnsembleBuilder] \ # Function called with argument: (61.... - # [DEBUG] [2020-11-30 11:53:47,069:EnsembleBuilder] Function called with argument: - # (28.246965646743774, 1, False), {} + # [DEBUG] [2020-11-30 11:53:47,069:EnsembleBuilder] \ + # Function called with argument: (28.246965646743774, 1, False), {} match = re.search( - r'EnsembleBuilder]\s+Function called with argument:\s+\(\d+\.\d+, (\d+), \w+', - line) + r"EnsembleBuilder]\s+Function called with argument:\s+\(\d+\.\d+, (\d+), \w+", # noqa: E501 + line, + ) if match: iterations.append(int(match.group(1))) @@ -143,19 +160,15 @@ def count_ensembler_iterations(self) -> int: # time left: 61.266255 # [DEBUG] [2020-11-27 20:27:28,044:EnsembleBuilder] Starting iteration 2, # time left: 10.603252 - match = re.search( - r'EnsembleBuilder]\s+Starting iteration (\d+)', - line) + match = re.search(r"EnsembleBuilder]\s+Starting iteration (\d+)", line) if match: iterations_from_inside_ensemble_builder.append(int(match.group(1))) # The ensemble builder might not be called if there is no time. # Here we expect the msg: - # [DEBUG] [2020-11-27 20:27:28,044:EnsembleBuilder] Not starting iteration 2, - # as time left: 1.59324 - match = re.search( - r'EnsembleBuilder]\s+Not starting iteration (\d+)', - line) + # [DEBUG] [2020-11-27 20:27:28,044:EnsembleBuilder] \ + # Not starting iteration 2, as time left: 1.59324 + match = re.search(r"EnsembleBuilder]\s+Not starting iteration (\d+)", line) if match: iterations_from_inside_ensemble_builder.append(int(match.group(1))) @@ -174,49 +187,80 @@ def count_ensembler_success_pynisher_calls(self) -> int: # [DEBUG] [2020-11-30 11:54:05,984:EnsembleBuilder] return value: # (([{'Timestamp': Timestamp('2020-11- 30 11:54:05.983837'), # 'ensemble_optimization_score': 0.9787234042553191}], 50, None, None, None), 0) - return_msgs = len([line for line in self.lines if re.search( - r'EnsembleBuilder]\s+return value:.*Timestamp', line)]) + return_msgs = len( + [ + line + for line in self.lines + if re.search(r"EnsembleBuilder]\s+return value:.*Timestamp", line) + ] + ) return return_msgs def count_tae_pynisher_calls(self) -> int: # We expect the return msg to be something like: - # [DEBUG] [2020-12-16 11:57:08,987:Client-pynisher] Function called with argument: () - # , {'queue': , 'config': 1 - # [DEBUG] [2020-12-16 11:57:10,537:Client-pynisher] Function called with argument: () - # , {'queue': , - # 'config': Configuration: + """ + [DEBUG] [2020-12-16 11:57:08,987:Client-pynisher] Function called with argument: (), + {'queue': , 'config': 1 + [DEBUG] [2020-12-16 11:57:10,537:Client-pynisher] Function called with argument: (), + {'queue': , 'config': Configuration: + """ # noqa: E501 # Only the parenthesis below need to be escaped, ] and { do not. - call_msgs = len([line for line in self.lines if re.search( - r'pynisher]\s+Function called with argument: \(\), {', line)]) + call_msgs = len( + [ + line + for line in self.lines + if re.search( + r"pynisher]\s+Function called with argument: \(\), {", line + ) + ] + ) return call_msgs def count_tae_pynisher_returns(self) -> int: # We expect the return msg to be something like: # [DEBUG] [2020-11-30 11:53:11,264:pynisher] return value: (None, 0) # [DEBUG] [2020-11-30 11:53:13,768:pynisher] return value: (None, 0) - return_msgs = len([line for line in self.lines if re.search( - r'pynisher]\s+return value:\s+', line)]) + return_msgs = len( + [ + line + for line in self.lines + if re.search(r"pynisher]\s+return value:\s+", line) + ] + ) # When the pynisher pipe is prematurely closed, we also expect: # Your function call closed the pipe prematurely # -> Subprocess probably got an uncatchable signal # We expect the return msg to be something like: # OR # Something else went wrong, sorry. - premature_msgs = len([line for line in self.lines if re.search( - r'pynisher]\s+Your function call closed the pipe prematurely', line)]) - failure_msgs = len([line for line in self.lines if re.search( - r'pynisher]\s+Something else went wrong, sorry.', line)]) + premature_msgs = len( + [ + line + for line in self.lines + if re.search( + r"pynisher]\s+Your function call closed the pipe prematurely", line + ) + ] + ) + failure_msgs = len( + [ + line + for line in self.lines + if re.search(r"pynisher]\s+Something else went wrong, sorry.", line) + ] + ) return return_msgs + premature_msgs + failure_msgs def get_automl_setting_from_log(self, dataset_name: str, setting: str) -> str: for line in self.lines: # We expect messages of the form - # [DEBUG] [2020-11-30 11:53:10,457:AutoML(5):breast_cancer] ensemble_size: 50 - # [DEBUG] [2020-11-30 11:53:10,457:AutoML(5):breast_cancer] ensemble_nbest: 50 - match = re.search( - f"{dataset_name}]\\s*{setting}\\s*:\\s*(\\w+)", - line) + """ + [DEBUG] [2020-11-30 11:53:10,457:AutoML(5):breast_cancer] ensemble_size: 50 + [DEBUG] [2020-11-30 11:53:10,457:AutoML(5):breast_cancer] ensemble_nbest: 50 + """ # noqa: E501 + match = re.search(f"{dataset_name}]\\s*{setting}\\s*:\\s*(\\w+)", line) if match: return match.group(1) + return None diff --git a/test/test_automl/test_automl.py b/test/test_automl/test_automl.py index 4e509d8755..37040f0560 100644 --- a/test/test_automl/test_automl.py +++ b/test/test_automl/test_automl.py @@ -1,12 +1,11 @@ # -*- encoding: utf-8 -*- from typing import Dict, List, Union +import glob import itertools import os import pickle -import sys import time -import glob import unittest import unittest.mock import warnings @@ -14,35 +13,46 @@ import numpy as np import pandas as pd import pytest -from scipy.sparse import csr_matrix, spmatrix import sklearn.datasets -from sklearn.ensemble import VotingRegressor, VotingClassifier -from smac.scenario.scenario import Scenario +from scipy.sparse import csr_matrix, spmatrix +from sklearn.ensemble import VotingClassifier, VotingRegressor from smac.facade.roar_facade import ROAR +from smac.scenario.scenario import Scenario +from smac.tae import StatusType -from autosklearn.automl import AutoML, AutoMLClassifier, AutoMLRegressor, _model_predict -from autosklearn.data.validation import InputValidator import autosklearn.automl -from autosklearn.data.xy_data_manager import XYDataManager -from autosklearn.metrics import ( - accuracy, log_loss, balanced_accuracy, default_metric_for_task -) -from autosklearn.evaluation.abstract_evaluator import MyDummyClassifier, MyDummyRegressor -from autosklearn.util.data import default_dataset_compression_arg -from autosklearn.util.logging_ import PickableLoggerAdapter import autosklearn.pipeline.util as putil +from autosklearn.automl import AutoML, AutoMLClassifier, AutoMLRegressor, _model_predict from autosklearn.constants import ( - MULTICLASS_CLASSIFICATION, BINARY_CLASSIFICATION, + CLASSIFICATION_TASKS, + MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION, - REGRESSION, MULTIOUTPUT_REGRESSION, - CLASSIFICATION_TASKS, + REGRESSION, ) -from smac.tae import StatusType +from autosklearn.data.validation import InputValidator +from autosklearn.data.xy_data_manager import XYDataManager +from autosklearn.evaluation.abstract_evaluator import ( + MyDummyClassifier, + MyDummyRegressor, +) +from autosklearn.metrics import ( + accuracy, + balanced_accuracy, + default_metric_for_task, + log_loss, +) +from autosklearn.util.data import default_dataset_compression_arg +from autosklearn.util.logging_ import PickableLoggerAdapter -sys.path.append(os.path.dirname(__file__)) -from automl_utils import print_debug_information, count_succeses, AutoMLLogParser, includes_all_scores, includes_train_scores, performance_over_time_is_plausible # noqa (E402: module level import not at top of file) +from test.test_automl.automl_utils import ( + AutoMLLogParser, + count_succeses, + includes_train_scores, + performance_over_time_is_plausible, + print_debug_information, +) class AutoMLStub(AutoML): @@ -57,7 +67,7 @@ def __del__(self): def test_fit(dask_client): - X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') + X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") automl = autosklearn.automl.AutoML( seed=0, time_left_for_this_task=30, @@ -80,13 +90,7 @@ def test_fit(dask_client): def test_fit_roar(dask_client_single_worker): def get_roar_object_callback( - scenario_dict, - seed, - ta, - ta_kwargs, - dask_client, - n_jobs, - **kwargs + scenario_dict, seed, ta, ta_kwargs, dask_client, n_jobs, **kwargs ): """Random online adaptive racing. @@ -101,7 +105,7 @@ def get_roar_object_callback( n_jobs=n_jobs, ) - X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') + X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") automl = autosklearn.automl.AutoML( time_left_for_this_task=30, per_run_time_limit=5, @@ -126,8 +130,7 @@ def test_refit_shuffle_on_fail(dask_client): failing_model = unittest.mock.Mock() failing_model.fit.side_effect = [ValueError(), ValueError(), None] - failing_model.fit_transformer.side_effect = [ - ValueError(), ValueError(), (None, {})] + failing_model.fit_transformer.side_effect = [ValueError(), ValueError(), (None, {})] failing_model.get_max_iter.return_value = 100 auto = AutoML(30, 5, dask_client=dask_client) @@ -135,7 +138,7 @@ def test_refit_shuffle_on_fail(dask_client): ensemble_mock.get_selected_model_identifiers.return_value = [(1, 1, 50.0)] auto.ensemble_ = ensemble_mock auto.InputValidator = InputValidator() - for budget_type in [None, 'iterations']: + for budget_type in [None, "iterations"]: auto._budget_type = budget_type auto.models_ = {(1, 1, 50.0): failing_model} @@ -153,12 +156,11 @@ def test_refit_shuffle_on_fail(dask_client): def test_only_loads_ensemble_models(automl_stub): - def side_effect(ids, *args, **kwargs): return models if ids is identifiers else {} # Add a resampling strategy as this is required by load_models - automl_stub._resampling_strategy = 'holdout' + automl_stub._resampling_strategy = "holdout" identifiers = [(1, 2), (3, 4)] models = [42] @@ -171,7 +173,7 @@ def side_effect(ids, *args, **kwargs): assert models == automl_stub.models_ assert automl_stub.cv_models_ is None - automl_stub._resampling_strategy = 'cv' + automl_stub._resampling_strategy = "cv" models = [42] automl_stub._backend.load_cv_models_by_identifiers.side_effect = side_effect @@ -192,7 +194,7 @@ def test_check_for_models_if_no_ensemble(automl_stub): def test_raises_if_no_models(automl_stub): automl_stub._backend.load_ensemble.return_value = None automl_stub._backend.list_all_models.return_value = [] - automl_stub._resampling_strategy = 'holdout' + automl_stub._resampling_strategy = "holdout" automl_stub._disable_evaluator_output = False with pytest.raises(ValueError): @@ -205,7 +207,7 @@ def test_raises_if_no_models(automl_stub): def test_delete_non_candidate_models(dask_client): seed = 555 - X, Y, _, _ = putil.get_dataset('iris') + X, Y, _, _ = putil.get_dataset("iris") automl = autosklearn.automl.AutoML( delete_tmp_folder_after_terminate=False, time_left_for_this_task=60, @@ -213,11 +215,8 @@ def test_delete_non_candidate_models(dask_client): ensemble_nbest=3, seed=seed, initial_configurations_via_metalearning=0, - resampling_strategy='holdout', - include={ - 'classifier': ['sgd'], - 'feature_preprocessor': ['no_preprocessing'] - }, + resampling_strategy="holdout", + include={"classifier": ["sgd"], "feature_preprocessor": ["no_preprocessing"]}, metric=accuracy, dask_client=dask_client, # Force model to be deleted. That is, from 50 which is the @@ -229,23 +228,31 @@ def test_delete_non_candidate_models(dask_client): # Assert at least one model file has been deleted and that there were no # deletion errors - log_file_path = glob.glob(os.path.join( - automl._backend.temporary_directory, 'AutoML(' + str(seed) + '):*.log')) + log_file_path = glob.glob( + os.path.join( + automl._backend.temporary_directory, "AutoML(" + str(seed) + "):*.log" + ) + ) with open(log_file_path[0]) as log_file: log_content = log_file.read() - assert 'Deleted files of non-candidate model' in log_content, log_content - assert 'Failed to delete files of non-candidate model' not in log_content, log_content - assert 'Failed to lock model' not in log_content, log_content + assert "Deleted files of non-candidate model" in log_content, log_content + assert ( + "Failed to delete files of non-candidate model" not in log_content + ), log_content + assert "Failed to lock model" not in log_content, log_content # Assert that the files of the models used by the ensemble weren't deleted model_files = automl._backend.list_all_models(seed=seed) model_files_idx = set() for m_file in model_files: # Extract the model identifiers from the filename - m_file = os.path.split(m_file)[1].replace('.model', '').split('.', 2) + m_file = os.path.split(m_file)[1].replace(".model", "").split(".", 2) model_files_idx.add((int(m_file[0]), int(m_file[1]), float(m_file[2]))) ensemble_members_idx = set(automl.ensemble_.identifiers_) - assert ensemble_members_idx.issubset(model_files_idx), (ensemble_members_idx, model_files_idx) + assert ensemble_members_idx.issubset(model_files_idx), ( + ensemble_members_idx, + model_files_idx, + ) del automl @@ -257,17 +264,23 @@ def test_binary_score_and_include(dask_client): """ data = sklearn.datasets.make_classification( - n_samples=400, n_features=10, n_redundant=1, n_informative=3, - n_repeated=1, n_clusters_per_class=2, random_state=1) + n_samples=400, + n_features=10, + n_redundant=1, + n_informative=3, + n_repeated=1, + n_clusters_per_class=2, + random_state=1, + ) X_train = data[0][:200] Y_train = data[1][:200] X_test = data[0][200:] Y_test = data[1][200:] automl = autosklearn.automl.AutoML( - 20, 5, - include={'classifier': ['sgd'], - 'feature_preprocessor': ['no_preprocessing']}, + 20, + 5, + include={"classifier": ["sgd"], "feature_preprocessor": ["no_preprocessing"]}, metric=accuracy, dask_client=dask_client, ) @@ -286,10 +299,11 @@ def test_binary_score_and_include(dask_client): def test_automl_outputs(dask_client): - X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') - name = 'iris' + X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") + name = "iris" auto = autosklearn.automl.AutoML( - 30, 5, + 30, + 5, initial_configurations_via_metalearning=0, seed=100, metric=accuracy, @@ -307,59 +321,70 @@ def test_automl_outputs(dask_client): ) data_manager_file = os.path.join( - auto._backend.temporary_directory, - '.auto-sklearn', - 'datamanager.pkl' + auto._backend.temporary_directory, ".auto-sklearn", "datamanager.pkl" ) # pickled data manager (without one hot encoding!) - with open(data_manager_file, 'rb') as fh: + with open(data_manager_file, "rb") as fh: D = pickle.load(fh) - assert np.allclose(D.data['X_train'], X_train) + assert np.allclose(D.data["X_train"], X_train) # Check that all directories are there fixture = [ - 'true_targets_ensemble.npy', - 'start_time_100', - 'datamanager.pkl', - 'ensemble_read_preds.pkl', - 'ensemble_read_losses.pkl', - 'runs', - 'ensembles', - 'ensemble_history.json', + "true_targets_ensemble.npy", + "start_time_100", + "datamanager.pkl", + "ensemble_read_preds.pkl", + "ensemble_read_losses.pkl", + "runs", + "ensembles", + "ensemble_history.json", ] - assert ( - sorted(os.listdir(os.path.join(auto._backend.temporary_directory, - '.auto-sklearn'))) - == sorted(fixture) - ) + assert sorted( + os.listdir(os.path.join(auto._backend.temporary_directory, ".auto-sklearn")) + ) == sorted(fixture) # At least one ensemble, one validation, one test prediction and one # model and one ensemble - fixture = glob.glob(os.path.join( - auto._backend.temporary_directory, - '.auto-sklearn', 'runs', '*', 'predictions_ensemble*npy', - )) + fixture = glob.glob( + os.path.join( + auto._backend.temporary_directory, + ".auto-sklearn", + "runs", + "*", + "predictions_ensemble*npy", + ) + ) assert len(fixture) > 0 - fixture = glob.glob(os.path.join(auto._backend.temporary_directory, '.auto-sklearn', - 'runs', '*', '100.*.model')) + fixture = glob.glob( + os.path.join( + auto._backend.temporary_directory, + ".auto-sklearn", + "runs", + "*", + "100.*.model", + ) + ) assert len(fixture) > 0 - fixture = os.listdir(os.path.join(auto._backend.temporary_directory, - '.auto-sklearn', 'ensembles')) - assert '100.0000000000.ensemble' in fixture + fixture = os.listdir( + os.path.join(auto._backend.temporary_directory, ".auto-sklearn", "ensembles") + ) + assert "100.0000000000.ensemble" in fixture # Start time - start_time_file_path = os.path.join(auto._backend.temporary_directory, - '.auto-sklearn', "start_time_100") - with open(start_time_file_path, 'r') as fh: + start_time_file_path = os.path.join( + auto._backend.temporary_directory, ".auto-sklearn", "start_time_100" + ) + with open(start_time_file_path, "r") as fh: start_time = float(fh.read()) assert time.time() - start_time >= 10, print_debug_information(auto) # Then check that the logger matches the run expectation - logfile = glob.glob(os.path.join( - auto._backend.temporary_directory, 'AutoML*.log'))[0] + logfile = glob.glob(os.path.join(auto._backend.temporary_directory, "AutoML*.log"))[ + 0 + ] parser = AutoMLLogParser(logfile) # The number of ensemble trajectories properly in log file @@ -381,42 +406,61 @@ def test_automl_outputs(dask_client): # Dummy not in run history total_calls_to_pynisher_log = parser.count_tae_pynisher_calls() - 1 total_returns_from_pynisher_log = parser.count_tae_pynisher_returns() - 1 - total_elements_rh = len([run_value for run_value in auto.runhistory_.data.values( - ) if run_value.status == StatusType.RUNNING]) + total_elements_rh = len( + [ + run_value + for run_value in auto.runhistory_.data.values() + if run_value.status == StatusType.RUNNING + ] + ) # Make sure we register all calls to pynisher # The less than or equal here is added as a WA as # https://github.com/automl/SMAC3/pull/712 is not yet integrated - assert total_elements_rh <= total_calls_to_pynisher_log, print_debug_information(auto) + assert total_elements_rh <= total_calls_to_pynisher_log, print_debug_information( + auto + ) # Make sure we register all returns from pynisher - assert total_elements_rh <= total_returns_from_pynisher_log, print_debug_information(auto) + assert ( + total_elements_rh <= total_returns_from_pynisher_log + ), print_debug_information(auto) # Lastly check that settings are print to logfile - ensemble_size = parser.get_automl_setting_from_log(auto._dataset_name, 'ensemble_size') + ensemble_size = parser.get_automl_setting_from_log( + auto._dataset_name, "ensemble_size" + ) assert auto._ensemble_size == int(ensemble_size) del auto -@pytest.mark.parametrize("datasets", [('breast_cancer', BINARY_CLASSIFICATION), - ('wine', MULTICLASS_CLASSIFICATION), - ('diabetes', REGRESSION)]) +@pytest.mark.parametrize( + "datasets", + [ + ("breast_cancer", BINARY_CLASSIFICATION), + ("wine", MULTICLASS_CLASSIFICATION), + ("diabetes", REGRESSION), + ], +) def test_do_dummy_prediction(dask_client, datasets): name, task = datasets X_train, Y_train, X_test, Y_test = putil.get_dataset(name) datamanager = XYDataManager( - X_train, Y_train, - X_test, Y_test, + X_train, + Y_train, + X_test, + Y_test, task=task, dataset_name=name, - feat_type={i: 'numerical' for i in range(X_train.shape[1])}, + feat_type={i: "numerical" for i in range(X_train.shape[1])}, ) auto = autosklearn.automl.AutoML( - 20, 5, + 20, + 5, initial_configurations_via_metalearning=25, metric=accuracy, dask_client=dask_client, @@ -433,18 +477,18 @@ def test_do_dummy_prediction(dask_client, datasets): D = auto._backend.load_datamanager() # Check if data manager is correcly loaded - assert D.info['task'] == datamanager.info['task'] + assert D.info["task"] == datamanager.info["task"] auto._do_dummy_prediction(D, 1) # Ensure that the dummy predictions are not in the current working # directory, but in the temporary directory. - unexpected_directory = os.path.join(os.getcwd(), '.auto-sklearn') + unexpected_directory = os.path.join(os.getcwd(), ".auto-sklearn") expected_directory = os.path.join( auto._backend.temporary_directory, - '.auto-sklearn', - 'runs', - '1_1_0.0', - 'predictions_ensemble_1_1_0.0.npy' + ".auto-sklearn", + "runs", + "1_1_0.0", + "predictions_ensemble_1_1_0.0.npy", ) assert not os.path.exists(unexpected_directory) assert os.path.exists(expected_directory) @@ -454,27 +498,30 @@ def test_do_dummy_prediction(dask_client, datasets): del auto -@unittest.mock.patch('autosklearn.evaluation.ExecuteTaFuncWithQueue.run') +@unittest.mock.patch("autosklearn.evaluation.ExecuteTaFuncWithQueue.run") def test_fail_if_dummy_prediction_fails(ta_run_mock, dask_client): - X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') + X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") datamanager = XYDataManager( - X_train, Y_train, - X_test, Y_test, + X_train, + Y_train, + X_test, + Y_test, task=2, - feat_type={i: 'Numerical' for i in range(X_train.shape[1])}, - dataset_name='iris', + feat_type={i: "Numerical" for i in range(X_train.shape[1])}, + dataset_name="iris", ) time_for_this_task = 30 per_run_time = 10 - auto = autosklearn.automl.AutoML(time_for_this_task, - per_run_time, - initial_configurations_via_metalearning=25, - metric=accuracy, - dask_client=dask_client, - delete_tmp_folder_after_terminate=False, - ) + auto = autosklearn.automl.AutoML( + time_for_this_task, + per_run_time, + initial_configurations_via_metalearning=25, + metric=accuracy, + dask_client=dask_client, + delete_tmp_folder_after_terminate=False, + ) auto._backend = auto._create_backend() auto._backend._make_internals_directory() auto._backend.save_datamanager(datamanager) @@ -497,55 +544,55 @@ def test_fail_if_dummy_prediction_fails(ta_run_mock, dask_client): auto._do_dummy_prediction(datamanager, 1) except ValueError: raised = True - assert not raised, 'Exception raised' + assert not raised, "Exception raised" # Case 2. Check that if statustype returned by ta.run() != success, # the function raises error. ta_run_mock.return_value = StatusType.CRASHED, None, None, {} with pytest.raises( ValueError, - match='Dummy prediction failed with run state StatusType.CRASHED and additional output: {}.' # noqa + match="Dummy prediction failed with run state StatusType.CRASHED and additional output: {}.", # noqa ): auto._do_dummy_prediction(datamanager, 1) ta_run_mock.return_value = StatusType.ABORT, None, None, {} with pytest.raises( ValueError, - match='Dummy prediction failed with run state StatusType.ABORT ' - 'and additional output: {}.', + match="Dummy prediction failed with run state StatusType.ABORT " + "and additional output: {}.", ): auto._do_dummy_prediction(datamanager, 1) ta_run_mock.return_value = StatusType.TIMEOUT, None, None, {} with pytest.raises( ValueError, - match='Dummy prediction failed with run state StatusType.TIMEOUT ' - 'and additional output: {}.' + match="Dummy prediction failed with run state StatusType.TIMEOUT " + "and additional output: {}.", ): auto._do_dummy_prediction(datamanager, 1) ta_run_mock.return_value = StatusType.MEMOUT, None, None, {} with pytest.raises( ValueError, - match='Dummy prediction failed with run state StatusType.MEMOUT ' - 'and additional output: {}.', + match="Dummy prediction failed with run state StatusType.MEMOUT " + "and additional output: {}.", ): auto._do_dummy_prediction(datamanager, 1) ta_run_mock.return_value = StatusType.CAPPED, None, None, {} with pytest.raises( ValueError, - match='Dummy prediction failed with run state StatusType.CAPPED ' - 'and additional output: {}.' + match="Dummy prediction failed with run state StatusType.CAPPED " + "and additional output: {}.", ): auto._do_dummy_prediction(datamanager, 1) - ta_run_mock.return_value = StatusType.CRASHED, None, None, {'exitcode': -6} + ta_run_mock.return_value = StatusType.CRASHED, None, None, {"exitcode": -6} with pytest.raises( ValueError, - match='The error suggests that the provided memory limits were too tight.', + match="The error suggests that the provided memory limits are too tight.", ): auto._do_dummy_prediction(datamanager, 1) -@unittest.mock.patch('autosklearn.smbo.AutoMLSMBO.run_smbo') +@unittest.mock.patch("autosklearn.smbo.AutoMLSMBO.run_smbo") def test_exceptions_inside_log_in_smbo(smbo_run_mock, dask_client): # Below importing and shutdown is a workaround, to make sure @@ -553,6 +600,7 @@ def test_exceptions_inside_log_in_smbo(smbo_run_mock, dask_client): # this test with multiple other test at the same time causes this # test to fail. This resets the singletons of the logging class import logging + logging.shutdown() automl = autosklearn.automl.AutoML( @@ -563,15 +611,15 @@ def test_exceptions_inside_log_in_smbo(smbo_run_mock, dask_client): delete_tmp_folder_after_terminate=False, ) - dataset_name = 'test_exceptions_inside_log' + dataset_name = "test_exceptions_inside_log" # Create a custom exception to prevent other errors to slip in class MyException(Exception): pass - X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') + X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") # The first call is on dummy predictor failure - message = str(np.random.randint(100)) + '_run_smbo' + message = str(np.random.randint(100)) + "_run_smbo" smbo_run_mock.side_effect = MyException(message) with pytest.raises(MyException): @@ -583,10 +631,12 @@ class MyException(Exception): ) # make sure that the logfile was created - logger_name = 'AutoML(%d):%s' % (1, dataset_name) + logger_name = "AutoML(%d):%s" % (1, dataset_name) logger = logging.getLogger(logger_name) - logfile = os.path.join(automl._backend.temporary_directory, logger_name + '.log') - assert os.path.exists(logfile), print_debug_information(automl) + str(automl._clean_logger()) + logfile = os.path.join(automl._backend.temporary_directory, logger_name + ".log") + assert os.path.exists(logfile), print_debug_information(automl) + str( + automl._clean_logger() + ) # Give some time for the error message to be printed in the # log file @@ -604,19 +654,21 @@ class MyException(Exception): automl._clean_logger() if not found_message: - pytest.fail("Did not find {} in the log file {} for logger {}/{}/{}".format( - message, - print_debug_information(automl), - vars(automl._logger.logger), - vars(logger), - vars(logging.getLogger()) - )) + pytest.fail( + "Did not find {} in the log file {} for logger {}/{}/{}".format( + message, + print_debug_information(automl), + vars(automl._logger.logger), + vars(logger), + vars(logging.getLogger()), + ) + ) @pytest.mark.parametrize("metric", [log_loss, balanced_accuracy]) def test_load_best_individual_model(metric, dask_client): - X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') + X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") automl = autosklearn.automl.AutoML( time_left_for_this_task=30, per_run_time_limit=5, @@ -645,9 +697,9 @@ def test_load_best_individual_model(metric, dask_client): assert get_models_with_weights[0][0] == 1.0 # Match a toy dataset - if metric.name == 'balanced_accuracy': + if metric.name == "balanced_accuracy": assert automl.score(X_test, Y_test) > 0.9 - elif metric.name == 'log_loss': + elif metric.name == "log_loss": # Seen values in github actions of 0.6978304740364537 assert automl.score(X_test, Y_test) < 0.7 else: @@ -667,17 +719,18 @@ def test_fail_if_feat_type_on_pandas_input(dask_client): dask_client=dask_client, ) - X_train = pd.DataFrame({'a': [1, 1], 'c': [1, 2]}) + X_train = pd.DataFrame({"a": [1, 1], "c": [1, 2]}) y_train = [1, 0] - with pytest.raises( - ValueError, - match="" - "providing the option feat_type to the fit method is not supported when using a Dataframe" - ): + msg = ( + "providing the option feat_type to the fit method is not supported" + " when using a Dataframe." + ) + with pytest.raises(ValueError, match=msg): automl.fit( - X_train, y_train, + X_train, + y_train, task=BINARY_CLASSIFICATION, - feat_type={1: 'Categorical', 2: 'Numerical'}, + feat_type={1: "Categorical", 2: "Numerical"}, ) @@ -686,7 +739,7 @@ def data_input_and_target_types(): # Create valid inputs X_ndarray = np.random.random(size=(n_rows, 5)) - X_ndarray[X_ndarray < .9] = 0 + X_ndarray[X_ndarray < 0.9] = 0 # Binary Classificaiton y_binary_ndarray = np.random.random(size=n_rows) @@ -696,7 +749,9 @@ def data_input_and_target_types(): # Multiclass classification y_multiclass_ndarray = np.random.random(size=n_rows) y_multiclass_ndarray[y_multiclass_ndarray > 0.66] = 2 - y_multiclass_ndarray[(y_multiclass_ndarray <= 0.66) & (y_multiclass_ndarray >= 0.33)] = 1 + y_multiclass_ndarray[ + (y_multiclass_ndarray <= 0.66) & (y_multiclass_ndarray >= 0.33) + ] = 1 y_multiclass_ndarray[y_multiclass_ndarray < 0.33] = 0 # Multilabel classificaiton @@ -789,11 +844,7 @@ def test_input_and_target_types(dask_client, X, y, X_test, y_test, task): # To save time fitting and only validate the inputs we only return # the configuration space automl.fit( - X=X, - y=y, - X_test=X_test, - y_test=y_test, - only_return_configuration_space=True + X=X, y=y, X_test=X_test, y_test=y_test, only_return_configuration_space=True ) assert automl._task == task assert automl._metric.name == default_metric_for_task[task].name @@ -801,21 +852,15 @@ def test_input_and_target_types(dask_client, X, y, X_test, y_test, task): def data_test_model_predict_outsputs_correct_shapes(): datasets = sklearn.datasets - binary = datasets.make_classification( - n_samples=5, n_classes=2, random_state=0 - ) + binary = datasets.make_classification(n_samples=5, n_classes=2, random_state=0) multiclass = datasets.make_classification( n_samples=5, n_informative=3, n_classes=3, random_state=0 ) multilabel = datasets.make_multilabel_classification( n_samples=5, n_classes=3, random_state=0 ) - regression = datasets.make_regression( - n_samples=5, random_state=0 - ) - multioutput = datasets.make_regression( - n_samples=5, n_targets=3, random_state=0 - ) + regression = datasets.make_regression(n_samples=5, random_state=0) + multioutput = datasets.make_regression(n_samples=5, n_targets=3, random_state=0) # TODO issue 1169 # While testing output shapes, realised all models are wrapped to provide @@ -841,17 +886,15 @@ def regressor(X, y): # How cross validation models are currently grouped together def voting_classifier(X, y): classifiers = [ - MyDummyClassifier(config=1, random_state=0).fit(X, y) - for _ in range(5) + MyDummyClassifier(config=1, random_state=0).fit(X, y) for _ in range(5) ] - vc = VotingClassifier(estimators=None, voting='soft') + vc = VotingClassifier(estimators=None, voting="soft") vc.estimators_ = classifiers return vc def voting_regressor(X, y): regressors = [ - MyDummyRegressor(config=1, random_state=0).fit(X, y) - for _ in range(5) + MyDummyRegressor(config=1, random_state=0).fit(X, y) for _ in range(5) ] vr = VotingRegressor(estimators=None) vr.estimators_ = regressors @@ -859,41 +902,41 @@ def voting_regressor(X, y): test_data = { BINARY_CLASSIFICATION: { - 'models': [classifier(*binary), voting_classifier(*binary)], - 'data': binary, + "models": [classifier(*binary), voting_classifier(*binary)], + "data": binary, # prob of false/true for the one class - 'expected_output_shape': (len(binary[0]), 2) + "expected_output_shape": (len(binary[0]), 2), }, MULTICLASS_CLASSIFICATION: { - 'models': [classifier(*multiclass), voting_classifier(*multiclass)], - 'data': multiclass, + "models": [classifier(*multiclass), voting_classifier(*multiclass)], + "data": multiclass, # prob of true for each possible class - 'expected_output_shape': (len(multiclass[0]), 3) + "expected_output_shape": (len(multiclass[0]), 3), }, MULTILABEL_CLASSIFICATION: { - 'models': [classifier(*multilabel), voting_classifier(*multilabel)], - 'data': multilabel, + "models": [classifier(*multilabel), voting_classifier(*multilabel)], + "data": multilabel, # probability of true for each binary label - 'expected_output_shape': (len(multilabel[0]), 3) # type: ignore + "expected_output_shape": (len(multilabel[0]), 3), # type: ignore }, REGRESSION: { - 'models': [regressor(*regression), voting_regressor(*regression)], - 'data': regression, + "models": [regressor(*regression), voting_regressor(*regression)], + "data": regression, # array of single outputs - 'expected_output_shape': (len(regression[0]), ) + "expected_output_shape": (len(regression[0]),), }, MULTIOUTPUT_REGRESSION: { - 'models': [regressor(*multioutput), voting_regressor(*multioutput)], - 'data': multioutput, + "models": [regressor(*multioutput), voting_regressor(*multioutput)], + "data": multioutput, # array of vector otuputs - 'expected_output_shape': (len(multioutput[0]), 3) - } + "expected_output_shape": (len(multioutput[0]), 3), + }, } return itertools.chain.from_iterable( [ - (model, cfg['data'], task, cfg['expected_output_shape']) - for model in cfg['models'] + (model, cfg["data"], task, cfg["expected_output_shape"]) + for model in cfg["models"] ] for task, cfg in test_data.items() ) @@ -901,7 +944,7 @@ def voting_regressor(X, y): @pytest.mark.parametrize( "model, data, task, expected_output_shape", - data_test_model_predict_outsputs_correct_shapes() + data_test_model_predict_outsputs_correct_shapes(), ) def test_model_predict_outputs_correct_shapes(model, data, task, expected_output_shape): X, y = data @@ -912,12 +955,12 @@ def test_model_predict_outputs_correct_shapes(model, data, task, expected_output def test_model_predict_outputs_warnings_to_logs(): X = list(range(20)) task = REGRESSION - logger = PickableLoggerAdapter('test_model_predict_correctly_outputs_warnings') + logger = PickableLoggerAdapter("test_model_predict_correctly_outputs_warnings") logger.warning = unittest.mock.Mock() class DummyModel: def predict(self, x): - warnings.warn('test warning', Warning) + warnings.warn("test warning", Warning) return x model = DummyModel() @@ -933,7 +976,7 @@ def test_model_predict_outputs_to_stdout_if_no_logger(): class DummyModel: def predict(self, x): - warnings.warn('test warning', Warning) + warnings.warn("test warning", Warning) return x model = DummyModel() @@ -959,7 +1002,7 @@ def test_param_dataset_compression_false(dataset_compression: bool) -> None: auto = AutoMLRegressor( time_left_for_this_task=30, per_run_time_limit=5, - dataset_compression=dataset_compression + dataset_compression=dataset_compression, ) assert auto._dataset_compression is None @@ -980,14 +1023,16 @@ def test_construction_param_dataset_compression_true(dataset_compression: bool) auto = AutoMLRegressor( time_left_for_this_task=30, per_run_time_limit=5, - dataset_compression=dataset_compression + dataset_compression=dataset_compression, ) assert auto._dataset_compression == default_dataset_compression_arg @pytest.mark.parametrize("dataset_compression", [{"memory_allocation": 0.2}]) -def test_construction_param_dataset_compression_valid_dict(dataset_compression: Dict) -> None: +def test_construction_param_dataset_compression_valid_dict( + dataset_compression: Dict, +) -> None: """ Parameters ---------- @@ -1001,7 +1046,7 @@ def test_construction_param_dataset_compression_valid_dict(dataset_compression: auto = AutoMLRegressor( time_left_for_this_task=30, per_run_time_limit=5, - dataset_compression=dataset_compression + dataset_compression=dataset_compression, ) expected_memory_allocation = dataset_compression["memory_allocation"] @@ -1012,7 +1057,9 @@ def test_construction_param_dataset_compression_valid_dict(dataset_compression: assert auto._dataset_compression["methods"] == expected_methods -@pytest.mark.parametrize("dataset_compression", [{"methods": ["precision", "subsample"]}]) +@pytest.mark.parametrize( + "dataset_compression", [{"methods": ["precision", "subsample"]}] +) @pytest.mark.parametrize("X", [np.ones((100, 10), dtype=int)]) @pytest.mark.parametrize("y", [np.random.random((100,))]) @unittest.mock.patch("autosklearn.automl.reduce_dataset_size_if_too_large") @@ -1020,7 +1067,7 @@ def test_fit_performs_dataset_compression_without_precision_with_int( mock_reduce_dataset: unittest.mock.MagicMock, dataset_compression: Dict, X: np.ndarray, - y: np.ndarray + y: np.ndarray, ) -> None: """We can't reduce the precision of ints as we do with floats. Suppose someone was to pass a column with `max_int64` and `min_int64`, any reduction of bits will @@ -1053,7 +1100,7 @@ def test_fit_performs_dataset_compression_without_precision_with_int( auto = AutoMLRegressor( time_left_for_this_task=30, # not used but required per_run_time_limit=5, # not used but required - dataset_compression=dataset_compression + dataset_compression=dataset_compression, ) # To prevent fitting anything we use `only_return_configuration_space` @@ -1066,36 +1113,48 @@ def test_fit_performs_dataset_compression_without_precision_with_int( @pytest.mark.parametrize("dataset_compression", [True]) -@pytest.mark.parametrize("X", [ - np.empty((10, 10)), - csr_matrix(np.identity(10)), - pytest.param( - np.empty((10, 10)).tolist(), - marks=pytest.mark.xfail(reason="Converted to dataframe by InputValidator") - ), - pytest.param( - pd.DataFrame(np.empty((10, 10))), - marks=pytest.mark.xfail(reason="No pandas support yet for dataset compression") - ) -]) -@pytest.mark.parametrize("y", [ - np.random.random((10, 1)), - np.random.random((10, 1)).tolist(), - pytest.param( - pd.Series(np.random.random((10,))), - marks=pytest.mark.xfail(reason="No pandas support yet for dataset compression") - ), - pytest.param( - pd.DataFrame(np.random.random((10, 10))), - marks=pytest.mark.xfail(reason="No pandas support yet for dataset compression") - ) -]) +@pytest.mark.parametrize( + "X", + [ + np.empty((10, 10)), + csr_matrix(np.identity(10)), + pytest.param( + np.empty((10, 10)).tolist(), + marks=pytest.mark.xfail(reason="Converted to dataframe by InputValidator"), + ), + pytest.param( + pd.DataFrame(np.empty((10, 10))), + marks=pytest.mark.xfail( + reason="No pandas support yet for dataset compression" + ), + ), + ], +) +@pytest.mark.parametrize( + "y", + [ + np.random.random((10, 1)), + np.random.random((10, 1)).tolist(), + pytest.param( + pd.Series(np.random.random((10,))), + marks=pytest.mark.xfail( + reason="No pandas support yet for dataset compression" + ), + ), + pytest.param( + pd.DataFrame(np.random.random((10, 10))), + marks=pytest.mark.xfail( + reason="No pandas support yet for dataset compression" + ), + ), + ], +) @unittest.mock.patch("autosklearn.automl.reduce_dataset_size_if_too_large") def test_fit_performs_dataset_compression( mock_reduce_dataset: unittest.mock.MagicMock, dataset_compression: bool, X: Union[np.ndarray, spmatrix, List, pd.DataFrame], - y: Union[np.ndarray, List, pd.Series, pd.DataFrame] + y: Union[np.ndarray, List, pd.Series, pd.DataFrame], ) -> None: """ Parameters @@ -1122,7 +1181,7 @@ def test_fit_performs_dataset_compression( auto = AutoMLRegressor( time_left_for_this_task=30, # not used but required per_run_time_limit=5, # not used but required - dataset_compression=dataset_compression + dataset_compression=dataset_compression, ) # To prevent fitting anything we use `only_return_configuration_space` diff --git a/test/test_automl/test_estimators.py b/test/test_automl/test_estimators.py index b32d1d0026..ac60e51472 100644 --- a/test/test_automl/test_estimators.py +++ b/test/test_automl/test_estimators.py @@ -3,67 +3,74 @@ import copy import glob import importlib -import os import inspect import itertools +import os import pickle import re import sys import tempfile import unittest import unittest.mock -import pytest -from ConfigSpace.configuration_space import Configuration import joblib -from joblib import cpu_count import numpy as np import numpy.ma as npma import pandas as pd +import pytest import sklearn -import sklearn.model_selection as model_selection -import sklearn.dummy import sklearn.datasets -from sklearn.base import clone -from sklearn.base import ClassifierMixin, RegressorMixin -from sklearn.base import is_classifier -from smac.tae import StatusType +import sklearn.dummy +import sklearn.model_selection as model_selection +from ConfigSpace.configuration_space import Configuration from dask.distributed import Client +from joblib import cpu_count +from sklearn.base import ClassifierMixin, RegressorMixin, clone, is_classifier +from smac.tae import StatusType -from autosklearn.data.validation import InputValidator +import autosklearn.estimators # noqa F401 import autosklearn.pipeline.util as putil +from autosklearn.automl import AutoMLClassifier +from autosklearn.data.validation import InputValidator from autosklearn.ensemble_builder import MODEL_FN_RE -import autosklearn.estimators # noqa F401 from autosklearn.estimators import ( - AutoSklearnEstimator, AutoSklearnRegressor, AutoSklearnClassifier + AutoSklearnClassifier, + AutoSklearnEstimator, + AutoSklearnRegressor, ) -from autosklearn.metrics import accuracy, f1_macro, mean_squared_error, r2 -from autosklearn.automl import AutoMLClassifier from autosklearn.experimental.askl2 import AutoSklearn2Classifier +from autosklearn.metrics import accuracy, f1_macro, mean_squared_error, r2 from autosklearn.smbo import get_smac_object sys.path.append(os.path.dirname(__file__)) -from automl_utils import print_debug_information, count_succeses, includes_train_scores, includes_all_scores, include_single_scores, performance_over_time_is_plausible # noqa (E402: module level import not at top of file) +from automl_utils import ( # noqa (E402: module level import not at top of file) + count_succeses, + include_single_scores, + includes_all_scores, + includes_train_scores, + performance_over_time_is_plausible, + print_debug_information, +) def test_fit_n_jobs(tmp_dir): - X_train, Y_train, X_test, Y_test = putil.get_dataset('breast_cancer') + X_train, Y_train, X_test, Y_test = putil.get_dataset("breast_cancer") # test parallel Classifier to predict classes, not only indices Y_train += 1 Y_test += 1 class get_smac_object_wrapper: - def __call__(self, *args, **kwargs): - self.n_jobs = kwargs['n_jobs'] + self.n_jobs = kwargs["n_jobs"] smac = get_smac_object(*args, **kwargs) self.dask_n_jobs = smac.solver.tae_runner.n_workers self.dask_client_n_jobs = len( - smac.solver.tae_runner.client.scheduler_info()['workers'] + smac.solver.tae_runner.client.scheduler_info()["workers"] ) return smac + get_smac_object_wrapper_instance = get_smac_object_wrapper() automl = AutoSklearnClassifier( @@ -75,8 +82,7 @@ def __call__(self, *args, **kwargs): initial_configurations_via_metalearning=0, ensemble_size=5, n_jobs=2, - include={'classifier': ['sgd'], - 'feature_preprocessor': ['no_preprocessing']}, + include={"classifier": ["sgd"], "feature_preprocessor": ["no_preprocessing"]}, get_smac_object_callback=get_smac_object_wrapper_instance, max_models_on_disc=None, ) @@ -84,17 +90,24 @@ def __call__(self, *args, **kwargs): automl.fit(X_train, Y_train) # Test that the argument is correctly passed to SMAC - assert getattr(get_smac_object_wrapper_instance, 'n_jobs') == 2 - assert getattr(get_smac_object_wrapper_instance, 'dask_n_jobs') == 2 - assert getattr(get_smac_object_wrapper_instance, 'dask_client_n_jobs') == 2 + assert getattr(get_smac_object_wrapper_instance, "n_jobs") == 2 + assert getattr(get_smac_object_wrapper_instance, "dask_n_jobs") == 2 + assert getattr(get_smac_object_wrapper_instance, "dask_client_n_jobs") == 2 available_num_runs = set() for run_key, run_value in automl.automl_.runhistory_.data.items(): - if run_value.additional_info is not None and 'num_run' in run_value.additional_info: - available_num_runs.add(run_value.additional_info['num_run']) + if ( + run_value.additional_info is not None + and "num_run" in run_value.additional_info + ): + available_num_runs.add(run_value.additional_info["num_run"]) available_predictions = set() predictions = glob.glob( - os.path.join(automl.automl_._backend.get_runs_directory(), '*', 'predictions_ensemble*.npy') + os.path.join( + automl.automl_._backend.get_runs_directory(), + "*", + "predictions_ensemble*.npy", + ) ) seeds = set() for prediction in predictions: @@ -117,7 +130,7 @@ def __call__(self, *args, **kwargs): seeds = set() for ensemble_file in ensembles: - seeds.add(int(ensemble_file.split('.')[0].split('_')[0])) + seeds.add(int(ensemble_file.split(".")[0].split("_")[0])) assert len(seeds) == 1 assert count_succeses(automl.cv_results_) > 0 @@ -132,7 +145,7 @@ def test_feat_type_wrong_arguments(): # Every Auto-Sklearn estimator has a backend, that allows a single # call to fit X = np.zeros((100, 100)) - y = np.zeros((100, )) + y = np.zeros((100,)) cls = AutoSklearnClassifier(ensemble_size=0) expected_msg = r".*feat_type does not have same number of " @@ -143,43 +156,55 @@ def test_feat_type_wrong_arguments(): cls = AutoSklearnClassifier(ensemble_size=0) expected_msg = r".*feat_type must only contain strings.*" with pytest.raises(ValueError, match=expected_msg): - cls.fit(X=X, y=y, feat_type=[True]*100) + cls.fit(X=X, y=y, feat_type=[True] * 100) cls = AutoSklearnClassifier(ensemble_size=0) expected_msg = r".*Only `Categorical`, `Numerical` and `String` are" "valid feature types, you passed `Car`.*" with pytest.raises(ValueError, match=expected_msg): - cls.fit(X=X, y=y, feat_type=['Car']*100) + cls.fit(X=X, y=y, feat_type=["Car"] * 100) # Mock AutoSklearnEstimator.fit so the test doesn't actually run fit(). -@unittest.mock.patch('autosklearn.estimators.AutoSklearnEstimator.fit') +@unittest.mock.patch("autosklearn.estimators.AutoSklearnEstimator.fit") def test_type_of_target(mock_estimator): # Test that classifier raises error for illegal target types. - X = np.array([[1, 2], - [2, 3], - [3, 4], - [4, 5], - ]) + X = np.array( + [ + [1, 2], + [2, 3], + [3, 4], + [4, 5], + ] + ) # Possible target types y_binary = np.array([0, 0, 1, 1]) y_continuous = np.array([0.1, 1.3, 2.1, 4.0]) y_multiclass = np.array([0, 1, 2, 0]) - y_multilabel = np.array([[0, 1], - [1, 1], - [1, 0], - [0, 0], - ]) - y_multiclass_multioutput = np.array([[0, 1], - [1, 3], - [2, 2], - [5, 3], - ]) - y_continuous_multioutput = np.array([[0.1, 1.5], - [1.2, 3.5], - [2.7, 2.7], - [5.5, 3.9], - ]) + y_multilabel = np.array( + [ + [0, 1], + [1, 1], + [1, 0], + [0, 0], + ] + ) + y_multiclass_multioutput = np.array( + [ + [0, 1], + [1, 3], + [2, 2], + [5, 3], + ] + ) + y_continuous_multioutput = np.array( + [ + [0.1, 1.5], + [1.2, 3.5], + [2.7, 2.7], + [5.5, 3.9], + ] + ) cls = AutoSklearnClassifier(ensemble_size=0) cls.automl_ = unittest.mock.Mock() @@ -208,20 +233,19 @@ def test_type_of_target(mock_estimator): try: cls.fit(X, y_binary) except ValueError: - pytest.fail("cls.fit() raised ValueError while fitting " - "binary targets") + pytest.fail("cls.fit() raised ValueError while fitting " "binary targets") try: cls.fit(X, y_multiclass) except ValueError: - pytest.fail("cls.fit() raised ValueError while fitting " - "multiclass targets") + pytest.fail("cls.fit() raised ValueError while fitting " "multiclass targets") try: cls.fit(X, y_multilabel) except ValueError: - pytest.fail("cls.fit() raised ValueError while fitting " - "multilabel-indicator targets") + pytest.fail( + "cls.fit() raised ValueError while fitting " "multilabel-indicator targets" + ) # Test that regressor raises error for illegal target types. reg = AutoSklearnRegressor(ensemble_size=0) @@ -230,12 +254,18 @@ def test_type_of_target(mock_estimator): expected_msg = r".*Regression with data of type" " multilabel-indicator is not supported.*" with pytest.raises(ValueError, match=expected_msg): - reg.fit(X=X, y=y_multilabel,) + reg.fit( + X=X, + y=y_multilabel, + ) expected_msg = r".*Regression with data of type" " multiclass-multioutput is not supported.*" with pytest.raises(ValueError, match=expected_msg): - reg.fit(X=X, y=y_multiclass_multioutput,) + reg.fit( + X=X, + y=y_multiclass_multioutput, + ) # Legal target types: continuous, multiclass, # continuous-multioutput, @@ -243,37 +273,38 @@ def test_type_of_target(mock_estimator): try: reg.fit(X, y_continuous) except ValueError: - pytest.fail("reg.fit() raised ValueError while fitting " - "continuous targets") + pytest.fail("reg.fit() raised ValueError while fitting " "continuous targets") try: reg.fit(X, y_multiclass) except ValueError: - pytest.fail("reg.fit() raised ValueError while fitting " - "multiclass targets") + pytest.fail("reg.fit() raised ValueError while fitting " "multiclass targets") try: reg.fit(X, y_continuous_multioutput) except ValueError: - pytest.fail("reg.fit() raised ValueError while fitting " - "continuous_multioutput targets") + pytest.fail( + "reg.fit() raised ValueError while fitting " + "continuous_multioutput targets" + ) try: reg.fit(X, y_binary) except ValueError: - pytest.fail("reg.fit() raised ValueError while fitting " - "binary targets") + pytest.fail("reg.fit() raised ValueError while fitting " "binary targets") def test_performance_over_time_no_ensemble(tmp_dir): - X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') + X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") - cls = AutoSklearnClassifier(time_left_for_this_task=30, - per_run_time_limit=5, - tmp_folder=tmp_dir, - seed=1, - initial_configurations_via_metalearning=0, - ensemble_size=0,) + cls = AutoSklearnClassifier( + time_left_for_this_task=30, + per_run_time_limit=5, + tmp_folder=tmp_dir, + seed=1, + initial_configurations_via_metalearning=0, + ensemble_size=0, + ) cls.fit(X_train, Y_train, X_test, Y_test) @@ -285,16 +316,17 @@ def test_performance_over_time_no_ensemble(tmp_dir): def test_cv_results(tmp_dir): # TODO restructure and actually use real SMAC output from a long run # to do this unittest! - X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') + X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") - cls = AutoSklearnClassifier(time_left_for_this_task=30, - per_run_time_limit=5, - tmp_folder=tmp_dir, - seed=1, - initial_configurations_via_metalearning=0, - ensemble_size=0, - scoring_functions=[autosklearn.metrics.precision, - autosklearn.metrics.roc_auc]) + cls = AutoSklearnClassifier( + time_left_for_this_task=30, + per_run_time_limit=5, + tmp_folder=tmp_dir, + seed=1, + initial_configurations_via_metalearning=0, + ensemble_size=0, + scoring_functions=[autosklearn.metrics.precision, autosklearn.metrics.roc_auc], + ) params = cls.get_params() original_params = copy.deepcopy(params) @@ -303,23 +335,27 @@ def test_cv_results(tmp_dir): cv_results = cls.cv_results_ assert isinstance(cv_results, dict), type(cv_results) - assert isinstance(cv_results['mean_test_score'], np.ndarray), type( - cv_results['mean_test_score']) - assert isinstance(cv_results['mean_fit_time'], np.ndarray), type( - cv_results['mean_fit_time'] + assert isinstance(cv_results["mean_test_score"], np.ndarray), type( + cv_results["mean_test_score"] ) - assert isinstance(cv_results['params'], list), type(cv_results['params']) - assert isinstance(cv_results['rank_test_scores'], np.ndarray), type( - cv_results['rank_test_scores'] + assert isinstance(cv_results["mean_fit_time"], np.ndarray), type( + cv_results["mean_fit_time"] ) - assert isinstance(cv_results['metric_precision'], npma.MaskedArray), type( - cv_results['metric_precision'] + assert isinstance(cv_results["params"], list), type(cv_results["params"]) + assert isinstance(cv_results["rank_test_scores"], np.ndarray), type( + cv_results["rank_test_scores"] ) - assert isinstance(cv_results['metric_roc_auc'], npma.MaskedArray), type( - cv_results['metric_roc_auc'] + assert isinstance(cv_results["metric_precision"], npma.MaskedArray), type( + cv_results["metric_precision"] ) - cv_result_items = [isinstance(val, npma.MaskedArray) for key, val in - cv_results.items() if key.startswith('param_')] + assert isinstance(cv_results["metric_roc_auc"], npma.MaskedArray), type( + cv_results["metric_roc_auc"] + ) + cv_result_items = [ + isinstance(val, npma.MaskedArray) + for key, val in cv_results.items() + if key.startswith("param_") + ] assert all(cv_result_items), cv_results.items() # Compare the state of the model parameters with the original parameters @@ -337,21 +373,20 @@ def test_cv_results(tmp_dir): assert joblib.hash(new_value) == joblib.hash(original_value), ( "Estimator %s should not change or mutate " " the parameter %s from %s to %s during fit." - % (cls, param_name, original_value, new_value)) + % (cls, param_name, original_value, new_value) + ) # Comply with https://scikit-learn.org/dev/glossary.html#term-classes is_classifier(cls) - assert hasattr(cls, 'classes_') + assert hasattr(cls, "classes_") -@pytest.mark.parametrize('estimator_type,dataset_name', [ - (AutoSklearnClassifier, 'iris'), - (AutoSklearnRegressor, 'boston') -]) +@pytest.mark.parametrize( + "estimator_type,dataset_name", + [(AutoSklearnClassifier, "iris"), (AutoSklearnRegressor, "boston")], +) def test_leaderboard( - tmp_dir: str, - estimator_type: Type[AutoSklearnEstimator], - dataset_name: str + tmp_dir: str, estimator_type: Type[AutoSklearnEstimator], dataset_name: str ): # Comprehensive test tasks a substantial amount of time, manually set if # required. @@ -361,16 +396,16 @@ def test_leaderboard( # Create a dict of all possible param values for each param # with some invalid one's of the incorrect type include_combinations = itertools.chain( - itertools.combinations(column_types['all'], item_count) + itertools.combinations(column_types["all"], item_count) for item_count in range(1, MAX_COMBO_SIZE_FOR_INCLUDE_PARAM) ) valid_params = { - 'detailed': [True, False], - 'ensemble_only': [True, False], - 'top_k': [-10, 0, 1, 10, 'all'], - 'sort_by': [*column_types['all'], 'invalid'], - 'sort_order': ['ascending', 'descending', 'auto', 'invalid', None], - 'include': itertools.chain([None, 'invalid', 'type'], include_combinations), + "detailed": [True, False], + "ensemble_only": [True, False], + "top_k": [-10, 0, 1, 10, "all"], + "sort_by": [*column_types["all"], "invalid"], + "sort_order": ["ascending", "descending", "auto", "invalid", None], + "include": itertools.chain([None, "invalid", "type"], include_combinations), } # Create a generator of all possible combinations of valid_params @@ -381,55 +416,49 @@ def test_leaderboard( X_train, Y_train, _, _ = putil.get_dataset(dataset_name) model = estimator_type( - time_left_for_this_task=30, - per_run_time_limit=5, - tmp_folder=tmp_dir, - seed=1 + time_left_for_this_task=30, per_run_time_limit=5, tmp_folder=tmp_dir, seed=1 ) model.fit(X_train, Y_train) for params in params_generator: # Convert from iterator to solid list - if params['include'] is not None and not isinstance(params['include'], str): - params['include'] = list(params['include']) + if params["include"] is not None and not isinstance(params["include"], str): + params["include"] = list(params["include"]) # Invalid top_k should raise an error, is a positive int or 'all' - if not (params['top_k'] == 'all' or params['top_k'] > 0): + if not (params["top_k"] == "all" or params["top_k"] > 0): with pytest.raises(ValueError): model.leaderboard(**params) # Invalid sort_by column - elif params['sort_by'] not in column_types['all']: + elif params["sort_by"] not in column_types["all"]: with pytest.raises(ValueError): model.leaderboard(**params) # Shouldn't accept an invalid sort order - elif params['sort_order'] not in ['ascending', 'descending', 'auto']: + elif params["sort_order"] not in ["ascending", "descending", "auto"]: with pytest.raises(ValueError): model.leaderboard(**params) # include is single str but not valid elif ( - isinstance(params['include'], str) - and params['include'] not in column_types['all'] + isinstance(params["include"], str) + and params["include"] not in column_types["all"] ): with pytest.raises(ValueError): model.leaderboard(**params) # Crash if include is list but contains invalid column elif ( - isinstance(params['include'], list) - and len(set(params['include']) - set(column_types['all'])) != 0 + isinstance(params["include"], list) + and len(set(params["include"]) - set(column_types["all"])) != 0 ): with pytest.raises(ValueError): model.leaderboard(**params) # Can't have just model_id, in both single str and list case - elif ( - params['include'] == 'model_id' - or params['include'] == ['model_id'] - ): + elif params["include"] == "model_id" or params["include"] == ["model_id"]: with pytest.raises(ValueError): model.leaderboard(**params) @@ -439,8 +468,8 @@ def test_leaderboard( # top_k should never be less than the rows given back # It can however be larger - if isinstance(params['top_k'], int): - assert params['top_k'] >= len(leaderboard) + if isinstance(params["top_k"], int): + assert params["top_k"] >= len(leaderboard) # Check the right columns are present and in the right order # The model_id is set as the index, not included in pandas columns @@ -449,43 +478,47 @@ def test_leaderboard( def exclude(lst, s): return [x for x in lst if x != s] - if params['include'] is not None: + if params["include"] is not None: # Include with only single str should be the only column - if isinstance(params['include'], str): - assert params['include'] in columns and len(columns) == 1 + if isinstance(params["include"], str): + assert params["include"] in columns and len(columns) == 1 # Include as a list should have all the columns without model_id else: - assert columns == exclude(params['include'], 'model_id') - elif params['detailed']: - assert columns == exclude(column_types['detailed'], 'model_id') + assert columns == exclude(params["include"], "model_id") + elif params["detailed"]: + assert columns == exclude(column_types["detailed"], "model_id") else: - assert columns == exclude(column_types['simple'], 'model_id') + assert columns == exclude(column_types["simple"], "model_id") # Ensure that if it's ensemble only # Can only check if 'ensemble_weight' is present - if ( - params['ensemble_only'] - and 'ensemble_weight' in columns - ): - assert all(leaderboard['ensemble_weight'] > 0) - - -@pytest.mark.parametrize('estimator', [AutoSklearnRegressor]) -@pytest.mark.parametrize('resampling_strategy', ['holdout']) -@pytest.mark.parametrize('X', [ - np.asarray([[1.0, 1.0, 1.0]] * 25 + [[2.0, 2.0, 2.0]] * 25 + - [[3.0, 3.0, 3.0]] * 25 + [[4.0, 4.0, 4.0]] * 25) -]) -@pytest.mark.parametrize('y', [ - np.asarray([1.0] * 25 + [2.0] * 25 + [3.0] * 25 + [4.0] * 25) -]) + if params["ensemble_only"] and "ensemble_weight" in columns: + assert all(leaderboard["ensemble_weight"] > 0) + + +@pytest.mark.parametrize("estimator", [AutoSklearnRegressor]) +@pytest.mark.parametrize("resampling_strategy", ["holdout"]) +@pytest.mark.parametrize( + "X", + [ + np.asarray( + [[1.0, 1.0, 1.0]] * 25 + + [[2.0, 2.0, 2.0]] * 25 + + [[3.0, 3.0, 3.0]] * 25 + + [[4.0, 4.0, 4.0]] * 25 + ) + ], +) +@pytest.mark.parametrize( + "y", [np.asarray([1.0] * 25 + [2.0] * 25 + [3.0] * 25 + [4.0] * 25)] +) def test_show_models_with_holdout( tmp_dir: str, dask_client: Client, estimator: AutoSklearnEstimator, resampling_strategy: str, X: np.ndarray, - y: np.ndarray + y: np.ndarray, ) -> None: """ Parameters @@ -521,39 +554,44 @@ def test_show_models_with_holdout( per_run_time_limit=5, tmp_folder=tmp_dir, resampling_strategy=resampling_strategy, - dask_client=dask_client + dask_client=dask_client, ) automl.fit(X, y) models = automl.show_models().values() - model_keys = set([ - 'model_id', 'rank', 'cost', 'ensemble_weight', - 'data_preprocessor', 'feature_preprocessor', - 'regressor', 'sklearn_regressor' - ]) + model_keys = set( + [ + "model_id", + "rank", + "cost", + "ensemble_weight", + "data_preprocessor", + "feature_preprocessor", + "regressor", + "sklearn_regressor", + ] + ) assert all([model_keys == set(model.keys()) for model in models]) - assert all([model['regressor'] for model in models]) - assert all([model['sklearn_regressor'] for model in models]) + assert all([model["regressor"] for model in models]) + assert all([model["sklearn_regressor"] for model in models]) assert not any([None in model.values() for model in models]) -@pytest.mark.parametrize('estimator', [AutoSklearnClassifier]) -@pytest.mark.parametrize('resampling_strategy', ['cv']) -@pytest.mark.parametrize('X', [ - np.asarray([[1.0, 1.0, 1.0]] * 50 + [[2.0, 2.0, 2.0]] * 50) -]) -@pytest.mark.parametrize('y', [ - np.asarray([1] * 50 + [2] * 50) -]) +@pytest.mark.parametrize("estimator", [AutoSklearnClassifier]) +@pytest.mark.parametrize("resampling_strategy", ["cv"]) +@pytest.mark.parametrize( + "X", [np.asarray([[1.0, 1.0, 1.0]] * 50 + [[2.0, 2.0, 2.0]] * 50)] +) +@pytest.mark.parametrize("y", [np.asarray([1] * 50 + [2] * 50)]) def test_show_models_with_cv( tmp_dir: str, dask_client: Client, estimator: AutoSklearnEstimator, resampling_strategy: str, X: np.ndarray, - y: np.ndarray + y: np.ndarray, ) -> None: """ Parameters @@ -578,12 +616,12 @@ def test_show_models_with_cv( Expects ------- - * Expects all the model dictionaries to have ``model_keys`` - * Expects no model to have any ``None`` value - * Expects all the estimators in a model to have ``estimator_keys`` - * Expects all model estimators to have an auto-sklearn wrapped model ``classifier`` - * Expects all model estimators to have a sklearn wrapped model ``sklearn_classifier`` - * Expects no estimator to have ``None`` value + * Expects all the model dictionaries to have `model_keys` + * Expects no model to have any `None` value + * Expects all the estimators in a model to have `estimator_keys` + * Expects all model estimators to have an auto-sklearn wrapped model `classifier` + * Expects all model estimators to have a sklearn wrapped model `sklearn_classifier` + * Expects no estimator to have None """ automl = estimator( @@ -591,37 +629,59 @@ def test_show_models_with_cv( per_run_time_limit=5, tmp_folder=tmp_dir, resampling_strategy=resampling_strategy, - dask_client=dask_client + dask_client=dask_client, ) automl.fit(X, y) models = automl.show_models().values() - model_keys = set([ - 'model_id', 'rank', - 'cost', 'ensemble_weight', - 'voting_model', 'estimators' - ]) + model_keys = set( + ["model_id", "rank", "cost", "ensemble_weight", "voting_model", "estimators"] + ) - estimator_keys = set([ - 'data_preprocessor', 'balancing', - 'feature_preprocessor', 'classifier', - 'sklearn_classifier' - ]) + estimator_keys = set( + [ + "data_preprocessor", + "balancing", + "feature_preprocessor", + "classifier", + "sklearn_classifier", + ] + ) assert all([model_keys == set(model.keys()) for model in models]) assert not any([None in model.values() for model in models]) - assert all([estimator_keys == set(estimator.keys()) - for model in models for estimator in model['estimators']]) - assert all([estimator['classifier'] - for model in models for estimator in model['estimators']]) - assert all([estimator['sklearn_classifier'] - for model in models for estimator in model['estimators']]) - assert not any([None in estimator.values() - for model in models for estimator in model['estimators']]) + assert all( + [ + estimator_keys == set(estimator.keys()) + for model in models + for estimator in model["estimators"] + ] + ) + assert all( + [ + estimator["classifier"] + for model in models + for estimator in model["estimators"] + ] + ) + assert all( + [ + estimator["sklearn_classifier"] + for model in models + for estimator in model["estimators"] + ] + ) + assert not any( + [ + None in estimator.values() + for model in models + for estimator in model["estimators"] + ] + ) -@unittest.mock.patch('autosklearn.estimators.AutoSklearnEstimator.build_automl') +@unittest.mock.patch("autosklearn.estimators.AutoSklearnEstimator.build_automl") def test_fit_n_jobs_negative(build_automl_patch): n_cores = cpu_count() cls = AutoSklearnEstimator(n_jobs=-1, ensemble_size=0) @@ -634,12 +694,17 @@ def test_get_number_of_available_cores(): assert n_cores >= 1, n_cores -@unittest.mock.patch('autosklearn.automl.AutoML.predict') +@unittest.mock.patch("autosklearn.automl.AutoML.predict") def test_multiclass_prediction(predict_mock, dask_client): - predicted_probabilities = [[0, 0, 0.99], [0, 0.99, 0], [0.99, 0, 0], - [0, 0.99, 0], [0, 0, 0.99]] + predicted_probabilities = [ + [0, 0, 0.99], + [0, 0.99, 0], + [0.99, 0, 0], + [0, 0.99, 0], + [0, 0, 0.99], + ] predicted_indexes = [2, 1, 0, 1, 2] - expected_result = ['c', 'b', 'a', 'b', 'c'] + expected_result = ["c", "b", "a", "b", "c"] predict_mock.return_value = np.array(predicted_probabilities) @@ -650,7 +715,7 @@ def test_multiclass_prediction(predict_mock, dask_client): ) classifier.InputValidator = InputValidator(is_classification=True) classifier.InputValidator.target_validator.fit( - pd.DataFrame(expected_result, dtype='category'), + pd.DataFrame(expected_result, dtype="category"), ) classifier.InputValidator._is_fitted = True @@ -659,13 +724,15 @@ def test_multiclass_prediction(predict_mock, dask_client): np.testing.assert_array_equal(expected_result, actual_result) -@unittest.mock.patch('autosklearn.automl.AutoML.predict') +@unittest.mock.patch("autosklearn.automl.AutoML.predict") def test_multilabel_prediction(predict_mock, dask_client): - predicted_probabilities = [[0.99, 0], - [0.99, 0], - [0, 0.99], - [0.99, 0.99], - [0.99, 0.99]] + predicted_probabilities = [ + [0.99, 0], + [0.99, 0], + [0, 0.99], + [0.99, 0.99], + [0.99, 0.99], + ] predicted_indexes = np.array([[1, 0], [1, 0], [0, 1], [1, 1], [1, 1]]) predict_mock.return_value = np.array(predicted_probabilities) @@ -677,11 +744,14 @@ def test_multilabel_prediction(predict_mock, dask_client): ) classifier.InputValidator = InputValidator(is_classification=True) classifier.InputValidator.target_validator.fit( - pd.DataFrame(predicted_indexes, dtype='int64'), + pd.DataFrame(predicted_indexes, dtype="int64"), ) classifier.InputValidator._is_fitted = True - assert classifier.InputValidator.target_validator.type_of_target == 'multilabel-indicator' + assert ( + classifier.InputValidator.target_validator.type_of_target + == "multilabel-indicator" + ) actual_result = classifier.predict([None] * len(predicted_indexes)) @@ -689,68 +759,66 @@ def test_multilabel_prediction(predict_mock, dask_client): def test_can_pickle_classifier(tmp_dir, dask_client): - X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') - automl = AutoSklearnClassifier(time_left_for_this_task=30, - delete_tmp_folder_after_terminate=False, - per_run_time_limit=5, - tmp_folder=tmp_dir, - dask_client=dask_client, - ) + X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") + automl = AutoSklearnClassifier( + time_left_for_this_task=30, + delete_tmp_folder_after_terminate=False, + per_run_time_limit=5, + tmp_folder=tmp_dir, + dask_client=dask_client, + ) automl.fit(X_train, Y_train) initial_predictions = automl.predict(X_test) - initial_accuracy = sklearn.metrics.accuracy_score(Y_test, - initial_predictions) + initial_accuracy = sklearn.metrics.accuracy_score(Y_test, initial_predictions) assert initial_accuracy >= 0.75 assert count_succeses(automl.cv_results_) > 0 assert includes_train_scores(automl.performance_over_time_.columns) is True assert performance_over_time_is_plausible(automl.performance_over_time_) is True # Test pickle - dump_file = os.path.join(tmp_dir, 'automl.dump.pkl') + dump_file = os.path.join(tmp_dir, "automl.dump.pkl") - with open(dump_file, 'wb') as f: + with open(dump_file, "wb") as f: pickle.dump(automl, f) - with open(dump_file, 'rb') as f: + with open(dump_file, "rb") as f: restored_automl = pickle.load(f) restored_predictions = restored_automl.predict(X_test) - restored_accuracy = sklearn.metrics.accuracy_score(Y_test, - restored_predictions) + restored_accuracy = sklearn.metrics.accuracy_score(Y_test, restored_predictions) assert restored_accuracy >= 0.75 assert initial_accuracy == restored_accuracy # Test joblib - dump_file = os.path.join(tmp_dir, 'automl.dump.joblib') + dump_file = os.path.join(tmp_dir, "automl.dump.joblib") joblib.dump(automl, dump_file) restored_automl = joblib.load(dump_file) restored_predictions = restored_automl.predict(X_test) - restored_accuracy = sklearn.metrics.accuracy_score(Y_test, - restored_predictions) + restored_accuracy = sklearn.metrics.accuracy_score(Y_test, restored_predictions) assert restored_accuracy >= 0.75 assert initial_accuracy == restored_accuracy def test_multilabel(tmp_dir, dask_client): - X_train, Y_train, X_test, Y_test = putil.get_dataset( - 'iris', make_multilabel=True) - automl = AutoSklearnClassifier(time_left_for_this_task=30, - per_run_time_limit=5, - tmp_folder=tmp_dir, - dask_client=dask_client, - ) + X_train, Y_train, X_test, Y_test = putil.get_dataset("iris", make_multilabel=True) + automl = AutoSklearnClassifier( + time_left_for_this_task=30, + per_run_time_limit=5, + tmp_folder=tmp_dir, + dask_client=dask_client, + ) automl.fit(X_train, Y_train) predictions = automl.predict(X_test) assert predictions.shape == (50, 3), print_debug_information(automl) - assert count_succeses(automl.cv_results_) > 0, print_debug_information(automl) + assert count_succeses(automl.cv_results_) > 0, print_debug_information(automl) assert includes_train_scores(automl.performance_over_time_.columns) is True assert performance_over_time_is_plausible(automl.performance_over_time_) is True @@ -763,20 +831,25 @@ def test_multilabel(tmp_dir, dask_client): def test_binary(tmp_dir, dask_client): - X_train, Y_train, X_test, Y_test = putil.get_dataset( - 'iris', make_binary=True) - automl = AutoSklearnClassifier(time_left_for_this_task=40, - delete_tmp_folder_after_terminate=False, - per_run_time_limit=10, - tmp_folder=tmp_dir, - dask_client=dask_client, - ) + X_train, Y_train, X_test, Y_test = putil.get_dataset("iris", make_binary=True) + automl = AutoSklearnClassifier( + time_left_for_this_task=40, + delete_tmp_folder_after_terminate=False, + per_run_time_limit=10, + tmp_folder=tmp_dir, + dask_client=dask_client, + ) - automl.fit(X_train, Y_train, X_test=X_test, y_test=Y_test, - dataset_name='binary_test_dataset') + automl.fit( + X_train, + Y_train, + X_test=X_test, + y_test=Y_test, + dataset_name="binary_test_dataset", + ) predictions = automl.predict(X_test) - assert predictions.shape == (50, ), print_debug_information(automl) + assert predictions.shape == (50,), print_debug_information(automl) score = accuracy(Y_test, predictions) assert score > 0.9, print_debug_information(automl) @@ -794,7 +867,7 @@ def test_classification_pandas_support(tmp_dir, dask_client): ) # Drop NAN!! - X = X.dropna(axis='columns') + X = X.dropna(axis="columns") # This test only make sense if input is dataframe assert isinstance(X, pd.DataFrame) @@ -802,7 +875,7 @@ def test_classification_pandas_support(tmp_dir, dask_client): automl = AutoSklearnClassifier( time_left_for_this_task=30, per_run_time_limit=5, - exclude={'classifier': ['libsvm_svc']}, + exclude={"classifier": ["libsvm_svc"]}, dask_client=dask_client, seed=5, tmp_folder=tmp_dir, @@ -828,12 +901,13 @@ def test_classification_pandas_support(tmp_dir, dask_client): def test_regression(tmp_dir, dask_client): - X_train, Y_train, X_test, Y_test = putil.get_dataset('boston') - automl = AutoSklearnRegressor(time_left_for_this_task=30, - per_run_time_limit=5, - tmp_folder=tmp_dir, - dask_client=dask_client, - ) + X_train, Y_train, X_test, Y_test = putil.get_dataset("boston") + automl = AutoSklearnRegressor( + time_left_for_this_task=30, + per_run_time_limit=5, + tmp_folder=tmp_dir, + dask_client=dask_client, + ) automl.fit(X_train, Y_train) @@ -842,8 +916,9 @@ def test_regression(tmp_dir, dask_client): score = mean_squared_error(Y_test, predictions) # On average np.sqrt(30) away from the target -> ~5.5 on average - # Results with select rates drops avg score to a range of -32.40 to -37, on 30 seconds - # constraint. With more time_left_for_this_task this is no longer an issue + # Results with select rates drops avg score to a range of -32.40 to -37, + # on 30 seconds constraint. + # With more time_left_for_this_task this is no longer an issue assert score >= -37, print_debug_information(automl) assert count_succeses(automl.cv_results_) > 0 assert includes_train_scores(automl.performance_over_time_.columns) is True @@ -856,13 +931,16 @@ def test_cv_regression(tmp_dir, dask_client): a regressor """ - X_train, Y_train, X_test, Y_test = putil.get_dataset('boston', train_size_maximum=300) - automl = AutoSklearnRegressor(time_left_for_this_task=60, - per_run_time_limit=10, - resampling_strategy='cv', - tmp_folder=tmp_dir, - dask_client=dask_client, - ) + X_train, Y_train, X_test, Y_test = putil.get_dataset( + "boston", train_size_maximum=300 + ) + automl = AutoSklearnRegressor( + time_left_for_this_task=60, + per_run_time_limit=10, + resampling_strategy="cv", + tmp_folder=tmp_dir, + dask_client=dask_client, + ) automl.fit(X_train, Y_train) @@ -913,13 +991,15 @@ def test_autosklearn_classification_methods_returns_self(dask_client): Currently this method only tests that the methods of AutoSklearnClassifier is able to fit using fit(), fit_ensemble() and refit() """ - X_train, y_train, X_test, y_test = putil.get_dataset('iris') - automl = AutoSklearnClassifier(time_left_for_this_task=60, - delete_tmp_folder_after_terminate=False, - per_run_time_limit=10, - ensemble_size=0, - dask_client=dask_client, - exclude={'feature_preprocessor': ['fast_ica']}) + X_train, y_train, X_test, y_test = putil.get_dataset("iris") + automl = AutoSklearnClassifier( + time_left_for_this_task=60, + delete_tmp_folder_after_terminate=False, + per_run_time_limit=10, + ensemble_size=0, + dask_client=dask_client, + exclude={"feature_preprocessor": ["fast_ica"]}, + ) automl_fitted = automl.fit(X_train, y_train) @@ -936,12 +1016,14 @@ def test_autosklearn_classification_methods_returns_self(dask_client): # Currently this class only tests that the methods of AutoSklearnRegressor # that should return self actually return self. def test_autosklearn_regression_methods_returns_self(dask_client): - X_train, y_train, X_test, y_test = putil.get_dataset('boston') - automl = AutoSklearnRegressor(time_left_for_this_task=30, - delete_tmp_folder_after_terminate=False, - per_run_time_limit=5, - dask_client=dask_client, - ensemble_size=0) + X_train, y_train, X_test, y_test = putil.get_dataset("boston") + automl = AutoSklearnRegressor( + time_left_for_this_task=30, + delete_tmp_folder_after_terminate=False, + per_run_time_limit=5, + dask_client=dask_client, + ensemble_size=0, + ) automl_fitted = automl.fit(X_train, y_train) assert automl is automl_fitted @@ -954,10 +1036,13 @@ def test_autosklearn_regression_methods_returns_self(dask_client): def test_autosklearn2_classification_methods_returns_self(dask_client): - X_train, y_train, X_test, y_test = putil.get_dataset('iris') - automl = AutoSklearn2Classifier(time_left_for_this_task=60, ensemble_size=0, - delete_tmp_folder_after_terminate=False, - dask_client=dask_client) + X_train, y_train, X_test, y_test = putil.get_dataset("iris") + automl = AutoSklearn2Classifier( + time_left_for_this_task=60, + ensemble_size=0, + delete_tmp_folder_after_terminate=False, + dask_client=dask_client, + ) automl_fitted = automl.fit(X_train, y_train) @@ -971,18 +1056,23 @@ def test_autosklearn2_classification_methods_returns_self(dask_client): assert automl is automl_refitted predictions = automl_fitted.predict(X_test) - assert sklearn.metrics.accuracy_score( - y_test, predictions - ) >= 2 / 3, print_debug_information(automl) + assert ( + sklearn.metrics.accuracy_score(y_test, predictions) >= 2 / 3 + ), print_debug_information(automl) pickle.dumps(automl_fitted) def test_autosklearn2_classification_methods_returns_self_sparse(dask_client): - X_train, y_train, X_test, y_test = putil.get_dataset('breast_cancer', make_sparse=True) - automl = AutoSklearn2Classifier(time_left_for_this_task=60, ensemble_size=0, - delete_tmp_folder_after_terminate=False, - dask_client=dask_client) + X_train, y_train, X_test, y_test = putil.get_dataset( + "breast_cancer", make_sparse=True + ) + automl = AutoSklearn2Classifier( + time_left_for_this_task=60, + ensemble_size=0, + delete_tmp_folder_after_terminate=False, + dask_client=dask_client, + ) automl_fitted = automl.fit(X_train, y_train) @@ -996,32 +1086,39 @@ def test_autosklearn2_classification_methods_returns_self_sparse(dask_client): assert automl is automl_refitted predictions = automl_fitted.predict(X_test) - assert sklearn.metrics.accuracy_score( - y_test, predictions - ) >= 2 / 3, print_debug_information(automl) + assert ( + sklearn.metrics.accuracy_score(y_test, predictions) >= 2 / 3 + ), print_debug_information(automl) assert "boosting" not in str(automl.get_configuration_space(X=X_train, y=y_train)) pickle.dumps(automl_fitted) -@pytest.mark.parametrize("class_", [AutoSklearnClassifier, AutoSklearnRegressor, - AutoSklearn2Classifier]) +@pytest.mark.parametrize( + "class_", [AutoSklearnClassifier, AutoSklearnRegressor, AutoSklearn2Classifier] +) def test_check_estimator_signature(class_): # Make sure signature is store in self - expected_subclass = ClassifierMixin if 'Classifier' in str(class_) else RegressorMixin + expected_subclass = ( + ClassifierMixin if "Classifier" in str(class_) else RegressorMixin + ) assert issubclass(class_, expected_subclass) estimator = class_() for expected in list(inspect.signature(class_).parameters): assert hasattr(estimator, expected) -@pytest.mark.parametrize("selector_path", [None, # No XDG_CACHE_HOME provided - '/', # XDG_CACHE_HOME has no permission - tempfile.gettempdir(), # in the user cache - ]) +@pytest.mark.parametrize( + "selector_path", + [ + None, # No XDG_CACHE_HOME provided + "/", # XDG_CACHE_HOME has no permission + tempfile.gettempdir(), # in the user cache + ], +) def test_selector_file_askl2_can_be_created(selector_path): - with unittest.mock.patch('os.environ.get') as mock_foo: + with unittest.mock.patch("os.environ.get") as mock_foo: mock_foo.return_value = selector_path if selector_path is not None and not os.access(selector_path, os.W_OK): with pytest.raises(PermissionError): @@ -1029,7 +1126,9 @@ def test_selector_file_askl2_can_be_created(selector_path): else: importlib.reload(autosklearn.experimental.askl2) for metric in autosklearn.experimental.askl2.metrics: - assert os.path.exists(autosklearn.experimental.askl2.selector_files[metric.name]) + assert os.path.exists( + autosklearn.experimental.askl2.selector_files[metric.name] + ) if selector_path is None or not os.access(selector_path, os.W_OK): # We default to home in worst case assert os.path.expanduser("~") in str( @@ -1047,34 +1146,38 @@ def test_selector_file_askl2_can_be_created(selector_path): def test_check_askl2_same_arguments_as_askl() -> None: """Check the asklearn2 has the same args as asklearn1 - This test is useful for when adding args to asklearn1 to make sure we update asklearn2 - Expects ------- - * The set of arguments for AutoSklearnClassifier is the same as AutoSklearn2Classifier - except for a few expected arugments + * The set of arguments for AutoSklearnClassifier is the same as + AutoSklearn2Classifier except for a few expected arugments. """ - autosklearn1_classifier_args = set(inspect.getfullargspec(AutoSklearnEstimator.__init__).args) - autosklearn2_classifier_args = set(inspect.getfullargspec(AutoSklearn2Classifier.__init__).args) + autosklearn1_classifier_args = set( + inspect.getfullargspec(AutoSklearnEstimator.__init__).args + ) + autosklearn2_classifier_args = set( + inspect.getfullargspec(AutoSklearn2Classifier.__init__).args + ) extra_arguments = autosklearn1_classifier_args - autosklearn2_classifier_args - expected_extra_args = set([ - 'exclude', - 'include', - 'resampling_strategy_arguments', - 'get_smac_object_callback', - 'initial_configurations_via_metalearning', - 'resampling_strategy', - 'metadata_directory', - 'get_trials_callback', - ]) + expected_extra_args = set( + [ + "exclude", + "include", + "resampling_strategy_arguments", + "get_smac_object_callback", + "initial_configurations_via_metalearning", + "resampling_strategy", + "metadata_directory", + "get_trials_callback", + ] + ) unexpected_args = extra_arguments - expected_extra_args assert len(unexpected_args) == 0, unexpected_args -@pytest.mark.parametrize("task_type", ['classification', 'regression']) -@pytest.mark.parametrize("resampling_strategy", ['test', 'cv', 'holdout']) +@pytest.mark.parametrize("task_type", ["classification", "regression"]) +@pytest.mark.parametrize("resampling_strategy", ["test", "cv", "holdout"]) @pytest.mark.parametrize("disable_file_output", [True, False]) def test_fit_pipeline(dask_client, task_type, resampling_strategy, disable_file_output): """ @@ -1082,14 +1185,16 @@ def test_fit_pipeline(dask_client, task_type, resampling_strategy, disable_file_ space, fit a classification pipeline with an acceptable score """ X_train, y_train, X_test, y_test = putil.get_dataset( - 'iris' if task_type == 'classification' else 'boston' + "iris" if task_type == "classification" else "boston" + ) + estimator = ( + AutoSklearnClassifier if task_type == "classification" else AutoSklearnRegressor ) - estimator = AutoSklearnClassifier if task_type == 'classification' else AutoSklearnRegressor seed = 3 if task_type == "classification": - include = {'classifier': ['random_forest']} + include = {"classifier": ["random_forest"]} else: - include = {'regressor': ['random_forest']} + include = {"regressor": ["random_forest"]} automl = estimator( delete_tmp_folder_after_terminate=False, time_left_for_this_task=120, @@ -1101,11 +1206,16 @@ def test_fit_pipeline(dask_client, task_type, resampling_strategy, disable_file_ include=include, seed=seed, # We cannot get the configuration space with 'test' not fit with it - resampling_strategy=resampling_strategy if resampling_strategy != 'test' else 'holdout', + resampling_strategy=resampling_strategy + if resampling_strategy != "test" + else "holdout", ) - config = automl.get_configuration_space(X_train, y_train, - X_test=X_test, y_test=y_test, - ).get_default_configuration() + config = automl.get_configuration_space( + X_train, + y_train, + X_test=X_test, + y_test=y_test, + ).get_default_configuration() pipeline, run_info, run_value = automl.fit_pipeline( X=X_train, @@ -1114,7 +1224,7 @@ def test_fit_pipeline(dask_client, task_type, resampling_strategy, disable_file_ X_test=X_test, y_test=y_test, disable_file_output=disable_file_output, - resampling_strategy=resampling_strategy + resampling_strategy=resampling_strategy, ) assert isinstance(run_info.config, Configuration) @@ -1124,20 +1234,20 @@ def test_fit_pipeline(dask_client, task_type, resampling_strategy, disable_file_ assert run_value.cost < 0.2 # Make sure that the pipeline can be pickled - dump_file = os.path.join(tempfile.gettempdir(), 'automl.dump.pkl') - with open(dump_file, 'wb') as f: + dump_file = os.path.join(tempfile.gettempdir(), "automl.dump.pkl") + with open(dump_file, "wb") as f: pickle.dump(pipeline, f) - if resampling_strategy == 'test' or disable_file_output: + if resampling_strategy == "test" or disable_file_output: # We do not produce a pipeline in 'test' assert pipeline is None - elif resampling_strategy == 'cv': + elif resampling_strategy == "cv": # We should have fitted a Voting estimator - assert hasattr(pipeline, 'estimators_') + assert hasattr(pipeline, "estimators_") else: # We should have fitted a pipeline with named_steps - assert hasattr(pipeline, 'named_steps') - assert 'RandomForest' in pipeline.steps[-1][-1].choice.__class__.__name__ + assert hasattr(pipeline, "named_steps") + assert "RandomForest" in pipeline.steps[-1][-1].choice.__class__.__name__ # Num run should be 2, as 1 is for dummy classifier and we have not launch # another pipeline @@ -1145,25 +1255,30 @@ def test_fit_pipeline(dask_client, task_type, resampling_strategy, disable_file_ # Check the re-sampling strategy num_run_dir = automl.automl_._backend.get_numrun_directory( - seed, num_run, budget=0.0) - cv_model_path = os.path.join(num_run_dir, automl.automl_._backend.get_cv_model_filename( - seed, num_run, budget=0.0)) - model_path = os.path.join(num_run_dir, automl.automl_._backend.get_model_filename( - seed, num_run, budget=0.0)) - if resampling_strategy == 'test' or disable_file_output: + seed, num_run, budget=0.0 + ) + cv_model_path = os.path.join( + num_run_dir, + automl.automl_._backend.get_cv_model_filename(seed, num_run, budget=0.0), + ) + model_path = os.path.join( + num_run_dir, + automl.automl_._backend.get_model_filename(seed, num_run, budget=0.0), + ) + if resampling_strategy == "test" or disable_file_output: # No file output is expected assert not os.path.exists(num_run_dir) else: # We expect the model path always # And the cv model only on 'cv' assert os.path.exists(model_path) - if resampling_strategy == 'cv': + if resampling_strategy == "cv": assert os.path.exists(cv_model_path) - elif resampling_strategy == 'holdout': + elif resampling_strategy == "holdout": assert not os.path.exists(cv_model_path) -@pytest.mark.parametrize("data_type", ['pandas', 'numpy']) +@pytest.mark.parametrize("data_type", ["pandas", "numpy"]) @pytest.mark.parametrize("include_categorical", [True, False]) def test_pass_categorical_and_numeric_columns_to_pipeline( dask_client, data_type, include_categorical @@ -1179,17 +1294,17 @@ def test_pass_categorical_and_numeric_columns_to_pipeline( if include_categorical: X = np.insert(X, n_features, values=0, axis=1) - if data_type == 'pandas': + if data_type == "pandas": X = pd.DataFrame(X) y = pd.DataFrame(y, dtype="category") # Set the last column to categorical if include_categorical: - X.loc[:, n_features] = X.loc[:, n_features].astype('category') # type: ignore + X.loc[:, n_features] = X.loc[:, n_features].astype("category") # Specify the feature_types - if data_type == 'numpy' and include_categorical: - feat_type = ['numerical'] * n_features + ['categorical'] + if data_type == "numpy" and include_categorical: + feat_type = ["numerical"] * n_features + ["categorical"] else: feat_type = None @@ -1207,17 +1322,25 @@ def test_pass_categorical_and_numeric_columns_to_pipeline( ensemble_size=0, seed=0, dask_client=dask_client, - include={'classifier': ['random_forest']}, + include={"classifier": ["random_forest"]}, ) config_space = automl.get_configuration_space( - X_train, y_train, X_test=X_test, y_test=y_test, feat_type=feat_type, + X_train, + y_train, + X_test=X_test, + y_test=y_test, + feat_type=feat_type, ) config = config_space.get_default_configuration() pipeline, _, run_value = automl.fit_pipeline( - X=X_train, y=y_train, X_test=X_test, y_test=y_test, - config=config, feat_type=feat_type, + X=X_train, + y=y_train, + X_test=X_test, + y_test=y_test, + config=config, + feat_type=feat_type, ) assert pipeline is not None, "Expected a pipeline from automl.fit_pipeline" @@ -1237,18 +1360,17 @@ def test_pass_categorical_and_numeric_columns_to_pipeline( if include_categorical: expected_feat_types = { i: feature_type - for i, feature_type - in enumerate(['numerical'] * (n_columns-1) + ['categorical']) + for i, feature_type in enumerate( + ["numerical"] * (n_columns - 1) + ["categorical"] + ) } else: expected_feat_types = { - i: feature_type - for i, feature_type - in enumerate(['numerical'] * n_columns) + i: feature_type for i, feature_type in enumerate(["numerical"] * n_columns) } - pipeline_feat_types = pipeline.named_steps['data_preprocessor'].choice.feat_type + pipeline_feat_types = pipeline.named_steps["data_preprocessor"].choice.feat_type assert expected_feat_types == pipeline_feat_types @@ -1260,20 +1382,27 @@ def test_autosklearn_anneal(as_frame): so is a good testcase for unit-testing """ X, y = sklearn.datasets.fetch_openml(data_id=2, return_X_y=True, as_frame=as_frame) - automl = AutoSklearnClassifier(time_left_for_this_task=60, ensemble_size=0, - delete_tmp_folder_after_terminate=False, - initial_configurations_via_metalearning=0, - smac_scenario_args={'runcount_limit': 6}, - resampling_strategy='holdout-iterative-fit') + automl = AutoSklearnClassifier( + time_left_for_this_task=60, + ensemble_size=0, + delete_tmp_folder_after_terminate=False, + initial_configurations_via_metalearning=0, + smac_scenario_args={"runcount_limit": 6}, + resampling_strategy="holdout-iterative-fit", + ) if as_frame: # Let autosklearn calculate the feat types automl_fitted = automl.fit(X, y) else: - X_, y_ = sklearn.datasets.fetch_openml(data_id=2, return_X_y=True, as_frame=True) - feat_type = ['categorical' if X_[col].dtype.name == 'category' else 'numerical' - for col in X_.columns] + X_, y_ = sklearn.datasets.fetch_openml( + data_id=2, return_X_y=True, as_frame=True + ) + feat_type = [ + "categorical" if X_[col].dtype.name == "category" else "numerical" + for col in X_.columns + ] automl_fitted = automl.fit(X, y, feat_type=feat_type) @@ -1289,9 +1418,9 @@ def test_autosklearn_anneal(as_frame): assert automl_fitted.score(X, y) > 0.75 -@pytest.mark.parametrize("dataset_compression", [ - False, True, {"memory_allocation": 0.2} -]) +@pytest.mark.parametrize( + "dataset_compression", [False, True, {"memory_allocation": 0.2}] +) def test_param_dataset_compression(dataset_compression: Union[bool, Dict[str, Any]]): """We expect this does not get parsed and modified until it gets to the AutoML class, In the meantime, it's value remains whatever was passed in. diff --git a/test/test_data/__init__.py b/test/test_data/__init__.py index cc3cd7becd..e298f0f075 100644 --- a/test/test_data/__init__.py +++ b/test/test_data/__init__.py @@ -1,2 +1,2 @@ # -*- encoding: utf-8 -*- -__author__ = 'feurerm' +__author__ = "feurerm" diff --git a/test/test_data/test_feature_validator.py b/test/test_data/test_feature_validator.py index 012ef1a179..0414cd31b4 100644 --- a/test/test_data/test_feature_validator.py +++ b/test/test_data/test_feature_validator.py @@ -1,14 +1,10 @@ import numpy as np - import pandas as pd -from pandas.api.types import is_numeric_dtype, is_categorical_dtype, is_string_dtype - import pytest - -from scipy import sparse - import sklearn.datasets import sklearn.model_selection +from pandas.api.types import is_categorical_dtype, is_numeric_dtype, is_string_dtype +from scipy import sparse from autosklearn.data.feature_validator import FeatureValidator @@ -16,118 +12,135 @@ # Fixtures to be used in this class. By default all elements have 100 datapoints @pytest.fixture def input_data_featuretest(request): - if request.param == 'numpy_categoricalonly_nonan': + if request.param == "numpy_categoricalonly_nonan": return np.random.randint(10, size=(100, 10)) - elif request.param == 'numpy_numericalonly_nonan': + elif request.param == "numpy_numericalonly_nonan": return np.random.uniform(10, size=(100, 10)) - elif request.param == 'numpy_mixed_nonan': - return np.column_stack([ - np.random.uniform(10, size=(100, 3)), - np.random.randint(10, size=(100, 3)), - np.random.uniform(10, size=(100, 3)), - np.random.randint(10, size=(100, 1)), - ]) - elif request.param == 'numpy_string_nonan': - return np.array([ - ['a', 'b', 'c', 'a', 'b', 'c'], - ['a', 'b', 'd', 'r', 'b', 'c'], - ]) - elif request.param == 'numpy_categoricalonly_nan': - array = np.random.randint(10, size=(100, 10)).astype('float') + elif request.param == "numpy_mixed_nonan": + return np.column_stack( + [ + np.random.uniform(10, size=(100, 3)), + np.random.randint(10, size=(100, 3)), + np.random.uniform(10, size=(100, 3)), + np.random.randint(10, size=(100, 1)), + ] + ) + elif request.param == "numpy_string_nonan": + return np.array( + [ + ["a", "b", "c", "a", "b", "c"], + ["a", "b", "d", "r", "b", "c"], + ] + ) + elif request.param == "numpy_categoricalonly_nan": + array = np.random.randint(10, size=(100, 10)).astype("float") array[50, 0:5] = np.nan return array - elif request.param == 'numpy_numericalonly_nan': - array = np.random.uniform(10, size=(100, 10)).astype('float') + elif request.param == "numpy_numericalonly_nan": + array = np.random.uniform(10, size=(100, 10)).astype("float") array[50, 0:5] = np.nan # Somehow array is changed to dtype object after np.nan - return array.astype('float') - elif request.param == 'numpy_mixed_nan': - array = np.column_stack([ - np.random.uniform(10, size=(100, 3)), - np.random.randint(10, size=(100, 3)), - np.random.uniform(10, size=(100, 3)), - np.random.randint(10, size=(100, 1)), - ]) + return array.astype("float") + elif request.param == "numpy_mixed_nan": + array = np.column_stack( + [ + np.random.uniform(10, size=(100, 3)), + np.random.randint(10, size=(100, 3)), + np.random.uniform(10, size=(100, 3)), + np.random.randint(10, size=(100, 1)), + ] + ) array[50, 0:5] = np.nan return array - elif request.param == 'numpy_string_nan': - return np.array([ - ['a', 'b', 'c', 'a', 'b', 'c'], - [np.nan, 'b', 'd', 'r', 'b', 'c'], - ]) - elif request.param == 'pandas_categoricalonly_nonan': - return pd.DataFrame([ - {'A': 1, 'B': 2}, - {'A': 3, 'B': 4}, - ], dtype='category') - elif request.param == 'pandas_numericalonly_nonan': - return pd.DataFrame([ - {'A': 1, 'B': 2}, - {'A': 3, 'B': 4}, - ], dtype='float') - elif request.param == 'pandas_mixed_nonan': - frame = pd.DataFrame([ - {'A': 1, 'B': 2}, - {'A': 3, 'B': 4}, - ], dtype='category') - frame['B'] = pd.to_numeric(frame['B']) + elif request.param == "numpy_string_nan": + return np.array( + [ + ["a", "b", "c", "a", "b", "c"], + [np.nan, "b", "d", "r", "b", "c"], + ] + ) + elif request.param == "pandas_categoricalonly_nonan": + return pd.DataFrame( + [ + {"A": 1, "B": 2}, + {"A": 3, "B": 4}, + ], + dtype="category", + ) + elif request.param == "pandas_numericalonly_nonan": + return pd.DataFrame( + [ + {"A": 1, "B": 2}, + {"A": 3, "B": 4}, + ], + dtype="float", + ) + elif request.param == "pandas_mixed_nonan": + frame = pd.DataFrame( + [ + {"A": 1, "B": 2}, + {"A": 3, "B": 4}, + ], + dtype="category", + ) + frame["B"] = pd.to_numeric(frame["B"]) return frame - elif request.param == 'pandas_categoricalonly_nan': - return pd.DataFrame([ - {'A': 1, 'B': 2, 'C': np.nan}, - {'A': 3, 'C': np.nan}, - ], dtype='category') - elif request.param == 'pandas_numericalonly_nan': - return pd.DataFrame([ - {'A': 1, 'B': 2, 'C': np.nan}, - {'A': 3, 'C': np.nan}, - ], dtype='float') - elif request.param == 'pandas_mixed_nan': - frame = pd.DataFrame([ - {'A': 1, 'B': 2, 'C': 8}, - {'A': 3, 'B': 4}, - ], dtype='category') - frame['B'] = pd.to_numeric(frame['B']) + elif request.param == "pandas_categoricalonly_nan": + return pd.DataFrame( + [ + {"A": 1, "B": 2, "C": np.nan}, + {"A": 3, "C": np.nan}, + ], + dtype="category", + ) + elif request.param == "pandas_numericalonly_nan": + return pd.DataFrame( + [ + {"A": 1, "B": 2, "C": np.nan}, + {"A": 3, "C": np.nan}, + ], + dtype="float", + ) + elif request.param == "pandas_mixed_nan": + frame = pd.DataFrame( + [ + {"A": 1, "B": 2, "C": 8}, + {"A": 3, "B": 4}, + ], + dtype="category", + ) + frame["B"] = pd.to_numeric(frame["B"]) return frame - elif request.param == 'pandas_string_nonan': - return pd.DataFrame([ - {'A': 1, 'B': 2}, - {'A': 3, 'B': 4}, - ], dtype='string') - elif request.param == 'list_categoricalonly_nonan': - return [ - ['a', 'b', 'c', 'd'], - ['e', 'f', 'c', 'd'], - ] - elif request.param == 'list_numericalonly_nonan': - return [ - [1, 2, 3, 4], - [5, 6, 7, 8] - ] - elif request.param == 'list_mixed_nonan': - return [ - ['a', 2, 3, 4], - ['b', 6, 7, 8] - ] - elif request.param == 'list_categoricalonly_nan': - return [ - ['a', 'b', 'c', np.nan], - ['e', 'f', 'c', 'd'], - ] - elif request.param == 'list_numericalonly_nan': + elif request.param == "pandas_string_nonan": + return pd.DataFrame( + [ + {"A": 1, "B": 2}, + {"A": 3, "B": 4}, + ], + dtype="string", + ) + elif request.param == "list_categoricalonly_nonan": return [ - [1, 2, 3, np.nan], - [5, 6, 7, 8] + ["a", "b", "c", "d"], + ["e", "f", "c", "d"], ] - elif request.param == 'list_mixed_nan': + elif request.param == "list_numericalonly_nonan": + return [[1, 2, 3, 4], [5, 6, 7, 8]] + elif request.param == "list_mixed_nonan": + return [["a", 2, 3, 4], ["b", 6, 7, 8]] + elif request.param == "list_categoricalonly_nan": return [ - ['a', np.nan, 3, 4], - ['b', 6, 7, 8] + ["a", "b", "c", np.nan], + ["e", "f", "c", "d"], ] - elif 'sparse' in request.param: + elif request.param == "list_numericalonly_nan": + return [[1, 2, 3, np.nan], [5, 6, 7, 8]] + elif request.param == "list_mixed_nan": + return [["a", np.nan, 3, 4], ["b", 6, 7, 8]] + elif "sparse" in request.param: # We expect the names to be of the type sparse_csc_nonan - sparse_, type_, nan_ = request.param.split('_') - if 'nonan' in nan_: + sparse_, type_, nan_ = request.param.split("_") + if "nonan" in nan_: data = np.ones(3) else: data = np.array([1, 2, np.nan]) @@ -135,26 +148,27 @@ def input_data_featuretest(request): # Then the type of sparse row_ind = np.array([0, 1, 2]) col_ind = np.array([1, 2, 1]) - if 'csc' in type_: + if "csc" in type_: return sparse.csc_matrix((data, (row_ind, col_ind))) - elif 'csr' in type_: + elif "csr" in type_: return sparse.csr_matrix((data, (row_ind, col_ind))) - elif 'coo' in type_: + elif "coo" in type_: return sparse.coo_matrix((data, (row_ind, col_ind))) - elif 'bsr' in type_: + elif "bsr" in type_: return sparse.bsr_matrix((data, (row_ind, col_ind))) - elif 'lil' in type_: + elif "lil" in type_: return sparse.lil_matrix((data)) - elif 'dok' in type_: + elif "dok" in type_: return sparse.dok_matrix(np.vstack((data, data, data))) - elif 'dia' in type_: + elif "dia" in type_: return sparse.dia_matrix(np.vstack((data, data, data))) else: ValueError("Unsupported indirect fixture {}".format(request.param)) - elif 'openml' in request.param: - _, openml_id = request.param.split('_') - X, y = sklearn.datasets.fetch_openml(data_id=int(openml_id), - return_X_y=True, as_frame=True) + elif "openml" in request.param: + _, openml_id = request.param.split("_") + X, y = sklearn.datasets.fetch_openml( + data_id=int(openml_id), return_X_y=True, as_frame=True + ) return X else: ValueError("Unsupported indirect fixture {}".format(request.param)) @@ -162,37 +176,37 @@ def input_data_featuretest(request): # Actual checks for the features @pytest.mark.parametrize( - 'input_data_featuretest', + "input_data_featuretest", ( - 'numpy_categoricalonly_nonan', - 'numpy_numericalonly_nonan', - 'numpy_mixed_nonan', - 'numpy_categoricalonly_nan', - 'numpy_numericalonly_nan', - 'numpy_mixed_nan', - 'pandas_categoricalonly_nonan', - 'pandas_numericalonly_nonan', - 'pandas_mixed_nonan', - 'pandas_numericalonly_nan', - 'list_numericalonly_nonan', - 'list_numericalonly_nan', - 'sparse_bsr_nonan', - 'sparse_bsr_nan', - 'sparse_coo_nonan', - 'sparse_coo_nan', - 'sparse_csc_nonan', - 'sparse_csc_nan', - 'sparse_csr_nonan', - 'sparse_csr_nan', - 'sparse_dia_nonan', - 'sparse_dia_nan', - 'sparse_dok_nonan', - 'sparse_dok_nan', - 'sparse_lil_nonan', - 'sparse_lil_nan', - 'openml_40981', # Australian + "numpy_categoricalonly_nonan", + "numpy_numericalonly_nonan", + "numpy_mixed_nonan", + "numpy_categoricalonly_nan", + "numpy_numericalonly_nan", + "numpy_mixed_nan", + "pandas_categoricalonly_nonan", + "pandas_numericalonly_nonan", + "pandas_mixed_nonan", + "pandas_numericalonly_nan", + "list_numericalonly_nonan", + "list_numericalonly_nan", + "sparse_bsr_nonan", + "sparse_bsr_nan", + "sparse_coo_nonan", + "sparse_coo_nan", + "sparse_csc_nonan", + "sparse_csc_nan", + "sparse_csr_nonan", + "sparse_csr_nan", + "sparse_dia_nonan", + "sparse_dia_nan", + "sparse_dok_nonan", + "sparse_dok_nan", + "sparse_lil_nonan", + "sparse_lil_nan", + "openml_40981", # Australian ), - indirect=True + indirect=True, ) def test_featurevalidator_supported_types(input_data_featuretest): validator = FeatureValidator() @@ -209,43 +223,45 @@ def test_featurevalidator_supported_types(input_data_featuretest): @pytest.mark.parametrize( - 'input_data_featuretest', + "input_data_featuretest", ( - 'numpy_string_nonan', - 'numpy_string_nan', + "numpy_string_nonan", + "numpy_string_nan", ), - indirect=True + indirect=True, ) def test_featurevalidator_unsupported_numpy(input_data_featuretest): validator = FeatureValidator() - with pytest.raises(ValueError, match=r".*When providing a numpy array.*not supported."): + with pytest.raises( + ValueError, match=r".*When providing a numpy array.*not supported." + ): validator.fit(input_data_featuretest) @pytest.mark.parametrize( - 'input_data_featuretest', + "input_data_featuretest", ( - 'numpy_categoricalonly_nonan', - 'numpy_mixed_nonan', - 'numpy_categoricalonly_nan', - 'numpy_mixed_nan', - 'pandas_categoricalonly_nonan', - 'pandas_mixed_nonan', - 'sparse_bsr_nonan', - 'sparse_bsr_nan', - 'sparse_coo_nonan', - 'sparse_coo_nan', - 'sparse_csc_nonan', - 'sparse_csc_nan', - 'sparse_csr_nonan', - 'sparse_csr_nan', - 'sparse_dia_nonan', - 'sparse_dia_nan', - 'sparse_dok_nonan', - 'sparse_dok_nan', - 'sparse_lil_nonan', + "numpy_categoricalonly_nonan", + "numpy_mixed_nonan", + "numpy_categoricalonly_nan", + "numpy_mixed_nan", + "pandas_categoricalonly_nonan", + "pandas_mixed_nonan", + "sparse_bsr_nonan", + "sparse_bsr_nan", + "sparse_coo_nonan", + "sparse_coo_nan", + "sparse_csc_nonan", + "sparse_csc_nan", + "sparse_csr_nonan", + "sparse_csr_nan", + "sparse_dia_nonan", + "sparse_dia_nan", + "sparse_dok_nonan", + "sparse_dok_nan", + "sparse_lil_nonan", ), - indirect=True + indirect=True, ) def test_featurevalidator_fitontypeA_transformtypeB(input_data_featuretest): """ @@ -276,20 +292,24 @@ def test_featurevalidatorget_feat_type_from_columns(): """ validator = FeatureValidator() - df = pd.DataFrame([ - {'int': 1, 'float': 1.0, 'category': 'one', 'bool': True}, - {'int': 2, 'float': 2.0, 'category': 'two', 'bool': False}, - ]) + df = pd.DataFrame( + [ + {"int": 1, "float": 1.0, "category": "one", "bool": True}, + {"int": 2, "float": 2.0, "category": "two", "bool": False}, + ] + ) for col in df.columns: df[col] = df[col].astype(col) feature_types = validator.get_feat_type_from_columns(df) - assert feature_types == {'int': 'numerical', - 'float': 'numerical', - 'category': 'categorical', - 'bool': 'categorical'} + assert feature_types == { + "int": "numerical", + "float": "numerical", + "category": "categorical", + "bool": "categorical", + } def test_features_unsupported_calls_are_raised(): @@ -300,28 +320,37 @@ def test_features_unsupported_calls_are_raised(): """ validator = FeatureValidator() with pytest.raises(ValueError, match=r"Auto-sklearn does not support time"): + validator.fit(pd.DataFrame({"datetime": [pd.Timestamp("20180310")]})) + with pytest.raises( + ValueError, match=r"Auto-sklearn only supports.*yet, the provided input" + ): + validator.fit({"input1": 1, "input2": 2}) + validator = FeatureValidator() + with pytest.raises( + ValueError, match=r"The feature dimensionality of the train and test" + ): validator.fit( - pd.DataFrame({'datetime': [pd.Timestamp('20180310')]}) + X_train=np.array([[1, 2, 3], [4, 5, 6]]), + X_test=np.array([[1, 2, 3, 4], [4, 5, 6, 7]]), ) - with pytest.raises(ValueError, match=r"Auto-sklearn only supports.*yet, the provided input"): - validator.fit({'input1': 1, 'input2': 2}) - validator = FeatureValidator() - with pytest.raises(ValueError, match=r"The feature dimensionality of the train and test"): - validator.fit(X_train=np.array([[1, 2, 3], [4, 5, 6]]), - X_test=np.array([[1, 2, 3, 4], [4, 5, 6, 7]]), - ) - with pytest.raises(ValueError, match=r"Cannot call transform on a validator that is not fit"): + with pytest.raises( + ValueError, match=r"Cannot call transform on a validator that is not fit" + ): validator.transform(np.array([[1, 2, 3], [4, 5, 6]])) - validator = FeatureValidator(feat_type=['Numerical']) - with pytest.raises(ValueError, match=r"providing the option feat_type to the fit method is.*"): + validator = FeatureValidator(feat_type=["Numerical"]) + with pytest.raises( + ValueError, match=r"providing the option feat_type to the fit method is.*" + ): validator.fit(pd.DataFrame([[1, 2, 3], [4, 5, 6]])) with pytest.raises(ValueError, match=r"feat_type does not have same number of.*"): validator.fit(np.array([[1, 2, 3], [4, 5, 6]])) validator = FeatureValidator(feat_type=[1, 2, 3]) with pytest.raises(ValueError, match=r"feat_type must only contain strings.*"): validator.fit(np.array([[1, 2, 3], [4, 5, 6]])) - validator = FeatureValidator(feat_type=['1', '2', '3']) - with pytest.raises(ValueError, match=r"Only `Categorical`, `Numerical` and `String` are.*"): + validator = FeatureValidator(feat_type=["1", "2", "3"]) + with pytest.raises( + ValueError, match=r"Only `Categorical`, `Numerical` and `String` are.*" + ): validator.fit(np.array([[1, 2, 3], [4, 5, 6]])) @@ -331,16 +360,16 @@ def test_no_new_category_after_fit(): without throwing an error """ # Then make sure we catch categorical extra categories - x = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}, dtype='category') + x = pd.DataFrame({"A": [1, 2, 3, 4], "B": [5, 6, 7, 8]}, dtype="category") validator = FeatureValidator() validator.fit(x) - x['A'] = x['A'].apply(lambda x: x*x) + x["A"] = x["A"].apply(lambda x: x * x) validator.transform(x) # Actual checks for the features @pytest.mark.parametrize( - 'openml_id', + "openml_id", ( 40981, # Australian 3, # kr-vs-kp @@ -349,32 +378,37 @@ def test_no_new_category_after_fit(): 40984, # Segment ), ) -@pytest.mark.parametrize('train_data_type', ('numpy', 'pandas', 'list')) -@pytest.mark.parametrize('test_data_type', ('numpy', 'pandas', 'list')) -def test_featurevalidator_new_data_after_fit(openml_id, - train_data_type, test_data_type): +@pytest.mark.parametrize("train_data_type", ("numpy", "pandas", "list")) +@pytest.mark.parametrize("test_data_type", ("numpy", "pandas", "list")) +def test_featurevalidator_new_data_after_fit( + openml_id, train_data_type, test_data_type +): # List is currently not supported as infer_objects # cast list objects to type objects - if train_data_type == 'list' or test_data_type == 'list': + if train_data_type == "list" or test_data_type == "list": pytest.skip() validator = FeatureValidator() - if train_data_type == 'numpy': - X, y = sklearn.datasets.fetch_openml(data_id=openml_id, - return_X_y=True, as_frame=False) - elif train_data_type == 'pandas': - X, y = sklearn.datasets.fetch_openml(data_id=openml_id, - return_X_y=True, as_frame=True) + if train_data_type == "numpy": + X, y = sklearn.datasets.fetch_openml( + data_id=openml_id, return_X_y=True, as_frame=False + ) + elif train_data_type == "pandas": + X, y = sklearn.datasets.fetch_openml( + data_id=openml_id, return_X_y=True, as_frame=True + ) else: - X, y = sklearn.datasets.fetch_openml(data_id=openml_id, - return_X_y=True, as_frame=True) + X, y = sklearn.datasets.fetch_openml( + data_id=openml_id, return_X_y=True, as_frame=True + ) X = X.values.tolist() y = y.values.tolist() X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( - X, y, random_state=1) + X, y, random_state=1 + ) validator.fit(X_train) @@ -391,7 +425,7 @@ def test_featurevalidator_new_data_after_fit(openml_id, @pytest.mark.parametrize( - 'openml_id', + "openml_id", ( 40981, # Australian 3, # kr-vs-kp @@ -403,10 +437,12 @@ def test_featurevalidator_new_data_after_fit(openml_id, ) def test_list_to_dataframe(openml_id): - X_pandas, y_pandas = sklearn.datasets.fetch_openml(data_id=openml_id, - return_X_y=True, as_frame=True) + X_pandas, y_pandas = sklearn.datasets.fetch_openml( + data_id=openml_id, return_X_y=True, as_frame=True + ) X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( - X_pandas, y_pandas, random_state=1) + X_pandas, y_pandas, random_state=1 + ) X_list = X_train.values.tolist() validator = FeatureValidator() @@ -439,24 +475,24 @@ def test_list_to_dataframe(openml_id): @pytest.mark.parametrize( - 'input_data_featuretest', + "input_data_featuretest", ( - 'sparse_bsr_nonan', - 'sparse_bsr_nan', - 'sparse_coo_nonan', - 'sparse_coo_nan', - 'sparse_csc_nonan', - 'sparse_csc_nan', - 'sparse_csr_nonan', - 'sparse_csr_nan', - 'sparse_dia_nonan', - 'sparse_dia_nan', - 'sparse_dok_nonan', - 'sparse_dok_nan', - 'sparse_lil_nonan', - 'sparse_lil_nan', + "sparse_bsr_nonan", + "sparse_bsr_nan", + "sparse_coo_nonan", + "sparse_coo_nan", + "sparse_csc_nonan", + "sparse_csc_nan", + "sparse_csr_nonan", + "sparse_csr_nan", + "sparse_dia_nonan", + "sparse_dia_nan", + "sparse_dok_nonan", + "sparse_dok_nan", + "sparse_lil_nonan", + "sparse_lil_nan", ), - indirect=True + indirect=True, ) def test_sparse_output_is_csr(input_data_featuretest): validator = FeatureValidator() @@ -467,7 +503,9 @@ def test_sparse_output_is_csr(input_data_featuretest): def test_unsupported_dataframe_sparse(): - df = pd.DataFrame({'A': pd.Series(pd.arrays.SparseArray(np.random.randn(10)))}) + df = pd.DataFrame({"A": pd.Series(pd.arrays.SparseArray(np.random.randn(10)))}) validator = FeatureValidator() - with pytest.raises(ValueError, match=r"Auto-sklearn does not yet support sparse pandas"): + with pytest.raises( + ValueError, match=r"Auto-sklearn does not yet support sparse pandas" + ): validator.fit(df) diff --git a/test/test_data/test_target_validator.py b/test/test_data/test_target_validator.py index 09e075b85f..e57f464c72 100644 --- a/test/test_data/test_target_validator.py +++ b/test/test_data/test_target_validator.py @@ -1,14 +1,10 @@ import numpy as np - import pandas as pd - import pytest -from pandas.api.types import is_numeric_dtype, is_bool_dtype - -from scipy import sparse - import sklearn.datasets import sklearn.model_selection +from pandas.api.types import is_bool_dtype, is_numeric_dtype +from scipy import sparse from sklearn.utils.multiclass import type_of_target from autosklearn.data.target_validator import TargetValidator @@ -17,80 +13,85 @@ # Fixtures to be used in this class. By default all elements have 100 datapoints @pytest.fixture def input_data_targettest(request): - if request.param == 'series_binary': + if request.param == "series_binary": return pd.Series([1, -1, -1, 1]) - elif request.param == 'series_multiclass': + elif request.param == "series_multiclass": return pd.Series([1, 0, 2]) - elif request.param == 'series_multilabel': + elif request.param == "series_multilabel": return pd.Series([[1, 0], [0, 1]]) - elif request.param == 'series_continuous': + elif request.param == "series_continuous": return pd.Series([0.1, 0.6, 0.7]) - elif request.param == 'series_continuous-multioutput': + elif request.param == "series_continuous-multioutput": return pd.Series([[1.5, 2.0], [3.0, 1.6]]) - elif request.param == 'pandas_binary': + elif request.param == "pandas_binary": return pd.DataFrame([1, -1, -1, 1]) - elif request.param == 'pandas_multiclass': + elif request.param == "pandas_multiclass": return pd.DataFrame([1, 0, 2]) - elif request.param == 'pandas_multilabel': + elif request.param == "pandas_multilabel": return pd.DataFrame([[1, 0], [0, 1]]) - elif request.param == 'pandas_continuous': + elif request.param == "pandas_continuous": return pd.DataFrame([0.1, 0.6, 0.7]) - elif request.param == 'pandas_continuous-multioutput': + elif request.param == "pandas_continuous-multioutput": return pd.DataFrame([[1.5, 2.0], [3.0, 1.6]]) - elif request.param == 'numpy_binary': + elif request.param == "numpy_binary": return np.array([1, -1, -1, 1]) - elif request.param == 'numpy_multiclass': + elif request.param == "numpy_multiclass": return np.array([1, 0, 2]) - elif request.param == 'numpy_multilabel': + elif request.param == "numpy_multilabel": return np.array([[1, 0], [0, 1]]) - elif request.param == 'numpy_continuous': + elif request.param == "numpy_continuous": return np.array([0.1, 0.6, 0.7]) - elif request.param == 'numpy_continuous-multioutput': + elif request.param == "numpy_continuous-multioutput": return np.array([[1.5, 2.0], [3.0, 1.6]]) - elif request.param == 'list_binary': + elif request.param == "list_binary": return [1, -1, -1, 1] - elif request.param == 'list_multiclass': + elif request.param == "list_multiclass": return [1, 0, 2] - elif request.param == 'list_multilabel': + elif request.param == "list_multilabel": return [[0, 1], [1, 0]] - elif request.param == 'list_continuous': + elif request.param == "list_continuous": return [0.1, 0.6, 0.7] - elif request.param == 'list_continuous-multioutput': + elif request.param == "list_continuous-multioutput": return [[1.5, 2.0], [3.0, 1.6]] - elif 'openml' in request.param: - _, openml_id = request.param.split('_') - X, y = sklearn.datasets.fetch_openml(data_id=int(openml_id), - return_X_y=True, as_frame=True) - if len(y.shape) > 1 and y.shape[1] > 1 and np.any(y.eq('TRUE').any(1).to_numpy()): + elif "openml" in request.param: + _, openml_id = request.param.split("_") + X, y = sklearn.datasets.fetch_openml( + data_id=int(openml_id), return_X_y=True, as_frame=True + ) + if ( + len(y.shape) > 1 + and y.shape[1] > 1 + and np.any(y.eq("TRUE").any(1).to_numpy()) + ): # This 'if' is only asserted for multi-label data # Force the downloaded data to be interpreted as multilabel y = y.dropna() - y.replace('FALSE', 0, inplace=True) - y.replace('TRUE', 1, inplace=True) + y.replace("FALSE", 0, inplace=True) + y.replace("TRUE", 1, inplace=True) y = y.astype(int) return y - elif 'sparse' in request.param: + elif "sparse" in request.param: # We expect the names to be of the type sparse_csc_nonan - sparse_, type_, nan_ = request.param.split('_') - if 'nonan' in nan_: + sparse_, type_, nan_ = request.param.split("_") + if "nonan" in nan_: data = np.ones(3) else: data = np.array([1, 2, np.nan]) # Then the type of sparse - if 'csc' in type_: + if "csc" in type_: return sparse.csc_matrix(data) - elif 'csr' in type_: + elif "csr" in type_: return sparse.csr_matrix(data) - elif 'coo' in type_: + elif "coo" in type_: return sparse.coo_matrix(data) - elif 'bsr' in type_: + elif "bsr" in type_: return sparse.bsr_matrix(data) - elif 'lil' in type_: + elif "lil" in type_: return sparse.lil_matrix(data) - elif 'dok' in type_: + elif "dok" in type_: return sparse.dok_matrix(np.vstack((data, data, data))) - elif 'dia' in type_: + elif "dia" in type_: return sparse.dia_matrix(np.vstack((data, data, data))) else: ValueError("Unsupported indirect fixture {}".format(request.param)) @@ -100,29 +101,29 @@ def input_data_targettest(request): # Actual checks for the targets @pytest.mark.parametrize( - 'input_data_targettest', + "input_data_targettest", ( - 'series_binary', - 'series_multiclass', - 'series_continuous', - 'pandas_binary', - 'pandas_multiclass', - 'pandas_multilabel', - 'pandas_continuous', - 'pandas_continuous-multioutput', - 'numpy_binary', - 'numpy_multiclass', - 'numpy_multilabel', - 'numpy_continuous', - 'numpy_continuous-multioutput', - 'list_binary', - 'list_multiclass', - 'list_multilabel', - 'list_continuous', - 'list_continuous-multioutput', - 'openml_204', + "series_binary", + "series_multiclass", + "series_continuous", + "pandas_binary", + "pandas_multiclass", + "pandas_multilabel", + "pandas_continuous", + "pandas_continuous-multioutput", + "numpy_binary", + "numpy_multiclass", + "numpy_multilabel", + "numpy_continuous", + "numpy_continuous-multioutput", + "list_binary", + "list_multiclass", + "list_multilabel", + "list_continuous", + "list_continuous-multioutput", + "openml_204", ), - indirect=True + indirect=True, ) def test_targetvalidator_supported_types_noclassification(input_data_targettest): y = input_data_targettest @@ -146,19 +147,19 @@ def test_targetvalidator_supported_types_noclassification(input_data_targettest) @pytest.mark.parametrize( - 'input_data_targettest', + "input_data_targettest", ( - 'series_binary', - 'series_multiclass', - 'pandas_binary', - 'pandas_multiclass', - 'numpy_binary', - 'numpy_multiclass', - 'list_binary', - 'list_multiclass', - 'openml_2', + "series_binary", + "series_multiclass", + "pandas_binary", + "pandas_multiclass", + "numpy_binary", + "numpy_multiclass", + "list_binary", + "list_multiclass", + "openml_2", ), - indirect=True + indirect=True, ) def test_targetvalidator_supported_types_classification(input_data_targettest): y = input_data_targettest # Just to remove visual clutter @@ -177,10 +178,7 @@ def test_targetvalidator_supported_types_classification(input_data_targettest): assert isinstance(y_inverse, np.ndarray) # Assert that y_encoded is numeric and not boolean - assert ( - is_numeric_dtype(y_encoded.dtype) - and not is_bool_dtype(y_encoded.dtype) - ) + assert is_numeric_dtype(y_encoded.dtype) and not is_bool_dtype(y_encoded.dtype) # Assert dtype is presevered with y -> y_encoded -> y_inverse def dtype(arr): @@ -205,7 +203,7 @@ def dtype(arr): if len(shape) == 2 and shape[1] == 1: # For cases where y = [[1], [2], [3]], # we expect y_inverse, y_encodedd to have been flattened to [1,2,3] - expected_shape = (shape[0], ) + expected_shape = (shape[0],) else: expected_shape = shape @@ -221,7 +219,7 @@ def dtype(arr): # # As a result of this, we don't encode 'multilabel-indicator' labels and # there is nothing else to check here - if validator.type_of_target == 'multilabel-indicator': + if validator.type_of_target == "multilabel-indicator": assert validator.encoder is None else: @@ -242,112 +240,112 @@ def dtype(arr): @pytest.mark.parametrize( - 'input_data_targettest', + "input_data_targettest", ( - 'series_binary', - 'pandas_binary', - 'numpy_binary', - 'list_binary', - 'openml_1066', + "series_binary", + "pandas_binary", + "numpy_binary", + "list_binary", + "openml_1066", ), - indirect=True + indirect=True, ) def test_targetvalidator_binary(input_data_targettest): - assert type_of_target(input_data_targettest) == 'binary' + assert type_of_target(input_data_targettest) == "binary" validator = TargetValidator(is_classification=True) # Test the X_test also! validator.fit(input_data_targettest, input_data_targettest) transformed_y = validator.transform(input_data_targettest) - assert type_of_target(transformed_y) == 'binary' + assert type_of_target(transformed_y) == "binary" @pytest.mark.parametrize( - 'input_data_targettest', + "input_data_targettest", ( - 'series_multiclass', - 'pandas_multiclass', - 'numpy_multiclass', - 'list_multiclass', - 'openml_54', + "series_multiclass", + "pandas_multiclass", + "numpy_multiclass", + "list_multiclass", + "openml_54", ), - indirect=True + indirect=True, ) def test_targetvalidator_multiclass(input_data_targettest): - assert type_of_target(input_data_targettest) == 'multiclass' + assert type_of_target(input_data_targettest) == "multiclass" validator = TargetValidator(is_classification=True) # Test the X_test also! validator.fit(input_data_targettest, input_data_targettest) transformed_y = validator.transform(input_data_targettest) - assert type_of_target(transformed_y) == 'multiclass' + assert type_of_target(transformed_y) == "multiclass" @pytest.mark.parametrize( - 'input_data_targettest', + "input_data_targettest", ( - 'pandas_multilabel', - 'numpy_multilabel', - 'list_multilabel', - 'openml_40594', + "pandas_multilabel", + "numpy_multilabel", + "list_multilabel", + "openml_40594", ), - indirect=True + indirect=True, ) def test_targetvalidator_multilabel(input_data_targettest): - assert type_of_target(input_data_targettest) == 'multilabel-indicator' + assert type_of_target(input_data_targettest) == "multilabel-indicator" validator = TargetValidator(is_classification=True) # Test the X_test also! validator.fit(input_data_targettest, input_data_targettest) transformed_y = validator.transform(input_data_targettest) - assert type_of_target(transformed_y) == 'multilabel-indicator' + assert type_of_target(transformed_y) == "multilabel-indicator" @pytest.mark.parametrize( - 'input_data_targettest', + "input_data_targettest", ( - 'series_continuous', - 'pandas_continuous', - 'numpy_continuous', - 'list_continuous', - 'openml_531', + "series_continuous", + "pandas_continuous", + "numpy_continuous", + "list_continuous", + "openml_531", ), - indirect=True + indirect=True, ) def test_targetvalidator_continuous(input_data_targettest): - assert type_of_target(input_data_targettest) == 'continuous' + assert type_of_target(input_data_targettest) == "continuous" validator = TargetValidator(is_classification=False) # Test the X_test also! validator.fit(input_data_targettest, input_data_targettest) transformed_y = validator.transform(input_data_targettest) - assert type_of_target(transformed_y) == 'continuous' + assert type_of_target(transformed_y) == "continuous" @pytest.mark.parametrize( - 'input_data_targettest', + "input_data_targettest", ( - 'pandas_continuous-multioutput', - 'numpy_continuous-multioutput', - 'list_continuous-multioutput', - 'openml_41483', + "pandas_continuous-multioutput", + "numpy_continuous-multioutput", + "list_continuous-multioutput", + "openml_41483", ), - indirect=True + indirect=True, ) def test_targetvalidator_continuous_multioutput(input_data_targettest): - assert type_of_target(input_data_targettest) == 'continuous-multioutput' + assert type_of_target(input_data_targettest) == "continuous-multioutput" validator = TargetValidator(is_classification=False) # Test the X_test also! validator.fit(input_data_targettest, input_data_targettest) transformed_y = validator.transform(input_data_targettest) - assert type_of_target(transformed_y) == 'continuous-multioutput' + assert type_of_target(transformed_y) == "continuous-multioutput" @pytest.mark.parametrize( - 'input_data_targettest', + "input_data_targettest", ( - 'series_binary', - 'pandas_binary', - 'numpy_binary', - 'list_binary', + "series_binary", + "pandas_binary", + "numpy_binary", + "list_binary", ), - indirect=True + indirect=True, ) def test_targetvalidator_fitontypeA_transformtypeB(input_data_targettest): """ @@ -370,12 +368,12 @@ def test_targetvalidator_fitontypeA_transformtypeB(input_data_targettest): @pytest.mark.parametrize( - 'input_data_targettest', + "input_data_targettest", ( - 'series_multilabel', - 'series_continuous-multioutput', + "series_multilabel", + "series_continuous-multioutput", ), - indirect=True + indirect=True, ) def test_type_of_target_unsupported(input_data_targettest): """ @@ -393,43 +391,63 @@ def test_target_unsupported(): when providing not supported data input """ validator = TargetValidator(is_classification=True) - with pytest.raises(ValueError, match=r"The dimensionality of the train and test targets"): + with pytest.raises( + ValueError, match=r"The dimensionality of the train and test targets" + ): validator.fit( np.array([[0, 1, 0], [0, 1, 1]]), np.array([[0, 1, 0, 0], [0, 1, 1, 1]]), ) - with pytest.raises(ValueError, match=r"Train and test targets must both have the same dtypes"): + with pytest.raises( + ValueError, match=r"Train and test targets must both have the same dtypes" + ): validator.fit( - pd.DataFrame({'a': [1, 2, 3]}), - pd.DataFrame({'a': [True, False, False]}), + pd.DataFrame({"a": [1, 2, 3]}), + pd.DataFrame({"a": [True, False, False]}), ) with pytest.raises(ValueError, match=r"Provided targets are not supported.*"): validator.fit( np.array([[0, 1, 2], [0, 3, 4]]), np.array([[0, 1, 2, 5], [0, 3, 4, 6]]), ) - with pytest.raises(ValueError, match="Train and test targets must both have the same"): + with pytest.raises( + ValueError, match="Train and test targets must both have the same" + ): validator.fit( - pd.DataFrame({'string': ['foo']}), - pd.DataFrame({'int': [1]}), + pd.DataFrame({"string": ["foo"]}), + pd.DataFrame({"int": [1]}), ) - with pytest.raises(ValueError, match=r"Auto-sklearn only supports Numpy arrays, .*"): - validator.fit({'input1': 1, 'input2': 2}) - with pytest.raises(ValueError, match=r"arget values cannot contain missing/NaN values"): + with pytest.raises( + ValueError, match=r"Auto-sklearn only supports Numpy arrays, .*" + ): + validator.fit({"input1": 1, "input2": 2}) + with pytest.raises( + ValueError, match=r"arget values cannot contain missing/NaN values" + ): validator.fit(np.array([np.nan, 1, 2])) - with pytest.raises(ValueError, match=r"arget values cannot contain missing/NaN values"): + with pytest.raises( + ValueError, match=r"arget values cannot contain missing/NaN values" + ): validator.fit(sparse.csr_matrix(np.array([1, 2, np.nan]))) - with pytest.raises(ValueError, match=r"TargetValidator must have fit\(\) called first"): + with pytest.raises( + ValueError, match=r"TargetValidator must have fit\(\) called first" + ): validator.transform(np.array([1, 2, 3])) - with pytest.raises(ValueError, match=r"TargetValidator must have fit\(\) called first"): + with pytest.raises( + ValueError, match=r"TargetValidator must have fit\(\) called first" + ): validator.inverse_transform(np.array([1, 2, 3])) - with pytest.raises(ValueError, match=r"Multi-dimensional classification is not yet supported"): + with pytest.raises( + ValueError, match=r"Multi-dimensional classification is not yet supported" + ): validator._fit(np.array([[1, 2, 3], [1, 5, 6]])) # Dia/ DOK are not supported as type of target makes calls len on the array # which causes TypeError: len() of unsized object. Basically, sparse data as # multi-label is the only thing that makes sense in this format. - with pytest.raises(ValueError, match=r"The provided data could not be interpreted by Sklearn"): + with pytest.raises( + ValueError, match=r"The provided data could not be interpreted by Sklearn" + ): validator.fit(sparse.dia_matrix(np.array([1, 2, 3]))) validator.fit(np.array([[0, 1, 0], [0, 1, 1]])) @@ -443,22 +461,21 @@ def test_targetvalidator_inversetransform(): """ validator = TargetValidator(is_classification=True) validator.fit( - pd.DataFrame(data=['a', 'a', 'b', 'c', 'a'], dtype='category'), + pd.DataFrame(data=["a", "a", "b", "c", "a"], dtype="category"), ) y = validator.transform( - pd.DataFrame(data=['a', 'a', 'b', 'c', 'a'], dtype='category'), + pd.DataFrame(data=["a", "a", "b", "c", "a"], dtype="category"), ) np.testing.assert_array_almost_equal(np.array([0, 0, 1, 2, 0]), y) y_decoded = validator.inverse_transform(y) - assert ['a', 'a', 'b', 'c', 'a'] == y_decoded.tolist() + assert ["a", "a", "b", "c", "a"] == y_decoded.tolist() - assert validator.classes_.tolist() == ['a', 'b', 'c'] + assert validator.classes_.tolist() == ["a", "b", "c"] validator = TargetValidator(is_classification=True) multi_label = pd.DataFrame( - np.array([[1, 0, 0, 1], [0, 0, 1, 1], [0, 0, 0, 0]]), - dtype=bool + np.array([[1, 0, 0, 1], [0, 0, 1, 1], [0, 0, 0, 0]]), dtype=bool ) validator.fit(multi_label) y = validator.transform(multi_label) @@ -473,18 +490,18 @@ def test_targetvalidator_inversetransform(): # Actual checks for the targets @pytest.mark.parametrize( - 'input_data_targettest', + "input_data_targettest", ( - 'series_binary', - 'series_multiclass', - 'pandas_binary', - 'pandas_multiclass', - 'numpy_binary', - 'numpy_multiclass', - 'list_binary', - 'list_multiclass', + "series_binary", + "series_multiclass", + "pandas_binary", + "pandas_multiclass", + "numpy_binary", + "numpy_multiclass", + "list_binary", + "list_multiclass", ), - indirect=True + indirect=True, ) def test_unknown_categories_in_targets(input_data_targettest): validator = TargetValidator(is_classification=True) diff --git a/test/test_data/test_validation.py b/test/test_data/test_validation.py index 7bc2cb3dc5..4d09c65075 100644 --- a/test/test_data/test_validation.py +++ b/test/test_data/test_validation.py @@ -1,34 +1,33 @@ import numpy as np - import pandas as pd - import pytest - -from scipy import sparse - import sklearn.datasets import sklearn.model_selection +from scipy import sparse from autosklearn.data.validation import InputValidator -@pytest.mark.parametrize('openmlid', [2, 40975, 40984]) -@pytest.mark.parametrize('as_frame', [True, False]) +@pytest.mark.parametrize("openmlid", [2, 40975, 40984]) +@pytest.mark.parametrize("as_frame", [True, False]) def test_data_validation_for_classification(openmlid, as_frame): - x, y = sklearn.datasets.fetch_openml(data_id=openmlid, return_X_y=True, as_frame=as_frame) + x, y = sklearn.datasets.fetch_openml( + data_id=openmlid, return_X_y=True, as_frame=as_frame + ) validator = InputValidator(is_classification=True) if as_frame: # NaN is not supported in categories, so # drop columns with them. nan_cols = [i for i in x.columns if x[i].isnull().any()] - cat_cols = [i for i in x.columns if x[i].dtype.name in ['category', 'bool']] + cat_cols = [i for i in x.columns if x[i].dtype.name in ["category", "bool"]] unsupported_columns = list(set(nan_cols) & set(cat_cols)) if len(unsupported_columns) > 0: x.drop(unsupported_columns, axis=1, inplace=True) X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( - x, y, test_size=0.33, random_state=0) + x, y, test_size=0.33, random_state=0 + ) validator.fit(X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test) @@ -49,23 +48,26 @@ def test_data_validation_for_classification(openmlid, as_frame): validator.feature_validator.feat_type is not None -@pytest.mark.parametrize('openmlid', [505, 546, 531]) -@pytest.mark.parametrize('as_frame', [True, False]) +@pytest.mark.parametrize("openmlid", [505, 546, 531]) +@pytest.mark.parametrize("as_frame", [True, False]) def test_data_validation_for_regression(openmlid, as_frame): - x, y = sklearn.datasets.fetch_openml(data_id=openmlid, return_X_y=True, as_frame=as_frame) + x, y = sklearn.datasets.fetch_openml( + data_id=openmlid, return_X_y=True, as_frame=as_frame + ) validator = InputValidator(is_classification=False) if as_frame: # NaN is not supported in categories, so # drop columns with them. nan_cols = [i for i in x.columns if x[i].isnull().any()] - cat_cols = [i for i in x.columns if x[i].dtype.name in ['category', 'bool']] + cat_cols = [i for i in x.columns if x[i].dtype.name in ["category", "bool"]] unsupported_columns = list(set(nan_cols) & set(cat_cols)) if len(unsupported_columns) > 0: x.drop(unsupported_columns, axis=1, inplace=True) X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( - x, y, test_size=0.33, random_state=0) + x, y, test_size=0.33, random_state=0 + ) validator.fit(X_train=X_train, y_train=y_train) @@ -83,7 +85,9 @@ def test_data_validation_for_regression(openmlid, as_frame): def test_sparse_data_validation_for_regression(): - X, y = sklearn.datasets.make_regression(n_samples=100, n_features=50, random_state=0) + X, y = sklearn.datasets.make_regression( + n_samples=100, n_features=50, random_state=0 + ) X_sp = sparse.coo_matrix(X) validator = InputValidator(is_classification=False) @@ -118,7 +122,9 @@ def test_validation_unsupported(): X_test=np.array([[0, 1, 0], [0, 1, 1]]), y_test=np.array([0, 1, 0, 0, 0, 0]), ) - with pytest.raises(ValueError, match=r"Cannot call transform on a validator .*fitted"): + with pytest.raises( + ValueError, match=r"Cannot call transform on a validator .*fitted" + ): validator.transform( X=np.array([[0, 1, 0], [0, 1, 1]]), y=np.array([0, 1]), diff --git a/test/test_ensemble_builder/__init__.py b/test/test_ensemble_builder/__init__.py index 51b8efdf22..b74c2a5ccb 100644 --- a/test/test_ensemble_builder/__init__.py +++ b/test/test_ensemble_builder/__init__.py @@ -1,2 +1,2 @@ # -*- encoding: utf-8 -*- -__author__ = 'mlindauer' +__author__ = "mlindauer" diff --git a/test/test_ensemble_builder/ensemble_utils.py b/test/test_ensemble_builder/ensemble_utils.py index b98021c7bd..fa0f22e9e7 100644 --- a/test/test_ensemble_builder/ensemble_utils.py +++ b/test/test_ensemble_builder/ensemble_utils.py @@ -5,47 +5,55 @@ import numpy as np -from autosklearn.automl_common.common.ensemble_building.abstract_ensemble import AbstractEnsemble - -from autosklearn.metrics import make_scorer +from autosklearn.automl_common.common.ensemble_building.abstract_ensemble import ( + AbstractEnsemble, +) from autosklearn.ensemble_builder import EnsembleBuilder +from autosklearn.metrics import make_scorer def scorer_function(a, b): return 0.9 -MockMetric = make_scorer('mock', scorer_function) +MockMetric = make_scorer("mock", scorer_function) class BackendMock(object): - def __init__(self, target_directory): - this_directory = os.path.abspath( - os.path.dirname(__file__) + this_directory = os.path.abspath(os.path.dirname(__file__)) + shutil.copytree( + os.path.join(this_directory, "data"), os.path.join(target_directory) ) - shutil.copytree(os.path.join(this_directory, 'data'), os.path.join(target_directory)) self.temporary_directory = target_directory - self.internals_directory = os.path.join(self.temporary_directory, '.auto-sklearn') + self.internals_directory = os.path.join( + self.temporary_directory, ".auto-sklearn" + ) def load_datamanager(self): manager = unittest.mock.Mock() manager.__reduce__ = lambda self: (unittest.mock.MagicMock, ()) - array = np.load(os.path.join( - self.temporary_directory, - '.auto-sklearn', - 'runs', '0_3_100.0', - 'predictions_test_0_3_100.0.npy' - )) + array = np.load( + os.path.join( + self.temporary_directory, + ".auto-sklearn", + "runs", + "0_3_100.0", + "predictions_test_0_3_100.0.npy", + ) + ) manager.data.get.return_value = array return manager def load_targets_ensemble(self): - with open(os.path.join( - self.temporary_directory, - ".auto-sklearn", - "predictions_ensemble_true.npy" - ), "rb") as fp: + with open( + os.path.join( + self.temporary_directory, + ".auto-sklearn", + "predictions_ensemble_true.npy", + ), + "rb", + ) as fp: y = np.load(fp, allow_pickle=True) return y @@ -56,13 +64,15 @@ def save_predictions_as_txt(self, predictions, subset, idx, prefix, precision): return def get_runs_directory(self) -> str: - return os.path.join(self.temporary_directory, '.auto-sklearn', 'runs') + return os.path.join(self.temporary_directory, ".auto-sklearn", "runs") def get_numrun_directory(self, seed: int, num_run: int, budget: float) -> str: - return os.path.join(self.get_runs_directory(), '%d_%d_%s' % (seed, num_run, budget)) + return os.path.join( + self.get_runs_directory(), "%d_%d_%s" % (seed, num_run, budget) + ) def get_model_filename(self, seed: int, idx: int, budget: float) -> str: - return '%s.%s.%s.model' % (seed, idx, budget) + return "%s.%s.%s.model" % (seed, idx, budget) def compare_read_preds(read_preds1, read_preds2): @@ -91,13 +101,15 @@ def compare_read_preds(read_preds1, read_preds2): class EnsembleBuilderMemMock(EnsembleBuilder): - def fit_ensemble(self, selected_keys): return True - def predict(self, set_: str, - ensemble: AbstractEnsemble, - selected_keys: list, - n_preds: int, - index_run: int): + def predict( + self, + set_: str, + ensemble: AbstractEnsemble, + selected_keys: list, + n_preds: int, + index_run: int, + ): np.ones([10000000, 1000000]) diff --git a/test/test_ensemble_builder/test_ensemble.py b/test/test_ensemble_builder/test_ensemble.py index 335c07eca2..3533da37cd 100644 --- a/test/test_ensemble_builder/test_ensemble.py +++ b/test/test_ensemble_builder/test_ensemble.py @@ -1,35 +1,40 @@ import os +import pickle +import shutil import sys import time import unittest.mock -import pickle -import pytest -import shutil import dask.distributed import numpy as np import pandas as pd -from smac.runhistory.runhistory import RunValue, RunKey, RunHistory +import pytest +from smac.runhistory.runhistory import RunHistory, RunKey, RunValue -from autosklearn.constants import MULTILABEL_CLASSIFICATION, BINARY_CLASSIFICATION -from autosklearn.metrics import roc_auc, accuracy, log_loss +from autosklearn.constants import BINARY_CLASSIFICATION, MULTILABEL_CLASSIFICATION from autosklearn.ensemble_builder import ( - EnsembleBuilder, - EnsembleBuilderManager, Y_ENSEMBLE, - Y_VALID, Y_TEST, + Y_VALID, + EnsembleBuilder, + EnsembleBuilderManager, ) from autosklearn.ensembles.singlebest_ensemble import SingleBest +from autosklearn.metrics import accuracy, log_loss, roc_auc this_directory = os.path.dirname(__file__) sys.path.append(this_directory) -from ensemble_utils import BackendMock, compare_read_preds, EnsembleBuilderMemMock, MockMetric # noqa (E402: module level import not at top of file) +from ensemble_utils import ( # noqa (E402: module level import not at top of file) + BackendMock, + EnsembleBuilderMemMock, + MockMetric, + compare_read_preds, +) @pytest.fixture(scope="function") def ensemble_backend(request): - test_id = '%s_%s' % (request.module.__name__, request.node.name) + test_id = "%s_%s" % (request.module.__name__, request.node.name) test_dir = os.path.join(this_directory, test_id) try: @@ -46,7 +51,9 @@ def session_run_at_end(): shutil.rmtree(test_dir) except: # noqa E722 pass + return session_run_at_end + request.addfinalizer(get_finalizer(backend)) return backend @@ -58,10 +65,7 @@ def ensemble_run_history(request): run_history = RunHistory() run_history._add( RunKey( - config_id=3, - instance_id='{"task_id": "breast_cancer"}', - seed=1, - budget=3.0 + config_id=3, instance_id='{"task_id": "breast_cancer"}', seed=1, budget=3.0 ), RunValue( cost=0.11347517730496459, @@ -70,30 +74,29 @@ def ensemble_run_history(request): starttime=time.time(), endtime=time.time(), additional_info={ - 'duration': 0.20323538780212402, - 'num_run': 3, - 'configuration_origin': 'Random Search'} + "duration": 0.20323538780212402, + "num_run": 3, + "configuration_origin": "Random Search", + }, ), status=None, origin=None, ) run_history._add( RunKey( - config_id=6, - instance_id='{"task_id": "breast_cancer"}', - seed=1, - budget=6.0 + config_id=6, instance_id='{"task_id": "breast_cancer"}', seed=1, budget=6.0 ), RunValue( - cost=2*0.11347517730496459, - time=2*0.21858787536621094, + cost=2 * 0.11347517730496459, + time=2 * 0.21858787536621094, status=None, starttime=time.time(), endtime=time.time(), additional_info={ - 'duration': 0.20323538780212402, - 'num_run': 6, - 'configuration_origin': 'Random Search'} + "duration": 0.20323538780212402, + "num_run": 6, + "configuration_origin": "Random Search", + }, ), status=None, origin=None, @@ -118,13 +121,13 @@ def testRead(ensemble_backend): filename = os.path.join( ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy" + ".auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy", ) assert ensbuilder.read_losses[filename]["ens_loss"] == 0.5 filename = os.path.join( ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy" + ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy", ) assert ensbuilder.read_losses[filename]["ens_loss"] == 0.0 @@ -132,13 +135,13 @@ def testRead(ensemble_backend): @pytest.mark.parametrize( "ensemble_nbest,max_models_on_disc,exp", ( - (1, None, 1), - (1.0, None, 2), - (0.1, None, 1), - (0.9, None, 1), - (1, 2, 1), - (2, 1, 1), - ) + (1, None, 1), + (1.0, None, 2), + (0.1, None, 1), + (0.9, None, 1), + (1, 2, 1), + (2, 1, 1), + ), ) def testNBest(ensemble_backend, ensemble_nbest, max_models_on_disc, exp): ensbuilder = EnsembleBuilder( @@ -158,26 +161,29 @@ def testNBest(ensemble_backend, ensemble_nbest, max_models_on_disc, exp): fixture = os.path.join( ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy" + ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy", ) assert sel_keys[0] == fixture -@pytest.mark.parametrize("test_case,exp", [ - # If None, no reduction - (None, 2), - # If Int, limit only on exceed - (4, 2), - (1, 1), - # If Float, translate float to # models. - # below, mock of each file is 100 Mb and 4 files .model and .npy (test/val/pred) exist - # per run (except for run3, there they are 5). Now, it takes 500MB for run 3 and - # another 500 MB of slack because we keep as much space as the largest model - # available as slack - (1499.0, 1), - (1500.0, 2), - (9999.0, 2), -]) +@pytest.mark.parametrize( + "test_case,exp", + [ + # If None, no reduction + (None, 2), + # If Int, limit only on exceed + (4, 2), + (1, 1), + # If Float, translate float to # models. + # below, mock of each file is 100 Mb and 4 files .model and .npy (test/val/pred) + # per run (except for run3, there they are 5). Now, it takes 500MB for run 3 and + # another 500 MB of slack because we keep as much space as the largest model + # available as slack + (1499.0, 1), + (1500.0, 2), + (9999.0, 2), + ], +) def testMaxModelsOnDisc(ensemble_backend, test_case, exp): ensemble_nbest = 4 ensbuilder = EnsembleBuilder( @@ -190,8 +196,8 @@ def testMaxModelsOnDisc(ensemble_backend, test_case, exp): max_models_on_disc=test_case, ) - with unittest.mock.patch('os.path.getsize') as mock: - mock.return_value = 100*1024*1024 + with unittest.mock.patch("os.path.getsize") as mock: + mock.return_value = 100 * 1024 * 1024 ensbuilder.compute_loss_per_model() sel_keys = ensbuilder.get_n_best_preds() assert len(sel_keys) == exp, test_case @@ -211,26 +217,26 @@ def testMaxModelsOnDisc2(ensemble_backend): ) ensbuilder.read_preds = {} for i in range(50): - ensbuilder.read_losses['pred'+str(i)] = { - 'ens_loss': -i*10, - 'num_run': i, - 'loaded': 1, + ensbuilder.read_losses["pred" + str(i)] = { + "ens_loss": -i * 10, + "num_run": i, + "loaded": 1, "seed": 1, - "disc_space_cost_mb": 50*i, + "disc_space_cost_mb": 50 * i, } - ensbuilder.read_preds['pred'+str(i)] = {Y_ENSEMBLE: True} + ensbuilder.read_preds["pred" + str(i)] = {Y_ENSEMBLE: True} sel_keys = ensbuilder.get_n_best_preds() - assert ['pred49', 'pred48', 'pred47'] == sel_keys + assert ["pred49", "pred48", "pred47"] == sel_keys # Make sure at least one model is kept alive ensbuilder.max_models_on_disc = 0.0 sel_keys = ensbuilder.get_n_best_preds() - assert ['pred49'] == sel_keys + assert ["pred49"] == sel_keys @pytest.mark.parametrize( "performance_range_threshold,exp", - ((0.0, 4), (0.1, 4), (0.3, 3), (0.5, 2), (0.6, 2), (0.8, 1), (1.0, 1), (1, 1)) + ((0.0, 4), (0.1, 4), (0.3, 3), (0.5, 2), (0.6, 2), (0.8, 1), (1.0, 1), (1, 1)), ) def testPerformanceRangeThreshold(ensemble_backend, performance_range_threshold, exp): ensbuilder = EnsembleBuilder( @@ -240,14 +246,14 @@ def testPerformanceRangeThreshold(ensemble_backend, performance_range_threshold, metric=roc_auc, seed=0, # important to find the test files ensemble_nbest=100, - performance_range_threshold=performance_range_threshold + performance_range_threshold=performance_range_threshold, ) ensbuilder.read_losses = { - 'A': {'ens_loss': -1, 'num_run': 1, 'loaded': -1, "seed": 1}, - 'B': {'ens_loss': -2, 'num_run': 2, 'loaded': -1, "seed": 1}, - 'C': {'ens_loss': -3, 'num_run': 3, 'loaded': -1, "seed": 1}, - 'D': {'ens_loss': -4, 'num_run': 4, 'loaded': -1, "seed": 1}, - 'E': {'ens_loss': -5, 'num_run': 5, 'loaded': -1, "seed": 1}, + "A": {"ens_loss": -1, "num_run": 1, "loaded": -1, "seed": 1}, + "B": {"ens_loss": -2, "num_run": 2, "loaded": -1, "seed": 1}, + "C": {"ens_loss": -3, "num_run": 3, "loaded": -1, "seed": 1}, + "D": {"ens_loss": -4, "num_run": 4, "loaded": -1, "seed": 1}, + "E": {"ens_loss": -5, "num_run": 5, "loaded": -1, "seed": 1}, } ensbuilder.read_preds = { key: {key_2: True for key_2 in (Y_ENSEMBLE, Y_VALID, Y_TEST)} @@ -261,12 +267,19 @@ def testPerformanceRangeThreshold(ensemble_backend, performance_range_threshold, @pytest.mark.parametrize( "performance_range_threshold,ensemble_nbest,exp", ( - (0.0, 1, 1), (0.0, 1.0, 4), (0.1, 2, 2), (0.3, 4, 3), - (0.5, 1, 1), (0.6, 10, 2), (0.8, 0.5, 1), (1, 1.0, 1) - ) + (0.0, 1, 1), + (0.0, 1.0, 4), + (0.1, 2, 2), + (0.3, 4, 3), + (0.5, 1, 1), + (0.6, 10, 2), + (0.8, 0.5, 1), + (1, 1.0, 1), + ), ) -def testPerformanceRangeThresholdMaxBest(ensemble_backend, performance_range_threshold, - ensemble_nbest, exp): +def testPerformanceRangeThresholdMaxBest( + ensemble_backend, performance_range_threshold, ensemble_nbest, exp +): ensbuilder = EnsembleBuilder( backend=ensemble_backend, dataset_name="TEST", @@ -278,11 +291,11 @@ def testPerformanceRangeThresholdMaxBest(ensemble_backend, performance_range_thr max_models_on_disc=None, ) ensbuilder.read_losses = { - 'A': {'ens_loss': -1, 'num_run': 1, 'loaded': -1, "seed": 1}, - 'B': {'ens_loss': -2, 'num_run': 2, 'loaded': -1, "seed": 1}, - 'C': {'ens_loss': -3, 'num_run': 3, 'loaded': -1, "seed": 1}, - 'D': {'ens_loss': -4, 'num_run': 4, 'loaded': -1, "seed": 1}, - 'E': {'ens_loss': -5, 'num_run': 5, 'loaded': -1, "seed": 1}, + "A": {"ens_loss": -1, "num_run": 1, "loaded": -1, "seed": 1}, + "B": {"ens_loss": -2, "num_run": 2, "loaded": -1, "seed": 1}, + "C": {"ens_loss": -3, "num_run": 3, "loaded": -1, "seed": 1}, + "D": {"ens_loss": -4, "num_run": 4, "loaded": -1, "seed": 1}, + "E": {"ens_loss": -5, "num_run": 5, "loaded": -1, "seed": 1}, } ensbuilder.read_preds = { key: {key_2: True for key_2 in (Y_ENSEMBLE, Y_VALID, Y_TEST)} @@ -295,13 +308,14 @@ def testPerformanceRangeThresholdMaxBest(ensemble_backend, performance_range_thr def testFallBackNBest(ensemble_backend): - ensbuilder = EnsembleBuilder(backend=ensemble_backend, - dataset_name="TEST", - task_type=BINARY_CLASSIFICATION, - metric=roc_auc, - seed=0, # important to find the test files - ensemble_nbest=1 - ) + ensbuilder = EnsembleBuilder( + backend=ensemble_backend, + dataset_name="TEST", + task_type=BINARY_CLASSIFICATION, + metric=roc_auc, + seed=0, # important to find the test files + ensemble_nbest=1, + ) ensbuilder.compute_loss_per_model() print() @@ -311,19 +325,19 @@ def testFallBackNBest(ensemble_backend): filename = os.path.join( ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy" + ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy", ) ensbuilder.read_losses[filename]["ens_loss"] = -1 filename = os.path.join( ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_3_100.0/predictions_ensemble_0_3_100.0.npy" + ".auto-sklearn/runs/0_3_100.0/predictions_ensemble_0_3_100.0.npy", ) ensbuilder.read_losses[filename]["ens_loss"] = -1 filename = os.path.join( ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy" + ".auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy", ) ensbuilder.read_losses[filename]["ens_loss"] = -1 @@ -331,7 +345,7 @@ def testFallBackNBest(ensemble_backend): fixture = os.path.join( ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy" + ".auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy", ) assert len(sel_keys) == 1 assert sel_keys[0] == fixture @@ -339,13 +353,14 @@ def testFallBackNBest(ensemble_backend): def testGetValidTestPreds(ensemble_backend): - ensbuilder = EnsembleBuilder(backend=ensemble_backend, - dataset_name="TEST", - task_type=BINARY_CLASSIFICATION, - metric=roc_auc, - seed=0, # important to find the test files - ensemble_nbest=1 - ) + ensbuilder = EnsembleBuilder( + backend=ensemble_backend, + dataset_name="TEST", + task_type=BINARY_CLASSIFICATION, + metric=roc_auc, + seed=0, # important to find the test files + ensemble_nbest=1, + ) ensbuilder.compute_loss_per_model() @@ -353,15 +368,15 @@ def testGetValidTestPreds(ensemble_backend): # different name. num_run=2 is selected when doing sorted() d1 = os.path.join( ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy" + ".auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy", ) d2 = os.path.join( ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy" + ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy", ) d3 = os.path.join( ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_3_100.0/predictions_ensemble_0_3_100.0.npy" + ".auto-sklearn/runs/0_3_100.0/predictions_ensemble_0_3_100.0.npy", ) sel_keys = ensbuilder.get_n_best_preds() @@ -371,10 +386,13 @@ def testGetValidTestPreds(ensemble_backend): # Number of read files should be three and # predictions_ensemble_0_4_0.0.npy must not be in there assert len(ensbuilder.read_preds) == 3 - assert os.path.join( + assert ( + os.path.join( ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_4_0.0/predictions_ensemble_0_4_0.0.npy" - ) not in ensbuilder.read_preds + ".auto-sklearn/runs/0_4_0.0/predictions_ensemble_0_4_0.0.npy", + ) + not in ensbuilder.read_preds + ) # not selected --> should still be None assert ensbuilder.read_preds[d1][Y_VALID] is None @@ -403,7 +421,7 @@ def testEntireEnsembleBuilder(ensemble_backend): d2 = os.path.join( ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy" + ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy", ) sel_keys = ensbuilder.get_n_best_preds() @@ -454,11 +472,13 @@ def test_main(ensemble_backend): seed=0, # important to find the test files ensemble_nbest=2, max_models_on_disc=None, - ) + ) ensbuilder.SAVE2DISC = False run_history, ensemble_nbest, _, _, _ = ensbuilder.main( - time_left=np.inf, iteration=1, return_predictions=False, + time_left=np.inf, + iteration=1, + return_predictions=False, ) assert len(ensbuilder.read_preds) == 3 @@ -473,26 +493,26 @@ def test_main(ensemble_backend): # As the data loader loads the same val/train/test # we expect 1.0 as score and all keys available expected_performance = { - 'ensemble_val_score': 1.0, - 'ensemble_test_score': 1.0, - 'ensemble_optimization_score': 1.0, + "ensemble_val_score": 1.0, + "ensemble_test_score": 1.0, + "ensemble_optimization_score": 1.0, } # Make sure that expected performance is a subset of the run history assert all(item in run_history[0].items() for item in expected_performance.items()) - assert 'Timestamp' in run_history[0] - assert isinstance(run_history[0]['Timestamp'], pd.Timestamp) + assert "Timestamp" in run_history[0] + assert isinstance(run_history[0]["Timestamp"], pd.Timestamp) assert os.path.exists( - os.path.join(ensemble_backend.internals_directory, 'ensemble_read_preds.pkl') + os.path.join(ensemble_backend.internals_directory, "ensemble_read_preds.pkl") ), os.listdir(ensemble_backend.internals_directory) assert os.path.exists( - os.path.join(ensemble_backend.internals_directory, 'ensemble_read_losses.pkl') + os.path.join(ensemble_backend.internals_directory, "ensemble_read_losses.pkl") ), os.listdir(ensemble_backend.internals_directory) def test_run_end_at(ensemble_backend): - with unittest.mock.patch('pynisher.enforce_limits') as pynisher_mock: + with unittest.mock.patch("pynisher.enforce_limits") as pynisher_mock: ensbuilder = EnsembleBuilder( backend=ensemble_backend, dataset_name="TEST", @@ -501,69 +521,74 @@ def test_run_end_at(ensemble_backend): seed=0, # important to find the test files ensemble_nbest=2, max_models_on_disc=None, - ) + ) ensbuilder.SAVE2DISC = False current_time = time.time() - ensbuilder.run(end_at=current_time + 10, iteration=1, pynisher_context='forkserver') - # 4 seconds left because: 10 seconds - 5 seconds overhead - very little overhead, + ensbuilder.run( + end_at=current_time + 10, iteration=1, pynisher_context="forkserver" + ) + # 4 seconds left because: 10 seconds - 5 seconds overhead - little overhead # but then rounded to an integer assert pynisher_mock.call_args_list[0][1]["wall_time_in_s"], 4 def testLimit(ensemble_backend): - ensbuilder = EnsembleBuilderMemMock(backend=ensemble_backend, - dataset_name="TEST", - task_type=BINARY_CLASSIFICATION, - metric=roc_auc, - seed=0, # important to find the test files - ensemble_nbest=10, - # small to trigger MemoryException - memory_limit=100, - ) + ensbuilder = EnsembleBuilderMemMock( + backend=ensemble_backend, + dataset_name="TEST", + task_type=BINARY_CLASSIFICATION, + metric=roc_auc, + seed=0, # important to find the test files + ensemble_nbest=10, + # small to trigger MemoryException + memory_limit=100, + ) ensbuilder.SAVE2DISC = False read_losses_file = os.path.join( - ensemble_backend.internals_directory, - 'ensemble_read_losses.pkl' + ensemble_backend.internals_directory, "ensemble_read_losses.pkl" ) read_preds_file = os.path.join( - ensemble_backend.internals_directory, - 'ensemble_read_preds.pkl' + ensemble_backend.internals_directory, "ensemble_read_preds.pkl" ) def mtime_mock(filename): mtimes = { - 'predictions_ensemble_0_1_0.0.npy': 0, - 'predictions_valid_0_1_0.0.npy': 0.1, - 'predictions_test_0_1_0.0.npy': 0.2, - 'predictions_ensemble_0_2_0.0.npy': 1, - 'predictions_valid_0_2_0.0.npy': 1.1, - 'predictions_test_0_2_0.0.npy': 1.2, - 'predictions_ensemble_0_3_100.0.npy': 2, - 'predictions_valid_0_3_100.0.npy': 2.1, - 'predictions_test_0_3_100.0.npy': 2.2, + "predictions_ensemble_0_1_0.0.npy": 0, + "predictions_valid_0_1_0.0.npy": 0.1, + "predictions_test_0_1_0.0.npy": 0.2, + "predictions_ensemble_0_2_0.0.npy": 1, + "predictions_valid_0_2_0.0.npy": 1.1, + "predictions_test_0_2_0.0.npy": 1.2, + "predictions_ensemble_0_3_100.0.npy": 2, + "predictions_valid_0_3_100.0.npy": 2.1, + "predictions_test_0_3_100.0.npy": 2.2, } return mtimes[os.path.split(filename)[1]] - with unittest.mock.patch('logging.getLogger') as get_logger_mock, \ - unittest.mock.patch('logging.config.dictConfig') as _, \ - unittest.mock.patch('os.path.getmtime') as mtime: + with unittest.mock.patch( + "logging.getLogger" + ) as get_logger_mock, unittest.mock.patch( + "logging.config.dictConfig" + ) as _, unittest.mock.patch( + "os.path.getmtime" + ) as mtime: logger_mock = unittest.mock.Mock() logger_mock.handlers = [] get_logger_mock.return_value = logger_mock mtime.side_effect = mtime_mock - ensbuilder.run(time_left=1000, iteration=0, pynisher_context='fork') + ensbuilder.run(time_left=1000, iteration=0, pynisher_context="fork") assert os.path.exists(read_losses_file) assert not os.path.exists(read_preds_file) assert logger_mock.warning.call_count == 1 - ensbuilder.run(time_left=1000, iteration=0, pynisher_context='fork') + ensbuilder.run(time_left=1000, iteration=0, pynisher_context="fork") assert os.path.exists(read_losses_file) assert not os.path.exists(read_preds_file) assert logger_mock.warning.call_count == 2 - ensbuilder.run(time_left=1000, iteration=0, pynisher_context='fork') + ensbuilder.run(time_left=1000, iteration=0, pynisher_context="fork") assert os.path.exists(read_losses_file) assert not os.path.exists(read_preds_file) assert logger_mock.warning.call_count == 3 @@ -571,7 +596,7 @@ def mtime_mock(filename): # it should try to reduce ensemble_nbest until it also failed at 2 assert ensbuilder.ensemble_nbest == 1 - ensbuilder.run(time_left=1000, iteration=0, pynisher_context='fork') + ensbuilder.run(time_left=1000, iteration=0, pynisher_context="fork") assert os.path.exists(read_losses_file) assert not os.path.exists(read_preds_file) assert logger_mock.warning.call_count == 4 @@ -579,9 +604,9 @@ def mtime_mock(filename): # it should next reduce the number of models to read at most assert ensbuilder.read_at_most == 1 - # And then it still runs, but basically won't do anything any more except for raising error - # messages via the logger - ensbuilder.run(time_left=1000, iteration=0, pynisher_context='fork') + # And then it still runs, but basically won't do anything any more except for + # raising error messages via the logger + ensbuilder.run(time_left=1000, iteration=0, pynisher_context="fork") assert os.path.exists(read_losses_file) assert not os.path.exists(read_preds_file) assert logger_mock.warning.call_count == 4 @@ -592,8 +617,9 @@ def mtime_mock(filename): logger_mock.error.call_args_list ) for i in range(len(logger_mock.error.call_args_list)): - assert 'Memory Exception -- Unable to further reduce' in str( - logger_mock.error.call_args_list[i]) + assert "Memory Exception -- Unable to further reduce" in str( + logger_mock.error.call_args_list[i] + ) def test_read_pickle_read_preds(ensemble_backend): @@ -610,15 +636,14 @@ def test_read_pickle_read_preds(ensemble_backend): seed=0, # important to find the test files ensemble_nbest=2, max_models_on_disc=None, - ) + ) ensbuilder.SAVE2DISC = False ensbuilder.main(time_left=np.inf, iteration=1, return_predictions=False) # Check that the memory was created ensemble_memory_file = os.path.join( - ensemble_backend.internals_directory, - 'ensemble_read_preds.pkl' + ensemble_backend.internals_directory, "ensemble_read_preds.pkl" ) assert os.path.exists(ensemble_memory_file) @@ -630,8 +655,7 @@ def test_read_pickle_read_preds(ensemble_backend): assert last_hash == ensbuilder.last_hash ensemble_memory_file = os.path.join( - ensemble_backend.internals_directory, - 'ensemble_read_losses.pkl' + ensemble_backend.internals_directory, "ensemble_read_losses.pkl" ) assert os.path.exists(ensemble_memory_file) @@ -650,21 +674,23 @@ def test_read_pickle_read_preds(ensemble_backend): seed=0, # important to find the test files ensemble_nbest=2, max_models_on_disc=None, - ) + ) compare_read_preds(ensbuilder2.read_preds, ensbuilder.read_preds) compare_read_preds(ensbuilder2.read_losses, ensbuilder.read_losses) assert ensbuilder2.last_hash == ensbuilder.last_hash @pytest.mark.parametrize("metric", [log_loss, accuracy]) -@unittest.mock.patch('os.path.exists') -def test_get_identifiers_from_run_history(exists, metric, ensemble_run_history, ensemble_backend): +@unittest.mock.patch("os.path.exists") +def test_get_identifiers_from_run_history( + exists, metric, ensemble_run_history, ensemble_backend +): exists.return_value = True ensemble = SingleBest( - metric=log_loss, - seed=1, - run_history=ensemble_run_history, - backend=ensemble_backend, + metric=log_loss, + seed=1, + run_history=ensemble_run_history, + backend=ensemble_backend, ) # Just one model @@ -682,7 +708,7 @@ def test_ensemble_builder_process_realrun(dask_client_single_worker, ensemble_ba start_time=time.time(), time_left_for_ensembles=1000, backend=ensemble_backend, - dataset_name='Test', + dataset_name="Test", task=BINARY_CLASSIFICATION, metric=MockMetric, ensemble_size=50, @@ -701,12 +727,12 @@ def test_ensemble_builder_process_realrun(dask_client_single_worker, ensemble_ba result = future.result() history, _, _, _, _ = result - assert 'ensemble_optimization_score' in history[0] - assert history[0]['ensemble_optimization_score'] == 0.9 - assert 'ensemble_val_score' in history[0] - assert history[0]['ensemble_val_score'] == 0.9 - assert 'ensemble_test_score' in history[0] - assert history[0]['ensemble_test_score'] == 0.9 + assert "ensemble_optimization_score" in history[0] + assert history[0]["ensemble_optimization_score"] == 0.9 + assert "ensemble_val_score" in history[0] + assert history[0]["ensemble_val_score"] == 0.9 + assert "ensemble_test_score" in history[0] + assert history[0]["ensemble_test_score"] == 0.9 def test_ensemble_builder_nbest_remembered( @@ -722,7 +748,7 @@ def test_ensemble_builder_nbest_remembered( start_time=time.time(), time_left_for_ensembles=1000, backend=ensemble_backend, - dataset_name='Test', + dataset_name="Test", task=MULTILABEL_CLASSIFICATION, metric=roc_auc, ensemble_size=50, @@ -740,7 +766,9 @@ def test_ensemble_builder_nbest_remembered( future = manager.futures[0] dask.distributed.wait([future]) # wait for the ensemble process to finish assert future.result() == ([], 5, None, None, None) - file_path = os.path.join(ensemble_backend.internals_directory, 'ensemble_read_preds.pkl') + file_path = os.path.join( + ensemble_backend.internals_directory, "ensemble_read_preds.pkl" + ) assert not os.path.exists(file_path) manager.build_ensemble(dask_client_single_worker, unit_test=True) diff --git a/test/test_ensemble_builder/test_ensemble_selection.py b/test/test_ensemble_builder/test_ensemble_selection.py index c03060c037..44e00229fb 100644 --- a/test/test_ensemble_builder/test_ensemble_selection.py +++ b/test/test_ensemble_builder/test_ensemble_selection.py @@ -1,5 +1,4 @@ import numpy as np - import pytest from autosklearn.constants import BINARY_CLASSIFICATION, REGRESSION @@ -12,10 +11,12 @@ def testEnsembleSelection(): Makes sure ensemble selection fit method creates an ensemble correctly """ - ensemble = EnsembleSelection(ensemble_size=10, - task_type=REGRESSION, - random_state=0, - metric=root_mean_squared_error) + ensemble = EnsembleSelection( + ensemble_size=10, + task_type=REGRESSION, + random_state=0, + metric=root_mean_squared_error, + ) # We create a problem such that we encourage the addition of members to the ensemble # Fundamentally, the average of 10 sequential number is 5.5 @@ -23,24 +24,57 @@ def testEnsembleSelection(): predictions = [] for i in range(1, 20): pred = np.full((100), i, dtype=np.float32) - pred[i*5:5*(i+1)] = 5.5 * i + pred[i * 5 : 5 * (i + 1)] = 5.5 * i predictions.append(pred) ensemble.fit(predictions, y_true, identifiers=[(i, i, i) for i in range(20)]) - np.testing.assert_array_equal(ensemble.weights_, - np.array([0.1, 0.2, 0.2, 0.1, 0.1, 0.1, 0.1, 0.1, - 0., 0., 0., 0., 0., 0., 0., 0., - 0., 0., 0.])) + np.testing.assert_array_equal( + ensemble.weights_, + np.array( + [ + 0.1, + 0.2, + 0.2, + 0.1, + 0.1, + 0.1, + 0.1, + 0.1, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + ] + ), + ) assert ensemble.identifiers_ == [(i, i, i) for i in range(20)] - np.testing.assert_array_almost_equal(np.array(ensemble.trajectory_), - np.array([3.462296925452813, 2.679202306657711, - 2.2748626436960375, 2.065717187806695, - 1.7874562615598728, 1.6983448128441783, - 1.559451106330085, 1.5316326052614575, - 1.3801950121782542, 1.3554980575295374])) + np.testing.assert_array_almost_equal( + np.array(ensemble.trajectory_), + np.array( + [ + 3.462296925452813, + 2.679202306657711, + 2.2748626436960375, + 2.065717187806695, + 1.7874562615598728, + 1.6983448128441783, + 1.559451106330085, + 1.5316326052614575, + 1.3801950121782542, + 1.3554980575295374, + ] + ), + ) def testPredict(): @@ -54,52 +88,38 @@ def testPredict(): # we first exclude all occurrences of zero in self.weights_, and then # apply the weights. # If none of the above is the case, predict() raises Error. - ensemble = EnsembleSelection(ensemble_size=3, - task_type=BINARY_CLASSIFICATION, - random_state=0, - metric=accuracy, - ) + ensemble = EnsembleSelection( + ensemble_size=3, + task_type=BINARY_CLASSIFICATION, + random_state=0, + metric=accuracy, + ) # Test for case 1. Create (3, 2, 2) predictions. - per_model_pred = np.array([ - [[0.9, 0.1], - [0.4, 0.6]], - [[0.8, 0.2], - [0.3, 0.7]], - [[1.0, 0.0], - [0.1, 0.9]] - ]) + per_model_pred = np.array( + [[[0.9, 0.1], [0.4, 0.6]], [[0.8, 0.2], [0.3, 0.7]], [[1.0, 0.0], [0.1, 0.9]]] + ) # Weights of 3 hypothetical models ensemble.weights_ = [0.7, 0.2, 0.1] pred = ensemble.predict(per_model_pred) - truth = np.array([[0.89, 0.11], # This should be the true prediction. - [0.35, 0.65]]) + truth = np.array( + [[0.89, 0.11], [0.35, 0.65]] # This should be the true prediction. + ) assert np.allclose(pred, truth) # Test for case 2. - per_model_pred = np.array([ - [[0.9, 0.1], - [0.4, 0.6]], - [[0.8, 0.2], - [0.3, 0.7]], - [[1.0, 0.0], - [0.1, 0.9]] - ]) + per_model_pred = np.array( + [[[0.9, 0.1], [0.4, 0.6]], [[0.8, 0.2], [0.3, 0.7]], [[1.0, 0.0], [0.1, 0.9]]] + ) # The third model now has weight of zero. ensemble.weights_ = [0.7, 0.2, 0.0, 0.1] pred = ensemble.predict(per_model_pred) - truth = np.array([[0.89, 0.11], - [0.35, 0.65]]) + truth = np.array([[0.89, 0.11], [0.35, 0.65]]) assert np.allclose(pred, truth) # Test for error case. - per_model_pred = np.array([ - [[0.9, 0.1], - [0.4, 0.6]], - [[0.8, 0.2], - [0.3, 0.7]], - [[1.0, 0.0], - [0.1, 0.9]] - ]) + per_model_pred = np.array( + [[[0.9, 0.1], [0.4, 0.6]], [[0.8, 0.2], [0.3, 0.7]], [[1.0, 0.0], [0.1, 0.9]]] + ) # Now the weights have 2 zero weights and 2 non-zero weights, # which is incompatible. ensemble.weights_ = [0.6, 0.0, 0.0, 0.4] diff --git a/test/test_evaluation/__init__.py b/test/test_evaluation/__init__.py index cc3cd7becd..e298f0f075 100644 --- a/test/test_evaluation/__init__.py +++ b/test/test_evaluation/__init__.py @@ -1,2 +1,2 @@ # -*- encoding: utf-8 -*- -__author__ = 'feurerm' +__author__ = "feurerm" diff --git a/test/test_evaluation/evaluation_util.py b/test/test_evaluation/evaluation_util.py index e8ba4edf07..d8bf017c35 100644 --- a/test/test_evaluation/evaluation_util.py +++ b/test/test_evaluation/evaluation_util.py @@ -1,28 +1,53 @@ import functools -import traceback import tempfile +import traceback import unittest import numpy as np -from numpy.linalg import LinAlgError import sklearn.datasets -from sklearn import preprocessing import sklearn.model_selection +from numpy.linalg import LinAlgError +from sklearn import preprocessing from autosklearn.automl_common.common.utils.backend import Backend - -from autosklearn.constants import \ - MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION, BINARY_CLASSIFICATION, REGRESSION -from autosklearn.util.data import convert_to_bin +from autosklearn.constants import ( + BINARY_CLASSIFICATION, + MULTICLASS_CLASSIFICATION, + MULTILABEL_CLASSIFICATION, + REGRESSION, +) from autosklearn.data.xy_data_manager import XYDataManager +from autosklearn.metrics import ( + accuracy, + balanced_accuracy, + f1_macro, + f1_micro, + f1_weighted, + log_loss, + precision_macro, + precision_micro, + precision_weighted, + recall_macro, + recall_micro, + recall_weighted, +) from autosklearn.pipeline.util import get_dataset -from autosklearn.metrics import accuracy, balanced_accuracy, f1_macro, f1_micro, f1_weighted, \ - log_loss, precision_macro, precision_micro, precision_weighted, recall_macro, \ - recall_micro, recall_weighted +from autosklearn.util.data import convert_to_bin -SCORER_LIST = [accuracy, balanced_accuracy, f1_macro, f1_micro, f1_weighted, log_loss, - precision_macro, precision_micro, precision_weighted, recall_macro, - recall_micro, recall_weighted] +SCORER_LIST = [ + accuracy, + balanced_accuracy, + f1_macro, + f1_micro, + f1_weighted, + log_loss, + precision_macro, + precision_micro, + precision_weighted, + recall_macro, + recall_micro, + recall_weighted, +] N_TEST_RUNS = 5 @@ -32,14 +57,16 @@ def get_evaluation_backend(): backend_mock.temporary_directory = tempfile.gettempdir() # Assign a default data - backend_mock.load_datamanager.return_value = get_multiclass_classification_datamanager() + backend_mock.load_datamanager.return_value = ( + get_multiclass_classification_datamanager() + ) return backend_mock class Dummy(object): def __init__(self): - self.name = 'Dummy' + self.name = "Dummy" class BaseEvaluatorTest(unittest.TestCase): @@ -61,82 +88,85 @@ def __fit(self, function_handle): function_handle() return True except KeyError as e: - if 'Floating-point under-/overflow occurred at epoch' in \ - e.args[0] or \ - 'removed all features' in e.args[0] or \ - 'failed to create intent' in e.args[0]: + if ( + "Floating-point under-/overflow occurred at epoch" in e.args[0] + or "removed all features" in e.args[0] + or "failed to create intent" in e.args[0] + ): pass else: traceback.print_exc() raise e except ValueError as e: - if 'Floating-point under-/overflow occurred at epoch' in e.args[ - 0] or \ - 'removed all features' in e.args[0] or \ - 'failed to create intent' in e.args[0]: + if ( + "Floating-point under-/overflow occurred at epoch" in e.args[0] + or "removed all features" in e.args[0] + or "failed to create intent" in e.args[0] + ): pass else: raise e except LinAlgError as e: - if 'not positive definite, even with jitter' in e.args[0]: + if "not positive definite, even with jitter" in e.args[0]: pass else: raise e except RuntimeWarning as e: - if 'invalid value encountered in sqrt' in e.args[0]: + if "invalid value encountered in sqrt" in e.args[0]: pass - elif 'divide by zero encountered in divide' in e.args[0]: + elif "divide by zero encountered in divide" in e.args[0]: pass else: raise e except UserWarning as e: - if 'FastICA did not converge' in e.args[0]: + if "FastICA did not converge" in e.args[0]: pass else: raise e def get_multiclass_classification_datamanager(): - X_train, Y_train, X_test, Y_test = get_dataset('iris') + X_train, Y_train, X_test, Y_test = get_dataset("iris") indices = list(range(X_train.shape[0])) np.random.seed(1) np.random.shuffle(indices) X_train = X_train[indices] Y_train = Y_train[indices] - X_valid = X_test[:25, ] - Y_valid = Y_test[:25, ] - X_test = X_test[25:, ] - Y_test = Y_test[25:, ] + X_valid = X_test[ + :25, + ] + Y_valid = Y_test[ + :25, + ] + X_test = X_test[ + 25:, + ] + Y_test = Y_test[ + 25:, + ] D = Dummy() - D.info = { - 'task': MULTICLASS_CLASSIFICATION, - 'is_sparse': False, - 'label_num': 3 - } + D.info = {"task": MULTICLASS_CLASSIFICATION, "is_sparse": False, "label_num": 3} D.data = { - 'X_train': X_train, - 'Y_train': Y_train, - 'X_valid': X_valid, - 'Y_valid': Y_valid, - 'X_test': X_test, - 'Y_test': Y_test + "X_train": X_train, + "Y_train": Y_train, + "X_valid": X_valid, + "Y_valid": Y_valid, + "X_test": X_test, + "Y_test": Y_test, } - D.feat_type = {0: 'numerical', - 1: 'Numerical', - 2: 'numerical', - 3: 'numerical'} + D.feat_type = {0: "numerical", 1: "Numerical", 2: "numerical", 3: "numerical"} return D def get_abalone_datamanager(): # https://www.openml.org/d/183 - dataset_name = 'abalone' + dataset_name = "abalone" data = sklearn.datasets.fetch_openml(data_id=183, as_frame=True) feat_type = { - i: 'Categorical' if x.name == 'category' else 'Numerical' - for i, x in enumerate(data['data'].dtypes) + i: "Categorical" if x.name == "category" else "Numerical" + for i, x in enumerate(data["data"].dtypes) } X, y = sklearn.datasets.fetch_openml(data_id=183, return_X_y=True, as_frame=False) y = preprocessing.LabelEncoder().fit_transform(y) @@ -145,17 +175,19 @@ def get_abalone_datamanager(): ) D = XYDataManager( - X_train, y_train, - X_test, y_test, + X_train, + y_train, + X_test, + y_test, MULTICLASS_CLASSIFICATION, feat_type, - dataset_name + dataset_name, ) return D def get_multilabel_classification_datamanager(): - X_train, Y_train, X_test, Y_test = get_dataset('iris') + X_train, Y_train, X_test, Y_test = get_dataset("iris") indices = list(range(X_train.shape[0])) np.random.seed(1) np.random.shuffle(indices) @@ -171,34 +203,35 @@ def get_multilabel_classification_datamanager(): # Y_test_[:, Y_test[i]] = 1 # Y_test = Y_test_ - X_valid = X_test[:25, ] - Y_valid = Y_test[:25, ] - X_test = X_test[25:, ] - Y_test = Y_test[25:, ] + X_valid = X_test[ + :25, + ] + Y_valid = Y_test[ + :25, + ] + X_test = X_test[ + 25:, + ] + Y_test = Y_test[ + 25:, + ] D = Dummy() - D.info = { - 'task': MULTILABEL_CLASSIFICATION, - 'is_sparse': False, - 'label_num': 3 - } + D.info = {"task": MULTILABEL_CLASSIFICATION, "is_sparse": False, "label_num": 3} D.data = { - 'X_train': X_train, - 'Y_train': Y_train, - 'X_valid': X_valid, - 'Y_valid': Y_valid, - 'X_test': X_test, - 'Y_test': Y_test + "X_train": X_train, + "Y_train": Y_train, + "X_valid": X_valid, + "Y_valid": Y_valid, + "X_test": X_test, + "Y_test": Y_test, } - D.feat_type = {0: 'numerical', - 1: 'Numerical', - 2: 'numerical', - 3: 'numerical'} + D.feat_type = {0: "numerical", 1: "Numerical", 2: "numerical", 3: "numerical"} return D def get_binary_classification_datamanager(): - X_train, Y_train, X_test, Y_test = get_dataset('iris') + X_train, Y_train, X_test, Y_test = get_dataset("iris") indices = list(range(X_train.shape[0])) np.random.seed(1) np.random.shuffle(indices) @@ -213,99 +246,108 @@ def get_binary_classification_datamanager(): X_test = X_test[eliminate_class_two] Y_test = Y_test[eliminate_class_two] - X_valid = X_test[:25, ] - Y_valid = Y_test[:25, ] - X_test = X_test[25:, ] - Y_test = Y_test[25:, ] + X_valid = X_test[ + :25, + ] + Y_valid = Y_test[ + :25, + ] + X_test = X_test[ + 25:, + ] + Y_test = Y_test[ + 25:, + ] D = Dummy() - D.info = { - 'task': BINARY_CLASSIFICATION, - 'is_sparse': False, - 'label_num': 2 - } + D.info = {"task": BINARY_CLASSIFICATION, "is_sparse": False, "label_num": 2} D.data = { - 'X_train': X_train, - 'Y_train': Y_train.reshape((-1, 1)), - 'X_valid': X_valid, - 'Y_valid': Y_valid.reshape((-1, 1)), - 'X_test': X_test, - 'Y_test': Y_test.reshape((-1, 1)) + "X_train": X_train, + "Y_train": Y_train.reshape((-1, 1)), + "X_valid": X_valid, + "Y_valid": Y_valid.reshape((-1, 1)), + "X_test": X_test, + "Y_test": Y_test.reshape((-1, 1)), } - D.feat_type = {0: 'numerical', - 1: 'Numerical', - 2: 'numerical', - 3: 'numerical'} + D.feat_type = {0: "numerical", 1: "Numerical", 2: "numerical", 3: "numerical"} return D def get_regression_datamanager(): - X_train, Y_train, X_test, Y_test = get_dataset('boston') + X_train, Y_train, X_test, Y_test = get_dataset("boston") indices = list(range(X_train.shape[0])) np.random.seed(1) np.random.shuffle(indices) X_train = X_train[indices] Y_train = Y_train[indices] - X_valid = X_test[:200, ] - Y_valid = Y_test[:200, ] - X_test = X_test[200:, ] - Y_test = Y_test[200:, ] + X_valid = X_test[ + :200, + ] + Y_valid = Y_test[ + :200, + ] + X_test = X_test[ + 200:, + ] + Y_test = Y_test[ + 200:, + ] D = Dummy() - D.info = { - 'task': REGRESSION, - 'is_sparse': False, - 'label_num': 1 - } + D.info = {"task": REGRESSION, "is_sparse": False, "label_num": 1} D.data = { - 'X_train': X_train, - 'Y_train': Y_train.reshape((-1, 1)), - 'X_valid': X_valid, - 'Y_valid': Y_valid.reshape((-1, 1)), - 'X_test': X_test, - 'Y_test': Y_test.reshape((-1, 1)) + "X_train": X_train, + "Y_train": Y_train.reshape((-1, 1)), + "X_valid": X_valid, + "Y_valid": Y_valid.reshape((-1, 1)), + "X_test": X_test, + "Y_test": Y_test.reshape((-1, 1)), } - D.feat_type = {i: 'numerical' for i in range(X_train.shape[1])} + D.feat_type = {i: "numerical" for i in range(X_train.shape[1])} return D def get_500_classes_datamanager(): weights = ([0.002] * 475) + ([0.001] * 25) - X, Y = sklearn.datasets.make_classification(n_samples=1000, - n_features=20, - n_classes=500, - n_clusters_per_class=1, - n_informative=15, - n_redundant=5, - n_repeated=0, - weights=weights, - flip_y=0, - class_sep=1.0, - hypercube=True, - shift=None, - scale=1.0, - shuffle=True, - random_state=1) + X, Y = sklearn.datasets.make_classification( + n_samples=1000, + n_features=20, + n_classes=500, + n_clusters_per_class=1, + n_informative=15, + n_redundant=5, + n_repeated=0, + weights=weights, + flip_y=0, + class_sep=1.0, + hypercube=True, + shift=None, + scale=1.0, + shuffle=True, + random_state=1, + ) D = Dummy() - D.info = { - 'task': MULTICLASS_CLASSIFICATION, - 'is_sparse': False, - 'label_num': 500 + D.info = {"task": MULTICLASS_CLASSIFICATION, "is_sparse": False, "label_num": 500} + D.data = { + "X_train": X[:700], + "Y_train": Y[:700], + "X_valid": X[700:710], + "Y_valid": Y[700:710], + "X_test": X[710:], + "Y_test": Y[710:], } - D.data = {'X_train': X[:700], 'Y_train': Y[:700], - 'X_valid': X[700:710], 'Y_valid': Y[700:710], - 'X_test': X[710:], 'Y_test': Y[710:] - } - D.feat_type = {i: 'numerical' for i in range(20)} + D.feat_type = {i: "numerical" for i in range(20)} return D def get_dataset_getters(): - return [get_binary_classification_datamanager, - get_multiclass_classification_datamanager, - get_multilabel_classification_datamanager, - get_500_classes_datamanager, - get_abalone_datamanager, - get_regression_datamanager] + return [ + get_binary_classification_datamanager, + get_multiclass_classification_datamanager, + get_multilabel_classification_datamanager, + get_500_classes_datamanager, + get_abalone_datamanager, + get_regression_datamanager, + ] diff --git a/test/test_evaluation/test_abstract_evaluator.py b/test/test_evaluation/test_abstract_evaluator.py index f51820221b..c668a82ffd 100644 --- a/test/test_evaluation/test_abstract_evaluator.py +++ b/test/test_evaluation/test_abstract_evaluator.py @@ -3,19 +3,18 @@ import os import shutil import sys +import tempfile import unittest import unittest.mock -import tempfile import numpy as np import sklearn.dummy +from smac.tae import StatusType from autosklearn.automl_common.common.utils.backend import Backend, BackendContext - from autosklearn.evaluation.abstract_evaluator import AbstractEvaluator -from autosklearn.pipeline.components.base import _addons from autosklearn.metrics import accuracy -from smac.tae import StatusType +from autosklearn.pipeline.components.base import _addons this_directory = os.path.dirname(__file__) sys.path.append(this_directory) @@ -29,7 +28,7 @@ def setUp(self): """ Creates a backend mock """ - self.ev_path = os.path.join(this_directory, '.tmp_evaluations') + self.ev_path = os.path.join(this_directory, ".tmp_evaluations") if not os.path.exists(self.ev_path): os.mkdir(self.ev_path) dummy_model_files = [os.path.join(self.ev_path, str(n)) for n in range(100)] @@ -46,7 +45,7 @@ def setUp(self): self.port = logging.handlers.DEFAULT_TCP_LOGGING_PORT - self.working_directory = os.path.join(this_directory, '.tmp_%s' % self.id()) + self.working_directory = os.path.join(this_directory, ".tmp_%s" % self.id()) def tearDown(self): if os.path.exists(self.ev_path): @@ -56,16 +55,18 @@ def tearDown(self): pass def test_finish_up_model_predicts_NaN(self): - '''Tests by handing in predictions which contain NaNs''' + """Tests by handing in predictions which contain NaNs""" rs = np.random.RandomState(1) queue_mock = unittest.mock.Mock() - ae = AbstractEvaluator(backend=self.backend_mock, - port=self.port, - output_y_hat_optimization=False, - queue=queue_mock, metric=accuracy, - additional_components=dict(), - ) + ae = AbstractEvaluator( + backend=self.backend_mock, + port=self.port, + output_y_hat_optimization=False, + queue=queue_mock, + metric=accuracy, + additional_components=dict(), + ) ae.Y_optimization = rs.rand(33, 3) predictions_ensemble = rs.rand(33, 3) predictions_test = rs.rand(25, 3) @@ -85,9 +86,10 @@ def test_finish_up_model_predicts_NaN(self): status=StatusType.SUCCESS, ) self.assertEqual(loss, 1.0) - self.assertEqual(additional_run_info, - {'error': 'Model predictions for optimization set ' - 'contains NaNs.'}) + self.assertEqual( + additional_run_info, + {"error": "Model predictions for optimization set " "contains NaNs."}, + ) # NaNs in prediction validation predictions_ensemble[5, 2] = 0.5 @@ -104,9 +106,10 @@ def test_finish_up_model_predicts_NaN(self): status=StatusType.SUCCESS, ) self.assertEqual(loss, 1.0) - self.assertEqual(additional_run_info, - {'error': 'Model predictions for validation set ' - 'contains NaNs.'}) + self.assertEqual( + additional_run_info, + {"error": "Model predictions for validation set " "contains NaNs."}, + ) # NaNs in prediction test predictions_valid[5, 2] = 0.5 @@ -123,9 +126,10 @@ def test_finish_up_model_predicts_NaN(self): status=StatusType.SUCCESS, ) self.assertEqual(loss, 1.0) - self.assertEqual(additional_run_info, - {'error': 'Model predictions for test set contains ' - 'NaNs.'}) + self.assertEqual( + additional_run_info, + {"error": "Model predictions for test set contains " "NaNs."}, + ) self.assertEqual(self.backend_mock.save_predictions_as_npy.call_count, 0) @@ -147,12 +151,10 @@ def test_disable_file_output(self): predictions_test = rs.rand(25, 3) predictions_valid = rs.rand(25, 3) - loss_, additional_run_info_ = ( - ae.file_output( - predictions_ensemble, - predictions_valid, - predictions_test, - ) + loss_, additional_run_info_ = ae.file_output( + predictions_ensemble, + predictions_valid, + predictions_test, ) self.assertIsNone(loss_) @@ -160,7 +162,7 @@ def test_disable_file_output(self): # This function is never called as there is a return before self.assertEqual(self.backend_mock.save_numrun_to_dir.call_count, 0) - for call_count, disable in enumerate(['model', 'cv_model'], start=1): + for call_count, disable in enumerate(["model", "cv_model"], start=1): ae = AbstractEvaluator( backend=self.backend_mock, output_y_hat_optimization=False, @@ -174,38 +176,49 @@ def test_disable_file_output(self): ae.model = unittest.mock.Mock() ae.models = [unittest.mock.Mock()] - loss_, additional_run_info_ = ( - ae.file_output( - predictions_ensemble, - predictions_valid, - predictions_test, - ) + loss_, additional_run_info_ = ae.file_output( + predictions_ensemble, + predictions_valid, + predictions_test, ) self.assertIsNone(loss_) self.assertEqual(additional_run_info_, {}) - self.assertEqual(self.backend_mock.save_numrun_to_dir.call_count, call_count) - if disable == 'model': + self.assertEqual( + self.backend_mock.save_numrun_to_dir.call_count, call_count + ) + if disable == "model": self.assertIsNone( - self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]['model']) + self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]["model"] + ) self.assertIsNotNone( - self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]['cv_model']) + self.backend_mock.save_numrun_to_dir.call_args_list[-1][1][ + "cv_model" + ] + ) else: self.assertIsNotNone( - self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]['model']) + self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]["model"] + ) self.assertIsNone( - self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]['cv_model']) + self.backend_mock.save_numrun_to_dir.call_args_list[-1][1][ + "cv_model" + ] + ) self.assertIsNotNone( self.backend_mock.save_numrun_to_dir.call_args_list[-1][1][ - 'ensemble_predictions'] + "ensemble_predictions" + ] ) self.assertIsNotNone( self.backend_mock.save_numrun_to_dir.call_args_list[-1][1][ - 'valid_predictions'] + "valid_predictions" + ] ) self.assertIsNotNone( self.backend_mock.save_numrun_to_dir.call_args_list[-1][1][ - 'test_predictions'] + "test_predictions" + ] ) ae = AbstractEvaluator( @@ -213,20 +226,18 @@ def test_disable_file_output(self): output_y_hat_optimization=False, queue=queue_mock, metric=accuracy, - disable_file_output=['y_optimization'], + disable_file_output=["y_optimization"], port=self.port, additional_components=dict(), ) ae.Y_optimization = predictions_ensemble - ae.model = 'model' + ae.model = "model" ae.models = [unittest.mock.Mock()] - loss_, additional_run_info_ = ( - ae.file_output( - predictions_ensemble, - predictions_valid, - predictions_test, - ) + loss_, additional_run_info_ = ae.file_output( + predictions_ensemble, + predictions_valid, + predictions_test, ) self.assertIsNone(loss_) @@ -234,15 +245,18 @@ def test_disable_file_output(self): self.assertIsNone( self.backend_mock.save_numrun_to_dir.call_args_list[-1][1][ - 'ensemble_predictions'] + "ensemble_predictions" + ] ) self.assertIsNotNone( self.backend_mock.save_numrun_to_dir.call_args_list[-1][1][ - 'valid_predictions'] + "valid_predictions" + ] ) self.assertIsNotNone( self.backend_mock.save_numrun_to_dir.call_args_list[-1][1][ - 'test_predictions'] + "test_predictions" + ] ) def test_file_output(self): @@ -252,14 +266,18 @@ def test_file_output(self): queue_mock = unittest.mock.Mock() context = BackendContext( - temporary_directory=os.path.join(self.working_directory, 'tmp'), - output_directory=os.path.join(self.working_directory, 'tmp_output'), + temporary_directory=os.path.join(self.working_directory, "tmp"), + output_directory=os.path.join(self.working_directory, "tmp_output"), delete_tmp_folder_after_terminate=True, delete_output_folder_after_terminate=True, - prefix="auto-sklearn" + prefix="auto-sklearn", ) - with unittest.mock.patch.object(Backend, 'load_datamanager') as load_datamanager_mock: - load_datamanager_mock.return_value = get_multiclass_classification_datamanager() + with unittest.mock.patch.object( + Backend, "load_datamanager" + ) as load_datamanager_mock: + load_datamanager_mock.return_value = ( + get_multiclass_classification_datamanager() + ) backend = Backend(context, prefix="auto-sklearn") @@ -285,8 +303,17 @@ def test_file_output(self): Y_test_pred=predictions_test, ) - self.assertTrue(os.path.exists(os.path.join(self.working_directory, 'tmp', - '.auto-sklearn', 'runs', '1_0_None'))) + self.assertTrue( + os.path.exists( + os.path.join( + self.working_directory, + "tmp", + ".auto-sklearn", + "runs", + "1_0_None", + ) + ) + ) shutil.rmtree(self.working_directory, ignore_errors=True) @@ -297,26 +324,34 @@ def test_add_additional_components(self): queue_mock = unittest.mock.Mock() context = BackendContext( - temporary_directory=os.path.join(self.working_directory, 'tmp'), - output_directory=os.path.join(self.working_directory, 'tmp_output'), + temporary_directory=os.path.join(self.working_directory, "tmp"), + output_directory=os.path.join(self.working_directory, "tmp_output"), delete_tmp_folder_after_terminate=True, delete_output_folder_after_terminate=True, - prefix="auto-sklearn" + prefix="auto-sklearn", ) - with unittest.mock.patch.object(Backend, 'load_datamanager') as load_datamanager_mock: - load_datamanager_mock.return_value = get_multiclass_classification_datamanager() + with unittest.mock.patch.object( + Backend, "load_datamanager" + ) as load_datamanager_mock: + load_datamanager_mock.return_value = ( + get_multiclass_classification_datamanager() + ) backend = Backend(context, prefix="auto-sklearn") - with unittest.mock.patch.object(_addons['classification'], 'add_component') as _: + with unittest.mock.patch.object( + _addons["classification"], "add_component" + ) as _: - # If the components in the argument `additional_components` are an empty dict - # there is no call to `add_component`, if there's something in it, `add_component - # is called (2nd case) - for fixture, case in ((0, dict()), (1, dict(abc='def'))): + # If the components in the argument `additional_components` are an + # empty dict there is no call to `add_component`, + # if there's something in it, `add_component is called (2nd case) + for fixture, case in ((0, dict()), (1, dict(abc="def"))): thirdparty_components_patch = unittest.mock.Mock() thirdparty_components_patch.components = case - additional_components = dict(classification=thirdparty_components_patch) + additional_components = dict( + classification=thirdparty_components_patch + ) AbstractEvaluator( backend=backend, output_y_hat_optimization=False, @@ -325,4 +360,6 @@ def test_add_additional_components(self): port=self.port, additional_components=additional_components, ) - self.assertEqual(_addons['classification'].add_component.call_count, fixture) + self.assertEqual( + _addons["classification"].add_component.call_count, fixture + ) diff --git a/test/test_evaluation/test_custom_splitters.py b/test/test_evaluation/test_custom_splitters.py index 4922442228..64f9dc2f18 100644 --- a/test/test_evaluation/test_custom_splitters.py +++ b/test/test_evaluation/test_custom_splitters.py @@ -1,37 +1,44 @@ -import pytest - import numpy as np +import pytest -from autosklearn.evaluation.splitter import CustomStratifiedShuffleSplit from autosklearn.constants import ( - BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION + BINARY_CLASSIFICATION, + MULTICLASS_CLASSIFICATION, + MULTILABEL_CLASSIFICATION, ) +from autosklearn.evaluation.splitter import CustomStratifiedShuffleSplit -@pytest.mark.parametrize("task, X, y", [ - ( - BINARY_CLASSIFICATION, - np.asarray(10000 * [[1, 1, 1, 1, 1]]), - np.asarray(9999 * [0] + 1 * [1]) - ), - ( - MULTICLASS_CLASSIFICATION, - np.asarray(10000 * [[1, 1, 1, 1, 1]]), - np.asarray(4999 * [1] + 4999 * [2] + 1 * [3] + 1 * [4])), - ( - MULTILABEL_CLASSIFICATION, - np.asarray(10000 * [[1, 1, 1, 1, 1]]), - np.asarray(4999 * [[0, 1, 1]] + 4999 * [[1, 1, 0]] + 1 * [[1, 0, 1]] + 1 * [[0, 0, 0]]) - ) -]) -@pytest.mark.parametrize('train_size', [100, 0.5, 200, 0.75]) +@pytest.mark.parametrize( + "task, X, y", + [ + ( + BINARY_CLASSIFICATION, + np.asarray(10000 * [[1, 1, 1, 1, 1]]), + np.asarray(9999 * [0] + 1 * [1]), + ), + ( + MULTICLASS_CLASSIFICATION, + np.asarray(10000 * [[1, 1, 1, 1, 1]]), + np.asarray(4999 * [1] + 4999 * [2] + 1 * [3] + 1 * [4]), + ), + ( + MULTILABEL_CLASSIFICATION, + np.asarray(10000 * [[1, 1, 1, 1, 1]]), + np.asarray( + 4999 * [[0, 1, 1]] + + 4999 * [[1, 1, 0]] + + 1 * [[1, 0, 1]] + + 1 * [[0, 0, 0]] + ), + ), + ], +) +@pytest.mark.parametrize("train_size", [100, 0.5, 200, 0.75]) def test_custom_stratified_shuffle_split_returns_unique_labels_and_maintains_size( task, X, y, train_size ): - splitter = CustomStratifiedShuffleSplit( - train_size=train_size, - random_state=1 - ) + splitter = CustomStratifiedShuffleSplit(train_size=train_size, random_state=1) left_idxs, _ = next(splitter.split(X=X, y=y)) y_sampled = y[left_idxs] X_sampled = X[left_idxs] @@ -46,5 +53,6 @@ def test_custom_stratified_shuffle_split_returns_unique_labels_and_maintains_siz assert len(X_sampled) == n_samples # Assert all the unique labels are present in the training set - assert all(label in np.unique(y_sampled) for label in np.unique(y)), \ - f"{task} failed, {np.unique(y)} != {np.unique(y_sampled)}" + assert all( + label in np.unique(y_sampled) for label in np.unique(y) + ), f"{task} failed, {np.unique(y)} != {np.unique(y_sampled)}" diff --git a/test/test_evaluation/test_dummy_pipelines.py b/test/test_evaluation/test_dummy_pipelines.py index ed7c499711..3d5f1d0f59 100644 --- a/test/test_evaluation/test_dummy_pipelines.py +++ b/test/test_evaluation/test_dummy_pipelines.py @@ -1,20 +1,21 @@ import numpy as np - import pytest - from sklearn.base import clone from sklearn.datasets import make_classification, make_regression from sklearn.utils.validation import check_is_fitted -from autosklearn.evaluation.abstract_evaluator import MyDummyClassifier, MyDummyRegressor +from autosklearn.evaluation.abstract_evaluator import ( + MyDummyClassifier, + MyDummyRegressor, +) -@pytest.mark.parametrize("task_type", ['classification', 'regression']) +@pytest.mark.parametrize("task_type", ["classification", "regression"]) def test_dummy_pipeline(task_type): - if task_type == 'classification': + if task_type == "classification": estimator_class = MyDummyClassifier data_maker = make_classification - elif task_type == 'regression': + elif task_type == "regression": estimator_class = MyDummyRegressor data_maker = make_regression else: diff --git a/test/test_evaluation/test_evaluation.py b/test/test_evaluation/test_evaluation.py index 77f6e5c4bf..67d9e0ca8b 100644 --- a/test/test_evaluation/test_evaluation.py +++ b/test/test_evaluation/test_evaluation.py @@ -1,6 +1,6 @@ -import os import logging import logging.handlers +import os import shutil import sys import time @@ -19,20 +19,21 @@ this_directory = os.path.dirname(__file__) sys.path.append(this_directory) -from evaluation_util import get_multiclass_classification_datamanager, get_evaluation_backend # noqa E402 +from evaluation_util import ( # noqa E402 + get_evaluation_backend, + get_multiclass_classification_datamanager, +) def safe_eval_success_mock(*args, **kwargs): - queue = kwargs['queue'] - queue.put({'status': StatusType.SUCCESS, - 'loss': 0.5, - 'additional_run_info': ''}) + queue = kwargs["queue"] + queue.put({"status": StatusType.SUCCESS, "loss": 0.5, "additional_run_info": ""}) class EvaluationTest(unittest.TestCase): def setUp(self): self.datamanager = get_multiclass_classification_datamanager() - self.tmp = os.path.join(os.getcwd(), '.test_evaluation') + self.tmp = os.path.join(os.getcwd(), ".test_evaluation") self.logger = logging.getLogger() scenario_mock = unittest.mock.Mock() scenario_mock.wallclock_limit = 10 @@ -72,142 +73,211 @@ def test_pynisher_timeout(self): def run_over_time(): time.sleep(2) - safe_eval = pynisher.enforce_limits(wall_time_in_s=1, - grace_period_in_s=0)(run_over_time) + safe_eval = pynisher.enforce_limits(wall_time_in_s=1, grace_period_in_s=0)( + run_over_time + ) safe_eval() self.assertEqual(safe_eval.exit_status, pynisher.TimeoutException) ############################################################################ # Test ExecuteTaFuncWithQueue.run_wrapper() - @unittest.mock.patch('autosklearn.evaluation.train_evaluator.eval_holdout') + @unittest.mock.patch("autosklearn.evaluation.train_evaluator.eval_holdout") def test_eval_with_limits_holdout(self, pynisher_mock): pynisher_mock.side_effect = safe_eval_success_mock config = unittest.mock.Mock() config.config_id = 198 - ta = ExecuteTaFuncWithQueue(backend=self.backend, autosklearn_seed=1, - port=self.logger_port, - resampling_strategy='holdout', - stats=self.stats, - memory_limit=3072, - metric=accuracy, - cost_for_crash=get_cost_of_crash(accuracy), - abort_on_first_run_crash=False, - pynisher_context='fork', - ) - info = ta.run_wrapper(RunInfo(config=config, cutoff=30, instance=None, - instance_specific=None, seed=1, capped=False)) + ta = ExecuteTaFuncWithQueue( + backend=self.backend, + autosklearn_seed=1, + port=self.logger_port, + resampling_strategy="holdout", + stats=self.stats, + memory_limit=3072, + metric=accuracy, + cost_for_crash=get_cost_of_crash(accuracy), + abort_on_first_run_crash=False, + pynisher_context="fork", + ) + info = ta.run_wrapper( + RunInfo( + config=config, + cutoff=30, + instance=None, + instance_specific=None, + seed=1, + capped=False, + ) + ) self.assertEqual(info[0].config.config_id, 198) self.assertEqual(info[1].status, StatusType.SUCCESS) self.assertEqual(info[1].cost, 0.5) self.assertIsInstance(info[1].time, float) - @unittest.mock.patch('pynisher.enforce_limits') + @unittest.mock.patch("pynisher.enforce_limits") def test_zero_or_negative_cutoff(self, pynisher_mock): config = unittest.mock.Mock() config.config_id = 198 - ta = ExecuteTaFuncWithQueue(backend=self.backend, autosklearn_seed=1, - port=self.logger_port, - resampling_strategy='holdout', - stats=self.stats, - metric=accuracy, - cost_for_crash=get_cost_of_crash(accuracy), - abort_on_first_run_crash=False, - pynisher_context='forkserver', - ) + ta = ExecuteTaFuncWithQueue( + backend=self.backend, + autosklearn_seed=1, + port=self.logger_port, + resampling_strategy="holdout", + stats=self.stats, + metric=accuracy, + cost_for_crash=get_cost_of_crash(accuracy), + abort_on_first_run_crash=False, + pynisher_context="forkserver", + ) self.scenario.wallclock_limit = 5 self.stats.submitted_ta_runs += 1 - run_info, run_value = ta.run_wrapper(RunInfo(config=config, cutoff=9, instance=None, - instance_specific=None, seed=1, capped=False)) + run_info, run_value = ta.run_wrapper( + RunInfo( + config=config, + cutoff=9, + instance=None, + instance_specific=None, + seed=1, + capped=False, + ) + ) self.assertEqual(run_value.status, StatusType.STOP) - @unittest.mock.patch('pynisher.enforce_limits') + @unittest.mock.patch("pynisher.enforce_limits") def test_cutoff_lower_than_remaining_time(self, pynisher_mock): config = unittest.mock.Mock() config.config_id = 198 - ta = ExecuteTaFuncWithQueue(backend=self.backend, autosklearn_seed=1, - port=self.logger_port, - resampling_strategy='holdout', - stats=self.stats, - metric=accuracy, - cost_for_crash=get_cost_of_crash(accuracy), - abort_on_first_run_crash=False, - pynisher_context='forkserver', - ) + ta = ExecuteTaFuncWithQueue( + backend=self.backend, + autosklearn_seed=1, + port=self.logger_port, + resampling_strategy="holdout", + stats=self.stats, + metric=accuracy, + cost_for_crash=get_cost_of_crash(accuracy), + abort_on_first_run_crash=False, + pynisher_context="forkserver", + ) self.stats.ta_runs = 1 - ta.run_wrapper(RunInfo(config=config, cutoff=30, instance=None, instance_specific=None, - seed=1, capped=False)) - self.assertEqual(pynisher_mock.call_args[1]['wall_time_in_s'], 4) - self.assertIsInstance(pynisher_mock.call_args[1]['wall_time_in_s'], int) + ta.run_wrapper( + RunInfo( + config=config, + cutoff=30, + instance=None, + instance_specific=None, + seed=1, + capped=False, + ) + ) + self.assertEqual(pynisher_mock.call_args[1]["wall_time_in_s"], 4) + self.assertIsInstance(pynisher_mock.call_args[1]["wall_time_in_s"], int) - @unittest.mock.patch('autosklearn.evaluation.train_evaluator.eval_holdout') + @unittest.mock.patch("autosklearn.evaluation.train_evaluator.eval_holdout") def test_eval_with_limits_holdout_fail_silent(self, pynisher_mock): pynisher_mock.return_value = None config = unittest.mock.Mock() - config.origin = 'MOCK' + config.origin = "MOCK" config.config_id = 198 - ta = ExecuteTaFuncWithQueue(backend=self.backend, autosklearn_seed=1, - port=self.logger_port, - resampling_strategy='holdout', - stats=self.stats, - memory_limit=3072, - metric=accuracy, - cost_for_crash=get_cost_of_crash(accuracy), - abort_on_first_run_crash=False, - pynisher_context='fork', - ) + ta = ExecuteTaFuncWithQueue( + backend=self.backend, + autosklearn_seed=1, + port=self.logger_port, + resampling_strategy="holdout", + stats=self.stats, + memory_limit=3072, + metric=accuracy, + cost_for_crash=get_cost_of_crash(accuracy), + abort_on_first_run_crash=False, + pynisher_context="fork", + ) # The following should not fail because abort on first config crashed is false - info = ta.run_wrapper(RunInfo(config=config, cutoff=60, instance=None, - instance_specific=None, seed=1, capped=False)) + info = ta.run_wrapper( + RunInfo( + config=config, + cutoff=60, + instance=None, + instance_specific=None, + seed=1, + capped=False, + ) + ) self.assertEqual(info[1].status, StatusType.CRASHED) self.assertEqual(info[1].cost, 1.0) self.assertIsInstance(info[1].time, float) - self.assertEqual(info[1].additional_info, {'configuration_origin': 'MOCK', - 'error': "Result queue is empty", - 'exit_status': 0, - 'exitcode': 0, - 'subprocess_stdout': '', - 'subprocess_stderr': ''}) + self.assertEqual( + info[1].additional_info, + { + "configuration_origin": "MOCK", + "error": "Result queue is empty", + "exit_status": 0, + "exitcode": 0, + "subprocess_stdout": "", + "subprocess_stderr": "", + }, + ) self.stats.submitted_ta_runs += 1 - info = ta.run_wrapper(RunInfo(config=config, cutoff=30, instance=None, - instance_specific=None, seed=1, capped=False)) + info = ta.run_wrapper( + RunInfo( + config=config, + cutoff=30, + instance=None, + instance_specific=None, + seed=1, + capped=False, + ) + ) self.assertEqual(info[1].status, StatusType.CRASHED) self.assertEqual(info[1].cost, 1.0) self.assertIsInstance(info[1].time, float) - self.assertEqual(info[1].additional_info, {'configuration_origin': 'MOCK', - 'error': "Result queue is empty", - 'exit_status': 0, - 'exitcode': 0, - 'subprocess_stdout': '', - 'subprocess_stderr': ''}) - - @unittest.mock.patch('autosklearn.evaluation.train_evaluator.eval_holdout') + self.assertEqual( + info[1].additional_info, + { + "configuration_origin": "MOCK", + "error": "Result queue is empty", + "exit_status": 0, + "exitcode": 0, + "subprocess_stdout": "", + "subprocess_stderr": "", + }, + ) + + @unittest.mock.patch("autosklearn.evaluation.train_evaluator.eval_holdout") def test_eval_with_limits_holdout_fail_memory_error(self, pynisher_mock): pynisher_mock.side_effect = MemoryError config = unittest.mock.Mock() config.config_id = 198 - ta = ExecuteTaFuncWithQueue(backend=self.backend, autosklearn_seed=1, - port=self.logger_port, - resampling_strategy='holdout', - stats=self.stats, - memory_limit=3072, - metric=log_loss, - cost_for_crash=get_cost_of_crash(log_loss), - abort_on_first_run_crash=False, - pynisher_context='fork', - ) - info = ta.run_wrapper(RunInfo(config=config, cutoff=30, instance=None, - instance_specific=None, seed=1, capped=False)) + ta = ExecuteTaFuncWithQueue( + backend=self.backend, + autosklearn_seed=1, + port=self.logger_port, + resampling_strategy="holdout", + stats=self.stats, + memory_limit=3072, + metric=log_loss, + cost_for_crash=get_cost_of_crash(log_loss), + abort_on_first_run_crash=False, + pynisher_context="fork", + ) + info = ta.run_wrapper( + RunInfo( + config=config, + cutoff=30, + instance=None, + instance_specific=None, + seed=1, + capped=False, + ) + ) self.assertEqual(info[1].status, StatusType.MEMOUT) # For logloss, worst possible result is MAXINT worst_possible_result = MAXINT self.assertEqual(info[1].cost, worst_possible_result) self.assertIsInstance(info[1].time, float) - self.assertNotIn('exitcode', info[1].additional_info) + self.assertNotIn("exitcode", info[1].additional_info) - @unittest.mock.patch('pynisher.enforce_limits') + @unittest.mock.patch("pynisher.enforce_limits") def test_eval_with_limits_holdout_fail_timeout(self, pynisher_mock): config = unittest.mock.Mock() config.config_id = 198 @@ -218,33 +288,46 @@ def test_eval_with_limits_holdout_fail_timeout(self, pynisher_mock): pynisher_mock.return_value = m1 m2.exit_status = pynisher.TimeoutException m2.wall_clock_time = 30 - ta = ExecuteTaFuncWithQueue(backend=self.backend, autosklearn_seed=1, - port=self.logger_port, - resampling_strategy='holdout', - stats=self.stats, - memory_limit=3072, - metric=accuracy, - cost_for_crash=get_cost_of_crash(accuracy), - abort_on_first_run_crash=False, - pynisher_context='forkserver', - ) - info = ta.run_wrapper(RunInfo(config=config, cutoff=30, instance=None, - instance_specific=None, seed=1, capped=False)) + ta = ExecuteTaFuncWithQueue( + backend=self.backend, + autosklearn_seed=1, + port=self.logger_port, + resampling_strategy="holdout", + stats=self.stats, + memory_limit=3072, + metric=accuracy, + cost_for_crash=get_cost_of_crash(accuracy), + abort_on_first_run_crash=False, + pynisher_context="forkserver", + ) + info = ta.run_wrapper( + RunInfo( + config=config, + cutoff=30, + instance=None, + instance_specific=None, + seed=1, + capped=False, + ) + ) self.assertEqual(info[1].status, StatusType.TIMEOUT) self.assertEqual(info[1].cost, 1.0) self.assertIsInstance(info[1].time, float) - self.assertNotIn('exitcode', info[1].additional_info) + self.assertNotIn("exitcode", info[1].additional_info) - @unittest.mock.patch('pynisher.enforce_limits') - def test_eval_with_limits_holdout_timeout_with_results_in_queue(self, pynisher_mock): + @unittest.mock.patch("pynisher.enforce_limits") + def test_eval_with_limits_holdout_timeout_with_results_in_queue( + self, pynisher_mock + ): config = unittest.mock.Mock() config.config_id = 198 def side_effect(**kwargs): - queue = kwargs['queue'] - queue.put({'status': StatusType.SUCCESS, - 'loss': 0.5, - 'additional_run_info': {}}) + queue = kwargs["queue"] + queue.put( + {"status": StatusType.SUCCESS, "loss": 0.5, "additional_run_info": {}} + ) + m1 = unittest.mock.Mock() m2 = unittest.mock.Mock() m1.return_value = m2 @@ -254,137 +337,194 @@ def side_effect(**kwargs): m2.wall_clock_time = 30 # Test for a succesful run - ta = ExecuteTaFuncWithQueue(backend=self.backend, autosklearn_seed=1, - port=self.logger_port, - resampling_strategy='holdout', - stats=self.stats, - memory_limit=3072, - metric=accuracy, - cost_for_crash=get_cost_of_crash(accuracy), - abort_on_first_run_crash=False, - pynisher_context='forkserver', - ) - info = ta.run_wrapper(RunInfo(config=config, cutoff=30, instance=None, - instance_specific=None, seed=1, capped=False)) + ta = ExecuteTaFuncWithQueue( + backend=self.backend, + autosklearn_seed=1, + port=self.logger_port, + resampling_strategy="holdout", + stats=self.stats, + memory_limit=3072, + metric=accuracy, + cost_for_crash=get_cost_of_crash(accuracy), + abort_on_first_run_crash=False, + pynisher_context="forkserver", + ) + info = ta.run_wrapper( + RunInfo( + config=config, + cutoff=30, + instance=None, + instance_specific=None, + seed=1, + capped=False, + ) + ) self.assertEqual(info[1].status, StatusType.SUCCESS) self.assertEqual(info[1].cost, 0.5) self.assertIsInstance(info[1].time, float) - self.assertNotIn('exitcode', info[1].additional_info) + self.assertNotIn("exitcode", info[1].additional_info) # And a crashed run which is in the queue def side_effect(**kwargs): - queue = kwargs['queue'] - queue.put({'status': StatusType.CRASHED, - 'loss': 2.0, - 'additional_run_info': {}}) + queue = kwargs["queue"] + queue.put( + {"status": StatusType.CRASHED, "loss": 2.0, "additional_run_info": {}} + ) + m2.side_effect = side_effect - ta = ExecuteTaFuncWithQueue(backend=self.backend, autosklearn_seed=1, - port=self.logger_port, - resampling_strategy='holdout', - stats=self.stats, - memory_limit=3072, - metric=accuracy, - cost_for_crash=get_cost_of_crash(accuracy), - abort_on_first_run_crash=False, - pynisher_context='forkserver', - ) - info = ta.run_wrapper(RunInfo(config=config, cutoff=30, instance=None, - instance_specific=None, seed=1, capped=False)) + ta = ExecuteTaFuncWithQueue( + backend=self.backend, + autosklearn_seed=1, + port=self.logger_port, + resampling_strategy="holdout", + stats=self.stats, + memory_limit=3072, + metric=accuracy, + cost_for_crash=get_cost_of_crash(accuracy), + abort_on_first_run_crash=False, + pynisher_context="forkserver", + ) + info = ta.run_wrapper( + RunInfo( + config=config, + cutoff=30, + instance=None, + instance_specific=None, + seed=1, + capped=False, + ) + ) self.assertEqual(info[1].status, StatusType.CRASHED) self.assertEqual(info[1].cost, 1.0) self.assertIsInstance(info[1].time, float) - self.assertNotIn('exitcode', info[1].additional_info) + self.assertNotIn("exitcode", info[1].additional_info) - @unittest.mock.patch('autosklearn.evaluation.train_evaluator.eval_holdout') + @unittest.mock.patch("autosklearn.evaluation.train_evaluator.eval_holdout") def test_eval_with_limits_holdout_2(self, eval_houldout_mock): config = unittest.mock.Mock() config.config_id = 198 def side_effect(*args, **kwargs): - queue = kwargs['queue'] - queue.put({'status': StatusType.SUCCESS, - 'loss': 0.5, - 'additional_run_info': kwargs['instance']}) + queue = kwargs["queue"] + queue.put( + { + "status": StatusType.SUCCESS, + "loss": 0.5, + "additional_run_info": kwargs["instance"], + } + ) + eval_houldout_mock.side_effect = side_effect - ta = ExecuteTaFuncWithQueue(backend=self.backend, autosklearn_seed=1, - port=self.logger_port, - resampling_strategy='holdout', - stats=self.stats, - memory_limit=3072, - metric=accuracy, - cost_for_crash=get_cost_of_crash(accuracy), - abort_on_first_run_crash=False, - pynisher_context='fork', - ) + ta = ExecuteTaFuncWithQueue( + backend=self.backend, + autosklearn_seed=1, + port=self.logger_port, + resampling_strategy="holdout", + stats=self.stats, + memory_limit=3072, + metric=accuracy, + cost_for_crash=get_cost_of_crash(accuracy), + abort_on_first_run_crash=False, + pynisher_context="fork", + ) self.scenario.wallclock_limit = 180 instance = "{'subsample': 30}" - info = ta.run_wrapper(RunInfo(config=config, cutoff=30, instance=instance, - instance_specific=None, seed=1, capped=False)) + info = ta.run_wrapper( + RunInfo( + config=config, + cutoff=30, + instance=instance, + instance_specific=None, + seed=1, + capped=False, + ) + ) self.assertEqual(info[1].status, StatusType.SUCCESS) self.assertEqual(len(info[1].additional_info), 2) - self.assertIn('configuration_origin', info[1].additional_info) - self.assertEqual(info[1].additional_info['message'], "{'subsample': 30}") + self.assertIn("configuration_origin", info[1].additional_info) + self.assertEqual(info[1].additional_info["message"], "{'subsample': 30}") - @unittest.mock.patch('autosklearn.evaluation.train_evaluator.eval_holdout') + @unittest.mock.patch("autosklearn.evaluation.train_evaluator.eval_holdout") def test_exception_in_target_function(self, eval_holdout_mock): config = unittest.mock.Mock() config.config_id = 198 eval_holdout_mock.side_effect = ValueError - ta = ExecuteTaFuncWithQueue(backend=self.backend, autosklearn_seed=1, - port=self.logger_port, - resampling_strategy='holdout', - stats=self.stats, - memory_limit=3072, - metric=accuracy, - cost_for_crash=get_cost_of_crash(accuracy), - abort_on_first_run_crash=False, - pynisher_context='fork', - ) + ta = ExecuteTaFuncWithQueue( + backend=self.backend, + autosklearn_seed=1, + port=self.logger_port, + resampling_strategy="holdout", + stats=self.stats, + memory_limit=3072, + metric=accuracy, + cost_for_crash=get_cost_of_crash(accuracy), + abort_on_first_run_crash=False, + pynisher_context="fork", + ) self.stats.submitted_ta_runs += 1 - info = ta.run_wrapper(RunInfo(config=config, cutoff=30, instance=None, - instance_specific=None, seed=1, capped=False)) + info = ta.run_wrapper( + RunInfo( + config=config, + cutoff=30, + instance=None, + instance_specific=None, + seed=1, + capped=False, + ) + ) self.assertEqual(info[1].status, StatusType.CRASHED) self.assertEqual(info[1].cost, 1.0) self.assertIsInstance(info[1].time, float) - self.assertEqual(info[1].additional_info['error'], 'ValueError()') - self.assertIn('traceback', info[1].additional_info) - self.assertNotIn('exitcode', info[1].additional_info) + self.assertEqual(info[1].additional_info["error"], "ValueError()") + self.assertIn("traceback", info[1].additional_info) + self.assertNotIn("exitcode", info[1].additional_info) def test_silent_exception_in_target_function(self): config = unittest.mock.Mock() config.config_id = 198 - delattr(self.backend, 'save_targets_ensemble') - ta = ExecuteTaFuncWithQueue(backend=self.backend, - port=self.logger_port, - autosklearn_seed=1, - resampling_strategy='holdout', - stats=self.stats, - memory_limit=3072, - metric=accuracy, - cost_for_crash=get_cost_of_crash(accuracy), - abort_on_first_run_crash=False, - iterative=False, - pynisher_context='fork', - ) + delattr(self.backend, "save_targets_ensemble") + ta = ExecuteTaFuncWithQueue( + backend=self.backend, + port=self.logger_port, + autosklearn_seed=1, + resampling_strategy="holdout", + stats=self.stats, + memory_limit=3072, + metric=accuracy, + cost_for_crash=get_cost_of_crash(accuracy), + abort_on_first_run_crash=False, + iterative=False, + pynisher_context="fork", + ) ta.pynisher_logger = unittest.mock.Mock() self.stats.submitted_ta_runs += 1 - info = ta.run_wrapper(RunInfo(config=config, cutoff=3000, instance=None, - instance_specific=None, seed=1, capped=False)) - self.assertEqual(info[1].status, StatusType.CRASHED, msg=str(info[1].additional_info)) + info = ta.run_wrapper( + RunInfo( + config=config, + cutoff=3000, + instance=None, + instance_specific=None, + seed=1, + capped=False, + ) + ) + self.assertEqual( + info[1].status, StatusType.CRASHED, msg=str(info[1].additional_info) + ) self.assertEqual(info[1].cost, 1.0) self.assertIsInstance(info[1].time, float) self.assertIn( - info[1].additional_info['error'], + info[1].additional_info["error"], ( """AttributeError("'BackendMock' object has no attribute """ """'save_targets_ensemble'",)""", """AttributeError("'BackendMock' object has no attribute """ """'save_targets_ensemble'")""", - """AttributeError('save_targets_ensemble')""" - ) + """AttributeError('save_targets_ensemble')""", + ), ) - self.assertNotIn('exitcode', info[1].additional_info) - self.assertNotIn('exit_status', info[1].additional_info) - self.assertNotIn('traceback', info[1]) + self.assertNotIn("exitcode", info[1].additional_info) + self.assertNotIn("exit_status", info[1].additional_info) + self.assertNotIn("traceback", info[1]) diff --git a/test/test_evaluation/test_test_evaluator.py b/test/test_evaluation/test_test_evaluator.py index 93ea0c2265..0a1b67faa9 100644 --- a/test/test_evaluation/test_test_evaluator.py +++ b/test/test_evaluation/test_test_evaluator.py @@ -14,24 +14,26 @@ from smac.tae import StatusType from autosklearn.automl_common.common.utils.backend import Backend - -from autosklearn.constants import MULTILABEL_CLASSIFICATION, BINARY_CLASSIFICATION, \ - MULTICLASS_CLASSIFICATION, REGRESSION +from autosklearn.constants import ( + BINARY_CLASSIFICATION, + MULTICLASS_CLASSIFICATION, + MULTILABEL_CLASSIFICATION, + REGRESSION, +) from autosklearn.evaluation.test_evaluator import TestEvaluator, eval_t from autosklearn.evaluation.util import read_queue +from autosklearn.metrics import accuracy, f1_macro, r2 from autosklearn.util.pipeline import get_configuration_space -from autosklearn.metrics import accuracy, r2, f1_macro this_directory = os.path.dirname(__file__) sys.path.append(this_directory) from evaluation_util import ( # noqa (E402: module level import not at top of file) - get_evaluation_backend, - get_dataset_getters, + SCORER_LIST, BaseEvaluatorTest, + get_dataset_getters, + get_evaluation_backend, get_multiclass_classification_datamanager, - SCORER_LIST -) # noqa (E402: module level import not at top of file) - +) N_TEST_RUNS = 3 @@ -45,28 +47,31 @@ class TestEvaluator_Test(BaseEvaluatorTest, unittest.TestCase): def test_datasets(self): for getter in get_dataset_getters(): - testname = '%s_%s' % (os.path.basename(__file__). - replace('.pyc', '').replace('.py', ''), - getter.__name__) + testname = "%s_%s" % ( + os.path.basename(__file__).replace(".pyc", "").replace(".py", ""), + getter.__name__, + ) with self.subTest(testname): backend_mock = get_evaluation_backend() D = getter() D_ = copy.deepcopy(D) - y = D.data['Y_train'] + y = D.data["Y_train"] if len(y.shape) == 2 and y.shape[1] == 1: - D_.data['Y_train'] = y.flatten() + D_.data["Y_train"] = y.flatten() backend_mock.load_datamanager.return_value = D_ - metric_lookup = {MULTILABEL_CLASSIFICATION: f1_macro, - BINARY_CLASSIFICATION: accuracy, - MULTICLASS_CLASSIFICATION: accuracy, - REGRESSION: r2} + metric_lookup = { + MULTILABEL_CLASSIFICATION: f1_macro, + BINARY_CLASSIFICATION: accuracy, + MULTICLASS_CLASSIFICATION: accuracy, + REGRESSION: r2, + } queue_ = multiprocessing.Queue() evaluator = TestEvaluator( backend_mock, queue_, - metric=metric_lookup[D.info['task']], + metric=metric_lookup[D.info["task"]], port=logging.handlers.DEFAULT_TCP_LOGGING_PORT, additional_components=dict(), ) @@ -75,22 +80,21 @@ def test_datasets(self): rval = read_queue(evaluator.queue) self.assertEqual(len(rval), 1) self.assertEqual(len(rval[0]), 3) - self.assertTrue(np.isfinite(rval[0]['loss'])) + self.assertTrue(np.isfinite(rval[0]["loss"])) class FunctionsTest(unittest.TestCase): def setUp(self): self.queue = multiprocessing.Queue() self.configuration = get_configuration_space( - {'task': MULTICLASS_CLASSIFICATION, - 'is_sparse': False}).get_default_configuration() + {"task": MULTICLASS_CLASSIFICATION, "is_sparse": False} + ).get_default_configuration() self.data = get_multiclass_classification_datamanager() - self.tmp_dir = os.path.join(os.path.dirname(__file__), - '.test_cv_functions') + self.tmp_dir = os.path.join(os.path.dirname(__file__), ".test_cv_functions") self.backend = unittest.mock.Mock(spec=Backend) self.backend.temporary_directory = tempfile.gettempdir() self.backend.load_datamanager.return_value = self.data - self.dataset_name = json.dumps({'task_id': 'test'}) + self.dataset_name = json.dumps({"task_id": "test"}) self.port = logging.handlers.DEFAULT_TCP_LOGGING_PORT @@ -106,7 +110,8 @@ def test_eval_test(self): backend=self.backend, config=self.configuration, metric=accuracy, - seed=1, num_run=1, + seed=1, + num_run=1, scoring_functions=None, output_y_hat_optimization=False, include=None, @@ -118,9 +123,9 @@ def test_eval_test(self): ) rval = read_queue(self.queue) self.assertEqual(len(rval), 1) - self.assertAlmostEqual(rval[0]['loss'], 0.040000000000000036) - self.assertEqual(rval[0]['status'], StatusType.SUCCESS) - self.assertNotIn('bac_metric', rval[0]['additional_run_info']) + self.assertAlmostEqual(rval[0]["loss"], 0.040000000000000036) + self.assertEqual(rval[0]["status"], StatusType.SUCCESS) + self.assertNotIn("bac_metric", rval[0]["additional_run_info"]) def test_eval_test_all_loss_functions(self): eval_t( @@ -128,7 +133,8 @@ def test_eval_test_all_loss_functions(self): backend=self.backend, config=self.configuration, metric=accuracy, - seed=1, num_run=1, + seed=1, + num_run=1, scoring_functions=SCORER_LIST, output_y_hat_optimization=False, include=None, @@ -142,25 +148,30 @@ def test_eval_test_all_loss_functions(self): self.assertEqual(len(rval), 1) # Note: All metric here should be minimized - fixture = {'accuracy': 0.040000000000000036, - 'balanced_accuracy': 0.02777777777777779, - 'f1_macro': 0.0341005967604433, - 'f1_micro': 0.040000000000000036, - 'f1_weighted': 0.039693094629155934, - 'log_loss': 0.13966929787769913, - 'precision_macro': 0.03703703703703709, - 'precision_micro': 0.040000000000000036, - 'precision_weighted': 0.03555555555555556, - 'recall_macro': 0.02777777777777779, - 'recall_micro': 0.040000000000000036, - 'recall_weighted': 0.040000000000000036, - 'num_run': -1} - - additional_run_info = rval[0]['additional_run_info'] + fixture = { + "accuracy": 0.040000000000000036, + "balanced_accuracy": 0.02777777777777779, + "f1_macro": 0.0341005967604433, + "f1_micro": 0.040000000000000036, + "f1_weighted": 0.039693094629155934, + "log_loss": 0.13966929787769913, + "precision_macro": 0.03703703703703709, + "precision_micro": 0.040000000000000036, + "precision_weighted": 0.03555555555555556, + "recall_macro": 0.02777777777777779, + "recall_micro": 0.040000000000000036, + "recall_weighted": 0.040000000000000036, + "num_run": -1, + } + + additional_run_info = rval[0]["additional_run_info"] for key, value in fixture.items(): self.assertAlmostEqual(additional_run_info[key], fixture[key], msg=key) - self.assertEqual(len(additional_run_info), len(fixture) + 1, - msg=sorted(additional_run_info.items())) - self.assertIn('duration', additional_run_info) - self.assertAlmostEqual(rval[0]['loss'], 0.040000000000000036) - self.assertEqual(rval[0]['status'], StatusType.SUCCESS) + self.assertEqual( + len(additional_run_info), + len(fixture) + 1, + msg=sorted(additional_run_info.items()), + ) + self.assertIn("duration", additional_run_info) + self.assertAlmostEqual(rval[0]["loss"], 0.040000000000000036) + self.assertEqual(rval[0]["status"], StatusType.SUCCESS) diff --git a/test/test_evaluation/test_train_evaluator.py b/test/test_evaluation/test_train_evaluator.py index 28bddcdb09..92e3cfcc10 100644 --- a/test/test_evaluation/test_train_evaluator.py +++ b/test/test_evaluation/test_train_evaluator.py @@ -1,49 +1,73 @@ import copy import json import logging.handlers -import queue import multiprocessing import os -import tempfile +import queue import shutil import sys +import tempfile import unittest import unittest.mock -from ConfigSpace import Configuration import numpy as np -from sklearn.model_selection import GroupKFold, GroupShuffleSplit, \ - KFold, LeaveOneGroupOut, LeavePGroupsOut, LeaveOneOut, LeavePOut, \ - PredefinedSplit, RepeatedKFold, RepeatedStratifiedKFold, ShuffleSplit, \ - StratifiedKFold, StratifiedShuffleSplit, TimeSeriesSplit import sklearn.model_selection +from ConfigSpace import Configuration +from sklearn.model_selection import ( + GroupKFold, + GroupShuffleSplit, + KFold, + LeaveOneGroupOut, + LeaveOneOut, + LeavePGroupsOut, + LeavePOut, + PredefinedSplit, + RepeatedKFold, + RepeatedStratifiedKFold, + ShuffleSplit, + StratifiedKFold, + StratifiedShuffleSplit, + TimeSeriesSplit, +) from smac.tae import StatusType, TAEAbortException -from autosklearn.automl_common.common.utils import backend - import autosklearn.evaluation.splitter +from autosklearn.automl_common.common.utils import backend +from autosklearn.constants import ( + BINARY_CLASSIFICATION, + MULTICLASS_CLASSIFICATION, + MULTILABEL_CLASSIFICATION, + MULTIOUTPUT_REGRESSION, + REGRESSION, +) from autosklearn.data.abstract_data_manager import AbstractDataManager +from autosklearn.evaluation.train_evaluator import ( + TrainEvaluator, + eval_cv, + eval_holdout, + eval_iterative_holdout, + eval_partial_cv, + subsample_indices, +) from autosklearn.evaluation.util import read_queue -from autosklearn.evaluation.train_evaluator import TrainEvaluator, \ - eval_holdout, eval_iterative_holdout, eval_cv, eval_partial_cv, subsample_indices +from autosklearn.metrics import accuracy, f1_macro, r2 from autosklearn.util.pipeline import get_configuration_space -from autosklearn.constants import BINARY_CLASSIFICATION, \ - MULTILABEL_CLASSIFICATION,\ - MULTICLASS_CLASSIFICATION,\ - REGRESSION,\ - MULTIOUTPUT_REGRESSION -from autosklearn.metrics import accuracy, r2, f1_macro this_directory = os.path.dirname(__file__) sys.path.append(this_directory) -from evaluation_util import get_regression_datamanager, BaseEvaluatorTest, \ - get_binary_classification_datamanager, get_dataset_getters, \ - get_multiclass_classification_datamanager, SCORER_LIST # noqa (E402: module level import not at top of file) +from evaluation_util import ( # noqa (E402: module level import not at top of file) + SCORER_LIST, + BaseEvaluatorTest, + get_binary_classification_datamanager, + get_dataset_getters, + get_multiclass_classification_datamanager, + get_regression_datamanager, +) class Dummy(object): def __init__(self): - self.name = 'dummy' + self.name = "dummy" class TestTrainEvaluator(BaseEvaluatorTest, unittest.TestCase): @@ -54,13 +78,15 @@ def setUp(self): Creates a backend mock """ tmp_dir_name = self.id() - self.ev_path = os.path.join(this_directory, '.tmp_evaluations', tmp_dir_name) + self.ev_path = os.path.join(this_directory, ".tmp_evaluations", tmp_dir_name) if os.path.exists(self.ev_path): shutil.rmtree(self.ev_path) os.makedirs(self.ev_path, exist_ok=False) dummy_model_files = [os.path.join(self.ev_path, str(n)) for n in range(100)] dummy_pred_files = [os.path.join(self.ev_path, str(n)) for n in range(100, 200)] - dummy_cv_model_files = [os.path.join(self.ev_path, str(n)) for n in range(200, 300)] + dummy_cv_model_files = [ + os.path.join(self.ev_path, str(n)) for n in range(200, 300) + ] backend_mock = unittest.mock.Mock() backend_mock.temporary_directory = tempfile.gettempdir() backend_mock.get_model_dir.return_value = self.ev_path @@ -70,7 +96,7 @@ def setUp(self): backend_mock.get_prediction_output_path.side_effect = dummy_pred_files self.backend_mock = backend_mock - self.tmp_dir = os.path.join(self.ev_path, 'tmp_dir') + self.tmp_dir = os.path.join(self.ev_path, "tmp_dir") self.port = logging.handlers.DEFAULT_TCP_LOGGING_PORT @@ -78,15 +104,18 @@ def tearDown(self): if os.path.exists(self.ev_path): shutil.rmtree(self.ev_path) - @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') + @unittest.mock.patch( + "autosklearn.pipeline.classification.SimpleClassificationPipeline" + ) def test_holdout(self, pipeline_mock): # Binary iris, contains 69 train samples, 25 validation samples, # 6 test samples D = get_binary_classification_datamanager() - D.name = 'test' + D.name = "test" - pipeline_mock.predict_proba.side_effect = \ - lambda X, batch_size=None: np.tile([0.6, 0.4], (len(X), 1)) + pipeline_mock.predict_proba.side_effect = lambda X, batch_size=None: np.tile( + [0.6, 0.4], (len(X), 1) + ) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock pipeline_mock.get_additional_run_info.return_value = None pipeline_mock.get_max_iter.return_value = 1 @@ -96,21 +125,23 @@ def test_holdout(self, pipeline_mock): backend_api = backend.create( temporary_directory=self.tmp_dir, output_directory=None, - prefix="auto-sklearn" + prefix="auto-sklearn", ) backend_api.load_datamanager = lambda: D queue_ = multiprocessing.Queue() - evaluator = TrainEvaluator(backend_api, queue_, - configuration=configuration, - resampling_strategy='holdout', - resampling_strategy_args={'train_size': 0.66}, - scoring_functions=None, - output_y_hat_optimization=True, - metric=accuracy, - port=self.port, - additional_components=dict(), - ) + evaluator = TrainEvaluator( + backend_api, + queue_, + configuration=configuration, + resampling_strategy="holdout", + resampling_strategy_args={"train_size": 0.66}, + scoring_functions=None, + output_y_hat_optimization=True, + metric=accuracy, + port=self.port, + additional_components=dict(), + ) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) evaluator.file_output.return_value = (None, {}) @@ -118,7 +149,7 @@ def test_holdout(self, pipeline_mock): rval = read_queue(evaluator.queue) self.assertEqual(len(rval), 1) - result = rval[0]['loss'] + result = rval[0]["loss"] self.assertEqual(len(rval[0]), 3) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) @@ -129,17 +160,21 @@ def test_holdout(self, pipeline_mock): self.assertEqual(pipeline_mock.predict_proba.call_count, 4) self.assertEqual(evaluator.file_output.call_count, 1) self.assertEqual(evaluator.file_output.call_args[0][0].shape[0], 24) - self.assertEqual(evaluator.file_output.call_args[0][1].shape[0], - D.data['Y_valid'].shape[0]) - self.assertEqual(evaluator.file_output.call_args[0][2].shape[0], - D.data['Y_test'].shape[0]) + self.assertEqual( + evaluator.file_output.call_args[0][1].shape[0], D.data["Y_valid"].shape[0] + ) + self.assertEqual( + evaluator.file_output.call_args[0][2].shape[0], D.data["Y_test"].shape[0] + ) self.assertEqual(evaluator.model.fit.call_count, 1) - @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') + @unittest.mock.patch( + "autosklearn.pipeline.classification.SimpleClassificationPipeline" + ) def test_iterative_holdout(self, pipeline_mock): # Regular fitting D = get_binary_classification_datamanager() - D.name = 'test' + D.name = "test" class SideEffect(object): def __init__(self): @@ -152,55 +187,100 @@ def configuration_fully_fitted(self): # final call to iterative fit return self.fully_fitted_call_count > 18 - Xt_fixture = 'Xt_fixture' + Xt_fixture = "Xt_fixture" pipeline_mock.estimator_supports_iterative_fit.return_value = True - pipeline_mock.configuration_fully_fitted.side_effect = \ + pipeline_mock.configuration_fully_fitted.side_effect = ( SideEffect().configuration_fully_fitted + ) pipeline_mock.fit_transformer.return_value = Xt_fixture, {} - pipeline_mock.predict_proba.side_effect = \ - lambda X, batch_size=None: np.tile([0.6, 0.4], (len(X), 1)) + pipeline_mock.predict_proba.side_effect = lambda X, batch_size=None: np.tile( + [0.6, 0.4], (len(X), 1) + ) pipeline_mock.get_additional_run_info.return_value = None pipeline_mock.side_effect = lambda **kwargs: pipeline_mock pipeline_mock.get_max_iter.return_value = 512 - pipeline_mock.get_current_iter.side_effect = (2, 4, 8, 16, 32, 64, 128, 256, 512) + pipeline_mock.get_current_iter.side_effect = ( + 2, + 4, + 8, + 16, + 32, + 64, + 128, + 256, + 512, + ) configuration = unittest.mock.Mock(spec=Configuration) backend_api = backend.create( temporary_directory=self.tmp_dir, output_directory=None, - prefix="auto-sklearn" + prefix="auto-sklearn", ) backend_api.load_datamanager = lambda: D queue_ = multiprocessing.Queue() - evaluator = TrainEvaluator(backend_api, queue_, - port=self.port, - configuration=configuration, - resampling_strategy='holdout', - scoring_functions=None, - output_y_hat_optimization=True, - metric=accuracy, - budget=0.0, - additional_components=dict(),) + evaluator = TrainEvaluator( + backend_api, + queue_, + port=self.port, + configuration=configuration, + resampling_strategy="holdout", + scoring_functions=None, + output_y_hat_optimization=True, + metric=accuracy, + budget=0.0, + additional_components=dict(), + ) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) evaluator.file_output.return_value = (None, {}) class LossSideEffect(object): def __init__(self): - self.losses = [1.0, 1.0, 1.0, 1.0, - 0.9, 0.9, 0.9, 0.9, - 0.8, 0.8, 0.8, 0.8, - 0.7, 0.7, 0.7, 0.7, - 0.6, 0.6, 0.6, 0.6, - 0.5, 0.5, 0.5, 0.5, - 0.4, 0.4, 0.4, 0.4, - 0.3, 0.3, 0.3, 0.3, - 0.2, 0.2, 0.2, 0.2] + self.losses = [ + 1.0, + 1.0, + 1.0, + 1.0, + 0.9, + 0.9, + 0.9, + 0.9, + 0.8, + 0.8, + 0.8, + 0.8, + 0.7, + 0.7, + 0.7, + 0.7, + 0.6, + 0.6, + 0.6, + 0.6, + 0.5, + 0.5, + 0.5, + 0.5, + 0.4, + 0.4, + 0.4, + 0.4, + 0.3, + 0.3, + 0.3, + 0.3, + 0.2, + 0.2, + 0.2, + 0.2, + ] self.iteration = 0 def side_effect(self, *args, **kwargs): self.iteration += 1 return self.losses[self.iteration - 1] + evaluator._loss = unittest.mock.Mock() evaluator._loss.side_effect = LossSideEffect().side_effect @@ -209,38 +289,42 @@ def side_effect(self, *args, **kwargs): for i in range(1, 10): rval = evaluator.queue.get(timeout=1) - result = rval['loss'] + result = rval["loss"] self.assertAlmostEqual(result, 1.0 - (0.1 * (i - 1))) if i < 9: - self.assertEqual(rval['status'], StatusType.DONOTADVANCE) + self.assertEqual(rval["status"], StatusType.DONOTADVANCE) self.assertEqual(len(rval), 3) else: - self.assertEqual(rval['status'], StatusType.SUCCESS) + self.assertEqual(rval["status"], StatusType.SUCCESS) self.assertEqual(len(rval), 4) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(pipeline_mock.iterative_fit.call_count, 9) self.assertEqual( - [cal[1]['n_iter'] for cal in pipeline_mock.iterative_fit.call_args_list], - [2, 2, 4, 8, 16, 32, 64, 128, 256] + [cal[1]["n_iter"] for cal in pipeline_mock.iterative_fit.call_args_list], + [2, 2, 4, 8, 16, 32, 64, 128, 256], ) # 20 calls because of train, holdout, validation and test set # and a total of five calls because of five iterations of fitting self.assertEqual(evaluator.model.predict_proba.call_count, 36) # 1/3 of 69 self.assertEqual(evaluator.file_output.call_args[0][0].shape[0], 23) - self.assertEqual(evaluator.file_output.call_args[0][1].shape[0], - D.data['Y_valid'].shape[0]) - self.assertEqual(evaluator.file_output.call_args[0][2].shape[0], - D.data['Y_test'].shape[0]) + self.assertEqual( + evaluator.file_output.call_args[0][1].shape[0], D.data["Y_valid"].shape[0] + ) + self.assertEqual( + evaluator.file_output.call_args[0][2].shape[0], D.data["Y_test"].shape[0] + ) self.assertEqual(evaluator.file_output.call_count, 9) self.assertEqual(evaluator.model.fit.call_count, 0) - @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') + @unittest.mock.patch( + "autosklearn.pipeline.classification.SimpleClassificationPipeline" + ) def test_iterative_holdout_interuption(self, pipeline_mock): # Regular fitting D = get_binary_classification_datamanager() - D.name = 'test' + D.name = "test" class SideEffect(object): def __init__(self): @@ -252,61 +336,93 @@ def configuration_fully_fitted(self): # if we need to add a special indicator to show that this is the # final call to iterative fit if self.fully_fitted_call_count == 5: - raise ValueError('fixture') + raise ValueError("fixture") return self.fully_fitted_call_count > 10 - Xt_fixture = 'Xt_fixture' + Xt_fixture = "Xt_fixture" pipeline_mock.estimator_supports_iterative_fit.return_value = True - pipeline_mock.configuration_fully_fitted.side_effect = \ + pipeline_mock.configuration_fully_fitted.side_effect = ( SideEffect().configuration_fully_fitted + ) pipeline_mock.fit_transformer.return_value = Xt_fixture, {} - pipeline_mock.predict_proba.side_effect = \ - lambda X, batch_size=None: np.tile([0.6, 0.4], (len(X), 1)) + pipeline_mock.predict_proba.side_effect = lambda X, batch_size=None: np.tile( + [0.6, 0.4], (len(X), 1) + ) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock pipeline_mock.get_additional_run_info.return_value = None pipeline_mock.get_max_iter.return_value = 512 - pipeline_mock.get_current_iter.side_effect = (2, 4, 8, 16, 32, 64, 128, 256, 512) + pipeline_mock.get_current_iter.side_effect = ( + 2, + 4, + 8, + 16, + 32, + 64, + 128, + 256, + 512, + ) configuration = unittest.mock.Mock(spec=Configuration) backend_api = backend.create( temporary_directory=self.tmp_dir, output_directory=None, - prefix="auto-sklearn" + prefix="auto-sklearn", ) backend_api.load_datamanager = lambda: D queue_ = multiprocessing.Queue() - evaluator = TrainEvaluator(backend_api, queue_, - port=self.port, - configuration=configuration, - resampling_strategy='holdout-iterative-fit', - scoring_functions=None, - output_y_hat_optimization=True, - metric=accuracy, - budget=0.0, - additional_components=dict(), - ) + evaluator = TrainEvaluator( + backend_api, + queue_, + port=self.port, + configuration=configuration, + resampling_strategy="holdout-iterative-fit", + scoring_functions=None, + output_y_hat_optimization=True, + metric=accuracy, + budget=0.0, + additional_components=dict(), + ) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) evaluator.file_output.return_value = (None, {}) class LossSideEffect(object): def __init__(self): - self.losses = [0.8, 0.8, 0.8, 0.8, - 0.6, 0.6, 0.6, 0.6, - 0.4, 0.4, 0.4, 0.4, - 0.2, 0.2, 0.2, 0.2, - 0.0, 0.0, 0.0, 0.0] + self.losses = [ + 0.8, + 0.8, + 0.8, + 0.8, + 0.6, + 0.6, + 0.6, + 0.6, + 0.4, + 0.4, + 0.4, + 0.4, + 0.2, + 0.2, + 0.2, + 0.2, + 0.0, + 0.0, + 0.0, + 0.0, + ] self.iteration = 0 def side_effect(self, *args, **kwargs): self.iteration += 1 return self.losses[self.iteration - 1] + evaluator._loss = unittest.mock.Mock() evaluator._loss.side_effect = LossSideEffect().side_effect self.assertRaisesRegex( ValueError, - 'fixture', + "fixture", evaluator.fit_predict_and_loss, iterative=True, ) @@ -314,7 +430,7 @@ def side_effect(self, *args, **kwargs): for i in range(1, 3): rval = evaluator.queue.get(timeout=1) - self.assertAlmostEqual(rval['loss'], 1.0 - (0.2 * i)) + self.assertAlmostEqual(rval["loss"], 1.0 - (0.2 * i)) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(pipeline_mock.iterative_fit.call_count, 2) @@ -322,24 +438,29 @@ def side_effect(self, *args, **kwargs): # and a total of two calls each because of two iterations of fitting self.assertEqual(evaluator.model.predict_proba.call_count, 8) self.assertEqual(evaluator.file_output.call_args[0][0].shape[0], 23) - self.assertEqual(evaluator.file_output.call_args[0][1].shape[0], - D.data['Y_valid'].shape[0]) - self.assertEqual(evaluator.file_output.call_args[0][2].shape[0], - D.data['Y_test'].shape[0]) + self.assertEqual( + evaluator.file_output.call_args[0][1].shape[0], D.data["Y_valid"].shape[0] + ) + self.assertEqual( + evaluator.file_output.call_args[0][2].shape[0], D.data["Y_test"].shape[0] + ) self.assertEqual(evaluator.file_output.call_count, 2) self.assertEqual(evaluator.model.fit.call_count, 0) - @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') + @unittest.mock.patch( + "autosklearn.pipeline.classification.SimpleClassificationPipeline" + ) def test_iterative_holdout_not_iterative(self, pipeline_mock): # Regular fitting D = get_binary_classification_datamanager() - D.name = 'test' + D.name = "test" - Xt_fixture = 'Xt_fixture' + Xt_fixture = "Xt_fixture" pipeline_mock.estimator_supports_iterative_fit.return_value = False pipeline_mock.fit_transformer.return_value = Xt_fixture, {} - pipeline_mock.predict_proba.side_effect = \ - lambda X, batch_size=None: np.tile([0.6, 0.4], (len(X), 1)) + pipeline_mock.predict_proba.side_effect = lambda X, batch_size=None: np.tile( + [0.6, 0.4], (len(X), 1) + ) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock pipeline_mock.get_additional_run_info.return_value = None @@ -347,20 +468,22 @@ def test_iterative_holdout_not_iterative(self, pipeline_mock): backend_api = backend.create( temporary_directory=self.tmp_dir, output_directory=None, - prefix="auto-sklearn" + prefix="auto-sklearn", ) backend_api.load_datamanager = lambda: D queue_ = multiprocessing.Queue() - evaluator = TrainEvaluator(backend_api, queue_, - port=self.port, - configuration=configuration, - resampling_strategy='holdout-iterative-fit', - scoring_functions=None, - output_y_hat_optimization=True, - metric=accuracy, - additional_components=dict(), - ) + evaluator = TrainEvaluator( + backend_api, + queue_, + port=self.port, + configuration=configuration, + resampling_strategy="holdout-iterative-fit", + scoring_functions=None, + output_y_hat_optimization=True, + metric=accuracy, + additional_components=dict(), + ) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) evaluator.file_output.return_value = (None, {}) @@ -368,26 +491,31 @@ def test_iterative_holdout_not_iterative(self, pipeline_mock): self.assertEqual(evaluator.file_output.call_count, 1) rval = evaluator.queue.get(timeout=1) - self.assertAlmostEqual(rval['loss'], 0.47826086956521741) + self.assertAlmostEqual(rval["loss"], 0.47826086956521741) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(pipeline_mock.iterative_fit.call_count, 0) # four calls for train, opt, valid and test self.assertEqual(evaluator.model.predict_proba.call_count, 4) self.assertEqual(evaluator.file_output.call_args[0][0].shape[0], 23) - self.assertEqual(evaluator.file_output.call_args[0][1].shape[0], - D.data['Y_valid'].shape[0]) - self.assertEqual(evaluator.file_output.call_args[0][2].shape[0], - D.data['Y_test'].shape[0]) + self.assertEqual( + evaluator.file_output.call_args[0][1].shape[0], D.data["Y_valid"].shape[0] + ) + self.assertEqual( + evaluator.file_output.call_args[0][2].shape[0], D.data["Y_test"].shape[0] + ) self.assertEqual(evaluator.file_output.call_count, 1) self.assertEqual(evaluator.model.fit.call_count, 1) - @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') + @unittest.mock.patch( + "autosklearn.pipeline.classification.SimpleClassificationPipeline" + ) def test_cv(self, pipeline_mock): D = get_binary_classification_datamanager() - pipeline_mock.predict_proba.side_effect = \ - lambda X, batch_size=None: np.tile([0.6, 0.4], (len(X), 1)) + pipeline_mock.predict_proba.side_effect = lambda X, batch_size=None: np.tile( + [0.6, 0.4], (len(X), 1) + ) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock pipeline_mock.get_additional_run_info.return_value = None @@ -395,21 +523,23 @@ def test_cv(self, pipeline_mock): backend_api = backend.create( temporary_directory=self.tmp_dir, output_directory=None, - prefix="auto-sklearn" + prefix="auto-sklearn", ) backend_api.load_datamanager = lambda: D queue_ = multiprocessing.Queue() - evaluator = TrainEvaluator(backend_api, queue_, - port=self.port, - configuration=configuration, - resampling_strategy='cv', - resampling_strategy_args={'folds': 5}, - scoring_functions=None, - output_y_hat_optimization=True, - metric=accuracy, - additional_components=dict(), - ) + evaluator = TrainEvaluator( + backend_api, + queue_, + port=self.port, + configuration=configuration, + resampling_strategy="cv", + resampling_strategy_args={"folds": 5}, + scoring_functions=None, + output_y_hat_optimization=True, + metric=accuracy, + additional_components=dict(), + ) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) evaluator.file_output.return_value = (None, {}) @@ -417,7 +547,7 @@ def test_cv(self, pipeline_mock): rval = read_queue(evaluator.queue) self.assertEqual(len(rval), 1) - result = rval[0]['loss'] + result = rval[0]["loss"] self.assertEqual(len(rval[0]), 3) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) @@ -427,49 +557,57 @@ def test_cv(self, pipeline_mock): # Fifteen calls because of the training, holdout, validation and # test set (4 sets x 5 folds = 20) self.assertEqual(pipeline_mock.predict_proba.call_count, 20) - self.assertEqual(evaluator.file_output.call_args[0][0].shape[0], - D.data['Y_train'].shape[0]) - self.assertEqual(evaluator.file_output.call_args[0][1].shape[0], - D.data['Y_valid'].shape[0]) - self.assertEqual(evaluator.file_output.call_args[0][2].shape[0], - D.data['Y_test'].shape[0]) + self.assertEqual( + evaluator.file_output.call_args[0][0].shape[0], D.data["Y_train"].shape[0] + ) + self.assertEqual( + evaluator.file_output.call_args[0][1].shape[0], D.data["Y_valid"].shape[0] + ) + self.assertEqual( + evaluator.file_output.call_args[0][2].shape[0], D.data["Y_test"].shape[0] + ) # The model prior to fitting is saved, this cannot be directly tested # because of the way the mock module is used. Instead, we test whether # the if block in which model assignment is done is accessed self.assertTrue(evaluator._added_empty_model) - @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') + @unittest.mock.patch( + "autosklearn.pipeline.classification.SimpleClassificationPipeline" + ) def test_partial_cv(self, pipeline_mock): D = get_binary_classification_datamanager() - pipeline_mock.predict_proba.side_effect = \ - lambda X, batch_size=None: np.tile([0.6, 0.4], (len(X), 1)) + pipeline_mock.predict_proba.side_effect = lambda X, batch_size=None: np.tile( + [0.6, 0.4], (len(X), 1) + ) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock pipeline_mock.get_additional_run_info.return_value = None pipeline_mock.get_max_iter.return_value = 1 pipeline_mock.get_current_iter.return_value = 1 D = get_binary_classification_datamanager() - D.name = 'test' + D.name = "test" configuration = unittest.mock.Mock(spec=Configuration) backend_api = backend.create( temporary_directory=self.tmp_dir, output_directory=None, - prefix="auto-sklearn" + prefix="auto-sklearn", ) backend_api.load_datamanager = lambda: D queue_ = multiprocessing.Queue() - evaluator = TrainEvaluator(backend_api, queue_, - port=self.port, - configuration=configuration, - resampling_strategy='partial-cv', - resampling_strategy_args={'folds': 5}, - scoring_functions=None, - output_y_hat_optimization=True, - metric=accuracy, - additional_components=dict(), - ) + evaluator = TrainEvaluator( + backend_api, + queue_, + port=self.port, + configuration=configuration, + resampling_strategy="partial-cv", + resampling_strategy_args={"folds": 5}, + scoring_functions=None, + output_y_hat_optimization=True, + metric=accuracy, + additional_components=dict(), + ) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) evaluator.file_output.return_value = (None, {}) @@ -480,19 +618,21 @@ def test_partial_cv(self, pipeline_mock): self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(evaluator.file_output.call_count, 0) - self.assertEqual(rval['loss'], 0.5) + self.assertEqual(rval["loss"], 0.5) self.assertEqual(pipeline_mock.fit.call_count, 1) self.assertEqual(pipeline_mock.predict_proba.call_count, 4) # The model prior to fitting is saved, this cannot be directly tested # because of the way the mock module is used. Instead, we test whether # the if block in which model assignment is done is accessed - self.assertTrue(hasattr(evaluator, 'model')) + self.assertTrue(hasattr(evaluator, "model")) - @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') + @unittest.mock.patch( + "autosklearn.pipeline.classification.SimpleClassificationPipeline" + ) def test_iterative_partial_cv(self, pipeline_mock): # Regular fitting D = get_binary_classification_datamanager() - D.name = 'test' + D.name = "test" class SideEffect(object): def __init__(self): @@ -505,57 +645,101 @@ def configuration_fully_fitted(self): # final call to iterative fit return self.fully_fitted_call_count > 18 - Xt_fixture = 'Xt_fixture' + Xt_fixture = "Xt_fixture" pipeline_mock.estimator_supports_iterative_fit.return_value = True - pipeline_mock.configuration_fully_fitted.side_effect = \ + pipeline_mock.configuration_fully_fitted.side_effect = ( SideEffect().configuration_fully_fitted + ) pipeline_mock.fit_transformer.return_value = Xt_fixture, {} - pipeline_mock.predict_proba.side_effect = \ - lambda X, batch_size=None: np.tile([0.6, 0.4], (len(X), 1)) + pipeline_mock.predict_proba.side_effect = lambda X, batch_size=None: np.tile( + [0.6, 0.4], (len(X), 1) + ) pipeline_mock.get_additional_run_info.return_value = None pipeline_mock.side_effect = lambda **kwargs: pipeline_mock pipeline_mock.get_max_iter.return_value = 512 - pipeline_mock.get_current_iter.side_effect = (2, 4, 8, 16, 32, 64, 128, 256, 512) + pipeline_mock.get_current_iter.side_effect = ( + 2, + 4, + 8, + 16, + 32, + 64, + 128, + 256, + 512, + ) configuration = unittest.mock.Mock(spec=Configuration) backend_api = backend.create( temporary_directory=self.tmp_dir, output_directory=None, - prefix="auto-sklearn" + prefix="auto-sklearn", ) backend_api.load_datamanager = lambda: D queue_ = multiprocessing.Queue() - evaluator = TrainEvaluator(backend_api, queue_, - port=self.port, - configuration=configuration, - resampling_strategy='partial-cv-iterative-fit', - resampling_strategy_args={'folds': 5}, - scoring_functions=None, - output_y_hat_optimization=True, - metric=accuracy, - budget=0.0, - additional_components=dict(), - ) + evaluator = TrainEvaluator( + backend_api, + queue_, + port=self.port, + configuration=configuration, + resampling_strategy="partial-cv-iterative-fit", + resampling_strategy_args={"folds": 5}, + scoring_functions=None, + output_y_hat_optimization=True, + metric=accuracy, + budget=0.0, + additional_components=dict(), + ) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) evaluator.file_output.return_value = (None, {}) class LossSideEffect(object): def __init__(self): - self.losses = [1.0, 1.0, 1.0, 1.0, - 0.9, 0.9, 0.9, 0.9, - 0.8, 0.8, 0.8, 0.8, - 0.7, 0.7, 0.7, 0.7, - 0.6, 0.6, 0.6, 0.6, - 0.5, 0.5, 0.5, 0.5, - 0.4, 0.4, 0.4, 0.4, - 0.3, 0.3, 0.3, 0.3, - 0.2, 0.2, 0.2, 0.2] + self.losses = [ + 1.0, + 1.0, + 1.0, + 1.0, + 0.9, + 0.9, + 0.9, + 0.9, + 0.8, + 0.8, + 0.8, + 0.8, + 0.7, + 0.7, + 0.7, + 0.7, + 0.6, + 0.6, + 0.6, + 0.6, + 0.5, + 0.5, + 0.5, + 0.5, + 0.4, + 0.4, + 0.4, + 0.4, + 0.3, + 0.3, + 0.3, + 0.3, + 0.2, + 0.2, + 0.2, + 0.2, + ] self.iteration = 0 def side_effect(self, *args, **kwargs): self.iteration += 1 return self.losses[self.iteration - 1] + evaluator._loss = unittest.mock.Mock() evaluator._loss.side_effect = LossSideEffect().side_effect @@ -565,118 +749,145 @@ def side_effect(self, *args, **kwargs): for i in range(1, 10): rval = evaluator.queue.get(timeout=1) - self.assertAlmostEqual(rval['loss'], 1.0 - (0.1 * (i - 1))) + self.assertAlmostEqual(rval["loss"], 1.0 - (0.1 * (i - 1))) if i < 9: - self.assertEqual(rval['status'], StatusType.DONOTADVANCE) + self.assertEqual(rval["status"], StatusType.DONOTADVANCE) else: - self.assertEqual(rval['status'], StatusType.SUCCESS) + self.assertEqual(rval["status"], StatusType.SUCCESS) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(pipeline_mock.iterative_fit.call_count, 9) self.assertEqual( - [cal[1]['n_iter'] for cal in pipeline_mock.iterative_fit.call_args_list], - [2, 2, 4, 8, 16, 32, 64, 128, 256] + [cal[1]["n_iter"] for cal in pipeline_mock.iterative_fit.call_args_list], + [2, 2, 4, 8, 16, 32, 64, 128, 256], ) # fifteen calls because of the holdout, the validation and the test set # and a total of five calls because of five iterations of fitting - self.assertTrue(hasattr(evaluator, 'model')) + self.assertTrue(hasattr(evaluator, "model")) self.assertEqual(pipeline_mock.iterative_fit.call_count, 9) # 20 calls because of train, holdout, the validation and the test set # and a total of five calls because of five iterations of fitting self.assertEqual(pipeline_mock.predict_proba.call_count, 36) - @unittest.mock.patch.object(TrainEvaluator, '_loss') - @unittest.mock.patch.object(TrainEvaluator, '_get_model') + @unittest.mock.patch.object(TrainEvaluator, "_loss") + @unittest.mock.patch.object(TrainEvaluator, "_get_model") def test_file_output(self, loss_mock, model_mock): D = get_regression_datamanager() - D.name = 'test' + D.name = "test" self.backend_mock.load_datamanager.return_value = D configuration = unittest.mock.Mock(spec=Configuration) queue_ = multiprocessing.Queue() loss_mock.return_value = None model_mock.return_value = None - evaluator = TrainEvaluator(self.backend_mock, queue=queue_, - port=self.port, - configuration=configuration, - resampling_strategy='cv', - resampling_strategy_args={'folds': 5}, - scoring_functions=SCORER_LIST, - output_y_hat_optimization=True, - metric=accuracy, - additional_components=dict(),) + evaluator = TrainEvaluator( + self.backend_mock, + queue=queue_, + port=self.port, + configuration=configuration, + resampling_strategy="cv", + resampling_strategy_args={"folds": 5}, + scoring_functions=SCORER_LIST, + output_y_hat_optimization=True, + metric=accuracy, + additional_components=dict(), + ) self.backend_mock.get_model_dir.return_value = True - evaluator.model = 'model' - evaluator.Y_optimization = D.data['Y_train'] + evaluator.model = "model" + evaluator.Y_optimization = D.data["Y_train"] rval = evaluator.file_output( - D.data['Y_train'], - D.data['Y_valid'], - D.data['Y_test'], + D.data["Y_train"], + D.data["Y_valid"], + D.data["Y_test"], ) self.assertEqual(rval, (None, {})) self.assertEqual(self.backend_mock.save_targets_ensemble.call_count, 1) self.assertEqual(self.backend_mock.save_numrun_to_dir.call_count, 1) - self.assertEqual(self.backend_mock.save_numrun_to_dir.call_args_list[-1][1].keys(), - {'seed', 'idx', 'budget', 'model', 'cv_model', - 'ensemble_predictions', 'valid_predictions', 'test_predictions'}) - self.assertIsNotNone(self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]['model']) - self.assertIsNone(self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]['cv_model']) + self.assertEqual( + self.backend_mock.save_numrun_to_dir.call_args_list[-1][1].keys(), + { + "seed", + "idx", + "budget", + "model", + "cv_model", + "ensemble_predictions", + "valid_predictions", + "test_predictions", + }, + ) + self.assertIsNotNone( + self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]["model"] + ) + self.assertIsNone( + self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]["cv_model"] + ) - evaluator.models = ['model2', 'model2'] + evaluator.models = ["model2", "model2"] rval = evaluator.file_output( - D.data['Y_train'], - D.data['Y_valid'], - D.data['Y_test'], + D.data["Y_train"], + D.data["Y_valid"], + D.data["Y_test"], ) self.assertEqual(rval, (None, {})) self.assertEqual(self.backend_mock.save_targets_ensemble.call_count, 2) self.assertEqual(self.backend_mock.save_numrun_to_dir.call_count, 2) - self.assertEqual(self.backend_mock.save_numrun_to_dir.call_args_list[-1][1].keys(), - {'seed', 'idx', 'budget', 'model', 'cv_model', - 'ensemble_predictions', 'valid_predictions', 'test_predictions'}) - self.assertIsNotNone(self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]['model']) - self.assertIsNotNone(self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]['cv_model']) + self.assertEqual( + self.backend_mock.save_numrun_to_dir.call_args_list[-1][1].keys(), + { + "seed", + "idx", + "budget", + "model", + "cv_model", + "ensemble_predictions", + "valid_predictions", + "test_predictions", + }, + ) + self.assertIsNotNone( + self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]["model"] + ) + self.assertIsNotNone( + self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]["cv_model"] + ) # Check for not containing NaNs - that the models don't predict nonsense # for unseen data - D.data['Y_valid'][0] = np.NaN + D.data["Y_valid"][0] = np.NaN rval = evaluator.file_output( - D.data['Y_train'], - D.data['Y_valid'], - D.data['Y_test'], + D.data["Y_train"], + D.data["Y_valid"], + D.data["Y_test"], ) self.assertEqual( rval, ( 1.0, - { - 'error': - 'Model predictions for validation set contains NaNs.' - }, - ) + {"error": "Model predictions for validation set contains NaNs."}, + ), ) - D.data['Y_train'][0] = np.NaN + D.data["Y_train"][0] = np.NaN rval = evaluator.file_output( - D.data['Y_train'], - D.data['Y_valid'], - D.data['Y_test'], + D.data["Y_train"], + D.data["Y_valid"], + D.data["Y_test"], ) self.assertEqual( rval, ( 1.0, - { - 'error': - 'Model predictions for optimization set contains NaNs.' - }, - ) + {"error": "Model predictions for optimization set contains NaNs."}, + ), ) - @unittest.mock.patch('autosklearn.automl_common.common.utils.backend.Backend') - @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') + @unittest.mock.patch("autosklearn.automl_common.common.utils.backend.Backend") + @unittest.mock.patch( + "autosklearn.pipeline.classification.SimpleClassificationPipeline" + ) def test_subsample_indices_classification(self, mock, backend_mock): configuration = unittest.mock.Mock(spec=Configuration) @@ -684,26 +895,32 @@ def test_subsample_indices_classification(self, mock, backend_mock): D = get_binary_classification_datamanager() backend_mock.load_datamanager.return_value = D backend_mock.temporary_directory = tempfile.gettempdir() - evaluator = TrainEvaluator(backend_mock, queue_, - port=self.port, - configuration=configuration, - resampling_strategy='cv', - resampling_strategy_args={'folds': 10}, - metric=accuracy, - additional_components=dict(), - ) + evaluator = TrainEvaluator( + backend_mock, + queue_, + port=self.port, + configuration=configuration, + resampling_strategy="cv", + resampling_strategy_args={"folds": 10}, + metric=accuracy, + additional_components=dict(), + ) train_indices = np.arange(69, dtype=int) train_indices1 = subsample_indices( - train_indices, 0.1449, evaluator.task_type, evaluator.Y_train) + train_indices, 0.1449, evaluator.task_type, evaluator.Y_train + ) evaluator.subsample = 20 train_indices2 = subsample_indices( - train_indices, 0.2898, evaluator.task_type, evaluator.Y_train) + train_indices, 0.2898, evaluator.task_type, evaluator.Y_train + ) evaluator.subsample = 30 train_indices3 = subsample_indices( - train_indices, 0.4347, evaluator.task_type, evaluator.Y_train) + train_indices, 0.4347, evaluator.task_type, evaluator.Y_train + ) evaluator.subsample = 67 train_indices4 = subsample_indices( - train_indices, 0.971, evaluator.task_type, evaluator.Y_train) + train_indices, 0.971, evaluator.task_type, evaluator.Y_train + ) # Common cases for ti in train_indices1: self.assertIn(ti, train_indices2) @@ -714,62 +931,98 @@ def test_subsample_indices_classification(self, mock, backend_mock): # Corner cases self.assertRaisesRegex( - ValueError, 'train_size=0.0 should be either positive and smaller than the ' - r'number of samples 69 or a float in the \(0, 1\) range', - subsample_indices, train_indices, 0.0, evaluator.task_type, evaluator.Y_train) + ValueError, + "train_size=0.0 should be either positive and smaller than the " + r"number of samples 69 or a float in the \(0, 1\) range", + subsample_indices, + train_indices, + 0.0, + evaluator.task_type, + evaluator.Y_train, + ) # With equal or greater it should return a non-shuffled array of indices train_indices5 = subsample_indices( - train_indices, 1.0, evaluator.task_type, evaluator.Y_train) + train_indices, 1.0, evaluator.task_type, evaluator.Y_train + ) self.assertTrue(np.all(train_indices5 == train_indices)) evaluator.subsample = 68 self.assertRaisesRegex( - ValueError, 'The test_size = 1 should be greater or equal to the number of ' - 'classes = 2', subsample_indices, train_indices, 0.9999, evaluator.task_type, - evaluator.Y_train) + ValueError, + "The test_size = 1 should be greater or equal to the number of " + "classes = 2", + subsample_indices, + train_indices, + 0.9999, + evaluator.task_type, + evaluator.Y_train, + ) - @unittest.mock.patch('autosklearn.automl_common.common.utils.backend.Backend') - @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') + @unittest.mock.patch("autosklearn.automl_common.common.utils.backend.Backend") + @unittest.mock.patch( + "autosklearn.pipeline.classification.SimpleClassificationPipeline" + ) def test_subsample_indices_regression(self, mock, backend_mock): configuration = unittest.mock.Mock(spec=Configuration) queue_ = multiprocessing.Queue() backend_mock.temporary_directory = tempfile.gettempdir() - evaluator = TrainEvaluator(backend_mock, queue_, - port=self.port, - configuration=configuration, - resampling_strategy='cv', - resampling_strategy_args={'folds': 10}, - metric=accuracy, - additional_components=dict(), - ) + evaluator = TrainEvaluator( + backend_mock, + queue_, + port=self.port, + configuration=configuration, + resampling_strategy="cv", + resampling_strategy_args={"folds": 10}, + metric=accuracy, + additional_components=dict(), + ) train_indices = np.arange(69, dtype=int) - train_indices3 = subsample_indices(train_indices, subsample=0.4347, - task_type=evaluator.task_type, - Y_train=evaluator.Y_train) + train_indices3 = subsample_indices( + train_indices, + subsample=0.4347, + task_type=evaluator.task_type, + Y_train=evaluator.Y_train, + ) evaluator.subsample = 67 - train_indices4 = subsample_indices(train_indices, subsample=0.4347, - task_type=evaluator.task_type, - Y_train=evaluator.Y_train) + train_indices4 = subsample_indices( + train_indices, + subsample=0.4347, + task_type=evaluator.task_type, + Y_train=evaluator.Y_train, + ) # Common cases for ti in train_indices3: self.assertIn(ti, train_indices4) # Corner cases self.assertRaisesRegex( - ValueError, 'train_size=0.0 should be either positive and smaller than the ' - r'number of samples 69 or a float in the \(0, 1\) range', - subsample_indices, train_indices, 0.0, - evaluator.task_type, evaluator.Y_train) + ValueError, + "train_size=0.0 should be either positive and smaller than the " + r"number of samples 69 or a float in the \(0, 1\) range", + subsample_indices, + train_indices, + 0.0, + evaluator.task_type, + evaluator.Y_train, + ) self.assertRaisesRegex( - ValueError, 'Subsample must not be larger than 1, but is 1.000100', - subsample_indices, train_indices, 1.0001, - evaluator.task_type, evaluator.Y_train) + ValueError, + "Subsample must not be larger than 1, but is 1.000100", + subsample_indices, + train_indices, + 1.0001, + evaluator.task_type, + evaluator.Y_train, + ) # With equal or greater it should return a non-shuffled array of indices - train_indices6 = subsample_indices(train_indices, 1.0, evaluator.task_type, - evaluator.Y_train) + train_indices6 = subsample_indices( + train_indices, 1.0, evaluator.task_type, evaluator.Y_train + ) np.testing.assert_allclose(train_indices6, train_indices) - @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') + @unittest.mock.patch( + "autosklearn.pipeline.classification.SimpleClassificationPipeline" + ) def test_predict_proba_binary_classification(self, mock): D = get_binary_classification_datamanager() self.backend_mock.load_datamanager.return_value = D @@ -781,30 +1034,38 @@ def test_predict_proba_binary_classification(self, mock): configuration = unittest.mock.Mock(spec=Configuration) queue_ = multiprocessing.Queue() - evaluator = TrainEvaluator(self.backend_mock, queue_, - port=self.port, - configuration=configuration, - resampling_strategy='cv', - resampling_strategy_args={'folds': 10}, - output_y_hat_optimization=False, - metric=accuracy, - additional_components=dict(), - ) + evaluator = TrainEvaluator( + self.backend_mock, + queue_, + port=self.port, + configuration=configuration, + resampling_strategy="cv", + resampling_strategy_args={"folds": 10}, + output_y_hat_optimization=False, + metric=accuracy, + additional_components=dict(), + ) evaluator.fit_predict_and_loss() Y_optimization_pred = self.backend_mock.save_numrun_to_dir.call_args_list[0][1][ - 'ensemble_predictions'] + "ensemble_predictions" + ] for i in range(7): self.assertEqual(0.9, Y_optimization_pred[i][1]) - @unittest.mock.patch.object(TrainEvaluator, 'file_output') - @unittest.mock.patch.object(TrainEvaluator, '_partial_fit_and_predict_standard') - @unittest.mock.patch('autosklearn.automl_common.common.utils.backend.Backend') - @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') + @unittest.mock.patch.object(TrainEvaluator, "file_output") + @unittest.mock.patch.object(TrainEvaluator, "_partial_fit_and_predict_standard") + @unittest.mock.patch("autosklearn.automl_common.common.utils.backend.Backend") + @unittest.mock.patch( + "autosklearn.pipeline.classification.SimpleClassificationPipeline" + ) def test_fit_predict_and_loss_standard_additional_run_info( - self, mock, backend_mock, _partial_fit_and_predict_mock, - file_output_mock, + self, + mock, + backend_mock, + _partial_fit_and_predict_mock, + file_output_mock, ): D = get_binary_classification_datamanager() backend_mock.load_datamanager.return_value = D @@ -815,7 +1076,7 @@ def test_fit_predict_and_loss_standard_additional_run_info( np.array([[0.1, 0.9]] * 23), np.array([[0.1, 0.9]] * 25), np.array([[0.1, 0.9]] * 6), - {'a': 5}, + {"a": 5}, ) file_output_mock.return_value = (None, {}) @@ -823,10 +1084,11 @@ def test_fit_predict_and_loss_standard_additional_run_info( queue_ = multiprocessing.Queue() evaluator = TrainEvaluator( - backend_mock, queue_, + backend_mock, + queue_, port=self.port, configuration=configuration, - resampling_strategy='holdout', + resampling_strategy="holdout", output_y_hat_optimization=False, metric=accuracy, additional_components=dict(), @@ -840,8 +1102,8 @@ def test_fit_predict_and_loss_standard_additional_run_info( rval = evaluator.fit_predict_and_loss(iterative=False) self.assertIsNone(rval) element = queue_.get() - self.assertEqual(element['status'], StatusType.SUCCESS) - self.assertEqual(element['additional_run_info']['a'], 5) + self.assertEqual(element["status"], StatusType.SUCCESS) + self.assertEqual(element["additional_run_info"]["a"], 5) self.assertEqual(_partial_fit_and_predict_mock.call_count, 1) class SideEffect(object): @@ -856,7 +1118,7 @@ def __call__(self, *args, **kwargs): np.array([[0.1, 0.9]] * 35), np.array([[0.1, 0.9]] * 25), np.array([[0.1, 0.9]] * 6), - {'a': 5} + {"a": 5}, ) else: return ( @@ -864,15 +1126,17 @@ def __call__(self, *args, **kwargs): np.array([[0.1, 0.9]] * 34), np.array([[0.1, 0.9]] * 25), np.array([[0.1, 0.9]] * 6), - {'a': 5} + {"a": 5}, ) + _partial_fit_and_predict_mock.side_effect = SideEffect() evaluator = TrainEvaluator( - backend_mock, queue_, + backend_mock, + queue_, port=self.port, configuration=configuration, - resampling_strategy='cv', - resampling_strategy_args={'folds': 2}, + resampling_strategy="cv", + resampling_strategy_args={"folds": 2}, output_y_hat_optimization=False, metric=accuracy, additional_components=dict(), @@ -885,28 +1149,34 @@ def __call__(self, *args, **kwargs): self.assertRaisesRegex( TAEAbortException, - 'Found additional run info "{\'a\': 5}" in fold 1, ' - 'but cannot handle additional run info if fold >= 1.', + "Found additional run info \"{'a': 5}\" in fold 1, " + "but cannot handle additional run info if fold >= 1.", evaluator.fit_predict_and_loss, - iterative=False + iterative=False, ) - @unittest.mock.patch.object(TrainEvaluator, '_loss') - @unittest.mock.patch.object(TrainEvaluator, 'finish_up') - @unittest.mock.patch('autosklearn.automl_common.common.utils.backend.Backend') - @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') + @unittest.mock.patch.object(TrainEvaluator, "_loss") + @unittest.mock.patch.object(TrainEvaluator, "finish_up") + @unittest.mock.patch("autosklearn.automl_common.common.utils.backend.Backend") + @unittest.mock.patch( + "autosklearn.pipeline.classification.SimpleClassificationPipeline" + ) def test_fit_predict_and_loss_iterative_additional_run_info( - self, mock, backend_mock, finish_up_mock, loss_mock, + self, + mock, + backend_mock, + finish_up_mock, + loss_mock, ): - class Counter: counter = 0 def __call__(self): self.counter += 1 return False if self.counter <= 1 else True + mock.estimator_supports_iterative_fit.return_value = True - mock.fit_transformer.return_value = ('Xt', {}) + mock.fit_transformer.return_value = ("Xt", {}) mock.configuration_fully_fitted.side_effect = Counter() mock.get_current_iter.side_effect = Counter() mock.get_max_iter.return_value = 1 @@ -922,10 +1192,11 @@ def __call__(self): queue_ = multiprocessing.Queue() evaluator = TrainEvaluator( - backend_mock, queue_, + backend_mock, + queue_, port=self.port, configuration=configuration, - resampling_strategy='holdout', + resampling_strategy="holdout", output_y_hat_optimization=False, metric=accuracy, budget=0.0, @@ -938,17 +1209,23 @@ def __call__(self): rval = evaluator.fit_predict_and_loss(iterative=True) self.assertIsNone(rval) self.assertEqual(finish_up_mock.call_count, 1) - self.assertEqual(finish_up_mock.call_args[1]['additional_run_info'], 14678) - - @unittest.mock.patch.object(TrainEvaluator, '_loss') - @unittest.mock.patch.object(TrainEvaluator, 'finish_up') - @unittest.mock.patch('autosklearn.automl_common.common.utils.backend.Backend') - @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') + self.assertEqual(finish_up_mock.call_args[1]["additional_run_info"], 14678) + + @unittest.mock.patch.object(TrainEvaluator, "_loss") + @unittest.mock.patch.object(TrainEvaluator, "finish_up") + @unittest.mock.patch("autosklearn.automl_common.common.utils.backend.Backend") + @unittest.mock.patch( + "autosklearn.pipeline.classification.SimpleClassificationPipeline" + ) def test_fit_predict_and_loss_iterative_noniterativemodel_additional_run_info( - self, mock, backend_mock, finish_up_mock, loss_mock, + self, + mock, + backend_mock, + finish_up_mock, + loss_mock, ): mock.estimator_supports_iterative_fit.return_value = False - mock.fit_transformer.return_value = ('Xt', {}) + mock.fit_transformer.return_value = ("Xt", {}) mock.get_additional_run_info.return_value = 14678 loss_mock.return_value = 0.5 @@ -961,10 +1238,11 @@ def test_fit_predict_and_loss_iterative_noniterativemodel_additional_run_info( queue_ = multiprocessing.Queue() evaluator = TrainEvaluator( - backend_mock, queue_, + backend_mock, + queue_, port=self.port, configuration=configuration, - resampling_strategy='holdout', + resampling_strategy="holdout", output_y_hat_optimization=False, metric=accuracy, additional_components=dict(), @@ -977,14 +1255,20 @@ def test_fit_predict_and_loss_iterative_noniterativemodel_additional_run_info( rval = evaluator.fit_predict_and_loss(iterative=True) self.assertIsNone(rval) self.assertEqual(finish_up_mock.call_count, 1) - self.assertEqual(finish_up_mock.call_args[1]['additional_run_info'], 14678) - - @unittest.mock.patch.object(TrainEvaluator, '_loss') - @unittest.mock.patch.object(TrainEvaluator, 'finish_up') - @unittest.mock.patch('autosklearn.automl_common.common.utils.backend.Backend') - @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') + self.assertEqual(finish_up_mock.call_args[1]["additional_run_info"], 14678) + + @unittest.mock.patch.object(TrainEvaluator, "_loss") + @unittest.mock.patch.object(TrainEvaluator, "finish_up") + @unittest.mock.patch("autosklearn.automl_common.common.utils.backend.Backend") + @unittest.mock.patch( + "autosklearn.pipeline.classification.SimpleClassificationPipeline" + ) def test_fit_predict_and_loss_budget_additional_run_info( - self, mock, backend_mock, finish_up_mock, loss_mock, + self, + mock, + backend_mock, + finish_up_mock, + loss_mock, ): class Counter: counter = 0 @@ -992,12 +1276,13 @@ class Counter: def __call__(self): self.counter += 1 return False if self.counter <= 1 else True + mock.configuration_fully_fitted.side_effect = Counter() mock.get_current_iter.side_effect = Counter() mock.get_max_iter.return_value = 1 mock.estimator_supports_iterative_fit.return_value = True - mock.fit_transformer.return_value = ('Xt', {}) - mock.get_additional_run_info.return_value = {'val': 14678} + mock.fit_transformer.return_value = ("Xt", {}) + mock.get_additional_run_info.return_value = {"val": 14678} mock.get_max_iter.return_value = 512 loss_mock.return_value = 0.5 @@ -1010,13 +1295,14 @@ def __call__(self): queue_ = multiprocessing.Queue() evaluator = TrainEvaluator( - backend_mock, queue_, + backend_mock, + queue_, port=self.port, configuration=configuration, - resampling_strategy='holdout', + resampling_strategy="holdout", output_y_hat_optimization=False, metric=accuracy, - budget_type='iterations', + budget_type="iterations", budget=50, additional_components=dict(), ) @@ -1028,18 +1314,26 @@ def __call__(self): rval = evaluator.fit_predict_and_loss(iterative=False) self.assertIsNone(rval) self.assertEqual(finish_up_mock.call_count, 1) - self.assertEqual(finish_up_mock.call_args[1]['additional_run_info'], {'val': 14678}) + self.assertEqual( + finish_up_mock.call_args[1]["additional_run_info"], {"val": 14678} + ) - @unittest.mock.patch.object(TrainEvaluator, '_loss') - @unittest.mock.patch.object(TrainEvaluator, 'finish_up') - @unittest.mock.patch('autosklearn.automl_common.common.utils.backend.Backend') - @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') + @unittest.mock.patch.object(TrainEvaluator, "_loss") + @unittest.mock.patch.object(TrainEvaluator, "finish_up") + @unittest.mock.patch("autosklearn.automl_common.common.utils.backend.Backend") + @unittest.mock.patch( + "autosklearn.pipeline.classification.SimpleClassificationPipeline" + ) def test_fit_predict_and_loss_budget_2_additional_run_info( - self, mock, backend_mock, finish_up_mock, loss_mock, + self, + mock, + backend_mock, + finish_up_mock, + loss_mock, ): mock.estimator_supports_iterative_fit.return_value = False - mock.fit_transformer.return_value = ('Xt', {}) - mock.get_additional_run_info.return_value = {'val': 14678} + mock.fit_transformer.return_value = ("Xt", {}) + mock.get_additional_run_info.return_value = {"val": 14678} loss_mock.return_value = 0.5 D = get_binary_classification_datamanager() @@ -1051,13 +1345,14 @@ def test_fit_predict_and_loss_budget_2_additional_run_info( queue_ = multiprocessing.Queue() evaluator = TrainEvaluator( - backend_mock, queue_, + backend_mock, + queue_, port=self.port, configuration=configuration, - resampling_strategy='holdout', + resampling_strategy="holdout", output_y_hat_optimization=False, metric=accuracy, - budget_type='subsample', + budget_type="subsample", budget=50, additional_components=dict(), ) @@ -1069,7 +1364,9 @@ def test_fit_predict_and_loss_budget_2_additional_run_info( rval = evaluator.fit_predict_and_loss(iterative=False) self.assertIsNone(rval) self.assertEqual(finish_up_mock.call_count, 1) - self.assertEqual(finish_up_mock.call_args[1]['additional_run_info'], {'val': 14678}) + self.assertEqual( + finish_up_mock.call_args[1]["additional_run_info"], {"val": 14678} + ) def test_get_results(self): queue_ = multiprocessing.Queue() @@ -1082,33 +1379,39 @@ def test_get_results(self): def test_datasets(self): for getter in get_dataset_getters(): - testname = '%s_%s' % (os.path.basename(__file__). - replace('.pyc', '').replace('.py', ''), - getter.__name__) + testname = "%s_%s" % ( + os.path.basename(__file__).replace(".pyc", "").replace(".py", ""), + getter.__name__, + ) with self.subTest(testname): D = getter() D_ = copy.deepcopy(D) - y = D.data['Y_train'] + y = D.data["Y_train"] if len(y.shape) == 2 and y.shape[1] == 1: - D_.data['Y_train'] = y.flatten() + D_.data["Y_train"] = y.flatten() self.backend_mock.load_datamanager.return_value = D_ queue_ = multiprocessing.Queue() - metric_lookup = {MULTILABEL_CLASSIFICATION: f1_macro, - BINARY_CLASSIFICATION: accuracy, - MULTICLASS_CLASSIFICATION: accuracy, - REGRESSION: r2} - evaluator = TrainEvaluator(self.backend_mock, queue_, - port=self.port, - resampling_strategy='cv', - resampling_strategy_args={'folds': 2}, - output_y_hat_optimization=False, - metric=metric_lookup[D.info['task']], - additional_components=dict(),) + metric_lookup = { + MULTILABEL_CLASSIFICATION: f1_macro, + BINARY_CLASSIFICATION: accuracy, + MULTICLASS_CLASSIFICATION: accuracy, + REGRESSION: r2, + } + evaluator = TrainEvaluator( + self.backend_mock, + queue_, + port=self.port, + resampling_strategy="cv", + resampling_strategy_args={"folds": 2}, + output_y_hat_optimization=False, + metric=metric_lookup[D.info["task"]], + additional_components=dict(), + ) evaluator.fit_predict_and_loss() rval = evaluator.queue.get(timeout=1) - self.assertTrue(np.isfinite(rval['loss'])) + self.assertTrue(np.isfinite(rval["loss"])) ############################################################################ # Test obtaining a splitter object from scikit-learn @@ -1122,147 +1425,142 @@ def test_get_splitter(self, te_mock): # holdout, binary classification evaluator = TrainEvaluator() - evaluator.resampling_strategy = 'holdout' + evaluator.resampling_strategy = "holdout" evaluator.resampling_strategy_args = {} cv = evaluator.get_splitter(D) - self.assertIsInstance(cv, - sklearn.model_selection.StratifiedShuffleSplit) + self.assertIsInstance(cv, sklearn.model_selection.StratifiedShuffleSplit) # holdout, binary classification, no shuffle evaluator = TrainEvaluator() - evaluator.resampling_strategy = 'holdout' - evaluator.resampling_strategy_args = {'shuffle': False} + evaluator.resampling_strategy = "holdout" + evaluator.resampling_strategy_args = {"shuffle": False} cv = evaluator.get_splitter(D) - self.assertIsInstance(cv, - sklearn.model_selection.PredefinedSplit) + self.assertIsInstance(cv, sklearn.model_selection.PredefinedSplit) # holdout, binary classification, fallback to custom shuffle split - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1, 2]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1, 2]) evaluator = TrainEvaluator() - evaluator.resampling_strategy = 'holdout' + evaluator.resampling_strategy = "holdout" evaluator.resampling_strategy_args = {} cv = evaluator.get_splitter(D) - self.assertIsInstance(cv, - autosklearn.evaluation.splitter.CustomStratifiedShuffleSplit) + self.assertIsInstance( + cv, autosklearn.evaluation.splitter.CustomStratifiedShuffleSplit + ) # cv, binary classification - D.data['Y_train'] = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1]) evaluator = TrainEvaluator() - evaluator.resampling_strategy = 'cv' - evaluator.resampling_strategy_args = {'folds': 5} + evaluator.resampling_strategy = "cv" + evaluator.resampling_strategy_args = {"folds": 5} cv = evaluator.get_splitter(D) - self.assertIsInstance(cv, - sklearn.model_selection._split.StratifiedKFold) + self.assertIsInstance(cv, sklearn.model_selection._split.StratifiedKFold) # cv, binary classification, shuffle is True - D.data['Y_train'] = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1]) evaluator = TrainEvaluator() - evaluator.resampling_strategy = 'cv' - evaluator.resampling_strategy_args = {'folds': 5} + evaluator.resampling_strategy = "cv" + evaluator.resampling_strategy_args = {"folds": 5} cv = evaluator.get_splitter(D) - self.assertIsInstance(cv, - sklearn.model_selection._split.StratifiedKFold) + self.assertIsInstance(cv, sklearn.model_selection._split.StratifiedKFold) self.assertTrue(cv.shuffle) # cv, binary classification, shuffle is False - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) evaluator = TrainEvaluator() - evaluator.resampling_strategy = 'cv' - evaluator.resampling_strategy_args = {'folds': 5, 'shuffle': False} + evaluator.resampling_strategy = "cv" + evaluator.resampling_strategy_args = {"folds": 5, "shuffle": False} cv = evaluator.get_splitter(D) - self.assertIsInstance(cv, - sklearn.model_selection._split.KFold) + self.assertIsInstance(cv, sklearn.model_selection._split.KFold) self.assertFalse(cv.shuffle) # cv, binary classification, fallback to custom splitter - D.data['Y_train'] = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2]) + D.data["Y_train"] = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2]) evaluator = TrainEvaluator() - evaluator.resampling_strategy = 'cv' - evaluator.resampling_strategy_args = {'folds': 5} + evaluator.resampling_strategy = "cv" + evaluator.resampling_strategy_args = {"folds": 5} cv = evaluator.get_splitter(D) - self.assertIsInstance(cv, - autosklearn.evaluation.splitter.CustomStratifiedKFold) + self.assertIsInstance(cv, autosklearn.evaluation.splitter.CustomStratifiedKFold) # regression, shuffle split - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() - evaluator.resampling_strategy = 'holdout' + evaluator.resampling_strategy = "holdout" evaluator.resampling_strategy_args = {} cv = evaluator.get_splitter(D) - self.assertIsInstance(cv, - sklearn.model_selection._split.ShuffleSplit) + self.assertIsInstance(cv, sklearn.model_selection._split.ShuffleSplit) # regression, no shuffle - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() - evaluator.resampling_strategy = 'holdout' - evaluator.resampling_strategy_args = {'shuffle': False} + evaluator.resampling_strategy = "holdout" + evaluator.resampling_strategy_args = {"shuffle": False} cv = evaluator.get_splitter(D) - self.assertIsInstance(cv, - sklearn.model_selection._split.PredefinedSplit) + self.assertIsInstance(cv, sklearn.model_selection._split.PredefinedSplit) # regression cv, KFold - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() - evaluator.resampling_strategy = 'cv' - evaluator.resampling_strategy_args = {'folds': 5} + evaluator.resampling_strategy = "cv" + evaluator.resampling_strategy_args = {"folds": 5} cv = evaluator.get_splitter(D) self.assertIsInstance(cv, sklearn.model_selection._split.KFold) self.assertTrue(cv.shuffle) # regression cv, KFold, no shuffling - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() - evaluator.resampling_strategy = 'cv' - evaluator.resampling_strategy_args = {'folds': 5, 'shuffle': False} + evaluator.resampling_strategy = "cv" + evaluator.resampling_strategy_args = {"folds": 5, "shuffle": False} cv = evaluator.get_splitter(D) self.assertIsInstance(cv, sklearn.model_selection._split.KFold) self.assertFalse(cv.shuffle) # multioutput regression, shuffle split - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() - evaluator.resampling_strategy = 'holdout' + evaluator.resampling_strategy = "holdout" evaluator.resampling_strategy_args = {} cv = evaluator.get_splitter(D) - self.assertIsInstance(cv, - sklearn.model_selection._split.ShuffleSplit) + self.assertIsInstance(cv, sklearn.model_selection._split.ShuffleSplit) # multioutput regression, no shuffle - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() - evaluator.resampling_strategy = 'holdout' - evaluator.resampling_strategy_args = {'shuffle': False} + evaluator.resampling_strategy = "holdout" + evaluator.resampling_strategy_args = {"shuffle": False} cv = evaluator.get_splitter(D) - self.assertIsInstance(cv, - sklearn.model_selection._split.PredefinedSplit) + self.assertIsInstance(cv, sklearn.model_selection._split.PredefinedSplit) # multioutput regression cv, KFold - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() - evaluator.resampling_strategy = 'cv' - evaluator.resampling_strategy_args = {'folds': 5} + evaluator.resampling_strategy = "cv" + evaluator.resampling_strategy_args = {"folds": 5} cv = evaluator.get_splitter(D) self.assertIsInstance(cv, sklearn.model_selection._split.KFold) self.assertTrue(cv.shuffle) # multioutput regression cv, KFold, no shuffling - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() - evaluator.resampling_strategy = 'cv' - evaluator.resampling_strategy_args = {'folds': 5, 'shuffle': False} + evaluator.resampling_strategy = "cv" + evaluator.resampling_strategy_args = {"folds": 5, "shuffle": False} cv = evaluator.get_splitter(D) self.assertIsInstance(cv, sklearn.model_selection._split.KFold) self.assertFalse(cv.shuffle) @@ -1276,19 +1574,26 @@ def test_get_splitter_cv_object(self, te_mock): D.feat_type = {} # GroupKFold, classification with args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) - D.data['X_train'] = np.array([0, 0, 0, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) + D.data["X_train"] = np.array([0, 0, 0, 1, 1, 1]) evaluator = TrainEvaluator() evaluator.resampling_strategy = GroupKFold(n_splits=2) - evaluator.resampling_strategy_args = {'groups': np.array([1, 1, 2, 1, 2, 2])} + evaluator.resampling_strategy_args = {"groups": np.array([1, 1, 2, 1, 2, 2])} cv = evaluator.get_splitter(D) self.assertIsInstance(cv, GroupKFold) - self.assertEqual(cv.get_n_splits(groups=evaluator.resampling_strategy_args['groups']), 2) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 2 + ) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # GroupKFold, classification no args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) evaluator = TrainEvaluator() evaluator.resampling_strategy = GroupKFold(n_splits=2) evaluator.resampling_strategy_args = None @@ -1296,23 +1601,31 @@ def test_get_splitter_cv_object(self, te_mock): ValueError, "The 'groups' parameter should not be None", evaluator.get_splitter, - D) + D, + ) # GroupKFold, regression with args - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy = GroupKFold(n_splits=2) - evaluator.resampling_strategy_args = {'groups': np.array([1, 1, 2, 1, 2, 2])} + evaluator.resampling_strategy_args = {"groups": np.array([1, 1, 2, 1, 2, 2])} cv = evaluator.get_splitter(D) self.assertIsInstance(cv, GroupKFold) - self.assertEqual(cv.get_n_splits(groups=evaluator.resampling_strategy_args['groups']), 2) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 2 + ) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # GroupKFold, regression no args - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy = GroupKFold(n_splits=2) evaluator.resampling_strategy_args = None @@ -1320,25 +1633,35 @@ def test_get_splitter_cv_object(self, te_mock): ValueError, "The 'groups' parameter should not be None", evaluator.get_splitter, - D) + D, + ) # GroupKFold, multi-output regression with args - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy = GroupKFold(n_splits=2) - evaluator.resampling_strategy_args = {'groups': np.array([1, 1, 2, 1, 2, 2])} + evaluator.resampling_strategy_args = {"groups": np.array([1, 1, 2, 1, 2, 2])} cv = evaluator.get_splitter(D) self.assertIsInstance(cv, GroupKFold) - self.assertEqual(cv.get_n_splits(groups=evaluator.resampling_strategy_args['groups']), 2) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 2 + ) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # GroupKFold, multi-output regression no args - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy = GroupKFold(n_splits=2) evaluator.resampling_strategy_args = None @@ -1346,110 +1669,154 @@ def test_get_splitter_cv_object(self, te_mock): ValueError, "The 'groups' parameter should not be None", evaluator.get_splitter, - D) + D, + ) # KFold, classification with args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None evaluator.resampling_strategy = KFold(n_splits=4, shuffle=True, random_state=5) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, KFold) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 4) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 4 + ) self.assertTrue(cv.shuffle) self.assertEqual(cv.random_state, 5) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # KFold, classification no args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None evaluator.resampling_strategy = KFold(n_splits=3) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, KFold) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 3) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 3 + ) self.assertFalse(cv.shuffle) self.assertIsNone(cv.random_state) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # KFold, regression with args - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None evaluator.resampling_strategy = KFold(n_splits=4, shuffle=True, random_state=5) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, KFold) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 4) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 4 + ) self.assertTrue(cv.shuffle) self.assertEqual(cv.random_state, 5) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # KFold, regression no args - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None evaluator.resampling_strategy = KFold(n_splits=3) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, KFold) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 3) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 3 + ) self.assertFalse(cv.shuffle) self.assertIsNone(cv.random_state) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # KFold, multi-output regression with args - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None evaluator.resampling_strategy = KFold(n_splits=4, shuffle=True, random_state=5) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, KFold) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 4) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 4 + ) self.assertTrue(cv.shuffle) self.assertEqual(cv.random_state, 5) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # KFold, multi-output regression no args - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None evaluator.resampling_strategy = KFold(n_splits=3) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, KFold) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 3) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 3 + ) self.assertFalse(cv.shuffle) self.assertIsNone(cv.random_state) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # LeaveOneGroupOut, classification with args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) evaluator = TrainEvaluator() evaluator.resampling_strategy = LeaveOneGroupOut() - evaluator.resampling_strategy_args = {'groups': np.array([1, 1, 2, 1, 2, 2])} + evaluator.resampling_strategy_args = {"groups": np.array([1, 1, 2, 1, 2, 2])} cv = evaluator.get_splitter(D) self.assertIsInstance(cv, LeaveOneGroupOut) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # LeaveOneGroupOut, classification no args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) evaluator = TrainEvaluator() evaluator.resampling_strategy = LeaveOneGroupOut() evaluator.resampling_strategy_args = None @@ -1457,22 +1824,28 @@ def test_get_splitter_cv_object(self, te_mock): ValueError, "The 'groups' parameter should not be None", evaluator.get_splitter, - D) + D, + ) # LeaveOneGroupOut, regression with args - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy = LeaveOneGroupOut() - evaluator.resampling_strategy_args = {'groups': np.array([1, 1, 2, 1, 2, 2])} + evaluator.resampling_strategy_args = {"groups": np.array([1, 1, 2, 1, 2, 2])} cv = evaluator.get_splitter(D) self.assertIsInstance(cv, LeaveOneGroupOut) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # LeaveOneGroupOut, regression no args - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy = LeaveOneGroupOut() evaluator.resampling_strategy_args = None @@ -1480,24 +1853,32 @@ def test_get_splitter_cv_object(self, te_mock): ValueError, "The 'groups' parameter should not be None", evaluator.get_splitter, - D) + D, + ) # LeaveOneGroupOut, multi-output regression with args - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy = LeaveOneGroupOut() - evaluator.resampling_strategy_args = {'groups': np.array([1, 1, 2, 1, 2, 2])} + evaluator.resampling_strategy_args = {"groups": np.array([1, 1, 2, 1, 2, 2])} cv = evaluator.get_splitter(D) self.assertIsInstance(cv, LeaveOneGroupOut) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # LeaveOneGroupOut, multi-output regression no args - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy = LeaveOneGroupOut() evaluator.resampling_strategy_args = None @@ -1505,21 +1886,27 @@ def test_get_splitter_cv_object(self, te_mock): ValueError, "The 'groups' parameter should not be None", evaluator.get_splitter, - D) + D, + ) # LeavePGroupsOut, classification with args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) evaluator = TrainEvaluator() evaluator.resampling_strategy = LeavePGroupsOut(n_groups=1) - evaluator.resampling_strategy_args = {'groups': np.array([1, 1, 2, 1, 2, 2])} + evaluator.resampling_strategy_args = {"groups": np.array([1, 1, 2, 1, 2, 2])} cv = evaluator.get_splitter(D) self.assertIsInstance(cv, LeavePGroupsOut) self.assertEqual(cv.n_groups, 1) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # LeavePGroupsOut, classification no args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) evaluator = TrainEvaluator() evaluator.resampling_strategy = LeaveOneGroupOut() evaluator.resampling_strategy_args = None @@ -1527,23 +1914,29 @@ def test_get_splitter_cv_object(self, te_mock): ValueError, "The 'groups' parameter should not be None", evaluator.get_splitter, - D) + D, + ) # LeavePGroupsOut, regression with args - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy = LeavePGroupsOut(n_groups=1) - evaluator.resampling_strategy_args = {'groups': np.array([1, 1, 2, 1, 2, 2])} + evaluator.resampling_strategy_args = {"groups": np.array([1, 1, 2, 1, 2, 2])} cv = evaluator.get_splitter(D) self.assertIsInstance(cv, LeavePGroupsOut) self.assertEqual(cv.n_groups, 1) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # LeavePGroupsOut, regression no args - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy = LeavePGroupsOut(n_groups=1) evaluator.resampling_strategy_args = None @@ -1551,25 +1944,33 @@ def test_get_splitter_cv_object(self, te_mock): ValueError, "The 'groups' parameter should not be None", evaluator.get_splitter, - D) + D, + ) # LeavePGroupsOut, multi-output regression with args - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy = LeavePGroupsOut(n_groups=1) - evaluator.resampling_strategy_args = {'groups': np.array([1, 1, 2, 1, 2, 2])} + evaluator.resampling_strategy_args = {"groups": np.array([1, 1, 2, 1, 2, 2])} cv = evaluator.get_splitter(D) self.assertIsInstance(cv, LeavePGroupsOut) self.assertEqual(cv.n_groups, 1) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # LeavePGroupsOut, multi-output regression no args - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy = LeavePGroupsOut(n_groups=1) evaluator.resampling_strategy_args = None @@ -1577,384 +1978,567 @@ def test_get_splitter_cv_object(self, te_mock): ValueError, "The 'groups' parameter should not be None", evaluator.get_splitter, - D) + D, + ) # LeaveOneOut, classification - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) evaluator = TrainEvaluator() evaluator.resampling_strategy = LeaveOneOut() evaluator.resampling_strategy_args = None cv = evaluator.get_splitter(D) self.assertIsInstance(cv, LeaveOneOut) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # LeaveOneOut, regression - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy = LeaveOneOut() evaluator.resampling_strategy_args = None cv = evaluator.get_splitter(D) self.assertIsInstance(cv, LeaveOneOut) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # LeaveOneOut, multi-output regression - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy = LeaveOneOut() evaluator.resampling_strategy_args = None cv = evaluator.get_splitter(D) self.assertIsInstance(cv, LeaveOneOut) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # LeavePOut, classification with args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None evaluator.resampling_strategy = LeavePOut(p=3) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, LeavePOut) self.assertEqual(cv.p, 3) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # LeavePOut, classification no args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) evaluator = TrainEvaluator() evaluator.resampling_strategy = LeavePOut(p=2) evaluator.resampling_strategy_args = None cv = evaluator.get_splitter(D) self.assertIsInstance(cv, LeavePOut) self.assertEqual(cv.p, 2) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # LeavePOut, regression with args - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None evaluator.resampling_strategy = LeavePOut(p=3) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, LeavePOut) self.assertEqual(cv.p, 3) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # LeavePOut, regression no args - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy = LeavePOut(p=2) evaluator.resampling_strategy_args = None cv = evaluator.get_splitter(D) self.assertIsInstance(cv, LeavePOut) self.assertEqual(cv.p, 2) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # LeavePOut, multi-output regression with args - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None evaluator.resampling_strategy = LeavePOut(p=3) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, LeavePOut) self.assertEqual(cv.p, 3) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # LeavePOut, multi-output regression no args - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy = LeavePOut(p=2) evaluator.resampling_strategy_args = None cv = evaluator.get_splitter(D) self.assertIsInstance(cv, LeavePOut) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # PredefinedSplit, classification with args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None - evaluator.resampling_strategy = PredefinedSplit(test_fold=np.array([0, 1, 0, 1, 0, 1])) + evaluator.resampling_strategy = PredefinedSplit( + test_fold=np.array([0, 1, 0, 1, 0, 1]) + ) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, PredefinedSplit) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # PredefinedSplit, regression with args - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None - evaluator.resampling_strategy = PredefinedSplit(test_fold=np.array([0, 1, 0, 1, 0, 1])) + evaluator.resampling_strategy = PredefinedSplit( + test_fold=np.array([0, 1, 0, 1, 0, 1]) + ) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, PredefinedSplit) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # PredefinedSplit, multi-output regression with args - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None - evaluator.resampling_strategy = PredefinedSplit(test_fold=np.array([0, 1, 0, 1, 0, 1])) + evaluator.resampling_strategy = PredefinedSplit( + test_fold=np.array([0, 1, 0, 1, 0, 1]) + ) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, PredefinedSplit) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # RepeatedKFold, classification with args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None - evaluator.resampling_strategy = RepeatedKFold(n_splits=4, n_repeats=3, random_state=5) + evaluator.resampling_strategy = RepeatedKFold( + n_splits=4, n_repeats=3, random_state=5 + ) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, RepeatedKFold) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 4*3) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 4 * 3 + ) self.assertEqual(cv.n_repeats, 3) self.assertEqual(cv.random_state, 5) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # RepeatedKFold, classification no args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) evaluator = TrainEvaluator() evaluator.resampling_strategy = RepeatedKFold(n_splits=5, n_repeats=10) evaluator.resampling_strategy_args = None cv = evaluator.get_splitter(D) self.assertIsInstance(cv, RepeatedKFold) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 5*10) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 5 * 10 + ) self.assertEqual(cv.n_repeats, 10) self.assertIsNone(cv.random_state) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # RepeatedKFold, regression with args - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None - evaluator.resampling_strategy = RepeatedKFold(n_splits=4, n_repeats=3, random_state=5) + evaluator.resampling_strategy = RepeatedKFold( + n_splits=4, n_repeats=3, random_state=5 + ) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, RepeatedKFold) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 4*3) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 4 * 3 + ) self.assertEqual(cv.n_repeats, 3) self.assertEqual(cv.random_state, 5) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # RepeatedKFold, regression no args - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None evaluator.resampling_strategy = RepeatedKFold(n_splits=5, n_repeats=10) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, RepeatedKFold) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 5*10) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 5 * 10 + ) self.assertEqual(cv.n_repeats, 10) self.assertIsNone(cv.random_state) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # RepeatedKFold, multi-output regression with args - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None - evaluator.resampling_strategy = RepeatedKFold(n_splits=4, n_repeats=3, random_state=5) + evaluator.resampling_strategy = RepeatedKFold( + n_splits=4, n_repeats=3, random_state=5 + ) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, RepeatedKFold) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 4*3) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 4 * 3 + ) self.assertEqual(cv.n_repeats, 3) self.assertEqual(cv.random_state, 5) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # RepeatedKFold, multi-output regression no args - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None evaluator.resampling_strategy = RepeatedKFold(n_splits=5, n_repeats=10) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, RepeatedKFold) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 5*10) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 5 * 10 + ) self.assertEqual(cv.n_repeats, 10) self.assertIsNone(cv.random_state) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # RepeatedStratifiedKFold, classification with args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None evaluator.resampling_strategy = RepeatedStratifiedKFold( - n_splits=2, n_repeats=3, random_state=5) + n_splits=2, n_repeats=3, random_state=5 + ) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, RepeatedStratifiedKFold) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 2*3) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 2 * 3 + ) self.assertEqual(cv.n_repeats, 3) self.assertEqual(cv.random_state, 5) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # RepeatedStratifiedKFold, classification no args - D.data['Y_train'] = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1]) - D.data['X_train'] = D.data['Y_train'] + D.data["Y_train"] = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1]) + D.data["X_train"] = D.data["Y_train"] evaluator = TrainEvaluator() - evaluator.resampling_strategy = RepeatedStratifiedKFold(n_splits=5, n_repeats=10) + evaluator.resampling_strategy = RepeatedStratifiedKFold( + n_splits=5, n_repeats=10 + ) evaluator.resampling_strategy_args = None cv = evaluator.get_splitter(D) self.assertIsInstance(cv, RepeatedStratifiedKFold) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 5*10) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 5 * 10 + ) self.assertEqual(cv.n_repeats, 10) self.assertIsNone(cv.random_state) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # StratifiedKFold, classification with args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) - D.data['X_train'] = D.data['Y_train'] + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) + D.data["X_train"] = D.data["Y_train"] evaluator = TrainEvaluator() evaluator.resampling_strategy = StratifiedKFold evaluator.resampling_strategy_args = None - evaluator.resampling_strategy = StratifiedKFold(n_splits=2, shuffle=True, random_state=5) + evaluator.resampling_strategy = StratifiedKFold( + n_splits=2, shuffle=True, random_state=5 + ) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, StratifiedKFold) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 2) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 2 + ) self.assertTrue(cv.shuffle) self.assertEqual(cv.random_state, 5) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # StratifiedKFold, classification no args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) evaluator = TrainEvaluator() evaluator.resampling_strategy = StratifiedKFold(n_splits=3, shuffle=False) evaluator.resampling_strategy_args = None cv = evaluator.get_splitter(D) self.assertIsInstance(cv, StratifiedKFold) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 3) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 3 + ) self.assertFalse(cv.shuffle) self.assertIsNone(cv.random_state) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # TimeSeriesSplit, multi-output regression with args - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None evaluator.resampling_strategy = TimeSeriesSplit(n_splits=4, max_train_size=3) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, TimeSeriesSplit) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 4) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 4 + ) self.assertEqual(cv.max_train_size, 3) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # TimeSeriesSplit, multi-output regression with args - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy = TimeSeriesSplit(n_splits=3) evaluator.resampling_strategy_args = None cv = evaluator.get_splitter(D) self.assertIsInstance(cv, TimeSeriesSplit) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 3) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 3 + ) self.assertIsNone(cv.max_train_size) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # TimeSeriesSplit, regression with args - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None evaluator.resampling_strategy = TimeSeriesSplit(n_splits=4, max_train_size=3) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, TimeSeriesSplit) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 4) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 4 + ) self.assertEqual(cv.max_train_size, 3) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # TimeSeriesSplit, regression no args - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy = TimeSeriesSplit(n_splits=3) evaluator.resampling_strategy_args = None cv = evaluator.get_splitter(D) self.assertIsInstance(cv, TimeSeriesSplit) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 3) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 3 + ) self.assertIsNone(cv.max_train_size) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # StratifiedKFold, classification no args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) evaluator = TrainEvaluator() evaluator.resampling_strategy = StratifiedKFold(n_splits=3) evaluator.resampling_strategy_args = None cv = evaluator.get_splitter(D) self.assertIsInstance(cv, StratifiedKFold) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 3) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 3 + ) self.assertFalse(cv.shuffle) self.assertIsNone(cv.random_state) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # GroupShuffleSplit, classification with args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) evaluator = TrainEvaluator() - evaluator.resampling_strategy_args = {'groups': np.array([1, 1, 2, 1, 2, 2])} - evaluator.resampling_strategy = GroupShuffleSplit(n_splits=2, test_size=0.3, - random_state=5) + evaluator.resampling_strategy_args = {"groups": np.array([1, 1, 2, 1, 2, 2])} + evaluator.resampling_strategy = GroupShuffleSplit( + n_splits=2, test_size=0.3, random_state=5 + ) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, GroupShuffleSplit) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 2) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 2 + ) self.assertEqual(cv.test_size, 0.3) self.assertEqual(cv.random_state, 5) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # GroupShuffleSplit, classification no args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) evaluator = TrainEvaluator() evaluator.resampling_strategy = GroupShuffleSplit(n_splits=5) evaluator.resampling_strategy_args = None @@ -1962,27 +2546,35 @@ def test_get_splitter_cv_object(self, te_mock): ValueError, "The 'groups' parameter should not be None", evaluator.get_splitter, - D) + D, + ) # GroupShuffleSplit, regression with args - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() - evaluator.resampling_strategy_args = {'groups': np.array([1, 1, 2, 1, 2, 2])} - evaluator.resampling_strategy = GroupShuffleSplit(n_splits=2, test_size=0.3, - random_state=5) + evaluator.resampling_strategy_args = {"groups": np.array([1, 1, 2, 1, 2, 2])} + evaluator.resampling_strategy = GroupShuffleSplit( + n_splits=2, test_size=0.3, random_state=5 + ) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, GroupShuffleSplit) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 2) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 2 + ) self.assertEqual(cv.test_size, 0.3) self.assertEqual(cv.random_state, 5) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # GroupShuffleSplit, regression no args - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy = GroupShuffleSplit(n_splits=5) evaluator.resampling_strategy_args = None @@ -1990,29 +2582,39 @@ def test_get_splitter_cv_object(self, te_mock): ValueError, "The 'groups' parameter should not be None", evaluator.get_splitter, - D) + D, + ) # GroupShuffleSplit, multi-output regression with args - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() - evaluator.resampling_strategy_args = {'groups': np.array([1, 1, 2, 1, 2, 2])} - evaluator.resampling_strategy = GroupShuffleSplit(n_splits=2, test_size=0.3, - random_state=5) + evaluator.resampling_strategy_args = {"groups": np.array([1, 1, 2, 1, 2, 2])} + evaluator.resampling_strategy = GroupShuffleSplit( + n_splits=2, test_size=0.3, random_state=5 + ) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, GroupShuffleSplit) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 2) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 2 + ) self.assertEqual(cv.test_size, 0.3) self.assertEqual(cv.random_state, 5) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # GroupShuffleSplit, multi-output regression no args - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy = GroupShuffleSplit(n_splits=5) evaluator.resampling_strategy_args = None @@ -2020,129 +2622,188 @@ def test_get_splitter_cv_object(self, te_mock): ValueError, "The 'groups' parameter should not be None", evaluator.get_splitter, - D) + D, + ) # StratifiedShuffleSplit, classification with args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None evaluator.resampling_strategy = StratifiedShuffleSplit( - n_splits=2, test_size=0.3, random_state=5) + n_splits=2, test_size=0.3, random_state=5 + ) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, StratifiedShuffleSplit) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 2) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 2 + ) self.assertEqual(cv.test_size, 0.3) self.assertEqual(cv.random_state, 5) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # StratifiedShuffleSplit, classification no args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, - 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1]) - D.data['X_train'] = D.data['Y_train'] + D.data["Y_train"] = np.array( + [0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1] + ) + D.data["X_train"] = D.data["Y_train"] evaluator = TrainEvaluator() evaluator.resampling_strategy = StratifiedShuffleSplit(n_splits=10) evaluator.resampling_strategy_args = None cv = evaluator.get_splitter(D) self.assertIsInstance(cv, StratifiedShuffleSplit) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 10) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 10 + ) self.assertIsNone(cv.test_size) self.assertIsNone(cv.random_state) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # ShuffleSplit, classification with args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) - D.data['X_train'] = D.data['Y_train'] + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) + D.data["X_train"] = D.data["Y_train"] evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None - evaluator.resampling_strategy = ShuffleSplit(n_splits=2, test_size=0.3, random_state=5) + evaluator.resampling_strategy = ShuffleSplit( + n_splits=2, test_size=0.3, random_state=5 + ) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, ShuffleSplit) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 2) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 2 + ) self.assertEqual(cv.test_size, 0.3) self.assertEqual(cv.random_state, 5) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # ShuffleSplit, classification no args - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) + D.data["Y_train"] = np.array([0, 0, 0, 1, 1, 1]) evaluator = TrainEvaluator() evaluator.resampling_strategy = ShuffleSplit(n_splits=10) evaluator.resampling_strategy_args = None cv = evaluator.get_splitter(D) self.assertIsInstance(cv, ShuffleSplit) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 10) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 10 + ) self.assertIsNone(cv.test_size) self.assertIsNone(cv.random_state) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # ShuffleSplit, regression with args - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None - evaluator.resampling_strategy = ShuffleSplit(n_splits=2, test_size=0.3, random_state=5) + evaluator.resampling_strategy = ShuffleSplit( + n_splits=2, test_size=0.3, random_state=5 + ) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, ShuffleSplit) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 2) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 2 + ) self.assertEqual(cv.test_size, 0.3) self.assertEqual(cv.random_state, 5) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # ShuffleSplit, regression no args - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION + D.data["Y_train"] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info["task"] = REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy = ShuffleSplit(n_splits=10) evaluator.resampling_strategy_args = None cv = evaluator.get_splitter(D) self.assertIsInstance(cv, ShuffleSplit) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 10) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 10 + ) self.assertIsNone(cv.test_size) self.assertIsNone(cv.random_state) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # ShuffleSplit, multi-output regression with args - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy_args = None - evaluator.resampling_strategy = ShuffleSplit(n_splits=2, test_size=0.3, random_state=5) + evaluator.resampling_strategy = ShuffleSplit( + n_splits=2, test_size=0.3, random_state=5 + ) cv = evaluator.get_splitter(D) self.assertIsInstance(cv, ShuffleSplit) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 2) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 2 + ) self.assertEqual(cv.test_size, 0.3) self.assertEqual(cv.random_state, 5) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) # ShuffleSplit, multi-output regression no args - D.data['Y_train'] = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], - [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]]) - D.info['task'] = MULTIOUTPUT_REGRESSION + D.data["Y_train"] = np.array( + [[0.0, 0.1], [0.2, 0.3], [0.4, 0.5], [1.0, 1.1], [1.2, 1.3], [1.4, 1.5]] + ) + D.info["task"] = MULTIOUTPUT_REGRESSION evaluator = TrainEvaluator() evaluator.resampling_strategy = ShuffleSplit(n_splits=10) evaluator.resampling_strategy_args = None cv = evaluator.get_splitter(D) self.assertIsInstance(cv, ShuffleSplit) - self.assertEqual(cv.get_n_splits( - groups=evaluator.resampling_strategy_args['groups']), 10) + self.assertEqual( + cv.get_n_splits(groups=evaluator.resampling_strategy_args["groups"]), 10 + ) self.assertIsNone(cv.test_size) self.assertIsNone(cv.random_state) - next(cv.split(D.data['Y_train'], D.data['Y_train'], - groups=evaluator.resampling_strategy_args['groups'])) + next( + cv.split( + D.data["Y_train"], + D.data["Y_train"], + groups=evaluator.resampling_strategy_args["groups"], + ) + ) @unittest.mock.patch.object(TrainEvaluator, "__init__") def test_holdout_split_size(self, te_mock): @@ -2151,102 +2812,119 @@ def test_holdout_split_size(self, te_mock): D.feat_type = {} evaluator = TrainEvaluator() - evaluator.resampling_strategy = 'holdout' + evaluator.resampling_strategy = "holdout" # Exact Ratio D.data = dict(Y_train=np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])) D.info = dict(task=BINARY_CLASSIFICATION) - evaluator.resampling_strategy_args = {'shuffle': True, - 'train_size': 0.7} + evaluator.resampling_strategy_args = {"shuffle": True, "train_size": 0.7} cv = evaluator.get_splitter(D) self.assertEqual(cv.get_n_splits(), 1) - train_samples, test_samples = next(cv.split(D.data['Y_train'], - D.data['Y_train'])) + train_samples, test_samples = next( + cv.split(D.data["Y_train"], D.data["Y_train"]) + ) self.assertEqual(len(train_samples), 7) self.assertEqual(len(test_samples), 3) # No Shuffle - evaluator.resampling_strategy_args = {'shuffle': False, - 'train_size': 0.7} + evaluator.resampling_strategy_args = {"shuffle": False, "train_size": 0.7} cv = evaluator.get_splitter(D) self.assertEqual(cv.get_n_splits(), 1) - train_samples, test_samples = next(cv.split(D.data['Y_train'], - D.data['Y_train'])) + train_samples, test_samples = next( + cv.split(D.data["Y_train"], D.data["Y_train"]) + ) self.assertEqual(len(train_samples), 7) self.assertEqual(len(test_samples), 3) # Rounded Ratio D.data = dict(Y_train=np.array([0, 0, 0, 0, 0, 1, 1, 1, 1])) - evaluator.resampling_strategy_args = {'shuffle': True, - 'train_size': 0.7} + evaluator.resampling_strategy_args = {"shuffle": True, "train_size": 0.7} cv = evaluator.get_splitter(D) self.assertEqual(cv.get_n_splits(), 1) - train_samples, test_samples = next(cv.split(D.data['Y_train'], - D.data['Y_train'])) + train_samples, test_samples = next( + cv.split(D.data["Y_train"], D.data["Y_train"]) + ) self.assertEqual(len(train_samples), 6) self.assertEqual(len(test_samples), 3) # Rounded Ratio No Shuffle - evaluator.resampling_strategy_args = {'shuffle': False, - 'train_size': 0.7} + evaluator.resampling_strategy_args = {"shuffle": False, "train_size": 0.7} cv = evaluator.get_splitter(D) self.assertEqual(cv.get_n_splits(), 1) - train_samples, test_samples = next(cv.split(D.data['Y_train'], - D.data['Y_train'])) + train_samples, test_samples = next( + cv.split(D.data["Y_train"], D.data["Y_train"]) + ) self.assertEqual(len(train_samples), 6) self.assertEqual(len(test_samples), 3) # More data - evaluator.resampling_strategy_args = {'shuffle': True, - 'train_size': 0.7} + evaluator.resampling_strategy_args = {"shuffle": True, "train_size": 0.7} D.data = dict(Y_train=np.zeros((900, 1))) cv = evaluator.get_splitter(D) self.assertEqual(cv.get_n_splits(), 1) - train_samples, test_samples = next(cv.split(D.data['Y_train'], - D.data['Y_train'])) + train_samples, test_samples = next( + cv.split(D.data["Y_train"], D.data["Y_train"]) + ) self.assertEqual(len(train_samples), 630) self.assertEqual(len(test_samples), 270) - evaluator.resampling_strategy_args = {'train_size': 0.752} + evaluator.resampling_strategy_args = {"train_size": 0.752} D.data = dict(Y_train=np.zeros((900, 1))) cv = evaluator.get_splitter(D) self.assertEqual(cv.get_n_splits(), 1) - train_samples, test_samples = next(cv.split(D.data['Y_train'], - D.data['Y_train'])) + train_samples, test_samples = next( + cv.split(D.data["Y_train"], D.data["Y_train"]) + ) self.assertEqual(len(train_samples), 676) self.assertEqual(len(test_samples), 224) # Multilabel Exact Ratio - D.data = dict(Y_train=np.array([[0, 0], [0, 1], [1, 1], [1, 0], [1, 1], - [1, 1], [1, 1], [1, 0], [1, 1], [1, 1]] - )) + D.data = dict( + Y_train=np.array( + [ + [0, 0], + [0, 1], + [1, 1], + [1, 0], + [1, 1], + [1, 1], + [1, 1], + [1, 0], + [1, 1], + [1, 1], + ] + ) + ) D.info = dict(task=MULTILABEL_CLASSIFICATION) - evaluator.resampling_strategy_args = {'shuffle': True, - 'train_size': 0.7} + evaluator.resampling_strategy_args = {"shuffle": True, "train_size": 0.7} cv = evaluator.get_splitter(D) self.assertEqual(cv.get_n_splits(), 1) - train_samples, test_samples = next(cv.split(D.data['Y_train'], - D.data['Y_train'])) + train_samples, test_samples = next( + cv.split(D.data["Y_train"], D.data["Y_train"]) + ) self.assertEqual(len(train_samples), 7) self.assertEqual(len(test_samples), 3) # Multilabel No Shuffle - D.data = dict(Y_train=np.array([[0, 0], [0, 1], [1, 1], [1, 0], [1, 1], - [1, 1], [1, 1], [1, 0], [1, 1]])) - evaluator.resampling_strategy_args = {'shuffle': False, - 'train_size': 0.7} + D.data = dict( + Y_train=np.array( + [[0, 0], [0, 1], [1, 1], [1, 0], [1, 1], [1, 1], [1, 1], [1, 0], [1, 1]] + ) + ) + evaluator.resampling_strategy_args = {"shuffle": False, "train_size": 0.7} cv = evaluator.get_splitter(D) self.assertEqual(cv.get_n_splits(), 1) - train_samples, test_samples = next(cv.split(D.data['Y_train'], - D.data['Y_train'])) + train_samples, test_samples = next( + cv.split(D.data["Y_train"], D.data["Y_train"]) + ) self.assertEqual(len(train_samples), 6) self.assertEqual(len(test_samples), 3) @@ -2255,16 +2933,17 @@ class FunctionsTest(unittest.TestCase): def setUp(self): self.queue = multiprocessing.Queue() self.configuration = get_configuration_space( - {'task': MULTICLASS_CLASSIFICATION, - 'is_sparse': False}).get_default_configuration() + {"task": MULTICLASS_CLASSIFICATION, "is_sparse": False} + ).get_default_configuration() self.data = get_multiclass_classification_datamanager() - self.tmp_dir = os.path.join(os.path.dirname(__file__), - '.test_holdout_functions') - self.n = len(self.data.data['Y_train']) - self.y = self.data.data['Y_train'].flatten() + self.tmp_dir = os.path.join( + os.path.dirname(__file__), ".test_holdout_functions" + ) + self.n = len(self.data.data["Y_train"]) + self.y = self.data.data["Y_train"].flatten() tmp_dir_name = self.id() - self.ev_path = os.path.join(this_directory, '.tmp_evaluations', tmp_dir_name) + self.ev_path = os.path.join(this_directory, ".tmp_evaluations", tmp_dir_name) if os.path.exists(self.ev_path): shutil.rmtree(self.ev_path) os.makedirs(self.ev_path, exist_ok=False) @@ -2274,12 +2953,14 @@ def setUp(self): self.backend.get_cv_model_dir.return_value = self.ev_path dummy_model_files = [os.path.join(self.ev_path, str(n)) for n in range(100)] dummy_pred_files = [os.path.join(self.ev_path, str(n)) for n in range(100, 200)] - dummy_cv_model_files = [os.path.join(self.ev_path, str(n)) for n in range(200, 300)] + dummy_cv_model_files = [ + os.path.join(self.ev_path, str(n)) for n in range(200, 300) + ] self.backend.get_model_path.side_effect = dummy_model_files self.backend.get_cv_model_path.side_effect = dummy_cv_model_files self.backend.get_prediction_output_path.side_effect = dummy_pred_files self.backend.load_datamanager.return_value = self.data - self.dataset_name = json.dumps({'task_id': 'test'}) + self.dataset_name = json.dumps({"task_id": "test"}) self.port = logging.handlers.DEFAULT_TCP_LOGGING_PORT def tearDown(self): @@ -2292,7 +2973,7 @@ def test_eval_holdout(self): port=self.port, config=self.configuration, backend=self.backend, - resampling_strategy='holdout', + resampling_strategy="holdout", resampling_strategy_args=None, seed=1, num_run=1, @@ -2307,9 +2988,9 @@ def test_eval_holdout(self): ) info = read_queue(self.queue) self.assertEqual(len(info), 1) - self.assertAlmostEqual(info[0]['loss'], 0.030303030303030276, places=3) - self.assertEqual(info[0]['status'], StatusType.SUCCESS) - self.assertNotIn('bac_metric', info[0]['additional_run_info']) + self.assertAlmostEqual(info[0]["loss"], 0.030303030303030276, places=3) + self.assertEqual(info[0]["status"], StatusType.SUCCESS) + self.assertNotIn("bac_metric", info[0]["additional_run_info"]) def test_eval_holdout_all_loss_functions(self): eval_holdout( @@ -2317,7 +2998,7 @@ def test_eval_holdout_all_loss_functions(self): port=self.port, config=self.configuration, backend=self.backend, - resampling_strategy='holdout', + resampling_strategy="holdout", resampling_strategy_args=None, seed=1, num_run=1, @@ -2334,34 +3015,36 @@ def test_eval_holdout_all_loss_functions(self): self.assertEqual(len(rval), 1) fixture = { - 'accuracy': 0.030303030303030276, - 'balanced_accuracy': 0.033333333333333326, - 'f1_macro': 0.032036613272311221, - 'f1_micro': 0.030303030303030276, - 'f1_weighted': 0.030441716940572849, - 'log_loss': 0.06376745642134637, - 'precision_macro': 0.02777777777777779, - 'precision_micro': 0.030303030303030276, - 'precision_weighted': 0.027777777777777901, - 'recall_macro': 0.033333333333333326, - 'recall_micro': 0.030303030303030276, - 'recall_weighted': 0.030303030303030276, - 'num_run': 1, - 'validation_loss': 0.0, - 'test_loss': 0.04, - 'train_loss': 0.0, + "accuracy": 0.030303030303030276, + "balanced_accuracy": 0.033333333333333326, + "f1_macro": 0.032036613272311221, + "f1_micro": 0.030303030303030276, + "f1_weighted": 0.030441716940572849, + "log_loss": 0.06376745642134637, + "precision_macro": 0.02777777777777779, + "precision_micro": 0.030303030303030276, + "precision_weighted": 0.027777777777777901, + "recall_macro": 0.033333333333333326, + "recall_micro": 0.030303030303030276, + "recall_weighted": 0.030303030303030276, + "num_run": 1, + "validation_loss": 0.0, + "test_loss": 0.04, + "train_loss": 0.0, } - additional_run_info = rval[0]['additional_run_info'] + additional_run_info = rval[0]["additional_run_info"] for key, value in fixture.items(): - self.assertAlmostEqual(additional_run_info[key], fixture[key], - msg=key) - self.assertIn('duration', additional_run_info) - self.assertEqual(len(additional_run_info), len(fixture) + 1, - msg=sorted(additional_run_info.items())) + self.assertAlmostEqual(additional_run_info[key], fixture[key], msg=key) + self.assertIn("duration", additional_run_info) + self.assertEqual( + len(additional_run_info), + len(fixture) + 1, + msg=sorted(additional_run_info.items()), + ) - self.assertAlmostEqual(rval[0]['loss'], 0.030303030303030276, places=3) - self.assertEqual(rval[0]['status'], StatusType.SUCCESS) + self.assertAlmostEqual(rval[0]["loss"], 0.030303030303030276, places=3) + self.assertEqual(rval[0]["status"], StatusType.SUCCESS) def test_eval_holdout_iterative_fit_no_timeout(self): eval_iterative_holdout( @@ -2369,7 +3052,7 @@ def test_eval_holdout_iterative_fit_no_timeout(self): port=self.port, config=self.configuration, backend=self.backend, - resampling_strategy='holdout', + resampling_strategy="holdout", resampling_strategy_args=None, seed=1, num_run=1, @@ -2384,9 +3067,9 @@ def test_eval_holdout_iterative_fit_no_timeout(self): ) rval = read_queue(self.queue) self.assertEqual(len(rval), 9) - self.assertAlmostEqual(rval[-1]['loss'], 0.030303030303030276) - self.assertEqual(rval[0]['status'], StatusType.DONOTADVANCE) - self.assertEqual(rval[-1]['status'], StatusType.SUCCESS) + self.assertAlmostEqual(rval[-1]["loss"], 0.030303030303030276) + self.assertEqual(rval[0]["status"], StatusType.DONOTADVANCE) + self.assertEqual(rval[-1]["status"], StatusType.SUCCESS) def test_eval_holdout_budget_iterations(self): eval_holdout( @@ -2394,7 +3077,7 @@ def test_eval_holdout_budget_iterations(self): port=self.port, config=self.configuration, backend=self.backend, - resampling_strategy='holdout', + resampling_strategy="holdout", resampling_strategy_args=None, seed=1, num_run=1, @@ -2406,45 +3089,45 @@ def test_eval_holdout_budget_iterations(self): instance=self.dataset_name, metric=accuracy, budget=1, - budget_type='iterations', + budget_type="iterations", additional_components=dict(), ) info = read_queue(self.queue) self.assertEqual(len(info), 1) - self.assertAlmostEqual(info[0]['loss'], 0.06060606060606055, places=3) - self.assertEqual(info[0]['status'], StatusType.SUCCESS) - self.assertNotIn('bac_metric', info[0]['additional_run_info']) + self.assertAlmostEqual(info[0]["loss"], 0.06060606060606055, places=3) + self.assertEqual(info[0]["status"], StatusType.SUCCESS) + self.assertNotIn("bac_metric", info[0]["additional_run_info"]) def test_eval_holdout_budget_iterations_converged(self): configuration = get_configuration_space( - exclude={'classifier': ['random_forest', 'liblinear_svc']}, - info={'task': MULTICLASS_CLASSIFICATION, 'is_sparse': False}, + exclude={"classifier": ["random_forest", "liblinear_svc"]}, + info={"task": MULTICLASS_CLASSIFICATION, "is_sparse": False}, ).get_default_configuration() eval_holdout( queue=self.queue, port=self.port, config=configuration, backend=self.backend, - resampling_strategy='holdout', + resampling_strategy="holdout", resampling_strategy_args=None, seed=1, num_run=1, scoring_functions=None, output_y_hat_optimization=True, include=None, - exclude={'classifier': ['random_forest', 'liblinear_svc']}, + exclude={"classifier": ["random_forest", "liblinear_svc"]}, disable_file_output=False, instance=self.dataset_name, metric=accuracy, budget=80, - budget_type='iterations', + budget_type="iterations", additional_components=dict(), ) info = read_queue(self.queue) self.assertEqual(len(info), 1) - self.assertAlmostEqual(info[0]['loss'], 0.18181818181818177, places=3) - self.assertEqual(info[0]['status'], StatusType.DONOTADVANCE) - self.assertNotIn('bac_metric', info[0]['additional_run_info']) + self.assertAlmostEqual(info[0]["loss"], 0.18181818181818177, places=3) + self.assertEqual(info[0]["status"], StatusType.DONOTADVANCE) + self.assertNotIn("bac_metric", info[0]["additional_run_info"]) def test_eval_holdout_budget_subsample(self): eval_holdout( @@ -2452,7 +3135,7 @@ def test_eval_holdout_budget_subsample(self): port=self.port, config=self.configuration, backend=self.backend, - resampling_strategy='holdout', + resampling_strategy="holdout", resampling_strategy_args=None, seed=1, num_run=1, @@ -2464,14 +3147,14 @@ def test_eval_holdout_budget_subsample(self): instance=self.dataset_name, metric=accuracy, budget=30, - budget_type='subsample', + budget_type="subsample", additional_components=dict(), ) info = read_queue(self.queue) self.assertEqual(len(info), 1) - self.assertAlmostEqual(info[0]['loss'], 0.0) - self.assertEqual(info[0]['status'], StatusType.SUCCESS) - self.assertNotIn('bac_metric', info[0]['additional_run_info']) + self.assertAlmostEqual(info[0]["loss"], 0.0) + self.assertEqual(info[0]["status"], StatusType.SUCCESS) + self.assertNotIn("bac_metric", info[0]["additional_run_info"]) def test_eval_holdout_budget_mixed_iterations(self): print(self.configuration) @@ -2480,7 +3163,7 @@ def test_eval_holdout_budget_mixed_iterations(self): port=self.port, config=self.configuration, backend=self.backend, - resampling_strategy='holdout', + resampling_strategy="holdout", resampling_strategy_args=None, seed=1, num_run=1, @@ -2492,44 +3175,44 @@ def test_eval_holdout_budget_mixed_iterations(self): instance=self.dataset_name, metric=accuracy, budget=1, - budget_type='mixed', - additional_components=dict() + budget_type="mixed", + additional_components=dict(), ) info = read_queue(self.queue) self.assertEqual(len(info), 1) - self.assertAlmostEqual(info[0]['loss'], 0.06060606060606055) + self.assertAlmostEqual(info[0]["loss"], 0.06060606060606055) def test_eval_holdout_budget_mixed_subsample(self): configuration = get_configuration_space( - exclude={'classifier': ['random_forest']}, - info={'task': MULTICLASS_CLASSIFICATION, 'is_sparse': False}, + exclude={"classifier": ["random_forest"]}, + info={"task": MULTICLASS_CLASSIFICATION, "is_sparse": False}, ).get_default_configuration() - self.assertEqual(configuration['classifier:__choice__'], 'liblinear_svc') + self.assertEqual(configuration["classifier:__choice__"], "liblinear_svc") eval_holdout( queue=self.queue, port=self.port, config=configuration, backend=self.backend, - resampling_strategy='holdout', + resampling_strategy="holdout", resampling_strategy_args=None, seed=1, num_run=1, scoring_functions=None, output_y_hat_optimization=True, include=None, - exclude={'classifier': ['random_forest']}, + exclude={"classifier": ["random_forest"]}, disable_file_output=False, instance=self.dataset_name, metric=accuracy, budget=40, - budget_type='mixed', + budget_type="mixed", additional_components=dict(), ) info = read_queue(self.queue) self.assertEqual(len(info), 1) - self.assertAlmostEqual(info[0]['loss'], 0.06060606060606055) - self.assertEqual(info[0]['status'], StatusType.SUCCESS) - self.assertNotIn('bac_metric', info[0]['additional_run_info']) + self.assertAlmostEqual(info[0]["loss"], 0.06060606060606055) + self.assertEqual(info[0]["status"], StatusType.SUCCESS) + self.assertNotIn("bac_metric", info[0]["additional_run_info"]) def test_eval_cv(self): eval_cv( @@ -2539,8 +3222,8 @@ def test_eval_cv(self): backend=self.backend, seed=1, num_run=1, - resampling_strategy='cv', - resampling_strategy_args={'folds': 3}, + resampling_strategy="cv", + resampling_strategy_args={"folds": 3}, scoring_functions=None, output_y_hat_optimization=True, include=None, @@ -2552,9 +3235,9 @@ def test_eval_cv(self): ) rval = read_queue(self.queue) self.assertEqual(len(rval), 1) - self.assertAlmostEqual(rval[0]['loss'], 0.04999999999999997) - self.assertEqual(rval[0]['status'], StatusType.SUCCESS) - self.assertNotIn('bac_metric', rval[0]['additional_run_info']) + self.assertAlmostEqual(rval[0]["loss"], 0.04999999999999997) + self.assertEqual(rval[0]["status"], StatusType.SUCCESS) + self.assertNotIn("bac_metric", rval[0]["additional_run_info"]) def test_eval_cv_all_loss_functions(self): eval_cv( @@ -2564,8 +3247,8 @@ def test_eval_cv_all_loss_functions(self): backend=self.backend, seed=1, num_run=1, - resampling_strategy='cv', - resampling_strategy_args={'folds': 3}, + resampling_strategy="cv", + resampling_strategy_args={"folds": 3}, scoring_functions=SCORER_LIST, output_y_hat_optimization=True, include=None, @@ -2579,33 +3262,36 @@ def test_eval_cv_all_loss_functions(self): self.assertEqual(len(rval), 1) fixture = { - 'accuracy': 0.04999999999999997, - 'balanced_accuracy': 0.05130303030303027, - 'f1_macro': 0.052793650793650775, - 'f1_micro': 0.04999999999999997, - 'f1_weighted': 0.050090909090909096, - 'log_loss': 0.12108563414774837, - 'precision_macro': 0.04963636363636359, - 'precision_micro': 0.04999999999999997, - 'precision_weighted': 0.045757575757575664, - 'recall_macro': 0.05130303030303027, - 'recall_micro': 0.04999999999999997, - 'recall_weighted': 0.04999999999999997, - 'num_run': 1, - 'validation_loss': 0.04, - 'test_loss': 0.04, - 'train_loss': 0.0, + "accuracy": 0.04999999999999997, + "balanced_accuracy": 0.05130303030303027, + "f1_macro": 0.052793650793650775, + "f1_micro": 0.04999999999999997, + "f1_weighted": 0.050090909090909096, + "log_loss": 0.12108563414774837, + "precision_macro": 0.04963636363636359, + "precision_micro": 0.04999999999999997, + "precision_weighted": 0.045757575757575664, + "recall_macro": 0.05130303030303027, + "recall_micro": 0.04999999999999997, + "recall_weighted": 0.04999999999999997, + "num_run": 1, + "validation_loss": 0.04, + "test_loss": 0.04, + "train_loss": 0.0, } - additional_run_info = rval[0]['additional_run_info'] + additional_run_info = rval[0]["additional_run_info"] for key, value in fixture.items(): self.assertAlmostEqual(additional_run_info[key], fixture[key], msg=key) - self.assertIn('duration', additional_run_info) - self.assertEqual(len(additional_run_info), len(fixture) + 1, - msg=sorted(additional_run_info.items())) + self.assertIn("duration", additional_run_info) + self.assertEqual( + len(additional_run_info), + len(fixture) + 1, + msg=sorted(additional_run_info.items()), + ) - self.assertAlmostEqual(rval[0]['loss'], 0.04999999999999997) - self.assertEqual(rval[0]['status'], StatusType.SUCCESS) + self.assertAlmostEqual(rval[0]["loss"], 0.04999999999999997) + self.assertEqual(rval[0]["status"], StatusType.SUCCESS) # def test_eval_cv_on_subset(self): # backend_api = backend.create(self.tmp_dir, self.tmp_dir) @@ -2619,13 +3305,15 @@ def test_eval_cv_all_loss_functions(self): # self.assertEqual(info[2], 1) def test_eval_partial_cv(self): - results = [0.050000000000000044, - 0.0, - 0.09999999999999998, - 0.09999999999999998, - 0.050000000000000044] + results = [ + 0.050000000000000044, + 0.0, + 0.09999999999999998, + 0.09999999999999998, + 0.050000000000000044, + ] for fold in range(5): - instance = json.dumps({'task_id': 'data', 'fold': fold}) + instance = json.dumps({"task_id": "data", "fold": fold}) eval_partial_cv( port=self.port, queue=self.queue, @@ -2634,8 +3322,8 @@ def test_eval_partial_cv(self): seed=1, num_run=1, instance=instance, - resampling_strategy='partial-cv', - resampling_strategy_args={'folds': 5}, + resampling_strategy="partial-cv", + resampling_strategy_args={"folds": 5}, scoring_functions=None, output_y_hat_optimization=True, include=None, @@ -2646,5 +3334,5 @@ def test_eval_partial_cv(self): ) rval = read_queue(self.queue) self.assertEqual(len(rval), 1) - self.assertAlmostEqual(rval[0]['loss'], results[fold]) - self.assertEqual(rval[0]['status'], StatusType.SUCCESS) + self.assertAlmostEqual(rval[0]["loss"], results[fold]) + self.assertEqual(rval[0]["status"], StatusType.SUCCESS) diff --git a/test/test_metalearning/__init__.py b/test/test_metalearning/__init__.py index cc3cd7becd..e298f0f075 100644 --- a/test/test_metalearning/__init__.py +++ b/test/test_metalearning/__init__.py @@ -1,2 +1,2 @@ # -*- encoding: utf-8 -*- -__author__ = 'feurerm' +__author__ = "feurerm" diff --git a/test/test_metalearning/pyMetaLearn/__init__.py b/test/test_metalearning/pyMetaLearn/__init__.py index 8f0ce6cb7c..92bf78f389 100644 --- a/test/test_metalearning/pyMetaLearn/__init__.py +++ b/test/test_metalearning/pyMetaLearn/__init__.py @@ -1 +1 @@ -__author__ = 'feurerm' +__author__ = "feurerm" diff --git a/test/test_metalearning/pyMetaLearn/metalearning/test_kND.py b/test/test_metalearning/pyMetaLearn/metalearning/test_kND.py index 6733dca93f..4877379440 100644 --- a/test/test_metalearning/pyMetaLearn/metalearning/test_kND.py +++ b/test/test_metalearning/pyMetaLearn/metalearning/test_kND.py @@ -1,8 +1,9 @@ import logging import unittest -import numpy as np +import numpy as np import pandas as pd + from autosklearn.metalearning.metalearning.kNearestDatasets.kND import KNearestDatasets from autosklearn.metalearning.metalearning.metrics.misc import get_random_metric @@ -11,15 +12,35 @@ class kNDTest(unittest.TestCase): _multiprocess_can_split_ = True def setUp(self): - self.anneal = pd.Series({"number_of_instances": 898., "number_of_classes": 5., - "number_of_features": 38.}, name=232) - self.krvskp = pd.Series({"number_of_instances": 3196., "number_of_classes": - 2., "number_of_features": 36.}, name=233) - self.labor = pd.Series({"number_of_instances": 57., "number_of_classes": - 2., "number_of_features": 16.}, name=234) - self.runs = {232: [0.1, 0.5, 0.7], - 233: [np.NaN, 0.1, 0.7], - 234: [0.5, 0.7, 0.1]} + self.anneal = pd.Series( + { + "number_of_instances": 898.0, + "number_of_classes": 5.0, + "number_of_features": 38.0, + }, + name=232, + ) + self.krvskp = pd.Series( + { + "number_of_instances": 3196.0, + "number_of_classes": 2.0, + "number_of_features": 36.0, + }, + name=233, + ) + self.labor = pd.Series( + { + "number_of_instances": 57.0, + "number_of_classes": 2.0, + "number_of_features": 16.0, + }, + name=234, + ) + self.runs = { + 232: [0.1, 0.5, 0.7], + 233: [np.NaN, 0.1, 0.7], + 234: [0.5, 0.7, 0.1], + } self.runs = pd.DataFrame(self.runs) self.logger = logging.getLogger() @@ -30,43 +51,47 @@ def test_fit_l1_distance(self): self.assertEqual(kND.best_configuration_per_dataset[232], 0) self.assertEqual(kND.best_configuration_per_dataset[233], 1) self.assertEqual(kND.best_configuration_per_dataset[234], 2) - self.assertTrue((kND.metafeatures == - pd.DataFrame([self.anneal, self.krvskp, self.labor])).all().all()) + self.assertTrue( + (kND.metafeatures == pd.DataFrame([self.anneal, self.krvskp, self.labor])) + .all() + .all() + ) # TODO: rename to kNearestTasks or something def test_kNearestDatasets(self): kND = KNearestDatasets(logger=self.logger) - kND.fit(pd.DataFrame([self.krvskp, self.labor]), - self.runs.loc[:, [233, 234]]) + kND.fit(pd.DataFrame([self.krvskp, self.labor]), self.runs.loc[:, [233, 234]]) neighbor = kND.kNearestDatasets(self.anneal, 1) self.assertEqual([233], neighbor) - neighbor, distance = kND.kNearestDatasets(self.anneal, 1, - return_distance=True) + neighbor, distance = kND.kNearestDatasets(self.anneal, 1, return_distance=True) self.assertEqual([233], neighbor) np.testing.assert_array_almost_equal([3.8320802803440586], distance) neighbors = kND.kNearestDatasets(self.anneal, 2) self.assertEqual([233, 234], neighbors) - neighbors, distance = kND.kNearestDatasets(self.anneal, 2, - return_distance=True) + neighbors, distance = kND.kNearestDatasets(self.anneal, 2, return_distance=True) self.assertEqual([233, 234], neighbors) - np.testing.assert_array_almost_equal([3.8320802803440586, 4.367919719655942], distance) + np.testing.assert_array_almost_equal( + [3.8320802803440586, 4.367919719655942], distance + ) neighbors = kND.kNearestDatasets(self.anneal, -1) self.assertEqual([233, 234], neighbors) - neighbors, distance = kND.kNearestDatasets(self.anneal, -1, - return_distance=True) + neighbors, distance = kND.kNearestDatasets( + self.anneal, -1, return_distance=True + ) self.assertEqual([233, 234], neighbors) - np.testing.assert_array_almost_equal([3.8320802803440586, 4.367919719655942], distance) + np.testing.assert_array_almost_equal( + [3.8320802803440586, 4.367919719655942], distance + ) self.assertRaises(ValueError, kND.kNearestDatasets, self.anneal, 0) self.assertRaises(ValueError, kND.kNearestDatasets, self.anneal, -2) def test_kBestSuggestions(self): kND = KNearestDatasets(logger=self.logger) - kND.fit(pd.DataFrame([self.krvskp, self.labor]), - self.runs.loc[:, [233, 234]]) + kND.fit(pd.DataFrame([self.krvskp, self.labor]), self.runs.loc[:, [233, 234]]) neighbor = kND.kBestSuggestions(self.anneal, 1) np.testing.assert_array_almost_equal( [(233, 3.8320802803440586, 1)], @@ -87,10 +112,10 @@ def test_kBestSuggestions(self): self.assertRaises(ValueError, kND.kBestSuggestions, self.anneal, -2) def test_random_metric(self): - kND = KNearestDatasets(logger=self.logger, - metric=get_random_metric(random_state=1)) - kND.fit(pd.DataFrame([self.krvskp, self.labor]), - self.runs.loc[:, [233, 234]]) + kND = KNearestDatasets( + logger=self.logger, metric=get_random_metric(random_state=1) + ) + kND.fit(pd.DataFrame([self.krvskp, self.labor]), self.runs.loc[:, [233, 234]]) distances = [] for i in range(20): neighbor = kND.kBestSuggestions(self.anneal, 1) diff --git a/test/test_metalearning/pyMetaLearn/test_meta_base.py b/test/test_metalearning/pyMetaLearn/test_meta_base.py index b1ac39ee2a..1c6788e816 100644 --- a/test/test_metalearning/pyMetaLearn/test_meta_base.py +++ b/test/test_metalearning/pyMetaLearn/test_meta_base.py @@ -14,7 +14,7 @@ class MetaBaseTest(unittest.TestCase): def setUp(self): self.cwd = os.getcwd() data_dir = os.path.dirname(__file__) - data_dir = os.path.join(data_dir, 'test_meta_base_data') + data_dir = os.path.join(data_dir, "test_meta_base_data") os.chdir(data_dir) pipeline = autosklearn.pipeline.classification.SimpleClassificationPipeline() @@ -33,33 +33,34 @@ def test_get_all_runs(self): self.assertEqual((125, 125), runs.shape) def test_get_runs(self): - runs = self.base.get_runs('233') + runs = self.base.get_runs("233") # TODO update this ASAP self.assertEqual(125, len(runs)) self.assertIsInstance(runs, pd.Series) def test_get_metafeatures_single_dataset(self): - mf = self.base.get_metafeatures('233') + mf = self.base.get_metafeatures("233") self.assertIsInstance(mf, pd.Series) - self.assertEqual(mf.name, '233') - self.assertEqual(mf.loc['NumberOfInstances'], 2142.0) + self.assertEqual(mf.name, "233") + self.assertEqual(mf.loc["NumberOfInstances"], 2142.0) def test_get_metafeatures_single_feature(self): - mf = self.base.get_metafeatures(features='NumberOfInstances') + mf = self.base.get_metafeatures(features="NumberOfInstances") self.assertIsInstance(mf, pd.Series) - self.assertEqual(mf.shape, (132, )) + self.assertEqual(mf.shape, (132,)) def test_get_metafeatures_single_dataset_and_single_feature(self): - mf = self.base.get_metafeatures('233', features='NumberOfInstances') + mf = self.base.get_metafeatures("233", features="NumberOfInstances") self.assertEqual(mf.shape, ()) def test_get_metafeatures_multiple_datasets(self): - mf = self.base.get_metafeatures(['233', '236']) + mf = self.base.get_metafeatures(["233", "236"]) self.assertIsInstance(mf, pd.DataFrame) self.assertEqual(mf.shape, (2, 46)) def test_get_metafeatures_multiple_features(self): - mf = self.base.get_metafeatures(features=['NumberOfInstances', - 'NumberOfClasses']) + mf = self.base.get_metafeatures( + features=["NumberOfInstances", "NumberOfClasses"] + ) self.assertIsInstance(mf, pd.DataFrame) self.assertEqual(mf.shape, (132, 2)) diff --git a/test/test_metalearning/pyMetaLearn/test_meta_features.py b/test/test_metalearning/pyMetaLearn/test_meta_features.py index d31f3d0227..6a9bec4dcf 100644 --- a/test/test_metalearning/pyMetaLearn/test_meta_features.py +++ b/test/test_metalearning/pyMetaLearn/test_meta_features.py @@ -3,25 +3,21 @@ import tempfile import unittest +import arff +import numpy as np import pandas as pd - import pytest - -import arff from joblib import Memory -import numpy as np -from sklearn.datasets import make_multilabel_classification, fetch_openml +from sklearn.datasets import fetch_openml, make_multilabel_classification -from autosklearn.pipeline.components.data_preprocessing.feature_type \ - import FeatTypeSplit -from autosklearn.metalearning.metafeatures.metafeature import MetaFeatureValue import autosklearn.metalearning.metafeatures.metafeatures as meta_features +from autosklearn.metalearning.metafeatures.metafeature import MetaFeatureValue +from autosklearn.pipeline.components.data_preprocessing.feature_type import ( + FeatTypeSplit, +) -@pytest.fixture( - scope='class', - params=('pandas', 'numpy') -) +@pytest.fixture(scope="class", params=("pandas", "numpy")) def multilabel_train_data(request): cache = Memory(location=tempfile.gettempdir()) cached_func = cache.cache(make_multilabel_classification) @@ -31,20 +27,17 @@ def multilabel_train_data(request): n_classes=5, n_labels=5, return_indicator=True, - random_state=1 + random_state=1, ) - if request.param == 'numpy': + if request.param == "numpy": return X, y - elif request.param == 'pandas': + elif request.param == "pandas": return pd.DataFrame(X), y else: raise ValueError(request.param) -@pytest.fixture( - scope='class', - params=('pandas', 'numpy') -) +@pytest.fixture(scope="class", params=("pandas", "numpy")) def meta_train_data(request): tests_dir = __file__ os.chdir(os.path.dirname(tests_dir)) @@ -55,40 +48,41 @@ def meta_train_data(request): # -1 because the last attribute is the class attribute_types = [ - 'numeric' if type(type_) != list else 'nominal' - for name, type_ in dataset['attributes'][:-1]] + "numeric" if type(type_) != list else "nominal" + for name, type_ in dataset["attributes"][:-1] + ] - categorical = {i: True if attribute == 'nominal' else False - for i, attribute in enumerate(attribute_types)} + categorical = { + i: True if attribute == "nominal" else False + for i, attribute in enumerate(attribute_types) + } - data = np.array(dataset['data'], dtype=np.float64) + data = np.array(dataset["data"], dtype=np.float64) X = data[:, :-1] y = data[:, -1].reshape((-1,)) - logger = logging.getLogger('Meta') + logger = logging.getLogger("Meta") meta_features.helper_functions.set_value( - "MissingValues", meta_features.helper_functions["MissingValues"](X, y, logger, categorical), - ) + "MissingValues", + meta_features.helper_functions["MissingValues"](X, y, logger, categorical), + ) meta_features.helper_functions.set_value( "NumSymbols", - meta_features.helper_functions["NumSymbols"](X, y, logger, categorical), + meta_features.helper_functions["NumSymbols"](X, y, logger, categorical), ) meta_features.helper_functions.set_value( "ClassOccurences", meta_features.helper_functions["ClassOccurences"](X, y, logger), ) - if request.param == 'numpy': + if request.param == "numpy": return X, y, categorical - elif request.param == 'pandas': + elif request.param == "pandas": return pd.DataFrame(X), y, categorical else: raise ValueError(request.param) -@pytest.fixture( - scope='class', - params=('pandas', 'numpy') -) +@pytest.fixture(scope="class", params=("pandas", "numpy")) def meta_train_data_transformed(request): tests_dir = __file__ os.chdir(os.path.dirname(tests_dir)) @@ -99,53 +93,67 @@ def meta_train_data_transformed(request): # -1 because the last attribute is the class attribute_types = [ - 'numeric' if type(type_) != list else 'nominal' - for name, type_ in dataset['attributes'][:-1]] - categorical = {i: True if attribute == 'nominal' else False - for i, attribute in enumerate(attribute_types)} + "numeric" if type(type_) != list else "nominal" + for name, type_ in dataset["attributes"][:-1] + ] + categorical = { + i: True if attribute == "nominal" else False + for i, attribute in enumerate(attribute_types) + } - data = np.array(dataset['data'], dtype=np.float64) + data = np.array(dataset["data"], dtype=np.float64) X = data[:, :-1] y = data[:, -1].reshape((-1,)) - logger = logging.getLogger('Meta') + logger = logging.getLogger("Meta") meta_features.helper_functions.set_value( - "MissingValues", meta_features.helper_functions["MissingValues"](X, y, logger, categorical), - ) + "MissingValues", + meta_features.helper_functions["MissingValues"](X, y, logger, categorical), + ) meta_features.helper_functions.set_value( "NumSymbols", - meta_features.helper_functions["NumSymbols"](X, y, logger, categorical), + meta_features.helper_functions["NumSymbols"](X, y, logger, categorical), ) meta_features.helper_functions.set_value( "ClassOccurences", meta_features.helper_functions["ClassOccurences"](X, y, logger), ) - DPP = FeatTypeSplit(feat_type={ - col: 'categorical' if category else 'numerical' for col, category in categorical.items() - }) + DPP = FeatTypeSplit( + feat_type={ + col: "categorical" if category else "numerical" + for col, category in categorical.items() + } + ) X_transformed = DPP.fit_transform(X) number_numerical = np.sum(~np.array(list(categorical.values()))) - categorical_transformed = {i: True if i < (X_transformed.shape[1] - number_numerical) else False - for i in range(X_transformed.shape[1])} + categorical_transformed = { + i: True if i < (X_transformed.shape[1] - number_numerical) else False + for i in range(X_transformed.shape[1]) + } # pre-compute values for transformed inputs meta_features.helper_functions.set_value( - "PCA", meta_features.helper_functions["PCA"](X_transformed, y, logger), + "PCA", + meta_features.helper_functions["PCA"](X_transformed, y, logger), ) meta_features.helper_functions.set_value( - "Skewnesses", meta_features.helper_functions["Skewnesses"]( - X_transformed, y, logger, categorical_transformed), + "Skewnesses", + meta_features.helper_functions["Skewnesses"]( + X_transformed, y, logger, categorical_transformed + ), ) meta_features.helper_functions.set_value( - "Kurtosisses", meta_features.helper_functions["Kurtosisses"]( - X_transformed, y, logger, categorical_transformed) + "Kurtosisses", + meta_features.helper_functions["Kurtosisses"]( + X_transformed, y, logger, categorical_transformed + ), ) - if request.param == 'numpy': + if request.param == "numpy": return X_transformed, y, categorical_transformed - elif request.param == 'pandas': + elif request.param == "pandas": return pd.DataFrame(X_transformed), y, categorical_transformed else: raise ValueError(request.param) @@ -154,7 +162,8 @@ def meta_train_data_transformed(request): def test_number_of_instance(meta_train_data): X, y, categorical = meta_train_data mf = meta_features.metafeatures["NumberOfInstances"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) assert mf.value == 898 assert isinstance(mf, MetaFeatureValue) @@ -162,7 +171,8 @@ def test_number_of_instance(meta_train_data): def test_number_of_classes(meta_train_data): X, y, categorical = meta_train_data mf = meta_features.metafeatures["NumberOfClasses"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) assert mf.value == 5 assert isinstance(mf, MetaFeatureValue) @@ -170,7 +180,8 @@ def test_number_of_classes(meta_train_data): def test_number_of_features(meta_train_data): X, y, categorical = meta_train_data mf = meta_features.metafeatures["NumberOfFeatures"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) assert mf.value == 38 assert isinstance(mf, MetaFeatureValue) @@ -178,8 +189,9 @@ def test_number_of_features(meta_train_data): def test_missing_values(meta_train_data): X, y, categorical = meta_train_data mf = meta_features.helper_functions["MissingValues"]( - X, y, logging.getLogger('Meta'), categorical) - assert isinstance(mf.value, pd.DataFrame if hasattr(X, 'iloc') else np.ndarray) + X, y, logging.getLogger("Meta"), categorical + ) + assert isinstance(mf.value, pd.DataFrame if hasattr(X, "iloc") else np.ndarray) assert mf.value.shape == X.shape assert 22175 == np.count_nonzero(mf.value) @@ -187,7 +199,8 @@ def test_missing_values(meta_train_data): def test_number_of_Instances_with_missing_values(meta_train_data): X, y, categorical = meta_train_data mf = meta_features.metafeatures["NumberOfInstancesWithMissingValues"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) assert mf.value == 898 assert isinstance(mf, MetaFeatureValue) @@ -197,10 +210,12 @@ def test_percentage_of_Instances_with_missing_values(meta_train_data): meta_features.metafeatures.set_value( "NumberOfInstancesWithMissingValues", meta_features.metafeatures["NumberOfInstancesWithMissingValues"]( - X, y, logging.getLogger('Meta'), categorical), - ) + X, y, logging.getLogger("Meta"), categorical + ), + ) mf = meta_features.metafeatures["PercentageOfInstancesWithMissingValues"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) assert pytest.approx(mf.value) == 1.0 assert isinstance(mf, MetaFeatureValue) @@ -208,7 +223,8 @@ def test_percentage_of_Instances_with_missing_values(meta_train_data): def test_number_of_features_with_missing_values(meta_train_data): X, y, categorical = meta_train_data mf = meta_features.metafeatures["NumberOfFeaturesWithMissingValues"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) assert mf.value == 29 assert isinstance(mf, MetaFeatureValue) @@ -218,18 +234,22 @@ def test_percentage_of_features_with_missing_values(meta_train_data): meta_features.metafeatures.set_value( "NumberOfFeaturesWithMissingValues", meta_features.metafeatures["NumberOfFeaturesWithMissingValues"]( - X, y, logging.getLogger('Meta'), categorical)) + X, y, logging.getLogger("Meta"), categorical + ), + ) mf = meta_features.metafeatures["PercentageOfFeaturesWithMissingValues"]( - X, y, logging.getLogger('Meta'), categorical) - assert pytest.approx(mf.value) == float(29)/float(38) + X, y, logging.getLogger("Meta"), categorical + ) + assert pytest.approx(mf.value) == float(29) / float(38) assert isinstance(mf, MetaFeatureValue) def test_number_of_missing_values(meta_train_data): X, y, categorical = meta_train_data - np.save('/tmp/debug', X) + np.save("/tmp/debug", X) mf = meta_features.metafeatures["NumberOfMissingValues"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) assert mf.value == 22175 assert isinstance(mf, MetaFeatureValue) @@ -237,18 +257,23 @@ def test_number_of_missing_values(meta_train_data): def test_percentage_missing_values(meta_train_data): X, y, categorical = meta_train_data meta_features.metafeatures.set_value( - "NumberOfMissingValues", meta_features.metafeatures["NumberOfMissingValues"]( - X, y, logging.getLogger('Meta'), categorical)) + "NumberOfMissingValues", + meta_features.metafeatures["NumberOfMissingValues"]( + X, y, logging.getLogger("Meta"), categorical + ), + ) mf = meta_features.metafeatures["PercentageOfMissingValues"]( - X, y, logging.getLogger('Meta'), categorical) - assert pytest.approx(mf.value) == (float(22175)/float(38*898)) + X, y, logging.getLogger("Meta"), categorical + ) + assert pytest.approx(mf.value) == (float(22175) / float(38 * 898)) assert isinstance(mf, MetaFeatureValue) def test_number_of_numeric_features(meta_train_data): X, y, categorical = meta_train_data mf = meta_features.metafeatures["NumberOfNumericFeatures"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) assert mf.value == 6 assert isinstance(mf, MetaFeatureValue) @@ -256,7 +281,8 @@ def test_number_of_numeric_features(meta_train_data): def test_number_of_categorical_features(meta_train_data): X, y, categorical = meta_train_data mf = meta_features.metafeatures["NumberOfCategoricalFeatures"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) assert mf.value == 32 assert isinstance(mf, MetaFeatureValue) @@ -264,62 +290,70 @@ def test_number_of_categorical_features(meta_train_data): def test_ratio_numerical_to_categorical(meta_train_data): X, y, categorical = meta_train_data mf = meta_features.metafeatures["RatioNumericalToNominal"]( - X, y, logging.getLogger('Meta'), categorical) - assert pytest.approx(mf.value) == (float(6)/float(32)) + X, y, logging.getLogger("Meta"), categorical + ) + assert pytest.approx(mf.value) == (float(6) / float(32)) assert isinstance(mf, MetaFeatureValue) def test_ratio_categorical_to_numerical(meta_train_data): X, y, categorical = meta_train_data mf = meta_features.metafeatures["RatioNominalToNumerical"]( - X, y, logging.getLogger('Meta'), categorical) - assert pytest.approx(mf.value) == (float(32)/float(6)) + X, y, logging.getLogger("Meta"), categorical + ) + assert pytest.approx(mf.value) == (float(32) / float(6)) assert isinstance(mf, MetaFeatureValue) def test_dataset_ratio(meta_train_data): X, y, categorical = meta_train_data mf = meta_features.metafeatures["DatasetRatio"]( - X, y, logging.getLogger('Meta'), categorical) - assert pytest.approx(mf.value) == (float(38)/float(898)) + X, y, logging.getLogger("Meta"), categorical + ) + assert pytest.approx(mf.value) == (float(38) / float(898)) assert isinstance(mf, MetaFeatureValue) def test_inverse_dataset_ratio(meta_train_data): X, y, categorical = meta_train_data mf = meta_features.metafeatures["InverseDatasetRatio"]( - X, y, logging.getLogger('Meta'), categorical) - assert pytest.approx(mf.value) == (float(898)/float(38)) + X, y, logging.getLogger("Meta"), categorical + ) + assert pytest.approx(mf.value) == (float(898) / float(38)) assert isinstance(mf, MetaFeatureValue) def test_class_occurences(meta_train_data): X, y, categorical = meta_train_data mf = meta_features.helper_functions["ClassOccurences"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) assert mf.value == {0.0: 8.0, 1.0: 99.0, 2.0: 684.0, 4.0: 67.0, 5.0: 40.0} def test_class_probability_min(meta_train_data): X, y, categorical = meta_train_data mf = meta_features.metafeatures["ClassProbabilityMin"]( - X, y, logging.getLogger('Meta'), categorical) - assert pytest.approx(mf.value) == (float(8)/float(898)) + X, y, logging.getLogger("Meta"), categorical + ) + assert pytest.approx(mf.value) == (float(8) / float(898)) assert isinstance(mf, MetaFeatureValue) def test_class_probability_max(meta_train_data): X, y, categorical = meta_train_data mf = meta_features.metafeatures["ClassProbabilityMax"]( - X, y, logging.getLogger('Meta'), categorical) - assert pytest.approx(mf.value) == (float(684)/float(898)) + X, y, logging.getLogger("Meta"), categorical + ) + assert pytest.approx(mf.value) == (float(684) / float(898)) assert isinstance(mf, MetaFeatureValue) def test_class_probability_mean(meta_train_data): X, y, categorical = meta_train_data mf = meta_features.metafeatures["ClassProbabilityMean"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) classes = np.array((8, 99, 684, 67, 40), dtype=np.float64) prob_mean = (classes / float(898)).mean() assert pytest.approx(mf.value) == prob_mean @@ -329,7 +363,8 @@ def test_class_probability_mean(meta_train_data): def test_class_probability_std(meta_train_data): X, y, categorical = meta_train_data mf = meta_features.metafeatures["ClassProbabilitySTD"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) classes = np.array((8, 99, 684, 67, 40), dtype=np.float64) prob_std = (classes / float(898)).std() assert pytest.approx(mf.value) == prob_std @@ -339,53 +374,148 @@ def test_class_probability_std(meta_train_data): def test_num_symbols(meta_train_data): X, y, categorical = meta_train_data mf = meta_features.helper_functions["NumSymbols"]( - X, y, logging.getLogger('Meta'), categorical) - symbol_frequency = [2, 1, 7, 1, 2, 4, 1, 1, 4, 2, 1, 1, 1, 2, 1, 0, - 1, 1, 1, 0, 1, 1, 0, 3, 1, 0, 0, 0, 2, 2, 3, 2] + X, y, logging.getLogger("Meta"), categorical + ) + symbol_frequency = [ + 2, + 1, + 7, + 1, + 2, + 4, + 1, + 1, + 4, + 2, + 1, + 1, + 1, + 2, + 1, + 0, + 1, + 1, + 1, + 0, + 1, + 1, + 0, + 3, + 1, + 0, + 0, + 0, + 2, + 2, + 3, + 2, + ] assert mf.value == symbol_frequency def test_symbols_min(meta_train_data): X, y, categorical = meta_train_data - mf = meta_features.metafeatures["SymbolsMin"](X, y, logging.getLogger('Meta'), categorical) + mf = meta_features.metafeatures["SymbolsMin"]( + X, y, logging.getLogger("Meta"), categorical + ) assert mf.value == 1 def test_symbols_max(meta_train_data): X, y, categorical = meta_train_data # this is attribute steel - mf = meta_features.metafeatures["SymbolsMax"](X, y, logging.getLogger('Meta'), categorical) + mf = meta_features.metafeatures["SymbolsMax"]( + X, y, logging.getLogger("Meta"), categorical + ) assert mf.value == 7 def test_symbols_mean(meta_train_data): X, y, categorical = meta_train_data mf = meta_features.metafeatures["SymbolsMean"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) # Empty looking spaces denote empty attributes - symbol_frequency = [2, 1, 7, 1, 2, 4, 1, 1, 4, 2, 1, 1, 1, 2, 1, # - 1, 1, 1, 1, 1, 3, 1, 2, 2, 3, 2] + symbol_frequency = [ + 2, + 1, + 7, + 1, + 2, + 4, + 1, + 1, + 4, + 2, + 1, + 1, + 1, + 2, + 1, # + 1, + 1, + 1, + 1, + 1, + 3, + 1, + 2, + 2, + 3, + 2, + ] assert pytest.approx(mf.value) == np.mean(symbol_frequency) def test_symbols_std(meta_train_data): X, y, categorical = meta_train_data - mf = meta_features.metafeatures["SymbolsSTD"](X, y, logging.getLogger('Meta'), categorical) - symbol_frequency = [2, 1, 7, 1, 2, 4, 1, 1, 4, 2, 1, 1, 1, 2, 1, # - 1, 1, 1, 1, 1, 3, 1, 2, 2, 3, 2] + mf = meta_features.metafeatures["SymbolsSTD"]( + X, y, logging.getLogger("Meta"), categorical + ) + symbol_frequency = [ + 2, + 1, + 7, + 1, + 2, + 4, + 1, + 1, + 4, + 2, + 1, + 1, + 1, + 2, + 1, # + 1, + 1, + 1, + 1, + 1, + 3, + 1, + 2, + 2, + 3, + 2, + ] assert pytest.approx(mf.value) == np.std(symbol_frequency) def test_symbols_sum(meta_train_data): X, y, categorical = meta_train_data - mf = meta_features.metafeatures["SymbolsSum"](X, y, logging.getLogger('Meta'), categorical) + mf = meta_features.metafeatures["SymbolsSum"]( + X, y, logging.getLogger("Meta"), categorical + ) assert mf.value == 49 def test_class_entropy(meta_train_data): X, y, categorical = meta_train_data mf = meta_features.metafeatures["ClassEntropy"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) classes = np.array((8, 99, 684, 67, 40), dtype=np.float64) classes = classes / sum(classes) entropy = -np.sum([c * np.log2(c) for c in classes]) @@ -396,15 +526,17 @@ def test_class_entropy(meta_train_data): def test_calculate_all_metafeatures(meta_train_data): X, y, categorical = meta_train_data mf = meta_features.calculate_all_metafeatures( - X, y, categorical, "2", logger=logging.getLogger('Meta')) + X, y, categorical, "2", logger=logging.getLogger("Meta") + ) assert 52 == len(mf.metafeature_values) - assert mf.metafeature_values['NumberOfCategoricalFeatures'].value == 32 + assert mf.metafeature_values["NumberOfCategoricalFeatures"].value == 32 def test_kurtosisses(meta_train_data_transformed): X_transformed, y, categorical_transformed = meta_train_data_transformed mf = meta_features.helper_functions["Kurtosisses"]( - X_transformed, y, logging.getLogger('Meta'), categorical_transformed) + X_transformed, y, logging.getLogger("Meta"), categorical_transformed + ) assert 6 == len(mf.value) @@ -412,34 +544,39 @@ def test_kurtosis_min(meta_train_data_transformed): X_transformed, y, categorical_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["KurtosisMin"]( - X_transformed, y, logging.getLogger('Meta'), categorical_transformed) + X_transformed, y, logging.getLogger("Meta"), categorical_transformed + ) def test_kurtosis_max(meta_train_data_transformed): X_transformed, y, categorical_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["KurtosisMax"]( - X_transformed, y, logging.getLogger('Meta'), categorical_transformed) + X_transformed, y, logging.getLogger("Meta"), categorical_transformed + ) def test_kurtosis_mean(meta_train_data_transformed): X_transformed, y, categorical_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["KurtosisMean"]( - X_transformed, y, logging.getLogger('Meta'), categorical_transformed) + X_transformed, y, logging.getLogger("Meta"), categorical_transformed + ) def test_kurtosis_std(meta_train_data_transformed): X_transformed, y, categorical_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["KurtosisSTD"]( - X_transformed, y, logging.getLogger('Meta'), categorical_transformed) + X_transformed, y, logging.getLogger("Meta"), categorical_transformed + ) def test_skewnesses(meta_train_data_transformed): X_transformed, y, categorical_transformed = meta_train_data_transformed mf = meta_features.helper_functions["Skewnesses"]( - X_transformed, y, logging.getLogger('Meta'), categorical_transformed) + X_transformed, y, logging.getLogger("Meta"), categorical_transformed + ) assert 6 == len(mf.value) @@ -447,62 +584,72 @@ def test_skewness_min(meta_train_data_transformed): X_transformed, y, categorical_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["SkewnessMin"]( - X_transformed, y, logging.getLogger('Meta'), categorical_transformed) + X_transformed, y, logging.getLogger("Meta"), categorical_transformed + ) def test_skewness_max(meta_train_data_transformed): X_transformed, y, categorical_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["SkewnessMax"]( - X_transformed, y, logging.getLogger('Meta'), categorical_transformed) + X_transformed, y, logging.getLogger("Meta"), categorical_transformed + ) def test_skewness_mean(meta_train_data_transformed): X_transformed, y, categorical_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["SkewnessMean"]( - X_transformed, y, logging.getLogger('Meta'), categorical_transformed) + X_transformed, y, logging.getLogger("Meta"), categorical_transformed + ) def test_skewness_std(meta_train_data_transformed): X_transformed, y, categorical_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["SkewnessSTD"]( - X_transformed, y, logging.getLogger('Meta'), categorical_transformed) + X_transformed, y, logging.getLogger("Meta"), categorical_transformed + ) def test_landmark_lda(meta_train_data_transformed): X_transformed, y, categorical_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? - meta_features.metafeatures["LandmarkLDA"](X_transformed, y, logging.getLogger('Meta')) + meta_features.metafeatures["LandmarkLDA"]( + X_transformed, y, logging.getLogger("Meta") + ) def test_landmark_naive_bayes(meta_train_data_transformed): X_transformed, y, categorical_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["LandmarkNaiveBayes"]( - X_transformed, y, logging.getLogger('Meta')) + X_transformed, y, logging.getLogger("Meta") + ) def test_landmark_decision_tree(meta_train_data_transformed): X_transformed, y, categorical_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["LandmarkDecisionTree"]( - X_transformed, y, logging.getLogger('Meta')) + X_transformed, y, logging.getLogger("Meta") + ) def test_decision_node(meta_train_data_transformed): X_transformed, y, categorical_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["LandmarkDecisionNodeLearner"]( - X_transformed, y, logging.getLogger('Meta')) + X_transformed, y, logging.getLogger("Meta") + ) def test_random_node(meta_train_data_transformed): X_transformed, y, categorical_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["LandmarkRandomNodeLearner"]( - X_transformed, y, logging.getLogger('Meta')) + X_transformed, y, logging.getLogger("Meta") + ) @unittest.skip("Currently not implemented!") @@ -510,57 +657,72 @@ def test_worst_node(meta_train_data_transformed): X_transformed, y, categorical_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["LandmarkWorstNodeLearner"]( - X_transformed, y, logging.getLogger('Meta')) + X_transformed, y, logging.getLogger("Meta") + ) def test_1NN(meta_train_data_transformed): X_transformed, y, categorical_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? - meta_features.metafeatures["Landmark1NN"](X_transformed, y, logging.getLogger('Meta')) + meta_features.metafeatures["Landmark1NN"]( + X_transformed, y, logging.getLogger("Meta") + ) def test_pca(meta_train_data_transformed): X_transformed, y, categorical_transformed = meta_train_data_transformed - meta_features.helper_functions["PCA"](X_transformed, y, logging.getLogger('Meta')) + meta_features.helper_functions["PCA"](X_transformed, y, logging.getLogger("Meta")) def test_pca_95percent(meta_train_data_transformed): X_transformed, y, categorical_transformed = meta_train_data_transformed mf = meta_features.metafeatures["PCAFractionOfComponentsFor95PercentVariance"]( - X_transformed, y, logging.getLogger('Meta')) + X_transformed, y, logging.getLogger("Meta") + ) assert pytest.approx(0.2716049382716049) == mf.value def test_pca_kurtosis_first_pc(meta_train_data_transformed): X_transformed, y, categorical_transformed = meta_train_data_transformed mf = meta_features.metafeatures["PCAKurtosisFirstPC"]( - X_transformed, y, logging.getLogger('Meta')) + X_transformed, y, logging.getLogger("Meta") + ) assert pytest.approx(-0.702850) != mf.value def test_pca_skewness_first_pc(meta_train_data_transformed): X_transformed, y, categorical_transformed = meta_train_data_transformed mf = meta_features.metafeatures["PCASkewnessFirstPC"]( - X_transformed, y, logging.getLogger('Meta')) + X_transformed, y, logging.getLogger("Meta") + ) assert pytest.approx(0.051210) != mf.value def test_class_occurences_multilabel(multilabel_train_data): X, y = multilabel_train_data - mf = meta_features.helper_functions["ClassOccurences"](X, y, logging.getLogger('Meta')) - assert mf.value == [{0: 16.0, 1: 84.0}, - {0: 8.0, 1: 92.0}, - {0: 68.0, 1: 32.0}, - {0: 15.0, 1: 85.0}, - {0: 28.0, 1: 72.0}] + mf = meta_features.helper_functions["ClassOccurences"]( + X, y, logging.getLogger("Meta") + ) + assert mf.value == [ + {0: 16.0, 1: 84.0}, + {0: 8.0, 1: 92.0}, + {0: 68.0, 1: 32.0}, + {0: 15.0, 1: 85.0}, + {0: 28.0, 1: 72.0}, + ] def test_class_probability_min_multilabel(multilabel_train_data): X, y = multilabel_train_data meta_features.helper_functions.set_value( - "ClassOccurences", meta_features.helper_functions["ClassOccurences"]( - X, y, logging.getLogger('Meta'))) - mf = meta_features.metafeatures["ClassProbabilityMin"](X, y, logging.getLogger('Meta')) + "ClassOccurences", + meta_features.helper_functions["ClassOccurences"]( + X, y, logging.getLogger("Meta") + ), + ) + mf = meta_features.metafeatures["ClassProbabilityMin"]( + X, y, logging.getLogger("Meta") + ) assert pytest.approx(mf.value) == (float(8) / float(100)) assert isinstance(mf, MetaFeatureValue) @@ -568,9 +730,14 @@ def test_class_probability_min_multilabel(multilabel_train_data): def test_class_probability_max_multilabel(multilabel_train_data): X, y = multilabel_train_data meta_features.helper_functions.set_value( - "ClassOccurences", meta_features.helper_functions["ClassOccurences"]( - X, y, logging.getLogger('Meta'))) - mf = meta_features.metafeatures["ClassProbabilityMax"](X, y, logging.getLogger('Meta')) + "ClassOccurences", + meta_features.helper_functions["ClassOccurences"]( + X, y, logging.getLogger("Meta") + ), + ) + mf = meta_features.metafeatures["ClassProbabilityMax"]( + X, y, logging.getLogger("Meta") + ) assert pytest.approx(mf.value) == (float(92) / float(100)) assert isinstance(mf, MetaFeatureValue) @@ -578,9 +745,14 @@ def test_class_probability_max_multilabel(multilabel_train_data): def test_class_probability_mean_multilabel(multilabel_train_data): X, y = multilabel_train_data meta_features.helper_functions.set_value( - "ClassOccurences", meta_features.helper_functions["ClassOccurences"]( - X, y, logging.getLogger('Meta'))) - mf = meta_features.metafeatures["ClassProbabilityMean"](X, y, logging.getLogger('Meta')) + "ClassOccurences", + meta_features.helper_functions["ClassOccurences"]( + X, y, logging.getLogger("Meta") + ), + ) + mf = meta_features.metafeatures["ClassProbabilityMean"]( + X, y, logging.getLogger("Meta") + ) classes = [(16, 84), (8, 92), (68, 32), (15, 85), (28, 72)] probas = np.mean([np.mean(np.array(cls_)) / 100 for cls_ in classes]) assert mf.value == pytest.approx(probas) @@ -589,7 +761,7 @@ def test_class_probability_mean_multilabel(multilabel_train_data): def test_number_of_classes_multilabel(multilabel_train_data): X, y = multilabel_train_data - mf = meta_features.metafeatures["NumberOfClasses"](X, y, logging.getLogger('Meta')) + mf = meta_features.metafeatures["NumberOfClasses"](X, y, logging.getLogger("Meta")) assert mf.value == 5 assert isinstance(mf, MetaFeatureValue) @@ -597,18 +769,23 @@ def test_number_of_classes_multilabel(multilabel_train_data): def test_class_probability_std_multilabel(multilabel_train_data): X, y = multilabel_train_data meta_features.helper_functions.set_value( - "ClassOccurences", meta_features.helper_functions["ClassOccurences"]( - X, y, logging.getLogger('Meta'))) - mf = meta_features.metafeatures["ClassProbabilitySTD"](X, y, logging.getLogger('Meta')) + "ClassOccurences", + meta_features.helper_functions["ClassOccurences"]( + X, y, logging.getLogger("Meta") + ), + ) + mf = meta_features.metafeatures["ClassProbabilitySTD"]( + X, y, logging.getLogger("Meta") + ) classes = [(16, 84), (8, 92), (68, 32), (15, 85), (28, 72)] - probas = np.mean([np.std(np.array(cls_) / 100.) for cls_ in classes]) + probas = np.mean([np.std(np.array(cls_) / 100.0) for cls_ in classes]) assert pytest.approx(mf.value) == probas assert isinstance(mf, MetaFeatureValue) def test_class_entropy_multilabel(multilabel_train_data): X, y = multilabel_train_data - mf = meta_features.metafeatures["ClassEntropy"](X, y, logging.getLogger('Meta')) + mf = meta_features.metafeatures["ClassEntropy"](X, y, logging.getLogger("Meta")) classes = [(16, 84), (8, 92), (68, 32), (15, 85), (28, 72)] entropies = [] @@ -623,39 +800,45 @@ def test_class_entropy_multilabel(multilabel_train_data): def test_landmark_lda_multilabel(multilabel_train_data): X, y = multilabel_train_data - mf = meta_features.metafeatures["LandmarkLDA"](X, y, logging.getLogger('Meta')) + mf = meta_features.metafeatures["LandmarkLDA"](X, y, logging.getLogger("Meta")) assert np.isfinite(mf.value) def test_landmark_naive_bayes_multilabel(multilabel_train_data): X, y = multilabel_train_data - mf = meta_features.metafeatures["LandmarkNaiveBayes"](X, y, logging.getLogger('Meta')) + mf = meta_features.metafeatures["LandmarkNaiveBayes"]( + X, y, logging.getLogger("Meta") + ) assert np.isfinite(mf.value) def test_landmark_decision_tree_multilabel(multilabel_train_data): X, y = multilabel_train_data - mf = meta_features.metafeatures["LandmarkDecisionTree"](X, y, logging.getLogger('Meta')) + mf = meta_features.metafeatures["LandmarkDecisionTree"]( + X, y, logging.getLogger("Meta") + ) assert np.isfinite(mf.value) def test_landmark_decision_node_multilabel(multilabel_train_data): X, y = multilabel_train_data mf = meta_features.metafeatures["LandmarkDecisionNodeLearner"]( - X, y, logging.getLogger('Meta')) + X, y, logging.getLogger("Meta") + ) assert np.isfinite(mf.value) def test_landmark_random_node_multilabel(multilabel_train_data): X, y = multilabel_train_data mf = meta_features.metafeatures["LandmarkRandomNodeLearner"]( - X, y, logging.getLogger('Meta')) + X, y, logging.getLogger("Meta") + ) assert np.isfinite(mf.value) def test_1NN_multilabel(multilabel_train_data): X, y = multilabel_train_data - mf = meta_features.metafeatures["Landmark1NN"](X, y, logging.getLogger('TestMeta')) + mf = meta_features.metafeatures["Landmark1NN"](X, y, logging.getLogger("TestMeta")) assert np.isfinite(mf.value) @@ -664,7 +847,8 @@ def test_calculate_all_metafeatures_multilabel(multilabel_train_data): X, y = multilabel_train_data categorical = {i: False for i in range(10)} mf = meta_features.calculate_all_metafeatures( - X, y, categorical, "Generated", logger=logging.getLogger('TestMeta')) + X, y, categorical, "Generated", logger=logging.getLogger("TestMeta") + ) assert 52 == len(mf.metafeature_values) @@ -675,77 +859,84 @@ def test_calculate_all_metafeatures_same_results_across_datatypes(): all metafeatures work in this complex dataset """ X, y = fetch_openml(data_id=2, return_X_y=True, as_frame=True) - categorical = {col: True if X[col].dtype.name == 'category' else False - for col in X.columns} + categorical = { + col: True if X[col].dtype.name == "category" else False for col in X.columns + } mf = meta_features.calculate_all_metafeatures( - X, y, categorical, "2", logger=logging.getLogger('Meta')) + X, y, categorical, "2", logger=logging.getLogger("Meta") + ) assert 52 == len(mf.metafeature_values) expected = { - 'PCASkewnessFirstPC': 0.41897660337677867, - 'PCAKurtosisFirstPC': -0.677692541156901, - 'PCAFractionOfComponentsFor95PercentVariance': 0.2716049382716049, - 'ClassEntropy': 1.1898338562043977, - 'SkewnessSTD': 7.540418815675546, - 'SkewnessMean': 1.47397188548894, - 'SkewnessMax': 29.916569235579203, - 'SkewnessMin': -29.916569235579203, - 'KurtosisSTD': 153.0563504598898, - 'KurtosisMean': 56.998860939761165, - 'KurtosisMax': 893.0011148272025, - 'KurtosisMin': -3.0, - 'SymbolsSum': 49, - 'SymbolsSTD': 1.3679553264445183, - 'SymbolsMean': 1.8846153846153846, - 'SymbolsMax': 7, - 'SymbolsMin': 1, - 'ClassProbabilitySTD': 0.28282850691819206, - 'ClassProbabilityMean': 0.2, - 'ClassProbabilityMax': 0.7616926503340757, - 'ClassProbabilityMin': 0.008908685968819599, - 'InverseDatasetRatio': 23.63157894736842, - 'DatasetRatio': 0.042316258351893093, - 'RatioNominalToNumerical': 5.333333333333333, - 'RatioNumericalToNominal': 0.1875, - 'NumberOfCategoricalFeatures': 32, - 'NumberOfNumericFeatures': 6, - 'NumberOfMissingValues': 22175.0, - 'NumberOfFeaturesWithMissingValues': 29.0, - 'NumberOfInstancesWithMissingValues': 898.0, - 'NumberOfFeatures': 38.0, - 'NumberOfClasses': 5.0, - 'NumberOfInstances': 898.0, - 'LogInverseDatasetRatio': 3.162583908575814, - 'LogDatasetRatio': -3.162583908575814, - 'PercentageOfMissingValues': 0.6498358926268901, - 'PercentageOfFeaturesWithMissingValues': 0.7631578947368421, - 'PercentageOfInstancesWithMissingValues': 1.0, - 'LogNumberOfFeatures': 3.6375861597263857, - 'LogNumberOfInstances': 6.8001700683022, + "PCASkewnessFirstPC": 0.41897660337677867, + "PCAKurtosisFirstPC": -0.677692541156901, + "PCAFractionOfComponentsFor95PercentVariance": 0.2716049382716049, + "ClassEntropy": 1.1898338562043977, + "SkewnessSTD": 7.540418815675546, + "SkewnessMean": 1.47397188548894, + "SkewnessMax": 29.916569235579203, + "SkewnessMin": -29.916569235579203, + "KurtosisSTD": 153.0563504598898, + "KurtosisMean": 56.998860939761165, + "KurtosisMax": 893.0011148272025, + "KurtosisMin": -3.0, + "SymbolsSum": 49, + "SymbolsSTD": 1.3679553264445183, + "SymbolsMean": 1.8846153846153846, + "SymbolsMax": 7, + "SymbolsMin": 1, + "ClassProbabilitySTD": 0.28282850691819206, + "ClassProbabilityMean": 0.2, + "ClassProbabilityMax": 0.7616926503340757, + "ClassProbabilityMin": 0.008908685968819599, + "InverseDatasetRatio": 23.63157894736842, + "DatasetRatio": 0.042316258351893093, + "RatioNominalToNumerical": 5.333333333333333, + "RatioNumericalToNominal": 0.1875, + "NumberOfCategoricalFeatures": 32, + "NumberOfNumericFeatures": 6, + "NumberOfMissingValues": 22175.0, + "NumberOfFeaturesWithMissingValues": 29.0, + "NumberOfInstancesWithMissingValues": 898.0, + "NumberOfFeatures": 38.0, + "NumberOfClasses": 5.0, + "NumberOfInstances": 898.0, + "LogInverseDatasetRatio": 3.162583908575814, + "LogDatasetRatio": -3.162583908575814, + "PercentageOfMissingValues": 0.6498358926268901, + "PercentageOfFeaturesWithMissingValues": 0.7631578947368421, + "PercentageOfInstancesWithMissingValues": 1.0, + "LogNumberOfFeatures": 3.6375861597263857, + "LogNumberOfInstances": 6.8001700683022, } assert {k: mf[k].value for k in expected.keys()} == pytest.approx(expected) expected_landmarks = { - 'Landmark1NN': 0.9721601489757914, - 'LandmarkRandomNodeLearner': 0.7616945996275606, - 'LandmarkDecisionNodeLearner': 0.7827932960893855, - 'LandmarkDecisionTree': 0.9899875853507139, - 'LandmarkNaiveBayes': 0.9287150837988827, - 'LandmarkLDA': 0.9610242085661079, + "Landmark1NN": 0.9721601489757914, + "LandmarkRandomNodeLearner": 0.7616945996275606, + "LandmarkDecisionNodeLearner": 0.7827932960893855, + "LandmarkDecisionTree": 0.9899875853507139, + "LandmarkNaiveBayes": 0.9287150837988827, + "LandmarkLDA": 0.9610242085661079, } assert {k: mf[k].value for k in expected_landmarks.keys()} == pytest.approx( - expected_landmarks, rel=1e-5) + expected_landmarks, rel=1e-5 + ) # Then do numpy! X, y = fetch_openml(data_id=2, return_X_y=True, as_frame=False) - categorical = {i: True if category else False - for i, category in enumerate(categorical.values())} + categorical = { + i: True if category else False + for i, category in enumerate(categorical.values()) + } mf = meta_features.calculate_all_metafeatures( - X, y, categorical, "2", logger=logging.getLogger('Meta')) + X, y, categorical, "2", logger=logging.getLogger("Meta") + ) assert {k: mf[k].value for k in expected.keys()} == pytest.approx(expected) # The column-reorder of pandas and numpy array are different after # the data preprocessing. So we cannot directly compare, and landmarking is # sensible to column order - expected_landmarks['LandmarkDecisionTree'] = 0.9922098075729361 + expected_landmarks["LandmarkDecisionTree"] = 0.9922098075729361 assert {k: mf[k].value for k in expected_landmarks.keys()} == pytest.approx( - expected_landmarks, rel=1e-5) + expected_landmarks, rel=1e-5 + ) diff --git a/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py b/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py index f5b9308971..856fd595cb 100644 --- a/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py +++ b/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py @@ -2,19 +2,16 @@ import os import arff - import numpy as np - import pytest - from scipy import sparse - from sklearn.impute import SimpleImputer from sklearn.preprocessing import StandardScaler -from autosklearn.pipeline.components.data_preprocessing.feature_type \ - import FeatTypeSplit import autosklearn.metalearning.metafeatures.metafeatures as meta_features +from autosklearn.pipeline.components.data_preprocessing.feature_type import ( + FeatTypeSplit, +) @pytest.fixture @@ -28,12 +25,15 @@ def sparse_data(): # -1 because the last attribute is the class attribute_types = [ - 'numeric' if type(type_) != list else 'nominal' - for name, type_ in dataset['attributes'][:-1]] - categorical = {i: True if attribute == 'nominal' else False - for i, attribute in enumerate(attribute_types)} + "numeric" if type(type_) != list else "nominal" + for name, type_ in dataset["attributes"][:-1] + ] + categorical = { + i: True if attribute == "nominal" else False + for i, attribute in enumerate(attribute_types) + } - data = np.array(dataset['data'], dtype=np.float64) + data = np.array(dataset["data"], dtype=np.float64) X = data[:, :-1] y = data[:, -1].reshape((-1,)) @@ -53,19 +53,19 @@ def sparse_data(): helpers.set_value( "MissingValues", helpers["MissingValues"](X, y, logger, categorical), - ) + ) mf.set_value( "NumberOfMissingValues", mf["NumberOfMissingValues"](X, y, logger, categorical), - ) + ) helpers.set_value( "NumSymbols", helpers["NumSymbols"](X, y, logger, categorical), - ) + ) helpers.set_value( "ClassOccurences", helpers["ClassOccurences"](X, y, logger), - ) + ) return X, y, categorical @@ -80,12 +80,15 @@ def sparse_data_transformed(): # -1 because the last attribute is the class attribute_types = [ - 'numeric' if type(type_) != list else 'nominal' - for name, type_ in dataset['attributes'][:-1]] - categorical = {i: True if attribute == 'nominal' else False - for i, attribute in enumerate(attribute_types)} + "numeric" if type(type_) != list else "nominal" + for name, type_ in dataset["attributes"][:-1] + ] + categorical = { + i: True if attribute == "nominal" else False + for i, attribute in enumerate(attribute_types) + } - data = np.array(dataset['data'], dtype=np.float64) + data = np.array(dataset["data"], dtype=np.float64) X = data[:, :-1] y = data[:, -1].reshape((-1,)) @@ -96,10 +99,12 @@ def sparse_data_transformed(): X_sparse[NaNs] = 0 X_sparse = sparse.csr_matrix(X_sparse) - ohe = FeatTypeSplit(feat_type={ - col: 'categorical' if category else 'numerical' - for col, category in categorical.items() - }) + ohe = FeatTypeSplit( + feat_type={ + col: "categorical" if category else "numerical" + for col, category in categorical.items() + } + ) X_transformed = X_sparse.copy() X_transformed = ohe.fit_transform(X_transformed) imp = SimpleImputer(copy=False) @@ -109,8 +114,10 @@ def sparse_data_transformed(): # Transform the array which indicates the categorical metafeatures number_numerical = np.sum(~np.array(list(categorical.values()))) - categorical_transformed = {i: True if i < (X_transformed.shape[1] - number_numerical) else False - for i in range(X_transformed.shape[1])} + categorical_transformed = { + i: True if i < (X_transformed.shape[1] - number_numerical) else False + for i in range(X_transformed.shape[1]) + } X = X_sparse X_transformed = X_transformed @@ -123,28 +130,27 @@ def sparse_data_transformed(): helpers.set_value( "PCA", helpers["PCA"](X_transformed, y, logger), - ) + ) helpers.set_value( "MissingValues", helpers["MissingValues"](X, y, logger, categorical), - ) + ) mf.set_value( "NumberOfMissingValues", mf["NumberOfMissingValues"](X, y, logger, categorical), - ) + ) helpers.set_value( "NumSymbols", helpers["NumSymbols"](X, y, logger, categorical), - ) + ) helpers.set_value( "ClassOccurences", helpers["ClassOccurences"](X, y, logger), - ) + ) helpers.set_value( "Skewnesses", - helpers["Skewnesses"](X_transformed, y, logger, - categorical_transformed), - ) + helpers["Skewnesses"](X_transformed, y, logger, categorical_transformed), + ) helpers.set_value( "Kurtosisses", helpers["Kurtosisses"](X_transformed, y, logger, categorical_transformed), @@ -155,7 +161,8 @@ def sparse_data_transformed(): def test_missing_values(sparse_data): X, y, categorical = sparse_data mf = meta_features.helper_functions["MissingValues"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) assert sparse.issparse(mf.value) assert mf.value.shape == X.shape assert mf.value.dtype == bool @@ -165,21 +172,24 @@ def test_missing_values(sparse_data): def test_number_of_missing_values(sparse_data): X, y, categorical = sparse_data mf = meta_features.metafeatures["NumberOfMissingValues"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) assert 0 == mf.value def test_percentage_missing_values(sparse_data): X, y, categorical = sparse_data mf = meta_features.metafeatures["PercentageOfMissingValues"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) assert 0 == mf.value def test_number_of_Instances_with_missing_values(sparse_data): X, y, categorical = sparse_data mf = meta_features.metafeatures["NumberOfInstancesWithMissingValues"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) assert 0 == mf.value @@ -188,16 +198,20 @@ def test_percentage_of_Instances_with_missing_values(sparse_data): meta_features.metafeatures.set_value( "NumberOfInstancesWithMissingValues", meta_features.metafeatures["NumberOfInstancesWithMissingValues"]( - X, y, logging.getLogger('Meta'), categorical)) + X, y, logging.getLogger("Meta"), categorical + ), + ) mf = meta_features.metafeatures["PercentageOfInstancesWithMissingValues"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) assert pytest.approx(0) == mf.value def test_number_of_features_with_missing_values(sparse_data): X, y, categorical = sparse_data mf = meta_features.metafeatures["NumberOfFeaturesWithMissingValues"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) assert 0 == mf.value @@ -206,33 +220,72 @@ def test_percentage_of_features_with_missing_values(sparse_data): meta_features.metafeatures.set_value( "NumberOfFeaturesWithMissingValues", meta_features.metafeatures["NumberOfFeaturesWithMissingValues"]( - X, y, logging.getLogger('Meta'), categorical)) + X, y, logging.getLogger("Meta"), categorical + ), + ) mf = meta_features.metafeatures["PercentageOfFeaturesWithMissingValues"]( - X, y, logging.getLogger('Meta'), categorical) - assert mf.value == pytest.approx(0) + X, y, logging.getLogger("Meta"), categorical + ) + assert pytest.approx(0, mf.value) def test_num_symbols(sparse_data): X, y, categorical = sparse_data mf = meta_features.helper_functions["NumSymbols"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) - symbol_frequency = [2, 0, 6, 0, 1, 3, 0, 0, 3, 1, 0, 0, 0, 1, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 1, 2, 2] + symbol_frequency = [ + 2, + 0, + 6, + 0, + 1, + 3, + 0, + 0, + 3, + 1, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 2, + 0, + 0, + 0, + 0, + 1, + 1, + 2, + 2, + ] assert mf.value == symbol_frequency def test_symbols_max(sparse_data): X, y, categorical = sparse_data # this is attribute steel - mf = meta_features.metafeatures["SymbolsMax"](X, y, logging.getLogger('Meta'), categorical) + mf = meta_features.metafeatures["SymbolsMax"]( + X, y, logging.getLogger("Meta"), categorical + ) assert mf.value == 6 def test_symbols_mean(sparse_data): X, y, categorical = sparse_data mf = meta_features.metafeatures["SymbolsMean"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) # Empty looking spaces denote empty attributes symbol_frequency = [2, 6, 1, 3, 3, 1, 1, 2, 1, 1, 2, 2] assert pytest.approx(mf.value) == np.mean(symbol_frequency) @@ -241,7 +294,8 @@ def test_symbols_mean(sparse_data): def test_symbols_std(sparse_data): X, y, categorical = sparse_data mf = meta_features.metafeatures["SymbolsSTD"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) symbol_frequency = [2, 6, 1, 3, 3, 1, 1, 2, 1, 1, 2, 2] assert pytest.approx(mf.value) == np.std(symbol_frequency) @@ -249,19 +303,49 @@ def test_symbols_std(sparse_data): def test_symbols_sum(sparse_data): X, y, categorical = sparse_data mf = meta_features.metafeatures["SymbolsSum"]( - X, y, logging.getLogger('Meta'), categorical) + X, y, logging.getLogger("Meta"), categorical + ) assert mf.value == 25 def test_skewnesses(sparse_data_transformed): X_transformed, y, categorical_transformed = sparse_data_transformed fixture = [ - 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, - -0.696970849903357, 0.626346013011262, 0.38099875966240554, - 1.4762248835141032, 0.07687661087633788, 0.3688979783036015 + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + -0.696970849903357, + 0.626346013011262, + 0.38099875966240554, + 1.4762248835141032, + 0.07687661087633788, + 0.3688979783036015, ] - mf = meta_features.helper_functions["Skewnesses"](X_transformed, y, logging.getLogger('Meta')) + mf = meta_features.helper_functions["Skewnesses"]( + X_transformed, y, logging.getLogger("Meta") + ) print(mf.value) print(fixture) np.testing.assert_allclose(mf.value, fixture) @@ -269,13 +353,42 @@ def test_skewnesses(sparse_data_transformed): def test_kurtosisses(sparse_data_transformed): fixture = [ - -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, - -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, - -3.0, -1.1005836114255763, -1.1786325509475744, -1.23879983823279, - 1.3934382644137013, -0.9768209837948336, -1.7937072296512784 + -3.0, + -3.0, + -3.0, + -3.0, + -3.0, + -3.0, + -3.0, + -3.0, + -3.0, + -3.0, + -3.0, + -3.0, + -3.0, + -3.0, + -3.0, + -3.0, + -3.0, + -3.0, + -3.0, + -3.0, + -3.0, + -3.0, + -3.0, + -3.0, + -3.0, + -1.1005836114255763, + -1.1786325509475744, + -1.23879983823279, + 1.3934382644137013, + -0.9768209837948336, + -1.7937072296512784, ] X_transformed, y, categorical_transformed = sparse_data_transformed - mf = meta_features.helper_functions["Kurtosisses"](X_transformed, y, logging.getLogger('Meta')) + mf = meta_features.helper_functions["Kurtosisses"]( + X_transformed, y, logging.getLogger("Meta") + ) print(mf.value) np.testing.assert_allclose(mf.value, fixture) @@ -283,26 +396,30 @@ def test_kurtosisses(sparse_data_transformed): def test_pca_95percent(sparse_data_transformed): X_transformed, y, categorical_transformed = sparse_data_transformed mf = meta_features.metafeatures["PCAFractionOfComponentsFor95PercentVariance"]( - X_transformed, y, logging.getLogger('Meta')) + X_transformed, y, logging.getLogger("Meta") + ) assert pytest.approx(0.7741935483870968) == mf.value def test_pca_kurtosis_first_pc(sparse_data_transformed): X_transformed, y, categorical_transformed = sparse_data_transformed mf = meta_features.metafeatures["PCAKurtosisFirstPC"]( - X_transformed, y, logging.getLogger('Meta')) + X_transformed, y, logging.getLogger("Meta") + ) assert pytest.approx(-0.15444516166802469) == mf.value def test_pca_skewness_first_pc(sparse_data_transformed): X_transformed, y, categorical_transformed = sparse_data_transformed mf = meta_features.metafeatures["PCASkewnessFirstPC"]( - X_transformed, y, logging.getLogger('Meta')) + X_transformed, y, logging.getLogger("Meta") + ) assert pytest.approx(0.026514792083623905) == mf.value def test_calculate_all_metafeatures(sparse_data): X, y, categorical = sparse_data mf = meta_features.calculate_all_metafeatures( - X, y, categorical, "2", logger=logging.getLogger('Meta')) + X, y, categorical, "2", logger=logging.getLogger("Meta") + ) assert 52 == len(mf.metafeature_values) diff --git a/test/test_metalearning/pyMetaLearn/test_metalearner.py b/test/test_metalearning/pyMetaLearn/test_metalearner.py index 58f2ce800a..a8b7d604cb 100644 --- a/test/test_metalearning/pyMetaLearn/test_metalearner.py +++ b/test/test_metalearning/pyMetaLearn/test_metalearner.py @@ -1,14 +1,13 @@ import logging -import numpy as np import os import unittest +import numpy as np import pandas as pd - from ConfigSpace.configuration_space import Configuration -import autosklearn.pipeline.classification -import autosklearn.metalearning.optimizers.metalearn_optimizer.metalearner as metalearner +import autosklearn.metalearning.optimizers.metalearn_optimizer.metalearner as metalearner # noqa: E501 +import autosklearn.pipeline.classification from autosklearn.metalearning.metalearning.meta_base import MetaBase logging.basicConfig() @@ -20,7 +19,7 @@ class MetaLearnerTest(unittest.TestCase): def setUp(self): self.cwd = os.getcwd() data_dir = os.path.dirname(__file__) - data_dir = os.path.join(data_dir, 'test_meta_base_data') + data_dir = os.path.join(data_dir, "test_meta_base_data") os.chdir(data_dir) pipeline = autosklearn.pipeline.classification.SimpleClassificationPipeline() @@ -29,7 +28,8 @@ def setUp(self): self.logger = logging.getLogger() meta_base = MetaBase(self.cs, data_dir, logger=self.logger) self.meta_optimizer = metalearner.MetaLearningOptimizer( - '233', self.cs, meta_base, logger=self.logger) + "233", self.cs, meta_base, logger=self.logger + ) def tearDown(self): os.chdir(self.cwd) @@ -38,8 +38,8 @@ def test_metalearning_suggest_all(self): ret = self.meta_optimizer.metalearning_suggest_all() self.assertEqual(124, len(ret)) # Reduced to 17 as we changed QDA searchspace - self.assertEqual('gradient_boosting', ret[0]['classifier:__choice__']) - self.assertEqual('adaboost', ret[1]['classifier:__choice__']) + self.assertEqual("gradient_boosting", ret[0]["classifier:__choice__"]) + self.assertEqual("adaboost", ret[1]["classifier:__choice__"]) # There is no test for exclude_double_configuration as it's not present # in the test data @@ -48,17 +48,17 @@ def test_metalearning_suggest_all_nan_metafeatures(self): ret = self.meta_optimizer.metalearning_suggest_all() self.assertEqual(124, len(ret)) # Reduced to 17 as we changed QDA searchspace - self.assertEqual('gradient_boosting', ret[0]['classifier:__choice__']) - self.assertEqual('gradient_boosting', ret[1]['classifier:__choice__']) + self.assertEqual("gradient_boosting", ret[0]["classifier:__choice__"]) + self.assertEqual("gradient_boosting", ret[1]["classifier:__choice__"]) def test_metalearning_suggest(self): ret = self.meta_optimizer.metalearning_suggest([]) self.assertIsInstance(ret, Configuration) - self.assertEqual('gradient_boosting', ret['classifier:__choice__']) + self.assertEqual("gradient_boosting", ret["classifier:__choice__"]) ret2 = self.meta_optimizer.metalearning_suggest([ret]) self.assertIsInstance(ret2, Configuration) - self.assertEqual('adaboost', ret2['classifier:__choice__']) + self.assertEqual("adaboost", ret2["classifier:__choice__"]) def test_learn(self): # Test only some special cases which are probably not yet handled @@ -67,8 +67,10 @@ def test_learn(self): self.meta_optimizer._learn() def test_split_metafeature_array(self): - ds_metafeatures, other_metafeatures = self.meta_optimizer. \ - _split_metafeature_array() + ( + ds_metafeatures, + other_metafeatures, + ) = self.meta_optimizer._split_metafeature_array() self.assertIsInstance(ds_metafeatures, pd.Series) self.assertEqual(ds_metafeatures.shape, (46,)) self.assertIsInstance(other_metafeatures, pd.DataFrame) diff --git a/test/test_metalearning/pyMetaLearn/test_optimizer_base.py b/test/test_metalearning/pyMetaLearn/test_optimizer_base.py index a78a6a7f61..63dc2184da 100644 --- a/test/test_metalearning/pyMetaLearn/test_optimizer_base.py +++ b/test/test_metalearning/pyMetaLearn/test_optimizer_base.py @@ -1,5 +1,5 @@ -from collections import OrderedDict import unittest +from collections import OrderedDict from autosklearn.metalearning.optimizers import optimizer_base @@ -14,8 +14,9 @@ def setUp(self): def test_parse_hyperopt_string(self): hyperparameter_string = "x {-5, 0, 5, 10}\ny {0, 5, 10, 15}" - expected = OrderedDict([["x", ["-5", "0", "5", "10"]], - ["y", ["0", "5", "10", "15"]]]) + expected = OrderedDict( + [["x", ["-5", "0", "5", "10"]], ["y", ["0", "5", "10", "15"]]] + ) ret = optimizer_base.parse_hyperparameter_string(hyperparameter_string) self.assertEqual(ret, expected) @@ -28,8 +29,11 @@ def test_parse_hyperopt_string(self): self.assertEqual(ret, expected) hyperparameter_string = "x {-5, 0, 5, 10}\ny 0, 5, 10, 15} [5]" - self.assertRaises(ValueError, optimizer_base.parse_hyperparameter_string, - hyperparameter_string) + self.assertRaises( + ValueError, + optimizer_base.parse_hyperparameter_string, + hyperparameter_string, + ) def test_construct_cli_call(self): cli_call = optimizer_base.construct_cli_call("cv.py", {"x": -5, "y": 0}) diff --git a/test/test_metalearning/test_metalearning.py b/test/test_metalearning/test_metalearning.py index 6a7e87511d..3ec847a8f5 100644 --- a/test/test_metalearning/test_metalearning.py +++ b/test/test_metalearning/test_metalearning.py @@ -1,18 +1,17 @@ # -*- encoding: utf-8 -*- import unittest -from autosklearn.pipeline.util import get_dataset -from autosklearn.classification import AutoSklearnClassifier +from sklearn.datasets import load_breast_cancer -from autosklearn.smbo import _calculate_metafeatures, _calculate_metafeatures_encoded -from autosklearn.constants import REGRESSION, MULTICLASS_CLASSIFICATION +from autosklearn.classification import AutoSklearnClassifier +from autosklearn.constants import MULTICLASS_CLASSIFICATION, REGRESSION from autosklearn.metalearning.mismbo import suggest_via_metalearning +from autosklearn.pipeline.util import get_dataset +from autosklearn.smbo import _calculate_metafeatures, _calculate_metafeatures_encoded from autosklearn.util.pipeline import get_configuration_space -from sklearn.datasets import load_breast_cancer class MetafeatureValueDummy(object): - def __init__(self, name, value): self.name = name self.value = value @@ -22,83 +21,93 @@ class Test(unittest.TestCase): _multiprocess_can_split_ = True def setUp(self): - self.X_train, self.Y_train, self.X_test, self.Y_test = \ - get_dataset('iris') + self.X_train, self.Y_train, self.X_test, self.Y_test = get_dataset("iris") eliminate_class_two = self.Y_train != 2 self.X_train = self.X_train[eliminate_class_two] self.Y_train = self.Y_train[eliminate_class_two] - @unittest.skip('TODO refactor!') + @unittest.skip("TODO refactor!") def test_metalearning(self): - dataset_name_classification = 'digits' + dataset_name_classification = "digits" initial_challengers_classification = { - "ACC_METRIC": "--initial-challengers \" " - "-balancing:strategy 'weighting' " - "-classifier:__choice__ 'proj_logit'", - "AUC_METRIC": "--initial-challengers \" " - "-balancing:strategy 'weighting' " - "-classifier:__choice__ 'liblinear_svc'", - "BAC_METRIC": "--initial-challengers \" " - "-balancing:strategy 'weighting' " - "-classifier:__choice__ 'proj_logit'", - "F1_METRIC": "--initial-challengers \" " - "-balancing:strategy 'weighting' " - "-classifier:__choice__ 'proj_logit'", - "PAC_METRIC": "--initial-challengers \" " - "-balancing:strategy 'none' " - "-classifier:__choice__ 'random_forest'" + "ACC_METRIC": '--initial-challengers " ' + "-balancing:strategy 'weighting' " + "-classifier:__choice__ 'proj_logit'", + "AUC_METRIC": '--initial-challengers " ' + "-balancing:strategy 'weighting' " + "-classifier:__choice__ 'liblinear_svc'", + "BAC_METRIC": '--initial-challengers " ' + "-balancing:strategy 'weighting' " + "-classifier:__choice__ 'proj_logit'", + "F1_METRIC": '--initial-challengers " ' + "-balancing:strategy 'weighting' " + "-classifier:__choice__ 'proj_logit'", + "PAC_METRIC": '--initial-challengers " ' + "-balancing:strategy 'none' " + "-classifier:__choice__ 'random_forest'", } - dataset_name_regression = 'diabetes' + dataset_name_regression = "diabetes" initial_challengers_regression = { - "A_METRIC": "--initial-challengers \" " - "-imputation:strategy 'mean' " - "-one_hot_encoding:minimum_fraction '0.01' " - "-one_hot_encoding:use_minimum_fraction 'True' " - "-preprocessor:__choice__ 'no_preprocessing' " - "-regressor:__choice__ 'random_forest'", - "R2_METRIC": "--initial-challengers \" " - "-imputation:strategy 'mean' " - "-one_hot_encoding:minimum_fraction '0.01' " - "-one_hot_encoding:use_minimum_fraction 'True' " - "-preprocessor:__choice__ 'no_preprocessing' " - "-regressor:__choice__ 'random_forest'", + "A_METRIC": '--initial-challengers " ' + "-imputation:strategy 'mean' " + "-one_hot_encoding:minimum_fraction '0.01' " + "-one_hot_encoding:use_minimum_fraction 'True' " + "-preprocessor:__choice__ 'no_preprocessing' " + "-regressor:__choice__ 'random_forest'", + "R2_METRIC": '--initial-challengers " ' + "-imputation:strategy 'mean' " + "-one_hot_encoding:minimum_fraction '0.01' " + "-one_hot_encoding:use_minimum_fraction 'True' " + "-preprocessor:__choice__ 'no_preprocessing' " + "-regressor:__choice__ 'random_forest'", } for dataset_name, task, initial_challengers in [ (dataset_name_regression, REGRESSION, initial_challengers_regression), - (dataset_name_classification, MULTICLASS_CLASSIFICATION, - initial_challengers_classification)]: + ( + dataset_name_classification, + MULTICLASS_CLASSIFICATION, + initial_challengers_classification, + ), + ]: for metric in initial_challengers: configuration_space = get_configuration_space( - { - 'metric': metric, - 'task': task, - 'is_sparse': False - }, - include={'feature_preprocessor': ['no_preprocessing']}) + {"metric": metric, "task": task, "is_sparse": False}, + include={"feature_preprocessor": ["no_preprocessing"]}, + ) X_train, Y_train, X_test, Y_test = get_dataset(dataset_name) categorical = {i: False for i in range(X_train.shape[1])} meta_features_label = _calculate_metafeatures( - X_train, Y_train, categorical, dataset_name, task) + X_train, Y_train, categorical, dataset_name, task + ) meta_features_encoded_label = _calculate_metafeatures_encoded( - X_train, Y_train, categorical, dataset_name, task) - - initial_configuration_strings_for_smac = \ - suggest_via_metalearning( - meta_features_label, - meta_features_encoded_label, - configuration_space, dataset_name, metric, - task, False, 1, None) + X_train, Y_train, categorical, dataset_name, task + ) + + initial_configuration_strings_for_smac = suggest_via_metalearning( + meta_features_label, + meta_features_encoded_label, + configuration_space, + dataset_name, + metric, + task, + False, + 1, + None, + ) print(metric) print(initial_configuration_strings_for_smac[0]) - self.assertTrue(initial_configuration_strings_for_smac[ - 0].startswith(initial_challengers[metric])) + self.assertTrue( + initial_configuration_strings_for_smac[0].startswith( + initial_challengers[metric] + ) + ) def test_metadata_directory(self): # Test that metadata directory is set correctly (if user specifies, @@ -108,11 +117,10 @@ def test_metadata_directory(self): automl1 = AutoSklearnClassifier( time_left_for_this_task=30, per_run_time_limit=5, - metadata_directory="pyMetaLearn/metadata_dir", # user specified metadata_dir + metadata_directory="pyMetaLearn/metadata_dir", # user metadata_dir dask_client=dask_client, ) - self.assertEqual(automl1.metadata_directory, - "pyMetaLearn/metadata_dir") + self.assertEqual(automl1.metadata_directory, "pyMetaLearn/metadata_dir") automl2 = AutoSklearnClassifier( # default metadata_dir time_left_for_this_task=30, @@ -130,6 +138,11 @@ def test_metadata_directory(self): ensemble_size=0, ) X, y = load_breast_cancer(return_X_y=True) - self.assertRaisesRegex(ValueError, "The specified metadata directory " - "\'%s\' does not exist!" % nonexistent_dir, - automl3.fit, X=X, y=y) + self.assertRaisesRegex( + ValueError, + "The specified metadata directory " + "'%s' does not exist!" % nonexistent_dir, + automl3.fit, + X=X, + y=y, + ) diff --git a/test/test_metric/__init__.py b/test/test_metric/__init__.py index cc3cd7becd..e298f0f075 100644 --- a/test/test_metric/__init__.py +++ b/test/test_metric/__init__.py @@ -1,2 +1,2 @@ # -*- encoding: utf-8 -*- -__author__ = 'feurerm' +__author__ = "feurerm" diff --git a/test/test_metric/test_metrics.py b/test/test_metric/test_metrics.py index 3c6ff73c2b..334a485fe3 100644 --- a/test/test_metric/test_metrics.py +++ b/test/test_metric/test_metrics.py @@ -1,27 +1,24 @@ import unittest import warnings -import pytest - import numpy as np +import pytest import sklearn.metrics +from smac.utils.constants import MAXINT import autosklearn.metrics - -from autosklearn.metrics import calculate_score, calculate_loss, calculate_metric from autosklearn.constants import BINARY_CLASSIFICATION, REGRESSION - -from smac.utils.constants import MAXINT +from autosklearn.metrics import calculate_loss, calculate_metric, calculate_score class TestScorer(unittest.TestCase): - def test_predict_scorer_binary(self): y_true = np.array([0, 0, 1, 1]) y_pred = np.array([[1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0]]) scorer = autosklearn.metrics._PredictScorer( - 'accuracy', sklearn.metrics.accuracy_score, 1, 0, 1, {}) + "accuracy", sklearn.metrics.accuracy_score, 1, 0, 1, {} + ) score = scorer(y_true, y_pred) self.assertAlmostEqual(score, 1.0) @@ -35,15 +32,20 @@ def test_predict_scorer_binary(self): self.assertAlmostEqual(score, 0.5) scorer = autosklearn.metrics._PredictScorer( - 'bac', sklearn.metrics.balanced_accuracy_score, - 1, 0, 1, {}) + "bac", sklearn.metrics.balanced_accuracy_score, 1, 0, 1, {} + ) score = scorer(y_true, y_pred) self.assertAlmostEqual(score, 0.5) scorer = autosklearn.metrics._PredictScorer( - name='accuracy', score_func=sklearn.metrics.accuracy_score, - optimum=1, worst_possible_result=0, sign=-1, kwargs={}) + name="accuracy", + score_func=sklearn.metrics.accuracy_score, + optimum=1, + worst_possible_result=0, + sign=-1, + kwargs={}, + ) y_pred = np.array([[1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0]]) score = scorer(y_true, y_pred) @@ -54,7 +56,8 @@ def test_predict_scorer_multiclass(self): y_pred = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]) scorer = autosklearn.metrics._PredictScorer( - 'accuracy', sklearn.metrics.accuracy_score, 1, 0, 1, {}) + "accuracy", sklearn.metrics.accuracy_score, 1, 0, 1, {} + ) score = scorer(y_true, y_pred) self.assertAlmostEqual(score, 1.0) @@ -68,14 +71,15 @@ def test_predict_scorer_multiclass(self): self.assertAlmostEqual(score, 0.333333333) scorer = autosklearn.metrics._PredictScorer( - 'bac', sklearn.metrics.balanced_accuracy_score, - 1, 0, 1, {}) + "bac", sklearn.metrics.balanced_accuracy_score, 1, 0, 1, {} + ) score = scorer(y_true, y_pred) self.assertAlmostEqual(score, 0.333333333) scorer = autosklearn.metrics._PredictScorer( - 'accuracy', sklearn.metrics.accuracy_score, 1, 0, -1, {}) + "accuracy", sklearn.metrics.accuracy_score, 1, 0, -1, {} + ) y_pred = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]) score = scorer(y_true, y_pred) @@ -86,7 +90,8 @@ def test_predict_scorer_multilabel(self): y_pred = np.array([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]) scorer = autosklearn.metrics._PredictScorer( - 'accuracy', sklearn.metrics.accuracy_score, 1, 0, 1, {}) + "accuracy", sklearn.metrics.accuracy_score, 1, 0, 1, {} + ) score = scorer(y_true, y_pred) self.assertAlmostEqual(score, 1.0) @@ -100,7 +105,8 @@ def test_predict_scorer_multilabel(self): self.assertAlmostEqual(score, 0.25) scorer = autosklearn.metrics._PredictScorer( - 'accuracy', sklearn.metrics.accuracy_score, 1, 0, -1, {}) + "accuracy", sklearn.metrics.accuracy_score, 1, 0, -1, {} + ) y_pred = np.array([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]) score = scorer(y_true, y_pred) @@ -111,7 +117,8 @@ def test_predict_scorer_regression(self): y_pred = y_true.copy() scorer = autosklearn.metrics._PredictScorer( - 'r2', sklearn.metrics.r2_score, 1, 0, 1, {}) + "r2", sklearn.metrics.r2_score, 1, 0, 1, {} + ) score = scorer(y_true, y_pred) self.assertAlmostEqual(score, 1.0) @@ -125,7 +132,8 @@ def test_proba_scorer_binary(self): y_pred = [[1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0]] scorer = autosklearn.metrics._ProbaScorer( - 'log_loss', sklearn.metrics.log_loss, 0, MAXINT, 1, {}) + "log_loss", sklearn.metrics.log_loss, 0, MAXINT, 1, {} + ) score = scorer(y_true, y_pred) self.assertAlmostEqual(score, 0.0) @@ -139,7 +147,8 @@ def test_proba_scorer_binary(self): self.assertAlmostEqual(score, 0.69314718055994529) scorer = autosklearn.metrics._ProbaScorer( - 'log_loss', sklearn.metrics.log_loss, 0, MAXINT, -1, {}) + "log_loss", sklearn.metrics.log_loss, 0, MAXINT, -1, {} + ) y_pred = [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]] score = scorer(y_true, y_pred) @@ -150,7 +159,8 @@ def test_proba_scorer_multiclass(self): y_pred = [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]] scorer = autosklearn.metrics._ProbaScorer( - 'log_loss', sklearn.metrics.log_loss, 0, MAXINT, 1, {}) + "log_loss", sklearn.metrics.log_loss, 0, MAXINT, 1, {} + ) score = scorer(y_true, y_pred) self.assertAlmostEqual(score, 0.0) @@ -164,7 +174,8 @@ def test_proba_scorer_multiclass(self): self.assertAlmostEqual(score, 1.0986122886681096) scorer = autosklearn.metrics._ProbaScorer( - 'log_loss', sklearn.metrics.log_loss, 0, MAXINT, -1, {}) + "log_loss", sklearn.metrics.log_loss, 0, MAXINT, -1, {} + ) y_pred = [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]] score = scorer(y_true, y_pred) @@ -175,7 +186,8 @@ def test_proba_scorer_multilabel(self): y_pred = np.array([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]) scorer = autosklearn.metrics._ProbaScorer( - 'log_loss', sklearn.metrics.log_loss, 0, MAXINT, 1, {}) + "log_loss", sklearn.metrics.log_loss, 0, MAXINT, 1, {} + ) score = scorer(y_true, y_pred) self.assertAlmostEqual(score, 0.34657359027997314) @@ -189,7 +201,8 @@ def test_proba_scorer_multilabel(self): self.assertAlmostEqual(score, 0.69314718055994529) scorer = autosklearn.metrics._ProbaScorer( - 'log_loss', sklearn.metrics.log_loss, 0, MAXINT, -1, {}) + "log_loss", sklearn.metrics.log_loss, 0, MAXINT, -1, {} + ) y_pred = np.array([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]) score = scorer(y_true, y_pred) @@ -200,7 +213,8 @@ def test_threshold_scorer_binary(self): y_pred = np.array([[1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0]]) scorer = autosklearn.metrics._ThresholdScorer( - 'roc_auc', sklearn.metrics.roc_auc_score, 1, 0, 1, {}) + "roc_auc", sklearn.metrics.roc_auc_score, 1, 0, 1, {} + ) score = scorer(y_true, y_pred) self.assertAlmostEqual(score, 1.0) @@ -214,7 +228,8 @@ def test_threshold_scorer_binary(self): self.assertAlmostEqual(score, 0.5) scorer = autosklearn.metrics._ThresholdScorer( - 'roc_auc', sklearn.metrics.roc_auc_score, 1, 0, -1, {}) + "roc_auc", sklearn.metrics.roc_auc_score, 1, 0, -1, {} + ) y_pred = np.array([[1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0]]) score = scorer(y_true, y_pred) @@ -225,7 +240,8 @@ def test_threshold_scorer_multilabel(self): y_pred = np.array([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]) scorer = autosklearn.metrics._ThresholdScorer( - 'roc_auc', sklearn.metrics.roc_auc_score, 1, 0, 1, {}) + "roc_auc", sklearn.metrics.roc_auc_score, 1, 0, 1, {} + ) score = scorer(y_true, y_pred) self.assertAlmostEqual(score, 1.0) @@ -239,7 +255,8 @@ def test_threshold_scorer_multilabel(self): self.assertAlmostEqual(score, 0.5) scorer = autosklearn.metrics._ThresholdScorer( - 'roc_auc', sklearn.metrics.roc_auc_score, 1, 0, -1, {}) + "roc_auc", sklearn.metrics.roc_auc_score, 1, 0, -1, {} + ) y_pred = np.array([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]) score = scorer(y_true, y_pred) @@ -250,7 +267,8 @@ def test_sign_flip(self): y_pred = y_true.copy() scorer = autosklearn.metrics.make_scorer( - 'r2', sklearn.metrics.r2_score, greater_is_better=True) + "r2", sklearn.metrics.r2_score, greater_is_better=True + ) score = scorer(y_true, y_pred + 1.0) self.assertAlmostEqual(score, -9.0) @@ -262,7 +280,8 @@ def test_sign_flip(self): self.assertAlmostEqual(score, 1.0) scorer = autosklearn.metrics.make_scorer( - 'r2', sklearn.metrics.r2_score, greater_is_better=False) + "r2", sklearn.metrics.r2_score, greater_is_better=False + ) score = scorer(y_true, y_pred + 1.0) self.assertAlmostEqual(score, 9.0) @@ -275,49 +294,44 @@ def test_sign_flip(self): class TestMetricsDoNotAlterInput(unittest.TestCase): - def test_regression_metrics(self): for metric, scorer in autosklearn.metrics.REGRESSION_METRICS.items(): y_true = np.random.random(100).reshape((-1, 1)) y_pred = y_true.copy() + np.random.randn(100, 1) * 0.1 - if metric == 'mean_squared_log_error': + if metric == "mean_squared_log_error": y_true = np.abs(y_true) y_pred = np.abs(y_pred) y_true_2 = y_true.copy() y_pred_2 = y_pred.copy() self.assertTrue(np.isfinite(scorer(y_true_2, y_pred_2))) - np.testing.assert_array_almost_equal(y_true, y_true_2, - err_msg=metric) - np.testing.assert_array_almost_equal(y_pred, y_pred_2, - err_msg=metric) + np.testing.assert_array_almost_equal(y_true, y_true_2, err_msg=metric) + np.testing.assert_array_almost_equal(y_pred, y_pred_2, err_msg=metric) def test_classification_metrics(self): for metric, scorer in autosklearn.metrics.CLASSIFICATION_METRICS.items(): y_true = np.random.randint(0, 2, size=(100, 1)) y_pred = np.random.random(200).reshape((-1, 2)) - y_pred = np.array([y_pred[i] / np.sum(y_pred[i]) - for i in range(100)]) + y_pred = np.array([y_pred[i] / np.sum(y_pred[i]) for i in range(100)]) y_true_2 = y_true.copy() y_pred_2 = y_pred.copy() try: self.assertTrue(np.isfinite(scorer(y_true_2, y_pred_2))) - np.testing.assert_array_almost_equal(y_true, y_true_2, - err_msg=metric) - np.testing.assert_array_almost_equal(y_pred, y_pred_2, - err_msg=metric) + np.testing.assert_array_almost_equal(y_true, y_true_2, err_msg=metric) + np.testing.assert_array_almost_equal(y_pred, y_pred_2, err_msg=metric) except ValueError as e: - if e.args[0] == 'Samplewise metrics are not available outside' \ - ' of multilabel classification.': + if ( + e.args[0] == "Samplewise metrics are not available outside" + " of multilabel classification." + ): pass else: raise e class TestMetric(unittest.TestCase): - def test_regression_all(self): for metric, scorer in autosklearn.metrics.REGRESSION_METRICS.items(): @@ -331,7 +345,7 @@ def test_regression_all(self): current_score = scorer(y_true, y_pred) self.assertLess(current_score, previous_score) - if scorer.name == 'mean_squared_log_error': + if scorer.name == "mean_squared_log_error": continue y_pred = np.array([-1, 0, -1, 0]) @@ -352,31 +366,39 @@ def test_classification_binary(self): # TODO: but its behavior is not right. When y_pred is completely # TODO: wrong, it does return 0.5, but when it is not completely # TODO: wrong, it returns value smaller than 0.5. - if metric in ['average_precision', - 'precision_samples', 'recall_samples', 'f1_samples']: + if metric in [ + "average_precision", + "precision_samples", + "recall_samples", + "f1_samples", + ]: continue y_true = np.array([1.0, 1.0, 1.0, 0.0, 0.0, 0.0]) - y_pred = \ - np.array([[0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0]]) + y_pred = np.array( + [[0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0]] + ) previous_score = scorer._optimum current_score = scorer(y_true, y_pred) self.assertAlmostEqual(current_score, previous_score) - y_pred = \ - np.array([[0.0, 1.0], [1.0, 0.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0], [1.0, 0.0]]) + y_pred = np.array( + [[0.0, 1.0], [1.0, 0.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0], [1.0, 0.0]] + ) previous_score = current_score current_score = scorer(y_true, y_pred) self.assertLess(current_score, previous_score) - y_pred = \ - np.array([[0.0, 1.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0]]) + y_pred = np.array( + [[0.0, 1.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0]] + ) previous_score = current_score current_score = scorer(y_true, y_pred) self.assertLess(current_score, previous_score) - y_pred = \ - np.array([[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0]]) + y_pred = np.array( + [[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0]] + ) previous_score = current_score current_score = scorer(y_true, y_pred) self.assertLess(current_score, previous_score) @@ -390,76 +412,86 @@ def test_classification_multiclass(self): # # This test should be parameterized so we can identify which metrics # cause which warning specifically and rectify if needed. - ignored_warnings = [ - (UserWarning, 'y_pred contains classes not in y_true') - ] + ignored_warnings = [(UserWarning, "y_pred contains classes not in y_true")] for metric, scorer in autosklearn.metrics.CLASSIFICATION_METRICS.items(): # Skip functions not applicable for multiclass classification. - if metric in ['roc_auc', 'average_precision', - 'precision', 'recall', 'f1', 'precision_samples', - 'recall_samples', 'f1_samples']: + if metric in [ + "roc_auc", + "average_precision", + "precision", + "recall", + "f1", + "precision_samples", + "recall_samples", + "f1_samples", + ]: continue - y_true = np.array( - [0.0, 0.0, 1.0, 1.0, 2.0] - ) + y_true = np.array([0.0, 0.0, 1.0, 1.0, 2.0]) - y_pred = np.array([ - [1.0, 0.0, 0.0], - [1.0, 0.0, 0.0], - [0.0, 1.0, 0.0], - [0.0, 1.0, 0.0], - [0.0, 0.0, 1.0] - ]) + y_pred = np.array( + [ + [1.0, 0.0, 0.0], + [1.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, 0.0, 1.0], + ] + ) previous_score = scorer._optimum current_score = scorer(y_true, y_pred) self.assertAlmostEqual(current_score, previous_score) - y_pred = np.array([ - [1.0, 0.0, 0.0], - [1.0, 0.0, 0.0], - [1.0, 0.0, 0.0], - [0.0, 1.0, 0.0], - [0.0, 0.0, 1.0], - ]) + y_pred = np.array( + [ + [1.0, 0.0, 0.0], + [1.0, 0.0, 0.0], + [1.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, 0.0, 1.0], + ] + ) previous_score = current_score current_score = scorer(y_true, y_pred) self.assertLess(current_score, previous_score) - y_pred = np.array([ - [0.0, 0.0, 1.0], - [0.0, 1.0, 0.0], - [1.0, 0.0, 0.0], - [0.0, 1.0, 0.0], - [0.0, 1.0, 0.0] - ]) + y_pred = np.array( + [ + [0.0, 0.0, 1.0], + [0.0, 1.0, 0.0], + [1.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, 1.0, 0.0], + ] + ) previous_score = current_score current_score = scorer(y_true, y_pred) self.assertLess(current_score, previous_score) - y_pred = np.array([ - [0.0, 0.0, 1.0], - [0.0, 0.0, 1.0], - [1.0, 0.0, 0.0], - [1.0, 0.0, 0.0], - [0.0, 1.0, 0.0] - ]) + y_pred = np.array( + [ + [0.0, 0.0, 1.0], + [0.0, 0.0, 1.0], + [1.0, 0.0, 0.0], + [1.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + ] + ) previous_score = current_score current_score = scorer(y_true, y_pred) self.assertLess(current_score, previous_score) # less labels in the targets than in the predictions y_true = np.array([0.0, 0.0, 1.0, 1.0]) - y_pred = np.array([ - [1.0, 0.0, 0.0], [1.0, 0.0, 0.0], - [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]] + y_pred = np.array( + [[1.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]] ) with warnings.catch_warnings(): for category, message in ignored_warnings: warnings.filterwarnings( - 'ignore', category=category, message=message + "ignore", category=category, message=message ) score = scorer(y_true, y_pred) @@ -469,8 +501,14 @@ def test_classification_multilabel(self): for metric, scorer in autosklearn.metrics.CLASSIFICATION_METRICS.items(): # Skip functions not applicable for multi-label classification. - if metric in ['roc_auc', 'log_loss', - 'precision', 'recall', 'f1', 'balanced_accuracy']: + if metric in [ + "roc_auc", + "log_loss", + "precision", + "recall", + "f1", + "balanced_accuracy", + ]: continue y_true = np.array([[1, 0, 0], [1, 1, 0], [0, 1, 1], [1, 1, 1]]) y_pred = y_true.copy() @@ -495,11 +533,11 @@ def test_classification_multilabel(self): class TestCalculateScore(unittest.TestCase): - def test_unsupported_task_type(self): y_true = np.array([1.0, 1.0, 1.0, 0.0, 0.0, 0.0]) - y_pred = \ - np.array([[0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0]]) + y_pred = np.array( + [[0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0]] + ) scorer = autosklearn.metrics.accuracy raised = False @@ -513,17 +551,22 @@ def test_classification_scoring_functions(self): scoring_functions = list(autosklearn.metrics.CLASSIFICATION_METRICS.values()) scoring_functions.remove(autosklearn.metrics.accuracy) - fail_metrics = ['precision_samples', 'recall_samples', 'f1_samples'] + fail_metrics = ["precision_samples", "recall_samples", "f1_samples"] success_metrics = list(autosklearn.metrics.CLASSIFICATION_METRICS.keys()) for metric in fail_metrics: success_metrics.remove(metric) y_true = np.array([1.0, 1.0, 1.0, 0.0, 0.0, 0.0]) - y_pred = \ - np.array([[0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0]]) - score_dict = calculate_score(y_true, y_pred, BINARY_CLASSIFICATION, - autosklearn.metrics.accuracy, - scoring_functions) + y_pred = np.array( + [[0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0]] + ) + score_dict = calculate_score( + y_true, + y_pred, + BINARY_CLASSIFICATION, + autosklearn.metrics.accuracy, + scoring_functions, + ) self.assertIsInstance(score_dict, dict) self.assertTrue(len(success_metrics), len(score_dict)) @@ -531,8 +574,10 @@ def test_classification_scoring_functions(self): self.assertNotIn(metric, score_dict.keys()) for metric in success_metrics: self.assertIn(metric, score_dict.keys()) - self.assertAlmostEqual(autosklearn.metrics.CLASSIFICATION_METRICS[metric]._optimum, - score_dict[metric]) + self.assertAlmostEqual( + autosklearn.metrics.CLASSIFICATION_METRICS[metric]._optimum, + score_dict[metric], + ) def test_regression_scoring_functions(self): @@ -540,26 +585,33 @@ def test_regression_scoring_functions(self): scoring_functions.remove(autosklearn.metrics.root_mean_squared_error) metrics = list(autosklearn.metrics.REGRESSION_METRICS.keys()) - metrics.remove('mean_squared_log_error') + metrics.remove("mean_squared_log_error") y_true = np.array([1, 2, 3, -4]) y_pred = y_true.copy() - score_dict = calculate_score(y_true, y_pred, REGRESSION, - autosklearn.metrics.root_mean_squared_error, - scoring_functions) + score_dict = calculate_score( + y_true, + y_pred, + REGRESSION, + autosklearn.metrics.root_mean_squared_error, + scoring_functions, + ) self.assertIsInstance(score_dict, dict) self.assertTrue(len(metrics), len(score_dict)) for metric in metrics: self.assertIn(metric, score_dict.keys()) - self.assertAlmostEqual(autosklearn.metrics.REGRESSION_METRICS[metric]._optimum, - score_dict[metric]) + self.assertAlmostEqual( + autosklearn.metrics.REGRESSION_METRICS[metric]._optimum, + score_dict[metric], + ) def test_classification_only_metric(self): y_true = np.array([1.0, 1.0, 1.0, 0.0, 0.0, 0.0]) - y_pred = \ - np.array([[0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0]]) + y_pred = np.array( + [[0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0]] + ) scorer = autosklearn.metrics.accuracy score = calculate_score(y_true, y_pred, BINARY_CLASSIFICATION, scorer) @@ -602,22 +654,28 @@ def test_calculate_loss(): prediction=y_pred, task_type=BINARY_CLASSIFICATION, metric=autosklearn.metrics.accuracy, - scoring_functions=[autosklearn.metrics.accuracy, autosklearn.metrics.balanced_accuracy] + scoring_functions=[ + autosklearn.metrics.accuracy, + autosklearn.metrics.balanced_accuracy, + ], ) expected_score_dict = { - 'accuracy': 0.9, - 'balanced_accuracy': 0.9285714285714286, + "accuracy": 0.9, + "balanced_accuracy": 0.9285714285714286, } loss_dict = calculate_loss( solution=y_true, prediction=y_pred, task_type=BINARY_CLASSIFICATION, metric=autosklearn.metrics.accuracy, - scoring_functions=[autosklearn.metrics.accuracy, autosklearn.metrics.balanced_accuracy] + scoring_functions=[ + autosklearn.metrics.accuracy, + autosklearn.metrics.balanced_accuracy, + ], ) for expected_metric, expected_score in expected_score_dict.items(): assert pytest.approx(expected_score) == score_dict[expected_metric] - assert pytest.approx(1-expected_score) == loss_dict[expected_metric] + assert pytest.approx(1 - expected_score) == loss_dict[expected_metric] # Lastly make sure that metrics whose optimum is zero # are also properly working diff --git a/test/test_optimizer/test_smbo.py b/test/test_optimizer/test_smbo.py index 4b7f0ffd79..fafd7b5a42 100644 --- a/test/test_optimizer/test_smbo.py +++ b/test/test_optimizer/test_smbo.py @@ -1,36 +1,39 @@ import logging.handlers -from ConfigSpace.configuration_space import Configuration - import pytest +from ConfigSpace.configuration_space import Configuration import autosklearn.metrics -from autosklearn.smbo import AutoMLSMBO import autosklearn.pipeline.util as putil from autosklearn.automl import AutoML from autosklearn.constants import BINARY_CLASSIFICATION from autosklearn.data.xy_data_manager import XYDataManager +from autosklearn.smbo import AutoMLSMBO from autosklearn.util.stopwatch import StopWatch -@pytest.mark.parametrize("context", ['fork', 'forkserver']) +@pytest.mark.parametrize("context", ["fork", "forkserver"]) def test_smbo_metalearning_configurations(backend, context, dask_client): # Get the inputs to the optimizer - X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') - config_space = AutoML(delete_tmp_folder_after_terminate=False, - metric=autosklearn.metrics.accuracy, - time_left_for_this_task=20, - per_run_time_limit=5).fit( - X_train, Y_train, - task=BINARY_CLASSIFICATION, - only_return_configuration_space=True) + X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") + config_space = AutoML( + delete_tmp_folder_after_terminate=False, + metric=autosklearn.metrics.accuracy, + time_left_for_this_task=20, + per_run_time_limit=5, + ).fit( + X_train, + Y_train, + task=BINARY_CLASSIFICATION, + only_return_configuration_space=True, + ) watcher = StopWatch() # Create an optimizer smbo = AutoMLSMBO( config_space=config_space, - dataset_name='iris', + dataset_name="iris", backend=backend, total_walltime_limit=10, func_eval_time_limit=5, @@ -49,11 +52,13 @@ def test_smbo_metalearning_configurations(backend, context, dask_client): # Create the inputs to metalearning datamanager = XYDataManager( - X_train, Y_train, - X_test, Y_test, + X_train, + Y_train, + X_test, + Y_test, task=BINARY_CLASSIFICATION, - dataset_name='iris', - feat_type={i: 'numerical' for i in range(X_train.shape[1])}, + dataset_name="iris", + feat_type={i: "numerical" for i in range(X_train.shape[1])}, ) backend.save_datamanager(datamanager) smbo.task = BINARY_CLASSIFICATION diff --git a/test/test_pipeline/components/__init__.py b/test/test_pipeline/components/__init__.py index 8f0ce6cb7c..92bf78f389 100644 --- a/test/test_pipeline/components/__init__.py +++ b/test/test_pipeline/components/__init__.py @@ -1 +1 @@ -__author__ = 'feurerm' +__author__ = "feurerm" diff --git a/test/test_pipeline/components/classification/__init__.py b/test/test_pipeline/components/classification/__init__.py index 8f0ce6cb7c..92bf78f389 100644 --- a/test/test_pipeline/components/classification/__init__.py +++ b/test/test_pipeline/components/classification/__init__.py @@ -1 +1 @@ -__author__ = 'feurerm' +__author__ = "feurerm" diff --git a/test/test_pipeline/components/classification/test_adaboost.py b/test/test_pipeline/components/classification/test_adaboost.py index 3c0d96f9a6..f41ba3319f 100644 --- a/test/test_pipeline/components/classification/test_adaboost.py +++ b/test/test_pipeline/components/classification/test_adaboost.py @@ -1,7 +1,7 @@ import sklearn.ensemble -from autosklearn.pipeline.components.classification.adaboost import \ - AdaboostClassifier +from autosklearn.pipeline.components.classification.adaboost import AdaboostClassifier + from .test_base import BaseClassificationComponentTest diff --git a/test/test_pipeline/components/classification/test_base.py b/test/test_pipeline/components/classification/test_base.py index 4fc381af56..a524759bc5 100644 --- a/test/test_pipeline/components/classification/test_base.py +++ b/test/test_pipeline/components/classification/test_base.py @@ -1,15 +1,18 @@ -from typing import Optional, Dict +from typing import Dict, Optional import unittest -from autosklearn.pipeline.util import _test_classifier, \ - _test_classifier_predict_proba, _test_classifier_iterative_fit -from autosklearn.pipeline.constants import SPARSE - -import sklearn.metrics import numpy as np +import sklearn.metrics -from test.test_pipeline.ignored_warnings import ignore_warnings, classifier_warnings +from autosklearn.pipeline.constants import SPARSE +from autosklearn.pipeline.util import ( + _test_classifier, + _test_classifier_iterative_fit, + _test_classifier_predict_proba, +) + +from test.test_pipeline.ignored_warnings import classifier_warnings, ignore_warnings class BaseClassificationComponentTest(unittest.TestCase): @@ -29,14 +32,14 @@ def test_default_iris(self): return for i in range(2): - predictions, targets, n_calls = \ - _test_classifier(dataset="iris", - classifier=self.module) - self.assertAlmostEqual(self.res["default_iris"], - sklearn.metrics.accuracy_score(targets, - predictions), - places=self.res.get( - "default_iris_places", 7)) + predictions, targets, n_calls = _test_classifier( + dataset="iris", classifier=self.module + ) + self.assertAlmostEqual( + self.res["default_iris"], + sklearn.metrics.accuracy_score(targets, predictions), + places=self.res.get("default_iris_places", 7), + ) if self.res.get("iris_n_calls"): self.assertEqual(self.res["iris_n_calls"], n_calls) @@ -45,7 +48,7 @@ def test_get_max_iter(self): if self.__class__ == BaseClassificationComponentTest: return - if not hasattr(self.module, 'iterative_fit'): + if not hasattr(self.module, "iterative_fit"): return self.module.get_max_iter() @@ -55,23 +58,25 @@ def test_default_iris_iterative_fit(self): if self.__class__ == BaseClassificationComponentTest: return - if not hasattr(self.module, 'iterative_fit'): + if not hasattr(self.module, "iterative_fit"): return for i in range(2): - predictions, targets, classifier = \ - _test_classifier_iterative_fit(dataset="iris", - classifier=self.module) - self.assertAlmostEqual(self.res["default_iris_iterative"], - sklearn.metrics.accuracy_score(targets, - predictions), - places=self.res.get( - "default_iris_iterative_places", 7)) + predictions, targets, classifier = _test_classifier_iterative_fit( + dataset="iris", classifier=self.module + ) + self.assertAlmostEqual( + self.res["default_iris_iterative"], + sklearn.metrics.accuracy_score(targets, predictions), + places=self.res.get("default_iris_iterative_places", 7), + ) if self.step_hyperparameter is not None: self.assertEqual( - getattr(classifier.estimator, self.step_hyperparameter['name']), - self.res.get("iris_iterative_n_iter", self.step_hyperparameter['value']) + getattr(classifier.estimator, self.step_hyperparameter["name"]), + self.res.get( + "iris_iterative_n_iter", self.step_hyperparameter["value"] + ), ) def test_default_iris_predict_proba(self): @@ -86,7 +91,7 @@ def test_default_iris_predict_proba(self): self.assertAlmostEqual( self.res["default_iris_proba"], sklearn.metrics.log_loss(targets, predictions), - places=self.res.get("default_iris_proba_places", 7) + places=self.res.get("default_iris_proba_places", 7), ) def test_default_iris_sparse(self): @@ -98,15 +103,14 @@ def test_default_iris_sparse(self): return for i in range(2): - predictions, targets, _ = \ - _test_classifier(dataset="iris", - classifier=self.module, - sparse=True) - self.assertAlmostEqual(self.res["default_iris_sparse"], - sklearn.metrics.accuracy_score(targets, - predictions), - places=self.res.get( - "default_iris_sparse_places", 7)) + predictions, targets, _ = _test_classifier( + dataset="iris", classifier=self.module, sparse=True + ) + self.assertAlmostEqual( + self.res["default_iris_sparse"], + sklearn.metrics.accuracy_score(targets, predictions), + places=self.res.get("default_iris_sparse_places", 7), + ) def test_default_digits_binary(self): @@ -114,15 +118,14 @@ def test_default_digits_binary(self): return for i in range(2): - predictions, targets, _ = \ - _test_classifier(classifier=self.module, - dataset='digits', sparse=False, - make_binary=True) - self.assertAlmostEqual(self.res["default_digits_binary"], - sklearn.metrics.accuracy_score( - targets, predictions), - places=self.res.get( - "default_digits_binary_places", 7)) + predictions, targets, _ = _test_classifier( + classifier=self.module, dataset="digits", sparse=False, make_binary=True + ) + self.assertAlmostEqual( + self.res["default_digits_binary"], + sklearn.metrics.accuracy_score(targets, predictions), + places=self.res.get("default_digits_binary_places", 7), + ) def test_default_digits(self): @@ -130,14 +133,14 @@ def test_default_digits(self): return for i in range(2): - predictions, targets, n_calls = \ - _test_classifier(dataset="digits", - classifier=self.module) - self.assertAlmostEqual(self.res["default_digits"], - sklearn.metrics.accuracy_score(targets, - predictions), - places=self.res.get( - "default_digits_places", 7)) + predictions, targets, n_calls = _test_classifier( + dataset="digits", classifier=self.module + ) + self.assertAlmostEqual( + self.res["default_digits"], + sklearn.metrics.accuracy_score(targets, predictions), + places=self.res.get("default_digits_places", 7), + ) if self.res.get("digits_n_calls"): self.assertEqual(self.res["digits_n_calls"], n_calls) @@ -147,23 +150,25 @@ def test_default_digits_iterative_fit(self): if self.__class__ == BaseClassificationComponentTest: return - if not hasattr(self.module, 'iterative_fit'): + if not hasattr(self.module, "iterative_fit"): return for i in range(2): - predictions, targets, classifier = \ - _test_classifier_iterative_fit(dataset="digits", - classifier=self.module) - self.assertAlmostEqual(self.res["default_digits_iterative"], - sklearn.metrics.accuracy_score(targets, - predictions), - places=self.res.get( - "default_digits_iterative_places", 7)) + predictions, targets, classifier = _test_classifier_iterative_fit( + dataset="digits", classifier=self.module + ) + self.assertAlmostEqual( + self.res["default_digits_iterative"], + sklearn.metrics.accuracy_score(targets, predictions), + places=self.res.get("default_digits_iterative_places", 7), + ) if self.step_hyperparameter is not None: self.assertEqual( - getattr(classifier.estimator, self.step_hyperparameter['name']), - self.res.get("digits_iterative_n_iter", self.step_hyperparameter['value']) + getattr(classifier.estimator, self.step_hyperparameter["name"]), + self.res.get( + "digits_iterative_n_iter", self.step_hyperparameter["value"] + ), ) def test_default_digits_multilabel(self): @@ -176,15 +181,16 @@ def test_default_digits_multilabel(self): for _ in range(2): predictions, targets, _ = _test_classifier( - classifier=self.module, dataset='digits', make_multilabel=True + classifier=self.module, dataset="digits", make_multilabel=True ) score = sklearn.metrics.precision_score( - targets, predictions, average='macro', zero_division=0 + targets, predictions, average="macro", zero_division=0 ) self.assertAlmostEqual( - self.res["default_digits_multilabel"], score, - places=self.res.get("default_digits_multilabel_places", 7) + self.res["default_digits_multilabel"], + score, + places=self.res.get("default_digits_multilabel_places", 7), ) def test_default_digits_multilabel_predict_proba(self): @@ -196,15 +202,15 @@ def test_default_digits_multilabel_predict_proba(self): return for i in range(2): - predictions, targets = \ - _test_classifier_predict_proba(classifier=self.module, - make_multilabel=True) + predictions, targets = _test_classifier_predict_proba( + classifier=self.module, make_multilabel=True + ) self.assertEqual(predictions.shape, ((50, 3))) - self.assertAlmostEqual(self.res["default_digits_multilabel_proba"], - sklearn.metrics.roc_auc_score( - targets, predictions, average='macro'), - places=self.res.get( - "default_digits_multilabel_proba_places", 7)) + self.assertAlmostEqual( + self.res["default_digits_multilabel_proba"], + sklearn.metrics.roc_auc_score(targets, predictions, average="macro"), + places=self.res.get("default_digits_multilabel_proba_places", 7), + ) def test_target_algorithm_multioutput_multiclass_support(self): @@ -218,42 +224,66 @@ def test_target_algorithm_multioutput_multiclass_support(self): X = np.random.random((10, 10)) y = np.random.randint(0, 1, size=(10, 10)) self.assertRaisesRegex( - ValueError, - 'bad input shape \\(10, 10\\)', - cls.fit, - X, - y + ValueError, "bad input shape \\(10, 10\\)", cls.fit, X, y ) else: return def test_module_idempotent(self): - """ Fitting twice with the same config gives the same model params. + """Fitting twice with the same config gives the same model params. - This is only valid when the random_state passed is an int. If a - RandomState object is passed then repeated calls to fit will have - different results. See the section on "Controlling Randomness" in the - sklearn docs. + This is only valid when the random_state passed is an int. If a + RandomState object is passed then repeated calls to fit will have + different results. See the section on "Controlling Randomness" in the + sklearn docs. - https://scikit-learn.org/0.24/common_pitfalls.html#controlling-randomness + https://scikit-learn.org/0.24/common_pitfalls.html#controlling-randomness """ if self.__class__ == BaseClassificationComponentTest: return classifier_cls = self.module - X = np.array([ - [0, 0], [0, 1], [1, 0], [1, 1], - [0, 0], [0, 1], [1, 0], [1, 1], - [0, 0], [0, 1], [1, 0], [1, 1], - [0, 0], [0, 1], [1, 0], [1, 1], - ]) - y = np.array([ - 0, 1, 1, 0, - 0, 1, 1, 0, - 0, 1, 1, 0, - 0, 1, 1, 0, - ]) + X = np.array( + [ + [0, 0], + [0, 1], + [1, 0], + [1, 1], + [0, 0], + [0, 1], + [1, 0], + [1, 1], + [0, 0], + [0, 1], + [1, 0], + [1, 1], + [0, 0], + [0, 1], + [1, 0], + [1, 1], + ] + ) + y = np.array( + [ + 0, + 1, + 1, + 0, + 0, + 1, + 1, + 0, + 0, + 1, + 1, + 0, + 0, + 1, + 1, + 0, + ] + ) # There are certain errors we ignore so we wrap this in a function def fitted_params(model) -> Optional[Dict]: @@ -268,12 +298,18 @@ def is_QDA_error(err): # We are okay if the BaseClassifier in AdaBoostClassifier is worse # than random so no ensemble can be fit def is_AdaBoostClassifier_error(err): - return ("BaseClassifier in AdaBoostClassifier ensemble is worse" - + " than random, ensemble can not be fit." in err.args[0]) + return ( + "BaseClassifier in AdaBoostClassifier ensemble is worse" + + " than random, ensemble can not be fit." + in err.args[0] + ) def is_unset_param_raw_predictions_val_error(err): - return ("local variable 'raw_predictions_val' referenced before" - + " assignment" in err.args[0]) + return ( + "local variable 'raw_predictions_val' referenced before" + + " assignment" + in err.args[0] + ) try: with ignore_warnings(classifier_warnings): @@ -288,7 +324,7 @@ def is_unset_param_raw_predictions_val_error(err): return model.estimator.get_params() # We ignore certain keys when comparing - param_keys_ignored = ['base_estimator'] + param_keys_ignored = ["base_estimator"] # We use the default config + sampled ones configuration_space = classifier_cls.get_hyperparameter_search_space() @@ -302,12 +338,12 @@ def is_unset_param_raw_predictions_val_error(err): # Get the parameters on the first and second fit with config params params_first = fitted_params(classifier) - if hasattr(classifier.estimator, 'random_state'): + if hasattr(classifier.estimator, "random_state"): rs_1 = classifier.random_state rs_estimator_1 = classifier.estimator.random_state params_second = fitted_params(classifier) - if hasattr(classifier.estimator, 'random_state'): + if hasattr(classifier.estimator, "random_state"): rs_2 = classifier.random_state rs_estimator_2 = classifier.estimator.random_state @@ -322,10 +358,13 @@ def is_unset_param_raw_predictions_val_error(err): del params[key] # They should have equal parameters - self.assertEqual(params_first, params_second, - f"Failed with model args {model_args}") - if hasattr(classifier.estimator, 'random_state'): - assert all([ - seed == random_state - for random_state in [rs_1, rs_estimator_1, rs_2, rs_estimator_2] - ]) + self.assertEqual( + params_first, params_second, f"Failed with model args {model_args}" + ) + if hasattr(classifier.estimator, "random_state"): + assert all( + [ + seed == random_state + for random_state in [rs_1, rs_estimator_1, rs_2, rs_estimator_2] + ] + ) diff --git a/test/test_pipeline/components/classification/test_bernoulli_nb.py b/test/test_pipeline/components/classification/test_bernoulli_nb.py index 8384119393..2def3a385f 100644 --- a/test/test_pipeline/components/classification/test_bernoulli_nb.py +++ b/test/test_pipeline/components/classification/test_bernoulli_nb.py @@ -1,7 +1,6 @@ import sklearn.naive_bayes -from autosklearn.pipeline.components.classification.bernoulli_nb import \ - BernoulliNB +from autosklearn.pipeline.components.classification.bernoulli_nb import BernoulliNB from .test_base import BaseClassificationComponentTest diff --git a/test/test_pipeline/components/classification/test_decision_tree.py b/test/test_pipeline/components/classification/test_decision_tree.py index e32a6536c7..546040e645 100644 --- a/test/test_pipeline/components/classification/test_decision_tree.py +++ b/test/test_pipeline/components/classification/test_decision_tree.py @@ -1,7 +1,6 @@ import sklearn.tree -from autosklearn.pipeline.components.classification.decision_tree import \ - DecisionTree +from autosklearn.pipeline.components.classification.decision_tree import DecisionTree from .test_base import BaseClassificationComponentTest diff --git a/test/test_pipeline/components/classification/test_extra_trees.py b/test/test_pipeline/components/classification/test_extra_trees.py index e7b1935db0..213bfbd916 100644 --- a/test/test_pipeline/components/classification/test_extra_trees.py +++ b/test/test_pipeline/components/classification/test_extra_trees.py @@ -1,6 +1,8 @@ import sklearn.ensemble -from autosklearn.pipeline.components.classification.extra_trees import ExtraTreesClassifier +from autosklearn.pipeline.components.classification.extra_trees import ( + ExtraTreesClassifier, +) from .test_base import BaseClassificationComponentTest @@ -12,12 +14,12 @@ class ExtraTreesComponentTest(BaseClassificationComponentTest): res = dict() res["default_iris"] = 0.96 res["iris_n_calls"] = 9 - res["default_iris_iterative"] = res['default_iris'] + res["default_iris_iterative"] = res["default_iris"] res["default_iris_proba"] = 0.10053485167017469 res["default_iris_sparse"] = 0.74 res["default_digits"] = 0.9216757741347905 res["digits_n_calls"] = 9 - res["default_digits_iterative"] = res['default_digits'] + res["default_digits_iterative"] = res["default_digits"] res["default_digits_iterative_places"] = 3 res["default_digits_binary"] = 0.994535519125683 res["default_digits_multilabel"] = 0.9983621593291405 @@ -26,6 +28,6 @@ class ExtraTreesComponentTest(BaseClassificationComponentTest): sk_mod = sklearn.ensemble.ExtraTreesClassifier module = ExtraTreesClassifier step_hyperparameter = { - 'name': 'n_estimators', - 'value': module.get_max_iter(), + "name": "n_estimators", + "value": module.get_max_iter(), } diff --git a/test/test_pipeline/components/classification/test_gaussian_nb.py b/test/test_pipeline/components/classification/test_gaussian_nb.py index ea5ce7cc5b..2f813b4293 100644 --- a/test/test_pipeline/components/classification/test_gaussian_nb.py +++ b/test/test_pipeline/components/classification/test_gaussian_nb.py @@ -1,7 +1,6 @@ import sklearn.naive_bayes -from autosklearn.pipeline.components.classification.gaussian_nb import \ - GaussianNB +from autosklearn.pipeline.components.classification.gaussian_nb import GaussianNB from .test_base import BaseClassificationComponentTest diff --git a/test/test_pipeline/components/classification/test_gradient_boosting.py b/test/test_pipeline/components/classification/test_gradient_boosting.py index efa3a3cca8..4bfadfa74c 100644 --- a/test/test_pipeline/components/classification/test_gradient_boosting.py +++ b/test/test_pipeline/components/classification/test_gradient_boosting.py @@ -1,7 +1,8 @@ import sklearn.ensemble -from autosklearn.pipeline.components.classification.gradient_boosting import \ - GradientBoostingClassifier +from autosklearn.pipeline.components.classification.gradient_boosting import ( + GradientBoostingClassifier, +) from .test_base import BaseClassificationComponentTest @@ -24,6 +25,6 @@ class GradientBoostingComponentTest(BaseClassificationComponentTest): sk_mod = sklearn.ensemble.ExtraTreesClassifier module = GradientBoostingClassifier step_hyperparameter = { - 'name': 'max_iter', - 'value': module.get_max_iter(), + "name": "max_iter", + "value": module.get_max_iter(), } diff --git a/test/test_pipeline/components/classification/test_k_nearest_neighbor.py b/test/test_pipeline/components/classification/test_k_nearest_neighbor.py index 8209e2a674..d09512d07d 100644 --- a/test/test_pipeline/components/classification/test_k_nearest_neighbor.py +++ b/test/test_pipeline/components/classification/test_k_nearest_neighbor.py @@ -1,7 +1,8 @@ import sklearn.neighbors -from autosklearn.pipeline.components.classification.k_nearest_neighbors import \ - KNearestNeighborsClassifier +from autosklearn.pipeline.components.classification.k_nearest_neighbors import ( + KNearestNeighborsClassifier, +) from .test_base import BaseClassificationComponentTest diff --git a/test/test_pipeline/components/classification/test_liblinear.py b/test/test_pipeline/components/classification/test_liblinear.py index bb2d2a1894..1aec8e227e 100644 --- a/test/test_pipeline/components/classification/test_liblinear.py +++ b/test/test_pipeline/components/classification/test_liblinear.py @@ -1,7 +1,6 @@ import sklearn.svm -from autosklearn.pipeline.components.classification.liblinear_svc import \ - LibLinear_SVC +from autosklearn.pipeline.components.classification.liblinear_svc import LibLinear_SVC from .test_base import BaseClassificationComponentTest diff --git a/test/test_pipeline/components/classification/test_libsvm_svc.py b/test/test_pipeline/components/classification/test_libsvm_svc.py index dcab429fc1..6fe95f5b62 100644 --- a/test/test_pipeline/components/classification/test_libsvm_svc.py +++ b/test/test_pipeline/components/classification/test_libsvm_svc.py @@ -2,8 +2,7 @@ import sklearn.svm from autosklearn.pipeline.components.classification.libsvm_svc import LibSVM_SVC -from autosklearn.pipeline.util import get_dataset, \ - _test_classifier_predict_proba +from autosklearn.pipeline.util import _test_classifier_predict_proba, get_dataset from .test_base import BaseClassificationComponentTest @@ -30,22 +29,23 @@ def test_default_configuration_predict_proba_individual(self): # Leave this additional test here for i in range(2): predictions, targets = _test_classifier_predict_proba( - LibSVM_SVC, sparse=True, dataset='digits', - train_size_maximum=500) - self.assertAlmostEqual(5.273502056835706, - sklearn.metrics.log_loss(targets, - predictions)) + LibSVM_SVC, sparse=True, dataset="digits", train_size_maximum=500 + ) + self.assertAlmostEqual( + 5.273502056835706, sklearn.metrics.log_loss(targets, predictions) + ) for i in range(2): predictions, targets = _test_classifier_predict_proba( - LibSVM_SVC, sparse=True, dataset='iris') - self.assertAlmostEqual(0.8408320837510618, - sklearn.metrics.log_loss(targets, - predictions)) + LibSVM_SVC, sparse=True, dataset="iris" + ) + self.assertAlmostEqual( + 0.8408320837510618, sklearn.metrics.log_loss(targets, predictions) + ) # 2 class for i in range(2): - X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris') + X_train, Y_train, X_test, Y_test = get_dataset(dataset="iris") remove_training_data = Y_train == 2 remove_test_data = Y_test == 2 X_train = X_train[~remove_training_data] @@ -57,11 +57,19 @@ def test_default_configuration_predict_proba_individual(self): configuration_space = LibSVM_SVC.get_hyperparameter_search_space() default = configuration_space.get_default_configuration() - cls = LibSVM_SVC(random_state=1, **{hp_name: default[hp_name] - for hp_name in default - if default[hp_name] is not None}) + cls = LibSVM_SVC( + random_state=1, + **{ + hp_name: default[hp_name] + for hp_name in default + if default[hp_name] is not None + }, + ) cls = cls.fit(X_train, Y_train) prediction = cls.predict_proba(X_test) - self.assertAlmostEqual(sklearn.metrics.log_loss(Y_test, prediction), - 0.6927962762794081, places=4) + self.assertAlmostEqual( + sklearn.metrics.log_loss(Y_test, prediction), + 0.6927962762794081, + places=4, + ) diff --git a/test/test_pipeline/components/classification/test_mlp.py b/test/test_pipeline/components/classification/test_mlp.py index b8d559b1bc..e1c4286d83 100644 --- a/test/test_pipeline/components/classification/test_mlp.py +++ b/test/test_pipeline/components/classification/test_mlp.py @@ -43,6 +43,6 @@ class MLPComponentTest(BaseClassificationComponentTest): sk_mod = sklearn.neural_network.MLPClassifier module = MLPClassifier step_hyperparameter = { - 'name': 'n_iter_', - 'value': module.get_max_iter(), + "name": "n_iter_", + "value": module.get_max_iter(), } diff --git a/test/test_pipeline/components/classification/test_multinomial_nb.py b/test/test_pipeline/components/classification/test_multinomial_nb.py index 2c982c41ef..c82b938679 100644 --- a/test/test_pipeline/components/classification/test_multinomial_nb.py +++ b/test/test_pipeline/components/classification/test_multinomial_nb.py @@ -1,10 +1,8 @@ import numpy as np - import sklearn.naive_bayes import sklearn.preprocessing -from autosklearn.pipeline.components.classification.multinomial_nb import \ - MultinomialNB +from autosklearn.pipeline.components.classification.multinomial_nb import MultinomialNB from autosklearn.pipeline.util import get_dataset from .test_base import BaseClassificationComponentTest @@ -32,17 +30,21 @@ class MultinomialNBComponentTest(BaseClassificationComponentTest): def test_default_configuration_negative_values(self): # Custon preprocessing test to check if clipping to zero works - X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits') + X_train, Y_train, X_test, Y_test = get_dataset(dataset="digits") ss = sklearn.preprocessing.StandardScaler() X_train = ss.fit_transform(X_train) configuration_space = MultinomialNB.get_hyperparameter_search_space() default = configuration_space.get_default_configuration() - cls = MultinomialNB(random_state=1, **{hp_name: default[hp_name] - for hp_name in default - if default[hp_name] is not None}) + cls = MultinomialNB( + random_state=1, + **{ + hp_name: default[hp_name] + for hp_name in default + if default[hp_name] is not None + }, + ) cls = cls.fit(X_train, Y_train) prediction = cls.predict(X_test) - self.assertAlmostEqual(np.nanmean(prediction == Y_test), - 0.88888888888888884) + self.assertAlmostEqual(np.nanmean(prediction == Y_test), 0.88888888888888884) diff --git a/test/test_pipeline/components/classification/test_passive_aggressive.py b/test/test_pipeline/components/classification/test_passive_aggressive.py index d904f9e569..b83dbaf120 100644 --- a/test/test_pipeline/components/classification/test_passive_aggressive.py +++ b/test/test_pipeline/components/classification/test_passive_aggressive.py @@ -1,7 +1,8 @@ import sklearn.linear_model -from autosklearn.pipeline.components.classification.passive_aggressive import \ - PassiveAggressive +from autosklearn.pipeline.components.classification.passive_aggressive import ( + PassiveAggressive, +) from .test_base import BaseClassificationComponentTest @@ -13,13 +14,13 @@ class PassiveAggressiveComponentTest(BaseClassificationComponentTest): res = dict() res["default_iris"] = 0.98 res["iris_n_calls"] = 6 - res["default_iris_iterative"] = res['default_iris'] + res["default_iris_iterative"] = res["default_iris"] res["iris_iterative_n_iter"] = 64 res["default_iris_proba"] = 0.27840521921952033 res["default_iris_sparse"] = 0.48 res["default_digits"] = 0.9162112932604736 res["digits_n_calls"] = 6 - res["default_digits_iterative"] = res['default_digits'] + res["default_digits_iterative"] = res["default_digits"] res["digits_iterative_n_iter"] = 64 res["default_digits_binary"] = 0.99210686095932 res["default_digits_multilabel"] = 0.910908768565592 @@ -29,6 +30,6 @@ class PassiveAggressiveComponentTest(BaseClassificationComponentTest): module = PassiveAggressive step_hyperparameter = { - 'name': 'max_iter', - 'value': module.get_max_iter(), + "name": "max_iter", + "value": module.get_max_iter(), } diff --git a/test/test_pipeline/components/classification/test_random_forest.py b/test/test_pipeline/components/classification/test_random_forest.py index 8e2c1136d3..f96869c270 100644 --- a/test/test_pipeline/components/classification/test_random_forest.py +++ b/test/test_pipeline/components/classification/test_random_forest.py @@ -1,7 +1,6 @@ import sklearn.ensemble -from autosklearn.pipeline.components.classification.random_forest import \ - RandomForest +from autosklearn.pipeline.components.classification.random_forest import RandomForest from .test_base import BaseClassificationComponentTest @@ -13,12 +12,12 @@ class RandomForestComponentTest(BaseClassificationComponentTest): res = dict() res["default_iris"] = 0.96 res["iris_n_calls"] = 9 - res["default_iris_iterative"] = res['default_iris'] + res["default_iris_iterative"] = res["default_iris"] res["default_iris_proba"] = 0.0996785324703419 res["default_iris_sparse"] = 0.85999999999999999 res["default_digits"] = 0.8998178506375227 res["digits_n_calls"] = 9 - res["default_digits_iterative"] = res['default_digits'] + res["default_digits_iterative"] = res["default_digits"] res["default_digits_binary"] = 0.9896782027929569 res["default_digits_multilabel"] = 0.9973653110879388 res["default_digits_multilabel_proba"] = 0.9965660960196189 @@ -26,6 +25,6 @@ class RandomForestComponentTest(BaseClassificationComponentTest): sk_mod = sklearn.ensemble.RandomForestClassifier module = RandomForest step_hyperparameter = { - 'name': 'n_estimators', - 'value': module.get_max_iter(), + "name": "n_estimators", + "value": module.get_max_iter(), } diff --git a/test/test_pipeline/components/classification/test_sgd.py b/test/test_pipeline/components/classification/test_sgd.py index defe8af81d..8f1d7821e1 100644 --- a/test/test_pipeline/components/classification/test_sgd.py +++ b/test/test_pipeline/components/classification/test_sgd.py @@ -1,6 +1,7 @@ import sklearn.linear_model from autosklearn.pipeline.components.classification.sgd import SGD + from .test_base import BaseClassificationComponentTest @@ -11,12 +12,12 @@ class SGDComponentTest(BaseClassificationComponentTest): res = dict() res["default_iris"] = 0.69999999999999996 res["iris_n_calls"] = 9 - res["default_iris_iterative"] = res['default_iris'] + res["default_iris_iterative"] = res["default_iris"] res["default_iris_proba"] = 0.5996114465819011 res["default_iris_sparse"] = 0.54 res["default_digits"] = 0.9198542805100182 res["digits_n_calls"] = 7 - res["default_digits_iterative"] = res['default_digits'] + res["default_digits_iterative"] = res["default_digits"] res["default_digits_binary"] = 0.9951426836672739 res["default_digits_multilabel"] = -1 res["default_digits_multilabel_proba"] = -1 diff --git a/test/test_pipeline/components/data_preprocessing/__init__.py b/test/test_pipeline/components/data_preprocessing/__init__.py index 8f0ce6cb7c..92bf78f389 100644 --- a/test/test_pipeline/components/data_preprocessing/__init__.py +++ b/test/test_pipeline/components/data_preprocessing/__init__.py @@ -1 +1 @@ -__author__ = 'feurerm' +__author__ = "feurerm" diff --git a/test/test_pipeline/components/data_preprocessing/test_balancing.py b/test/test_pipeline/components/data_preprocessing/test_balancing.py index 268a8ea542..cf8dc103b8 100644 --- a/test/test_pipeline/components/data_preprocessing/test_balancing.py +++ b/test/test_pipeline/components/data_preprocessing/test_balancing.py @@ -1,4 +1,4 @@ -__author__ = 'feurerm' +__author__ = "feurerm" import copy import unittest @@ -7,86 +7,111 @@ import sklearn.datasets import sklearn.metrics -from autosklearn.pipeline.components.data_preprocessing.balancing.balancing \ - import Balancing from autosklearn.pipeline.classification import SimpleClassificationPipeline from autosklearn.pipeline.components.classification.adaboost import AdaboostClassifier from autosklearn.pipeline.components.classification.decision_tree import DecisionTree -from autosklearn.pipeline.components.classification.extra_trees import ExtraTreesClassifier -from autosklearn.pipeline.components.classification.random_forest import RandomForest +from autosklearn.pipeline.components.classification.extra_trees import ( + ExtraTreesClassifier, +) +from autosklearn.pipeline.components.classification.gradient_boosting import ( + GradientBoostingClassifier, +) from autosklearn.pipeline.components.classification.liblinear_svc import LibLinear_SVC from autosklearn.pipeline.components.classification.libsvm_svc import LibSVM_SVC +from autosklearn.pipeline.components.classification.passive_aggressive import ( + PassiveAggressive, +) +from autosklearn.pipeline.components.classification.random_forest import RandomForest from autosklearn.pipeline.components.classification.sgd import SGD -from autosklearn.pipeline.components.classification.gradient_boosting \ - import GradientBoostingClassifier -from autosklearn.pipeline.components.classification.passive_aggressive import PassiveAggressive -from autosklearn.pipeline.components.feature_preprocessing\ - .extra_trees_preproc_for_classification import ExtraTreesPreprocessorClassification -from autosklearn.pipeline.components.feature_preprocessing.liblinear_svc_preprocessor import \ - LibLinear_Preprocessor +from autosklearn.pipeline.components.data_preprocessing.balancing.balancing import ( + Balancing, +) +from autosklearn.pipeline.components.feature_preprocessing.extra_trees_preproc_for_classification import ( # noqa: E501 + ExtraTreesPreprocessorClassification, +) +from autosklearn.pipeline.components.feature_preprocessing.liblinear_svc_preprocessor import ( # noqa: E501 + LibLinear_Preprocessor, +) class BalancingComponentTest(unittest.TestCase): def test_balancing_get_weights_treed_single_label(self): Y = np.array([0] * 80 + [1] * 20) - balancing = Balancing(strategy='weighting') - init_params, fit_params = balancing.get_weights( - Y, 'adaboost', None, None, None) + balancing = Balancing(strategy="weighting") + init_params, fit_params = balancing.get_weights(Y, "adaboost", None, None, None) self.assertAlmostEqual( - np.mean(fit_params['classifier:sample_weight']), 1, + np.mean(fit_params["classifier:sample_weight"]), + 1, ) np.testing.assert_allclose( - fit_params['classifier:sample_weight'], + fit_params["classifier:sample_weight"], np.array([0.625] * 80 + [2.5] * 20), ) def test_balancing_get_weights_treed_multilabel(self): - Y = np.array([[0, 0, 0]] * 100 + [[1, 0, 0]] * 100 + [[0, 1, 0]] * 100 + - [[1, 1, 0]] * 100 + [[0, 0, 1]] * 100 + [[1, 0, 1]] * 10) - balancing = Balancing(strategy='weighting') - init_params, fit_params = balancing.get_weights( - Y, 'adaboost', None, None, None) - print(fit_params['classifier:sample_weight']) + Y = np.array( + [[0, 0, 0]] * 100 + + [[1, 0, 0]] * 100 + + [[0, 1, 0]] * 100 + + [[1, 1, 0]] * 100 + + [[0, 0, 1]] * 100 + + [[1, 0, 1]] * 10 + ) + balancing = Balancing(strategy="weighting") + init_params, fit_params = balancing.get_weights(Y, "adaboost", None, None, None) + print(fit_params["classifier:sample_weight"]) self.assertAlmostEqual( - np.mean(fit_params['classifier:sample_weight']), 1, + np.mean(fit_params["classifier:sample_weight"]), + 1, ) np.testing.assert_allclose( - fit_params['classifier:sample_weight'], + fit_params["classifier:sample_weight"], np.array([0.85] * 500 + [8.5] * 10), ) def test_balancing_get_weights_svm_sgd(self): Y = np.array([0] * 80 + [1] * 20) - balancing = Balancing(strategy='weighting') + balancing = Balancing(strategy="weighting") init_params, fit_params = balancing.get_weights( - Y, 'libsvm_svc', None, None, None) - self.assertEqual(("classifier:class_weight", "balanced"), - list(init_params.items())[0]) + Y, "libsvm_svc", None, None, None + ) + self.assertEqual( + ("classifier:class_weight", "balanced"), list(init_params.items())[0] + ) init_params, fit_params = balancing.get_weights( - Y, None, 'liblinear_svc_preprocessor', None, None) - self.assertEqual(("feature_preprocessor:class_weight", "balanced"), - list(init_params.items())[0]) + Y, None, "liblinear_svc_preprocessor", None, None + ) + self.assertEqual( + ("feature_preprocessor:class_weight", "balanced"), + list(init_params.items())[0], + ) def test_weighting_effect(self): data = sklearn.datasets.make_classification( - n_samples=200, n_features=10, n_redundant=2, n_informative=2, - n_repeated=2, n_clusters_per_class=2, weights=[0.8, 0.2], - random_state=1) + n_samples=200, + n_features=10, + n_redundant=2, + n_informative=2, + n_repeated=2, + n_clusters_per_class=2, + weights=[0.8, 0.2], + random_state=1, + ) for name, clf, acc_no_weighting, acc_weighting, places in [ - ('adaboost', AdaboostClassifier, 0.810, 0.735, 3), - ('decision_tree', DecisionTree, 0.780, 0.643, 3), - ('extra_trees', ExtraTreesClassifier, 0.78, 0.8, 3), - ('random_forest', RandomForest, 0.75, 0.789, 3), - ('libsvm_svc', LibSVM_SVC, 0.769, 0.72, 3), - ('liblinear_svc', LibLinear_SVC, 0.762, 0.735, 3), - ('passive_aggressive', PassiveAggressive, 0.16, 0.222, 3), - ('sgd', SGD, 0.818, 0.567, 2), - ('gradient_boosting', GradientBoostingClassifier, 0.666, 0.682, 2) - ]: + ("adaboost", AdaboostClassifier, 0.810, 0.735, 3), + ("decision_tree", DecisionTree, 0.780, 0.643, 3), + ("extra_trees", ExtraTreesClassifier, 0.78, 0.8, 3), + ("random_forest", RandomForest, 0.75, 0.789, 3), + ("libsvm_svc", LibSVM_SVC, 0.769, 0.72, 3), + ("liblinear_svc", LibLinear_SVC, 0.762, 0.735, 3), + ("passive_aggressive", PassiveAggressive, 0.16, 0.222, 3), + ("sgd", SGD, 0.818, 0.567, 2), + ("gradient_boosting", GradientBoostingClassifier, 0.666, 0.682, 2), + ]: for strategy, acc in [ - ('none', acc_no_weighting), - ('weighting', acc_weighting) + ("none", acc_no_weighting), + ("weighting", acc_weighting), ]: # Fit data_ = copy.copy(data) @@ -98,23 +123,25 @@ def test_weighting_effect(self): model_args = { "random_state": 1, "include": { - 'classifier': [name], - 'feature_preprocessor': ['no_preprocessing'] - } + "classifier": [name], + "feature_preprocessor": ["no_preprocessing"], + }, } classifier = SimpleClassificationPipeline(**model_args) cs = classifier.get_hyperparameter_search_space() default = cs.get_default_configuration() - default._values['balancing:strategy'] = strategy + default._values["balancing:strategy"] = strategy classifier = SimpleClassificationPipeline(config=default, **model_args) classifier.fit(X_train, Y_train) predictions1 = classifier.predict(X_test) self.assertAlmostEqual( - sklearn.metrics.f1_score(predictions1, Y_test), acc, - places=places, msg=(name, strategy) + sklearn.metrics.f1_score(predictions1, Y_test), + acc, + places=places, + msg=(name, strategy), ) # fit_transformer and fit_estimator @@ -130,39 +157,53 @@ def test_weighting_effect(self): predictions2 = classifier.predict(X_test) np.testing.assert_allclose( - predictions1, predictions2, - err_msg=f"name = {name}, strategy = {strategy}" + predictions1, + predictions2, + err_msg=f"name = {name}, strategy = {strategy}", ) self.assertAlmostEqual( - sklearn.metrics.f1_score(predictions2, Y_test), acc, - places=places, msg=(name, strategy) + sklearn.metrics.f1_score(predictions2, Y_test), + acc, + places=places, + msg=(name, strategy), ) - for name, pre, acc_no_weighting, acc_weighting in \ - [('extra_trees_preproc_for_classification', - ExtraTreesPreprocessorClassification, 0.810, 0.590), - ('liblinear_svc_preprocessor', LibLinear_Preprocessor, - 0.837, 0.562)]: - for strategy, acc in [('none', acc_no_weighting), - ('weighting', acc_weighting)]: + for name, pre, acc_no_weighting, acc_weighting in [ + ( + "extra_trees_preproc_for_classification", + ExtraTreesPreprocessorClassification, + 0.810, + 0.590, + ), + ("liblinear_svc_preprocessor", LibLinear_Preprocessor, 0.837, 0.562), + ]: + for strategy, acc in [ + ("none", acc_no_weighting), + ("weighting", acc_weighting), + ]: data_ = copy.copy(data) X_train = data_[0][:100] Y_train = data_[1][:100] X_test = data_[0][100:] Y_test = data_[1][100:] - include = {'classifier': ['sgd'], 'feature_preprocessor': [name]} + include = {"classifier": ["sgd"], "feature_preprocessor": [name]} - classifier = SimpleClassificationPipeline(random_state=1, include=include) + classifier = SimpleClassificationPipeline( + random_state=1, include=include + ) cs = classifier.get_hyperparameter_search_space() default = cs.get_default_configuration() - default._values['balancing:strategy'] = strategy + default._values["balancing:strategy"] = strategy classifier.set_hyperparameters(default) predictor = classifier.fit(X_train, Y_train) predictions = predictor.predict(X_test) self.assertAlmostEqual( - sklearn.metrics.f1_score(predictions, Y_test), acc, - places=3, msg=(name, strategy)) + sklearn.metrics.f1_score(predictions, Y_test), + acc, + places=3, + msg=(name, strategy), + ) # fit_transformer and fit_estimator data_ = copy.copy(data) @@ -171,11 +212,13 @@ def test_weighting_effect(self): X_test = data_[0][100:] Y_test = data_[1][100:] - default._values['balancing:strategy'] = strategy - classifier = SimpleClassificationPipeline(default, random_state=1, include=include) + default._values["balancing:strategy"] = strategy + classifier = SimpleClassificationPipeline( + default, random_state=1, include=include + ) Xt, fit_params = classifier.fit_transformer(X_train, Y_train) classifier.fit_estimator(Xt, Y_train, **fit_params) predictions = classifier.predict(X_test) self.assertAlmostEqual( - sklearn.metrics.f1_score(predictions, Y_test), acc, - places=3) + sklearn.metrics.f1_score(predictions, Y_test), acc, places=3 + ) diff --git a/test/test_pipeline/components/data_preprocessing/test_categorical_imputation.py b/test/test_pipeline/components/data_preprocessing/test_categorical_imputation.py index 2767093179..d50e8cf842 100644 --- a/test/test_pipeline/components/data_preprocessing/test_categorical_imputation.py +++ b/test/test_pipeline/components/data_preprocessing/test_categorical_imputation.py @@ -1,11 +1,11 @@ import numpy as np -from scipy import sparse - import pandas as pd import pytest +from scipy import sparse -from autosklearn.pipeline.components.data_preprocessing.imputation.categorical_imputation\ - import CategoricalImputation +from autosklearn.pipeline.components.data_preprocessing.imputation.categorical_imputation import ( # noqa: E501 + CategoricalImputation, +) @pytest.fixture @@ -14,15 +14,15 @@ def input_data_imputation(request): X = np.array(np.random.randint(3, 10, size=size), dtype=float) mask = np.logical_not(np.random.randint(0, 5, size=size), dtype=bool) X[mask] = np.nan - if request.param == 'numpy': + if request.param == "numpy": pass - elif request.param == 'pandas': + elif request.param == "pandas": X = pd.DataFrame(X) return X, mask -@pytest.mark.parametrize('input_data_imputation', ('numpy', 'pandas'), indirect=True) -@pytest.mark.parametrize('categorical', (True, False)) +@pytest.mark.parametrize("input_data_imputation", ("numpy", "pandas"), indirect=True) +@pytest.mark.parametrize("categorical", (True, False)) def test_default_imputation(input_data_imputation, categorical): """ Makes sure that imputation works for both numerical and categorical data. @@ -30,8 +30,8 @@ def test_default_imputation(input_data_imputation, categorical): """ X, mask = input_data_imputation if categorical: - imputation_value = 'missing_value' - X = X.astype('str').astype('object') + imputation_value = "missing_value" + X = X.astype("str").astype("object") X[mask] = np.nan else: imputation_value = min(np.unique(X)) - 1 @@ -42,15 +42,15 @@ def test_default_imputation(input_data_imputation, categorical): assert np.array_equal(Y != imputation_value, ~mask) -@pytest.mark.parametrize('format_type', ('numpy', 'pandas')) +@pytest.mark.parametrize("format_type", ("numpy", "pandas")) def test_nonzero_numerical_imputation(format_type): # First try with an array with 0 as only valid category. The imputation should # happen with -1 X = np.full(fill_value=np.nan, shape=(10, 10)) X[0, :] = 0 - if 'pandas' in format_type: + if "pandas" in format_type: X = pd.DataFrame(X) - elif 'numpy' in format_type: + elif "numpy" in format_type: pass else: pytest.fail(format_type) @@ -61,13 +61,13 @@ def test_nonzero_numerical_imputation(format_type): X = np.full(fill_value=np.nan, shape=(10, 10)) X[0, :] = 0 X[1, :] = -1 - if 'pandas' in format_type: + if "pandas" in format_type: X = pd.DataFrame(X) Y = CategoricalImputation().fit_transform(X.copy()) np.testing.assert_equal(np.nan_to_num(X, nan=-2, copy=True), Y) -@pytest.mark.parametrize('input_data_imputation', ('numpy'), indirect=True) +@pytest.mark.parametrize("input_data_imputation", ("numpy"), indirect=True) def test_default_sparse(input_data_imputation): X, mask = input_data_imputation X = sparse.csr_matrix(X) diff --git a/test/test_pipeline/components/data_preprocessing/test_category_shift.py b/test/test_pipeline/components/data_preprocessing/test_category_shift.py index d49e6a84f0..ce637f50d4 100644 --- a/test/test_pipeline/components/data_preprocessing/test_category_shift.py +++ b/test/test_pipeline/components/data_preprocessing/test_category_shift.py @@ -1,19 +1,21 @@ import unittest + import numpy as np import scipy.sparse -from autosklearn.pipeline.components.data_preprocessing.category_shift.\ - category_shift import CategoryShift +from autosklearn.pipeline.components.data_preprocessing.category_shift.category_shift import ( # noqa: E501 + CategoryShift, +) class CategoryShiftTest(unittest.TestCase): - def test_data_type_consistency(self): X = np.random.randint(0, 255, (3, 4)) Y = CategoryShift().fit_transform(X) self.assertFalse(scipy.sparse.issparse(Y)) X = scipy.sparse.csc_matrix( - ([1, 2, 0, 4], ([0, 1, 2, 1], [3, 2, 1, 0])), shape=(3, 4)) + ([1, 2, 0, 4], ([0, 1, 2, 1], [3, 2, 1, 0])), shape=(3, 4) + ) Y = CategoryShift().fit_transform(X) self.assertTrue(scipy.sparse.issparse(Y)) diff --git a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing.py b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing.py index 5e6f89ad3a..ac8e9abbe2 100644 --- a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing.py +++ b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing.py @@ -1,13 +1,14 @@ import unittest + import numpy as np from scipy import sparse -from autosklearn.pipeline.components.data_preprocessing.feature_type \ - import FeatTypeSplit +from autosklearn.pipeline.components.data_preprocessing.feature_type import ( + FeatTypeSplit, +) class PreprocessingPipelineTest(unittest.TestCase): - def do_a_fit_transform(self, sparse_input): # X will be the input and Y is what we expect after transform. categ_feat stores # indicators of feature type (True if categorical, False if numerical) @@ -21,58 +22,57 @@ def do_a_fit_transform(self, sparse_input): # This feature should be normalized by having its mean subtracted from all # elements and by having them divided by the standard deviation. categ_feat.append(False) - nf = np.array([1., 2., 3.]).reshape(3, 1) # mean = 2. - sdev = np.sqrt(2. / 3.) - shift = 0 if sparse_input else 2. # if sparse_input, there is no mean subtraction + nf = np.array([1.0, 2.0, 3.0]).reshape(3, 1) # mean = 2. + sdev = np.sqrt(2.0 / 3.0) + shift = ( + 0 if sparse_input else 2.0 + ) # if sparse_input, there is no mean subtraction nft = (nf - shift) / sdev X.append(nf) Y.append(nft) # Feature 3 (numerical): - # This feature has a missing value that should be imputed by the mean of the other - # values (2.). This feature should also be normalized as in the previous feature. + # This feature has a missing value that should be imputed by the mean of the + # other values (2.). + # This feature should also be normalized as in the previous feature. categ_feat.append(False) - X.append(np.array([1., np.nan, 3.]).reshape(3, 1)) + X.append(np.array([1.0, np.nan, 3.0]).reshape(3, 1)) Y.append(nft.copy()) # Feature 4 (categorical) # This feature should be one hot encoded. categ_feat.append(True) X.append(np.array([1, 3, 2]).reshape(3, 1)) - Y.append(np.array([ - [1, 0, 0], - [0, 0, 1], - [0, 1, 0]])) + Y.append(np.array([[1, 0, 0], [0, 0, 1], [0, 1, 0]])) # Feature 5 (categorical) # This feature should be one hot encoded. (A discontinuous category set or # a category 0 shouldn't be problems.) categ_feat.append(True) X.append(np.array([2, 1, 9]).reshape(3, 1)) - Y.append(np.array([ - [0, 1, 0], - [1, 0, 0], - [0, 0, 1]])) + Y.append(np.array([[0, 1, 0], [1, 0, 0], [0, 0, 1]])) # Feature 6 (categorical) # This feature should be one hot encoded. The missing value gets imputed as # a category on its own. categ_feat.append(True) X.append(np.array([1, 1, np.nan]).reshape(3, 1)) - Y.append(np.array([ - [0, 1], - [0, 1], - [1, 0]])) + Y.append(np.array([[0, 1], [0, 1], [1, 0]])) # Combine datasets and shuffle columns: n_feats = len(categ_feat) random_order = np.random.choice(np.arange(n_feats), size=n_feats, replace=False) # Shuffle X according to random_order X = np.array(X)[random_order] X_comb = np.hstack(X) - # Shuffle Y according to random_order and reorder it as the PreprocessingPipeline - # does (i.e. categorical features come first in Y). + # Shuffle Y according to random_order and reorder it as the + # PreprocessingPipeline does (i.e. categorical features come first in Y). - categ_feat = {i: 'categorical' if categ_feat[order] else 'numerical' - for i, order in enumerate(random_order)} - cat_to_left_order = [index for col, index in sorted( - [(col_type, i) for i, col_type in categ_feat.items()] - )] + categ_feat = { + i: "categorical" if categ_feat[order] else "numerical" + for i, order in enumerate(random_order) + } + cat_to_left_order = [ + index + for col, index in sorted( + [(col_type, i) for i, col_type in categ_feat.items()] + ) + ] # Sort so that Y Matches the random ordering Y = [Y[n] for n in random_order] # Then move the categorical columns to the left @@ -101,15 +101,21 @@ def test_fit_transform_sparse(self): def test_string_categories(self): # Numerical dataset (as used in NumericalPreprocessingPipelineTest) - X_num = np.array([ - [3.14, 1., 1.], # noqa : matrix legibility - [3.14, 2., np.nan], # noqa : matrix legibility - [3.14, 3., 3.]]) # noqa : matrix legibility + X_num = np.array( + [ + [3.14, 1.0, 1.0], # noqa : matrix legibility + [3.14, 2.0, np.nan], # noqa : matrix legibility + [3.14, 3.0, 3.0], + ] + ) # noqa : matrix legibility # Categorical string dataset - X_cat = np.array([ - ['red', 'medium', 'small'], - ['blue', 'short', 'big'], - ['white', 'tall', np.nan]]) + X_cat = np.array( + [ + ["red", "medium", "small"], + ["blue", "short", "big"], + ["white", "tall", np.nan], + ] + ) # Combined dataset with shuffled columns: X_comb = np.hstack((X_num, X_cat)) categ_feat = [False] * 3 + [True] * 3 @@ -118,6 +124,8 @@ def test_string_categories(self): categ_feat = [categ_feat[order] for order in random_order] # Strings are not allowed, therefore: with self.assertRaises(ValueError): - categ_feat = {i: 'categorical' if feat else 'numerical' - for i, feat in enumerate(categ_feat)} + categ_feat = { + i: "categorical" if feat else "numerical" + for i, feat in enumerate(categ_feat) + } FeatTypeSplit(feat_type=categ_feat).fit_transform(X_comb) diff --git a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_categorical.py b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_categorical.py index dbffe26f51..1d693eb150 100644 --- a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_categorical.py +++ b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_categorical.py @@ -1,34 +1,35 @@ import unittest -import numpy as np -from scipy import sparse +import numpy as np import pytest +from scipy import sparse -from autosklearn.pipeline.components.data_preprocessing.feature_type_categorical \ - import CategoricalPreprocessingPipeline +from autosklearn.pipeline.components.data_preprocessing.feature_type_categorical import ( # noqa: E501 + CategoricalPreprocessingPipeline, +) class CategoricalPreprocessingPipelineTest(unittest.TestCase): - def test_data_type_consistency(self): X = np.random.randint(3, 6, (3, 4)) Y = CategoricalPreprocessingPipeline().fit_transform(X) self.assertFalse(sparse.issparse(Y)) X = sparse.csc_matrix( - ([3, 6, 4, 5], ([0, 1, 2, 1], [3, 2, 1, 0])), shape=(3, 4)) + ([3, 6, 4, 5], ([0, 1, 2, 1], [3, 2, 1, 0])), shape=(3, 4) + ) Y = CategoricalPreprocessingPipeline().fit_transform(X) self.assertTrue(sparse.issparse(Y)) def test_fit_transform(self): - X = np.array([ - [1, 2, 1], - [3, 1, 1], - [2, 9, np.nan]]) - Y = np.array([ - [1, 0, 0, 0, 1, 0, 0, 1], - [0, 0, 1, 1, 0, 0, 0, 1], - [0, 1, 0, 0, 0, 1, 1, 0]]) + X = np.array([[1, 2, 1], [3, 1, 1], [2, 9, np.nan]]) + Y = np.array( + [ + [1, 0, 0, 0, 1, 0, 0, 1], + [0, 0, 1, 1, 0, 0, 0, 1], + [0, 1, 0, 0, 0, 1, 1, 0], + ] + ) # dense input # Notice the X.copy() here as the imputation # is in place to save resources @@ -41,30 +42,30 @@ def test_fit_transform(self): np.testing.assert_array_equal(Yt, Y) def test_transform(self): - X1 = np.array([ - [1, 2, 0], - [3, 0, 0], - [2, 9, np.nan]]) - Y1 = np.array([ - [1, 0, 0, 0, 1, 0, 0, 1], - [0, 0, 1, 1, 0, 0, 0, 1], - [0, 1, 0, 0, 0, 1, 1, 0]]) - X2 = np.array([ - [2, 2, 1], - [3, 0, 0], - [2, np.nan, np.nan]]) - Y2 = np.array([ - [0, 1, 0, 0, 1, 0, 0, 0], - [0, 0, 1, 1, 0, 0, 0, 1], - [0, 1, 0, 0, 0, 0, 1, 0]]) - X3 = np.array([ - [3, np.nan, 0], - [3, 9, np.nan], - [2, 2, 5]]) - Y3 = np.array([ - [0, 0, 1, 0, 0, 0, 0, 1], - [0, 0, 1, 0, 0, 1, 1, 0], - [0, 1, 0, 0, 1, 0, 0, 0]]) + X1 = np.array([[1, 2, 0], [3, 0, 0], [2, 9, np.nan]]) + Y1 = np.array( + [ + [1, 0, 0, 0, 1, 0, 0, 1], + [0, 0, 1, 1, 0, 0, 0, 1], + [0, 1, 0, 0, 0, 1, 1, 0], + ] + ) + X2 = np.array([[2, 2, 1], [3, 0, 0], [2, np.nan, np.nan]]) + Y2 = np.array( + [ + [0, 1, 0, 0, 1, 0, 0, 0], + [0, 0, 1, 1, 0, 0, 0, 1], + [0, 1, 0, 0, 0, 0, 1, 0], + ] + ) + X3 = np.array([[3, np.nan, 0], [3, 9, np.nan], [2, 2, 5]]) + Y3 = np.array( + [ + [0, 0, 1, 0, 0, 0, 0, 1], + [0, 0, 1, 0, 0, 1, 1, 0], + [0, 1, 0, 0, 1, 0, 0, 0], + ] + ) # "fit" CPPL = CategoricalPreprocessingPipeline() CPPL.fit_transform(X1) @@ -81,13 +82,15 @@ def test_transform(self): def test_transform_with_coalescence(self): # Generates an array with categories 0, 20, 5, 6, 10, and occurences of 60%, # 30%, 19% 0.5% and 0.5% respectively - X = np.vstack(( - np.ones((120, 10)) * 0, - np.ones((60, 10)) * 20, - np.ones((18, 10)) * 5, - np.ones((1, 10)) * 6, - np.ones((1, 10)) * 10, - )) + X = np.vstack( + ( + np.ones((120, 10)) * 0, + np.ones((60, 10)) * 20, + np.ones((18, 10)) * 5, + np.ones((1, 10)) * 6, + np.ones((1, 10)) * 10, + ) + ) for col in range(X.shape[1]): np.random.shuffle(X[:, col]) @@ -100,10 +103,12 @@ def test_transform_with_coalescence(self): Y2t = CPPL.transform(X) np.testing.assert_array_equal(Y1t, Y2t) - @pytest.mark.xfail(reason=( - "Encoding step does not support sparse matrices to convert negative labels to" - " positive ones as it does with non-sparse matrices" - )) + @pytest.mark.xfail( + reason=( + "Encoding step does not support sparse matrices to convert negative labels" + " to positive ones as it does with non-sparse matrices" + ) + ) def test_transform_with_sparse_column_with_negative_labels(self): X = sparse.csr_matrix([[0], [-1]]) CategoricalPreprocessingPipeline().fit_transform(X) diff --git a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_numerical.py b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_numerical.py index 6a0b9d37fc..5a0a840501 100644 --- a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_numerical.py +++ b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_numerical.py @@ -1,69 +1,64 @@ import unittest -import numpy as np +import numpy as np from scipy import sparse -from autosklearn.pipeline.components.data_preprocessing.feature_type_numerical \ - import NumericalPreprocessingPipeline +from autosklearn.pipeline.components.data_preprocessing.feature_type_numerical import ( + NumericalPreprocessingPipeline, +) class NumericalPreprocessingPipelineTest(unittest.TestCase): - def test_data_type_consistency(self): X = np.random.rand(3, 4) Y = NumericalPreprocessingPipeline().fit_transform(X) self.assertFalse(sparse.issparse(Y)) X = sparse.csc_matrix( - ([3., 6., 4., 5.], ([0, 1, 2, 1], [3, 2, 1, 0])), shape=(3, 4)) + ([3.0, 6.0, 4.0, 5.0], ([0, 1, 2, 1], [3, 2, 1, 0])), shape=(3, 4) + ) Y = NumericalPreprocessingPipeline().fit_transform(X) self.assertTrue(sparse.issparse(Y)) def test_fit_transform(self): - X = np.array([ - [3.14, 1., 1.], - [3.14, 2., np.nan], - [3.14, 3., 3.]]) # noqa : matrix legibility + X = np.array( + [[3.14, 1.0, 1.0], [3.14, 2.0, np.nan], [3.14, 3.0, 3.0]] + ) # noqa : matrix legibility # 1st column should be droped due to low variance # The 2nd should be be standardized (default rescaling algorithm) - # The 3rd will get a value imputed by the mean (2.), therefore the transformation - # here will have the same effect as on the the 2nd column + # The 3rd will get a value imputed by the mean (2.), therefore the + # transformation here will have the same effect as on the the 2nd column sdev = np.sqrt(2 / 3) - Y1 = np.array([ - [-1/sdev, -1/sdev], - [ 0., 0.], # noqa : matrix legibility - [ 1/sdev, 1/sdev]]) # noqa : matrix legibility + Y1 = np.array( + [ + [-1 / sdev, -1 / sdev], + [0.0, 0.0], # noqa : matrix legibility + [1 / sdev, 1 / sdev], + ] + ) # noqa : matrix legibility # dense input Yt = NumericalPreprocessingPipeline().fit_transform(X) np.testing.assert_array_almost_equal(Yt, Y1) # sparse input (uses with_mean=False) - Y2 = np.array([ - [1., 1.], - [2., 2.], - [3., 3.]]) / sdev + Y2 = np.array([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0]]) / sdev X_sparse = sparse.csc_matrix(X) Yt = NumericalPreprocessingPipeline().fit_transform(X_sparse) np.testing.assert_array_almost_equal(Yt.todense(), Y2) def test_transform(self): - X1 = np.array([ - [3.14, 1., 1.], - [3.14, 2., np.nan], - [3.14, 3., 3.]]) # noqa : matrix legibility + X1 = np.array( + [[3.14, 1.0, 1.0], [3.14, 2.0, np.nan], [3.14, 3.0, 3.0]] + ) # noqa : matrix legibility sdev = np.sqrt(2 / 3) # fit NPP = NumericalPreprocessingPipeline() NPP.fit_transform(X1) # transform - X2 = np.array([ - [1., 5., 8.], - [2., 6., 9.], - [3., 7., np.nan]]) + X2 = np.array([[1.0, 5.0, 8.0], [2.0, 6.0, 9.0], [3.0, 7.0, np.nan]]) Yt = NPP.transform(X2) # imputation, variance_threshold and rescaling are done using the data already # fitted, therefore: - Y2 = np.array([ - [3/sdev, 6/sdev], - [4/sdev, 7/sdev], - [5/sdev, 0.]]) # noqa : matrix legibility + Y2 = np.array( + [[3 / sdev, 6 / sdev], [4 / sdev, 7 / sdev], [5 / sdev, 0.0]] + ) # noqa : matrix legibility np.testing.assert_array_almost_equal(Yt, Y2) diff --git a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_text.py b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_text.py index 0e39c5d7e9..0a2e3d5188 100644 --- a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_text.py +++ b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_text.py @@ -1,33 +1,57 @@ import unittest + import numpy as np import pandas as pd -from autosklearn.pipeline.components.data_preprocessing.text_encoding.bag_of_word_encoding import \ - BagOfWordEncoder as BOW -from autosklearn.pipeline.components.data_preprocessing.\ - text_encoding.bag_of_word_encoding_distinct import BagOfWordEncoder as BOW_distinct +from autosklearn.pipeline.components.data_preprocessing.text_encoding.bag_of_word_encoding import ( # noqa: E501 + BagOfWordEncoder as BOW, +) +from autosklearn.pipeline.components.data_preprocessing.text_encoding.bag_of_word_encoding_distinct import ( # noqa: E501 + BagOfWordEncoder as BOW_distinct, +) class TextPreprocessingPipelineTest(unittest.TestCase): - def test_fit_transform(self): - X = pd.DataFrame({"col1": ["hello world", - "This is a test"], - "col2": ["hello mars", - "This is the second column"]}).astype({"col1": "string", - "col2": "string"}) - BOW_fitted = BOW(ngram_range=1, min_df_choice="min_df_absolute", min_df_absolute=0, - min_df_relative=0, random_state=1).fit(X.copy()) + X = pd.DataFrame( + { + "col1": ["hello world", "This is a test"], + "col2": ["hello mars", "This is the second column"], + } + ).astype({"col1": "string", "col2": "string"}) + BOW_fitted = BOW( + ngram_range=1, + min_df_choice="min_df_absolute", + min_df_absolute=0, + min_df_relative=0, + random_state=1, + ).fit(X.copy()) Yt = BOW_fitted.preprocessor.vocabulary_ - words = sorted(["hello", "world", "this", "is", "test", # "a" is not added, len(...)=1 - "mars", "the", "second", "column"]) # is ignored by CountVectorizer + words = sorted( + [ + "hello", + "world", + "this", + "is", + "test", # "a" is not added, len(...)=1 + "mars", + "the", + "second", + "column", + ] + ) # is ignored by CountVectorizer Y = {key: idx for idx, key in enumerate(words)} np.testing.assert_array_equal(Yt, Y) - BOW_fitted = BOW_distinct(ngram_range=1, min_df_choice="min_df_absolute", min_df_absolute=0, - min_df_relative=0, random_state=1).fit(X.copy()) + BOW_fitted = BOW_distinct( + ngram_range=1, + min_df_choice="min_df_absolute", + min_df_absolute=0, + min_df_relative=0, + random_state=1, + ).fit(X.copy()) for key in BOW_fitted.preprocessor: y = [] @@ -38,58 +62,89 @@ def test_fit_transform(self): np.testing.assert_array_equal(yt, y) def test_transform(self): - X = pd.DataFrame({"col1": ["hello world", - "this is a test"], - "col2": ["hello mars", - "this is the second column"]}).astype({"col1": "string", - "col2": "string"}) - X_t = BOW(ngram_range=1, min_df_choice="min_df_absolute", min_df_absolute=0, - min_df_relative=0, random_state=1).fit_transform(X.copy()) + X = pd.DataFrame( + { + "col1": ["hello world", "this is a test"], + "col2": ["hello mars", "this is the second column"], + } + ).astype({"col1": "string", "col2": "string"}) + X_t = BOW( + ngram_range=1, + min_df_choice="min_df_absolute", + min_df_absolute=0, + min_df_relative=0, + random_state=1, + ).fit_transform(X.copy()) # ['column', 'hello', 'is', 'mars', 'second', 'test', 'the', 'this', 'world'] - y = np.array([[0, 2, 0, 1, 0, 0, 0, 0, 1], - [1, 0, 2, 0, 1, 1, 1, 2, 0]]) + y = np.array([[0, 2, 0, 1, 0, 0, 0, 0, 1], [1, 0, 2, 0, 1, 1, 1, 2, 0]]) np.testing.assert_array_equal(X_t.toarray(), y) - X_t = BOW_distinct(ngram_range=1, min_df_choice="min_df_absolute", min_df_absolute=0, - min_df_relative=0, random_state=1).fit_transform(X.copy()) + X_t = BOW_distinct( + ngram_range=1, + min_df_choice="min_df_absolute", + min_df_absolute=0, + min_df_relative=0, + random_state=1, + ).fit_transform(X.copy()) # 'hello', 'is', 'test', 'this', 'world', # 'column', 'hello', 'is', 'mars', 'second', 'the', 'this' - y = np.array([[1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0], - [0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1]]) + y = np.array( + [[1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0], [0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1]] + ) np.testing.assert_array_equal(X_t.toarray(), y) def test_check_shape(self): - X = pd.DataFrame({"col1": ["hello world", - "this is test"], - "col2": ["test test", - "test test"]}).astype({"col1": "string", - "col2": "string"}) - X_t = BOW(ngram_range=1, min_df_choice="min_df_absolute", min_df_absolute=0, - min_df_relative=0, random_state=1).fit_transform(X.copy()) + X = pd.DataFrame( + { + "col1": ["hello world", "this is test"], + "col2": ["test test", "test test"], + } + ).astype({"col1": "string", "col2": "string"}) + X_t = BOW( + ngram_range=1, + min_df_choice="min_df_absolute", + min_df_absolute=0, + min_df_relative=0, + random_state=1, + ).fit_transform(X.copy()) self.assertEqual(X_t.shape, (2, 5)) - X_t = BOW_distinct(ngram_range=1, min_df_choice="min_df_absolute", min_df_absolute=0, - min_df_relative=0, random_state=1).fit_transform(X.copy()) + X_t = BOW_distinct( + ngram_range=1, + min_df_choice="min_df_absolute", + min_df_absolute=0, + min_df_relative=0, + random_state=1, + ).fit_transform(X.copy()) self.assertEqual(X_t.shape, (2, 6)) def test_check_nan(self): - X = pd.DataFrame({"col1": ["hello world", - "this is test", - None], - "col2": ["test test", - "test test", - "test"]}).astype({"col1": "string", - "col2": "string"}) - X_t = BOW(ngram_range=1, min_df_choice="min_df_absolute", min_df_absolute=0, - min_df_relative=0, random_state=1).fit_transform(X.copy()) + X = pd.DataFrame( + { + "col1": ["hello world", "this is test", None], + "col2": ["test test", "test test", "test"], + } + ).astype({"col1": "string", "col2": "string"}) + X_t = BOW( + ngram_range=1, + min_df_choice="min_df_absolute", + min_df_absolute=0, + min_df_relative=0, + random_state=1, + ).fit_transform(X.copy()) self.assertEqual(X_t.shape, (3, 5)) - X_t = BOW_distinct(ngram_range=1, min_df_choice="min_df_absolute", min_df_absolute=0, - min_df_relative=0, random_state=1).fit_transform(X.copy()) + X_t = BOW_distinct( + ngram_range=1, + min_df_choice="min_df_absolute", + min_df_absolute=0, + min_df_relative=0, + random_state=1, + ).fit_transform(X.copy()) self.assertEqual(X_t.shape, (3, 6)) diff --git a/test/test_pipeline/components/data_preprocessing/test_minority_coalescence.py b/test/test_pipeline/components/data_preprocessing/test_minority_coalescence.py index 7b3e35763e..8e73e963ab 100644 --- a/test/test_pipeline/components/data_preprocessing/test_minority_coalescence.py +++ b/test/test_pipeline/components/data_preprocessing/test_minority_coalescence.py @@ -1,23 +1,25 @@ import unittest -import numpy as np +import numpy as np import scipy.sparse -from autosklearn.pipeline.components.data_preprocessing.minority_coalescense\ - .minority_coalescer import MinorityCoalescer -from autosklearn.pipeline.components.data_preprocessing.minority_coalescense\ - .no_coalescense import NoCoalescence +from autosklearn.pipeline.components.data_preprocessing.minority_coalescense.minority_coalescer import ( # noqa: E501 + MinorityCoalescer, +) +from autosklearn.pipeline.components.data_preprocessing.minority_coalescense.no_coalescense import ( # noqa: E501 + NoCoalescence, +) class MinorityCoalescerTest(unittest.TestCase): - def test_data_type_consistency(self): X = np.random.randint(3, 6, (3, 4)) Y = MinorityCoalescer().fit_transform(X) self.assertFalse(scipy.sparse.issparse(Y)) X = scipy.sparse.csc_matrix( - ([3, 6, 4, 5], ([0, 1, 2, 1], [3, 2, 1, 0])), shape=(3, 4)) + ([3, 6, 4, 5], ([0, 1, 2, 1], [3, 2, 1, 0])), shape=(3, 4) + ) Y = MinorityCoalescer().fit_transform(X) self.assertTrue(scipy.sparse.issparse(Y)) diff --git a/test/test_pipeline/components/data_preprocessing/test_numerical_imputation.py b/test/test_pipeline/components/data_preprocessing/test_numerical_imputation.py index 35d9d23a6d..d3354c3730 100644 --- a/test/test_pipeline/components/data_preprocessing/test_numerical_imputation.py +++ b/test/test_pipeline/components/data_preprocessing/test_numerical_imputation.py @@ -1,8 +1,9 @@ from scipy import sparse -from autosklearn.pipeline.components.data_preprocessing.imputation.numerical_imputation\ - import NumericalImputation -from autosklearn.pipeline.util import _test_preprocessing, PreprocessingTestCase +from autosklearn.pipeline.components.data_preprocessing.imputation.numerical_imputation import ( # noqa: E501 + NumericalImputation, +) +from autosklearn.pipeline.util import PreprocessingTestCase, _test_preprocessing class NumericalImputationTest(PreprocessingTestCase): @@ -14,13 +15,13 @@ def test_default_configuration(self): self.assertTrue((transformation == original).all()) transformations.append(transformation) if len(transformations) > 1: - self.assertTrue( - (transformations[-1] == transformations[-2]).all()) + self.assertTrue((transformations[-1] == transformations[-2]).all()) def test_default_configuration_sparse_data(self): transformations = [] - transformation, original = _test_preprocessing(NumericalImputation, - make_sparse=True) + transformation, original = _test_preprocessing( + NumericalImputation, make_sparse=True + ) self.assertEqual(transformation.shape, original.shape) self.assertTrue((transformation.data == original.data).all()) self.assertIsInstance(transformation, sparse.csc_matrix) @@ -28,4 +29,5 @@ def test_default_configuration_sparse_data(self): def test_preprocessing_dtype(self): super(NumericalImputationTest, self)._test_preprocessing_dtype( - NumericalImputation, add_NaNs=True) + NumericalImputation, add_NaNs=True + ) diff --git a/test/test_pipeline/components/data_preprocessing/test_one_hot_encoding.py b/test/test_pipeline/components/data_preprocessing/test_one_hot_encoding.py index ba99724964..08d2cadd9e 100644 --- a/test/test_pipeline/components/data_preprocessing/test_one_hot_encoding.py +++ b/test/test_pipeline/components/data_preprocessing/test_one_hot_encoding.py @@ -3,10 +3,12 @@ import numpy as np from scipy import sparse -from autosklearn.pipeline.components.data_preprocessing.categorical_encoding.\ - one_hot_encoding import OneHotEncoder -from autosklearn.pipeline.components.data_preprocessing.categorical_encoding.\ - no_encoding import NoEncoding +from autosklearn.pipeline.components.data_preprocessing.categorical_encoding.no_encoding import ( # noqa: E501 + NoEncoding, +) +from autosklearn.pipeline.components.data_preprocessing.categorical_encoding.one_hot_encoding import ( # noqa: E501 + OneHotEncoder, +) from autosklearn.pipeline.util import _test_preprocessing @@ -18,7 +20,6 @@ def create_X(instances=1000, n_feats=10, categs_per_feat=5, seed=0): class OneHotEncoderTest(unittest.TestCase): - def setUp(self): self.X_train = create_X() @@ -28,7 +29,8 @@ def test_data_type_consistency(self): self.assertFalse(sparse.issparse(Y)) X = sparse.csc_matrix( - ([3, 6, 4, 5], ([0, 1, 2, 1], [3, 2, 1, 0])), shape=(3, 4)) + ([3, 6, 4, 5], ([0, 1, 2, 1], [3, 2, 1, 0])), shape=(3, 4) + ) Y = OneHotEncoder().fit_transform(X) self.assertTrue(sparse.issparse(Y)) @@ -54,8 +56,7 @@ def test_default_configuration_no_encoding(self): self.assertTrue((transformation == original).all()) transformations.append(transformation) if len(transformations) > 1: - self.assertTrue( - (transformations[-1] == transformations[-2]).all()) + self.assertTrue((transformations[-1] == transformations[-2]).all()) def test_default_configuration_sparse_data(self): transformations = [] @@ -74,17 +75,18 @@ def test_default_configuration_sparse_data(self): transformations.append(Xt) if len(transformations) > 1: self.assertEqual( - (transformations[-1] != transformations[-2]).count_nonzero(), 0) + (transformations[-1] != transformations[-2]).count_nonzero(), 0 + ) def test_default_configuration_sparse_no_encoding(self): transformations = [] for i in range(2): - transformation, original = _test_preprocessing(NoEncoding, - make_sparse=True) + transformation, original = _test_preprocessing(NoEncoding, make_sparse=True) self.assertEqual(transformation.shape, original.shape) self.assertTrue((transformation.todense() == original.todense()).all()) transformations.append(transformation) if len(transformations) > 1: self.assertEqual( - (transformations[-1] != transformations[-2]).count_nonzero(), 0) + (transformations[-1] != transformations[-2]).count_nonzero(), 0 + ) diff --git a/test/test_pipeline/components/data_preprocessing/test_scaling.py b/test/test_pipeline/components/data_preprocessing/test_scaling.py index f800930dda..7f8249e3f1 100644 --- a/test/test_pipeline/components/data_preprocessing/test_scaling.py +++ b/test/test_pipeline/components/data_preprocessing/test_scaling.py @@ -12,13 +12,14 @@ def _test_helper(self, Preprocessor, dataset=None, make_sparse=False): X_train, Y_train, X_test, Y_test = get_dataset( dataset=dataset, make_sparse=make_sparse, - ) + ) - dataset_properties = {'sparse': make_sparse} + dataset_properties = {"sparse": make_sparse} original_X_train = X_train.copy() - configuration_space = Preprocessor(dataset_properties).\ - get_hyperparameter_search_space(dataset_properties) + configuration_space = Preprocessor( + dataset_properties + ).get_hyperparameter_search_space(dataset_properties) default = configuration_space.get_default_configuration() preprocessor = Preprocessor(dataset_properties, random_state=1) @@ -28,31 +29,31 @@ def _test_helper(self, Preprocessor, dataset=None, make_sparse=False): return transformer.transform(X_train), original_X_train def test_boston_is_not_scaled(self): - data = sklearn.datasets.load_boston()['data'] + data = sklearn.datasets.load_boston()["data"] self.assertGreaterEqual(np.max(data), 100) def test_default_configuration(self): transformations = [] for i in range(2): - transformation, original = self._test_helper(RescalingChoice, - dataset='boston') + transformation, original = self._test_helper( + RescalingChoice, dataset="boston" + ) # The maximum is around 1.95 for the transformed array... self.assertAlmostEqual(np.mean(transformation), 0, places=5) self.assertAlmostEqual(np.std(transformation), 1, places=5) self.assertFalse((original == transformation).all()) transformations.append(transformation) if len(transformations) > 1: - self.assertTrue( - (transformations[-1] == transformations[-2]).all()) + self.assertTrue((transformations[-1] == transformations[-2]).all()) def test_default_configuration_with_sparse_data(self): - preprocessing = self._test_helper(RescalingChoice, dataset='boston', - make_sparse=True) + preprocessing = self._test_helper( + RescalingChoice, dataset="boston", make_sparse=True + ) transformation, original = preprocessing self.assertEqual(original.getnnz(), transformation.getnnz()) self.assertTrue(~np.allclose(original.data, transformation.data)) @unittest.skip("Does not work at the moment.") def test_preprocessing_dtype(self): - super(ScalingComponentTest, self)._test_helper( - RescalingChoice) + super(ScalingComponentTest, self)._test_helper(RescalingChoice) diff --git a/test/test_pipeline/components/data_preprocessing/test_variance_threshold.py b/test/test_pipeline/components/data_preprocessing/test_variance_threshold.py index 4da441828d..a9ba4083ca 100644 --- a/test/test_pipeline/components/data_preprocessing/test_variance_threshold.py +++ b/test/test_pipeline/components/data_preprocessing/test_variance_threshold.py @@ -1,8 +1,9 @@ from scipy import sparse -from autosklearn.pipeline.components.data_preprocessing.variance_threshold.variance_threshold \ - import VarianceThreshold -from autosklearn.pipeline.util import _test_preprocessing, PreprocessingTestCase +from autosklearn.pipeline.components.data_preprocessing.variance_threshold.variance_threshold import ( # noqa: E501 + VarianceThreshold, +) +from autosklearn.pipeline.util import PreprocessingTestCase, _test_preprocessing class VarianceThresholdTest(PreprocessingTestCase): @@ -14,13 +15,13 @@ def test_default_configuration(self): self.assertTrue((transformation == original).all()) transformations.append(transformation) if len(transformations) > 1: - self.assertTrue( - (transformations[-1] == transformations[-2]).all()) + self.assertTrue((transformations[-1] == transformations[-2]).all()) def test_default_configuration_sparse_data(self): transformations = [] - transformation, original = _test_preprocessing(VarianceThreshold, - make_sparse=True) + transformation, original = _test_preprocessing( + VarianceThreshold, make_sparse=True + ) self.assertEqual(transformation.shape, (100, 3)) self.assertTrue((transformation.toarray() == original.toarray()[:, 1:]).all()) self.assertIsInstance(transformation, sparse.csr_matrix) diff --git a/test/test_pipeline/components/dummy_components/dummy_component_1.py b/test/test_pipeline/components/dummy_components/dummy_component_1.py index 06074db983..0af3466787 100644 --- a/test/test_pipeline/components/dummy_components/dummy_component_1.py +++ b/test/test_pipeline/components/dummy_components/dummy_component_1.py @@ -5,7 +5,7 @@ # Add the parent directory to the path to import the parent component this_directory = os.path.dirname(os.path.abspath(__file__)) -parent_directory = os.path.abspath(os.path.join(this_directory, '..')) +parent_directory = os.path.abspath(os.path.join(this_directory, "..")) sys.path.append(parent_directory) diff --git a/test/test_pipeline/components/dummy_components/dummy_component_2.py b/test/test_pipeline/components/dummy_components/dummy_component_2.py index 9b67230e4c..f941dcdb40 100644 --- a/test/test_pipeline/components/dummy_components/dummy_component_2.py +++ b/test/test_pipeline/components/dummy_components/dummy_component_2.py @@ -6,7 +6,7 @@ # Add the parent directory to the path to import the parent component as # dummy_components.dummy_component_2.DummyComponent1 this_directory = os.path.dirname(os.path.abspath(__file__)) -parent_directory = os.path.abspath(os.path.join(this_directory, '..')) +parent_directory = os.path.abspath(os.path.join(this_directory, "..")) sys.path.append(parent_directory) diff --git a/test/test_pipeline/components/dummy_components/dummy_component_import.py b/test/test_pipeline/components/dummy_components/dummy_component_import.py index f7981a40a3..a4cb764215 100644 --- a/test/test_pipeline/components/dummy_components/dummy_component_import.py +++ b/test/test_pipeline/components/dummy_components/dummy_component_import.py @@ -1,2 +1,4 @@ -from autosklearn.pipeline.components.base import find_components # noqa -from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm # noqa +from autosklearn.pipeline.components.base import find_components # noqa +from autosklearn.pipeline.components.base import ( # noqa + AutoSklearnClassificationAlgorithm, +) diff --git a/test/test_pipeline/components/feature_preprocessing/__init__.py b/test/test_pipeline/components/feature_preprocessing/__init__.py index 8f0ce6cb7c..92bf78f389 100644 --- a/test/test_pipeline/components/feature_preprocessing/__init__.py +++ b/test/test_pipeline/components/feature_preprocessing/__init__.py @@ -1 +1 @@ -__author__ = 'feurerm' +__author__ = "feurerm" diff --git a/test/test_pipeline/components/feature_preprocessing/test_NoPreprocessing.py b/test/test_pipeline/components/feature_preprocessing/test_NoPreprocessing.py index 22811e75bb..440f2fd50d 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_NoPreprocessing.py +++ b/test/test_pipeline/components/feature_preprocessing/test_NoPreprocessing.py @@ -1,7 +1,9 @@ import numpy as np -from autosklearn.pipeline.components.feature_preprocessing.no_preprocessing import NoPreprocessing -from autosklearn.pipeline.util import _test_preprocessing, PreprocessingTestCase +from autosklearn.pipeline.components.feature_preprocessing.no_preprocessing import ( + NoPreprocessing, +) +from autosklearn.pipeline.util import PreprocessingTestCase, _test_preprocessing class NoneComponentTest(PreprocessingTestCase): diff --git a/test/test_pipeline/components/feature_preprocessing/test_choice.py b/test/test_pipeline/components/feature_preprocessing/test_choice.py index 525ec38356..516cf318bf 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_choice.py +++ b/test/test_pipeline/components/feature_preprocessing/test_choice.py @@ -6,27 +6,27 @@ class FeatureProcessingTest(unittest.TestCase): def test_get_available_components(self): # Target type - for target_type, num_values in [('classification', 15), - ('regression', 14)]: - data_properties = {'target_type': target_type} + for target_type, num_values in [("classification", 15), ("regression", 14)]: + data_properties = {"target_type": target_type} - available_components = fp.FeaturePreprocessorChoice(data_properties)\ - .get_available_components(data_properties) + available_components = fp.FeaturePreprocessorChoice( + data_properties + ).get_available_components(data_properties) self.assertEqual(len(available_components), num_values) # Multiclass - data_properties = {'target_type': 'classification', - 'multiclass': True} - available_components = fp.FeaturePreprocessorChoice(data_properties) \ - .get_available_components(data_properties) + data_properties = {"target_type": "classification", "multiclass": True} + available_components = fp.FeaturePreprocessorChoice( + data_properties + ).get_available_components(data_properties) self.assertEqual(len(available_components), 15) # Multilabel - data_properties = {'target_type': 'classification', - 'multilabel': True} - available_components = fp.FeaturePreprocessorChoice(data_properties) \ - .get_available_components(data_properties) + data_properties = {"target_type": "classification", "multilabel": True} + available_components = fp.FeaturePreprocessorChoice( + data_properties + ).get_available_components(data_properties) self.assertEqual(len(available_components), 12) diff --git a/test/test_pipeline/components/feature_preprocessing/test_densifier.py b/test/test_pipeline/components/feature_preprocessing/test_densifier.py index 6f02ee0e5b..9831a53e57 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_densifier.py +++ b/test/test_pipeline/components/feature_preprocessing/test_densifier.py @@ -1,7 +1,7 @@ import numpy as np from autosklearn.pipeline.components.feature_preprocessing.densifier import Densifier -from autosklearn.pipeline.util import _test_preprocessing, PreprocessingTestCase +from autosklearn.pipeline.util import PreprocessingTestCase, _test_preprocessing class DensifierComponentTest(PreprocessingTestCase): diff --git a/test/test_pipeline/components/feature_preprocessing/test_extra_trees_classification.py b/test/test_pipeline/components/feature_preprocessing/test_extra_trees_classification.py index 6b69462fec..2db52679c7 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_extra_trees_classification.py +++ b/test/test_pipeline/components/feature_preprocessing/test_extra_trees_classification.py @@ -1,29 +1,36 @@ -from sklearn.linear_model import RidgeClassifier -from autosklearn.pipeline.components.feature_preprocessing.\ - extra_trees_preproc_for_classification import \ - ExtraTreesPreprocessorClassification -from autosklearn.pipeline.util import _test_preprocessing, \ - PreprocessingTestCase, get_dataset import sklearn.metrics +from sklearn.linear_model import RidgeClassifier + +from autosklearn.pipeline.components.feature_preprocessing.extra_trees_preproc_for_classification import ( # noqa: E501 + ExtraTreesPreprocessorClassification, +) +from autosklearn.pipeline.util import ( + PreprocessingTestCase, + _test_preprocessing, + get_dataset, +) class ExtreTreesClassificationComponentTest(PreprocessingTestCase): def test_default_configuration(self): transformation, original = _test_preprocessing( - ExtraTreesPreprocessorClassification) + ExtraTreesPreprocessorClassification + ) self.assertEqual(transformation.shape[0], original.shape[0]) self.assertFalse((transformation == 0).all()) def test_default_configuration_classify(self): for i in range(2): - X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', - make_sparse=False) - configuration_space = ExtraTreesPreprocessorClassification.\ - get_hyperparameter_search_space() + X_train, Y_train, X_test, Y_test = get_dataset( + dataset="digits", make_sparse=False + ) + configuration_space = ( + ExtraTreesPreprocessorClassification.get_hyperparameter_search_space() + ) default = configuration_space.get_default_configuration() preprocessor = ExtraTreesPreprocessorClassification( - random_state=1, - **{hp_name: default[hp_name] for hp_name in default}) + random_state=1, **{hp_name: default[hp_name] for hp_name in default} + ) preprocessor.fit(X_train, Y_train) X_train_trans = preprocessor.transform(X_train) X_test_trans = preprocessor.transform(X_test) @@ -37,14 +44,16 @@ def test_default_configuration_classify(self): def test_default_configuration_classify_sparse(self): for i in range(2): - X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', - make_sparse=True) - configuration_space = ExtraTreesPreprocessorClassification.\ - get_hyperparameter_search_space() + X_train, Y_train, X_test, Y_test = get_dataset( + dataset="digits", make_sparse=True + ) + configuration_space = ( + ExtraTreesPreprocessorClassification.get_hyperparameter_search_space() + ) default = configuration_space.get_default_configuration() preprocessor = ExtraTreesPreprocessorClassification( - random_state=1, - **{hp_name: default[hp_name] for hp_name in default}) + random_state=1, **{hp_name: default[hp_name] for hp_name in default} + ) preprocessor.fit(X_train, Y_train) X_train_trans = preprocessor.transform(X_train) X_test_trans = preprocessor.transform(X_test) @@ -57,5 +66,6 @@ def test_default_configuration_classify_sparse(self): self.assertAlmostEqual(accuracy, 0.43715846994535518, places=2) def test_preprocessing_dtype(self): - super(ExtreTreesClassificationComponentTest, self).\ - _test_preprocessing_dtype(ExtraTreesPreprocessorClassification) + super(ExtreTreesClassificationComponentTest, self)._test_preprocessing_dtype( + ExtraTreesPreprocessorClassification + ) diff --git a/test/test_pipeline/components/feature_preprocessing/test_extra_trees_regression.py b/test/test_pipeline/components/feature_preprocessing/test_extra_trees_regression.py index b850d5aa99..cd6ae3dd21 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_extra_trees_regression.py +++ b/test/test_pipeline/components/feature_preprocessing/test_extra_trees_regression.py @@ -1,29 +1,34 @@ -from sklearn.ensemble import ExtraTreesRegressor -from autosklearn.pipeline.components.feature_preprocessing.\ - extra_trees_preproc_for_regression import \ - ExtraTreesPreprocessorRegression -from autosklearn.pipeline.util import _test_preprocessing, \ - PreprocessingTestCase, get_dataset import sklearn.metrics +from sklearn.ensemble import ExtraTreesRegressor + +from autosklearn.pipeline.components.feature_preprocessing.extra_trees_preproc_for_regression import ( # noqa: E501 + ExtraTreesPreprocessorRegression, +) +from autosklearn.pipeline.util import ( + PreprocessingTestCase, + _test_preprocessing, + get_dataset, +) class ExtraTreesRegressionComponentTest(PreprocessingTestCase): def test_default_configuration(self): - transformation, original = _test_preprocessing( - ExtraTreesPreprocessorRegression) + transformation, original = _test_preprocessing(ExtraTreesPreprocessorRegression) self.assertEqual(transformation.shape[0], original.shape[0]) self.assertFalse((transformation == 0).all()) def test_default_configuration_regression(self): for i in range(2): - X_train, Y_train, X_test, Y_test = get_dataset(dataset='boston', - make_sparse=False) - configuration_space = ExtraTreesPreprocessorRegression.\ - get_hyperparameter_search_space() + X_train, Y_train, X_test, Y_test = get_dataset( + dataset="boston", make_sparse=False + ) + configuration_space = ( + ExtraTreesPreprocessorRegression.get_hyperparameter_search_space() + ) default = configuration_space.get_default_configuration() preprocessor = ExtraTreesPreprocessorRegression( - random_state=1, - **{hp_name: default[hp_name] for hp_name in default}) + random_state=1, **{hp_name: default[hp_name] for hp_name in default} + ) preprocessor.fit(X_train, Y_train) X_train_trans = preprocessor.transform(X_train) X_test_trans = preprocessor.transform(X_test) @@ -37,14 +42,16 @@ def test_default_configuration_regression(self): def test_default_configuration_classify_sparse(self): for i in range(2): - X_train, Y_train, X_test, Y_test = get_dataset(dataset='boston', - make_sparse=True) - configuration_space = ExtraTreesPreprocessorRegression.\ - get_hyperparameter_search_space() + X_train, Y_train, X_test, Y_test = get_dataset( + dataset="boston", make_sparse=True + ) + configuration_space = ( + ExtraTreesPreprocessorRegression.get_hyperparameter_search_space() + ) default = configuration_space.get_default_configuration() preprocessor = ExtraTreesPreprocessorRegression( - random_state=1, - **{hp_name: default[hp_name] for hp_name in default}) + random_state=1, **{hp_name: default[hp_name] for hp_name in default} + ) preprocessor.fit(X_train, Y_train) X_train_trans = preprocessor.transform(X_train) X_test_trans = preprocessor.transform(X_test) @@ -57,5 +64,6 @@ def test_default_configuration_classify_sparse(self): self.assertAlmostEqual(error, 55.69613978965742, places=2) def test_preprocessing_dtype(self): - super(ExtraTreesRegressionComponentTest, self).\ - _test_preprocessing_dtype(ExtraTreesPreprocessorRegression) + super(ExtraTreesRegressionComponentTest, self)._test_preprocessing_dtype( + ExtraTreesPreprocessorRegression + ) diff --git a/test/test_pipeline/components/feature_preprocessing/test_fast_ica.py b/test/test_pipeline/components/feature_preprocessing/test_fast_ica.py index ae22d65c54..a38097a60e 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_fast_ica.py +++ b/test/test_pipeline/components/feature_preprocessing/test_fast_ica.py @@ -1,28 +1,30 @@ import unittest -from sklearn.linear_model import Ridge -from autosklearn.pipeline.components.feature_preprocessing.fast_ica import \ - FastICA -from autosklearn.pipeline.util import _test_preprocessing, PreprocessingTestCase, \ - get_dataset import sklearn.metrics +from sklearn.linear_model import Ridge + +from autosklearn.pipeline.components.feature_preprocessing.fast_ica import FastICA +from autosklearn.pipeline.util import ( + PreprocessingTestCase, + _test_preprocessing, + get_dataset, +) class FastICAComponentTest(PreprocessingTestCase): def test_default_configuration(self): - transformation, original = _test_preprocessing(FastICA, - dataset="diabetes") + transformation, original = _test_preprocessing(FastICA, dataset="diabetes") self.assertEqual(transformation.shape[0], original.shape[0]) self.assertFalse((transformation == 0).all()) def test_default_configuration_regression(self): for i in range(5): - X_train, Y_train, X_test, Y_test = get_dataset(dataset='diabetes') + X_train, Y_train, X_test, Y_test = get_dataset(dataset="diabetes") configuration_space = FastICA.get_hyperparameter_search_space() default = configuration_space.get_default_configuration() - preprocessor = FastICA(random_state=1, - **{hp_name: default[hp_name] for hp_name in - default}) + preprocessor = FastICA( + random_state=1, **{hp_name: default[hp_name] for hp_name in default} + ) preprocessor.fit(X_train, Y_train) X_train_trans = preprocessor.transform(X_train) X_test_trans = preprocessor.transform(X_test) @@ -36,5 +38,6 @@ def test_default_configuration_regression(self): @unittest.skip("Always returns float64") def test_preprocessing_dtype(self): - super(FastICAComponentTest, - self)._test_preprocessing_dtype(FastICA, dataset='diabetes') + super(FastICAComponentTest, self)._test_preprocessing_dtype( + FastICA, dataset="diabetes" + ) diff --git a/test/test_pipeline/components/feature_preprocessing/test_feature_agglomeration.py b/test/test_pipeline/components/feature_preprocessing/test_feature_agglomeration.py index 0cac9426d2..afccd79c31 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_feature_agglomeration.py +++ b/test/test_pipeline/components/feature_preprocessing/test_feature_agglomeration.py @@ -1,9 +1,14 @@ -from sklearn.ensemble import RandomForestClassifier -from autosklearn.pipeline.components.feature_preprocessing.feature_agglomeration import \ - FeatureAgglomeration -from autosklearn.pipeline.util import _test_preprocessing, PreprocessingTestCase, \ - get_dataset import sklearn.metrics +from sklearn.ensemble import RandomForestClassifier + +from autosklearn.pipeline.components.feature_preprocessing.feature_agglomeration import ( # noqa: E501 + FeatureAgglomeration, +) +from autosklearn.pipeline.util import ( + PreprocessingTestCase, + _test_preprocessing, + get_dataset, +) class FeatureAgglomerationComponentTest(PreprocessingTestCase): @@ -14,13 +19,14 @@ def test_default_configuration(self): def test_default_configuration_classify(self): for i in range(3): - X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', - make_sparse=False) + X_train, Y_train, X_test, Y_test = get_dataset( + dataset="digits", make_sparse=False + ) configuration_space = FeatureAgglomeration.get_hyperparameter_search_space() default = configuration_space.get_default_configuration() - preprocessor = FeatureAgglomeration(random_state=1, - **{hp_name: default[hp_name] for - hp_name in default}) + preprocessor = FeatureAgglomeration( + random_state=1, **{hp_name: default[hp_name] for hp_name in default} + ) preprocessor.fit(X_train, Y_train) X_train_trans = preprocessor.transform(X_train) X_test_trans = preprocessor.transform(X_test) @@ -33,6 +39,6 @@ def test_default_configuration_classify(self): self.assertAlmostEqual(accuracy, 0.8761384335154827) def test_preprocessing_dtype(self): - super(FeatureAgglomerationComponentTest, - self)._test_preprocessing_dtype(FeatureAgglomeration, - test_sparse=False) + super(FeatureAgglomerationComponentTest, self)._test_preprocessing_dtype( + FeatureAgglomeration, test_sparse=False + ) diff --git a/test/test_pipeline/components/feature_preprocessing/test_kernel_pca.py b/test/test_pipeline/components/feature_preprocessing/test_kernel_pca.py index 19b1368a49..2c5a8c865b 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_kernel_pca.py +++ b/test/test_pipeline/components/feature_preprocessing/test_kernel_pca.py @@ -1,38 +1,46 @@ import unittest -from sklearn.linear_model import RidgeClassifier -from autosklearn.pipeline.components.feature_preprocessing.kernel_pca import \ - KernelPCA -from autosklearn.pipeline.util import _test_preprocessing, PreprocessingTestCase, \ - get_dataset import sklearn.metrics +from sklearn.linear_model import RidgeClassifier + +from autosklearn.pipeline.components.feature_preprocessing.kernel_pca import KernelPCA +from autosklearn.pipeline.util import ( + PreprocessingTestCase, + _test_preprocessing, + get_dataset, +) class KernelPCAComponentTest(PreprocessingTestCase): def test_default_configuration(self): - transformation, original = _test_preprocessing(KernelPCA, - dataset='digits', - train_size_maximum=2000) + transformation, original = _test_preprocessing( + KernelPCA, dataset="digits", train_size_maximum=2000 + ) self.assertEqual(transformation.shape[0], original.shape[0]) self.assertFalse((transformation == 0).all()) def test_default_configuration_sparse(self): - transformation, original = _test_preprocessing(KernelPCA, - make_sparse=True, - dataset='digits') + transformation, original = _test_preprocessing( + KernelPCA, make_sparse=True, dataset="digits" + ) self.assertEqual(transformation.shape[0], original.shape[0]) self.assertFalse((transformation == 0).all()) def test_default_configuration_classify(self): for i in range(5): - X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', - make_sparse=False, - train_size_maximum=1000) + X_train, Y_train, X_test, Y_test = get_dataset( + dataset="digits", make_sparse=False, train_size_maximum=1000 + ) configuration_space = KernelPCA.get_hyperparameter_search_space() default = configuration_space.get_default_configuration() - preprocessor = KernelPCA(random_state=1, - **{hp_name: default[hp_name] for hp_name in - default if default[hp_name] is not None}) + preprocessor = KernelPCA( + random_state=1, + **{ + hp_name: default[hp_name] + for hp_name in default + if default[hp_name] is not None + }, + ) preprocessor.fit(X_train, Y_train) X_train_trans = preprocessor.transform(X_train) X_test_trans = preprocessor.transform(X_test) @@ -46,5 +54,4 @@ def test_default_configuration_classify(self): @unittest.skip("Always returns float64") def test_preprocessing_dtype(self): - super(KernelPCAComponentTest, - self)._test_preprocessing_dtype(KernelPCA) + super(KernelPCAComponentTest, self)._test_preprocessing_dtype(KernelPCA) diff --git a/test/test_pipeline/components/feature_preprocessing/test_kitchen_sinks.py b/test/test_pipeline/components/feature_preprocessing/test_kitchen_sinks.py index c94e6f9a55..16ef41198d 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_kitchen_sinks.py +++ b/test/test_pipeline/components/feature_preprocessing/test_kitchen_sinks.py @@ -1,7 +1,9 @@ import unittest -from autosklearn.pipeline.components.feature_preprocessing.kitchen_sinks import RandomKitchenSinks -from autosklearn.pipeline.util import _test_preprocessing, PreprocessingTestCase +from autosklearn.pipeline.components.feature_preprocessing.kitchen_sinks import ( + RandomKitchenSinks, +) +from autosklearn.pipeline.util import PreprocessingTestCase, _test_preprocessing class KitchenSinkComponent(PreprocessingTestCase): @@ -13,5 +15,4 @@ def test_default_configuration(self): @unittest.skip("Right now, the RBFSampler returns a float64 array!") def test_preprocessing_dtype(self): - super(KitchenSinkComponent, - self)._test_preprocessing_dtype(RandomKitchenSinks) + super(KitchenSinkComponent, self)._test_preprocessing_dtype(RandomKitchenSinks) diff --git a/test/test_pipeline/components/feature_preprocessing/test_liblinear.py b/test/test_pipeline/components/feature_preprocessing/test_liblinear.py index 19b56b6eac..0195dfb701 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_liblinear.py +++ b/test/test_pipeline/components/feature_preprocessing/test_liblinear.py @@ -1,15 +1,22 @@ -from sklearn.linear_model import RidgeClassifier -from autosklearn.pipeline.components.feature_preprocessing.liblinear_svc_preprocessor import \ - LibLinear_Preprocessor -from autosklearn.pipeline.util import _test_preprocessing, PreprocessingTestCase, \ - get_dataset import sklearn.metrics +from sklearn.linear_model import RidgeClassifier -from test.test_pipeline.ignored_warnings import ignore_warnings, feature_preprocessing_warnings +from autosklearn.pipeline.components.feature_preprocessing.liblinear_svc_preprocessor import ( # noqa: E501 + LibLinear_Preprocessor, +) +from autosklearn.pipeline.util import ( + PreprocessingTestCase, + _test_preprocessing, + get_dataset, +) +from test.test_pipeline.ignored_warnings import ( + feature_preprocessing_warnings, + ignore_warnings, +) -class LiblinearComponentTest(PreprocessingTestCase): +class LiblinearComponentTest(PreprocessingTestCase): def test_default_configuration(self): with ignore_warnings(feature_preprocessing_warnings): transformation, original = _test_preprocessing(LibLinear_Preprocessor) @@ -19,15 +26,21 @@ def test_default_configuration(self): def test_default_configuration_classify(self): for i in range(2): - X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', - make_sparse=False) - configuration_space = LibLinear_Preprocessor.get_hyperparameter_search_space() + X_train, Y_train, X_test, Y_test = get_dataset( + dataset="digits", make_sparse=False + ) + configuration_space = ( + LibLinear_Preprocessor.get_hyperparameter_search_space() + ) default = configuration_space.get_default_configuration() - preprocessor = LibLinear_Preprocessor(random_state=1, - **{hp_name: default[hp_name] - for hp_name in - default if default[ - hp_name] is not None}) + preprocessor = LibLinear_Preprocessor( + random_state=1, + **{ + hp_name: default[hp_name] + for hp_name in default + if default[hp_name] is not None + }, + ) with ignore_warnings(feature_preprocessing_warnings): preprocessor.fit(X_train, Y_train) diff --git a/test/test_pipeline/components/feature_preprocessing/test_nystroem_sampler.py b/test/test_pipeline/components/feature_preprocessing/test_nystroem_sampler.py index b3db49ebca..d6244c362f 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_nystroem_sampler.py +++ b/test/test_pipeline/components/feature_preprocessing/test_nystroem_sampler.py @@ -3,8 +3,9 @@ import numpy as np import sklearn.preprocessing -from autosklearn.pipeline.components.feature_preprocessing.nystroem_sampler import \ - Nystroem +from autosklearn.pipeline.components.feature_preprocessing.nystroem_sampler import ( + Nystroem, +) from autosklearn.pipeline.util import _test_preprocessing, get_dataset @@ -16,7 +17,7 @@ def test_default_configuration(self): self.assertFalse((transformation == 0).all()) # Custon preprocessing test to check if clipping to zero works - X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits') + X_train, Y_train, X_test, Y_test = get_dataset(dataset="digits") original_X_train = X_train.copy() ss = sklearn.preprocessing.StandardScaler() X_train = ss.fit_transform(X_train) @@ -25,12 +26,15 @@ def test_default_configuration(self): preprocessor = Nystroem( random_state=1, - **{hp_name: default[hp_name] for hp_name in default if default[hp_name] is not None}, - ) + **{ + hp_name: default[hp_name] + for hp_name in default + if default[hp_name] is not None + }, + ) transformer = preprocessor.fit(X_train, Y_train) - transformation, original = transformer.transform( - X_train), original_X_train + transformation, original = transformer.transform(X_train), original_X_train self.assertEqual(transformation.shape[0], original.shape[0]) self.assertEqual(transformation.shape[1], 100) @@ -46,7 +50,7 @@ def _test_preprocessing_dtype(self): preprocessor = Nystroem( random_state=1, **{hp.hyperparameter.name: hp.value for hp in default.values.values()}, - ) + ) preprocessor.fit(X_train) Xt = preprocessor.transform(X_train) self.assertEqual(Xt.dtype, np.float32) @@ -59,7 +63,7 @@ def _test_preprocessing_dtype(self): preprocessor = Nystroem( random_state=1, **{hp.hyperparameter.name: hp.value for hp in default.values.values()}, - ) + ) preprocessor.fit(X_train, Y_train) Xt = preprocessor.transform(X_train) self.assertEqual(Xt.dtype, np.float64) @@ -73,7 +77,7 @@ def _test_preprocessing_dtype(self): preprocessor = Nystroem( random_state=1, **{hp.hyperparameter.name: hp.value for hp in default.values.values()}, - ) + ) preprocessor.fit(X_train) Xt = preprocessor.transform(X_train) self.assertEqual(Xt.dtype, np.float32) @@ -86,7 +90,7 @@ def _test_preprocessing_dtype(self): preprocessor = Nystroem( random_state=1, **{hp.hyperparameter.name: hp.value for hp in default.values.values()}, - ) + ) preprocessor.fit(X_train) Xt = preprocessor.transform(X_train) self.assertEqual(Xt.dtype, np.float64) diff --git a/test/test_pipeline/components/feature_preprocessing/test_pca.py b/test/test_pipeline/components/feature_preprocessing/test_pca.py index 02ab8bdd0e..b73da8aa64 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_pca.py +++ b/test/test_pipeline/components/feature_preprocessing/test_pca.py @@ -1,7 +1,7 @@ import numpy as np from autosklearn.pipeline.components.feature_preprocessing.pca import PCA -from autosklearn.pipeline.util import _test_preprocessing, PreprocessingTestCase +from autosklearn.pipeline.util import PreprocessingTestCase, _test_preprocessing class PCAComponentTest(PreprocessingTestCase): @@ -13,9 +13,9 @@ def test_default_configuration(self): self.assertFalse((transformation == original).all()) transformations.append(transformation) if len(transformations) > 1: - np.testing.assert_allclose(transformations[-1], - transformations[-2], rtol=1e-4) + np.testing.assert_allclose( + transformations[-1], transformations[-2], rtol=1e-4 + ) def test_preprocessing_dtype(self): - super(PCAComponentTest, self)._test_preprocessing_dtype(PCA, - test_sparse=False) + super(PCAComponentTest, self)._test_preprocessing_dtype(PCA, test_sparse=False) diff --git a/test/test_pipeline/components/feature_preprocessing/test_polynomial.py b/test/test_pipeline/components/feature_preprocessing/test_polynomial.py index 28f84bc595..3c9e93a49c 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_polynomial.py +++ b/test/test_pipeline/components/feature_preprocessing/test_polynomial.py @@ -1,9 +1,14 @@ -from sklearn.tree import DecisionTreeClassifier -from autosklearn.pipeline.components.feature_preprocessing.polynomial import \ - PolynomialFeatures -from autosklearn.pipeline.util import _test_preprocessing, PreprocessingTestCase, \ - get_dataset import sklearn.metrics +from sklearn.tree import DecisionTreeClassifier + +from autosklearn.pipeline.components.feature_preprocessing.polynomial import ( + PolynomialFeatures, +) +from autosklearn.pipeline.util import ( + PreprocessingTestCase, + _test_preprocessing, + get_dataset, +) class PolynomialFeaturesComponentTest(PreprocessingTestCase): @@ -14,13 +19,14 @@ def test_default_configuration(self): def test_default_configuration_classify(self): for i in range(2): - X_train, Y_train, X_test, Y_test = get_dataset(dataset='breast_cancer', - make_sparse=False) + X_train, Y_train, X_test, Y_test = get_dataset( + dataset="breast_cancer", make_sparse=False + ) configuration_space = PolynomialFeatures.get_hyperparameter_search_space() default = configuration_space.get_default_configuration() - preprocessor = PolynomialFeatures(random_state=1, - **{hp_name: default[hp_name] for - hp_name in default}) + preprocessor = PolynomialFeatures( + random_state=1, **{hp_name: default[hp_name] for hp_name in default} + ) preprocessor.fit(X_train, Y_train) X_train_trans = preprocessor.transform(X_train) X_test_trans = preprocessor.transform(X_test) @@ -34,13 +40,14 @@ def test_default_configuration_classify(self): def test_default_configuration_classify_sparse(self): for i in range(2): - X_train, Y_train, X_test, Y_test = get_dataset(dataset='breast_cancer', - make_sparse=True) + X_train, Y_train, X_test, Y_test = get_dataset( + dataset="breast_cancer", make_sparse=True + ) configuration_space = PolynomialFeatures.get_hyperparameter_search_space() default = configuration_space.get_default_configuration() - preprocessor = PolynomialFeatures(random_state=1, - **{hp_name: default[hp_name] for - hp_name in default}) + preprocessor = PolynomialFeatures( + random_state=1, **{hp_name: default[hp_name] for hp_name in default} + ) preprocessor.fit(X_train, Y_train) X_train_trans = preprocessor.transform(X_train) X_test_trans = preprocessor.transform(X_test) @@ -53,6 +60,6 @@ def test_default_configuration_classify_sparse(self): self.assertAlmostEqual(accuracy, 0.8544152744630071, places=2) def test_preprocessing_dtype(self): - super(PolynomialFeaturesComponentTest, - self)._test_preprocessing_dtype(PolynomialFeatures, - test_sparse=False) + super(PolynomialFeaturesComponentTest, self)._test_preprocessing_dtype( + PolynomialFeatures, test_sparse=False + ) diff --git a/test/test_pipeline/components/feature_preprocessing/test_random_trees_embedding.py b/test/test_pipeline/components/feature_preprocessing/test_random_trees_embedding.py index 681c319830..f84675dc1a 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_random_trees_embedding.py +++ b/test/test_pipeline/components/feature_preprocessing/test_random_trees_embedding.py @@ -3,8 +3,9 @@ import numpy as np import scipy.sparse -from autosklearn.pipeline.components.feature_preprocessing.random_trees_embedding import \ - RandomTreesEmbedding +from autosklearn.pipeline.components.feature_preprocessing.random_trees_embedding import ( # noqa: E501 + RandomTreesEmbedding, +) from autosklearn.pipeline.util import _test_preprocessing, get_dataset @@ -26,10 +27,9 @@ def test_preprocessing_dtype(self): configuration_space = RandomTreesEmbedding.get_hyperparameter_search_space() default = configuration_space.get_default_configuration() - preprocessor = RandomTreesEmbedding(random_state=1, - **{hp_name: default[hp_name] for - hp_name in - default}) + preprocessor = RandomTreesEmbedding( + random_state=1, **{hp_name: default[hp_name] for hp_name in default} + ) preprocessor.fit(X_train) Xt = preprocessor.transform(X_train) @@ -40,10 +40,9 @@ def test_preprocessing_dtype(self): X_train = X_train.astype(np.float64) configuration_space = RandomTreesEmbedding.get_hyperparameter_search_space() default = configuration_space.get_default_configuration() - preprocessor = RandomTreesEmbedding(random_state=1, - **{hp_name: default[hp_name] for - hp_name in - default}) + preprocessor = RandomTreesEmbedding( + random_state=1, **{hp_name: default[hp_name] for hp_name in default} + ) preprocessor.fit(X_train, Y_train) Xt = preprocessor.transform(X_train) self.assertEqual(Xt.dtype, np.float64) diff --git a/test/test_pipeline/components/feature_preprocessing/test_select_percentile_classification.py b/test/test_pipeline/components/feature_preprocessing/test_select_percentile_classification.py index d7cde925b0..b177e4f4ba 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_select_percentile_classification.py +++ b/test/test_pipeline/components/feature_preprocessing/test_select_percentile_classification.py @@ -4,8 +4,9 @@ import scipy.sparse import sklearn.preprocessing -from autosklearn.pipeline.components.feature_preprocessing.select_percentile_classification \ - import SelectPercentileClassification +from autosklearn.pipeline.components.feature_preprocessing.select_percentile_classification import ( # noqa: E501 + SelectPercentileClassification, +) from autosklearn.pipeline.util import _test_preprocessing, get_dataset @@ -13,29 +14,35 @@ class SelectPercentileClassificationTest(unittest.TestCase): def test_default_configuration(self): transformation, original = _test_preprocessing(SelectPercentileClassification) self.assertEqual(transformation.shape[0], original.shape[0]) - self.assertEqual(transformation.shape[1], int(original.shape[1]/2)) + self.assertEqual(transformation.shape[1], int(original.shape[1] / 2)) self.assertFalse((transformation == 0).all()) transformation, original = _test_preprocessing( - SelectPercentileClassification, - make_sparse=True, - ) + SelectPercentileClassification, + make_sparse=True, + ) self.assertTrue(scipy.sparse.issparse(transformation)) self.assertEqual(transformation.shape[0], original.shape[0]) - self.assertEqual(transformation.shape[1], int(original.shape[1]/2)) + self.assertEqual(transformation.shape[1], int(original.shape[1] / 2)) # Custon preprocessing test to check if clipping to zero works - X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits') + X_train, Y_train, X_test, Y_test = get_dataset(dataset="digits") original_X_train = X_train.copy() ss = sklearn.preprocessing.StandardScaler() X_train = ss.fit_transform(X_train) - configuration_space = SelectPercentileClassification.get_hyperparameter_search_space() + configuration_space = ( + SelectPercentileClassification.get_hyperparameter_search_space() + ) default = configuration_space.get_default_configuration() preprocessor = SelectPercentileClassification( - random_state=1, - **{hp_name: default[hp_name] for hp_name in default if default[hp_name] is not None}, - ) + random_state=1, + **{ + hp_name: default[hp_name] + for hp_name in default + if default[hp_name] is not None + }, + ) transformer = preprocessor.fit(X_train, Y_train) transformation, original = transformer.transform(X_train), original_X_train @@ -48,11 +55,13 @@ def test_preprocessing_dtype(self): X_train, Y_train, X_test, Y_test = get_dataset("iris") self.assertEqual(X_train.dtype, np.float32) - configuration_space = SelectPercentileClassification.get_hyperparameter_search_space() + configuration_space = ( + SelectPercentileClassification.get_hyperparameter_search_space() + ) default = configuration_space.get_default_configuration() - preprocessor = SelectPercentileClassification(random_state=1, - **{hp_name: default[hp_name] - for hp_name in default}) + preprocessor = SelectPercentileClassification( + random_state=1, **{hp_name: default[hp_name] for hp_name in default} + ) preprocessor.fit(X_train, Y_train) Xt = preprocessor.transform(X_train) self.assertEqual(Xt.dtype, np.float32) @@ -60,11 +69,13 @@ def test_preprocessing_dtype(self): # np.float64 X_train, Y_train, X_test, Y_test = get_dataset("iris") X_train = X_train.astype(np.float64) - configuration_space = SelectPercentileClassification.get_hyperparameter_search_space() + configuration_space = ( + SelectPercentileClassification.get_hyperparameter_search_space() + ) default = configuration_space.get_default_configuration() - preprocessor = SelectPercentileClassification(random_state=1, - **{hp_name: default[hp_name] - for hp_name in default}) + preprocessor = SelectPercentileClassification( + random_state=1, **{hp_name: default[hp_name] for hp_name in default} + ) preprocessor.fit(X_train, Y_train) Xt = preprocessor.transform(X_train) self.assertEqual(Xt.dtype, np.float64) @@ -73,11 +84,13 @@ def test_preprocessing_dtype(self): # np.float32 X_train, Y_train, X_test, Y_test = get_dataset("iris", make_sparse=True) self.assertEqual(X_train.dtype, np.float32) - configuration_space = SelectPercentileClassification.get_hyperparameter_search_space() + configuration_space = ( + SelectPercentileClassification.get_hyperparameter_search_space() + ) default = configuration_space.get_default_configuration() - preprocessor = SelectPercentileClassification(random_state=1, - **{hp_name: default[hp_name] - for hp_name in default}) + preprocessor = SelectPercentileClassification( + random_state=1, **{hp_name: default[hp_name] for hp_name in default} + ) preprocessor.fit(X_train, Y_train) Xt = preprocessor.transform(X_train) self.assertEqual(Xt.dtype, np.float32) @@ -85,11 +98,13 @@ def test_preprocessing_dtype(self): # np.float64 X_train, Y_train, X_test, Y_test = get_dataset("iris", make_sparse=True) X_train = X_train.astype(np.float64) - configuration_space = SelectPercentileClassification.get_hyperparameter_search_space() + configuration_space = ( + SelectPercentileClassification.get_hyperparameter_search_space() + ) default = configuration_space.get_default_configuration() - preprocessor = SelectPercentileClassification(random_state=1, - **{hp_name: default[hp_name] - for hp_name in default}) + preprocessor = SelectPercentileClassification( + random_state=1, **{hp_name: default[hp_name] for hp_name in default} + ) preprocessor.fit(X_train, Y_train) Xt = preprocessor.transform(X_train) self.assertEqual(Xt.dtype, np.float64) diff --git a/test/test_pipeline/components/feature_preprocessing/test_select_percentile_regression.py b/test/test_pipeline/components/feature_preprocessing/test_select_percentile_regression.py index a76a15c5a3..0fd335fd83 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_select_percentile_regression.py +++ b/test/test_pipeline/components/feature_preprocessing/test_select_percentile_regression.py @@ -2,8 +2,9 @@ import numpy as np -from autosklearn.pipeline.components.feature_preprocessing.select_percentile_regression \ - import SelectPercentileRegression +from autosklearn.pipeline.components.feature_preprocessing.select_percentile_regression import ( # noqa: E501 + SelectPercentileRegression, +) from autosklearn.pipeline.util import _test_preprocessing, get_dataset @@ -12,9 +13,9 @@ def test_default_configuration(self): transformation, original = _test_preprocessing( dataset="boston", Preprocessor=SelectPercentileRegression, - ) + ) self.assertEqual(transformation.shape[0], original.shape[0]) - self.assertEqual(transformation.shape[1], int(original.shape[1]/2)) + self.assertEqual(transformation.shape[1], int(original.shape[1] / 2)) self.assertFalse((transformation == 0).all()) def test_preprocessing_dtype(self): @@ -23,11 +24,13 @@ def test_preprocessing_dtype(self): X_train, Y_train, X_test, Y_test = get_dataset("iris") self.assertEqual(X_train.dtype, np.float32) - configuration_space = SelectPercentileRegression.get_hyperparameter_search_space() + configuration_space = ( + SelectPercentileRegression.get_hyperparameter_search_space() + ) default = configuration_space.get_default_configuration() - preprocessor = SelectPercentileRegression(random_state=1, - **{hp_name: default[hp_name] - for hp_name in default}) + preprocessor = SelectPercentileRegression( + random_state=1, **{hp_name: default[hp_name] for hp_name in default} + ) preprocessor.fit(X_train, Y_train) Xt = preprocessor.transform(X_train) self.assertEqual(Xt.dtype, np.float32) @@ -35,11 +38,13 @@ def test_preprocessing_dtype(self): # np.float64 X_train, Y_train, X_test, Y_test = get_dataset("iris") X_train = X_train.astype(np.float64) - configuration_space = SelectPercentileRegression.get_hyperparameter_search_space() + configuration_space = ( + SelectPercentileRegression.get_hyperparameter_search_space() + ) default = configuration_space.get_default_configuration() - preprocessor = SelectPercentileRegression(random_state=1, - **{hp_name: default[hp_name] - for hp_name in default}) + preprocessor = SelectPercentileRegression( + random_state=1, **{hp_name: default[hp_name] for hp_name in default} + ) preprocessor.fit(X_train, Y_train) Xt = preprocessor.transform(X_train) self.assertEqual(Xt.dtype, np.float64) diff --git a/test/test_pipeline/components/feature_preprocessing/test_select_rates_classification.py b/test/test_pipeline/components/feature_preprocessing/test_select_rates_classification.py index 2497b5174a..2d1c2aaf78 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_select_rates_classification.py +++ b/test/test_pipeline/components/feature_preprocessing/test_select_rates_classification.py @@ -4,8 +4,9 @@ import scipy.sparse import sklearn.preprocessing -from autosklearn.pipeline.components.feature_preprocessing.select_rates_classification import \ - SelectClassificationRates +from autosklearn.pipeline.components.feature_preprocessing.select_rates_classification import ( # noqa: E501 + SelectClassificationRates, +) from autosklearn.pipeline.util import _test_preprocessing, get_dataset @@ -17,27 +18,33 @@ def test_default_configuration(self): self.assertFalse((transformation == 0).all()) transformation, original = _test_preprocessing( - SelectClassificationRates, make_sparse=True) + SelectClassificationRates, make_sparse=True + ) self.assertTrue(scipy.sparse.issparse(transformation)) self.assertEqual(transformation.shape[0], original.shape[0]) self.assertEqual(transformation.shape[1], int(original.shape[1] / 2)) # Custom preprocessing test to check if clipping to zero works - X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits') + X_train, Y_train, X_test, Y_test = get_dataset(dataset="digits") original_X_train = X_train.copy() ss = sklearn.preprocessing.StandardScaler() X_train = ss.fit_transform(X_train) - configuration_space = SelectClassificationRates.get_hyperparameter_search_space() + configuration_space = ( + SelectClassificationRates.get_hyperparameter_search_space() + ) default = configuration_space.get_default_configuration() - preprocessor = SelectClassificationRates(random_state=1, - **{hp_name: default[hp_name] - for hp_name in default - if default[hp_name] is not None}) + preprocessor = SelectClassificationRates( + random_state=1, + **{ + hp_name: default[hp_name] + for hp_name in default + if default[hp_name] is not None + }, + ) transformer = preprocessor.fit(X_train, Y_train) - transformation, original = transformer.transform( - X_train), original_X_train + transformation, original = transformer.transform(X_train), original_X_train self.assertEqual(transformation.shape[0], original.shape[0]) # I don't know why it's 52 here and not 32 which would be half of the # number of features. Seems to be related to a runtime warning raised @@ -50,11 +57,13 @@ def test_preprocessing_dtype(self): X_train, Y_train, X_test, Y_test = get_dataset("iris") self.assertEqual(X_train.dtype, np.float32) - configuration_space = SelectClassificationRates.get_hyperparameter_search_space() + configuration_space = ( + SelectClassificationRates.get_hyperparameter_search_space() + ) default = configuration_space.get_default_configuration() - preprocessor = SelectClassificationRates(random_state=1, - **{hp_name: default[hp_name] for hp_name in - default}) + preprocessor = SelectClassificationRates( + random_state=1, **{hp_name: default[hp_name] for hp_name in default} + ) preprocessor.fit(X_train, Y_train) Xt = preprocessor.transform(X_train) self.assertEqual(Xt.dtype, np.float32) @@ -62,11 +71,13 @@ def test_preprocessing_dtype(self): # np.float64 X_train, Y_train, X_test, Y_test = get_dataset("iris") X_train = X_train.astype(np.float64) - configuration_space = SelectClassificationRates.get_hyperparameter_search_space() + configuration_space = ( + SelectClassificationRates.get_hyperparameter_search_space() + ) default = configuration_space.get_default_configuration() - preprocessor = SelectClassificationRates(random_state=1, - **{hp_name: default[hp_name] for hp_name in - default}) + preprocessor = SelectClassificationRates( + random_state=1, **{hp_name: default[hp_name] for hp_name in default} + ) preprocessor.fit(X_train, Y_train) Xt = preprocessor.transform(X_train) self.assertEqual(Xt.dtype, np.float64) @@ -75,11 +86,13 @@ def test_preprocessing_dtype(self): # np.float32 X_train, Y_train, X_test, Y_test = get_dataset("iris", make_sparse=True) self.assertEqual(X_train.dtype, np.float32) - configuration_space = SelectClassificationRates.get_hyperparameter_search_space() + configuration_space = ( + SelectClassificationRates.get_hyperparameter_search_space() + ) default = configuration_space.get_default_configuration() - preprocessor = SelectClassificationRates(random_state=1, - **{hp_name: default[hp_name] for hp_name in - default}) + preprocessor = SelectClassificationRates( + random_state=1, **{hp_name: default[hp_name] for hp_name in default} + ) preprocessor.fit(X_train, Y_train) Xt = preprocessor.transform(X_train) self.assertEqual(Xt.dtype, np.float32) @@ -87,11 +100,13 @@ def test_preprocessing_dtype(self): # np.float64 X_train, Y_train, X_test, Y_test = get_dataset("iris", make_sparse=True) X_train = X_train.astype(np.float64) - configuration_space = SelectClassificationRates.get_hyperparameter_search_space() + configuration_space = ( + SelectClassificationRates.get_hyperparameter_search_space() + ) default = configuration_space.get_default_configuration() - preprocessor = SelectClassificationRates(random_state=1, - **{hp_name: default[hp_name] for hp_name in - default}) + preprocessor = SelectClassificationRates( + random_state=1, **{hp_name: default[hp_name] for hp_name in default} + ) preprocessor.fit(X_train, Y_train) Xt = preprocessor.transform(X_train) self.assertEqual(Xt.dtype, np.float64) diff --git a/test/test_pipeline/components/feature_preprocessing/test_select_rates_regression.py b/test/test_pipeline/components/feature_preprocessing/test_select_rates_regression.py index 573bab32ce..869d7fbee2 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_select_rates_regression.py +++ b/test/test_pipeline/components/feature_preprocessing/test_select_rates_regression.py @@ -4,8 +4,9 @@ import scipy.sparse import sklearn.preprocessing -from autosklearn.pipeline.components.feature_preprocessing.select_rates_regression import \ - SelectRegressionRates +from autosklearn.pipeline.components.feature_preprocessing.select_rates_regression import ( # noqa: E501 + SelectRegressionRates, +) from autosklearn.pipeline.util import _test_preprocessing, get_dataset @@ -17,34 +18,38 @@ def test_default_configuration(self): self.assertFalse((transformation == 0).all()) transformation, original = _test_preprocessing( - SelectRegressionRates, make_sparse=True) + SelectRegressionRates, make_sparse=True + ) self.assertTrue(scipy.sparse.issparse(transformation)) self.assertEqual(transformation.shape[0], original.shape[0]) self.assertEqual(transformation.shape[1], int(original.shape[1] / 2)) # Makes sure that the features are reduced, not the number of samples - X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits') + X_train, Y_train, X_test, Y_test = get_dataset(dataset="digits") original_X_train = X_train.copy() ss = sklearn.preprocessing.StandardScaler() X_train = ss.fit_transform(X_train) configuration_space = SelectRegressionRates.get_hyperparameter_search_space() default = configuration_space.get_default_configuration() - preprocessor = SelectRegressionRates(random_state=1, - **{hp_name: default[hp_name] - for hp_name in default - if default[hp_name] is not None}) + preprocessor = SelectRegressionRates( + random_state=1, + **{ + hp_name: default[hp_name] + for hp_name in default + if default[hp_name] is not None + }, + ) transformer = preprocessor.fit(X_train, Y_train) - transformation, original = transformer.transform( - X_train), original_X_train + transformation, original = transformer.transform(X_train), original_X_train self.assertEqual(transformation.shape[0], original.shape[0]) self.assertEqual(transformation.shape[1], 21) def test_default_configuration_regression(self): transformation, original = _test_preprocessing( SelectRegressionRates, - dataset='boston', + dataset="boston", ) self.assertEqual(transformation.shape[0], original.shape[0]) # From 13 to 12 features @@ -57,15 +62,15 @@ def test_preprocessing_dtype_regression(self): X_train, Y_train, X_test, Y_test = get_dataset("boston") self.assertEqual(X_train.dtype, np.float32) - dataset_properties = {'target_type': 'regression'} + dataset_properties = {"target_type": "regression"} configuration_space = SelectRegressionRates.get_hyperparameter_search_space( dataset_properties ) default = configuration_space.get_default_configuration() - preprocessor = SelectRegressionRates(random_state=1, - **{hp_name: default[hp_name] for hp_name in - default}) + preprocessor = SelectRegressionRates( + random_state=1, **{hp_name: default[hp_name] for hp_name in default} + ) preprocessor.fit(X_train, Y_train) Xt = preprocessor.transform(X_train) self.assertEqual(Xt.dtype, np.float32) @@ -77,9 +82,9 @@ def test_preprocessing_dtype_regression(self): dataset_properties ) default = configuration_space.get_default_configuration() - preprocessor = SelectRegressionRates(random_state=1, - **{hp_name: default[hp_name] for hp_name in - default}) + preprocessor = SelectRegressionRates( + random_state=1, **{hp_name: default[hp_name] for hp_name in default} + ) preprocessor.fit(X_train, Y_train) Xt = preprocessor.transform(X_train) self.assertEqual(Xt.dtype, np.float64) diff --git a/test/test_pipeline/components/feature_preprocessing/test_truncatedSVD.py b/test/test_pipeline/components/feature_preprocessing/test_truncatedSVD.py index df1f1d2fe6..7e16fa7fa5 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_truncatedSVD.py +++ b/test/test_pipeline/components/feature_preprocessing/test_truncatedSVD.py @@ -1,11 +1,16 @@ import unittest -from sklearn.linear_model import RidgeClassifier -from autosklearn.pipeline.components.feature_preprocessing.truncatedSVD import \ - TruncatedSVD -from autosklearn.pipeline.util import _test_preprocessing, PreprocessingTestCase, \ - get_dataset import sklearn.metrics +from sklearn.linear_model import RidgeClassifier + +from autosklearn.pipeline.components.feature_preprocessing.truncatedSVD import ( + TruncatedSVD, +) +from autosklearn.pipeline.util import ( + PreprocessingTestCase, + _test_preprocessing, + get_dataset, +) class TruncatedSVDComponentTest(PreprocessingTestCase): @@ -16,14 +21,19 @@ def test_default_configuration(self): def test_default_configuration_classify(self): for i in range(2): - X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', - make_sparse=True) + X_train, Y_train, X_test, Y_test = get_dataset( + dataset="digits", make_sparse=True + ) configuration_space = TruncatedSVD.get_hyperparameter_search_space() default = configuration_space.get_default_configuration() preprocessor = TruncatedSVD( random_state=1, - **{hp_name: default[hp_name] for hp_name in default if default[hp_name] is not None} - ) + **{ + hp_name: default[hp_name] + for hp_name in default + if default[hp_name] is not None + }, + ) preprocessor.fit(X_train, Y_train) X_train_trans = preprocessor.transform(X_train) X_test_trans = preprocessor.transform(X_test) @@ -37,6 +47,6 @@ def test_default_configuration_classify(self): @unittest.skip("Truncated SVD returns np.float64.") def test_preprocessing_dtype(self): - super(TruncatedSVDComponentTest, - self)._test_preprocessing_dtype(TruncatedSVD, - test_sparse=False) + super(TruncatedSVDComponentTest, self)._test_preprocessing_dtype( + TruncatedSVD, test_sparse=False + ) diff --git a/test/test_pipeline/components/regression/test_adaboost.py b/test/test_pipeline/components/regression/test_adaboost.py index c7f199d5ee..b62df4fd9b 100644 --- a/test/test_pipeline/components/regression/test_adaboost.py +++ b/test/test_pipeline/components/regression/test_adaboost.py @@ -1,7 +1,7 @@ import sklearn.ensemble -from autosklearn.pipeline.components.regression.adaboost import \ - AdaboostRegressor +from autosklearn.pipeline.components.regression.adaboost import AdaboostRegressor + from .test_base import BaseRegressionComponentTest diff --git a/test/test_pipeline/components/regression/test_ard_regression.py b/test/test_pipeline/components/regression/test_ard_regression.py index dac8d61349..829bf9b507 100644 --- a/test/test_pipeline/components/regression/test_ard_regression.py +++ b/test/test_pipeline/components/regression/test_ard_regression.py @@ -1,7 +1,7 @@ import sklearn.linear_model -from autosklearn.pipeline.components.regression.ard_regression import \ - ARDRegression +from autosklearn.pipeline.components.regression.ard_regression import ARDRegression + from .test_base import BaseRegressionComponentTest diff --git a/test/test_pipeline/components/regression/test_base.py b/test/test_pipeline/components/regression/test_base.py index 8ffc1d23fe..dcf7770332 100644 --- a/test/test_pipeline/components/regression/test_base.py +++ b/test/test_pipeline/components/regression/test_base.py @@ -1,19 +1,17 @@ -from typing import Type, Container +from typing import Container, Type import unittest -import pytest - import numpy as np +import pytest import sklearn.metrics -from autosklearn.pipeline.util import _test_regressor, _test_regressor_iterative_fit -from autosklearn.pipeline.constants import SPARSE +from autosklearn.pipeline.components.regression import RegressorChoice, _regressors from autosklearn.pipeline.components.regression.libsvm_svr import LibSVM_SVR +from autosklearn.pipeline.constants import SPARSE +from autosklearn.pipeline.util import _test_regressor, _test_regressor_iterative_fit -from autosklearn.pipeline.components.regression import _regressors, RegressorChoice - -from test.test_pipeline.ignored_warnings import regressor_warnings, ignore_warnings +from test.test_pipeline.ignored_warnings import ignore_warnings, regressor_warnings class BaseRegressionComponentTest(unittest.TestCase): @@ -37,8 +35,7 @@ def test_default_boston(self): with ignore_warnings(regressor_warnings): predictions, targets, n_calls = _test_regressor( - dataset="boston", - Regressor=self.module + dataset="boston", Regressor=self.module ) score = sklearn.metrics.r2_score(y_true=targets, y_pred=predictions) @@ -70,14 +67,13 @@ def test_default_boston_iterative_fit(self): if self.__class__ == BaseRegressionComponentTest: return - if not hasattr(self.module, 'iterative_fit'): + if not hasattr(self.module, "iterative_fit"): return for i in range(2): with ignore_warnings(regressor_warnings): predictions, targets, regressor = _test_regressor_iterative_fit( - dataset="boston", - Regressor=self.module + dataset="boston", Regressor=self.module ) score = sklearn.metrics.r2_score(targets, predictions) @@ -92,8 +88,8 @@ def test_default_boston_iterative_fit(self): self.assertAlmostEqual(fixture, score, places) if self.step_hyperparameter is not None: - param_name = self.step_hyperparameter['name'] - default = self.step_hyperparameter['value'] + param_name = self.step_hyperparameter["name"] + default = self.step_hyperparameter["value"] value = getattr(regressor.estimator, param_name) expected = self.res.get("boston_iterative_n_iter", default) @@ -110,7 +106,7 @@ def test_default_boston_iterative_sparse_fit(self): if self.__class__ == BaseRegressionComponentTest: return - if not hasattr(self.module, 'iterative_fit'): + if not hasattr(self.module, "iterative_fit"): return if SPARSE not in self.module.get_properties()["input"]: @@ -119,15 +115,13 @@ def test_default_boston_iterative_sparse_fit(self): for i in range(2): with ignore_warnings(regressor_warnings): predictions, targets, _ = _test_regressor_iterative_fit( - dataset="boston", - Regressor=self.module, - sparse=True + dataset="boston", Regressor=self.module, sparse=True ) - self.assertAlmostEqual(self.res["default_boston_iterative_sparse"], - sklearn.metrics.r2_score(targets, - predictions), - places=self.res.get( - "default_boston_iterative_sparse_places", 7)) + self.assertAlmostEqual( + self.res["default_boston_iterative_sparse"], + sklearn.metrics.r2_score(targets, predictions), + places=self.res.get("default_boston_iterative_sparse_places", 7), + ) def test_default_boston_sparse(self): @@ -140,16 +134,14 @@ def test_default_boston_sparse(self): for i in range(2): with ignore_warnings(regressor_warnings): predictions, targets, _ = _test_regressor( - dataset="boston", - Regressor=self.module, - sparse=True + dataset="boston", Regressor=self.module, sparse=True ) - self.assertAlmostEqual(self.res["default_boston_sparse"], - sklearn.metrics.r2_score(targets, - predictions), - places=self.res.get( - "default_boston_sparse_places", 7)) + self.assertAlmostEqual( + self.res["default_boston_sparse"], + sklearn.metrics.r2_score(targets, predictions), + places=self.res.get("default_boston_sparse_places", 7), + ) def test_default_diabetes(self): @@ -159,15 +151,14 @@ def test_default_diabetes(self): for i in range(2): with ignore_warnings(regressor_warnings): predictions, targets, n_calls = _test_regressor( - dataset="diabetes", - Regressor=self.module + dataset="diabetes", Regressor=self.module ) - self.assertAlmostEqual(self.res["default_diabetes"], - sklearn.metrics.r2_score(targets, - predictions), - places=self.res.get( - "default_diabetes_places", 7)) + self.assertAlmostEqual( + self.res["default_diabetes"], + sklearn.metrics.r2_score(targets, predictions), + places=self.res.get("default_diabetes_places", 7), + ) if self.res.get("diabetes_n_calls"): self.assertEqual(self.res["diabetes_n_calls"], n_calls) @@ -177,28 +168,27 @@ def test_default_diabetes_iterative_fit(self): if self.__class__ == BaseRegressionComponentTest: return - if not hasattr(self.module, 'iterative_fit'): + if not hasattr(self.module, "iterative_fit"): return for i in range(2): with ignore_warnings(regressor_warnings): predictions, targets, _ = _test_regressor_iterative_fit( - dataset="diabetes", - Regressor=self.module + dataset="diabetes", Regressor=self.module ) - self.assertAlmostEqual(self.res["default_diabetes_iterative"], - sklearn.metrics.r2_score(targets, - predictions), - places=self.res.get( - "default_diabetes_iterative_places", 7)) + self.assertAlmostEqual( + self.res["default_diabetes_iterative"], + sklearn.metrics.r2_score(targets, predictions), + places=self.res.get("default_diabetes_iterative_places", 7), + ) def test_default_diabetes_iterative_sparse_fit(self): if self.__class__ == BaseRegressionComponentTest: return - if not hasattr(self.module, 'iterative_fit'): + if not hasattr(self.module, "iterative_fit"): return if SPARSE not in self.module.get_properties()["input"]: @@ -207,21 +197,21 @@ def test_default_diabetes_iterative_sparse_fit(self): for i in range(2): with ignore_warnings(regressor_warnings): predictions, targets, regressor = _test_regressor_iterative_fit( - dataset="diabetes", - Regressor=self.module, - sparse=True + dataset="diabetes", Regressor=self.module, sparse=True ) - self.assertAlmostEqual(self.res["default_diabetes_iterative_sparse"], - sklearn.metrics.r2_score(targets, - predictions), - places=self.res.get( - "default_diabetes_iterative_sparse_places", 7)) + self.assertAlmostEqual( + self.res["default_diabetes_iterative_sparse"], + sklearn.metrics.r2_score(targets, predictions), + places=self.res.get("default_diabetes_iterative_sparse_places", 7), + ) if self.step_hyperparameter is not None: self.assertEqual( - getattr(regressor.estimator, self.step_hyperparameter['name']), - self.res.get("diabetes_iterative_n_iter", self.step_hyperparameter['value']) + getattr(regressor.estimator, self.step_hyperparameter["name"]), + self.res.get( + "diabetes_iterative_n_iter", self.step_hyperparameter["value"] + ), ) def test_default_diabetes_sparse(self): @@ -235,47 +225,73 @@ def test_default_diabetes_sparse(self): for i in range(2): with ignore_warnings(regressor_warnings): predictions, targets, _ = _test_regressor( - dataset="diabetes", - Regressor=self.module, - sparse=True + dataset="diabetes", Regressor=self.module, sparse=True ) - self.assertAlmostEqual(self.res["default_diabetes_sparse"], - sklearn.metrics.r2_score(targets, - predictions), - places=self.res.get( - "default_diabetes_sparse_places", 7)) + self.assertAlmostEqual( + self.res["default_diabetes_sparse"], + sklearn.metrics.r2_score(targets, predictions), + places=self.res.get("default_diabetes_sparse_places", 7), + ) def test_module_idempotent(self): - """ Fitting twice with the same config gives the same model params. + """Fitting twice with the same config gives the same model params. - This is only valid when the random_state passed is an int. If a - RandomState object is passed then repeated calls to fit will have - different results. See the section on "Controlling Randomness" in the - sklearn docs. + This is only valid when the random_state passed is an int. If a + RandomState object is passed then repeated calls to fit will have + different results. See the section on "Controlling Randomness" in the + sklearn docs. - https://scikit-learn.org/0.24/common_pitfalls.html#controlling-randomness + https://scikit-learn.org/0.24/common_pitfalls.html#controlling-randomness """ if self.__class__ == BaseRegressionComponentTest: return regressor_cls = self.module - X = np.array([ - [0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5], - [0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5], - [0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5], - [0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5], - ]) - y = np.array([ - 1, 1, 1, 1, - 1, 1, 1, 1, - 1, 1, 1, 1, - 1, 1, 1, 1, - ]) + X = np.array( + [ + [0.5, 0.5], + [0.5, 0.5], + [0.5, 0.5], + [0.5, 0.5], + [0.5, 0.5], + [0.5, 0.5], + [0.5, 0.5], + [0.5, 0.5], + [0.5, 0.5], + [0.5, 0.5], + [0.5, 0.5], + [0.5, 0.5], + [0.5, 0.5], + [0.5, 0.5], + [0.5, 0.5], + [0.5, 0.5], + ] + ) + y = np.array( + [ + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + ] + ) # We ignore certain keys when comparing - param_keys_ignored = ['base_estimator'] + param_keys_ignored = ["base_estimator"] # We use the default config + sampled ones configuration_space = regressor_cls.get_hyperparameter_search_space() @@ -292,14 +308,14 @@ def test_module_idempotent(self): with ignore_warnings(regressor_warnings): params_first = regressor.fit(X.copy(), y.copy()).estimator.get_params() - if hasattr(regressor.estimator, 'random_state'): + if hasattr(regressor.estimator, "random_state"): rs_1 = regressor.random_state rs_estimator_1 = regressor.estimator.random_state with ignore_warnings(regressor_warnings): params_second = regressor.fit(X.copy(), y.copy()).estimator.get_params() - if hasattr(regressor.estimator, 'random_state'): + if hasattr(regressor.estimator, "random_state"): rs_2 = regressor.random_state rs_estimator_2 = regressor.estimator.random_state @@ -310,27 +326,27 @@ def test_module_idempotent(self): del params[key] # They should have equal parameters - self.assertEqual(params_first, params_second, - f"Failed with model args {model_args}") - if ( - hasattr(regressor.estimator, 'random_state') - and not isinstance(regressor, LibSVM_SVR) + self.assertEqual( + params_first, params_second, f"Failed with model args {model_args}" + ) + if hasattr(regressor.estimator, "random_state") and not isinstance( + regressor, LibSVM_SVR ): # sklearn.svm.SVR has it as an attribute but does not use it and # defaults it to None, even if a value is passed in - assert all([ - seed == random_state - for random_state in [rs_1, rs_estimator_1, rs_2, rs_estimator_2] - ]) + assert all( + [ + seed == random_state + for random_state in [rs_1, rs_estimator_1, rs_2, rs_estimator_2] + ] + ) @pytest.mark.parametrize("regressor", _regressors.values()) @pytest.mark.parametrize("X", [np.array([[1, 2, 3]] * 20)]) @pytest.mark.parametrize("y", [np.array([1] * 20)]) def test_fit_and_predict_with_1d_targets_as_1d( - regressor: Type[RegressorChoice], - X: np.ndarray, - y: np.ndarray + regressor: Type[RegressorChoice], X: np.ndarray, y: np.ndarray ) -> None: """Test that all pipelines work with 1d target types @@ -371,9 +387,7 @@ def test_fit_and_predict_with_1d_targets_as_1d( @pytest.mark.parametrize("X", [np.array([[1, 2, 3]] * 20)]) @pytest.mark.parametrize("y", [np.array([[1]] * 20)]) def test_fit_and_predict_with_1d_targets_as_2d( - regressor: Type[RegressorChoice], - X: np.ndarray, - y: np.ndarray + regressor: Type[RegressorChoice], X: np.ndarray, y: np.ndarray ) -> None: """Test that all pipelines work with 1d target types when they are wrapped as 2d @@ -412,17 +426,18 @@ def test_fit_and_predict_with_1d_targets_as_2d( assert len(predictions) == len(y) -@pytest.mark.parametrize("regressor", [ - regressor - for regressor in _regressors.values() - if regressor.get_properties()['handles_multilabel'] -]) +@pytest.mark.parametrize( + "regressor", + [ + regressor + for regressor in _regressors.values() + if regressor.get_properties()["handles_multilabel"] + ], +) @pytest.mark.parametrize("X", [np.array([[1, 2, 3]] * 20)]) @pytest.mark.parametrize("y", [np.array([[1, 1, 1]] * 20)]) def test_fit_and_predict_with_2d_targets( - regressor: Type[RegressorChoice], - X: np.ndarray, - y: np.ndarray + regressor: Type[RegressorChoice], X: np.ndarray, y: np.ndarray ) -> None: """Test that all pipelines work with 2d target types diff --git a/test/test_pipeline/components/regression/test_decision_tree.py b/test/test_pipeline/components/regression/test_decision_tree.py index a5d2e53990..942b9db601 100644 --- a/test/test_pipeline/components/regression/test_decision_tree.py +++ b/test/test_pipeline/components/regression/test_decision_tree.py @@ -1,7 +1,7 @@ import sklearn.tree -from autosklearn.pipeline.components.regression.decision_tree import \ - DecisionTree +from autosklearn.pipeline.components.regression.decision_tree import DecisionTree + from .test_base import BaseRegressionComponentTest diff --git a/test/test_pipeline/components/regression/test_extra_trees.py b/test/test_pipeline/components/regression/test_extra_trees.py index 5d6f6d1acf..8d92fa30c8 100644 --- a/test/test_pipeline/components/regression/test_extra_trees.py +++ b/test/test_pipeline/components/regression/test_extra_trees.py @@ -1,7 +1,7 @@ import sklearn.ensemble -from autosklearn.pipeline.components.regression.extra_trees import \ - ExtraTreesRegressor +from autosklearn.pipeline.components.regression.extra_trees import ExtraTreesRegressor + from .test_base import BaseRegressionComponentTest @@ -12,18 +12,18 @@ class ExtraTreesComponentTest(BaseRegressionComponentTest): res = dict() res["default_boston"] = 0.8539264243687228 res["boston_n_calls"] = 9 - res["default_boston_iterative"] = res['default_boston'] + res["default_boston_iterative"] = res["default_boston"] res["default_boston_sparse"] = 0.411211701806908 - res["default_boston_iterative_sparse"] = res['default_boston_sparse'] + res["default_boston_iterative_sparse"] = res["default_boston_sparse"] res["default_diabetes"] = 0.3885150255877827 res["diabetes_n_calls"] = 9 - res["default_diabetes_iterative"] = res['default_diabetes'] + res["default_diabetes_iterative"] = res["default_diabetes"] res["default_diabetes_sparse"] = 0.2422804139169642 - res["default_diabetes_iterative_sparse"] = res['default_diabetes_sparse'] + res["default_diabetes_iterative_sparse"] = res["default_diabetes_sparse"] sk_mod = sklearn.ensemble.ExtraTreesRegressor module = ExtraTreesRegressor step_hyperparameter = { - 'name': 'n_estimators', - 'value': module.get_max_iter(), + "name": "n_estimators", + "value": module.get_max_iter(), } diff --git a/test/test_pipeline/components/regression/test_gaussian_process.py b/test/test_pipeline/components/regression/test_gaussian_process.py index d148d416df..0f766e22b1 100644 --- a/test/test_pipeline/components/regression/test_gaussian_process.py +++ b/test/test_pipeline/components/regression/test_gaussian_process.py @@ -1,7 +1,6 @@ import sklearn.gaussian_process -from autosklearn.pipeline.components.regression.gaussian_process import \ - GaussianProcess +from autosklearn.pipeline.components.regression.gaussian_process import GaussianProcess from .test_base import BaseRegressionComponentTest diff --git a/test/test_pipeline/components/regression/test_gradient_boosting.py b/test/test_pipeline/components/regression/test_gradient_boosting.py index 9fcb2cd623..6412fd0598 100644 --- a/test/test_pipeline/components/regression/test_gradient_boosting.py +++ b/test/test_pipeline/components/regression/test_gradient_boosting.py @@ -1,7 +1,8 @@ import sklearn.ensemble -from autosklearn.pipeline.components.regression.gradient_boosting import \ - GradientBoosting +from autosklearn.pipeline.components.regression.gradient_boosting import ( + GradientBoosting, +) from .test_base import BaseRegressionComponentTest diff --git a/test/test_pipeline/components/regression/test_k_nearest_neighbors.py b/test/test_pipeline/components/regression/test_k_nearest_neighbors.py index 40637c3ec8..19d0cf40f5 100644 --- a/test/test_pipeline/components/regression/test_k_nearest_neighbors.py +++ b/test/test_pipeline/components/regression/test_k_nearest_neighbors.py @@ -1,7 +1,9 @@ import sklearn.neighbors -from autosklearn.pipeline.components.regression.k_nearest_neighbors import \ - KNearestNeighborsRegressor +from autosklearn.pipeline.components.regression.k_nearest_neighbors import ( + KNearestNeighborsRegressor, +) + from .test_base import BaseRegressionComponentTest diff --git a/test/test_pipeline/components/regression/test_liblinear_svr.py b/test/test_pipeline/components/regression/test_liblinear_svr.py index 42b73bfba7..37b6552c9b 100644 --- a/test/test_pipeline/components/regression/test_liblinear_svr.py +++ b/test/test_pipeline/components/regression/test_liblinear_svr.py @@ -1,7 +1,7 @@ import sklearn.svm -from autosklearn.pipeline.components.regression.liblinear_svr import \ - LibLinear_SVR +from autosklearn.pipeline.components.regression.liblinear_svr import LibLinear_SVR + from .test_base import BaseRegressionComponentTest diff --git a/test/test_pipeline/components/regression/test_mlp.py b/test/test_pipeline/components/regression/test_mlp.py index c003037c76..9e2a92acac 100644 --- a/test/test_pipeline/components/regression/test_mlp.py +++ b/test/test_pipeline/components/regression/test_mlp.py @@ -64,6 +64,6 @@ class MLPComponentTest(BaseRegressionComponentTest): sk_mod = sklearn.neural_network.MLPRegressor module = MLPRegressor step_hyperparameter = { - 'name': 'n_iter_', - 'value': module.get_max_iter(), + "name": "n_iter_", + "value": module.get_max_iter(), } diff --git a/test/test_pipeline/components/regression/test_random_forests.py b/test/test_pipeline/components/regression/test_random_forests.py index ee6f342a8e..6e1634ff83 100644 --- a/test/test_pipeline/components/regression/test_random_forests.py +++ b/test/test_pipeline/components/regression/test_random_forests.py @@ -1,7 +1,7 @@ import sklearn.ensemble -from autosklearn.pipeline.components.regression.random_forest import \ - RandomForest +from autosklearn.pipeline.components.regression.random_forest import RandomForest + from .test_base import BaseRegressionComponentTest @@ -11,18 +11,18 @@ class RandomForestComponentTest(BaseRegressionComponentTest): res = dict() res["default_boston"] = 0.8410063895401654 res["boston_n_calls"] = 9 - res["default_boston_iterative"] = res['default_boston'] + res["default_boston_iterative"] = res["default_boston"] res["default_boston_sparse"] = 0.4194462097407078 - res["default_boston_iterative_sparse"] = res['default_boston_sparse'] + res["default_boston_iterative_sparse"] = res["default_boston_sparse"] res["default_diabetes"] = 0.3496051170409269 res["diabetes_n_calls"] = 9 - res["default_diabetes_iterative"] = res['default_diabetes'] + res["default_diabetes_iterative"] = res["default_diabetes"] res["default_diabetes_sparse"] = 0.2383300978781976 - res["default_diabetes_iterative_sparse"] = res['default_diabetes_sparse'] + res["default_diabetes_iterative_sparse"] = res["default_diabetes_sparse"] sk_mod = sklearn.ensemble.RandomForestRegressor module = RandomForest step_hyperparameter = { - 'name': 'n_estimators', - 'value': module.get_max_iter(), + "name": "n_estimators", + "value": module.get_max_iter(), } diff --git a/test/test_pipeline/components/regression/test_sgd.py b/test/test_pipeline/components/regression/test_sgd.py index df31b3e026..467f3519f1 100644 --- a/test/test_pipeline/components/regression/test_sgd.py +++ b/test/test_pipeline/components/regression/test_sgd.py @@ -1,6 +1,7 @@ import sklearn.linear_model from autosklearn.pipeline.components.regression.sgd import SGD + from .test_base import BaseRegressionComponentTest @@ -10,16 +11,16 @@ class SGDComponentTest(BaseRegressionComponentTest): # Values are extremely bad because the invscaling does not drop the # learning rate aggressively enough! res = dict() - res["default_boston"] = -1.1811672998629865e+28 + res["default_boston"] = -1.1811672998629865e28 res["boston_n_calls"] = 6 - res["default_boston_iterative"] = res['default_boston'] - res["default_boston_sparse"] = -1.1518512489347601e+28 - res["default_boston_iterative_sparse"] = res['default_boston_sparse'] + res["default_boston_iterative"] = res["default_boston"] + res["default_boston_sparse"] = -1.1518512489347601e28 + res["default_boston_iterative_sparse"] = res["default_boston_sparse"] res["default_diabetes"] = 0.27420813549185374 res["diabetes_n_calls"] = 10 - res["default_diabetes_iterative"] = res['default_diabetes'] + res["default_diabetes_iterative"] = res["default_diabetes"] res["default_diabetes_sparse"] = 0.034801785011824404 - res["default_diabetes_iterative_sparse"] = res['default_diabetes_sparse'] + res["default_diabetes_iterative_sparse"] = res["default_diabetes_sparse"] sk_mod = sklearn.linear_model.SGDRegressor module = SGD diff --git a/test/test_pipeline/components/regression/test_support_vector_regression.py b/test/test_pipeline/components/regression/test_support_vector_regression.py index 57cde050ed..84bea51da6 100644 --- a/test/test_pipeline/components/regression/test_support_vector_regression.py +++ b/test/test_pipeline/components/regression/test_support_vector_regression.py @@ -1,6 +1,7 @@ import sklearn.linear_model from autosklearn.pipeline.components.regression.libsvm_svr import LibSVM_SVR + from .test_base import BaseRegressionComponentTest diff --git a/test/test_pipeline/components/test_base.py b/test/test_pipeline/components/test_base.py index c53246cc77..1e6ddbbd14 100644 --- a/test/test_pipeline/components/test_base.py +++ b/test/test_pipeline/components/test_base.py @@ -2,20 +2,23 @@ import sys import unittest -from autosklearn.pipeline.components.base import find_components, \ - AutoSklearnClassificationAlgorithm +from autosklearn.pipeline.components.base import ( + AutoSklearnClassificationAlgorithm, + find_components, +) this_dir = os.path.dirname(os.path.abspath(__file__)) sys.path.append(this_dir) class TestBase(unittest.TestCase): - def test_find_components(self): - c = find_components('dummy_components', - os.path.join(this_dir, 'dummy_components'), - AutoSklearnClassificationAlgorithm) - print('COMPONENTS: %s' % repr(c)) + c = find_components( + "dummy_components", + os.path.join(this_dir, "dummy_components"), + AutoSklearnClassificationAlgorithm, + ) + print("COMPONENTS: %s" % repr(c)) self.assertEqual(len(c), 2) - self.assertEqual(c['dummy_component_1'].__name__, 'DummyComponent1') - self.assertEqual(c['dummy_component_2'].__name__, 'DummyComponent2') + self.assertEqual(c["dummy_component_1"].__name__, "DummyComponent1") + self.assertEqual(c["dummy_component_2"].__name__, "DummyComponent2") diff --git a/test/test_pipeline/ignored_warnings.py b/test/test_pipeline/ignored_warnings.py index 5b941281f9..715cacb6ba 100644 --- a/test/test_pipeline/ignored_warnings.py +++ b/test/test_pipeline/ignored_warnings.py @@ -1,106 +1,116 @@ -from contextlib import contextmanager -from typing import List, Iterator, Tuple +from typing import Iterator, List, Tuple, Type import warnings +from contextlib import contextmanager from sklearn.exceptions import ConvergenceWarning - regressor_warnings = [ ( - UserWarning, ( # From QuantileTransformer + UserWarning, + ( # From QuantileTransformer r"n_quantiles \(\d+\) is greater than the total number of samples \(\d+\)\." r" n_quantiles is set to n_samples\." - ) + ), ), ( - ConvergenceWarning, ( # From GaussianProcesses + ConvergenceWarning, + ( # From GaussianProcesses r"The optimal value found for dimension \d+ of parameter \w+ is close" r" to the specified (upper|lower) bound .*(Increasing|Decreasing) the bound" r" and calling fit again may find a better value." - ) + ), ), ( - UserWarning, ( # From FastICA - r"n_components is too large: it will be set to \d+" - ) + UserWarning, + (r"n_components is too large: it will be set to \d+"), # From FastICA ), ( - ConvergenceWarning, ( # From SGD - r"Maximum number of iteration reached before convergence\. Consider increasing" - r" max_iter to improve the fit\." - ) + ConvergenceWarning, + ( # From SGD + r"Maximum number of iteration reached before convergence\." + r" Consider increasing max_iter to improve the fit\." + ), ), ( - ConvergenceWarning, ( # From MLP + ConvergenceWarning, + ( # From MLP r"Stochastic Optimizer: Maximum iterations \(\d+\) reached and the" r" optimization hasn't converged yet\." - ) + ), ), ] classifier_warnings = [ ( - UserWarning, ( # From QuantileTransformer + UserWarning, + ( # From QuantileTransformer r"n_quantiles \(\d+\) is greater than the total number of samples \(\d+\)\." r" n_quantiles is set to n_samples\." - ) + ), ), ( - UserWarning, ( # From FastICA - r"n_components is too large: it will be set to \d+" - ) - + UserWarning, + (r"n_components is too large: it will be set to \d+"), # From FastICA ), ( - ConvergenceWarning, ( # From Liblinear + ConvergenceWarning, + ( # From Liblinear r"Liblinear failed to converge, increase the number of iterations\." - ) + ), ), ( - ConvergenceWarning, ( # From SGD - r"Maximum number of iteration reached before convergence\. Consider increasing" - r" max_iter to improve the fit\." - ) + ConvergenceWarning, + ( # From SGD + r"Maximum number of iteration reached before convergence\." + r"Consider increasing max_iter to improve the fit\." + ), ), ( - ConvergenceWarning, ( # From MLP + ConvergenceWarning, + ( # From MLP r"Stochastic Optimizer: Maximum iterations \(\d+\) reached and the" r" optimization hasn't converged yet\." - ) + ), ), ( - ConvergenceWarning, ( # From FastICA + ConvergenceWarning, + ( # From FastICA r"FastICA did not converge\." r" Consider increasing tolerance or the maximum number of iterations\." - ) + ), ), ( - UserWarning, ( # From LDA (Linear Discriminant Analysis) - r"Variables are collinear" - ) + UserWarning, + (r"Variables are collinear"), # From LDA (Linear Discriminant Analysis) ), ( - UserWarning, ( + UserWarning, + ( r"Clustering metrics expects discrete values but received continuous values" r" for label, and multiclass values for target" - ) - ) + ), + ), ] feature_preprocessing_warnings = [ ( - ConvergenceWarning, ( # From liblinear + ConvergenceWarning, + ( # From liblinear r"Liblinear failed to converge, increase the number of iterations." - ) + ), ) ] -ignored_warnings = regressor_warnings + classifier_warnings + feature_preprocessing_warnings +ignored_warnings = ( + regressor_warnings + classifier_warnings + feature_preprocessing_warnings +) @contextmanager -def ignore_warnings(to_ignore: List[Tuple[Exception, str]] = ignored_warnings) -> Iterator[None]: +def ignore_warnings( + to_ignore: List[Tuple[Type[Warning], str]] = ignored_warnings +) -> Iterator[None]: """A context manager to ignore warnings >>> with ignore_warnings(classifier_warnings): @@ -113,5 +123,5 @@ def ignore_warnings(to_ignore: List[Tuple[Exception, str]] = ignored_warnings) - """ with warnings.catch_warnings(): for category, message in to_ignore: - warnings.filterwarnings('ignore', category=category, message=message) + warnings.filterwarnings("ignore", category=category, message=message) yield diff --git a/test/test_pipeline/implementations/__init__.py b/test/test_pipeline/implementations/__init__.py index 8f0ce6cb7c..92bf78f389 100644 --- a/test/test_pipeline/implementations/__init__.py +++ b/test/test_pipeline/implementations/__init__.py @@ -1 +1 @@ -__author__ = 'feurerm' +__author__ = "feurerm" diff --git a/test/test_pipeline/implementations/test_CategoryShift.py b/test/test_pipeline/implementations/test_CategoryShift.py index 621d9b47cb..1b5e1451e6 100644 --- a/test/test_pipeline/implementations/test_CategoryShift.py +++ b/test/test_pipeline/implementations/test_CategoryShift.py @@ -1,4 +1,5 @@ import unittest + import numpy as np import scipy.sparse @@ -6,7 +7,6 @@ class CategoryShiftTest(unittest.TestCase): - def test_dense(self): X = np.random.randint(0, 255, (3, 4)) Y = CategoryShift().fit_transform(X) @@ -14,7 +14,8 @@ def test_dense(self): def test_sparse(self): X = scipy.sparse.csc_matrix( - ([1, 2, 0, 4], ([0, 1, 2, 1], [3, 2, 1, 0])), shape=(3, 4)) + ([1, 2, 0, 4], ([0, 1, 2, 1], [3, 2, 1, 0])), shape=(3, 4) + ) Y = CategoryShift().fit_transform(X) X.data += 3 self.assertTrue((Y.todense() == X.todense()).all()) @@ -29,6 +30,6 @@ def test_negative(self): CategoryShift().fit_transform(X) def test_string(self): - X = np.array([['a', 'b'], ['c', 'd']]) + X = np.array([["a", "b"], ["c", "d"]]) with self.assertRaises(ValueError): CategoryShift().fit_transform(X) diff --git a/test/test_pipeline/implementations/test_MinorityCoalescer.py b/test/test_pipeline/implementations/test_MinorityCoalescer.py index 73cbf9049a..7bdca8f1aa 100644 --- a/test/test_pipeline/implementations/test_MinorityCoalescer.py +++ b/test/test_pipeline/implementations/test_MinorityCoalescer.py @@ -1,24 +1,25 @@ import unittest -import numpy as np +import numpy as np import scipy.sparse from autosklearn.pipeline.implementations.MinorityCoalescer import MinorityCoalescer class MinorityCoalescerTest(unittest.TestCase): - @property def X1(self): # Generates an array with categories 3, 4, 5, 6, 7 and occurences of 30%, # 30%, 30%, 5% and 5% respectively - X = np.vstack(( - np.ones((30, 10)) * 3, - np.ones((30, 10)) * 4, - np.ones((30, 10)) * 5, - np.ones((5, 10)) * 6, - np.ones((5, 10)) * 7, - )) + X = np.vstack( + ( + np.ones((30, 10)) * 3, + np.ones((30, 10)) * 4, + np.ones((30, 10)) * 5, + np.ones((5, 10)) * 6, + np.ones((5, 10)) * 7, + ) + ) for col in range(X.shape[1]): np.random.shuffle(X[:, col]) return X @@ -27,13 +28,15 @@ def X1(self): def X2(self): # Generates an array with categories 3, 4, 5, 6, 7 and occurences of 5%, # 5%, 5%, 35% and 50% respectively - X = np.vstack(( - np.ones((5, 10)) * 3, - np.ones((5, 10)) * 4, - np.ones((5, 10)) * 5, - np.ones((35, 10)) * 6, - np.ones((50, 10)) * 7, - )) + X = np.vstack( + ( + np.ones((5, 10)) * 3, + np.ones((5, 10)) * 4, + np.ones((5, 10)) * 5, + np.ones((35, 10)) * 6, + np.ones((50, 10)) * 7, + ) + ) for col in range(X.shape[1]): np.random.shuffle(X[:, col]) return X @@ -48,7 +51,7 @@ def test_default(self): def test_coalesce_10_percent(self): X = self.X1 - Y = MinorityCoalescer(minimum_fraction=.1).fit_transform(X) + Y = MinorityCoalescer(minimum_fraction=0.1).fit_transform(X) for col in range(Y.shape[1]): hist = np.histogram(Y[:, col], bins=np.arange(1, 7)) np.testing.assert_array_almost_equal(hist[0], [10, 0, 30, 30, 30]) @@ -57,7 +60,7 @@ def test_coalesce_10_percent(self): def test_coalesce_10_percent_sparse(self): X = scipy.sparse.csc_matrix(self.X1) - Y = MinorityCoalescer(minimum_fraction=.1).fit_transform(X) + Y = MinorityCoalescer(minimum_fraction=0.1).fit_transform(X) # Assert no copies were made self.assertEqual(id(X), id(Y)) Y = Y.todense() @@ -75,7 +78,7 @@ def test_transform_after_fit(self): X_fit = self.X1 # Here categories 3, 4, 5 have ocurrence above 10% X_transf = self.X2 # Here it is the opposite, just categs 6 and 7 are above 10% - mc = MinorityCoalescer(minimum_fraction=.1).fit(X_fit) + mc = MinorityCoalescer(minimum_fraction=0.1).fit(X_fit) # transform() should coalesce categories as learned during fit. # Category distribution in X_transf should be irrelevant. diff --git a/test/test_pipeline/implementations/test_SparseOneHotEncoder.py b/test/test_pipeline/implementations/test_SparseOneHotEncoder.py index 731533637b..91f1827c06 100644 --- a/test/test_pipeline/implementations/test_SparseOneHotEncoder.py +++ b/test/test_pipeline/implementations/test_SparseOneHotEncoder.py @@ -1,38 +1,37 @@ import unittest import numpy as np - import scipy.sparse -import sklearn.tree import sklearn.datasets import sklearn.model_selection import sklearn.pipeline +import sklearn.tree from sklearn.impute import SimpleImputer from sklearn.tree import DecisionTreeClassifier -from autosklearn.pipeline.implementations.SparseOneHotEncoder import SparseOneHotEncoder from autosklearn.pipeline.implementations.CategoryShift import CategoryShift +from autosklearn.pipeline.implementations.SparseOneHotEncoder import SparseOneHotEncoder -sparse1 = scipy.sparse.csc_matrix(([3, 2, 1, 1, 2, 3], - ((1, 4, 5, 2, 3, 5), - (0, 0, 0, 1, 1, 1))), shape=(6, 2)) -sparse1_1h = scipy.sparse.csc_matrix(([1, 1, 1, 1, 1, 1], - ((5, 4, 1, 2, 3, 5), - (0, 1, 2, 3, 4, 5))), shape=(6, 6)) +sparse1 = scipy.sparse.csc_matrix( + ([3, 2, 1, 1, 2, 3], ((1, 4, 5, 2, 3, 5), (0, 0, 0, 1, 1, 1))), shape=(6, 2) +) +sparse1_1h = scipy.sparse.csc_matrix( + ([1, 1, 1, 1, 1, 1], ((5, 4, 1, 2, 3, 5), (0, 1, 2, 3, 4, 5))), shape=(6, 6) +) -sparse2 = scipy.sparse.csc_matrix(([2, 1, 0, 0, 0, 0], - ((1, 4, 5, 2, 3, 5), - (0, 0, 0, 1, 1, 1))), shape=(6, 2)) -sparse2_1h = scipy.sparse.csc_matrix(([1, 1, 1, 1, 1, 1], - ((5, 4, 1, 2, 3, 5), - (0, 1, 2, 3, 3, 3))), shape=(6, 4)) +sparse2 = scipy.sparse.csc_matrix( + ([2, 1, 0, 0, 0, 0], ((1, 4, 5, 2, 3, 5), (0, 0, 0, 1, 1, 1))), shape=(6, 2) +) +sparse2_1h = scipy.sparse.csc_matrix( + ([1, 1, 1, 1, 1, 1], ((5, 4, 1, 2, 3, 5), (0, 1, 2, 3, 3, 3))), shape=(6, 4) +) -sparse2_csr = scipy.sparse.csr_matrix(([2, 1, 0, 0, 0, 0], - ((1, 4, 5, 2, 3, 5), - (0, 0, 0, 1, 1, 1))), shape=(6, 2)) -sparse2_csr_1h = scipy.sparse.csr_matrix(([1, 1, 1, 1, 1, 1], - ((5, 4, 1, 2, 3, 5), - (0, 1, 2, 3, 3, 3))), shape=(6, 4)) +sparse2_csr = scipy.sparse.csr_matrix( + ([2, 1, 0, 0, 0, 0], ((1, 4, 5, 2, 3, 5), (0, 0, 0, 1, 1, 1))), shape=(6, 2) +) +sparse2_csr_1h = scipy.sparse.csr_matrix( + ([1, 1, 1, 1, 1, 1], ((5, 4, 1, 2, 3, 5), (0, 1, 2, 3, 3, 3))), shape=(6, 4) +) class TestSparseOneHotEncoder(unittest.TestCase): @@ -52,8 +51,7 @@ def _fit_then_transform(self, expected, input): transformation = ohe.fit_transform(input) self.assertIsInstance(transformation, scipy.sparse.csr_matrix) np.testing.assert_array_almost_equal( - expected.astype(float), - transformation.todense() + expected.astype(float), transformation.todense() ) self._check_arrays_equal(input, input_copy) @@ -90,23 +88,26 @@ def test_transform_with_unknown_value(self): self.assertEqual(3, np.sum(output)) def test_classification_workflow(self): - X, y = sklearn.datasets.fetch_openml(data_id=24, as_frame=False, return_X_y=True) + X, y = sklearn.datasets.fetch_openml( + data_id=24, as_frame=False, return_X_y=True + ) print(type(X)) - X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=3, - train_size=0.5, - test_size=0.5) + X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=3, train_size=0.5, test_size=0.5 + ) X_train = scipy.sparse.csc_matrix(X_train) X_test = scipy.sparse.csc_matrix(X_test) - pipeline = sklearn.pipeline.Pipeline(( - ('shift', CategoryShift()), - ('imput', SimpleImputer(strategy='constant', fill_value=2)), - ('ohe', SparseOneHotEncoder()), - ('tree', DecisionTreeClassifier(random_state=1)), - )) + pipeline = sklearn.pipeline.Pipeline( + ( + ("shift", CategoryShift()), + ("imput", SimpleImputer(strategy="constant", fill_value=2)), + ("ohe", SparseOneHotEncoder()), + ("tree", DecisionTreeClassifier(random_state=1)), + ) + ) pipeline.fit(X_train, y_train) pred_train = pipeline.predict(X_train) diff --git a/test/test_pipeline/implementations/test_util.py b/test/test_pipeline/implementations/test_util.py index 06f2a1eb2f..58412e0b0c 100644 --- a/test/test_pipeline/implementations/test_util.py +++ b/test/test_pipeline/implementations/test_util.py @@ -7,19 +7,44 @@ class UtilTest(unittest.TestCase): def test_softmax_binary(self): - df = np.array([-40.00643897, 34.69754581, 23.71181359, -29.89724287, - 27.06071791, -37.78334103, -40.15812461, 40.16139229, - -27.85887801, 42.67404756, -36.89753589, -36.45148009, - 54.68976306, 19.47886562, -49.99821027, -35.70205302, - -40.59639267, 32.96343916, -39.23777841, -37.86535019, - -33.10196906, 26.84144377, -36.8569686]) + df = np.array( + [ + -40.00643897, + 34.69754581, + 23.71181359, + -29.89724287, + 27.06071791, + -37.78334103, + -40.15812461, + 40.16139229, + -27.85887801, + 42.67404756, + -36.89753589, + -36.45148009, + 54.68976306, + 19.47886562, + -49.99821027, + -35.70205302, + -40.59639267, + 32.96343916, + -39.23777841, + -37.86535019, + -33.10196906, + 26.84144377, + -36.8569686, + ] + ) probas = softmax(df) - expected = [[1., 0.] if d < 0. else [0., 1.] for d in df] + expected = [[1.0, 0.0] if d < 0.0 else [0.0, 1.0] for d in df] np.testing.assert_array_almost_equal(expected, probas) def test_softmax(self): - df = np.array([[2.75021367e+10, -8.83772371e-01, -2.20516715e+27], - [-2.10848072e+11, 2.35024444e-01, 5.20106536e+25]]) + df = np.array( + [ + [2.75021367e10, -8.83772371e-01, -2.20516715e27], + [-2.10848072e11, 2.35024444e-01, 5.20106536e25], + ] + ) # With a numerically unstable softmax, the output would be something # like this: # [[ 0. 0. nan] @@ -30,6 +55,7 @@ def test_softmax(self): df = np.array([[0.1, 0.6, 0.3], [0.2, 0.3, 0.5]]) probas = softmax(df) - expected = np.array([[0.25838965, 0.42601251, 0.31559783], - [0.28943311, 0.31987306, 0.39069383]]) + expected = np.array( + [[0.25838965, 0.42601251, 0.31559783], [0.28943311, 0.31987306, 0.39069383]] + ) np.testing.assert_array_almost_equal(expected, probas) diff --git a/test/test_pipeline/test_base.py b/test/test_pipeline/test_base.py index 0d40bca0d1..f1efed23b4 100644 --- a/test/test_pipeline/test_base.py +++ b/test/test_pipeline/test_base.py @@ -5,8 +5,8 @@ import autosklearn.pipeline.base import autosklearn.pipeline.components.base -import autosklearn.pipeline.components.feature_preprocessing -import autosklearn.pipeline.components.classification +import autosklearn.pipeline.components.classification as classification +import autosklearn.pipeline.components.feature_preprocessing as feature_preprocessing class BasePipelineMock(autosklearn.pipeline.base.BasePipeline): @@ -17,42 +17,45 @@ def __init__(self): class BaseTest(unittest.TestCase): def test_get_hyperparameter_configuration_space_3choices(self): cs = ConfigSpace.configuration_space.ConfigurationSpace() - dataset_properties = {'target_type': 'classification'} + dataset_properties = {"target_type": "classification"} exclude = {} include = {} - pipeline = [('p0', - autosklearn.pipeline.components.feature_preprocessing - .FeaturePreprocessorChoice(dataset_properties)), - ('p1', - autosklearn.pipeline.components.feature_preprocessing - .FeaturePreprocessorChoice(dataset_properties)), - ('c', autosklearn.pipeline.components.classification - .ClassifierChoice(dataset_properties))] + pipeline = [ + ( + "p0", + feature_preprocessing.FeaturePreprocessorChoice(dataset_properties), + ), + ( + "p1", + feature_preprocessing.FeaturePreprocessorChoice(dataset_properties), + ), + ( + "c", + classification.ClassifierChoice(dataset_properties), + ), + ] base = BasePipelineMock() - cs = base._get_base_search_space(cs, dataset_properties, - exclude, include, pipeline) + cs = base._get_base_search_space( + cs, dataset_properties, exclude, include, pipeline + ) - self.assertEqual(len(cs.get_hyperparameter("p0:__choice__").choices), - 13) - self.assertEqual(len(cs.get_hyperparameter("p1:__choice__").choices), - 15) + self.assertEqual(len(cs.get_hyperparameter("p0:__choice__").choices), 13) + self.assertEqual(len(cs.get_hyperparameter("p1:__choice__").choices), 15) # for clause in sorted([str(clause) for clause in cs.forbidden_clauses]): # print(clause) self.assertEqual(148, len(cs.forbidden_clauses)) cs = ConfigSpace.configuration_space.ConfigurationSpace() - dataset_properties = {'target_type': 'classification', 'signed': True} - include = {'c': ['multinomial_nb']} - cs = base._get_base_search_space(cs, dataset_properties, - exclude, include, pipeline) - self.assertEqual(len(cs.get_hyperparameter("p0:__choice__").choices), - 13) - self.assertEqual(len(cs.get_hyperparameter("p1:__choice__").choices), - 10) - self.assertEqual(len(cs.get_hyperparameter("c:__choice__").choices), - 1) + dataset_properties = {"target_type": "classification", "signed": True} + include = {"c": ["multinomial_nb"]} + cs = base._get_base_search_space( + cs, dataset_properties, exclude, include, pipeline + ) + self.assertEqual(len(cs.get_hyperparameter("p0:__choice__").choices), 13) + self.assertEqual(len(cs.get_hyperparameter("p1:__choice__").choices), 10) + self.assertEqual(len(cs.get_hyperparameter("c:__choice__").choices), 1) # Mostly combinations of p0 making the data unsigned and p1 not # changing the values of the data points # for clause in sorted([str(clause) for clause in cs.forbidden_clauses]): @@ -60,42 +63,41 @@ def test_get_hyperparameter_configuration_space_3choices(self): self.assertEqual(64, len(cs.forbidden_clauses)) cs = ConfigSpace.configuration_space.ConfigurationSpace() - dataset_properties = {'target_type': 'classification', 'signed': True} + dataset_properties = {"target_type": "classification", "signed": True} include = {} - cs = base._get_base_search_space(cs, dataset_properties, - exclude, include, pipeline) - self.assertEqual(len(cs.get_hyperparameter("p0:__choice__").choices), - 13) - self.assertEqual(len(cs.get_hyperparameter("p1:__choice__").choices), - 15) - self.assertEqual(len(cs.get_hyperparameter("c:__choice__").choices), - 16) + cs = base._get_base_search_space( + cs, dataset_properties, exclude, include, pipeline + ) + self.assertEqual(len(cs.get_hyperparameter("p0:__choice__").choices), 13) + self.assertEqual(len(cs.get_hyperparameter("p1:__choice__").choices), 15) + self.assertEqual(len(cs.get_hyperparameter("c:__choice__").choices), 16) # for clause in sorted([str(clause) for clause in cs.forbidden_clauses]): # print(clause) self.assertEqual(110, len(cs.forbidden_clauses)) cs = ConfigSpace.configuration_space.ConfigurationSpace() - dataset_properties = {'target_type': 'classification', 'sparse': True} - cs = base._get_base_search_space(cs, dataset_properties, - exclude, include, pipeline) - self.assertEqual(len(cs.get_hyperparameter("p0:__choice__").choices), - 12) - self.assertEqual(len(cs.get_hyperparameter("p1:__choice__").choices), - 15) + dataset_properties = {"target_type": "classification", "sparse": True} + cs = base._get_base_search_space( + cs, dataset_properties, exclude, include, pipeline + ) + self.assertEqual(len(cs.get_hyperparameter("p0:__choice__").choices), 12) + self.assertEqual(len(cs.get_hyperparameter("p1:__choice__").choices), 15) # for clause in sorted([str(clause) for clause in cs.forbidden_clauses]): # print(clause) self.assertEqual(419, len(cs.forbidden_clauses)) cs = ConfigSpace.configuration_space.ConfigurationSpace() - dataset_properties = {'target_type': 'classification', - 'sparse': True, 'signed': True} - cs = base._get_base_search_space(cs, dataset_properties, - exclude, include, pipeline) - - self.assertEqual(len(cs.get_hyperparameter("p0:__choice__").choices), - 12) - self.assertEqual(len(cs.get_hyperparameter("p1:__choice__").choices), - 15) + dataset_properties = { + "target_type": "classification", + "sparse": True, + "signed": True, + } + cs = base._get_base_search_space( + cs, dataset_properties, exclude, include, pipeline + ) + + self.assertEqual(len(cs.get_hyperparameter("p0:__choice__").choices), 12) + self.assertEqual(len(cs.get_hyperparameter("p1:__choice__").choices), 15) # Data is guaranteed to be positive in cases like densifier, # extra_trees_preproc, multinomial_nb -> less constraints # for clause in sorted([str(clause) for clause in cs.forbidden_clauses]): @@ -123,52 +125,71 @@ def test_init_params_handling(self): for init_params, expected_init_params in [ ({}, {}), (None, None), - ({'M:key': 'value'}, {'key': 'value'}), + ({"M:key": "value"}, {"key": "value"}), ]: node = unittest.mock.Mock( spec=autosklearn.pipeline.components.base.AutoSklearnComponent ) node.get_hyperparameter_search_space.return_value = cs - node.key = 'value' - base.steps = [('M', node)] - base.set_hyperparameters(cs.sample_configuration(), init_params=init_params) - self.assertEqual(node.set_hyperparameters.call_args[1]['init_params'], - expected_init_params) + node.key = "value" + base.steps = [("M", node)] + base.set_hyperparameters( + cs.sample_configuration(), init_params=init_params + ) + self.assertEqual( + node.set_hyperparameters.call_args[1]["init_params"], + expected_init_params, + ) # Check for proper exception raising node = unittest.mock.Mock( spec=autosklearn.pipeline.components.base.AutoSklearnComponent ) node.get_hyperparameter_search_space.return_value = cs - base.steps = [('M', node)] - with self.assertRaisesRegex(ValueError, "Unsupported argument to init_params"): - base.set_hyperparameters(cs.sample_configuration(), init_params={'key': 'value'}) + base.steps = [("M", node)] + with self.assertRaisesRegex( + ValueError, "Unsupported argument to init_params" + ): + base.set_hyperparameters( + cs.sample_configuration(), init_params={"key": "value"} + ) # An invalid node name is passed - with self.assertRaisesRegex(ValueError, "The current node name specified via key"): - base.set_hyperparameters(cs.sample_configuration(), init_params={'N:key': 'value'}) + with self.assertRaisesRegex( + ValueError, "The current node name specified via key" + ): + base.set_hyperparameters( + cs.sample_configuration(), init_params={"N:key": "value"} + ) # The value was not properly set -- Here it happens because the # object is a magic mock, calling the method doesn't set a new parameter with self.assertRaisesRegex(ValueError, "Cannot properly set the pair"): - base.set_hyperparameters(cs.sample_configuration(), init_params={'M:key': 'value'}) + base.set_hyperparameters( + cs.sample_configuration(), init_params={"M:key": "value"} + ) def test_include_exclude_validation(self): - """ - Makes sure include and exclude arguments are validated and raises expected exception - on error + """Makes sure include and exclude arguments are validated and raises + expected exception on error """ base = BasePipelineMock() - dataset_properties = {'target_type': 'classification'} + dataset_properties = {"target_type": "classification"} base.dataset_properties = dataset_properties - base.steps = [('p0', - autosklearn.pipeline.components.feature_preprocessing - .FeaturePreprocessorChoice(dataset_properties)), - ('p1', - autosklearn.pipeline.components.feature_preprocessing - .FeaturePreprocessorChoice(dataset_properties)), - ('c', autosklearn.pipeline.components.classification - .ClassifierChoice(dataset_properties))] + base.steps = [ + ( + "p0", + feature_preprocessing.FeaturePreprocessorChoice(dataset_properties), + ), + ( + "p1", + feature_preprocessing.FeaturePreprocessorChoice(dataset_properties), + ), + ( + "c", + classification.ClassifierChoice(dataset_properties), + ), + ] def assert_value_error(include=None, exclude=None): base.include = include @@ -177,21 +198,21 @@ def assert_value_error(include=None, exclude=None): base._validate_include_exclude_params() # Same key in include and exclude argument - assert_value_error(include={'c': ['adaboost']}, exclude={'c': ['sgd']}) + assert_value_error(include={"c": ["adaboost"]}, exclude={"c": ["sgd"]}) # Invalid key in the exclude argument - assert_value_error(exclude={'p2': ['pca']}) + assert_value_error(exclude={"p2": ["pca"]}) # Invalid value type for the key in the include argument - assert_value_error(include={'c': ('adaboost', 'sgd')}, exclude=None) + assert_value_error(include={"c": ("adaboost", "sgd")}, exclude=None) # Empty list of the key in the include argument - assert_value_error(include={'c': []}) + assert_value_error(include={"c": []}) # Invalid component in the list value for the key in the include argument - assert_value_error(include={'c': ['pca']}) + assert_value_error(include={"c": ["pca"]}) # Case when all conditions passed for include and exclude - base.include = {'c': ['adaboost', 'sgd']} - base.exclude = {'p1': ['pca']} + base.include = {"c": ["adaboost", "sgd"]} + base.exclude = {"p1": ["pca"]} self.assertIsNone(base._validate_include_exclude_params()) diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index 5fb14eca8b..c214e10cfd 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -8,29 +8,37 @@ import unittest import unittest.mock -from joblib import Memory import numpy as np - -from sklearn.base import clone import sklearn.datasets import sklearn.decomposition -import sklearn.model_selection import sklearn.ensemble +import sklearn.model_selection import sklearn.svm -from sklearn.utils.validation import check_is_fitted - from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter +from joblib import Memory +from sklearn.base import clone +from sklearn.utils.validation import check_is_fitted -from autosklearn.pipeline.classification import SimpleClassificationPipeline -from autosklearn.pipeline.components.base import \ - AutoSklearnClassificationAlgorithm, AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.components.base import AutoSklearnComponent, AutoSklearnChoice, _addons import autosklearn.pipeline.components.classification as classification_components import autosklearn.pipeline.components.feature_preprocessing as preprocessing_components +from autosklearn.pipeline.classification import SimpleClassificationPipeline +from autosklearn.pipeline.components.base import ( + AutoSklearnChoice, + AutoSklearnClassificationAlgorithm, + AutoSklearnComponent, + AutoSklearnPreprocessingAlgorithm, + _addons, +) +from autosklearn.pipeline.constants import ( + DENSE, + INPUT, + PREDICTIONS, + SIGNED_DATA, + SPARSE, + UNSIGNED_DATA, +) from autosklearn.pipeline.util import get_dataset -from autosklearn.pipeline.constants import \ - DENSE, SPARSE, UNSIGNED_DATA, PREDICTIONS, SIGNED_DATA, INPUT from test.test_pipeline.ignored_warnings import classifier_warnings, ignore_warnings @@ -38,16 +46,18 @@ class DummyClassifier(AutoSklearnClassificationAlgorithm): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'AB', - 'name': 'AdaBoost Classifier', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (PREDICTIONS,)} + return { + "shortname": "AB", + "name": "AdaBoost Classifier", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (PREDICTIONS,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): @@ -58,16 +68,18 @@ def get_hyperparameter_search_space(dataset_properties=None): class DummyPreprocessor(AutoSklearnPreprocessingAlgorithm): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'AB', - 'name': 'AdaBoost Classifier', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,)} + return { + "shortname": "AB", + "name": "AdaBoost Classifier", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): @@ -81,16 +93,18 @@ def __init__(*args, **kwargs): @staticmethod def get_properties(dataset_properties=None): - return {'shortname': 'AB', - 'name': 'AdaBoost Classifier', - 'handles_regression': False, - 'handles_classification': True, - 'handles_multiclass': True, - 'handles_multilabel': True, - 'handles_multioutput': False, - 'is_deterministic': True, - 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,)} + return { + "shortname": "AB", + "name": "AdaBoost Classifier", + "handles_regression": False, + "handles_classification": True, + "handles_multiclass": True, + "handles_multilabel": True, + "handles_multioutput": False, + "is_deterministic": True, + "input": (DENSE, SPARSE, UNSIGNED_DATA), + "output": (INPUT,), + } def fit(self, X, y): raise ValueError("Make sure fit is called") @@ -116,21 +130,21 @@ def test_io_dict(self): if classifiers[c] == classification_components.ClassifierChoice: continue props = classifiers[c].get_properties() - self.assertIn('input', props) - self.assertIn('output', props) - inp = props['input'] - output = props['output'] + self.assertIn("input", props) + self.assertIn("output", props) + inp = props["input"] + output = props["output"] self.assertIsInstance(inp, tuple) self.assertIsInstance(output, tuple) for i in inp: self.assertIn(i, (SPARSE, DENSE, SIGNED_DATA, UNSIGNED_DATA)) self.assertEqual(output, (PREDICTIONS,)) - self.assertIn('handles_regression', props) - self.assertFalse(props['handles_regression']) - self.assertIn('handles_classification', props) - self.assertIn('handles_multiclass', props) - self.assertIn('handles_multilabel', props) + self.assertIn("handles_regression", props) + self.assertFalse(props["handles_regression"]) + self.assertIn("handles_classification", props) + self.assertIn("handles_multiclass", props) + self.assertIn("handles_multilabel", props) def test_find_classifiers(self): """Test that the classifier components can be found @@ -143,9 +157,11 @@ def test_find_classifiers(self): classifiers = classification_components._classifiers self.assertGreaterEqual(len(classifiers), 2) for key in classifiers: - if hasattr(classifiers[key], 'get_components'): + if hasattr(classifiers[key], "get_components"): continue - self.assertIn(AutoSklearnClassificationAlgorithm, classifiers[key].__bases__) + self.assertIn( + AutoSklearnClassificationAlgorithm, classifiers[key].__bases__ + ) def test_find_preprocessors(self): """Test that preproccesor components can be found @@ -156,20 +172,23 @@ def test_find_preprocessors(self): * The inherit from AutoSklearnPreprocessingAlgorithm """ preprocessors = preprocessing_components._preprocessors - self.assertGreaterEqual(len(preprocessors), 1) + self.assertGreaterEqual(len(preprocessors), 1) for key in preprocessors: - if hasattr(preprocessors[key], 'get_components'): + if hasattr(preprocessors[key], "get_components"): continue - self.assertIn(AutoSklearnPreprocessingAlgorithm, preprocessors[key].__bases__) + self.assertIn( + AutoSklearnPreprocessingAlgorithm, preprocessors[key].__bases__ + ) def test_default_configuration(self): """Test that seeded SimpleClassificaitonPipeline returns good results on iris Expects ------- - * The performance of configuration with fixed seed gets above 96% accuracy on iris + * The performance of configuration with fixed seed gets above 96% accuracy + on iris """ - X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris') + X_train, Y_train, X_test, Y_test = get_dataset(dataset="iris") auto = SimpleClassificationPipeline(random_state=1) @@ -190,11 +209,12 @@ def test_default_configuration_multilabel(self): * The performance of a random configuratino gets above 96% on a multilabel version of iris """ - X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris', make_multilabel=True) + X_train, Y_train, X_test, Y_test = get_dataset( + dataset="iris", make_multilabel=True + ) classifier = SimpleClassificationPipeline( - dataset_properties={'multilabel': True}, - random_state=0 + dataset_properties={"multilabel": True}, random_state=0 ) cs = classifier.get_hyperparameter_search_space() @@ -218,14 +238,14 @@ def test_default_configuration_iterative_fit(self): * Random forest pipeline can be fit iteratively * Test that its number of estimators is equal to the iteration count """ - X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris') + X_train, Y_train, X_test, Y_test = get_dataset(dataset="iris") classifier = SimpleClassificationPipeline( include={ - 'classifier': ['random_forest'], - 'feature_preprocessor': ['no_preprocessing'] + "classifier": ["random_forest"], + "feature_preprocessor": ["no_preprocessing"], }, - random_state=0 + random_state=0, ) classifier.fit_transformer(X_train, Y_train) @@ -256,9 +276,7 @@ def test_multilabel(self): * All configurations should fit, predict and predict_proba successfully """ cache = Memory(location=tempfile.gettempdir()) - cached_func = cache.cache( - sklearn.datasets.make_multilabel_classification - ) + cached_func = cache.cache(sklearn.datasets.make_multilabel_classification) X, Y = cached_func( n_samples=150, n_features=20, @@ -269,14 +287,16 @@ def test_multilabel(self): sparse=False, return_indicator=True, return_distributions=False, - random_state=1 + random_state=1, ) data = { - 'X_train': X[:100, :], - 'Y_train': Y[:100, :], - 'X_test': X[101:, :], - 'Y_test': Y[101:, ] + "X_train": X[:100, :], + "Y_train": Y[:100, :], + "X_test": X[101:, :], + "Y_test": Y[ + 101:, + ], } pipeline = SimpleClassificationPipeline(dataset_properties={"multilabel": True}) @@ -301,12 +321,14 @@ def test_configurations_signed_data(self): ------- * All configurations should fit, predict and predict_proba successfully """ - dataset_properties = {'signed': True} + dataset_properties = {"signed": True} cls = SimpleClassificationPipeline(dataset_properties=dataset_properties) cs = cls.get_hyperparameter_search_space() - self._test_configurations(configurations_space=cs, dataset_properties=dataset_properties) + self._test_configurations( + configurations_space=cs, dataset_properties=dataset_properties + ) def test_configurations_sparse(self): """Tests a non-seeded random set of configurations with sparse data @@ -315,7 +337,7 @@ def test_configurations_sparse(self): ------- * All configurations should fit, predict and predict_proba successfully """ - pipeline = SimpleClassificationPipeline(dataset_properties={'sparse': True}) + pipeline = SimpleClassificationPipeline(dataset_properties={"sparse": True}) cs = pipeline.get_hyperparameter_search_space() self._test_configurations(configurations_space=cs, make_sparse=True) @@ -330,41 +352,89 @@ def test_configurations_categorical_data(self): * All configurations should fit, predict and predict_proba successfully """ pipeline = SimpleClassificationPipeline( - dataset_properties={'sparse': False}, + dataset_properties={"sparse": False}, include={ - 'feature_preprocessor': ['no_preprocessing'], - 'classifier': ['sgd', 'adaboost'] - } + "feature_preprocessor": ["no_preprocessing"], + "classifier": ["sgd", "adaboost"], + }, ) cs = pipeline.get_hyperparameter_search_space() categorical_columns = [ - True, True, True, False, False, True, True, True, False, True, True, True, True, - True, True, True, True, True, True, True, True, True, True, True, True, True, - True, True, True, True, True, True, False, False, False, True, True, True + True, + True, + True, + False, + False, + True, + True, + True, + False, + True, + True, + True, + True, + True, + True, + True, + True, + True, + True, + True, + True, + True, + True, + True, + True, + True, + True, + True, + True, + True, + True, + True, + False, + False, + False, + True, + True, + True, ] categorical = { - i: 'categorical' if is_categorical else 'numerical' + i: "categorical" if is_categorical else "numerical" for i, is_categorical in enumerate(categorical_columns) } here = os.path.dirname(__file__) - dataset_path = os.path.join(here, "components", "data_preprocessing", "dataset.pkl") + dataset_path = os.path.join( + here, "components", "data_preprocessing", "dataset.pkl" + ) X = np.loadtxt(dataset_path) y = X[:, -1].copy() X = X[:, :-1] - X_train, X_test, Y_train, Y_test = sklearn.model_selection.train_test_split(X, y) + X_train, X_test, Y_train, Y_test = sklearn.model_selection.train_test_split( + X, y + ) - data = {'X_train': X_train, 'Y_train': Y_train, 'X_test': X_test, 'Y_test': Y_test} + data = { + "X_train": X_train, + "Y_train": Y_train, + "X_test": X_test, + "Y_test": Y_test, + } - init_params = {'data_preprocessor:feat_type': categorical} + init_params = {"data_preprocessor:feat_type": categorical} - self._test_configurations(configurations_space=cs, dataset=data, init_params=init_params) + self._test_configurations( + configurations_space=cs, dataset=data, init_params=init_params + ) - @unittest.mock.patch('autosklearn.pipeline.components.data_preprocessing' - '.DataPreprocessorChoice.set_hyperparameters') + @unittest.mock.patch( + "autosklearn.pipeline.components.data_preprocessing" + ".DataPreprocessorChoice.set_hyperparameters" + ) def test_categorical_passed_to_one_hot_encoder(self, ohe_mock): """Test that the feat_types arg is passed to the OneHotEncoder @@ -379,36 +449,38 @@ def test_categorical_passed_to_one_hot_encoder(self, ohe_mock): # Mock the _check_init_params_honored as there is no object created, # _check_init_params_honored will fail as a datapreprocessor was never created - with unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline' - '._check_init_params_honored'): + with unittest.mock.patch( + "autosklearn.pipeline.classification.SimpleClassificationPipeline" + "._check_init_params_honored" + ): # Check through construction - feat_types = {0: 'categorical', 1: 'numerical'} + feat_types = {0: "categorical", 1: "numerical"} cls = SimpleClassificationPipeline( - init_params={'data_preprocessor:feat_type': feat_types} + init_params={"data_preprocessor:feat_type": feat_types} ) - init_args = ohe_mock.call_args[1]['init_params'] - self.assertEqual(init_args, {'feat_type': feat_types}) + init_args = ohe_mock.call_args[1]["init_params"] + self.assertEqual(init_args, {"feat_type": feat_types}) # Check through `set_hyperparameters` - feat_types = {0: 'categorical', 1: 'categorical', 2: 'numerical'} + feat_types = {0: "categorical", 1: "categorical", 2: "numerical"} default = cls.get_hyperparameter_search_space().get_default_configuration() cls.set_hyperparameters( configuration=default, - init_params={'data_preprocessor:feat_type': feat_types}, + init_params={"data_preprocessor:feat_type": feat_types}, ) - init_args = ohe_mock.call_args[1]['init_params'] - self.assertEqual(init_args, {'feat_type': feat_types}) + init_args = ohe_mock.call_args[1]["init_params"] + self.assertEqual(init_args, {"feat_type": feat_types}) def _test_configurations( self, configurations_space: ConfigurationSpace, make_sparse: bool = False, - dataset: Union[str, Dict[str, Any]] = 'digits', + dataset: Union[str, Dict[str, Any]] = "digits", init_params: Dict[str, Any] = None, dataset_properties: Dict[str, Any] = None, n_samples: int = 10, @@ -448,53 +520,55 @@ def _test_configurations( config._populate_values() # Restrict configurations which could take too long on travis-ci - restrictions = {'classifier:passive_aggressive:n_iter': 5, - 'classifier:sgd:n_iter': 5, - 'classifier:adaboost:n_estimators': 50, - 'classifier:adaboost:max_depth': 1, - 'feature_preprocessor:kernel_pca:n_components': 10, - 'feature_preprocessor:kitchen_sinks:n_components': 50, - 'classifier:proj_logit:max_epochs': 1, - 'classifier:libsvm_svc:degree': 2, - 'regressor:libsvm_svr:degree': 2, - 'feature_preprocessor:truncatedSVD:target_dim': 10, - 'feature_preprocessor:polynomial:degree': 2, - 'classifier:lda:n_components': 10, - 'feature_preprocessor:nystroem_sampler:n_components': 50, - 'feature_preprocessor:feature_agglomeration:n_clusters': 2, - 'classifier:gradient_boosting:max_leaf_nodes': 64} - - config._values.update({ - param: value - for param, value in restrictions.items() - if param in config and config[param] is not None - }) + restrictions = { + "classifier:passive_aggressive:n_iter": 5, + "classifier:sgd:n_iter": 5, + "classifier:adaboost:n_estimators": 50, + "classifier:adaboost:max_depth": 1, + "feature_preprocessor:kernel_pca:n_components": 10, + "feature_preprocessor:kitchen_sinks:n_components": 50, + "classifier:proj_logit:max_epochs": 1, + "classifier:libsvm_svc:degree": 2, + "regressor:libsvm_svr:degree": 2, + "feature_preprocessor:truncatedSVD:target_dim": 10, + "feature_preprocessor:polynomial:degree": 2, + "classifier:lda:n_components": 10, + "feature_preprocessor:nystroem_sampler:n_components": 50, + "feature_preprocessor:feature_agglomeration:n_clusters": 2, + "classifier:gradient_boosting:max_leaf_nodes": 64, + } + + config._values.update( + { + param: value + for param, value in restrictions.items() + if param in config and config[param] is not None + } + ) if isinstance(dataset, str): X_train, Y_train, X_test, Y_test = get_dataset( - dataset=dataset, - make_sparse=make_sparse, - add_NaNs=True + dataset=dataset, make_sparse=make_sparse, add_NaNs=True ) else: - X_train = dataset['X_train'].copy() - Y_train = dataset['Y_train'].copy() - X_test = dataset['X_test'].copy() - dataset['Y_test'].copy() + X_train = dataset["X_train"].copy() + Y_train = dataset["Y_train"].copy() + X_test = dataset["X_test"].copy() + dataset["Y_test"].copy() init_params_ = copy.deepcopy(init_params) cls = SimpleClassificationPipeline( - dataset_properties=dataset_properties, - init_params=init_params_ + dataset_properties=dataset_properties, init_params=init_params_ ) cls.set_hyperparameters(config, init_params=init_params_) # First make sure that for this configuration, setting the parameters # does not mistakenly set the estimator as fitted for name, step in cls.named_steps.items(): - with self.assertRaisesRegex(sklearn.exceptions.NotFittedError, - "instance is not fitted yet"): + with self.assertRaisesRegex( + sklearn.exceptions.NotFittedError, "instance is not fitted yet" + ): check_is_fitted(step) try: @@ -526,15 +600,17 @@ def _test_configurations( continue elif "Numerical problems in QDA" in e.args[0]: continue - elif 'Bug in scikit-learn' in e.args[0]: + elif "Bug in scikit-learn" in e.args[0]: continue - elif 'The condensed distance matrix must contain only finite ' \ - 'values.' in e.args[0]: + elif ( + "The condensed distance matrix must contain only finite " + "values." in e.args[0] + ): continue - elif 'Internal work array size computation failed' in e.args[0]: + elif "Internal work array size computation failed" in e.args[0]: continue # Assumed to be caused by knn with preprocessor fast_ica with whiten - elif 'Input contains NaN, infinity or a value too large' in e.args[0]: + elif "Input contains NaN, infinity or a value too large" in e.args[0]: continue elif "zero-size array to reduction operation maximum which has no " \ "identity" in e.args[0]: @@ -584,14 +660,18 @@ def test_get_hyperparameter_search_space(self): cs = pipeline.get_hyperparameter_search_space() self.assertIsInstance(cs, ConfigurationSpace) - rescale_param = 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__' + rescale_param = ( + "data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__" + ) n_choices = len(cs.get_hyperparameter(rescale_param).choices) self.assertEqual(n_choices, 7) - n_classifiers = len(cs.get_hyperparameter('classifier:__choice__').choices) + n_classifiers = len(cs.get_hyperparameter("classifier:__choice__").choices) self.assertEqual(n_classifiers, 16) - n_preprocessors = len(cs.get_hyperparameter('feature_preprocessor:__choice__').choices) + n_preprocessors = len( + cs.get_hyperparameter("feature_preprocessor:__choice__").choices + ) self.assertEqual(n_preprocessors, 13) hyperparameters = cs.get_hyperparameters() @@ -613,69 +693,71 @@ def test_get_hyperparameter_search_space_include_exclude_models(self): Expects ------- - * Including a classifier choice has pipeline give back matching choice - * Excluding a classifier choice means it won't show up in the hyperparameter space + * Including a classifier has pipeline give back matching choice + * Excluding a classifier means it won't show up in the hyperparameter space * Including a feature preprocessor has pipeline give back matching choice - * Excluding a feature preprocessor means it won't show up in the hyperparameter space + * Excluding a feature preprocessor means it won't show up in the + hyperparameter space """ # include a classifier choice - pipeline = SimpleClassificationPipeline(include={'classifier': ['libsvm_svc']}) + pipeline = SimpleClassificationPipeline(include={"classifier": ["libsvm_svc"]}) cs = pipeline.get_hyperparameter_search_space() - expected = CategoricalHyperparameter('classifier:__choice__', ['libsvm_svc']) - returned = cs.get_hyperparameter('classifier:__choice__') + expected = CategoricalHyperparameter("classifier:__choice__", ["libsvm_svc"]) + returned = cs.get_hyperparameter("classifier:__choice__") self.assertEqual(returned, expected) # exclude a classifier choice - pipeline = SimpleClassificationPipeline(exclude={'classifier': ['libsvm_svc']}) + pipeline = SimpleClassificationPipeline(exclude={"classifier": ["libsvm_svc"]}) cs = pipeline.get_hyperparameter_search_space() - self.assertNotIn('libsvm_svc', str(cs)) + self.assertNotIn("libsvm_svc", str(cs)) # include a feature preprocessor pipeline = SimpleClassificationPipeline( - include={'feature_preprocessor': ['select_percentile_classification']} + include={"feature_preprocessor": ["select_percentile_classification"]} ) cs = pipeline.get_hyperparameter_search_space() - returned = cs.get_hyperparameter('feature_preprocessor:__choice__') + returned = cs.get_hyperparameter("feature_preprocessor:__choice__") expected = CategoricalHyperparameter( - 'feature_preprocessor:__choice__', - ['select_percentile_classification'] + "feature_preprocessor:__choice__", ["select_percentile_classification"] ) self.assertEqual(returned, expected) # exclude a feature preprocessor pipeline = SimpleClassificationPipeline( - exclude={'feature_preprocessor': ['select_percentile_classification']} + exclude={"feature_preprocessor": ["select_percentile_classification"]} ) cs = pipeline.get_hyperparameter_search_space() - self.assertNotIn('select_percentile_classification', str(cs)) + self.assertNotIn("select_percentile_classification", str(cs)) - def test_get_hyperparameter_search_space_preprocessor_contradicts_default_classifier(self): + def test_get_hyperparameter_search_space_preprocessor_contradicts_default( + self, + ): """Test that the default classifier gets updated based on the legal feature preprocessors that come before. Expects ------- - * With 'densifier' as only legal feature_preprocessor, 'qda' is default classifier - * With 'nystroem_sampler' as only legal feature_preprocessor, 'sgd' is default classifier + * With 'densifier' as only legal feature_preprocessor, 'qda' is default + * With 'nystroem_sampler' as only legal feature_preprocessor, 'sgd' is default """ pipeline = SimpleClassificationPipeline( - include={'feature_preprocessor': ['densifier']}, - dataset_properties={'sparse': True} + include={"feature_preprocessor": ["densifier"]}, + dataset_properties={"sparse": True}, ) cs = pipeline.get_hyperparameter_search_space() - default_choice = cs.get_hyperparameter('classifier:__choice__').default_value - self.assertEqual(default_choice, 'qda') + default_choice = cs.get_hyperparameter("classifier:__choice__").default_value + self.assertEqual(default_choice, "qda") pipeline = SimpleClassificationPipeline( - include={'feature_preprocessor': ['nystroem_sampler']} + include={"feature_preprocessor": ["nystroem_sampler"]} ) cs = pipeline.get_hyperparameter_search_space() - default_choice = cs.get_hyperparameter('classifier:__choice__').default_value - self.assertEqual(default_choice, 'sgd') + default_choice = cs.get_hyperparameter("classifier:__choice__").default_value + self.assertEqual(default_choice, "sgd") def test_get_hyperparameter_search_space_only_forbidden_combinations(self): """Test that invalid pipeline configurations raise errors @@ -689,43 +771,48 @@ def test_get_hyperparameter_search_space_only_forbidden_combinations(self): with self.assertRaisesRegex(AssertionError, "No valid pipeline found."): SimpleClassificationPipeline( include={ - 'classifier': ['multinomial_nb'], - 'feature_preprocessor': ['pca'] + "classifier": ["multinomial_nb"], + "feature_preprocessor": ["pca"], }, - dataset_properties={'sparse': True} + dataset_properties={"sparse": True}, ) - with self.assertRaisesRegex(ValueError, "Cannot find a legal default configuration."): + with self.assertRaisesRegex( + ValueError, "Cannot find a legal default configuration." + ): SimpleClassificationPipeline( include={ - 'classifier': ['liblinear_svc'], - 'feature_preprocessor': ['densifier'] + "classifier": ["liblinear_svc"], + "feature_preprocessor": ["densifier"], }, - dataset_properties={'sparse': True} + dataset_properties={"sparse": True}, ) @unittest.skip("Wait until ConfigSpace is fixed.") def test_get_hyperparameter_search_space_dataset_properties(self): cs_mc = SimpleClassificationPipeline.get_hyperparameter_search_space( - dataset_properties={'multiclass': True} + dataset_properties={"multiclass": True} ) - self.assertNotIn('bernoulli_nb', str(cs_mc)) + self.assertNotIn("bernoulli_nb", str(cs_mc)) cs_ml = SimpleClassificationPipeline.get_hyperparameter_search_space( - dataset_properties={'multilabel': True}) - self.assertNotIn('k_nearest_neighbors', str(cs_ml)) - self.assertNotIn('liblinear', str(cs_ml)) - self.assertNotIn('libsvm_svc', str(cs_ml)) - self.assertNotIn('sgd', str(cs_ml)) + dataset_properties={"multilabel": True} + ) + self.assertNotIn("k_nearest_neighbors", str(cs_ml)) + self.assertNotIn("liblinear", str(cs_ml)) + self.assertNotIn("libsvm_svc", str(cs_ml)) + self.assertNotIn("sgd", str(cs_ml)) cs_sp = SimpleClassificationPipeline.get_hyperparameter_search_space( - dataset_properties={'sparse': True}) - self.assertIn('extra_trees', str(cs_sp)) - self.assertIn('gradient_boosting', str(cs_sp)) - self.assertIn('random_forest', str(cs_sp)) + dataset_properties={"sparse": True} + ) + self.assertIn("extra_trees", str(cs_sp)) + self.assertIn("gradient_boosting", str(cs_sp)) + self.assertIn("random_forest", str(cs_sp)) cs_mc_ml = SimpleClassificationPipeline.get_hyperparameter_search_space( - dataset_properties={'multilabel': True, 'multiclass': True}) + dataset_properties={"multilabel": True, "multiclass": True} + ) self.assertEqual(cs_ml, cs_mc_ml) def test_predict_batched(self): @@ -736,12 +823,13 @@ def test_predict_batched(self): ------- * Should expect the output shape to match that of the digits dataset * Should expect a fixed call count each test run - * Should expect predict_proba with `batches` and predict_proba perform near identically + * Should expect predict_proba with `batches` and predict_proba + perform near identically """ - cls = SimpleClassificationPipeline(include={'classifier': ['sgd']}) + cls = SimpleClassificationPipeline(include={"classifier": ["sgd"]}) # Multiclass - X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits') + X_train, Y_train, X_test, Y_test = get_dataset(dataset="digits") with ignore_warnings(classifier_warnings): cls.fit(X_train, Y_train) @@ -767,15 +855,17 @@ def test_predict_batched_sparse(self): ------- * Should expect the output shape to match that of the digits dataset * Should expect a fixed call count each test run - * Should expect predict_proba with `batches` and predict_proba perform near identically + * Should expect predict_proba with `batches` and predict_proba + perform near identically """ cls = SimpleClassificationPipeline( - dataset_properties={'sparse': True}, - include={'classifier': ['sgd']} + dataset_properties={"sparse": True}, include={"classifier": ["sgd"]} ) # Multiclass - X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', make_sparse=True) + X_train, Y_train, X_test, Y_test = get_dataset( + dataset="digits", make_sparse=True + ) with ignore_warnings(classifier_warnings): cls.fit(X_train, Y_train) @@ -800,11 +890,12 @@ def test_predict_proba_batched(self): ------- * Should expect the output shape to match that of the digits dataset * Should expect a fixed call count each test run - * Should expect predict_proba with `batches` and predict_proba perform near identically + * Should expect predict_proba with `batches` and predict_proba + perform near identically """ # Multiclass - cls = SimpleClassificationPipeline(include={'classifier': ['sgd']}) - X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits') + cls = SimpleClassificationPipeline(include={"classifier": ["sgd"]}) + X_train, Y_train, X_test, Y_test = get_dataset(dataset="digits") with ignore_warnings(classifier_warnings): cls.fit(X_train, Y_train) @@ -823,10 +914,11 @@ def test_predict_proba_batched(self): np.testing.assert_array_almost_equal(prediction_, prediction) # Multilabel - cls = SimpleClassificationPipeline(include={'classifier': ['lda']}) - X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits') - Y_train = np.array(list([(list([1 if i != y else 0 for i in range(10)])) - for y in Y_train])) + cls = SimpleClassificationPipeline(include={"classifier": ["lda"]}) + X_train, Y_train, X_test, Y_test = get_dataset(dataset="digits") + Y_train = np.array( + list([(list([1 if i != y else 0 for i in range(10)])) for y in Y_train]) + ) with ignore_warnings(classifier_warnings): cls.fit(X_train, Y_train) @@ -852,15 +944,18 @@ def test_predict_proba_batched_sparse(self): ------- * Should expect the output shape to match that of the digits dataset * Should expect a fixed call count each test run - * Should expect predict_proba with `batches` and predict_proba perform near identically + * Should expect predict_proba with `batches` and predict_proba + perform near identically """ cls = SimpleClassificationPipeline( - dataset_properties={'sparse': True, 'multiclass': True}, - include={'classifier': ['sgd']} + dataset_properties={"sparse": True, "multiclass": True}, + include={"classifier": ["sgd"]}, ) # Multiclass - X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', make_sparse=True) + X_train, Y_train, X_test, Y_test = get_dataset( + dataset="digits", make_sparse=True + ) X_test_ = X_test.copy() with ignore_warnings(classifier_warnings): @@ -880,10 +975,12 @@ def test_predict_proba_batched_sparse(self): # Multilabel cls = SimpleClassificationPipeline( - dataset_properties={'sparse': True, 'multilabel': True}, - include={'classifier': ['lda']} + dataset_properties={"sparse": True, "multilabel": True}, + include={"classifier": ["lda"]}, + ) + X_train, Y_train, X_test, Y_test = get_dataset( + dataset="digits", make_sparse=True ) - X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', make_sparse=True) X_test_ = X_test.copy() Y_train = np.array([[1 if i != y else 0 for i in range(10)] for y in Y_train]) @@ -912,7 +1009,7 @@ def test_pipeline_clonability(self): * The cloned object can be constructed from theses params * The reconstructed clone and the original have the same param values """ - X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris') + X_train, Y_train, X_test, Y_test = get_dataset(dataset="iris") auto = SimpleClassificationPipeline() @@ -955,18 +1052,24 @@ def test_add_classifier(self): * There should be 1 component after adding a classifier * The classifier should be in the search space of the Pipeline after being added """ - self.assertEqual(len(classification_components.additional_components.components), 0) - self.assertEqual(len(_addons['classification'].components), 0) + self.assertEqual( + len(classification_components.additional_components.components), 0 + ) + self.assertEqual(len(_addons["classification"].components), 0) classification_components.add_classifier(DummyClassifier) - self.assertEqual(len(classification_components.additional_components.components), 1) - self.assertEqual(len(_addons['classification'].components), 1) + self.assertEqual( + len(classification_components.additional_components.components), 1 + ) + self.assertEqual(len(_addons["classification"].components), 1) cs = SimpleClassificationPipeline().get_hyperparameter_search_space() - self.assertIn('DummyClassifier', str(cs)) + self.assertIn("DummyClassifier", str(cs)) - del classification_components.additional_components.components['DummyClassifier'] + del classification_components.additional_components.components[ + "DummyClassifier" + ] def test_add_preprocessor(self): """Test that preprocessors can be added @@ -975,22 +1078,30 @@ def test_add_preprocessor(self): ------- * There should be 0 components initially * There should be 1 component after adding a preprocessor - * The preprocessor should be in the search space of the Pipeline after being added + * The preprocessor ii in the search space of the Pipeline after being added """ - self.assertEqual(len(preprocessing_components.additional_components.components), 0) - self.assertEqual(len(_addons['feature_preprocessing'].components), 0) + self.assertEqual( + len(preprocessing_components.additional_components.components), 0 + ) + self.assertEqual(len(_addons["feature_preprocessing"].components), 0) preprocessing_components.add_preprocessor(DummyPreprocessor) - self.assertEqual(len(preprocessing_components.additional_components.components), 1) - self.assertEqual(len(_addons['feature_preprocessing'].components), 1) + self.assertEqual( + len(preprocessing_components.additional_components.components), 1 + ) + self.assertEqual(len(_addons["feature_preprocessing"].components), 1) cs = SimpleClassificationPipeline().get_hyperparameter_search_space() - self.assertIn('DummyPreprocessor', str(cs)) + self.assertIn("DummyPreprocessor", str(cs)) - del preprocessing_components.additional_components.components['DummyPreprocessor'] + del preprocessing_components.additional_components.components[ + "DummyPreprocessor" + ] - def _test_set_hyperparameter_choice(self, expected_key, implementation, config_dict): + def _test_set_hyperparameter_choice( + self, expected_key, implementation, config_dict + ): """Given a configuration in config, this procedure makes sure that the given implementation, which should be a Choice component, honors the type of the object, and any hyperparameter associated to it @@ -1004,14 +1115,16 @@ def _test_set_hyperparameter_choice(self, expected_key, implementation, config_d # Are there further hyperparams? # A choice component might have attribute requirements that we need to check - expected_sub_key = expected_key.replace(':__choice__', ':') + implementation_type + expected_sub_key = ( + expected_key.replace(":__choice__", ":") + implementation_type + ) expected_attributes = {} - if 'data_preprocessor:__choice__' in expected_key: + if "data_preprocessor:__choice__" in expected_key: # We have to check both the numerical and categorical to_check = { - 'numerical_transformer': implementation.choice.numer_ppl.named_steps, - 'categorical_transformer': implementation.choice.categ_ppl.named_steps, - 'text_transformer': implementation.choice.txt_ppl.named_steps, + "numerical_transformer": implementation.choice.numer_ppl.named_steps, + "categorical_transformer": implementation.choice.categ_ppl.named_steps, + "text_transformer": implementation.choice.txt_ppl.named_steps, } for data_type, pipeline in to_check.items(): @@ -1019,8 +1132,7 @@ def _test_set_hyperparameter_choice(self, expected_key, implementation, config_d # If it is a Choice, make sure it is the correct one! if isinstance(sub_step, AutoSklearnChoice): key = "data_preprocessor:feature_type:{}:{}:__choice__".format( - data_type, - sub_name + data_type, sub_name ) keys_checked.extend( self._test_set_hyperparameter_choice( @@ -1032,10 +1144,10 @@ def _test_set_hyperparameter_choice(self, expected_key, implementation, config_d keys_checked.extend( self._test_set_hyperparameter_component( "data_preprocessor:feature_type:{}:{}".format( - data_type, - sub_name + data_type, sub_name ), - sub_step, config_dict + sub_step, + config_dict, ) ) else: @@ -1044,7 +1156,7 @@ def _test_set_hyperparameter_choice(self, expected_key, implementation, config_d for key, value in config_dict.items(): if key != expected_key and expected_sub_key in key: - expected_attributes[key.split(':')[-1]] = value + expected_attributes[key.split(":")[-1]] = value keys_checked.append(key) if expected_attributes: @@ -1056,7 +1168,9 @@ def _test_set_hyperparameter_choice(self, expected_key, implementation, config_d return keys_checked - def _test_set_hyperparameter_component(self, expected_key, implementation, config_dict): + def _test_set_hyperparameter_component( + self, expected_key, implementation, config_dict + ): """ Given a configuration in config, this procedure makes sure that the given implementation, which should be a autosklearn component, honors @@ -1070,15 +1184,14 @@ def _test_set_hyperparameter_component(self, expected_key, implementation, confi for key, value in config_dict.items(): if expected_key in key: keys_checked.append(key) - key = key.replace(expected_key + ':', '') - if ':' in key: - raise ValueError("This utility should only be called with a " - "matching string that produces leaf configurations, " - "that is no further colons are expected, yet key={}" - "".format( - key - ) - ) + key = key.replace(expected_key + ":", "") + if ":" in key: + raise ValueError( + "This utility should only be called with a " + "matching string that produces leaf configurations, " + "that is no further colons are expected, yet key={}" + "".format(key) + ) expected_attributes[key] = value # self.assertDictContainsSubset(expected_attributes, attributes) # Cannot check the whole dictionary, just names, as some @@ -1100,12 +1213,17 @@ def test_set_hyperparameters_honors_configuration(self): """ random_state = 1 all_combinations = list(itertools.product([True, False], repeat=4)) - for sparse, multilabel, signed, multiclass, in all_combinations: + for ( + sparse, + multilabel, + signed, + multiclass, + ) in all_combinations: dataset_properties = { - 'sparse': sparse, - 'multilabel': multilabel, - 'multiclass': multiclass, - 'signed': signed, + "sparse": sparse, + "multilabel": multilabel, + "multiclass": multiclass, + "signed": signed, } cls = SimpleClassificationPipeline( random_state=random_state, @@ -1124,36 +1242,37 @@ def test_set_hyperparameters_honors_configuration(self): keys_checked = [] for name, step in cls.named_steps.items(): - if name == 'data_preprocessor': + if name == "data_preprocessor": keys_checked.extend( self._test_set_hyperparameter_choice( - 'data_preprocessor:__choice__', step, config_dict + "data_preprocessor:__choice__", step, config_dict ) ) self.assertEqual(step.random_state, random_state) - elif name == 'balancing': + elif name == "balancing": keys_checked.extend( self._test_set_hyperparameter_component( - 'balancing', - step, config_dict + "balancing", step, config_dict ) ) - elif name == 'feature_preprocessor': + elif name == "feature_preprocessor": keys_checked.extend( self._test_set_hyperparameter_choice( - 'feature_preprocessor:__choice__', step, config_dict + "feature_preprocessor:__choice__", step, config_dict ) ) self.assertEqual(step.random_state, random_state) - elif name == 'classifier': + elif name == "classifier": keys_checked.extend( self._test_set_hyperparameter_choice( - 'classifier:__choice__', step, config_dict + "classifier:__choice__", step, config_dict ) ) self.assertEqual(step.random_state, random_state) else: - raise ValueError("Found another type of step! Need to update this check") + raise ValueError( + "Found another type of step! Need to update this check" + ) # Make sure we checked the whole configuration self.assertSetEqual(set(config_dict.keys()), set(keys_checked)) @@ -1165,18 +1284,18 @@ def test_fit_instantiates_component(self): # We reduce the search space as forbidden clauses prevent to instantiate # the user defined preprocessor manually - cls = SimpleClassificationPipeline( - include={'classifier': ['random_forest']} - ) + cls = SimpleClassificationPipeline(include={"classifier": ["random_forest"]}) cs = cls.get_hyperparameter_search_space() - self.assertIn('CrashPreprocessor', str(cs)) + self.assertIn("CrashPreprocessor", str(cs)) config = cs.sample_configuration() try: - config['feature_preprocessor:__choice__'] = 'CrashPreprocessor' + config["feature_preprocessor:__choice__"] = "CrashPreprocessor" except Exception as e: # In case of failure clean up the components and print enough information # to clean up with check in the future - del preprocessing_components.additional_components.components['CrashPreprocessor'] + del preprocessing_components.additional_components.components[ + "CrashPreprocessor" + ] self.fail("cs={} config={} Exception={}".format(cs, config, e)) cls.set_hyperparameters(config) @@ -1185,7 +1304,9 @@ def test_fit_instantiates_component(self): with ignore_warnings(classifier_warnings): cls.fit( X=np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]), - y=np.array([1, 0, 1, 1]) + y=np.array([1, 0, 1, 1]), ) - del preprocessing_components.additional_components.components['CrashPreprocessor'] + del preprocessing_components.additional_components.components[ + "CrashPreprocessor" + ] diff --git a/test/test_pipeline/test_create_searchspace_util_classification.py b/test/test_pipeline/test_create_searchspace_util_classification.py index 7bf1450979..a830430097 100644 --- a/test/test_pipeline/test_create_searchspace_util_classification.py +++ b/test/test_pipeline/test_create_searchspace_util_classification.py @@ -1,20 +1,23 @@ +import unittest from collections import OrderedDict -import unittest import numpy - from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter -from autosklearn.pipeline.components.classification.liblinear_svc import LibLinear_SVC +import autosklearn.pipeline.create_searchspace_util from autosklearn.pipeline.components.classification.lda import LDA - +from autosklearn.pipeline.components.classification.liblinear_svc import LibLinear_SVC +from autosklearn.pipeline.components.feature_preprocessing.no_preprocessing import ( + NoPreprocessing, +) from autosklearn.pipeline.components.feature_preprocessing.pca import PCA -from autosklearn.pipeline.components.feature_preprocessing.truncatedSVD import TruncatedSVD -from autosklearn.pipeline.components.feature_preprocessing.no_preprocessing import NoPreprocessing -from autosklearn.pipeline.components.feature_preprocessing.random_trees_embedding \ - import RandomTreesEmbedding -import autosklearn.pipeline.create_searchspace_util +from autosklearn.pipeline.components.feature_preprocessing.random_trees_embedding import ( # noqa: E501 + RandomTreesEmbedding, +) +from autosklearn.pipeline.components.feature_preprocessing.truncatedSVD import ( + TruncatedSVD, +) class TestCreateClassificationSearchspace(unittest.TestCase): @@ -23,9 +26,9 @@ class TestCreateClassificationSearchspace(unittest.TestCase): def test_get_match_array_sparse_and_dense(self): # preproc is empty preprocessors = OrderedDict() - preprocessors['pca'] = PCA + preprocessors["pca"] = PCA classifiers = OrderedDict() - classifiers['lda'] = LDA + classifiers["lda"] = LDA # Sparse + dense class Preprocessors(object): @@ -40,62 +43,69 @@ def get_available_components(self, *args, **kwargs): # Dense m = autosklearn.pipeline.create_searchspace_util.get_match_array( - pipeline=((0, PCA), (1, LDA)), dataset_properties={'sparse': True}) + pipeline=((0, PCA), (1, LDA)), dataset_properties={"sparse": True} + ) self.assertEqual(numpy.sum(m), 0) m = autosklearn.pipeline.create_searchspace_util.get_match_array( - pipeline=((0, PCA), (1, LDA)), dataset_properties={'sparse': False}) + pipeline=((0, PCA), (1, LDA)), dataset_properties={"sparse": False} + ) self.assertEqual(m, [[1]]) # Sparse - preprocessors['tSVD'] = TruncatedSVD + preprocessors["tSVD"] = TruncatedSVD m = autosklearn.pipeline.create_searchspace_util.get_match_array( - pipeline=((0, Preprocessors), (1, LDA)), - dataset_properties={'sparse': True}) + pipeline=((0, Preprocessors), (1, LDA)), dataset_properties={"sparse": True} + ) self.assertEqual(m[0], [0]) # pca self.assertEqual(m[1], [1]) # svd m = autosklearn.pipeline.create_searchspace_util.get_match_array( pipeline=((0, Preprocessors), (1, LDA)), - dataset_properties={'sparse': False}) + dataset_properties={"sparse": False}, + ) self.assertEqual(m[0], [1]) # pca self.assertEqual(m[1], [0]) # svd - preprocessors['none'] = NoPreprocessing + preprocessors["none"] = NoPreprocessing m = autosklearn.pipeline.create_searchspace_util.get_match_array( - pipeline=((0, Preprocessors), (1, LDA)), - dataset_properties={'sparse': True}) + pipeline=((0, Preprocessors), (1, LDA)), dataset_properties={"sparse": True} + ) self.assertEqual(m[0, :], [0]) # pca self.assertEqual(m[1, :], [1]) # tsvd self.assertEqual(m[2, :], [0]) # none m = autosklearn.pipeline.create_searchspace_util.get_match_array( pipeline=((0, Preprocessors), (1, LDA)), - dataset_properties={'sparse': False}) + dataset_properties={"sparse": False}, + ) self.assertEqual(m[0, :], [1]) # pca self.assertEqual(m[1, :], [0]) # tsvd self.assertEqual(m[2, :], [1]) # none - classifiers['libsvm'] = LibLinear_SVC + classifiers["libsvm"] = LibLinear_SVC m = autosklearn.pipeline.create_searchspace_util.get_match_array( pipeline=((0, Preprocessors), (1, Classifiers)), - dataset_properties={'sparse': False}) + dataset_properties={"sparse": False}, + ) self.assertListEqual(list(m[0, :]), [1, 1]) # pca self.assertListEqual(list(m[1, :]), [0, 0]) # tsvd self.assertListEqual(list(m[2, :]), [1, 1]) # none m = autosklearn.pipeline.create_searchspace_util.get_match_array( pipeline=((0, Preprocessors), (1, Classifiers)), - dataset_properties={'sparse': True}) + dataset_properties={"sparse": True}, + ) self.assertListEqual(list(m[0, :]), [0, 0]) # pca self.assertListEqual(list(m[1, :]), [1, 1]) # tsvd self.assertListEqual(list(m[2, :]), [0, 1]) # none # Do fancy 3d stuff - preprocessors['random_trees'] = RandomTreesEmbedding + preprocessors["random_trees"] = RandomTreesEmbedding m = autosklearn.pipeline.create_searchspace_util.get_match_array( pipeline=((0, Preprocessors), (1, Preprocessors), (2, Classifiers)), - dataset_properties={'sparse': False}) + dataset_properties={"sparse": False}, + ) # PCA followed by truncated SVD is forbidden self.assertEqual(list(m[0].flatten()), [1, 1, 0, 0, 1, 1, 0, 1]) # Truncated SVD is forbidden @@ -112,28 +122,38 @@ def test_get_match_array_signed_unsigned_and_binary(self): @unittest.skip("Not currently working.") def test_add_forbidden(self): m = numpy.ones([2, 3]) - preprocessors_list = ['pa', 'pb'] - classifier_list = ['ca', 'cb', 'cc'] + preprocessors_list = ["pa", "pb"] + classifier_list = ["ca", "cb", "cc"] cs = ConfigurationSpace() - preprocessor = CategoricalHyperparameter(name='feature_preprocessor', - choices=preprocessors_list) - classifier = CategoricalHyperparameter(name='classifier', - choices=classifier_list) + preprocessor = CategoricalHyperparameter( + name="feature_preprocessor", choices=preprocessors_list + ) + classifier = CategoricalHyperparameter( + name="classifier", choices=classifier_list + ) cs.add_hyperparameter(preprocessor) cs.add_hyperparameter(classifier) new_cs = autosklearn.pipeline.create_searchspace_util.add_forbidden( - conf_space=cs, node_0_list=preprocessors_list, - node_1_list=classifier_list, matches=m, - node_0_name='feature_preprocessor', node_1_name="classifier") + conf_space=cs, + node_0_list=preprocessors_list, + node_1_list=classifier_list, + matches=m, + node_0_name="feature_preprocessor", + node_1_name="classifier", + ) self.assertEqual(len(new_cs.forbidden_clauses), 0) self.assertIsInstance(new_cs, ConfigurationSpace) m[1, 1] = 0 new_cs = autosklearn.pipeline.create_searchspace_util.add_forbidden( - conf_space=cs, node_0_list=preprocessors_list, - node_1_list=classifier_list, matches=m, - node_0_name='feature_preprocessor', node_1_name="classifier") + conf_space=cs, + node_0_list=preprocessors_list, + node_1_list=classifier_list, + matches=m, + node_0_name="feature_preprocessor", + node_1_name="classifier", + ) self.assertEqual(len(new_cs.forbidden_clauses), 1) - self.assertEqual(new_cs.forbidden_clauses[0].components[0].value, 'cb') - self.assertEqual(new_cs.forbidden_clauses[0].components[1].value, 'pb') + self.assertEqual(new_cs.forbidden_clauses[0].components[0].value, "cb") + self.assertEqual(new_cs.forbidden_clauses[0].components[1].value, "pb") self.assertIsInstance(new_cs, ConfigurationSpace) diff --git a/test/test_pipeline/test_regression.py b/test/test_pipeline/test_regression.py index fccf59af67..501b73ec5d 100644 --- a/test/test_pipeline/test_regression.py +++ b/test/test_pipeline/test_regression.py @@ -5,28 +5,36 @@ import unittest import unittest.mock -from joblib import Memory import numpy as np import sklearn.datasets import sklearn.decomposition -from sklearn.base import clone import sklearn.ensemble import sklearn.svm -from sklearn.utils.validation import check_is_fitted - from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter +from joblib import Memory +from sklearn.base import clone +from sklearn.utils.validation import check_is_fitted -from autosklearn.pipeline.regression import SimpleRegressionPipeline -from autosklearn.pipeline.components.base import \ - AutoSklearnPreprocessingAlgorithm, AutoSklearnRegressionAlgorithm -import autosklearn.pipeline.components.regression as regression_components -from autosklearn.pipeline.components.base import AutoSklearnComponent, AutoSklearnChoice import autosklearn.pipeline.components.feature_preprocessing as preprocessing_components +import autosklearn.pipeline.components.regression as regression_components +from autosklearn.pipeline.components.base import ( + AutoSklearnChoice, + AutoSklearnComponent, + AutoSklearnPreprocessingAlgorithm, + AutoSklearnRegressionAlgorithm, +) +from autosklearn.pipeline.constants import ( + DENSE, + PREDICTIONS, + SIGNED_DATA, + SPARSE, + UNSIGNED_DATA, +) +from autosklearn.pipeline.regression import SimpleRegressionPipeline from autosklearn.pipeline.util import get_dataset -from autosklearn.pipeline.constants import SPARSE, DENSE, SIGNED_DATA, UNSIGNED_DATA, PREDICTIONS -from test.test_pipeline.ignored_warnings import regressor_warnings, ignore_warnings +from test.test_pipeline.ignored_warnings import ignore_warnings, regressor_warnings class SimpleRegressionPipelineTest(unittest.TestCase): @@ -38,41 +46,43 @@ def test_io_dict(self): if regressors[r] == regression_components.RegressorChoice: continue props = regressors[r].get_properties() - self.assertIn('input', props) - self.assertIn('output', props) - inp = props['input'] - output = props['output'] + self.assertIn("input", props) + self.assertIn("output", props) + inp = props["input"] + output = props["output"] self.assertIsInstance(inp, tuple) self.assertIsInstance(output, tuple) for i in inp: self.assertIn(i, (SPARSE, DENSE, SIGNED_DATA, UNSIGNED_DATA)) self.assertEqual(output, (PREDICTIONS,)) - self.assertIn('handles_regression', props) - self.assertTrue(props['handles_regression']) - self.assertIn('handles_classification', props) - self.assertIn('handles_multiclass', props) - self.assertIn('handles_multilabel', props) - self.assertIn('handles_multioutput', props) - self.assertFalse(props['handles_classification']) - self.assertFalse(props['handles_multiclass']) - self.assertFalse(props['handles_multilabel']) + self.assertIn("handles_regression", props) + self.assertTrue(props["handles_regression"]) + self.assertIn("handles_classification", props) + self.assertIn("handles_multiclass", props) + self.assertIn("handles_multilabel", props) + self.assertIn("handles_multioutput", props) + self.assertFalse(props["handles_classification"]) + self.assertFalse(props["handles_multiclass"]) + self.assertFalse(props["handles_multilabel"]) def test_find_regressors(self): regressors = regression_components._regressors self.assertGreaterEqual(len(regressors), 1) for key in regressors: - if hasattr(regressors[key], 'get_components'): + if hasattr(regressors[key], "get_components"): continue self.assertIn(AutoSklearnRegressionAlgorithm, regressors[key].__bases__) def test_find_preprocessors(self): preprocessors = preprocessing_components._preprocessors - self.assertGreaterEqual(len(preprocessors), 1) + self.assertGreaterEqual(len(preprocessors), 1) for key in preprocessors: - if hasattr(preprocessors[key], 'get_components'): + if hasattr(preprocessors[key], "get_components"): continue - self.assertIn(AutoSklearnPreprocessingAlgorithm, preprocessors[key].__bases__) + self.assertIn( + AutoSklearnPreprocessingAlgorithm, preprocessors[key].__bases__ + ) def test_configurations(self): cs = SimpleRegressionPipeline().get_hyperparameter_search_space() @@ -80,27 +90,28 @@ def test_configurations(self): self._test_configurations(cs) def test_configurations_signed_data(self): - dataset_properties = {'signed': True} - cs = SimpleRegressionPipeline(dataset_properties=dataset_properties).\ - get_hyperparameter_search_space() + dataset_properties = {"signed": True} + cs = SimpleRegressionPipeline( + dataset_properties=dataset_properties + ).get_hyperparameter_search_space() - self._test_configurations(configurations_space=cs, - dataset_properties=dataset_properties) + self._test_configurations( + configurations_space=cs, dataset_properties=dataset_properties + ) def test_configurations_sparse(self): - dataset_properties = {'sparse': True} + dataset_properties = {"sparse": True} cs = SimpleRegressionPipeline( dataset_properties=dataset_properties ).get_hyperparameter_search_space() - self._test_configurations(cs, make_sparse=True, - dataset_properties=dataset_properties) + self._test_configurations( + cs, make_sparse=True, dataset_properties=dataset_properties + ) def test_multioutput(self): cache = Memory(location=tempfile.gettempdir()) - cached_func = cache.cache( - sklearn.datasets.make_regression - ) + cached_func = cache.cache(sklearn.datasets.make_regression) X, Y = cached_func( n_samples=250, n_features=20, @@ -112,24 +123,33 @@ def test_multioutput(self): noise=0.3, shuffle=True, coef=False, - random_state=1 + random_state=1, ) X_train = X[:200, :] Y_train = Y[:200, :] X_test = X[200:, :] Y_test = Y[200:, :] - data = {'X_train': X_train, 'Y_train': Y_train, - 'X_test': X_test, 'Y_test': Y_test} + data = { + "X_train": X_train, + "Y_train": Y_train, + "X_test": X_test, + "Y_test": Y_test, + } - dataset_properties = {'multioutput': True} + dataset_properties = {"multioutput": True} pipeline = SimpleRegressionPipeline(dataset_properties=dataset_properties) cs = pipeline.get_hyperparameter_search_space() self._test_configurations(cs, data=data, dataset_properties=dataset_properties) - def _test_configurations(self, configurations_space, make_sparse=False, - data=None, dataset_properties=None): + def _test_configurations( + self, + configurations_space, + make_sparse=False, + data=None, + dataset_properties=None, + ): # Use a limit of ~4GiB limit = 3072 * 1024 * 1024 resource.setrlimit(resource.RLIMIT_AS, (limit, limit)) @@ -141,42 +161,48 @@ def _test_configurations(self, configurations_space, make_sparse=False, config._populate_values() # Restrict configurations which could take too long on travis-ci - restrictions = {'regressor:adaboost:n_estimators': 50, - 'regressor:adaboost:max_depth': 1, - 'feature_preprocessor:kernel_pca:n_components': 10, - 'feature_preprocessor:kitchen_sinks:n_components': 50, - 'regressor:libsvm_svc:degree': 2, - 'regressor:libsvm_svr:degree': 2, - 'regressor:libsvm_svr:C': 1., - 'feature_preprocessor:truncatedSVD:target_dim': 10, - 'feature_preprocessor:polynomial:degree': 2, - 'regressor:lda:n_components': 10} + restrictions = { + "regressor:adaboost:n_estimators": 50, + "regressor:adaboost:max_depth": 1, + "feature_preprocessor:kernel_pca:n_components": 10, + "feature_preprocessor:kitchen_sinks:n_components": 50, + "regressor:libsvm_svc:degree": 2, + "regressor:libsvm_svr:degree": 2, + "regressor:libsvm_svr:C": 1.0, + "feature_preprocessor:truncatedSVD:target_dim": 10, + "feature_preprocessor:polynomial:degree": 2, + "regressor:lda:n_components": 10, + } for restrict_parameter in restrictions: restrict_to = restrictions[restrict_parameter] - if restrict_parameter in config and config[restrict_parameter] is not None: + if ( + restrict_parameter in config + and config[restrict_parameter] is not None + ): config._values[restrict_parameter] = restrict_to if data is None: X_train, Y_train, X_test, Y_test = get_dataset( - dataset='boston', make_sparse=make_sparse, add_NaNs=True) + dataset="boston", make_sparse=make_sparse, add_NaNs=True + ) else: - X_train = data['X_train'].copy() - Y_train = data['Y_train'].copy() - X_test = data['X_test'].copy() - data['Y_test'].copy() + X_train = data["X_train"].copy() + Y_train = data["Y_train"].copy() + X_test = data["X_test"].copy() + data["Y_test"].copy() cls = SimpleRegressionPipeline( - random_state=1, - dataset_properties=dataset_properties + random_state=1, dataset_properties=dataset_properties ) cls.set_hyperparameters(config) # First make sure that for this configuration, setting the parameters # does not mistakenly set the estimator as fitted for name, step in cls.named_steps.items(): - with self.assertRaisesRegex(sklearn.exceptions.NotFittedError, - "instance is not fitted yet"): + with self.assertRaisesRegex( + sklearn.exceptions.NotFittedError, "instance is not fitted yet" + ): check_is_fitted(step) try: @@ -190,9 +216,9 @@ def _test_configurations(self, configurations_space, make_sparse=False, for name, step in cls.named_steps.items(): check_is_fitted(step) except sklearn.exceptions.NotFittedError: - self.fail("config={} raised NotFittedError unexpectedly!".format( - config - )) + self.fail( + "config={} raised NotFittedError unexpectedly!".format(config) + ) cls.predict(X_test) except MemoryError: @@ -200,8 +226,7 @@ def _test_configurations(self, configurations_space, make_sparse=False, except np.linalg.LinAlgError: continue except ValueError as e: - if "Floating-point under-/overflow occurred at epoch" in \ - e.args[0]: + if "Floating-point under-/overflow occurred at epoch" in e.args[0]: continue elif "removed all features" in e.args[0]: continue @@ -209,13 +234,17 @@ def _test_configurations(self, configurations_space, make_sparse=False, continue elif "Numerical problems in QDA" in e.args[0]: continue - elif 'Bug in scikit-learn' in e.args[0]: + elif "Bug in scikit-learn" in e.args[0]: continue - elif 'The condensed distance matrix must contain only finite ' \ - 'values.' in e.args[0]: + elif ( + "The condensed distance matrix must contain only finite " + "values." in e.args[0] + ): continue - elif "zero-size array to reduction operation maximum which has no " \ - "identity" in e.args[0]: + elif ( + "zero-size array to reduction operation maximum which has no " + "identity" in e.args[0] + ): continue else: e.args += (f"config={config}",) @@ -244,7 +273,10 @@ def _test_configurations(self, configurations_space, make_sparse=False, raise e except Exception as e: - if "Multiple input features cannot have the same target value" in e.args[0]: + if ( + "Multiple input features cannot have the same target value" + in e.args[0] + ): continue else: e.args += (f"config={config}",) @@ -252,7 +284,7 @@ def _test_configurations(self, configurations_space, make_sparse=False, def test_default_configuration(self): for i in range(2): - X_train, Y_train, X_test, Y_test = get_dataset(dataset='diabetes') + X_train, Y_train, X_test, Y_test = get_dataset(dataset="diabetes") auto = SimpleRegressionPipeline(random_state=1) auto = auto.fit(X_train, Y_train) predictions = auto.predict(copy.deepcopy(X_test)) @@ -266,16 +298,15 @@ def test_default_configuration_iterative_fit(self): regressor = SimpleRegressionPipeline( random_state=1, include={ - 'regressor': ['random_forest'], - 'feature_preprocessor': ['no_preprocessing'] - } + "regressor": ["random_forest"], + "feature_preprocessor": ["no_preprocessing"], + }, ) - X_train, Y_train, X_test, Y_test = get_dataset(dataset='boston') + X_train, Y_train, X_test, Y_test = get_dataset(dataset="boston") regressor.fit_transformer(X_train, Y_train) for i in range(1, 11): regressor.iterative_fit(X_train, Y_train) - self.assertEqual(regressor.steps[-1][-1].choice.estimator.n_estimators, - i) + self.assertEqual(regressor.steps[-1][-1].choice.estimator.n_estimators, i) def test_repr(self): representation = repr(SimpleRegressionPipeline()) @@ -293,56 +324,50 @@ def test_get_hyperparameter_search_space(self): self.assertEqual(len(forbiddens), 35) def test_get_hyperparameter_search_space_include_exclude_models(self): - regressor = SimpleRegressionPipeline( - include={'regressor': ['random_forest']} - ) + regressor = SimpleRegressionPipeline(include={"regressor": ["random_forest"]}) cs = regressor.get_hyperparameter_search_space() self.assertEqual( - cs.get_hyperparameter('regressor:__choice__'), - CategoricalHyperparameter('regressor:__choice__', ['random_forest']), + cs.get_hyperparameter("regressor:__choice__"), + CategoricalHyperparameter("regressor:__choice__", ["random_forest"]), ) # TODO add this test when more than one regressor is present - regressor = SimpleRegressionPipeline( - exclude={'regressor': ['random_forest']} - ) + regressor = SimpleRegressionPipeline(exclude={"regressor": ["random_forest"]}) cs = regressor.get_hyperparameter_search_space() - self.assertNotIn('random_forest', str(cs)) + self.assertNotIn("random_forest", str(cs)) - regressor = SimpleRegressionPipeline( - include={'feature_preprocessor': ['pca']} - ) + regressor = SimpleRegressionPipeline(include={"feature_preprocessor": ["pca"]}) cs = regressor.get_hyperparameter_search_space() - self.assertEqual(cs.get_hyperparameter( - 'feature_preprocessor:__choice__'), - CategoricalHyperparameter('feature_preprocessor:__choice__', ['pca'])) + self.assertEqual( + cs.get_hyperparameter("feature_preprocessor:__choice__"), + CategoricalHyperparameter("feature_preprocessor:__choice__", ["pca"]), + ) regressor = SimpleRegressionPipeline( - exclude={'feature_preprocessor': ['no_preprocessing']} + exclude={"feature_preprocessor": ["no_preprocessing"]} ) cs = regressor.get_hyperparameter_search_space() - self.assertNotIn('no_preprocessing', str(cs)) + self.assertNotIn("no_preprocessing", str(cs)) - def test_get_hyperparameter_search_space_preprocessor_contradicts_default_classifier( - self + def test_get_hyperparameter_search_space_preprocessor_contradicts_default( + self, ): regressor = SimpleRegressionPipeline( - include={'feature_preprocessor': ['densifier']}, - dataset_properties={'sparse': True} + include={"feature_preprocessor": ["densifier"]}, + dataset_properties={"sparse": True}, ) cs = regressor.get_hyperparameter_search_space() self.assertEqual( - cs.get_hyperparameter('regressor:__choice__').default_value, - 'gradient_boosting' + cs.get_hyperparameter("regressor:__choice__").default_value, + "gradient_boosting", ) regressor = SimpleRegressionPipeline( - include={'feature_preprocessor': ['nystroem_sampler']} + include={"feature_preprocessor": ["nystroem_sampler"]} ) cs = regressor.get_hyperparameter_search_space() self.assertEqual( - cs.get_hyperparameter('regressor:__choice__').default_value, - 'sgd' + cs.get_hyperparameter("regressor:__choice__").default_value, "sgd" ) def test_get_hyperparameter_search_space_only_forbidden_combinations(self): @@ -351,9 +376,9 @@ def test_get_hyperparameter_search_space_only_forbidden_combinations(self): "Cannot find a legal default configuration.", SimpleRegressionPipeline, include={ - 'regressor': ['random_forest'], - 'feature_preprocessor': ['kitchen_sinks'] - } + "regressor": ["random_forest"], + "feature_preprocessor": ["kitchen_sinks"], + }, ) # It must also be catched that no classifiers which can handle sparse @@ -363,14 +388,16 @@ def test_get_hyperparameter_search_space_only_forbidden_combinations(self): "Cannot find a legal default configuration", SimpleRegressionPipeline, include={ - 'regressor': ['extra_trees'], - 'feature_preprocessor': ['densifier'] + "regressor": ["extra_trees"], + "feature_preprocessor": ["densifier"], }, - dataset_properties={'sparse': True} + dataset_properties={"sparse": True}, ) - @unittest.skip("test_get_hyperparameter_search_space_dataset_properties" + - " Not yet Implemented") + @unittest.skip( + "test_get_hyperparameter_search_space_dataset_properties" + + " Not yet Implemented" + ) def test_get_hyperparameter_search_space_dataset_properties(self): # TODO: We do not have any dataset properties for regression, so this # test is somewhat stupid @@ -403,16 +430,14 @@ def test_get_hyperparameter_search_space_dataset_properties(self): """ def test_predict_batched(self): - include = {'regressor': ['decision_tree']} + include = {"regressor": ["decision_tree"]} cs = SimpleRegressionPipeline(include=include).get_hyperparameter_search_space() default = cs.get_default_configuration() regressor = SimpleRegressionPipeline( - config=default, - random_state=1, - include=include + config=default, random_state=1, include=include ) - X_train, Y_train, X_test, Y_test = get_dataset(dataset='boston') + X_train, Y_train, X_test, Y_test = get_dataset(dataset="boston") regressor.fit(X_train, Y_train) X_test_ = X_test.copy() prediction_ = regressor.predict(X_test_) @@ -424,12 +449,11 @@ def test_predict_batched(self): np.testing.assert_array_almost_equal(prediction_, prediction) def test_predict_batched_sparse(self): - dataset_properties = {'sparse': True} - include = {'regressor': ['decision_tree']} + dataset_properties = {"sparse": True} + include = {"regressor": ["decision_tree"]} cs = SimpleRegressionPipeline( - dataset_properties=dataset_properties, - include=include + dataset_properties=dataset_properties, include=include ).get_hyperparameter_search_space() default = cs.get_default_configuration() @@ -437,11 +461,12 @@ def test_predict_batched_sparse(self): config=default, random_state=1, dataset_properties=dataset_properties, - include=include + include=include, ) - X_train, Y_train, X_test, Y_test = get_dataset(dataset='boston', - make_sparse=True) + X_train, Y_train, X_test, Y_test = get_dataset( + dataset="boston", make_sparse=True + ) regressor.fit(X_train, Y_train) X_test_ = X_test.copy() prediction_ = regressor.predict(X_test_) @@ -465,7 +490,7 @@ def test_validate_input_Y(self): raise NotImplementedError() def test_pipeline_clonability(self): - X_train, Y_train, X_test, Y_test = get_dataset(dataset='boston') + X_train, Y_train, X_test, Y_test = get_dataset(dataset="boston") auto = SimpleRegressionPipeline(random_state=1) auto = auto.fit(X_train, Y_train) auto_clone = clone(auto) @@ -494,7 +519,9 @@ def test_set_params(self): def test_get_params(self): pass - def _test_set_hyperparameter_choice(self, expected_key, implementation, config_dict): + def _test_set_hyperparameter_choice( + self, expected_key, implementation, config_dict + ): """ Given a configuration in config, this procedure makes sure that the given implementation, which should be a Choice component, honors @@ -507,14 +534,16 @@ def _test_set_hyperparameter_choice(self, expected_key, implementation, config_d # Are there further hyperparams? # A choice component might have attribute requirements that we need to check - expected_sub_key = expected_key.replace(':__choice__', ':') + implementation_type + expected_sub_key = ( + expected_key.replace(":__choice__", ":") + implementation_type + ) expected_attributes = {} - if 'data_preprocessor:__choice__' in expected_key: + if "data_preprocessor:__choice__" in expected_key: # We have to check both the numerical and categorical to_check = { - 'numerical_transformer': implementation.choice.numer_ppl.named_steps, - 'categorical_transformer': implementation.choice.categ_ppl.named_steps, - 'text_transformer': implementation.choice.txt_ppl.named_steps, + "numerical_transformer": implementation.choice.numer_ppl.named_steps, + "categorical_transformer": implementation.choice.categ_ppl.named_steps, + "text_transformer": implementation.choice.txt_ppl.named_steps, } for data_type, pipeline in to_check.items(): @@ -522,8 +551,7 @@ def _test_set_hyperparameter_choice(self, expected_key, implementation, config_d # If it is a Choice, make sure it is the correct one! if isinstance(sub_step, AutoSklearnChoice): key = "data_preprocessor:feature_type:{}:{}:__choice__".format( - data_type, - sub_name + data_type, sub_name ) keys_checked.extend( self._test_set_hyperparameter_choice( @@ -535,10 +563,10 @@ def _test_set_hyperparameter_choice(self, expected_key, implementation, config_d keys_checked.extend( self._test_set_hyperparameter_component( "data_preprocessor:feature_type:{}:{}".format( - data_type, - sub_name + data_type, sub_name ), - sub_step, config_dict + sub_step, + config_dict, ) ) else: @@ -547,7 +575,7 @@ def _test_set_hyperparameter_choice(self, expected_key, implementation, config_d else: for key, value in config_dict.items(): if key != expected_key and expected_sub_key in key: - expected_attributes[key.split(':')[-1]] = value + expected_attributes[key.split(":")[-1]] = value keys_checked.append(key) if expected_attributes: attributes = vars(implementation.choice) @@ -557,7 +585,9 @@ def _test_set_hyperparameter_choice(self, expected_key, implementation, config_d self.assertIn(expected_attribute, attributes.keys()) return keys_checked - def _test_set_hyperparameter_component(self, expected_key, implementation, config_dict): + def _test_set_hyperparameter_component( + self, expected_key, implementation, config_dict + ): """ Given a configuration in config, this procedure makes sure that the given implementation, which should be a autosklearn component, honors @@ -569,15 +599,14 @@ def _test_set_hyperparameter_component(self, expected_key, implementation, confi for key, value in config_dict.items(): if expected_key in key: keys_checked.append(key) - key = key.replace(expected_key + ':', '') - if ':' in key: - raise ValueError("This utility should only be called with a " - "matching string that produces leaf configurations, " - "that is no further colons are expected, yet key={}" - "".format( - key - ) - ) + key = key.replace(expected_key + ":", "") + if ":" in key: + raise ValueError( + "This utility should only be called with a " + "matching string that produces leaf configurations, " + "that is no further colons are expected, yet key={}" + "".format(key) + ) expected_attributes[key] = value # Cannot check the whole dictionary, just names, as some # classes map the text hyperparameter directly to a function! @@ -598,12 +627,17 @@ def test_set_hyperparameters_honors_configuration(self): """ all_combinations = list(itertools.product([True, False], repeat=4)) - for sparse, multilabel, signed, multiclass, in all_combinations: + for ( + sparse, + multilabel, + signed, + multiclass, + ) in all_combinations: dataset_properties = { - 'sparse': sparse, - 'multilabel': multilabel, - 'multiclass': multiclass, - 'signed': signed, + "sparse": sparse, + "multilabel": multilabel, + "multiclass": multiclass, + "signed": signed, } random_state = 1 auto = SimpleRegressionPipeline( @@ -623,31 +657,32 @@ def test_set_hyperparameters_honors_configuration(self): keys_checked = [] for name, step in auto.named_steps.items(): - if name == 'data_preprocessor': + if name == "data_preprocessor": keys_checked.extend( self._test_set_hyperparameter_choice( - 'data_preprocessor:__choice__', step, config_dict + "data_preprocessor:__choice__", step, config_dict ) ) self.assertEqual(step.random_state, random_state) - elif name == 'feature_preprocessor': + elif name == "feature_preprocessor": keys_checked.extend( self._test_set_hyperparameter_choice( - 'feature_preprocessor:__choice__', step, config_dict + "feature_preprocessor:__choice__", step, config_dict ) ) self.assertEqual(step.random_state, random_state) - elif name == 'regressor': + elif name == "regressor": keys_checked.extend( self._test_set_hyperparameter_choice( - 'regressor:__choice__', step, config_dict + "regressor:__choice__", step, config_dict ) ) self.assertEqual(step.random_state, random_state) else: - raise ValueError("Found another type of step! Need to update this check" - " {}. ".format(name) - ) + raise ValueError( + "Found another type of step! Need to update this check" + " {}. ".format(name) + ) # Make sure we checked the whole configuration self.assertSetEqual(set(config_dict.keys()), set(keys_checked)) diff --git a/test/test_scripts/test_metadata_generation.py b/test/test_scripts/test_metadata_generation.py index 6cc4fad38d..6c6ba70ef5 100644 --- a/test/test_scripts/test_metadata_generation.py +++ b/test/test_scripts/test_metadata_generation.py @@ -13,27 +13,29 @@ class TestMetadataGeneration(unittest.TestCase): - def setUp(self): - self.working_directory = '/tmp/autosklearn-unittest-tmp-dir-%s-%d-%d' % ( - socket.gethostname(), os.getpid(), random.randint(0, 1000000)) + self.working_directory = "/tmp/autosklearn-unittest-tmp-dir-%s-%d-%d" % ( + socket.gethostname(), + os.getpid(), + random.randint(0, 1000000), + ) def print_files(self): - print('Existing files:') + print("Existing files:") for dirpath, dirnames, filenames in os.walk(self.working_directory): print(dirpath, dirnames, filenames) def test_metadata_generation(self): regression_task_id = 360029 - regression_dataset_name = 'SWD'.lower() + regression_dataset_name = "SWD".lower() classification_task_id = 245 - classification_dataset_name = 'breast-w'.lower() + classification_dataset_name = "breast-w".lower() current_directory = __file__ - scripts_directory = os.path.abspath(os.path.join(current_directory, - '..', '..', '..', - 'scripts')) + scripts_directory = os.path.abspath( + os.path.join(current_directory, "..", "..", "..", "scripts") + ) # 1. create working directory try: @@ -44,214 +46,293 @@ def test_metadata_generation(self): # 2. should be done by the person running the unit tests! # 3. create configuration commands - script_filename = os.path.join(scripts_directory, '01_create_commands.py') - cmd = 'python3 %s --working-directory %s --test' % (script_filename, self.working_directory) - rval = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + script_filename = os.path.join(scripts_directory, "01_create_commands.py") + cmd = "python3 %s --working-directory %s --test" % ( + script_filename, + self.working_directory, + ) + rval = subprocess.run( + cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE + ) self.assertEqual(rval.returncode, 0, msg=str(rval)) # 4. run one of the commands to get some data - commands_output_file = os.path.join(self.working_directory, 'metadata_commands.txt') + commands_output_file = os.path.join( + self.working_directory, "metadata_commands.txt" + ) self.assertTrue(os.path.exists(commands_output_file)) with open(commands_output_file) as fh: - cmds = fh.read().split('\n') - # 6 regression, 7 classification (roc_auc + task 258 is illegal), 1 empty line - self.assertEqual(len(cmds), 18, msg='\n'.join(cmds)) + cmds = fh.read().split("\n") + # 6 regression, 7 classification (roc_auc + task 258 is illegal), + # 1 empty line + self.assertEqual(len(cmds), 18, msg="\n".join(cmds)) for task_id, dataset_name, task_type, metric in ( ( classification_task_id, classification_dataset_name, - 'classification', - 'balanced_accuracy', + "classification", + "balanced_accuracy", ), - (regression_task_id, regression_dataset_name, 'regression', 'r2') + (regression_task_id, regression_dataset_name, "regression", "r2"), ): cmd = None with open(commands_output_file) as fh: while True: cmd = fh.readline() - if 'task-id %d' % task_id in cmd and metric in cmd: + if "task-id %d" % task_id in cmd and metric in cmd: break if cmd is None: - self.fail('Did not find a command for task_id %s and metric %s in %s' - % (task_id, metric, cmds)) + self.fail( + "Did not find a command for task_id %s and metric %s in %s" + % (task_id, metric, cmds) + ) - self.assertIn('time-limit 86400', cmd) - self.assertIn('per-run-time-limit 1800', cmd) - cmd = cmd.replace('time-limit 86400', 'time-limit 60').replace( - 'per-run-time-limit 1800', 'per-run-time-limit 5') + self.assertIn("time-limit 86400", cmd) + self.assertIn("per-run-time-limit 1800", cmd) + cmd = cmd.replace("time-limit 86400", "time-limit 60").replace( + "per-run-time-limit 1800", "per-run-time-limit 5" + ) # This tells the script to use the same memory limit for testing as # for training. In production, it would use twice as much! - cmd = cmd.replace('-s 1', '-s 1 --unittest') - print('COMMAND: %s' % cmd) - rval = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, - stderr=subprocess.PIPE) - print('STDOUT: %s' % repr(rval.stdout), flush=True) - print('STDERR: %s' % repr(rval.stderr), flush=True) + cmd = cmd.replace("-s 1", "-s 1 --unittest") + print("COMMAND: %s" % cmd) + rval = subprocess.run( + cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE + ) + print("STDOUT: %s" % repr(rval.stdout), flush=True) + print("STDERR: %s" % repr(rval.stderr), flush=True) self.print_files() - expected_output_directory = os.path.join(self.working_directory, - 'configuration', - task_type, - str(task_id), metric, - 'auto-sklearn-output') - self.assertTrue(os.path.exists(expected_output_directory), - msg=expected_output_directory) - smac_log = os.path.join(expected_output_directory, 'AutoML(1):%s.log' % dataset_name) + expected_output_directory = os.path.join( + self.working_directory, + "configuration", + task_type, + str(task_id), + metric, + "auto-sklearn-output", + ) + self.assertTrue( + os.path.exists(expected_output_directory), msg=expected_output_directory + ) + smac_log = os.path.join( + expected_output_directory, "AutoML(1):%s.log" % dataset_name + ) with open(smac_log) as fh: smac_output = fh.read() - self.assertEqual(rval.returncode, 0, msg=str(rval) + '\n' + smac_output) - expected_validation_output = os.path.join(expected_output_directory, '..', - 'validation_trajectory_1.json') + self.assertEqual(rval.returncode, 0, msg=str(rval) + "\n" + smac_output) + expected_validation_output = os.path.join( + expected_output_directory, "..", "validation_trajectory_1.json" + ) self.assertTrue(os.path.exists(expected_validation_output)) - trajectory = os.path.join(expected_output_directory, - 'smac3-output', 'run_1', 'trajectory.json') + trajectory = os.path.join( + expected_output_directory, "smac3-output", "run_1", "trajectory.json" + ) with open(expected_validation_output) as fh_validation: with open(trajectory) as fh_trajectory: traj = json.load(fh_trajectory) valid_traj = json.load(fh_validation) - print('Validation trajectory:') + print("Validation trajectory:") print(valid_traj) self.assertGreater(len(traj), 2, msg=str(valid_traj)) self.assertEqual(len(traj), len(valid_traj), msg=str(valid_traj)) for entry in valid_traj: - if task_type == 'classification': + if task_type == "classification": for metric in CLASSIFICATION_METRICS: # This is a multilabel metric - if metric in ('precision_samples', 'recall_samples', 'f1_samples'): + if metric in ( + "precision_samples", + "recall_samples", + "f1_samples", + ): continue self.assertIn(metric, entry[-1]) self.assertIsInstance(entry[-1][metric], float) - self.assertTrue(np.isfinite(entry[-1][metric]), - (metric, str(entry[-1][metric]))) + self.assertTrue( + np.isfinite(entry[-1][metric]), + (metric, str(entry[-1][metric])), + ) else: for metric in REGRESSION_METRICS: self.assertIn(metric, entry[-1]) self.assertIsInstance(entry[-1][metric], float) - self.assertTrue(np.isfinite(entry[-1][metric]), - (metric, str(entry[-1][metric]))) + self.assertTrue( + np.isfinite(entry[-1][metric]), + (metric, str(entry[-1][metric])), + ) # 5. Get the test performance of these configurations - script_filename = os.path.join(scripts_directory, '02_retrieve_metadata.py') - cmd = 'python3 %s --working-directory %s ' % (script_filename, self.working_directory) - print('COMMAND: %s' % cmd) - rval = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, - stderr=subprocess.PIPE) - print('STDOUT: %s' % repr(rval.stdout), flush=True) - print('STDERR: %s' % repr(rval.stderr), flush=True) + script_filename = os.path.join(scripts_directory, "02_retrieve_metadata.py") + cmd = "python3 %s --working-directory %s " % ( + script_filename, + self.working_directory, + ) + print("COMMAND: %s" % cmd) + rval = subprocess.run( + cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE + ) + print("STDOUT: %s" % repr(rval.stdout), flush=True) + print("STDERR: %s" % repr(rval.stderr), flush=True) self.assertEqual(rval.returncode, 0, msg=str(rval)) - for file in ['algorithm_runs.arff', 'configurations.csv', 'description.results.txt']: - for metric in ['accuracy', 'balanced_accuracy', 'log_loss', 'roc_auc']: + for file in [ + "algorithm_runs.arff", + "configurations.csv", + "description.results.txt", + ]: + for metric in ["accuracy", "balanced_accuracy", "log_loss", "roc_auc"]: path = os.path.join( self.working_directory, - 'configuration_results', - '%s_binary.classification_dense' % metric, + "configuration_results", + "%s_binary.classification_dense" % metric, file, ) self.assertTrue(os.path.exists(path), msg=path) - for file in ['algorithm_runs.arff', 'configurations.csv', 'description.results.txt']: - for metric in ['r2', 'mean_squared_error']: + for file in [ + "algorithm_runs.arff", + "configurations.csv", + "description.results.txt", + ]: + for metric in ["r2", "mean_squared_error"]: path = os.path.join( self.working_directory, - 'configuration_results', - '%s_regression_dense' % metric, + "configuration_results", + "%s_regression_dense" % metric, file, ) self.assertTrue(os.path.exists(path), msg=path) # 6. Calculate metafeatures - script_filename = os.path.join(scripts_directory, '03_calculate_metafeatures.py') - cmd = ( - 'python3 %s --working-directory %s --test-mode ' - % (script_filename, self.working_directory) + script_filename = os.path.join( + scripts_directory, "03_calculate_metafeatures.py" + ) + cmd = "python3 %s --working-directory %s --test-mode " % ( + script_filename, + self.working_directory, + ) + rval = subprocess.run( + cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE ) - rval = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, - stderr=subprocess.PIPE) self.assertEqual(rval.returncode, 0, msg=str(rval)) - for task_type in ('classification', 'regression'): - for file in ['calculation_times.csv', 'description.features.txt', - 'feature_costs.arff', 'feature_runstatus.arff', - 'feature_values.arff']: + for task_type in ("classification", "regression"): + for file in [ + "calculation_times.csv", + "description.features.txt", + "feature_costs.arff", + "feature_runstatus.arff", + "feature_values.arff", + ]: self.assertTrue( - os.path.exists(os.path.join( - self.working_directory, - 'metafeatures', - task_type, - file) + os.path.exists( + os.path.join( + self.working_directory, "metafeatures", task_type, file + ) ) ) with open( os.path.join( - self.working_directory, 'metafeatures', 'regression', 'feature_values.arff' + self.working_directory, + "metafeatures", + "regression", + "feature_values.arff", ) ) as fh: - metafeatures_arff = fh.read().split('\n') + metafeatures_arff = fh.read().split("\n") contains_regression_id = False for line in metafeatures_arff: - if line.startswith('fri_c4_500_25,'): + if line.startswith("fri_c4_500_25,"): contains_regression_id = True self.assertTrue(contains_regression_id, msg=metafeatures_arff) with open( - os.path.join( - self.working_directory, 'metafeatures', 'classification', 'feature_values.arff' - ) + os.path.join( + self.working_directory, + "metafeatures", + "classification", + "feature_values.arff", + ) ) as fh: - metafeatures_arff = fh.read().split('\n') + metafeatures_arff = fh.read().split("\n") contains_classification_id = False for line in metafeatures_arff: - if line.startswith('anneal,'): + if line.startswith("anneal,"): contains_classification_id = True self.assertTrue(contains_classification_id, msg=metafeatures_arff) # 7. Create aslib files - script_filename = os.path.join(scripts_directory, '04_create_aslib_files.py') - cmd = 'python3 %s --working-directory %s ' % ( - script_filename, self.working_directory) - rval = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, - stderr=subprocess.PIPE) + script_filename = os.path.join(scripts_directory, "04_create_aslib_files.py") + cmd = "python3 %s --working-directory %s " % ( + script_filename, + self.working_directory, + ) + rval = subprocess.run( + cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE + ) self.assertEqual(rval.returncode, 0, msg=str(rval)) for metric_, combination in ( - (metric, '%s_binary.classification_dense' % metric), - (metric, '%s_regression_dense' % metric), + (metric, "%s_binary.classification_dense" % metric), + (metric, "%s_regression_dense" % metric), ): if task_type not in combination: continue - for file in ['algorithm_runs.arff', 'configurations.csv', - 'description.txt', 'feature_costs.arff', - 'feature_runstatus.arff', 'feature_values.arff', - 'readme.txt']: + for file in [ + "algorithm_runs.arff", + "configurations.csv", + "description.txt", + "feature_costs.arff", + "feature_runstatus.arff", + "feature_values.arff", + "readme.txt", + ]: expected_path = os.path.join( - self.working_directory, 'metadata', combination, file, + self.working_directory, + "metadata", + combination, + file, ) self.assertTrue(os.path.exists(expected_path), msg=expected_path) - with open(os.path.join(self.working_directory, - 'metadata', - combination, - 'algorithm_runs.arff')) as fh: + with open( + os.path.join( + self.working_directory, + "metadata", + combination, + "algorithm_runs.arff", + ) + ) as fh: algorithm_runs = arff.load(fh) - self.assertEqual(algorithm_runs['attributes'], - [('instance_id', 'STRING'), - ('repetition', 'NUMERIC'), - ('algorithm', 'STRING'), - (metric_, 'NUMERIC'), - ('runstatus', - ['ok', 'timeout', 'memout', 'not_applicable', - 'crash', 'other'])]) - self.assertEqual(len(algorithm_runs['data']), 1) - self.assertEqual(len(algorithm_runs['data'][0]), 5) - self.assertLess(algorithm_runs['data'][0][3], 0.9) - self.assertEqual(algorithm_runs['data'][0][4], 'ok') + self.assertEqual( + algorithm_runs["attributes"], + [ + ("instance_id", "STRING"), + ("repetition", "NUMERIC"), + ("algorithm", "STRING"), + (metric_, "NUMERIC"), + ( + "runstatus", + [ + "ok", + "timeout", + "memout", + "not_applicable", + "crash", + "other", + ], + ), + ], + ) + self.assertEqual(len(algorithm_runs["data"]), 1) + self.assertEqual(len(algorithm_runs["data"][0]), 5) + self.assertLess(algorithm_runs["data"][0][3], 0.9) + self.assertEqual(algorithm_runs["data"][0][4], "ok") def tearDown(self): for i in range(5): diff --git a/test/test_util/__init__.py b/test/test_util/__init__.py index cc3cd7becd..e298f0f075 100644 --- a/test/test_util/__init__.py +++ b/test/test_util/__init__.py @@ -1,2 +1,2 @@ # -*- encoding: utf-8 -*- -__author__ = 'feurerm' +__author__ = "feurerm" diff --git a/test/test_util/test_StopWatch.py b/test/test_util/test_StopWatch.py index 14038c6820..d45ecbf55d 100644 --- a/test/test_util/test_StopWatch.py +++ b/test/test_util/test_StopWatch.py @@ -22,8 +22,8 @@ def test_stopwatch_overhead(self): cpu_start = time.process_time() watch = StopWatch() for i in range(1, 1000): - watch.start_task('task_%d' % i) - watch.stop_task('task_%d' % i) + watch.start_task("task_%d" % i) + watch.stop_task("task_%d" % i) cpu_stop = time.process_time() stop = time.time() dur = stop - start @@ -36,6 +36,6 @@ def test_stopwatch_overhead(self): self.assertLess(watch.cpu_sum(), 2 * watch.wall_sum()) -if __name__ == '__main__': +if __name__ == "__main__": # import sys;sys.argv = ['', 'Test.testName'] unittest.main() diff --git a/test/test_util/test_backend.py b/test/test_util/test_backend.py index a029aef4bb..0673370b97 100644 --- a/test/test_util/test_backend.py +++ b/test/test_util/test_backend.py @@ -7,48 +7,48 @@ class BackendModelsTest(unittest.TestCase): - class BackendStub(Backend): - def __init__(self): self.__class__ = Backend def setUp(self): self.backend = self.BackendStub() - self.backend.internals_directory = '/' + self.backend.internals_directory = "/" - @unittest.mock.patch('pickle.load') - @unittest.mock.patch('os.path.exists') + @unittest.mock.patch("pickle.load") + @unittest.mock.patch("os.path.exists") def test_load_model_by_seed_and_id(self, exists_mock, pickleLoadMock): exists_mock.return_value = False - open_mock = unittest.mock.mock_open(read_data='Data') + open_mock = unittest.mock.mock_open(read_data="Data") with unittest.mock.patch( - 'autosklearn.automl_common.common.utils.backend.open', + "autosklearn.automl_common.common.utils.backend.open", open_mock, create=True, ): seed = 13 idx = 17 budget = 50.0 - expected_model = self._setup_load_model_mocks(open_mock, - pickleLoadMock, - seed, idx, budget) + expected_model = self._setup_load_model_mocks( + open_mock, pickleLoadMock, seed, idx, budget + ) actual_model = self.backend.load_model_by_seed_and_id_and_budget( - seed, idx, budget) + seed, idx, budget + ) self.assertEqual(expected_model, actual_model) - @unittest.mock.patch('pickle.load') - @unittest.mock.patch.object(builtins, 'open') - @unittest.mock.patch('os.path.exists') + @unittest.mock.patch("pickle.load") + @unittest.mock.patch.object(builtins, "open") + @unittest.mock.patch("os.path.exists") def test_loads_models_by_identifiers(self, exists_mock, openMock, pickleLoadMock): exists_mock.return_value = True seed = 13 idx = 17 budget = 50.0 expected_model = self._setup_load_model_mocks( - openMock, pickleLoadMock, seed, idx, budget) + openMock, pickleLoadMock, seed, idx, budget + ) expected_dict = {(seed, idx, budget): expected_model} actual_dict = self.backend.load_models_by_identifiers([(seed, idx, budget)]) @@ -57,15 +57,25 @@ def test_loads_models_by_identifiers(self, exists_mock, openMock, pickleLoadMock self.assertDictEqual(expected_dict, actual_dict) def _setup_load_model_mocks(self, openMock, pickleLoadMock, seed, idx, budget): - model_path = '/runs/%s_%s_%s/%s.%s.%s.model' % (seed, idx, budget, seed, idx, budget) - file_handler = 'file_handler' - expected_model = 'model' + model_path = "/runs/%s_%s_%s/%s.%s.%s.model" % ( + seed, + idx, + budget, + seed, + idx, + budget, + ) + file_handler = "file_handler" + expected_model = "model" fileMock = unittest.mock.MagicMock() fileMock.__enter__.return_value = file_handler - openMock.side_effect = \ - lambda path, flag: fileMock if path == model_path and flag == 'rb' else None - pickleLoadMock.side_effect = lambda fh: expected_model if fh == file_handler else None + openMock.side_effect = ( + lambda path, flag: fileMock if path == model_path and flag == "rb" else None + ) + pickleLoadMock.side_effect = ( + lambda fh: expected_model if fh == file_handler else None + ) return expected_model diff --git a/test/test_util/test_common.py b/test/test_util/test_common.py index 740608969d..33fa4cee31 100644 --- a/test/test_util/test_common.py +++ b/test/test_util/test_common.py @@ -18,5 +18,5 @@ def test_check_pid(self): self.assertFalse(exists) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/test/test_util/test_data.py b/test/test_util/test_data.py index 87548b413f..2bceac804a 100644 --- a/test/test_util/test_data.py +++ b/test/test_util/test_data.py @@ -1,29 +1,33 @@ -from typing import Any, List, Dict, Union -from itertools import chain -import warnings +from typing import Any, Dict, List, Union -import pytest +import warnings +from itertools import chain import numpy as np import pandas as pd +import pytest import sklearn.datasets from scipy.sparse import csr_matrix, spmatrix from autosklearn.constants import ( - BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION, - REGRESSION, MULTIOUTPUT_REGRESSION, CLASSIFICATION_TASKS, REGRESSION_TASKS + BINARY_CLASSIFICATION, + CLASSIFICATION_TASKS, + MULTICLASS_CLASSIFICATION, + MULTILABEL_CLASSIFICATION, + MULTIOUTPUT_REGRESSION, + REGRESSION, + REGRESSION_TASKS, ) from autosklearn.util.data import ( - subsample, + default_dataset_compression_arg, reduce_dataset_size_if_too_large, reduce_precision, reduction_mapping, + subsample, supported_precision_reductions, validate_dataset_compression_arg, - default_dataset_compression_arg ) - parametrize = pytest.mark.parametrize @@ -68,11 +72,14 @@ def test_validate_dataset_compression_arg_returns_with_memory_allocation( assert validate_arg["methods"] == expected_methods -@parametrize("methods", [ - ["precision"], - ["precision", "subsample"], - ["precision", "precision", "subsample"] -]) +@parametrize( + "methods", + [ + ["precision"], + ["precision", "subsample"], + ["precision", "precision", "subsample"], + ], +) def test_validate_dataset_compression_arg_returns_with_same_methods( methods: List[str], ): @@ -125,17 +132,14 @@ def test_validate_dataset_compression_arg_raises_error_with_bad_key(key: str): ------- * Should raise a ValueError """ - bad_arg = { - **default_dataset_compression_arg, - key: 1337 - } + bad_arg = {**default_dataset_compression_arg, key: 1337} with pytest.raises(ValueError, match=r"Unknown key"): validate_dataset_compression_arg(bad_arg, memory_limit=10) @parametrize("memory_allocation", ["hello", {}, [1, 2, 3]]) def test_validate_dataset_compression_arg_raises_error_with_bad_memory_allocation_type( - memory_allocation: Any + memory_allocation: Any, ): """ Parameters @@ -148,13 +152,15 @@ def test_validate_dataset_compression_arg_raises_error_with_bad_memory_allocatio * Should raise a ValueError """ bad_arg = {"memory_allocation": memory_allocation} - with pytest.raises(ValueError, match=r"key 'memory_allocation' must be an `int` or `float`"): + with pytest.raises( + ValueError, match=r"key 'memory_allocation' must be an `int` or `float`" + ): validate_dataset_compression_arg(bad_arg, memory_limit=10) @parametrize("memory_allocation", [-0.5, 0.0, 1.0, 1.5]) def test_validate_dataset_compression_arg_raises_error_with_bad_memory_allocation_float( - memory_allocation: float + memory_allocation: float, ): """ Parameters @@ -168,16 +174,17 @@ def test_validate_dataset_compression_arg_raises_error_with_bad_memory_allocatio """ bad_arg = {"memory_allocation": memory_allocation} - with pytest.raises(ValueError, match=r"key 'memory_allocation' if float must be in \(0, 1\)"): + with pytest.raises( + ValueError, match=r"key 'memory_allocation' if float must be in \(0, 1\)" + ): validate_dataset_compression_arg(bad_arg, memory_limit=10) -@parametrize("memory_allocation, memory_limit", [ - (0, 10), (10, 10), (-20, 10), (20, 10) -]) +@parametrize( + "memory_allocation, memory_limit", [(0, 10), (10, 10), (-20, 10), (20, 10)] +) def test_validate_dataset_compression_arg_raises_error_with_bad_memory_allocation_int( - memory_allocation: int, - memory_limit: int + memory_allocation: int, memory_limit: int ): """ Parameters @@ -193,12 +200,16 @@ def test_validate_dataset_compression_arg_raises_error_with_bad_memory_allocatio * Should raise a ValueError """ bad_arg = {"memory_allocation": memory_allocation} - with pytest.raises(ValueError, match=r"key 'memory_allocation' if int must be in \(0,"): + with pytest.raises( + ValueError, match=r"key 'memory_allocation' if int must be in \(0," + ): validate_dataset_compression_arg(bad_arg, memory_limit=memory_limit) @parametrize("methods", [10, {"hello", "world"}, []]) -def test_validate_dataset_compression_arg_raises_error_with_bad_methods_type(methods: Any): +def test_validate_dataset_compression_arg_raises_error_with_bad_methods_type( + methods: Any, +): """ Parameters ---------- @@ -214,12 +225,17 @@ def test_validate_dataset_compression_arg_raises_error_with_bad_methods_type(met validate_dataset_compression_arg(bad_arg, memory_limit=10) -@parametrize("methods", [ - ["bad", "worse"], - ["precision", "kind_of_bad"], - ["still_bad", "precision", "subsample"] -]) -def test_validate_dataset_compression_arg_raises_error_with_bad_methods_entries(methods: Any): +@parametrize( + "methods", + [ + ["bad", "worse"], + ["precision", "kind_of_bad"], + ["still_bad", "precision", "subsample"], + ], +) +def test_validate_dataset_compression_arg_raises_error_with_bad_methods_entries( + methods: Any, +): """ Parameters ---------- @@ -235,11 +251,16 @@ def test_validate_dataset_compression_arg_raises_error_with_bad_methods_entries( validate_dataset_compression_arg(bad_arg, memory_limit=10) -@parametrize("y", [ - np.asarray(9999 * [0] + 1 * [1]), - np.asarray(4999 * [1] + 4999 * [2] + 1 * [3] + 1 * [4]), - np.asarray(4999 * [[0, 1, 1]] + 4999 * [[1, 1, 0]] + 1 * [[1, 0, 1]] + 1 * [[0, 0, 0]]) -]) +@parametrize( + "y", + [ + np.asarray(9999 * [0] + 1 * [1]), + np.asarray(4999 * [1] + 4999 * [2] + 1 * [3] + 1 * [4]), + np.asarray( + 4999 * [[0, 1, 1]] + 4999 * [[1, 1, 0]] + 1 * [[1, 0, 1]] + 1 * [[0, 0, 0]] + ), + ], +) @parametrize("random_state", list(range(5))) def test_subsample_classification_unique_labels_stay_in_training_set(y, random_state): n_samples = len(y) @@ -253,32 +274,37 @@ def test_subsample_classification_unique_labels_stay_in_training_set(y, random_s with warnings.catch_warnings(): warnings.simplefilter("ignore") X_sampled, y_sampled = subsample( - X, y, + X, + y, random_state=random_state, sample_size=sample_size, - is_classification=True + is_classification=True, ) assert X_sampled.dtype == X.dtype and y_sampled.dtype == y.dtype assert len(y_sampled) == sample_size - assert all(label in y_sampled for label in unique_labels), \ - f"sampled unique = {np.unique(y_sampled)}, original unique = {unique_labels}" + assert all( + label in y_sampled for label in unique_labels + ), f"sampled unique = {np.unique(y_sampled)}, original unique = {unique_labels}" @parametrize("X", [np.asarray([[1, 1, 1]] * 30)]) @parametrize("x_type", [list, np.ndarray, csr_matrix, pd.DataFrame]) -@parametrize("y, task", [ - (np.asarray([0] * 15 + [1] * 15), BINARY_CLASSIFICATION), - (np.asarray([0] * 10 + [1] * 10 + [2] * 10), MULTICLASS_CLASSIFICATION), - (np.asarray([[1, 0, 1]] * 30), MULTILABEL_CLASSIFICATION), - (np.asarray([1.0] * 30), REGRESSION), - (np.asarray([[1.0, 1.0, 1.0]] * 30), MULTIOUTPUT_REGRESSION), -]) +@parametrize( + "y, task", + [ + (np.asarray([0] * 15 + [1] * 15), BINARY_CLASSIFICATION), + (np.asarray([0] * 10 + [1] * 10 + [2] * 10), MULTICLASS_CLASSIFICATION), + (np.asarray([[1, 0, 1]] * 30), MULTILABEL_CLASSIFICATION), + (np.asarray([1.0] * 30), REGRESSION), + (np.asarray([[1.0, 1.0, 1.0]] * 30), MULTIOUTPUT_REGRESSION), + ], +) @parametrize("y_type", [list, np.ndarray, pd.DataFrame, pd.Series]) @parametrize("random_state", [0]) @parametrize("sample_size", [0.25, 0.5, 5, 10]) def test_subsample_validity(X, x_type, y, y_type, random_state, sample_size, task): - """ Asserts the validity of the function with all valid types + """Asserts the validity of the function with all valid types We want to make sure that `subsample` works correctly with all the types listed as x_type and y_type. @@ -289,10 +315,10 @@ def test_subsample_validity(X, x_type, y, y_type, random_state, sample_size, tas """ assert len(X) == len(y) # Make sure our test data is correct - if ( - y_type == pd.Series - and task in [MULTILABEL_CLASSIFICATION, MULTIOUTPUT_REGRESSION] - ): + if y_type == pd.Series and task in [ + MULTILABEL_CLASSIFICATION, + MULTIOUTPUT_REGRESSION, + ]: # We can't have a pd.Series with multiple values as it's 1 dimensional pytest.skip("Can't have pd.Series as y when task is n-dimensional") @@ -312,10 +338,11 @@ def convert(arr, objtype): with warnings.catch_warnings(): warnings.simplefilter("ignore") X_sampled, y_sampled = subsample( - X, y, + X, + y, random_state=random_state, sample_size=sample_size, - is_classification=task in CLASSIFICATION_TASKS + is_classification=task in CLASSIFICATION_TASKS, ) # Function to get the type of an obj @@ -359,9 +386,11 @@ def size(obj): assert size(X_sampled) == sample_size -@parametrize('X', [np.asarray([[0, 0, 1]] * 10)]) -@parametrize('dtype', supported_precision_reductions + [np.dtype('float32'), np.dtype('float64')]) -@parametrize('x_type', [np.ndarray, csr_matrix]) +@parametrize("X", [np.asarray([[0, 0, 1]] * 10)]) +@parametrize( + "dtype", supported_precision_reductions + [np.dtype("float32"), np.dtype("float64")] +) +@parametrize("x_type", [np.ndarray, csr_matrix]) def test_reduce_precision_correctly_reduces_precision(X, dtype, x_type): X = X.astype(dtype) if x_type == csr_matrix: @@ -376,13 +405,13 @@ def test_reduce_precision_correctly_reduces_precision(X, dtype, x_type): expected: Dict[type, type] = { np.float32: np.float32, np.float64: np.float32, - np.dtype('float32'): np.float32, - np.dtype('float64'): np.float32 + np.dtype("float32"): np.float32, + np.dtype("float64"): np.float32, } - if hasattr(np, 'float96'): + if hasattr(np, "float96"): expected[np.float96] = np.float64 - if hasattr(np, 'float128'): + if hasattr(np, "float128"): expected[np.float128] = np.float64 assert precision == expected[dtype] @@ -394,28 +423,40 @@ def test_reduce_precision_correctly_reduces_precision(X, dtype, x_type): assert type(X) == type(X_reduced) -@parametrize('X', [np.asarray([0, 0, 1]) * 10]) -@parametrize('dtype', [np.int32, np.int64, np.complex128]) +@parametrize("X", [np.asarray([0, 0, 1]) * 10]) +@parametrize("dtype", [np.int32, np.int64, np.complex128]) def test_reduce_precision_with_unsupported_dtypes(X, dtype): X = X.astype(dtype) with pytest.raises(ValueError) as err: reduce_precision(X) - expected = f"X.dtype = {X.dtype} not equal to any supported {supported_precision_reductions}" + expected = ( + f"X.dtype = {X.dtype} not equal to any supported " + f"{supported_precision_reductions}" + ) + assert err.value.args[0] == expected -@parametrize("X", [ - np.ones((100000, 10), dtype=np.float64) # Make it big for reductions to take place -]) +@parametrize( + "X", + [ + np.ones( + (100000, 10), dtype=np.float64 + ) # Make it big for reductions to take place + ], +) @parametrize("x_type", [csr_matrix, np.ndarray]) @parametrize("dtype", supported_precision_reductions) -@parametrize('y, is_classification', [ - (np.ones((100000,)), True), - (np.ones((100000,)), False), -]) -@parametrize('memory_allocation', [0.1, 1/5.2, 1/8, 1]) -@parametrize('operations', [['precision'], ['subsample'], ['precision', 'subsample']]) +@parametrize( + "y, is_classification", + [ + (np.ones((100000,)), True), + (np.ones((100000,)), False), + ], +) +@parametrize("memory_allocation", [0.1, 1 / 5.2, 1 / 8, 1]) +@parametrize("operations", [["precision"], ["subsample"], ["precision", "subsample"]]) def test_reduce_dataset_reduces_size_and_precision( X, x_type, dtype, y, is_classification, memory_allocation, operations ): @@ -444,13 +485,13 @@ def bytes(arr): return arr.nbytes if isinstance(arr, np.ndarray) else arr.data.nbytes # If we expect some precision reduction unless at float32 already - if 'precision' in operations and dtype != np.float32: + if "precision" in operations and dtype != np.float32: expected = reduction_mapping[X.dtype] assert X_out.dtype == expected assert bytes(X_out) < bytes(X) # If we expect some subsampling - if 'subsample' in operations: + if "subsample" in operations: assert X_out.shape[0] < X.shape[0] assert y_out.shape[0] < y.shape[0] assert bytes(X_out) < bytes(X) @@ -464,10 +505,10 @@ def test_reduce_dataset_invalid_dtype_for_precision_reduction(): reduce_dataset_size_if_too_large( X=X, y=X, - operations=['precision'], + operations=["precision"], memory_limit=1, memory_allocation=0.1, - is_classification=False + is_classification=False, ) expected_err = f"Unsupported type `{X.dtype}` for precision reduction" @@ -485,7 +526,7 @@ def test_reduce_dataset_invalid_operations(): operations=[invalid_op], memory_limit=1, memory_allocation=0.1, - is_classification=False + is_classification=False, ) expected_err = f"Unknown operation `{invalid_op}`" @@ -504,13 +545,15 @@ def test_reduce_dataset_invalid_memory_allocation_float(memory_allocation: float ------- * Should raise a ValueError """ - with pytest.raises(ValueError, match=r"memory_allocation if float must be in \(0, 1\)"): + with pytest.raises( + ValueError, match=r"memory_allocation if float must be in \(0, 1\)" + ): reduce_dataset_size_if_too_large( X=np.empty(1), y=np.empty(1), memory_limit=100, is_classification=True, - memory_allocation=memory_allocation + memory_allocation=memory_allocation, ) @@ -526,17 +569,19 @@ def test_reduce_dataset_invalid_memory_allocation_int(memory_allocation: int): ------- * Should raise a ValueError """ - with pytest.raises(ValueError, match=r"memory_allocation if int must be in \(0, memory_limit"): + with pytest.raises( + ValueError, match=r"memory_allocation if int must be in \(0, memory_limit" + ): reduce_dataset_size_if_too_large( X=np.empty(1), y=np.empty(1), is_classification=True, memory_limit=100, - memory_allocation=memory_allocation + memory_allocation=memory_allocation, ) -@parametrize("memory_allocation", ["100", {'a': 1}, [100]]) +@parametrize("memory_allocation", ["100", {"a": 1}, [100]]) def test_reduce_dataset_invalid_memory_allocation_type(memory_allocation: Any): """ Parameters @@ -554,25 +599,30 @@ def test_reduce_dataset_invalid_memory_allocation_type(memory_allocation: Any): y=np.empty(1), memory_limit=100, is_classification=True, - memory_allocation=memory_allocation + memory_allocation=memory_allocation, ) @pytest.mark.parametrize( - 'memory_limit,precision,task', + "memory_limit,precision,task", [ (memory_limit, precision, task) for task in chain(CLASSIFICATION_TASKS, REGRESSION_TASKS) for precision in (float, np.float32, np.float64, np.float128) for memory_limit in (1, 100) - ] + ], ) def test_reduce_dataset_subsampling_explicit_values(memory_limit, precision, task): random_state = 0 fixture = { BINARY_CLASSIFICATION: { 1: {float: 2621, np.float32: 2621, np.float64: 2621, np.float128: 1310}, - 100: {float: 12000, np.float32: 12000, np.float64: 12000, np.float128: 12000}, + 100: { + float: 12000, + np.float32: 12000, + np.float64: 12000, + np.float128: 12000, + }, }, MULTICLASS_CLASSIFICATION: { 1: {float: 409, np.float32: 409, np.float64: 409, np.float128: 204}, @@ -589,7 +639,7 @@ def test_reduce_dataset_subsampling_explicit_values(memory_limit, precision, tas MULTIOUTPUT_REGRESSION: { 1: {float: 1310, np.float32: 1310, np.float64: 1310, np.float128: 655}, 100: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000}, - } + }, } # Create the task and data @@ -620,12 +670,13 @@ def test_reduce_dataset_subsampling_explicit_values(memory_limit, precision, tas with warnings.catch_warnings(): warnings.simplefilter("ignore") X_new, y_new = reduce_dataset_size_if_too_large( - X=X, y=y, + X=X, + y=y, random_state=random_state, memory_limit=memory_limit, is_classification=task in CLASSIFICATION_TASKS, - operations=['precision', 'subsample'], - memory_allocation=0.1 + operations=["precision", "subsample"], + memory_allocation=0.1, ) # Assert the new number of samples diff --git a/test/test_util/test_dependencies.py b/test/test_util/test_dependencies.py index 53b2285750..1c59dad51b 100644 --- a/test/test_util/test_dependencies.py +++ b/test/test_util/test_dependencies.py @@ -1,30 +1,31 @@ -import unittest -import pkg_resources import re - -from unittest.mock import patch, Mock +import unittest +from unittest.mock import Mock, patch import numpy as np +import pkg_resources -from autosklearn.util.dependencies import verify_packages, MissingPackageError, \ - IncorrectPackageVersionError +from autosklearn.util.dependencies import ( + IncorrectPackageVersionError, + MissingPackageError, + verify_packages, +) -@patch('pkg_resources.get_distribution') +@patch("pkg_resources.get_distribution") class VerifyPackagesTests(unittest.TestCase): - def test_existing_package(self, getDistributionMock): - requirement = 'package' + requirement = "package" distribution_mock = unittest.mock.Mock() getDistributionMock.return_value = distribution_mock - distribution_mock.version = '1.0.0' + distribution_mock.version = "1.0.0" verify_packages(requirement) - getDistributionMock.assert_called_once_with('package') + getDistributionMock.assert_called_once_with("package") def test_missing_package(self, getDistributionMock): - requirement = 'package' + requirement = "package" getDistributionMock.side_effect = pkg_resources.DistributionNotFound() @@ -35,7 +36,7 @@ def test_missing_package(self, getDistributionMock): requirement, ) - @patch('importlib.import_module') + @patch("importlib.import_module") def test_package_can_only_be_imported(self, import_mock, getDistributionMock): getDistributionMock.side_effect = pkg_resources.DistributionNotFound() @@ -43,60 +44,64 @@ def test_package_can_only_be_imported(self, import_mock, getDistributionMock): package.__version__ = np.__version__ import_mock.return_value = package - verify_packages('numpy') + verify_packages("numpy") def test_correct_package_versions(self, getDistributionMock): - requirement = 'package==0.1.2\n' \ - 'package>0.1\n' \ - 'package>=0.1' + requirement = "package==0.1.2\n" "package>0.1\n" "package>=0.1" moduleMock = Mock() - moduleMock.version = '0.1.2' + moduleMock.version = "0.1.2" getDistributionMock.return_value = moduleMock verify_packages(requirement) - getDistributionMock.assert_called_with('package') + getDistributionMock.assert_called_with("package") self.assertEqual(3, len(getDistributionMock.call_args_list)) def test_wrong_package_version(self, getDistributionMock): - requirement = 'package>0.1.2' + requirement = "package>0.1.2" moduleMock = Mock() - moduleMock.version = '0.1.2' + moduleMock.version = "0.1.2" getDistributionMock.return_value = moduleMock self.assertRaisesRegex( IncorrectPackageVersionError, - re.escape("found 'package' version 0.1.2 but requires package version >0.1.2"), + re.escape( + "found 'package' version 0.1.2 but requires package version >0.1.2" + ), verify_packages, requirement, - ) + ) def test_outdated_requirement(self, getDistributionMock): - requirement = 'package>=0.1' + requirement = "package>=0.1" moduleMock = Mock() - moduleMock.version = '0.0.9' + moduleMock.version = "0.0.9" getDistributionMock.return_value = moduleMock self.assertRaisesRegex( IncorrectPackageVersionError, - re.escape("found 'package' version 0.0.9 but requires package version >=0.1"), + re.escape( + "found 'package' version 0.0.9 but requires package version >=0.1" + ), verify_packages, requirement, - ) + ) def test_too_fresh_requirement(self, getDistributionMock): - requirement = 'package==0.1.2' + requirement = "package==0.1.2" moduleMock = Mock() - moduleMock.version = '0.1.3' + moduleMock.version = "0.1.3" getDistributionMock.return_value = moduleMock self.assertRaisesRegex( IncorrectPackageVersionError, - re.escape("found 'package' version 0.1.3 but requires package version ==0.1.2"), + re.escape( + "found 'package' version 0.1.3 but requires package version ==0.1.2" + ), verify_packages, requirement, - ) + ) diff --git a/test/test_util/test_logging.py b/test/test_util/test_logging.py index 568593c7c8..d824aecc02 100644 --- a/test/test_util/test_logging.py +++ b/test/test_util/test_logging.py @@ -1,47 +1,46 @@ -import os -import unittest import logging import logging.config +import os import tempfile -import yaml +import unittest +import yaml from autosklearn.util import logging_ class LoggingTest(unittest.TestCase): - def test_setup_logger(self): # Test that setup_logger function correctly configures the logger # according to the given dictionary, and uses the default # logging.yaml file if logging_config is not specified. - with open(os.path.join(os.path.dirname(__file__), 'example_config.yaml'), 'r') as fh: + with open( + os.path.join(os.path.dirname(__file__), "example_config.yaml"), "r" + ) as fh: example_config = yaml.safe_load(fh) # Configure logger with example_config.yaml. - logging_.setup_logger(logging_config=example_config, - output_dir=tempfile.gettempdir()) + logging_.setup_logger( + logging_config=example_config, output_dir=tempfile.gettempdir() + ) # example_config sets the root logger's level to CRITICAL, # which corresponds to 50. self.assertEqual(logging.getLogger().getEffectiveLevel(), 50) # This time use the default configuration. - logging_.setup_logger(logging_config=None, - output_dir=tempfile.gettempdir()) + logging_.setup_logger(logging_config=None, output_dir=tempfile.gettempdir()) # default config sets the root logger's level to DEBUG, # which corresponds to 10. self.assertEqual(logging.getLogger().getEffectiveLevel(), 10) # Make sure we log to the desired directory - logging_.setup_logger(output_dir=os.path.dirname(__file__), - filename='test.log' - ) + logging_.setup_logger(output_dir=os.path.dirname(__file__), filename="test.log") logger = logging.getLogger() - logger.info('test_setup_logger') + logger.info("test_setup_logger") - with open(os.path.join(os.path.dirname(__file__), 'test.log')) as fh: - self.assertIn('test_setup_logger', ''.join(fh.readlines())) - os.remove(os.path.join(os.path.dirname(__file__), 'test.log')) + with open(os.path.join(os.path.dirname(__file__), "test.log")) as fh: + self.assertIn("test_setup_logger", "".join(fh.readlines())) + os.remove(os.path.join(os.path.dirname(__file__), "test.log")) diff --git a/test/test_util/test_single_thread_client.py b/test/test_util/test_single_thread_client.py index 34fe7736fe..770ff9f04a 100644 --- a/test/test_util/test_single_thread_client.py +++ b/test/test_util/test_single_thread_client.py @@ -1,8 +1,6 @@ import dask.distributed - -from distributed.utils_test import inc - import pytest +from distributed.utils_test import inc from autosklearn.util.single_thread_client import SingleThreadedClient diff --git a/test/test_util/test_trials_callback.py b/test/test_util/test_trials_callback.py index 3cda8ea204..d1bfe6b748 100644 --- a/test/test_util/test_trials_callback.py +++ b/test/test_util/test_trials_callback.py @@ -13,56 +13,62 @@ class AutoMLTrialsCallBack(IncorporateRunResultCallback): - def __init__(self, fname): self.trials_num = 1 self.fname = fname with open(fname, "w") as fp: - fp.write("TrialNo, " - "StartTime, " - "EndTime, " - "Status, " - "TrainLoss, " - "ValidLoss, " - "TestLoss, " - "Classifier") + fp.write( + "TrialNo, " + "StartTime, " + "EndTime, " + "Status, " + "TrainLoss, " + "ValidLoss, " + "TestLoss, " + "Classifier" + ) def __call__( - self, smbo: 'SMBO', - run_info: RunInfo, - result: RunValue, - time_left: float, + self, + smbo: "SMBO", + run_info: RunInfo, + result: RunValue, + time_left: float, ) -> None: train_loss, valid_loss, test_loss = None, None, None trial_start_time = result.starttime trial_end_time = result.endtime trial_status = result.status.name if trial_status == StatusType.SUCCESS.name: - train_loss = result.additional_info.get('train_loss') + train_loss = result.additional_info.get("train_loss") valid_loss = result.cost - test_loss = result.additional_info.get('test_loss') - trial_classifier = run_info.config.get_dictionary()['classifier:__choice__'] + test_loss = result.additional_info.get("test_loss") + trial_classifier = run_info.config.get_dictionary()["classifier:__choice__"] with open(self.fname, "a+") as fp: - fp.write(f"\n {self.trials_num}, {trial_start_time}, {trial_end_time}, {trial_status}, " - f"{train_loss}, {valid_loss}, {test_loss}, {trial_classifier}") + fp.write( + f"\n {self.trials_num}, {trial_start_time}, {trial_end_time}," + f" {trial_status}, {train_loss}, {valid_loss}, {test_loss}," + f" {trial_classifier}" + ) self.trials_num += 1 class VerifyTrialsCallBack(unittest.TestCase): - def test_trials_callback_execution(self): trials_summary_fname = os.path.join(tempfile.gettempdir(), "trials.csv") - X_train, Y_train, X_test, Y_test = putil.get_dataset('breast_cancer') - cls = AutoSklearnClassifier(time_left_for_this_task=30, - initial_configurations_via_metalearning=0, - per_run_time_limit=10, - memory_limit=1024, - delete_tmp_folder_after_terminate=False, - n_jobs=1, - include={'feature_preprocessor': ['pca'], - 'classifier': ['sgd']}, - get_trials_callback=AutoMLTrialsCallBack(trials_summary_fname) - ) + X_train, Y_train, X_test, Y_test = putil.get_dataset("breast_cancer") + cls = AutoSklearnClassifier( + time_left_for_this_task=30, + initial_configurations_via_metalearning=0, + per_run_time_limit=10, + memory_limit=1024, + delete_tmp_folder_after_terminate=False, + n_jobs=1, + include={"feature_preprocessor": ["pca"], "classifier": ["sgd"]}, + get_trials_callback=AutoMLTrialsCallBack(trials_summary_fname), + ) cls.fit(X_train, Y_train, X_test, Y_test) trials = pd.read_csv(trials_summary_fname) - assert trials.shape[0] > 0, f"Auto-Sklearn explored {trials.shape[0] - 1} trials" + assert ( + trials.shape[0] > 0 + ), f"Auto-Sklearn explored {trials.shape[0] - 1} trials" diff --git a/testcommand.sh b/testcommand.sh deleted file mode 100644 index 00c8fe8321..0000000000 --- a/testcommand.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/usr/bin/env bash -pytest -n 3 --durations=20 --timeout=300 --dist load --timeout-method=thread --fulltrace -v $1 From a5bd76519388b02ab278492d190d16b7ea2070b2 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Mon, 7 Feb 2022 23:12:50 +0100 Subject: [PATCH 027/118] Add: workflow for validating contribution.cff (#1390) --- .github/workflows/citation_cff.yml | 19 +++++++++++++++++++ CITATION.cff | 10 +++++----- 2 files changed, 24 insertions(+), 5 deletions(-) create mode 100644 .github/workflows/citation_cff.yml diff --git a/.github/workflows/citation_cff.yml b/.github/workflows/citation_cff.yml new file mode 100644 index 0000000000..707a71c4b2 --- /dev/null +++ b/.github/workflows/citation_cff.yml @@ -0,0 +1,19 @@ +name: cffconvert + +on: + push: + paths: + - CITATION.cff + +jobs: + validate: + name: "validate" + runs-on: ubuntu-latest + steps: + - name: Check out a copy of the repository + uses: actions/checkout@v2 + + - name: Check whether the citation metadata from CITATION.cff is valid + uses: citation-file-format/cffconvert-github-action@2.0.0 + with: + args: "--validate" diff --git a/CITATION.cff b/CITATION.cff index a4e1395c80..38777995f0 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -25,15 +25,15 @@ authors: affiliation: "University of Freiburg, Germany" - family-names: "Eggensperger" - given-names: "Katharina" + given-names: "Katharina" orcid: "https://orcid.org/0000-0002-0309-401X" affiliation: "University of Freiburg, Germany" - - family-name: "Bergman" + - family-names: "Bergman" given-names: "Edward" affiliation: "University of Freiburg, Germany" - - family-name: "Rivera Valverde" + - family-names: "Rivera Valverde" given-names: "Francisco" affiliation: "University of Freiburg, Germany" @@ -81,7 +81,7 @@ preferred-citation: authors: - family-names: "Feurer" - given-names: "Matthias" + given-names: "Matthias" orcid: "https://orcid.org/0000-0001-9611-8588" affiliation: "University of Freiburg, Germany" @@ -90,7 +90,7 @@ preferred-citation: affiliation: "University of Freiburg, Germany" - family-names: "Eggensperger" - given-names: "Katharina" + given-names: "Katharina" orcid: "https://orcid.org/0000-0002-0309-401X" affiliation: "University of Freiburg, Germany" From d9fec9081ba73019ab31fe309ec74c269ba363ee Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Tue, 8 Feb 2022 16:15:51 +0100 Subject: [PATCH 028/118] Fix: approx usage in pytest as illuminated with pytest/9061 (#1391) --- test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py b/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py index 856fd595cb..34a2c8e8d1 100644 --- a/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py +++ b/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py @@ -226,7 +226,7 @@ def test_percentage_of_features_with_missing_values(sparse_data): mf = meta_features.metafeatures["PercentageOfFeaturesWithMissingValues"]( X, y, logging.getLogger("Meta"), categorical ) - assert pytest.approx(0, mf.value) + assert mf.value == pytest.approx(0.0) def test_num_symbols(sparse_data): From 0ee3cd6b049ab17ac74d9456638d55cc8eab03e0 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Tue, 8 Feb 2022 16:16:13 +0100 Subject: [PATCH 029/118] Fix: Update codecov to 84% to account for fact `if` branches now covered (#1393) --- .codecov.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.codecov.yml b/.codecov.yml index fcf754bf7d..e2676824a2 100755 --- a/.codecov.yml +++ b/.codecov.yml @@ -10,12 +10,12 @@ coverage: # https://docs.codecov.com/docs/commit-status status: - # We want our total main project to always remain above 87% coverage, a + # We want our total main project to always remain above 84% coverage, a # drop of 0.20% is allowed. It should fail if coverage couldn't be uploaded # of the CI fails otherwise project: default: - target: 87% + target: 84% threshold: 0.20% if_not_found: failure if_ci_failed: error From f13f54b1ddee9339285c88b1557fc2304dd7f5a4 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Tue, 8 Feb 2022 16:16:24 +0100 Subject: [PATCH 030/118] Fix: Non-formatted file, update pre-commit to use defaults (#1392) --- .pre-commit-config.yaml | 5 ----- test/test_pipeline/test_classification.py | 6 ++++-- 2 files changed, 4 insertions(+), 7 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 4c8a59dc4e..f20c303b2f 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -9,12 +9,10 @@ repos: - id: isort name: isort imports autosklearn files: autosklearn/.* - args: [--check] - id: isort name: isort imports test files: test/.* - args: [--check] - repo: https://github.com/ambv/black rev: 22.1.0 @@ -22,17 +20,14 @@ repos: - id: black name: black formatter autosklearn files: autosklearn/.* - args: [--check] - id: black name: black formatter test files: test/.* - args: [--check] - id: black name: black formatter examples files: examples/.* - args: [--check] # This is disabled as most modules fail this - repo: https://github.com/pycqa/pydocstyle diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index c214e10cfd..1ce93ebb0d 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -612,8 +612,10 @@ def _test_configurations( # Assumed to be caused by knn with preprocessor fast_ica with whiten elif "Input contains NaN, infinity or a value too large" in e.args[0]: continue - elif "zero-size array to reduction operation maximum which has no " \ - "identity" in e.args[0]: + elif ( + "zero-size array to reduction operation maximum which has no " + "identity" in e.args[0] + ): continue else: e.args += (f"config={config}",) From 7482b74a9759180c6ab48cbced160e392826e8f2 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Tue, 8 Feb 2022 16:16:52 +0100 Subject: [PATCH 031/118] Update: Contributing guide with respect to #1358 (#1389) * Update: Contributing guide with respect to #1358 * Fix: Add line on manually running pre-commit * Add: Line for `isort` in contributing guide * Fix: uncomment `make examples` in overview --- CONTRIBUTING.md | 79 ++++++++++++++++++++++++++++++++----------------- 1 file changed, 52 insertions(+), 27 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 3e3e4cb181..aa3b656d34 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -87,7 +87,11 @@ Following that we'll tell you about how you can test your changes locally and th Fortunately, you can check out [pyenv](https://github.com/pyenv/pyenv) which lets you switch between Python versions on the fly! * Now that we have a virtual environment, it's time to install all the dependencies into it. + We've provided a simple `make` command to help do this. ```bash + make install-dev + + # Manually pip install -e .[test,examples,doc] # If you're using shells other than bash you'll need to use @@ -101,6 +105,8 @@ Following that we'll tell you about how you can test your changes locally and th You can check out what these are in the `setup.py` file. * If you're new to virtual environments, after performing all this, it's a great time to check out what actually exists in the `my-virtual-env` folder. +* You can check out some functionality we have captured in a `Makefile` by running `make help` + * Now it's time to make some changes, whether it be for [documentation](#documentation), a [bug fix](#bug-fixes) or a new [features](#features). ## Making Changes @@ -234,23 +240,18 @@ Lastly, if the feature really is a game changer or you're very proud of it, cons * Now we are going to use [sphinx](https://www.sphinx-doc.org/en/master/) to generate all the documentation and make sure there are no issues. ```bash - cd doc - make html + make doc ``` * If you're unfamiliar with sphinx, it's a documentation generator which can read comments and docstrings from within the code and generate html documentation. - * We also use sphinx-gallery which can take python files (such as those in the `examples` folder) and run them, creating html which shows the code and the output it generates. - Unfortunately this can take quite some time but you should only have to run this once. - * If you need to quickly check something, you can run `make html-noexamples` to prevent sphinx from running the examples. - ```bash - cd doc - make html-noexamples - ``` - * Sphinx also has a command `linkcheck` for making sure all the links correctly go to some destination. + * If you've added documentation, we also has a command `linkcheck` for making sure all the links correctly go to some destination. This helps tests for dead links or accidental typos. ```bash - cd doc make linkcheck ``` + * We also use sphinx-gallery which can take python files (such as those in the `examples` folder) and run them, creating html which shows the code and the output it generates. + ```bash + make examples + ``` * To view the documentation itself, make sure it is built with the above commands and then open `doc/build/html/index.html` with your favourite browser: ```bash # Firefox @@ -261,19 +262,33 @@ Lastly, if the feature really is a game changer or you're very proud of it, cons ``` * Once you've made all your changes and all the tests pass successfully, we need to make sure that the code fits a certain format and that the [typing](https://docs.python.org/3/library/typing.html) is correct. - To do this, we use a tool call `pre-commit` which runs `flake8`, a code checker and `mypy`, a static type checker against the code. + * Formatting and import sorting can helps keep things uniform across all coding styles. We use `black` and `isort` to do this for us. To automatically run these formatters across the code base, just run the following command: ```bash - pip install pre-commit - pre-commit run --all-files + make format ``` - * The reason we use a code standard like `flake8` is to make sure that when we review code: - * There are no extra blank spaces and blank lines. - * Lines don't end up too long - * Code from multiple source keeps a similar appearance. - * We perform static type checking with `mypy` as this can remove a majority of bugs, before a test is even run. - It points out programmer errors and is what makes compiled languages so safe. + * To then check for other issues with `mypy`, `flake8` and `pydocstyle`, run + ```bash + make check + ``` + * To do this checking automatically, we use `pre-commit` which if you already installed everything with `make install-dev` then this has been done for you. + This will happen every time you make a commit and warn you of any issues. + Otherwise you can run the following to install pre-commit. + ```bash + pre-commit install + ``` + * To run `pre-commit` manually: + ```bash + pre-commit run --al-files + ``` + * The reason we use tools like `flake8`, `mypy`, `black`, `isort` and `pydocstyle` is to make sure that when we review code: + * There are no extra blank spaces and blank lines. (`flake8`, `black`) + * Lines don't end up too long. (`flake8`, `black`) + * Code from multiple source keeps a similar appearance. (`black`) + * Importing things is consistently ordered. (`isort`) + * Functions are type annotated and correct with static type checking. (`mypy`) + * Function and classes have docstrings. (`pydocstyle`) If you are new to Python types, or stuck with how something should be 'typed', please feel free to push the pull request in the following steps and we should be able to help you out. - * If interested, the configuration for `pre-commit` can be found in `.pre-commit-config.yaml` + * If interested, the configuration for `pre-commit` can be found in `.pre-commit-config.yaml` with the other tools mainly being configured in `pyproject.toml` and `.flake8`. ## Creating the PR @@ -341,6 +356,7 @@ Lastly, if the feature really is a game changer or you're very proud of it, cons # Pull Request Overview * Create a [fork](https://docs.github.com/en/get-started/quickstart/fork-a-repo) of the [automl/auto-sklearn](https://github.com/automl/auto-sklearn) git repo +* Check out what's available by running `make help`. * Clone your own fork and create a new branch from the branch to work on ```bash git clone git@github.com:your-username/auto-sklearn.git @@ -355,15 +371,24 @@ Lastly, if the feature really is a game changer or you're very proud of it, cons python -m venv my-virtual-env source my-virtual-env/bin/activate - pip install -e .[test,docs,examples] # zsh users need quotes ".[test,...]" + make install-dev + # pip install -e ".[test,docs,examples]" # To manually install things # Edit files... + # Format code + make format + + # Check for any issues + make check + + # ... fix any issues + # If you changed documentation: - # This will generate all documentation, run examples and check links - cd doc - make html + # This will generate all documentation and check links + make doc make linkcheck + make examples # mainly needed if you modified some examples # ... fix any issues @@ -373,8 +398,8 @@ Lastly, if the feature really is a game changer or you're very proud of it, cons # ... fix any issues - # Use pre-commit for style and typing checks - pip install pre-commit + # If you want to run pre-commit, the formatting checks we run on github + pre-commit install pre-commit run --all-files # ... fix any issues From ea93372ef35b024cc27d6d253eabd86735d266c7 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Wed, 9 Feb 2022 14:40:18 +0100 Subject: [PATCH 032/118] Fix makefile (#1397) --- Makefile | 26 +++++++++++++++++--------- 1 file changed, 17 insertions(+), 9 deletions(-) diff --git a/Makefile b/Makefile index 495b1c2f24..2841ea9da0 100644 --- a/Makefile +++ b/Makefile @@ -33,10 +33,10 @@ MYPY ?= mypy PRECOMMIT ?= pre-commit FLAKE8 ?= flake8 -DIR := ${CURDIR} -DIST := ${CURDIR}/dist -DOCDIR := ${DIR}/doc -INDEX_HTML := file://${DOCDIR}/html/build/index.html +DIR := "${CURDIR}" +DIST := "${CURDIR}/dist"" +DOCDIR := "${DIR}/doc" +INDEX_HTML := "file://${DOCDIR}/build/html/index.html" install-dev: $(PIP) install -e ".[test,examples,docs]" @@ -88,7 +88,7 @@ clean: clean-doc clean-build # Build a distribution in ./dist build: - $(PYTHON) setup.py bdist + $(PYTHON) setup.py sdist doc: $(MAKE) -C ${DOCDIR} html-noexamples @@ -108,15 +108,23 @@ examples: # Publish to testpypi # Will echo the commands to actually publish to be run to publish to actual PyPi # This is done to prevent accidental publishing but provide the same conveniences -publish: clean-build build +publish: clean build $(PIP) install twine $(PYTHON) -m twine upload --repository testpypi ${DIST}/* @echo - @echo "Test with the following line:" - @echo "pip install --index-url https://test.pypi.org/simple/ auto-sklearn" + @echo "Test with the following:" + @echo "* Create a new virtual environment to install the uplaoded distribution into" + @echo "* Run the following:" + @echo + @echo " pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ autosklearn" + @echo + @echo "* Run this to make sure it can import correctly, plus whatever else you'd like to test:" + @echo + @echo " python -c 'import autosklearn'" @echo @echo "Once you have decided it works, publish to actual pypi with" - @echo "python -m twine upload dist/*" + @echo + @echo " python -m twine upload dist/*" test: $(PYTEST) test From d34231a98ce5bedff0015947fca95645302b697b Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Wed, 9 Feb 2022 16:12:59 +0100 Subject: [PATCH 033/118] Fix: use instead of for gitignore (#1400) --- .gitignore | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index 9d2b72965a..07d38690ff 100755 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,6 @@ # Documentation -docs/build/* -docs/examples +doc/build/* +doc/examples *.py[cod] From 3a1d8f25e0f6cf50cc9c57b78b9e0cde6c209540 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Wed, 9 Feb 2022 16:13:23 +0100 Subject: [PATCH 034/118] Update tool links in contribution guide and fix a typo (#1401) --- CONTRIBUTING.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index aa3b656d34..19aaca371e 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -262,11 +262,11 @@ Lastly, if the feature really is a game changer or you're very proud of it, cons ``` * Once you've made all your changes and all the tests pass successfully, we need to make sure that the code fits a certain format and that the [typing](https://docs.python.org/3/library/typing.html) is correct. - * Formatting and import sorting can helps keep things uniform across all coding styles. We use `black` and `isort` to do this for us. To automatically run these formatters across the code base, just run the following command: + * Formatting and import sorting can helps keep things uniform across all coding styles. We use [`black`](https://black.readthedocs.io/en/stable/) and [`isort`](https://isort.readthedocs.io/en/latest/) to do this for us. To automatically run these formatters across the code base, just run the following command: ```bash make format ``` - * To then check for other issues with `mypy`, `flake8` and `pydocstyle`, run + * To then check for issues using [`black`](https://black.readthedocs.io/en/stable/), [`isort`](https://isort.readthedocs.io/en/latest/), [`mypy`](http://mypy-lang.org/), [`flake8`](https://flake8.pycqa.org/en/latest/) and [`pydocstyle`](http://www.pydocstyle.org/en/stable/), run ```bash make check ``` @@ -278,9 +278,9 @@ Lastly, if the feature really is a game changer or you're very proud of it, cons ``` * To run `pre-commit` manually: ```bash - pre-commit run --al-files + pre-commit run --all-files ``` - * The reason we use tools like `flake8`, `mypy`, `black`, `isort` and `pydocstyle` is to make sure that when we review code: + * The reason we use tools like [`flake8`](https://flake8.pycqa.org/en/latest/), [`mypy`](http://mypy-lang.org/), [`black`](https://black.readthedocs.io/en/stable/), [`isort`](https://isort.readthedocs.io/en/latest/) and [`pydocstyle`](http://www.pydocstyle.org/en/stable/) is to make sure that when we review code: * There are no extra blank spaces and blank lines. (`flake8`, `black`) * Lines don't end up too long. (`flake8`, `black`) * Code from multiple source keeps a similar appearance. (`black`) From b90d61d69bc6dad8190cc73bc93795329c80c091 Mon Sep 17 00:00:00 2001 From: Basavasagar K Patil <59369344+bkpcoding@users.noreply.github.com> Date: Thu, 17 Feb 2022 01:12:21 +0530 Subject: [PATCH 035/118] Fix the ensemble_size == 0 error in automl.py (#1369) * Fix the ensemble == 0 error in fit_ensemble and show_models function by adding a valueError to the former and giving a warning and returning empty dictionary in the latter * Update automl.py * Two tests for ensemble_size == 0 cases Added two tests to check if the automl.fit_ensemble() raises error when ensemble_size == 0 and if show_models() returns empty dictionary when ensemble_size == 0 * Update automl.py * Update test_automl.py Test for checking if the show_models() functions raise an error if models are not fitted. * Update automl.py Add a function __sklearn_is_fitted__() which returns the boolean value of self.fitted(). And add the check for model fitting in show_models() function. * Update autosklearn/automl.py * Formatting changes to clear all the pre-commit tests Co-authored-by: Eddie Bergman --- autosklearn/automl.py | 28 +++++++++++++++++++--- test/test_automl/test_automl.py | 42 +++++++++++++++++++++++++++++++++ 2 files changed, 67 insertions(+), 3 deletions(-) diff --git a/autosklearn/automl.py b/autosklearn/automl.py index cee7c492f3..03829a7146 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -13,6 +13,7 @@ import time import unittest.mock import uuid +import warnings import dask import dask.distributed @@ -302,6 +303,7 @@ def __init__( # The ensemble performance history through time self.ensemble_performance_history = [] + self.fitted = False # Single core, local runs should use fork # to prevent the __main__ requirements in @@ -348,7 +350,7 @@ def _create_dask_client(self): processes=False, threads_per_worker=1, # We use the temporal directory to save the - # dask workers, because deleting workers + # dask workers, because deleting workers takes # more time than deleting backend directories # This prevent an error saying that the worker # file was deleted, so the client could not close @@ -562,7 +564,7 @@ def fit( # "multiclass" be mean either REGRESSION or MULTICLASS_CLASSIFICATION, # and so this is where the subclasses are used to determine which. # However, this could also be deduced from the `is_classification` - # paramaeter. + # parameter. # # In the future, there is little need for the subclasses of `AutoML` # and no need for the `task` parameter. The extra functionality @@ -1068,9 +1070,13 @@ def fit( self._logger.info("Finished loading models...") self._fit_cleanup() + self.fitted = True return self + def __sklearn_is_fitted__(self) -> bool: + return self.fitted + def _fit_cleanup(self): self._logger.info("Closing the dask infrastructure") self._close_dask_client() @@ -1481,6 +1487,10 @@ def fit_ensemble( ensemble_nbest=None, ensemble_size=None, ): + # check for the case when ensemble_size is less than 0 + if not ensemble_size > 0: + raise ValueError("ensemble_size must be greater than 0 for fit_ensemble") + # AutoSklearn does not handle sparse y for now y = convert_if_sparse(y) @@ -1971,9 +1981,21 @@ def show_models(self) -> Dict[int, Any]: ------- Dict(int, Any) : dictionary of length = number of models in the ensemble A dictionary of models in the ensemble, where ``model_id`` is the key. - """ # noqa: E501 ensemble_dict = {} + # check for condition whether autosklearn is fitted if not raise runtime error + if not self.__sklearn_is_fitted__(): + raise RuntimeError("AutoSklearn has not been fitted") + + # check for ensemble_size == 0 + if self._ensemble_size == 0: + warnings.warn("No models in the ensemble. Kindly check the ensemble size.") + return ensemble_dict + + # check for condition when ensemble_size > 0 but there is no ensemble to load + if self.ensemble_ is None: + warnings.warn("No ensemble found. Returning empty dictionary.") + return ensemble_dict def has_key(rv, key): return rv.additional_info and key in rv.additional_info diff --git a/test/test_automl/test_automl.py b/test/test_automl/test_automl.py index 37040f0560..3c62f9a4da 100644 --- a/test/test_automl/test_automl.py +++ b/test/test_automl/test_automl.py @@ -88,6 +88,48 @@ def test_fit(dask_client): del automl +def test_ensemble_size_zero(): + """Test if automl.fit_ensemble raises error when ensemble_size == 0""" + X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") + automl = autosklearn.automl.AutoML( + seed=0, + time_left_for_this_task=30, + per_run_time_limit=5, + metric=accuracy, + ensemble_size=0, + ) + automl.fit(X_train, Y_train, task=MULTICLASS_CLASSIFICATION) + with pytest.raises(ValueError): + automl.fit_ensemble(Y_test, ensemble_size=0) + + +def test_empty_dict_in_show_models(): + """Test if show_models() returns empty dictionary when ensemble_size == 0""" + X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") + automl = autosklearn.automl.AutoMLClassifier( + seed=0, + time_left_for_this_task=30, + per_run_time_limit=5, + metric=accuracy, + ensemble_size=0, + ) + automl.fit(X_train, Y_train) + assert automl.show_models() == {} + + +def test_fitted_models_in_show_models(): + X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") + automl = autosklearn.automl.AutoMLClassifier( + seed=0, + time_left_for_this_task=30, + per_run_time_limit=5, + metric=accuracy, + ensemble_size=0, + ) + with pytest.raises(RuntimeError, match="AutoSklearn has not been fitted"): + automl.show_models() + + def test_fit_roar(dask_client_single_worker): def get_roar_object_callback( scenario_dict, seed, ta, ta_kwargs, dask_client, n_jobs, **kwargs From 7185aedf5d87b8b6234d5fd414ed0725f150faf1 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Mon, 21 Feb 2022 11:14:52 +0100 Subject: [PATCH 036/118] Fix: Implement hotfix from #1407, aimed at master (#1408) * Fix: Implement hotfix from #1407, aimed at master * Add: Argument to ExecuteTaFuncWithQueue * Add: multi_objectives arg to tests * Fix: Two more locations of `multi_objectives` --- autosklearn/__version__.py | 2 +- autosklearn/automl.py | 2 ++ autosklearn/evaluation/__init__.py | 2 +- autosklearn/util/single_thread_client.py | 1 + doc/releases.rst | 43 ------------------------ test/test_evaluation/test_evaluation.py | 11 ++++++ 6 files changed, 16 insertions(+), 45 deletions(-) diff --git a/autosklearn/__version__.py b/autosklearn/__version__.py index 75e1328665..6928aee41e 100644 --- a/autosklearn/__version__.py +++ b/autosklearn/__version__.py @@ -1,4 +1,4 @@ """Version information.""" # The following line *must* be the last in the module, exactly as formatted: -__version__ = "0.14.7" +__version__ = "0.14.6" diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 03829a7146..222f9de727 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -487,6 +487,7 @@ def _do_dummy_prediction(self, datamanager: XYDataManager, num_run: int) -> int: ta = ExecuteTaFuncWithQueue( backend=self._backend, autosklearn_seed=self._seed, + multi_objectives=["cost"], resampling_strategy=self._resampling_strategy, initial_num_run=num_run, stats=stats, @@ -1347,6 +1348,7 @@ def fit_pipeline( backend=self._backend, autosklearn_seed=self._seed, abort_on_first_run_crash=False, + multi_objectives=["cost"], cost_for_crash=get_cost_of_crash(kwargs["metric"]), port=self._logger_port, **kwargs, diff --git a/autosklearn/evaluation/__init__.py b/autosklearn/evaluation/__init__.py index 51ad69fbb3..8eb997c571 100644 --- a/autosklearn/evaluation/__init__.py +++ b/autosklearn/evaluation/__init__.py @@ -131,6 +131,7 @@ def __init__( abort_on_first_run_crash: bool, port: int, pynisher_context: str, + multi_objectives: List[str], initial_num_run: int = 1, stats: Optional[Stats] = None, run_obj: str = "quality", @@ -146,7 +147,6 @@ def __init__( ta: Optional[Callable] = None, **resampling_strategy_args: Any, ): - if resampling_strategy == "holdout": eval_function = autosklearn.evaluation.train_evaluator.eval_holdout elif resampling_strategy == "holdout-iterative-fit": diff --git a/autosklearn/util/single_thread_client.py b/autosklearn/util/single_thread_client.py index c27794c328..fc0c238f8d 100644 --- a/autosklearn/util/single_thread_client.py +++ b/autosklearn/util/single_thread_client.py @@ -1,4 +1,5 @@ import typing +from typing import Any from pathlib import Path from typing import Any diff --git a/doc/releases.rst b/doc/releases.rst index ccaef5e9ec..456d7f60c7 100644 --- a/doc/releases.rst +++ b/doc/releases.rst @@ -9,16 +9,6 @@ Releases ======== - -Version 0.14.7 -============== - -* HOTFIX #1445: Locks `ConfigSpace` to `<0.5.0` and `smac` to `<1.3`. Adds upper bounds on `automl` packages to help prevent further issues. - -Contributors v0.14.7 -******************** -* Eddie Bergman - Version 0.14.6 ============== @@ -38,39 +28,6 @@ Contributors v0.14.5 ******************** * Eddie Bergman -Version 0.14.4 -============== - -* Fix #1356: SVR degree hyperparameter now only active with "poly" kernel. -* Add #1311: Black format checking (non-strict). -* Maint #1306: Run history is now saved every iteration -* Doc #1309: Updated the doc faqs to include many use cases and the manual for early introductions -* Doc #1322: Fix typo in contribution guide -* Maint #1326: Add isort checker (non-strict) -* Maint #1238, #1346, #1368, #1370: Update warnings in tests -* Maint #1325: Test workflow can now be manually triggered -* Maint #1332: Update docstring and typing of ``include`` and ``exclude`` params -* Add #1260: Support for Python 3.10 -* Add #1318: First update to use the shared backend in a new submodule `automl_common `_ -* Fix #1339: Resolve dependancy issues with ``sphinx_toolbox`` -* Fix #1335: Fix issue where some regression algorithm gave incorrect output dimensions as raised in #1297 -* Doc #1340: Update example for predefined splits -* Fix #1329: Fix random state not being passed to the ConfigurationSpace -* Maint #1348: Stop double triggering of github workflows -* Doc #1349: Rename OSX to macOS in docs -* Add #1321: Change ``show_models()`` to produce actual pipeline objects and not a ``str`` -* Maint #1361: Remove ``flaky`` dependency -* Maint #1366: Make ``SimpleClassificationPipeline`` tests more deterministic -* Maint #1367: Update test values for ``MLPRegressor`` with newer numpy - -Contributors v0.14.4 -******************** - -* Eddie Bergman -* Matthias Feurer -* Katharina Eggensperger -* UserFindingSelf -* partev Version 0.14.3 ============== diff --git a/test/test_evaluation/test_evaluation.py b/test/test_evaluation/test_evaluation.py index 67d9e0ca8b..5df1f5fe50 100644 --- a/test/test_evaluation/test_evaluation.py +++ b/test/test_evaluation/test_evaluation.py @@ -91,6 +91,7 @@ def test_eval_with_limits_holdout(self, pynisher_mock): autosklearn_seed=1, port=self.logger_port, resampling_strategy="holdout", + multi_objectives=["cost"], stats=self.stats, memory_limit=3072, metric=accuracy, @@ -121,6 +122,7 @@ def test_zero_or_negative_cutoff(self, pynisher_mock): backend=self.backend, autosklearn_seed=1, port=self.logger_port, + multi_objectives=["cost"], resampling_strategy="holdout", stats=self.stats, metric=accuracy, @@ -150,6 +152,7 @@ def test_cutoff_lower_than_remaining_time(self, pynisher_mock): backend=self.backend, autosklearn_seed=1, port=self.logger_port, + multi_objectives=["cost"], resampling_strategy="holdout", stats=self.stats, metric=accuracy, @@ -181,6 +184,7 @@ def test_eval_with_limits_holdout_fail_silent(self, pynisher_mock): backend=self.backend, autosklearn_seed=1, port=self.logger_port, + multi_objectives=["cost"], resampling_strategy="holdout", stats=self.stats, memory_limit=3072, @@ -251,6 +255,7 @@ def test_eval_with_limits_holdout_fail_memory_error(self, pynisher_mock): backend=self.backend, autosklearn_seed=1, port=self.logger_port, + multi_objectives=["cost"], resampling_strategy="holdout", stats=self.stats, memory_limit=3072, @@ -292,6 +297,7 @@ def test_eval_with_limits_holdout_fail_timeout(self, pynisher_mock): backend=self.backend, autosklearn_seed=1, port=self.logger_port, + multi_objectives=["cost"], resampling_strategy="holdout", stats=self.stats, memory_limit=3072, @@ -341,6 +347,7 @@ def side_effect(**kwargs): backend=self.backend, autosklearn_seed=1, port=self.logger_port, + multi_objectives=["cost"], resampling_strategy="holdout", stats=self.stats, memory_limit=3072, @@ -376,6 +383,7 @@ def side_effect(**kwargs): backend=self.backend, autosklearn_seed=1, port=self.logger_port, + multi_objectives=["cost"], resampling_strategy="holdout", stats=self.stats, memory_limit=3072, @@ -419,6 +427,7 @@ def side_effect(*args, **kwargs): backend=self.backend, autosklearn_seed=1, port=self.logger_port, + multi_objectives=["cost"], resampling_strategy="holdout", stats=self.stats, memory_limit=3072, @@ -454,6 +463,7 @@ def test_exception_in_target_function(self, eval_holdout_mock): backend=self.backend, autosklearn_seed=1, port=self.logger_port, + multi_objectives=["cost"], resampling_strategy="holdout", stats=self.stats, memory_limit=3072, @@ -490,6 +500,7 @@ def test_silent_exception_in_target_function(self): port=self.logger_port, autosklearn_seed=1, resampling_strategy="holdout", + multi_objectives=["cost"], stats=self.stats, memory_limit=3072, metric=accuracy, From c3d488548e2dd9c253b4640bbf6d1e03fbc5e596 Mon Sep 17 00:00:00 2001 From: Lukas Strack <73999018+Louquinze@users.noreply.github.com> Date: Wed, 2 Mar 2022 10:22:53 +0100 Subject: [PATCH 037/118] Change HP Name & Include Text example (#1410) * rename "ngram_range" to "ngram_upper_bound" this includes renaming it in all *csv and *json files for metalearning * rename "ngram_range" to "ngram_upper_bound" this includes renaming it in all *csv and *json files for metalearning * handle the following issue https://github.com/automl/auto-sklearn/issues/1373#issue-1108180296 this commit fixes the first 3 bullet points on the to do list. 1. rename hyperparameter "ngram_range" --> "ngram_upper_bound" this includes changing all *csv and *json files 2. Create a new textpreprocessing example_text_preprocessing.py, this new example features the 20Newsgroups dataset import in example_text_preprocessing.py to long, but i can not come up with a good solution * handle the following issue https://github.com/automl/auto-sklearn/issues/1373#issue-1108180296 this commit fixes the first 3 bullet points on the to do list. 1. rename hyperparameter "ngram_range" --> "ngram_upper_bound" this includes changing all *csv and *json files 2. Create a new textpreprocessing example_text_preprocessing.py, this new example features the 20Newsgroups dataset import in example_text_preprocessing.py to long, but i can not come up with a good solution include feedback from 02.24. * handle the following issue https://github.com/automl/auto-sklearn/issues/1373#issue-1108180296 this commit fixes the first 3 bullet points on the to do list. 1. rename hyperparameter "ngram_range" --> "ngram_upper_bound" this includes changing all *csv and *json files 2. Create a new textpreprocessing example_text_preprocessing.py, this new example features the 20Newsgroups dataset import in example_text_preprocessing.py to long, but i can not come up with a good solution include feedback from 02.24. * handle the following issue https://github.com/automl/auto-sklearn/issues/1373#issue-1108180296 this commit fixes the first 3 bullet points on the to do list. 1. rename hyperparameter "ngram_range" --> "ngram_upper_bound" this includes changing all *csv and *json files 2. Create a new textpreprocessing example_text_preprocessing.py, this new example features the 20Newsgroups dataset import in example_text_preprocessing.py to long, but i can not come up with a good solution include feedback from 02.24. * handle the following issue https://github.com/automl/auto-sklearn/issues/1373#issue-1108180296 this commit fixes the first 3 bullet points on the to do list. 1. rename hyperparameter "ngram_range" --> "ngram_upper_bound" this includes changing all *csv and *json files 2. Create a new textpreprocessing example_text_preprocessing.py, this new example features the 20Newsgroups dataset import in example_text_preprocessing.py to long, but i can not come up with a good solution include feedback from 02.24. * handle the following issue https://github.com/automl/auto-sklearn/issues/1373#issue-1108180296 this commit fixes the first 3 bullet points on the to do list. 1. rename hyperparameter "ngram_range" --> "ngram_upper_bound" this includes changing all *csv and *json files 2. Create a new textpreprocessing example_text_preprocessing.py, this new example features the 20Newsgroups dataset import in example_text_preprocessing.py to long, but i can not come up with a good solution include feedback from 02.24. * handle the following issue https://github.com/automl/auto-sklearn/issues/1373#issue-1108180296 this commit fixes the first 3 bullet points on the to do list. 1. rename hyperparameter "ngram_range" --> "ngram_upper_bound" this includes changing all *csv and *json files 2. Create a new textpreprocessing example_text_preprocessing.py, this new example features the 20Newsgroups dataset import in example_text_preprocessing.py to long, but i can not come up with a good solution include feedback from 02.24. * limit 20NG to 5 labels. automl.leaderboard has problems if the ensamble contains only one model. Therefore we reduced the problem complexity * limit 20NG to 5 labels. automl.leaderboard has problems if the ensamble contains only one model. Therefore we reduced the problem complexity * limit 20NG to 2 labels. automl.leaderboard has problems if the ensamble contains only one model. Therefore we reduced the problem complexity * limit 20NG to 2 labels. automl.leaderboard has problems if the ensamble contains only one model. 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autosklearn/metalearning/files/median_absolute_error_regression_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/precision_binary.classification_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/precision_binary.classification_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/precision_macro_binary.classification_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/precision_macro_binary.classification_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/precision_micro_binary.classification_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/precision_micro_binary.classification_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/precision_multiclass.classification_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/precision_multiclass.classification_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/precision_samples_binary.classification_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/precision_samples_binary.classification_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/precision_weighted_binary.classification_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/r2_regression_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/r2_regression_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/recall_binary.classification_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/recall_binary.classification_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/recall_macro_binary.classification_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/recall_macro_binary.classification_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/recall_micro_binary.classification_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/recall_micro_binary.classification_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/recall_multiclass.classification_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/recall_multiclass.classification_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/recall_samples_binary.classification_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/recall_samples_binary.classification_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/recall_weighted_binary.classification_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/roc_auc_binary.classification_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/roc_auc_binary.classification_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/root_mean_squared_error_regression_dense/configurations.csv mode change 100755 => 100644 autosklearn/metalearning/files/root_mean_squared_error_regression_sparse/configurations.csv diff --git a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_10CV_iterative_es_if.json b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_10CV_iterative_es_if.json index 5740a45656..615a7e2ed0 100644 --- a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_10CV_iterative_es_if.json +++ b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_10CV_iterative_es_if.json @@ -25,7 +25,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f1500cd": { "balancing:strategy": "weighting", @@ -52,7 +52,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3e1a332": { "balancing:strategy": "weighting", @@ -77,7 +77,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "22a29ef": { "balancing:strategy": "weighting", @@ -106,7 +106,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "5886349": { "balancing:strategy": "weighting", @@ -133,7 +133,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8f54538": { "balancing:strategy": "none", @@ -163,7 +163,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9b6c297": { "balancing:strategy": "weighting", @@ -190,7 +190,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4dffb08": { "balancing:strategy": "weighting", @@ -215,7 +215,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "73611bf": { "balancing:strategy": "weighting", @@ -242,7 +242,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4d5e802": { "balancing:strategy": "weighting", @@ -269,7 +269,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "43a743f": { "balancing:strategy": "none", @@ -297,7 +297,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "eac6b8e": { "balancing:strategy": "weighting", @@ -322,7 +322,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "78a0240": { "balancing:strategy": "weighting", @@ -347,7 +347,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "809eef4": { "balancing:strategy": "weighting", @@ -373,7 +373,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "44e2072": { "balancing:strategy": "weighting", @@ -398,7 +398,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f7d054b": { "balancing:strategy": "weighting", @@ -424,7 +424,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "87cff92": { "balancing:strategy": "none", @@ -451,7 +451,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8e0e269": { "balancing:strategy": "weighting", @@ -478,7 +478,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6b55ec8": { "balancing:strategy": "weighting", @@ -498,7 +498,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4e98e28": { "balancing:strategy": "weighting", @@ -525,7 +525,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3a5c250": { "balancing:strategy": "weighting", @@ -552,7 +552,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "0dd7e47": { "balancing:strategy": "weighting", @@ -580,7 +580,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7ff3956": { "balancing:strategy": "weighting", @@ -602,7 +602,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "77861ef": { "balancing:strategy": "weighting", @@ -629,7 +629,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "79812c6": { "balancing:strategy": "weighting", @@ -656,7 +656,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "85bfdfa": { "balancing:strategy": "weighting", @@ -681,7 +681,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ce012ed": { "balancing:strategy": "weighting", @@ -701,7 +701,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e71ff84": { "balancing:strategy": "none", @@ -733,7 +733,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d4abc34": { "balancing:strategy": "weighting", @@ -764,7 +764,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "283cac3": { "balancing:strategy": "weighting", @@ -788,7 +788,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "808d532": { "balancing:strategy": "none", @@ -814,7 +814,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d2d2039": { "balancing:strategy": "weighting", @@ -841,7 +841,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_3CV_iterative_es_if.json b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_3CV_iterative_es_if.json index 1f39b65dc8..4d163e4184 100644 --- a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_3CV_iterative_es_if.json +++ b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_3CV_iterative_es_if.json @@ -24,7 +24,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "1cdd4cf": { "balancing:strategy": "weighting", @@ -45,7 +45,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "08d784a": { "balancing:strategy": "weighting", @@ -73,7 +73,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9b1161c": { "balancing:strategy": "weighting", @@ -99,7 +99,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e6b6325": { "balancing:strategy": "weighting", @@ -126,7 +126,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c138595": { "balancing:strategy": "weighting", @@ -154,7 +154,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "53634b7": { "balancing:strategy": "none", @@ -183,7 +183,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d91f995": { "balancing:strategy": "weighting", @@ -208,7 +208,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ba079f7": { "balancing:strategy": "weighting", @@ -236,7 +236,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "5251c86": { "balancing:strategy": "none", @@ -262,7 +262,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "56a06e6": { "balancing:strategy": "weighting", @@ -286,7 +286,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "762f55d": { "balancing:strategy": "weighting", @@ -313,7 +313,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "2a3926a": { "balancing:strategy": "none", @@ -339,7 +339,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7fa43cd": { "balancing:strategy": "weighting", @@ -368,7 +368,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9616495": { "balancing:strategy": "none", @@ -400,7 +400,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "1a2077f": { "balancing:strategy": "weighting", @@ -425,7 +425,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6bdd0f7": { "balancing:strategy": "weighting", @@ -450,7 +450,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "fcd1bda": { "balancing:strategy": "none", @@ -482,7 +482,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3eada54": { "balancing:strategy": "weighting", @@ -502,7 +502,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4b06ddd": { "balancing:strategy": "weighting", @@ -526,7 +526,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8d8ad31": { "balancing:strategy": "weighting", @@ -552,7 +552,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3bdcff2": { "balancing:strategy": "weighting", @@ -582,7 +582,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "42ab0ad": { "balancing:strategy": "weighting", @@ -609,7 +609,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f28f4ab": { "balancing:strategy": "weighting", @@ -637,7 +637,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f8aba41": { "balancing:strategy": "weighting", @@ -657,7 +657,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "1a6eed2": { "balancing:strategy": "weighting", @@ -683,7 +683,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "aba43bb": { "balancing:strategy": "none", @@ -714,7 +714,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "15f5c2d": { "balancing:strategy": "weighting", @@ -744,7 +744,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d3402a5": { "balancing:strategy": "weighting", @@ -776,7 +776,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3e4ac17": { "balancing:strategy": "weighting", @@ -803,7 +803,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3e922f0": { "balancing:strategy": "weighting", @@ -831,7 +831,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "b2b7a37": { "balancing:strategy": "weighting", @@ -856,7 +856,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_5CV_iterative_es_if.json b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_5CV_iterative_es_if.json index a3a67396b1..7a4cba3e5b 100644 --- a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_5CV_iterative_es_if.json +++ b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_5CV_iterative_es_if.json @@ -27,7 +27,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "153cd48": { "balancing:strategy": "weighting", @@ -53,7 +53,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "517b840": { "balancing:strategy": "weighting", @@ -76,7 +76,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "251a3fa": { "balancing:strategy": "none", @@ -105,7 +105,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "65b7e1e": { "balancing:strategy": "weighting", @@ -129,7 +129,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d814f50": { "balancing:strategy": "none", @@ -156,7 +156,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "0c1ac11": { "balancing:strategy": "weighting", @@ -181,7 +181,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "17bd445": { "balancing:strategy": "weighting", @@ -209,7 +209,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c2a619b": { "balancing:strategy": "weighting", @@ -236,7 +236,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ddc3d8c": { "balancing:strategy": "none", @@ -268,7 +268,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8c3de7c": { "balancing:strategy": "weighting", @@ -292,7 +292,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "752352a": { "balancing:strategy": "none", @@ -317,7 +317,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "b546b81": { "balancing:strategy": "weighting", @@ -345,7 +345,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c3fe2a6": { "balancing:strategy": "none", @@ -370,7 +370,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8418723": { "balancing:strategy": "weighting", @@ -397,7 +397,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "986a1dd": { "balancing:strategy": "weighting", @@ -421,7 +421,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ec57057": { "balancing:strategy": "none", @@ -453,7 +453,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "54904c9": { "balancing:strategy": "weighting", @@ -478,7 +478,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "af700cf": { "balancing:strategy": "weighting", @@ -499,7 +499,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "b05c113": { "balancing:strategy": "weighting", @@ -524,7 +524,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "58aee7e": { "balancing:strategy": "weighting", @@ -550,7 +550,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "fa44888": { "balancing:strategy": "weighting", @@ -575,7 +575,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f0dcb8c": { "balancing:strategy": "weighting", @@ -599,7 +599,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "cc08355": { "balancing:strategy": "weighting", @@ -627,7 +627,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9a87fb9": { "balancing:strategy": "weighting", @@ -655,7 +655,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "2c33a1b": { "balancing:strategy": "weighting", @@ -680,7 +680,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "64ee1f9": { "balancing:strategy": "none", @@ -711,7 +711,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e9935d1": { "balancing:strategy": "weighting", @@ -736,7 +736,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "1ccb4e6": { "balancing:strategy": "none", @@ -766,7 +766,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "70fc8a2": { "balancing:strategy": "weighting", @@ -787,7 +787,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7c8dfce": { "balancing:strategy": "weighting", @@ -815,7 +815,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9aa60c4": { "balancing:strategy": "weighting", @@ -841,7 +841,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_holdout_iterative_es_if.json b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_holdout_iterative_es_if.json index 4430df5dcc..b2964e9f37 100644 --- a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_holdout_iterative_es_if.json +++ b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_holdout_iterative_es_if.json @@ -24,7 +24,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d38f297": { "balancing:strategy": "weighting", @@ -48,7 +48,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "2d4a38e": { "balancing:strategy": "weighting", @@ -74,7 +74,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "77764f9": { "balancing:strategy": "weighting", @@ -100,7 +100,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "099d657": { "balancing:strategy": "none", @@ -129,7 +129,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "1684610": { "balancing:strategy": "weighting", @@ -157,7 +157,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "58e7fea": { "balancing:strategy": "weighting", @@ -185,7 +185,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "05690ed": { "balancing:strategy": "none", @@ -212,7 +212,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "35e418d": { "balancing:strategy": "none", @@ -235,7 +235,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "074da43": { "balancing:strategy": "weighting", @@ -260,7 +260,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7cab112": { "balancing:strategy": "weighting", @@ -289,7 +289,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c2b918a": { "balancing:strategy": "weighting", @@ -318,7 +318,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "811cba1": { "balancing:strategy": "weighting", @@ -344,7 +344,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "05327ff": { "balancing:strategy": "weighting", @@ -374,7 +374,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "32974d1": { "balancing:strategy": "none", @@ -398,7 +398,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "a3ba205": { "balancing:strategy": "weighting", @@ -421,7 +421,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "864161c": { "balancing:strategy": "weighting", @@ -447,7 +447,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d033742": { "balancing:strategy": "weighting", @@ -477,7 +477,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "eb6f7f1": { "balancing:strategy": "weighting", @@ -505,7 +505,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c29f8bf": { "balancing:strategy": "weighting", @@ -532,7 +532,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c832fa9": { "balancing:strategy": "weighting", @@ -564,7 +564,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "5d877bc": { "balancing:strategy": "none", @@ -593,7 +593,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c7158d0": { "balancing:strategy": "weighting", @@ -623,7 +623,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d618703": { "balancing:strategy": "weighting", @@ -654,7 +654,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "82a2502": { "balancing:strategy": "weighting", @@ -681,7 +681,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8e1bfe6": { "balancing:strategy": "none", @@ -708,7 +708,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4ad9cee": { "balancing:strategy": "weighting", @@ -735,7 +735,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "2f6c8d6": { "balancing:strategy": "none", @@ -759,7 +759,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6be5932": { "balancing:strategy": "none", @@ -786,7 +786,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c5628ce": { "balancing:strategy": "weighting", @@ -818,7 +818,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "564103f": { "balancing:strategy": "weighting", @@ -848,7 +848,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9ce49a5": { "balancing:strategy": "none", @@ -874,7 +874,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json index 99d1ccda90..23f127343d 100644 --- a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json +++ b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json @@ -25,7 +25,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f1500cd": { "balancing:strategy": "weighting", @@ -52,7 +52,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3e1a332": { "balancing:strategy": "weighting", @@ -77,7 +77,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7500838": { "balancing:strategy": "weighting", @@ -105,7 +105,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "dd3a0e8": { "balancing:strategy": "weighting", @@ -131,7 +131,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "13d94b3": { "balancing:strategy": "weighting", @@ -162,7 +162,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "78a0240": { "balancing:strategy": "weighting", @@ -187,7 +187,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4dffb08": { "balancing:strategy": "weighting", @@ -212,7 +212,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "43a743f": { "balancing:strategy": "none", @@ -240,7 +240,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4d5e802": { "balancing:strategy": "weighting", @@ -267,7 +267,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9b6c297": { "balancing:strategy": "weighting", @@ -294,7 +294,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "5886349": { "balancing:strategy": "weighting", @@ -321,7 +321,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "5c6b775": { "balancing:strategy": "weighting", @@ -350,7 +350,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "87cff92": { "balancing:strategy": "none", @@ -377,7 +377,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "05cb256": { "balancing:strategy": "none", @@ -410,7 +410,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4e50c99": { "balancing:strategy": "weighting", @@ -437,7 +437,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "73611bf": { "balancing:strategy": "weighting", @@ -464,7 +464,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "a885804": { "balancing:strategy": "weighting", @@ -489,7 +489,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "44e2072": { "balancing:strategy": "weighting", @@ -514,7 +514,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4b79861": { "balancing:strategy": "none", @@ -541,7 +541,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "79812c6": { "balancing:strategy": "weighting", @@ -568,7 +568,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8e0e269": { "balancing:strategy": "weighting", @@ -595,7 +595,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "66c3cf8": { "balancing:strategy": "weighting", @@ -620,7 +620,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "bff26f9": { "balancing:strategy": "weighting", @@ -643,7 +643,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ce012ed": { "balancing:strategy": "weighting", @@ -663,7 +663,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d2d2039": { "balancing:strategy": "weighting", @@ -690,7 +690,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "eac6b8e": { "balancing:strategy": "weighting", @@ -715,7 +715,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "a31129f": { "balancing:strategy": "none", @@ -745,7 +745,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "da90184": { "balancing:strategy": "weighting", @@ -771,7 +771,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "85bfdfa": { "balancing:strategy": "weighting", @@ -796,7 +796,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d4abc34": { "balancing:strategy": "weighting", @@ -827,7 +827,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "acb3733": { "balancing:strategy": "weighting", @@ -855,7 +855,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json index c10793b4d2..606926fdeb 100644 --- a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json +++ b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json @@ -24,7 +24,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "08d784a": { "balancing:strategy": "weighting", @@ -52,7 +52,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d91f995": { "balancing:strategy": "weighting", @@ -77,7 +77,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9b1161c": { "balancing:strategy": "weighting", @@ -103,7 +103,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e6b6325": { "balancing:strategy": "weighting", @@ -130,7 +130,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "53634b7": { "balancing:strategy": "none", @@ -159,7 +159,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ba079f7": { "balancing:strategy": "weighting", @@ -187,7 +187,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "eb4397b": { "balancing:strategy": "weighting", @@ -213,7 +213,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8d8ad31": { "balancing:strategy": "weighting", @@ -239,7 +239,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "56a06e6": { "balancing:strategy": "weighting", @@ -263,7 +263,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "1a2077f": { "balancing:strategy": "weighting", @@ -288,7 +288,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "762f55d": { "balancing:strategy": "weighting", @@ -315,7 +315,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3eada54": { "balancing:strategy": "weighting", @@ -335,7 +335,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "42ab0ad": { "balancing:strategy": "weighting", @@ -362,7 +362,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6bdd0f7": { "balancing:strategy": "weighting", @@ -387,7 +387,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "aba43bb": { "balancing:strategy": "none", @@ -418,7 +418,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "fcd1bda": { "balancing:strategy": "none", @@ -450,7 +450,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "418cb3d": { "balancing:strategy": "weighting", @@ -477,7 +477,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6f053cf": { "balancing:strategy": "none", @@ -507,7 +507,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c138595": { "balancing:strategy": "weighting", @@ -535,7 +535,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "237eb9d": { "balancing:strategy": "weighting", @@ -562,7 +562,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "1a6eed2": { "balancing:strategy": "weighting", @@ -588,7 +588,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "fda1986": { "balancing:strategy": "none", @@ -616,7 +616,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "2036c61": { "balancing:strategy": "weighting", @@ -646,7 +646,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c8cc243": { "balancing:strategy": "weighting", @@ -671,7 +671,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "61b0988": { "balancing:strategy": "none", @@ -693,7 +693,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "2190cfc": { "balancing:strategy": "weighting", @@ -718,7 +718,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "15debe5": { "balancing:strategy": "weighting", @@ -748,7 +748,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "45b9541": { "balancing:strategy": "none", @@ -779,7 +779,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "48cd11d": { "balancing:strategy": "none", @@ -808,7 +808,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "95e43e6": { "balancing:strategy": "none", @@ -840,7 +840,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f8aba41": { "balancing:strategy": "weighting", @@ -860,7 +860,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json index f69b4779e6..5f7be31cd6 100644 --- a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json +++ b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json @@ -27,7 +27,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "153cd48": { "balancing:strategy": "weighting", @@ -53,7 +53,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "65b7e1e": { "balancing:strategy": "weighting", @@ -77,7 +77,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7c8dfce": { "balancing:strategy": "weighting", @@ -105,7 +105,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "64ee1f9": { "balancing:strategy": "none", @@ -136,7 +136,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8ceec1c": { "balancing:strategy": "weighting", @@ -162,7 +162,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "97dbfc8": { "balancing:strategy": "none", @@ -192,7 +192,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "aa37c31": { "balancing:strategy": "weighting", @@ -218,7 +218,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "0c1ac11": { "balancing:strategy": "weighting", @@ -243,7 +243,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8418723": { "balancing:strategy": "weighting", @@ -270,7 +270,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "752352a": { "balancing:strategy": "none", @@ -295,7 +295,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "b546b81": { "balancing:strategy": "weighting", @@ -323,7 +323,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "17bd445": { "balancing:strategy": "weighting", @@ -351,7 +351,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c3fe2a6": { "balancing:strategy": "none", @@ -376,7 +376,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "58aee7e": { "balancing:strategy": "weighting", @@ -402,7 +402,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4d1f885": { "balancing:strategy": "weighting", @@ -428,7 +428,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d814f50": { "balancing:strategy": "none", @@ -455,7 +455,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "b05c113": { "balancing:strategy": "weighting", @@ -480,7 +480,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f0dcb8c": { "balancing:strategy": "weighting", @@ -504,7 +504,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9b48748": { "balancing:strategy": "none", @@ -527,7 +527,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "986a1dd": { "balancing:strategy": "weighting", @@ -551,7 +551,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9a87fb9": { "balancing:strategy": "weighting", @@ -579,7 +579,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "fa44888": { "balancing:strategy": "weighting", @@ -604,7 +604,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7ed7866": { "balancing:strategy": "weighting", @@ -632,7 +632,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "517b840": { "balancing:strategy": "weighting", @@ -655,7 +655,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8ee7265": { "balancing:strategy": "weighting", @@ -680,7 +680,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "67da9ba": { "balancing:strategy": "weighting", @@ -705,7 +705,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e9935d1": { "balancing:strategy": "weighting", @@ -730,7 +730,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "70fc8a2": { "balancing:strategy": "weighting", @@ -751,7 +751,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6bc51e2": { "balancing:strategy": "weighting", @@ -783,7 +783,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ddebe02": { "balancing:strategy": "weighting", @@ -809,7 +809,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "af700cf": { "balancing:strategy": "weighting", @@ -830,7 +830,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json index d713b3a77f..235f4e7682 100644 --- a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json +++ b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json @@ -24,7 +24,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "eb6f7f1": { "balancing:strategy": "weighting", @@ -52,7 +52,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d033742": { "balancing:strategy": "weighting", @@ -82,7 +82,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d38f297": { "balancing:strategy": "weighting", @@ -106,7 +106,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e249036": { "balancing:strategy": "weighting", @@ -134,7 +134,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "978b964": { "balancing:strategy": "weighting", @@ -158,7 +158,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "07bebab": { "balancing:strategy": "weighting", @@ -186,7 +186,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "05690ed": { "balancing:strategy": "none", @@ -213,7 +213,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8cd6304": { "balancing:strategy": "none", @@ -235,7 +235,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "73fc45b": { "balancing:strategy": "weighting", @@ -261,7 +261,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "074da43": { "balancing:strategy": "weighting", @@ -286,7 +286,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c832fa9": { "balancing:strategy": "weighting", @@ -318,7 +318,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "a3ba205": { "balancing:strategy": "weighting", @@ -341,7 +341,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "68e6606": { "balancing:strategy": "weighting", @@ -365,7 +365,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f187687": { "balancing:strategy": "weighting", @@ -391,7 +391,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "a479a3d": { "balancing:strategy": "none", @@ -419,7 +419,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c2b918a": { "balancing:strategy": "weighting", @@ -448,7 +448,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8c75f1c": { "balancing:strategy": "weighting", @@ -468,7 +468,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "77764f9": { "balancing:strategy": "weighting", @@ -494,7 +494,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "05327ff": { "balancing:strategy": "weighting", @@ -524,7 +524,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "2d4a38e": { "balancing:strategy": "weighting", @@ -550,7 +550,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d618703": { "balancing:strategy": "weighting", @@ -581,7 +581,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c437bea": { "balancing:strategy": "none", @@ -607,7 +607,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "2f6c8d6": { "balancing:strategy": "none", @@ -631,7 +631,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "811cba1": { "balancing:strategy": "weighting", @@ -657,7 +657,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c29f8bf": { "balancing:strategy": "weighting", @@ -684,7 +684,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d54bc21": { "balancing:strategy": "weighting", @@ -716,7 +716,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "b8184c0": { "balancing:strategy": "weighting", @@ -741,7 +741,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7cab112": { "balancing:strategy": "weighting", @@ -770,7 +770,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "b86e0b2": { "balancing:strategy": "weighting", @@ -791,7 +791,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "a2734c9": { "balancing:strategy": "weighting", @@ -820,7 +820,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "564103f": { "balancing:strategy": "weighting", @@ -850,7 +850,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_10CV_iterative_es_if.json b/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_10CV_iterative_es_if.json index 2b124d73b5..ef78997fe9 100644 --- a/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_10CV_iterative_es_if.json +++ b/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_10CV_iterative_es_if.json @@ -26,7 +26,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "69a5aed": { "balancing:strategy": "weighting", @@ -56,7 +56,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "278a3b7": { "balancing:strategy": "weighting", @@ -79,7 +79,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "2b21643": { "balancing:strategy": "weighting", @@ -110,7 +110,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "186f4ea": { "balancing:strategy": "none", @@ -138,7 +138,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "bad65bc": { "balancing:strategy": "none", @@ -165,7 +165,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f4ccd76": { "balancing:strategy": "weighting", @@ -192,7 +192,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "eaafcc8": { "balancing:strategy": "weighting", @@ -223,7 +223,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "5d883d6": { "balancing:strategy": "none", @@ -254,7 +254,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "bacc15e": { "balancing:strategy": "weighting", @@ -286,7 +286,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6c9a546": { "balancing:strategy": "none", @@ -317,7 +317,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "fe1cf61": { "balancing:strategy": "weighting", @@ -345,7 +345,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "231dfac": { "balancing:strategy": "none", @@ -373,7 +373,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "2d37001": { "balancing:strategy": "weighting", @@ -403,7 +403,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c11fce2": { "balancing:strategy": "weighting", @@ -427,7 +427,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "040bd83": { "balancing:strategy": "none", @@ -459,7 +459,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "0ed94fb": { "balancing:strategy": "weighting", @@ -490,7 +490,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "72903c0": { "balancing:strategy": "none", @@ -515,7 +515,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "a82be94": { "balancing:strategy": "none", @@ -541,7 +541,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "395da55": { "balancing:strategy": "weighting", @@ -573,7 +573,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "121a4ef": { "balancing:strategy": "none", @@ -599,7 +599,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e403bce": { "balancing:strategy": "weighting", @@ -626,7 +626,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e1d88ff": { "balancing:strategy": "weighting", @@ -652,7 +652,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3b1767a": { "balancing:strategy": "none", @@ -679,7 +679,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7e1cc2b": { "balancing:strategy": "none", @@ -704,7 +704,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "5ac8b4c": { "balancing:strategy": "weighting", @@ -734,7 +734,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f3b27fb": { "balancing:strategy": "none", @@ -764,7 +764,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d70e605": { "balancing:strategy": "none", @@ -794,7 +794,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "db042ae": { "balancing:strategy": "none", @@ -824,7 +824,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "46148ef": { "balancing:strategy": "none", @@ -853,7 +853,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "b96cfb3": { "balancing:strategy": "none", @@ -883,7 +883,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "a69fc2a": { "balancing:strategy": "none", @@ -908,7 +908,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_3CV_iterative_es_if.json b/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_3CV_iterative_es_if.json index 35859a3ae8..336ae5522c 100644 --- a/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_3CV_iterative_es_if.json +++ b/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_3CV_iterative_es_if.json @@ -26,7 +26,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "a4cc318": { "balancing:strategy": "weighting", @@ -55,7 +55,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "5d80c27": { "balancing:strategy": "weighting", @@ -76,7 +76,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "772b3a8": { "balancing:strategy": "none", @@ -107,7 +107,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "aa1d8b0": { "balancing:strategy": "none", @@ -134,7 +134,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9650698": { "balancing:strategy": "weighting", @@ -160,7 +160,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f203d83": { "balancing:strategy": "weighting", @@ -185,7 +185,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "128ee5e": { "balancing:strategy": "weighting", @@ -212,7 +212,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6859bdd": { "balancing:strategy": "none", @@ -237,7 +237,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7cd518a": { "balancing:strategy": "none", @@ -262,7 +262,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "94dbc0f": { "balancing:strategy": "weighting", @@ -292,7 +292,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e623b07": { "balancing:strategy": "none", @@ -321,7 +321,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "05b30ee": { "balancing:strategy": "weighting", @@ -351,7 +351,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ab05de1": { "balancing:strategy": "weighting", @@ -377,7 +377,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "fb3d3cb": { "balancing:strategy": "none", @@ -406,7 +406,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "b351885": { "balancing:strategy": "weighting", @@ -432,7 +432,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e5e92a5": { "balancing:strategy": "none", @@ -458,7 +458,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "80918df": { "balancing:strategy": "none", @@ -489,7 +489,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "789a57a": { "balancing:strategy": "none", @@ -509,7 +509,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "09b2b34": { "balancing:strategy": "none", @@ -535,7 +535,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7d4aa2b": { "balancing:strategy": "none", @@ -562,7 +562,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9fc9b85": { "balancing:strategy": "none", @@ -594,7 +594,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "0a12676": { "balancing:strategy": "weighting", @@ -624,7 +624,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7cbf2e6": { "balancing:strategy": "none", @@ -651,7 +651,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "daaac58": { "balancing:strategy": "weighting", @@ -677,7 +677,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9605116": { "balancing:strategy": "weighting", @@ -703,7 +703,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6c51fe7": { "balancing:strategy": "none", @@ -733,7 +733,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "255fc3f": { "balancing:strategy": "weighting", @@ -763,7 +763,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "5c39af4": { "balancing:strategy": "none", @@ -792,7 +792,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3b71f29": { "balancing:strategy": "none", @@ -817,7 +817,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "1726d64": { "balancing:strategy": "weighting", @@ -843,7 +843,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ddf8743": { "balancing:strategy": "none", @@ -870,7 +870,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_5CV_iterative_es_if.json b/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_5CV_iterative_es_if.json index 83b82b308a..96bcad5987 100644 --- a/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_5CV_iterative_es_if.json +++ b/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_5CV_iterative_es_if.json @@ -25,7 +25,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "04ec8ba": { "balancing:strategy": "weighting", @@ -55,7 +55,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e26c1a0": { "balancing:strategy": "weighting", @@ -77,7 +77,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d0b9bfd": { "balancing:strategy": "none", @@ -107,7 +107,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f697bc3": { "balancing:strategy": "weighting", @@ -127,7 +127,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "91ff063": { "balancing:strategy": "weighting", @@ -157,7 +157,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "5b541c8": { "balancing:strategy": "weighting", @@ -182,7 +182,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6e089e5": { "balancing:strategy": "weighting", @@ -209,7 +209,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6ab4d4e": { "balancing:strategy": "none", @@ -235,7 +235,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "0ba913b": { "balancing:strategy": "weighting", @@ -264,7 +264,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "cf5bab1": { "balancing:strategy": "weighting", @@ -291,7 +291,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "297e609": { "balancing:strategy": "none", @@ -323,7 +323,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ace7e83": { "balancing:strategy": "none", @@ -353,7 +353,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "1e5b9a4": { "balancing:strategy": "none", @@ -381,7 +381,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f937be6": { "balancing:strategy": "none", @@ -411,7 +411,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "2ff9871": { "balancing:strategy": "weighting", @@ -442,7 +442,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4fe636f": { "balancing:strategy": "weighting", @@ -469,7 +469,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "35682b2": { "balancing:strategy": "weighting", @@ -496,7 +496,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "eb711e8": { "balancing:strategy": "none", @@ -522,7 +522,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7e9bd96": { "balancing:strategy": "none", @@ -555,7 +555,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "27587a2": { "balancing:strategy": "none", @@ -579,7 +579,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "10f054f": { "balancing:strategy": "none", @@ -610,7 +610,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "dc9d49c": { "balancing:strategy": "none", @@ -638,7 +638,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ca6d42d": { "balancing:strategy": "none", @@ -658,7 +658,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "b7fb7d2": { "balancing:strategy": "none", @@ -688,7 +688,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4f414ab": { "balancing:strategy": "weighting", @@ -721,7 +721,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f1c1dc4": { "balancing:strategy": "none", @@ -748,7 +748,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "83d9459": { "balancing:strategy": "weighting", @@ -775,7 +775,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "0824a55": { "balancing:strategy": "none", @@ -807,7 +807,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "82d949b": { "balancing:strategy": "weighting", @@ -834,7 +834,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "1892815": { "balancing:strategy": "weighting", @@ -863,7 +863,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "26b1b25": { "balancing:strategy": "none", @@ -890,7 +890,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_holdout_iterative_es_if.json b/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_holdout_iterative_es_if.json index fb3b1732b0..80dd79454e 100644 --- a/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_holdout_iterative_es_if.json +++ b/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_holdout_iterative_es_if.json @@ -27,7 +27,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "fac7a35": { "balancing:strategy": "weighting", @@ -48,7 +48,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "73f216b": { "balancing:strategy": "none", @@ -77,7 +77,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "b73823e": { "balancing:strategy": "weighting", @@ -103,7 +103,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "bdbd472": { "balancing:strategy": "none", @@ -130,7 +130,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e447ff7": { "balancing:strategy": "none", @@ -162,7 +162,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "2224037": { "balancing:strategy": "weighting", @@ -192,7 +192,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c7e9f6c": { "balancing:strategy": "weighting", @@ -216,7 +216,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6931b43": { "balancing:strategy": "weighting", @@ -245,7 +245,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "281561b": { "balancing:strategy": "none", @@ -272,7 +272,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "a1fef2a": { "balancing:strategy": "none", @@ -298,7 +298,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "999a070": { "balancing:strategy": "weighting", @@ -328,7 +328,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "440cf58": { "balancing:strategy": "weighting", @@ -360,7 +360,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "2656bee": { "balancing:strategy": "none", @@ -388,7 +388,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "aeb22c7": { "balancing:strategy": "weighting", @@ -415,7 +415,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8e0506b": { "balancing:strategy": "none", @@ -446,7 +446,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "857fdf6": { "balancing:strategy": "none", @@ -472,7 +472,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8f4c1a4": { "balancing:strategy": "none", @@ -498,7 +498,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d2d26b1": { "balancing:strategy": "none", @@ -528,7 +528,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4332765": { "balancing:strategy": "weighting", @@ -556,7 +556,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "92c8a99": { "balancing:strategy": "none", @@ -585,7 +585,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "84e34e8": { "balancing:strategy": "weighting", @@ -615,7 +615,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ca1afa4": { "balancing:strategy": "weighting", @@ -645,7 +645,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ca3d668": { "balancing:strategy": "weighting", @@ -675,7 +675,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "619947f": { "balancing:strategy": "none", @@ -702,7 +702,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "947b3e7": { "balancing:strategy": "none", @@ -727,7 +727,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d366b63": { "balancing:strategy": "weighting", @@ -754,7 +754,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e2da229": { "balancing:strategy": "none", @@ -786,7 +786,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "47c2f5a": { "balancing:strategy": "weighting", @@ -808,7 +808,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "2dca877": { "balancing:strategy": "weighting", @@ -840,7 +840,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "95b36c5": { "balancing:strategy": "weighting", @@ -866,7 +866,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "981e948": { "balancing:strategy": "none", @@ -891,7 +891,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json b/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json index c95d179335..5cafbcf25d 100644 --- a/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json +++ b/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json @@ -26,7 +26,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "69a5aed": { "balancing:strategy": "weighting", @@ -56,7 +56,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "278a3b7": { "balancing:strategy": "weighting", @@ -79,7 +79,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c708530": { "balancing:strategy": "weighting", @@ -107,7 +107,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "eaafcc8": { "balancing:strategy": "weighting", @@ -138,7 +138,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "11b4871": { "balancing:strategy": "weighting", @@ -164,7 +164,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "cccabeb": { "balancing:strategy": "weighting", @@ -193,7 +193,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "121a4ef": { "balancing:strategy": "none", @@ -219,7 +219,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "bad65bc": { "balancing:strategy": "none", @@ -246,7 +246,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6da0e1a": { "balancing:strategy": "weighting", @@ -278,7 +278,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "2d37001": { "balancing:strategy": "weighting", @@ -308,7 +308,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8285215": { "balancing:strategy": "weighting", @@ -335,7 +335,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "0ed94fb": { "balancing:strategy": "weighting", @@ -366,7 +366,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c11fce2": { "balancing:strategy": "weighting", @@ -390,7 +390,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "0c324d9": { "balancing:strategy": "weighting", @@ -416,7 +416,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "423e2f5": { "balancing:strategy": "none", @@ -444,7 +444,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "a82be94": { "balancing:strategy": "none", @@ -470,7 +470,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "040bd83": { "balancing:strategy": "none", @@ -502,7 +502,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "72903c0": { "balancing:strategy": "none", @@ -527,7 +527,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "5ac8b4c": { "balancing:strategy": "weighting", @@ -557,7 +557,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "dcc5375": { "balancing:strategy": "weighting", @@ -581,7 +581,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6c58cb5": { "balancing:strategy": "weighting", @@ -607,7 +607,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "494f574": { "balancing:strategy": "none", @@ -637,7 +637,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "395da55": { "balancing:strategy": "weighting", @@ -669,7 +669,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "bf2837d": { "balancing:strategy": "none", @@ -694,7 +694,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4cf2fa5": { "balancing:strategy": "none", @@ -721,7 +721,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "5fc1b32": { "balancing:strategy": "none", @@ -749,7 +749,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d87827f": { "balancing:strategy": "none", @@ -776,7 +776,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "596ffcf": { "balancing:strategy": "none", @@ -802,7 +802,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7e1cc2b": { "balancing:strategy": "none", @@ -827,7 +827,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e5c9543": { "balancing:strategy": "weighting", @@ -849,7 +849,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "231dfac": { "balancing:strategy": "none", @@ -877,7 +877,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json b/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json index 926a652f64..286b843ff4 100644 --- a/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json +++ b/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json @@ -26,7 +26,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "772b3a8": { "balancing:strategy": "none", @@ -57,7 +57,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "5d80c27": { "balancing:strategy": "weighting", @@ -78,7 +78,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6ddc39b": { "balancing:strategy": "weighting", @@ -107,7 +107,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e5c81ce": { "balancing:strategy": "weighting", @@ -135,7 +135,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e623b07": { "balancing:strategy": "none", @@ -164,7 +164,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "05b30ee": { "balancing:strategy": "weighting", @@ -194,7 +194,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f203d83": { "balancing:strategy": "weighting", @@ -219,7 +219,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6554e90": { "balancing:strategy": "none", @@ -245,7 +245,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "0e85550": { "balancing:strategy": "weighting", @@ -270,7 +270,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "50aa7ad": { "balancing:strategy": "weighting", @@ -300,7 +300,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7d4aa2b": { "balancing:strategy": "none", @@ -327,7 +327,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "1d66cc6": { "balancing:strategy": "none", @@ -356,7 +356,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ef2bd55": { "balancing:strategy": "weighting", @@ -385,7 +385,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6973c5f": { "balancing:strategy": "none", @@ -411,7 +411,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "789a57a": { "balancing:strategy": "none", @@ -431,7 +431,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "919a721": { "balancing:strategy": "none", @@ -458,7 +458,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3b71f29": { "balancing:strategy": "none", @@ -483,7 +483,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "a43e999": { "balancing:strategy": "weighting", @@ -513,7 +513,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6859bdd": { "balancing:strategy": "none", @@ -538,7 +538,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "a03ce20": { "balancing:strategy": "none", @@ -570,7 +570,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "172a64f": { "balancing:strategy": "none", @@ -597,7 +597,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "80918df": { "balancing:strategy": "none", @@ -628,7 +628,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "1726d64": { "balancing:strategy": "weighting", @@ -654,7 +654,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9605116": { "balancing:strategy": "weighting", @@ -680,7 +680,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ef6b017": { "balancing:strategy": "weighting", @@ -710,7 +710,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ce18bb7": { "balancing:strategy": "weighting", @@ -739,7 +739,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "255fc3f": { "balancing:strategy": "weighting", @@ -769,7 +769,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e5e92a5": { "balancing:strategy": "none", @@ -795,7 +795,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6d8d3a8": { "balancing:strategy": "weighting", @@ -827,7 +827,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "84f6f17": { "balancing:strategy": "weighting", @@ -853,7 +853,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6c51fe7": { "balancing:strategy": "none", @@ -883,7 +883,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json b/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json index 3a7005ce58..8b4afb13d8 100644 --- a/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json +++ b/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json @@ -25,7 +25,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "0ba913b": { "balancing:strategy": "weighting", @@ -54,7 +54,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e26c1a0": { "balancing:strategy": "weighting", @@ -76,7 +76,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "1bbdcf7": { "balancing:strategy": "weighting", @@ -103,7 +103,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f937be6": { "balancing:strategy": "none", @@ -133,7 +133,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "b174099": { "balancing:strategy": "weighting", @@ -153,7 +153,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "04ec8ba": { "balancing:strategy": "weighting", @@ -183,7 +183,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "403e74e": { "balancing:strategy": "weighting", @@ -206,7 +206,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "83d9459": { "balancing:strategy": "weighting", @@ -233,7 +233,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6ab4d4e": { "balancing:strategy": "none", @@ -259,7 +259,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6e089e5": { "balancing:strategy": "weighting", @@ -286,7 +286,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "2ff9871": { "balancing:strategy": "weighting", @@ -317,7 +317,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8933744": { "balancing:strategy": "weighting", @@ -342,7 +342,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "271d452": { "balancing:strategy": "weighting", @@ -367,7 +367,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8c9971b": { "balancing:strategy": "none", @@ -399,7 +399,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9eaeaab": { "balancing:strategy": "weighting", @@ -430,7 +430,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ace7e83": { "balancing:strategy": "none", @@ -460,7 +460,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "dc9d49c": { "balancing:strategy": "none", @@ -488,7 +488,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7e9bd96": { "balancing:strategy": "none", @@ -521,7 +521,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "0c11923": { "balancing:strategy": "none", @@ -547,7 +547,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ca6d42d": { "balancing:strategy": "none", @@ -567,7 +567,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "27587a2": { "balancing:strategy": "none", @@ -591,7 +591,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "5367b82": { "balancing:strategy": "none", @@ -618,7 +618,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "050fe7a": { "balancing:strategy": "none", @@ -644,7 +644,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4f50608": { "balancing:strategy": "weighting", @@ -675,7 +675,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4f414ab": { "balancing:strategy": "weighting", @@ -708,7 +708,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "26b1b25": { "balancing:strategy": "none", @@ -735,7 +735,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8f7d99e": { "balancing:strategy": "weighting", @@ -765,7 +765,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "cca9a17": { "balancing:strategy": "weighting", @@ -789,7 +789,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "28afd70": { "balancing:strategy": "weighting", @@ -819,7 +819,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "2373433": { "balancing:strategy": "weighting", @@ -849,7 +849,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "0a2cd4a": { "balancing:strategy": "none", @@ -877,7 +877,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json b/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json index 3823f941fb..fe0edab8da 100644 --- a/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json +++ b/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json @@ -27,7 +27,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "fac7a35": { "balancing:strategy": "weighting", @@ -48,7 +48,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "73f216b": { "balancing:strategy": "none", @@ -77,7 +77,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e447ff7": { "balancing:strategy": "none", @@ -109,7 +109,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "b73823e": { "balancing:strategy": "weighting", @@ -135,7 +135,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6931b43": { "balancing:strategy": "weighting", @@ -164,7 +164,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c7e9f6c": { "balancing:strategy": "weighting", @@ -188,7 +188,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "667d5f4": { "balancing:strategy": "weighting", @@ -216,7 +216,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "bdbd472": { "balancing:strategy": "none", @@ -243,7 +243,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "eff997e": { "balancing:strategy": "none", @@ -273,7 +273,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "aeb22c7": { "balancing:strategy": "weighting", @@ -300,7 +300,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "a1fef2a": { "balancing:strategy": "none", @@ -326,7 +326,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "2224037": { "balancing:strategy": "weighting", @@ -356,7 +356,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "a56e902": { "balancing:strategy": "none", @@ -386,7 +386,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "fa2f9f6": { "balancing:strategy": "none", @@ -414,7 +414,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "47c2f5a": { "balancing:strategy": "weighting", @@ -436,7 +436,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d2d26b1": { "balancing:strategy": "none", @@ -466,7 +466,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d366b63": { "balancing:strategy": "weighting", @@ -493,7 +493,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4be27b9": { "balancing:strategy": "none", @@ -521,7 +521,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8e0506b": { "balancing:strategy": "none", @@ -552,7 +552,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7fb1f2e": { "balancing:strategy": "none", @@ -579,7 +579,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4332765": { "balancing:strategy": "weighting", @@ -607,7 +607,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "95b36c5": { "balancing:strategy": "weighting", @@ -633,7 +633,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "947b3e7": { "balancing:strategy": "none", @@ -658,7 +658,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "aaa7355": { "balancing:strategy": "none", @@ -688,7 +688,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "619947f": { "balancing:strategy": "none", @@ -715,7 +715,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "84e34e8": { "balancing:strategy": "weighting", @@ -745,7 +745,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "440cf58": { "balancing:strategy": "weighting", @@ -777,7 +777,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "92a2b3e": { "balancing:strategy": "none", @@ -807,7 +807,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d4b1080": { "balancing:strategy": "weighting", @@ -834,7 +834,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6933af5": { "balancing:strategy": "weighting", @@ -866,7 +866,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c2bfd40": { "balancing:strategy": "none", @@ -887,7 +887,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_10CV_iterative_es_if.json b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_10CV_iterative_es_if.json index 33c50add9d..650fe8b4a7 100644 --- a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_10CV_iterative_es_if.json +++ b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_10CV_iterative_es_if.json @@ -25,7 +25,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "2e26e3e": { "balancing:strategy": "weighting", @@ -57,7 +57,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e457d9c": { "balancing:strategy": "weighting", @@ -84,7 +84,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ef55f97": { "balancing:strategy": "weighting", @@ -111,7 +111,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "1e0cf43": { "balancing:strategy": "none", @@ -140,7 +140,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "40266af": { "balancing:strategy": "weighting", @@ -166,7 +166,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7b353d7": { "balancing:strategy": "weighting", @@ -189,7 +189,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "5aab6e4": { "balancing:strategy": "weighting", @@ -220,7 +220,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "cb9ea50": { "balancing:strategy": "none", @@ -247,7 +247,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4a934ee": { "balancing:strategy": "weighting", @@ -272,7 +272,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c0e6259": { "balancing:strategy": "weighting", @@ -304,7 +304,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "14994aa": { "balancing:strategy": "weighting", @@ -324,7 +324,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d2c45b7": { "balancing:strategy": "none", @@ -351,7 +351,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "604e370": { "balancing:strategy": "none", @@ -375,7 +375,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "13bae60": { "balancing:strategy": "weighting", @@ -400,7 +400,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "bdb1c5c": { "balancing:strategy": "weighting", @@ -432,7 +432,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6dec707": { "balancing:strategy": "weighting", @@ -463,7 +463,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "5e8eb5f": { "balancing:strategy": "none", @@ -490,7 +490,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3373e00": { "balancing:strategy": "weighting", @@ -518,7 +518,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f37dd59": { "balancing:strategy": "none", @@ -548,7 +548,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7c0aca6": { "balancing:strategy": "weighting", @@ -577,7 +577,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8fda287": { "balancing:strategy": "weighting", @@ -600,7 +600,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3cc8774": { "balancing:strategy": "none", @@ -625,7 +625,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "b62b471": { "balancing:strategy": "weighting", @@ -653,7 +653,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "188a54d": { "balancing:strategy": "none", @@ -685,7 +685,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "929f30b": { "balancing:strategy": "weighting", @@ -712,7 +712,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "b03f092": { "balancing:strategy": "none", @@ -739,7 +739,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "27509eb": { "balancing:strategy": "none", @@ -766,7 +766,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f208775": { "balancing:strategy": "none", @@ -792,7 +792,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e3b07fe": { "balancing:strategy": "none", @@ -823,7 +823,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "32ab8ad": { "balancing:strategy": "none", @@ -850,7 +850,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "952e617": { "balancing:strategy": "none", @@ -874,7 +874,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_3CV_iterative_es_if.json b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_3CV_iterative_es_if.json index 28cf8bc1cd..a270e86217 100644 --- a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_3CV_iterative_es_if.json +++ b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_3CV_iterative_es_if.json @@ -24,7 +24,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "057a961": { "balancing:strategy": "none", @@ -54,7 +54,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "18b9a26": { "balancing:strategy": "none", @@ -81,7 +81,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "32b95ed": { "balancing:strategy": "weighting", @@ -108,7 +108,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3b9d012": { "balancing:strategy": "weighting", @@ -134,7 +134,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e94ffc2": { "balancing:strategy": "none", @@ -162,7 +162,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "611fcd2": { "balancing:strategy": "none", @@ -188,7 +188,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "2cc7fa6": { "balancing:strategy": "none", @@ -217,7 +217,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "a507a1e": { "balancing:strategy": "weighting", @@ -245,7 +245,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6270995": { "balancing:strategy": "weighting", @@ -276,7 +276,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "09c23ea": { "balancing:strategy": "none", @@ -303,7 +303,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8436422": { "balancing:strategy": "none", @@ -330,7 +330,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4fcd4ce": { "balancing:strategy": "weighting", @@ -351,7 +351,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "fbd4f3d": { "balancing:strategy": "none", @@ -381,7 +381,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "81f13b6": { "balancing:strategy": "none", @@ -414,7 +414,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "937efac": { "balancing:strategy": "none", @@ -446,7 +446,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "670fc33": { "balancing:strategy": "weighting", @@ -474,7 +474,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ccb5d37": { "balancing:strategy": "none", @@ -501,7 +501,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "0d34b7c": { "balancing:strategy": "weighting", @@ -533,7 +533,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ea94610": { "balancing:strategy": "none", @@ -558,7 +558,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "fb21bf8": { "balancing:strategy": "weighting", @@ -585,7 +585,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "83389d2": { "balancing:strategy": "weighting", @@ -611,7 +611,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "70d7673": { "balancing:strategy": "weighting", @@ -643,7 +643,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "b4eb798": { "balancing:strategy": "none", @@ -676,7 +676,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "0a5c673": { "balancing:strategy": "weighting", @@ -707,7 +707,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3c13b23": { "balancing:strategy": "none", @@ -737,7 +737,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7a1979d": { "balancing:strategy": "none", @@ -762,7 +762,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "db47f52": { "balancing:strategy": "none", @@ -789,7 +789,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "57c1d3d": { "balancing:strategy": "weighting", @@ -813,7 +813,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "330533a": { "balancing:strategy": "weighting", @@ -839,7 +839,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "31f9d24": { "balancing:strategy": "weighting", @@ -864,7 +864,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "b7a81a4": { "balancing:strategy": "none", @@ -892,7 +892,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_5CV_iterative_es_if.json b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_5CV_iterative_es_if.json index a26e4ec237..db7cd35f5c 100644 --- a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_5CV_iterative_es_if.json +++ b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_5CV_iterative_es_if.json @@ -28,7 +28,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "48e7d72": { "balancing:strategy": "none", @@ -61,7 +61,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3b5725c": { "balancing:strategy": "none", @@ -85,7 +85,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e8567fc": { "balancing:strategy": "weighting", @@ -108,7 +108,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8aac02c": { "balancing:strategy": "weighting", @@ -132,7 +132,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "0b0f56b": { "balancing:strategy": "none", @@ -162,7 +162,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "766431d": { "balancing:strategy": "none", @@ -188,7 +188,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9454294": { "balancing:strategy": "weighting", @@ -220,7 +220,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "b85628c": { "balancing:strategy": "none", @@ -250,7 +250,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "a8f7d08": { "balancing:strategy": "weighting", @@ -277,7 +277,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c5fe8c1": { "balancing:strategy": "weighting", @@ -305,7 +305,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7eb153b": { "balancing:strategy": "weighting", @@ -334,7 +334,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ed48b1e": { "balancing:strategy": "weighting", @@ -365,7 +365,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "fd4841b": { "balancing:strategy": "weighting", @@ -394,7 +394,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c3c5a39": { "balancing:strategy": "none", @@ -421,7 +421,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "aeea657": { "balancing:strategy": "weighting", @@ -454,7 +454,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d41cebb": { "balancing:strategy": "none", @@ -486,7 +486,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "546748f": { "balancing:strategy": "none", @@ -518,7 +518,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ca57fd6": { "balancing:strategy": "none", @@ -544,7 +544,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d2a3f8c": { "balancing:strategy": "none", @@ -576,7 +576,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "36cd396": { "balancing:strategy": "weighting", @@ -606,7 +606,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "09555d8": { "balancing:strategy": "none", @@ -633,7 +633,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8ffaacd": { "balancing:strategy": "none", @@ -658,7 +658,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4396ea3": { "balancing:strategy": "weighting", @@ -690,7 +690,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "1d4a4c2": { "balancing:strategy": "weighting", @@ -716,7 +716,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "038e453": { "balancing:strategy": "weighting", @@ -743,7 +743,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d2b0a20": { "balancing:strategy": "none", @@ -776,7 +776,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7308c48": { "balancing:strategy": "none", @@ -806,7 +806,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4e08230": { "balancing:strategy": "weighting", @@ -833,7 +833,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "a9c54cc": { "balancing:strategy": "none", @@ -860,7 +860,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c9ed268": { "balancing:strategy": "none", @@ -885,7 +885,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3995b86": { "balancing:strategy": "none", @@ -912,7 +912,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_holdout_iterative_es_if.json b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_holdout_iterative_es_if.json index 29c92e1ae2..882b8c6c2c 100644 --- a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_holdout_iterative_es_if.json +++ b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_holdout_iterative_es_if.json @@ -25,7 +25,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c85132e": { "balancing:strategy": "weighting", @@ -55,7 +55,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3ffac3d": { "balancing:strategy": "weighting", @@ -80,7 +80,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3861869": { "balancing:strategy": "weighting", @@ -109,7 +109,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "1d8ad41": { "balancing:strategy": "none", @@ -137,7 +137,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "93a94fc": { "balancing:strategy": "none", @@ -167,7 +167,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "88839ea": { "balancing:strategy": "weighting", @@ -194,7 +194,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "98dcda2": { "balancing:strategy": "weighting", @@ -224,7 +224,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f3b89ca": { "balancing:strategy": "none", @@ -249,7 +249,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "5da6d7e": { "balancing:strategy": "none", @@ -277,7 +277,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4522c5c": { "balancing:strategy": "none", @@ -308,7 +308,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3e86168": { "balancing:strategy": "none", @@ -337,7 +337,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e33f5d1": { "balancing:strategy": "weighting", @@ -360,7 +360,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6e537da": { "balancing:strategy": "none", @@ -390,7 +390,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e72a0d0": { "balancing:strategy": "none", @@ -418,7 +418,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "59fbdef": { "balancing:strategy": "none", @@ -441,7 +441,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "cbcd40f": { "balancing:strategy": "none", @@ -470,7 +470,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "04b6e1c": { "balancing:strategy": "weighting", @@ -490,7 +490,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4a96439": { "balancing:strategy": "none", @@ -514,7 +514,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "80bc363": { "balancing:strategy": "weighting", @@ -541,7 +541,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4464ce4": { "balancing:strategy": "none", @@ -565,7 +565,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3a1cf69": { "balancing:strategy": "weighting", @@ -595,7 +595,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3230065": { "balancing:strategy": "none", @@ -625,7 +625,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e5d8521": { "balancing:strategy": "weighting", @@ -657,7 +657,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ee1d407": { "balancing:strategy": "weighting", @@ -683,7 +683,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c9d403d": { "balancing:strategy": "none", @@ -709,7 +709,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "5ec319d": { "balancing:strategy": "none", @@ -736,7 +736,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "0c4f40a": { "balancing:strategy": "none", @@ -765,7 +765,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "87bd8ad": { "balancing:strategy": "weighting", @@ -797,7 +797,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "33ce15c": { "balancing:strategy": "none", @@ -824,7 +824,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7166357": { "balancing:strategy": "weighting", @@ -848,7 +848,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "fcfb9a5": { "balancing:strategy": "none", @@ -877,7 +877,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json index d5dc0fefb1..12c6cf2e01 100644 --- a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json +++ b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json @@ -25,7 +25,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "078caa9": { "balancing:strategy": "none", @@ -54,7 +54,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e457d9c": { "balancing:strategy": "weighting", @@ -81,7 +81,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ef55f97": { "balancing:strategy": "weighting", @@ -108,7 +108,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3aa9e49": { "balancing:strategy": "weighting", @@ -134,7 +134,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "2e26e3e": { "balancing:strategy": "weighting", @@ -166,7 +166,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9fbdad0": { "balancing:strategy": "weighting", @@ -191,7 +191,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f3d6f6e": { "balancing:strategy": "weighting", @@ -222,7 +222,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "344e35a": { "balancing:strategy": "none", @@ -248,7 +248,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "fd44352": { "balancing:strategy": "weighting", @@ -273,7 +273,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3c53750": { "balancing:strategy": "weighting", @@ -305,7 +305,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "5e8eb5f": { "balancing:strategy": "none", @@ -332,7 +332,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "48c6703": { "balancing:strategy": "none", @@ -363,7 +363,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "09b43c4": { "balancing:strategy": "weighting", @@ -392,7 +392,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "fc6f1b6": { "balancing:strategy": "weighting", @@ -414,7 +414,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "11c8920": { "balancing:strategy": "none", @@ -444,7 +444,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7b353d7": { "balancing:strategy": "weighting", @@ -467,7 +467,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "1e0cf43": { "balancing:strategy": "none", @@ -496,7 +496,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "cb9ea50": { "balancing:strategy": "none", @@ -523,7 +523,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "13bae60": { "balancing:strategy": "weighting", @@ -548,7 +548,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "14994aa": { "balancing:strategy": "weighting", @@ -568,7 +568,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3cc8774": { "balancing:strategy": "none", @@ -593,7 +593,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9f0a242": { "balancing:strategy": "weighting", @@ -623,7 +623,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9395997": { "balancing:strategy": "weighting", @@ -654,7 +654,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "8bb9a95": { "balancing:strategy": "weighting", @@ -685,7 +685,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "0c52278": { "balancing:strategy": "none", @@ -710,7 +710,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9c231f7": { "balancing:strategy": "none", @@ -732,7 +732,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "fd2205c": { "balancing:strategy": "weighting", @@ -761,7 +761,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9d1c409": { "balancing:strategy": "weighting", @@ -792,7 +792,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "bdb1c5c": { "balancing:strategy": "weighting", @@ -824,7 +824,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4a934ee": { "balancing:strategy": "weighting", @@ -849,7 +849,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "32ab8ad": { "balancing:strategy": "none", @@ -876,7 +876,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json index 6be0ae11eb..75f1a2aabd 100644 --- a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json +++ b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json @@ -24,7 +24,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4e2d8ae": { "balancing:strategy": "none", @@ -51,7 +51,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "a507a1e": { "balancing:strategy": "weighting", @@ -79,7 +79,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3b9d012": { "balancing:strategy": "weighting", @@ -105,7 +105,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "37ed3d0": { "balancing:strategy": "none", @@ -135,7 +135,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "09c23ea": { "balancing:strategy": "none", @@ -162,7 +162,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "70fe9d8": { "balancing:strategy": "weighting", @@ -188,7 +188,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "da2661d": { "balancing:strategy": "none", @@ -220,7 +220,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4134365": { "balancing:strategy": "none", @@ -251,7 +251,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "0823d35": { "balancing:strategy": "weighting", @@ -278,7 +278,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "b4eb798": { "balancing:strategy": "none", @@ -311,7 +311,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "499a242": { "balancing:strategy": "weighting", @@ -335,7 +335,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "86e9ac7": { "balancing:strategy": "none", @@ -360,7 +360,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4fcd4ce": { "balancing:strategy": "weighting", @@ -381,7 +381,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "81f13b6": { "balancing:strategy": "none", @@ -414,7 +414,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "1981b8a": { "balancing:strategy": "none", @@ -439,7 +439,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "32b95ed": { "balancing:strategy": "weighting", @@ -466,7 +466,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "611fcd2": { "balancing:strategy": "none", @@ -492,7 +492,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "0a5c673": { "balancing:strategy": "weighting", @@ -523,7 +523,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7d32be9": { "balancing:strategy": "none", @@ -551,7 +551,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "1f3235b": { "balancing:strategy": "none", @@ -581,7 +581,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c1bec76": { "balancing:strategy": "none", @@ -609,7 +609,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9bdce9a": { "balancing:strategy": "none", @@ -634,7 +634,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "18708a2": { "balancing:strategy": "weighting", @@ -660,7 +660,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "b54d05f": { "balancing:strategy": "none", @@ -693,7 +693,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3f02da0": { "balancing:strategy": "weighting", @@ -720,7 +720,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "21ff716": { "balancing:strategy": "weighting", @@ -751,7 +751,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f2e837c": { "balancing:strategy": "weighting", @@ -781,7 +781,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "fb21bf8": { "balancing:strategy": "weighting", @@ -808,7 +808,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d13d06f": { "balancing:strategy": "none", @@ -838,7 +838,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d95f74b": { "balancing:strategy": "none", @@ -864,7 +864,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7167f0c": { "balancing:strategy": "none", @@ -894,7 +894,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json index 42df526afa..d99c235d16 100644 --- a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json +++ b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json @@ -28,7 +28,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "48e7d72": { "balancing:strategy": "none", @@ -61,7 +61,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3b5725c": { "balancing:strategy": "none", @@ -85,7 +85,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e8567fc": { "balancing:strategy": "weighting", @@ -108,7 +108,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d3b1c98": { "balancing:strategy": "none", @@ -135,7 +135,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "49b696b": { "balancing:strategy": "none", @@ -162,7 +162,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "766431d": { "balancing:strategy": "none", @@ -188,7 +188,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "901862d": { "balancing:strategy": "weighting", @@ -218,7 +218,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9454294": { "balancing:strategy": "weighting", @@ -250,7 +250,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ed48b1e": { "balancing:strategy": "weighting", @@ -281,7 +281,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7eb153b": { "balancing:strategy": "weighting", @@ -310,7 +310,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d41cebb": { "balancing:strategy": "none", @@ -342,7 +342,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c3c5a39": { "balancing:strategy": "none", @@ -369,7 +369,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "09555d8": { "balancing:strategy": "none", @@ -396,7 +396,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c9ed268": { "balancing:strategy": "none", @@ -421,7 +421,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "07b7a65": { "balancing:strategy": "weighting", @@ -447,7 +447,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "fd4841b": { "balancing:strategy": "weighting", @@ -476,7 +476,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "a9c54cc": { "balancing:strategy": "none", @@ -503,7 +503,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "d2a3f8c": { "balancing:strategy": "none", @@ -535,7 +535,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "36cd396": { "balancing:strategy": "weighting", @@ -565,7 +565,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ed2b3cf": { "balancing:strategy": "weighting", @@ -591,7 +591,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "aeea657": { "balancing:strategy": "weighting", @@ -624,7 +624,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "1d4a4c2": { "balancing:strategy": "weighting", @@ -650,7 +650,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "5dbf9b3": { "balancing:strategy": "weighting", @@ -681,7 +681,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "225eba5": { "balancing:strategy": "none", @@ -713,7 +713,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "51f697f": { "balancing:strategy": "weighting", @@ -737,7 +737,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "b84962e": { "balancing:strategy": "weighting", @@ -764,7 +764,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "05ee5be": { "balancing:strategy": "none", @@ -794,7 +794,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "0ca3806": { "balancing:strategy": "weighting", @@ -827,7 +827,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "015ec3b": { "balancing:strategy": "none", @@ -854,7 +854,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "53af782": { "balancing:strategy": "none", @@ -885,7 +885,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "04ee8cd": { "balancing:strategy": "weighting", @@ -912,7 +912,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 } }, "cutoffs": [ diff --git a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json index fc060cb7f3..6bcc253cfd 100644 --- a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json +++ b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json @@ -25,7 +25,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c85132e": { "balancing:strategy": "weighting", @@ -55,7 +55,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3ffac3d": { "balancing:strategy": "weighting", @@ -80,7 +80,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3861869": { "balancing:strategy": "weighting", @@ -109,7 +109,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "1d8ad41": { "balancing:strategy": "none", @@ -137,7 +137,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e8c14b9": { "balancing:strategy": "weighting", @@ -164,7 +164,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "93a94fc": { "balancing:strategy": "none", @@ -194,7 +194,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ee1d407": { "balancing:strategy": "weighting", @@ -220,7 +220,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4464ce4": { "balancing:strategy": "none", @@ -244,7 +244,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "5da6d7e": { "balancing:strategy": "none", @@ -272,7 +272,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4522c5c": { "balancing:strategy": "none", @@ -303,7 +303,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "cbcd40f": { "balancing:strategy": "none", @@ -332,7 +332,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "7166357": { "balancing:strategy": "weighting", @@ -356,7 +356,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3a1cf69": { "balancing:strategy": "weighting", @@ -386,7 +386,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e99d884": { "balancing:strategy": "none", @@ -416,7 +416,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "1312886": { "balancing:strategy": "weighting", @@ -445,7 +445,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "59fbdef": { "balancing:strategy": "none", @@ -468,7 +468,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "33ce15c": { "balancing:strategy": "none", @@ -495,7 +495,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e72a0d0": { "balancing:strategy": "none", @@ -523,7 +523,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "5ec319d": { "balancing:strategy": "none", @@ -550,7 +550,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3230065": { "balancing:strategy": "none", @@ -580,7 +580,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "4a96439": { "balancing:strategy": "none", @@ -604,7 +604,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "970f3cb": { "balancing:strategy": "none", @@ -635,7 +635,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "3e86168": { "balancing:strategy": "none", @@ -664,7 +664,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "6e537da": { "balancing:strategy": "none", @@ -694,7 +694,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "e5d8521": { "balancing:strategy": "weighting", @@ -726,7 +726,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "ac7f6c4": { "balancing:strategy": "weighting", @@ -754,7 +754,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "f1d04fb": { "balancing:strategy": "none", @@ -784,7 +784,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "191b6ed": { "balancing:strategy": "none", @@ -811,7 +811,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "9c8ea9e": { "balancing:strategy": "weighting", @@ -835,7 +835,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "842e6d9": { "balancing:strategy": "none", @@ -864,7 +864,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_range": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 }, "c867baf": { "balancing:strategy": "weighting", @@ -889,7 +889,7 @@ "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, 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2,no_encoding,no_coalescense,,most_frequent,standardize,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,f_regression,gaussian_process,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.019645201003537352,1.1170068633409441e-07,130.632960269528,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type,100,bag_of_word_encoding,1,min_df_absolute,,,,,,,,,1 6,one_hot_encoding,minority_coalescer,0.000400759453878678,mean,minmax,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09988990258830989,fpr,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,3.0002886744200357e-06,0.015673535665067875,least_squares,255,None,7,23,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type,100,bag_of_word_encoding,1,min_df_absolute,,,,,,,,,1 7,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,809,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.01806079811541296,False,,0.015540128437738566,True,0.9326390049807726,constant,squared_loss,elasticnet,,0.00010576654619459039,feature_type,100,bag_of_word_encoding,1,min_df_absolute,,,,,,,,,1 diff --git a/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding.py b/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding.py index 47a80684f7..b8a62ccd89 100644 --- a/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding.py +++ b/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding.py @@ -17,13 +17,13 @@ class BagOfWordEncoder(AutoSklearnPreprocessingAlgorithm): def __init__( self, - ngram_range: int = 1, + ngram_upper_bound: int = 1, min_df_choice: str = "min_df_absolute", min_df_absolute: int = 0, min_df_relative: float = 0.01, random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> None: - self.ngram_range = ngram_range + self.ngram_upper_bound = ngram_upper_bound self.random_state = random_state self.min_df_choice = min_df_choice self.min_df_absolute = min_df_absolute @@ -46,13 +46,13 @@ def fit( if self.min_df_choice == "min_df_absolute": self.preprocessor = CountVectorizer( min_df=self.min_df_absolute, - ngram_range=(1, self.ngram_range), + ngram_range=(1, self.ngram_upper_bound), ) elif self.min_df_choice == "min_df_relative": self.preprocessor = CountVectorizer( min_df=self.min_df_relative, - ngram_range=(1, self.ngram_range), + ngram_range=(1, self.ngram_upper_bound), ) else: @@ -98,8 +98,8 @@ def get_hyperparameter_search_space( dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: cs = ConfigurationSpace() - hp_ngram_range = CSH.UniformIntegerHyperparameter( - name="ngram_range", lower=1, upper=3, default_value=1 + hp_ngram_upper_bound = CSH.UniformIntegerHyperparameter( + name="ngram_upper_bound", lower=1, upper=3, default_value=1 ) hp_min_df_choice_bow = CSH.CategoricalHyperparameter( "min_df_choice", choices=["min_df_absolute", "min_df_relative"] @@ -112,7 +112,7 @@ def get_hyperparameter_search_space( ) cs.add_hyperparameters( [ - hp_ngram_range, + hp_ngram_upper_bound, hp_min_df_choice_bow, hp_min_df_absolute_bow, hp_min_df_relative_bow, diff --git a/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding_distinct.py b/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding_distinct.py index 22a0be1088..90a43b0f48 100644 --- a/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding_distinct.py +++ b/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding_distinct.py @@ -16,13 +16,13 @@ class BagOfWordEncoder(AutoSklearnPreprocessingAlgorithm): def __init__( self, - ngram_range: int = 1, + ngram_upper_bound: int = 1, min_df_choice: str = "min_df_absolute", min_df_absolute: int = 0, min_df_relative: float = 0.01, random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> None: - self.ngram_range = ngram_range + self.ngram_upper_bound = ngram_upper_bound self.random_state = random_state self.min_df_choice = min_df_choice self.min_df_absolute = min_df_absolute @@ -40,7 +40,8 @@ def fit( for feature in X.columns: vectorizer = CountVectorizer( - min_df=self.min_df_absolute, ngram_range=(1, self.ngram_range) + min_df=self.min_df_absolute, + ngram_range=(1, self.ngram_upper_bound), ).fit(X[feature]) self.preprocessor[feature] = vectorizer @@ -50,7 +51,8 @@ def fit( for feature in X.columns: vectorizer = CountVectorizer( - min_df=self.min_df_relative, ngram_range=(1, self.ngram_range) + min_df=self.min_df_relative, + ngram_range=(1, self.ngram_upper_bound), ).fit(X[feature]) self.preprocessor[feature] = vectorizer else: @@ -102,8 +104,8 @@ def get_hyperparameter_search_space( dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: cs = ConfigurationSpace() - hp_ngram_range = CSH.UniformIntegerHyperparameter( - name="ngram_range", lower=1, upper=3, default_value=1 + hp_ngram_upper_bound = CSH.UniformIntegerHyperparameter( + name="ngram_upper_bound", lower=1, upper=3, default_value=1 ) hp_min_df_choice_bow = CSH.CategoricalHyperparameter( "min_df_choice", choices=["min_df_absolute", "min_df_relative"] @@ -116,7 +118,7 @@ def get_hyperparameter_search_space( ) cs.add_hyperparameters( [ - hp_ngram_range, + hp_ngram_upper_bound, hp_min_df_choice_bow, hp_min_df_absolute_bow, hp_min_df_relative_bow, diff --git a/autosklearn/pipeline/components/data_preprocessing/text_encoding/tfidf_encoding.py b/autosklearn/pipeline/components/data_preprocessing/text_encoding/tfidf_encoding.py index aea4a05906..f20d24f769 100644 --- a/autosklearn/pipeline/components/data_preprocessing/text_encoding/tfidf_encoding.py +++ b/autosklearn/pipeline/components/data_preprocessing/text_encoding/tfidf_encoding.py @@ -17,14 +17,14 @@ class TfidfEncoder(AutoSklearnPreprocessingAlgorithm): def __init__( self, - ngram_range: int = 1, + ngram_upper_bound: int = 1, use_idf: bool = True, min_df_choice: str = "min_df_absolute", min_df_absolute: int = 0, min_df_relative: float = 0.01, random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> None: - self.ngram_range = ngram_range + self.ngram_upper_bound = ngram_upper_bound self.random_state = random_state self.use_idf = use_idf self.min_df_choice = min_df_choice @@ -50,14 +50,14 @@ def fit( self.preprocessor = TfidfVectorizer( min_df=self.min_df_absolute, use_idf=self.use_idf, - ngram_range=(1, self.ngram_range), + ngram_range=(1, self.ngram_upper_bound), ) elif self.min_df_choice == "min_df_relative": self.preprocessor = TfidfVectorizer( min_df=self.min_df_relative, use_idf=self.use_idf, - ngram_range=(1, self.ngram_range), + ngram_range=(1, self.ngram_upper_bound), ) else: @@ -103,8 +103,8 @@ def get_hyperparameter_search_space( dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: cs = ConfigurationSpace() - hp_ngram_range = CSH.UniformIntegerHyperparameter( - name="ngram_range", lower=1, upper=3, default_value=1 + hp_ngram_upper_bound = CSH.UniformIntegerHyperparameter( + name="ngram_upper_bound", lower=1, upper=3, default_value=1 ) hp_use_idf = CSH.CategoricalHyperparameter("use_idf", choices=[False, True]) hp_min_df_choice = CSH.CategoricalHyperparameter( @@ -118,7 +118,7 @@ def get_hyperparameter_search_space( ) cs.add_hyperparameters( [ - hp_ngram_range, + hp_ngram_upper_bound, hp_use_idf, hp_min_df_choice, hp_min_df_absolute, diff --git a/examples/40_advanced/example_text_preprocessing.py b/examples/40_advanced/example_text_preprocessing.py index 76c2d91cfc..7c65825b7b 100644 --- a/examples/40_advanced/example_text_preprocessing.py +++ b/examples/40_advanced/example_text_preprocessing.py @@ -1,79 +1,84 @@ # -*- encoding: utf-8 -*- """ ================== -Text Preprocessing +Text preprocessing ================== -This example shows, how to use text features in *auto-sklearn*. *auto-sklearn* can automatically -encode text features if they are provided as string type in a pandas dataframe. -For processing text features you need a pandas dataframe and set the desired -text columns to string and the categorical columns to category. +The following example shows how to fit a simple NLP problem with +*auto-sklearn*. -*auto-sklearn* text embedding creates a bag of words count. +For an introduction to text preprocessing you can follow these links: + 1. https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html + 2. https://machinelearningmastery.com/clean-text-machine-learning-python/ """ +from pprint import pprint + +import pandas as pd import sklearn.metrics -import sklearn.datasets +from sklearn.datasets import fetch_20newsgroups + import autosklearn.classification ############################################################################ # Data Loading # ============ +cats = ["comp.sys.ibm.pc.hardware", "rec.sport.baseball"] +X_train, y_train = fetch_20newsgroups( + subset="train", # select train set + shuffle=True, # shuffle the data set for unbiased validation results + random_state=42, # set a random seed for reproducibility + categories=cats, # select only 2 out of 20 labels + return_X_y=True, # 20NG dataset consists of 2 columns X: the text data, y: the label +) # load this two columns separately as numpy array + +X_test, y_test = fetch_20newsgroups( + subset="test", # select test set for unbiased evaluation + categories=cats, # select only 2 out of 20 labels + return_X_y=True, # 20NG dataset consists of 2 columns X: the text data, y: the label +) # load this two columns separately as numpy array -X, y = sklearn.datasets.fetch_openml(data_id=40945, return_X_y=True) - -# by default, the columns which should be strings are not formatted as such -print(f"{X.info()}\n") - -# manually convert these to string columns -X = X.astype( - { - "name": "string", - "ticket": "string", - "cabin": "string", - "boat": "string", - "home.dest": "string", - } -) +############################################################################ +# Creating a pandas dataframe +# =========================== +# Both categorical and text features are often strings. Python Pandas stores python stings +# in the generic `object` type. Please ensure that the correct +# `dtype `_ is applied to the correct +# column. -# now *auto-sklearn* handles the string columns with its text feature preprocessing pipeline +# create a pandas dataframe for training labeling the "Text" column as sting +X_train = pd.DataFrame({"Text": pd.Series(X_train, dtype="string")}) -X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( - X, y, random_state=1 -) +# create a pandas dataframe for testing labeling the "Text" column as sting +X_test = pd.DataFrame({"Text": pd.Series(X_test, dtype="string")}) -cls = autosklearn.classification.AutoSklearnClassifier( - time_left_for_this_task=30, - # Bellow two flags are provided to speed up calculations - # Not recommended for a real implementation - initial_configurations_via_metalearning=0, - smac_scenario_args={"runcount_limit": 1}, +############################################################################ +# Build and fit a classifier +# ========================== + +# create an autosklearn Classifier or Regressor depending on your task at hand. +automl = autosklearn.classification.AutoSklearnClassifier( + time_left_for_this_task=60, + per_run_time_limit=30, + tmp_folder="/tmp/autosklearn_text_example_tmp", ) -cls.fit(X_train, y_train, X_test, y_test) - -predictions = cls.predict(X_test) -print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions)) +automl.fit(X_train, y_train, dataset_name="20_Newsgroups") # fit the automl model +############################################################################ +# View the models found by auto-sklearn +# ===================================== -X, y = sklearn.datasets.fetch_openml(data_id=40945, return_X_y=True, as_frame=True) -X = X.select_dtypes(exclude=["object"]) +print(automl.leaderboard()) -X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( - X, y, random_state=1 -) +############################################################################ +# Print the final ensemble constructed by auto-sklearn +# ==================================================== -cls = autosklearn.classification.AutoSklearnClassifier( - time_left_for_this_task=30, - # Bellow two flags are provided to speed up calculations - # Not recommended for a real implementation - initial_configurations_via_metalearning=0, - smac_scenario_args={"runcount_limit": 1}, -) +pprint(automl.show_models(), indent=4) -cls.fit(X_train, y_train, X_test, y_test) +########################################################################### +# Get the Score of the final ensemble +# =================================== -predictions = cls.predict(X_test) -print( - "Accuracy score without text preprocessing", - sklearn.metrics.accuracy_score(y_test, predictions), -) +predictions = automl.predict(X_test) +print("Accuracy score:", sklearn.metrics.accuracy_score(y_test, predictions)) diff --git a/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv b/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv index 0336e76f01..e36bd31d4a 100755 --- a/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv +++ b/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv @@ -1,4 +1,4 @@ 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3,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7811236762098946,None,0.0,15,9,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.2269858618750471,None,0.0,13,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type,100,bag_of_word_encoding,1,,min_df_absolute,,,,,,,,,1 diff --git a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_text.py b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_text.py index 0a2e3d5188..c0729b1dfc 100644 --- a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_text.py +++ b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_text.py @@ -20,7 +20,7 @@ def test_fit_transform(self): } ).astype({"col1": "string", "col2": "string"}) BOW_fitted = BOW( - ngram_range=1, + ngram_upper_bound=1, min_df_choice="min_df_absolute", min_df_absolute=0, min_df_relative=0, @@ -46,7 +46,7 @@ def test_fit_transform(self): np.testing.assert_array_equal(Yt, Y) BOW_fitted = BOW_distinct( - ngram_range=1, + ngram_upper_bound=1, min_df_choice="min_df_absolute", min_df_absolute=0, min_df_relative=0, @@ -69,7 +69,7 @@ def test_transform(self): } ).astype({"col1": "string", "col2": "string"}) X_t = BOW( - ngram_range=1, + ngram_upper_bound=1, min_df_choice="min_df_absolute", min_df_absolute=0, min_df_relative=0, @@ -81,7 +81,7 @@ def test_transform(self): np.testing.assert_array_equal(X_t.toarray(), y) X_t = BOW_distinct( - ngram_range=1, + ngram_upper_bound=1, min_df_choice="min_df_absolute", min_df_absolute=0, min_df_relative=0, @@ -103,7 +103,7 @@ def test_check_shape(self): } ).astype({"col1": "string", "col2": "string"}) X_t = BOW( - ngram_range=1, + ngram_upper_bound=1, min_df_choice="min_df_absolute", min_df_absolute=0, min_df_relative=0, @@ -113,7 +113,7 @@ def test_check_shape(self): self.assertEqual(X_t.shape, (2, 5)) X_t = BOW_distinct( - ngram_range=1, + ngram_upper_bound=1, min_df_choice="min_df_absolute", min_df_absolute=0, min_df_relative=0, @@ -130,7 +130,7 @@ def test_check_nan(self): } ).astype({"col1": "string", "col2": "string"}) X_t = BOW( - ngram_range=1, + ngram_upper_bound=1, min_df_choice="min_df_absolute", min_df_absolute=0, min_df_relative=0, @@ -140,7 +140,7 @@ def test_check_nan(self): self.assertEqual(X_t.shape, (3, 5)) X_t = BOW_distinct( - ngram_range=1, + ngram_upper_bound=1, min_df_choice="min_df_absolute", min_df_absolute=0, min_df_relative=0, From 0da939553da3edff83eedba090bca99512e23690 Mon Sep 17 00:00:00 2001 From: Lukas Strack <73999018+Louquinze@users.noreply.github.com> Date: Thu, 3 Mar 2022 13:36:00 +0100 Subject: [PATCH 038/118] rename feature_reduction to text_feature_reduction (#1414) * rename `auto-sklearn/autosklearn/pipeline/components/data_preprocessing/feature_reduction` to `auto-sklearn/autosklearn/pipeline/components/data_preprocessing/text_feature_reduction`. also rename corresponding feature reduction class FeatureReduction to TextFeatureReduction. `auto-sklearn/autosklearn/pipeline/components/data_preprocessing/text_feature_reduction/truncated_svd.py:TextFeatureReduction` This includes adapting all *csv and *json participating in metalearning The "real" changes are limited to 1. truncated_svd.py 2. feature_type_text.py * rename `auto-sklearn/autosklearn/pipeline/components/data_preprocessing/feature_reduction` to `auto-sklearn/autosklearn/pipeline/components/data_preprocessing/text_feature_reduction`. also rename corresponding feature reduction class FeatureReduction to TextFeatureReduction. `auto-sklearn/autosklearn/pipeline/components/data_preprocessing/text_feature_reduction/truncated_svd.py:TextFeatureReduction` This includes adapting all *csv and *json participating in metalearning The "real" changes are limited to 1. truncated_svd.py 2. feature_type_text.py --- .../RF_None_10CV_iterative_es_if.json | 128 ++++----- .../RF_None_3CV_iterative_es_if.json | 128 ++++----- .../RF_None_5CV_iterative_es_if.json | 128 ++++----- .../RF_None_holdout_iterative_es_if.json | 128 ++++----- .../RF_SH-eta4-i_10CV_iterative_es_if.json | 128 ++++----- .../RF_SH-eta4-i_3CV_iterative_es_if.json | 128 ++++----- .../RF_SH-eta4-i_5CV_iterative_es_if.json | 128 ++++----- .../RF_SH-eta4-i_holdout_iterative_es_if.json | 128 ++++----- .../RF_None_10CV_iterative_es_if.json | 128 ++++----- .../RF_None_3CV_iterative_es_if.json | 128 ++++----- .../RF_None_5CV_iterative_es_if.json | 128 ++++----- .../RF_None_holdout_iterative_es_if.json | 128 ++++----- .../RF_SH-eta4-i_10CV_iterative_es_if.json | 128 ++++----- .../RF_SH-eta4-i_3CV_iterative_es_if.json | 128 ++++----- .../RF_SH-eta4-i_5CV_iterative_es_if.json | 128 ++++----- .../RF_SH-eta4-i_holdout_iterative_es_if.json | 128 ++++----- .../RF_None_10CV_iterative_es_if.json | 128 ++++----- .../RF_None_3CV_iterative_es_if.json | 128 ++++----- .../RF_None_5CV_iterative_es_if.json | 128 ++++----- .../RF_None_holdout_iterative_es_if.json | 128 ++++----- .../RF_SH-eta4-i_10CV_iterative_es_if.json | 128 ++++----- .../RF_SH-eta4-i_3CV_iterative_es_if.json | 128 ++++----- .../RF_SH-eta4-i_5CV_iterative_es_if.json | 128 ++++----- .../RF_SH-eta4-i_holdout_iterative_es_if.json | 128 ++++----- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../r2_regression_dense/configurations.csv | 2 +- .../r2_regression_sparse/configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../configurations.csv | 2 +- .../data_preprocessing/feature_type_text.py | 11 +- .../__init__.py | 0 .../truncated_svd.py | 4 +- .../test_meta_base_data/configurations.csv | 258 +++++++++--------- 120 files changed, 1766 insertions(+), 1763 deletions(-) rename autosklearn/pipeline/components/data_preprocessing/{feature_reduction => text_feature_reduction}/__init__.py (100%) rename autosklearn/pipeline/components/data_preprocessing/{feature_reduction => text_feature_reduction}/truncated_svd.py (96%) diff --git a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_10CV_iterative_es_if.json b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_10CV_iterative_es_if.json index 615a7e2ed0..217c28e771 100644 --- a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_10CV_iterative_es_if.json +++ b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_10CV_iterative_es_if.json @@ -21,11 +21,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7668727846319956, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.28633349214759163, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "f1500cd": { "balancing:strategy": "weighting", @@ -48,11 +48,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0013138596672908974, "classifier:gradient_boosting:n_iter_no_change": 13, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "3e1a332": { "balancing:strategy": "weighting", @@ -73,11 +73,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1062, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "22a29ef": { "balancing:strategy": "weighting", @@ -102,11 +102,11 @@ "classifier:gradient_boosting:n_iter_no_change": 6, "classifier:gradient_boosting:validation_fraction": 0.11549557161401015, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "5886349": { "balancing:strategy": "weighting", @@ -129,11 +129,11 @@ "classifier:gradient_boosting:n_iter_no_change": 1, "classifier:gradient_boosting:validation_fraction": 0.16202792486532844, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "8f54538": { "balancing:strategy": "none", @@ -159,11 +159,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.004667222369408079, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "f7d054b": { "balancing:strategy": "weighting", @@ -420,11 +420,11 @@ "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.009713456035294782, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "6b55ec8": { "balancing:strategy": "weighting", @@ -494,11 +494,11 @@ "classifier:passive_aggressive:loss": "hinge", "classifier:passive_aggressive:tol": 6.434802101116561e-05, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "3a5c250": { "balancing:strategy": "weighting", @@ -548,11 +548,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.009333921831143037, "classifier:gradient_boosting:n_iter_no_change": 12, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "0dd7e47": { "balancing:strategy": "weighting", @@ -576,11 +576,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.702784013363627, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.06696502879142427, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "7ff3956": { "balancing:strategy": "weighting", @@ -598,11 +598,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 907, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "data_preprocessor:__choice__": "feature_type", - 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"data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "85bfdfa": { "balancing:strategy": "weighting", @@ -677,11 +677,11 @@ 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"ce012ed": { "balancing:strategy": "weighting", @@ -697,11 +697,11 @@ "classifier:passive_aggressive:loss": "squared_hinge", "classifier:passive_aggressive:tol": 0.009455868366528827, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "e71ff84": { "balancing:strategy": "none", @@ -729,11 +729,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.1913513845124594, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "d4abc34": { "balancing:strategy": "weighting", @@ -760,11 +760,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.748076585259295, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.23337355967199702, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "283cac3": { "balancing:strategy": "weighting", @@ -784,11 +784,11 @@ "classifier:extra_trees:min_samples_split": 15, "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "808d532": { "balancing:strategy": "none", @@ -810,11 +810,11 @@ "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.05184374776387598, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "d2d2039": { "balancing:strategy": "weighting", @@ -837,11 +837,11 @@ "classifier:gradient_boosting:n_iter_no_change": 19, "classifier:gradient_boosting:validation_fraction": 0.29656195578950684, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_3CV_iterative_es_if.json b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_3CV_iterative_es_if.json index 4d163e4184..023a79e741 100644 --- a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_3CV_iterative_es_if.json +++ b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_3CV_iterative_es_if.json @@ -20,11 +20,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1025, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "1cdd4cf": { "balancing:strategy": "weighting", @@ -41,11 +41,11 @@ "classifier:passive_aggressive:tol": 0.001538331512469445, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0020876231314801, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "08d784a": { "balancing:strategy": "weighting", @@ -69,11 +69,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "classifier:gradient_boosting:n_iter_no_change": 1, "data_preprocessor:__choice__": "feature_type", - 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"classifier:gradient_boosting:n_iter_no_change": 17, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "c138595": { "balancing:strategy": "weighting", @@ -150,11 +150,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 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"classifier:mlp:tol": 0.0001, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "d91f995": { "balancing:strategy": "weighting", @@ -204,11 +204,11 @@ "classifier:sgd:eta0": 0.0008683421715267357, "classifier:sgd:l1_ratio": 0.0037742853051687883, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "ba079f7": { "balancing:strategy": "weighting", @@ -232,11 +232,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7279628505501666, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.24610303193556213, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "5251c86": { "balancing:strategy": "none", @@ -258,11 +258,11 @@ "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.28959335532988506, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "56a06e6": { "balancing:strategy": "weighting", @@ -282,11 +282,11 @@ 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"data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "42ab0ad": { "balancing:strategy": "weighting", @@ -605,11 +605,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 839, 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"classifier:passive_aggressive:loss": "hinge", "classifier:passive_aggressive:tol": 0.010384183550709794, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "1a6eed2": { "balancing:strategy": "weighting", @@ -679,11 +679,11 @@ "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.04101667890187473, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "aba43bb": { "balancing:strategy": "none", @@ -710,11 +710,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.011827601847252461, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "15f5c2d": { "balancing:strategy": "weighting", @@ -740,11 +740,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.006279035684154215, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "d3402a5": { "balancing:strategy": "weighting", @@ -772,11 +772,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.17384885170243727, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "3e4ac17": { "balancing:strategy": "weighting", @@ -799,11 +799,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 869, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "3e922f0": { "balancing:strategy": "weighting", @@ -827,11 +827,11 @@ "classifier:gradient_boosting:n_iter_no_change": 4, "classifier:gradient_boosting:validation_fraction": 0.20573288758098512, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "b2b7a37": { "balancing:strategy": "weighting", @@ -852,11 +852,11 @@ "classifier:random_forest:min_weight_fraction_leaf": 0.0, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0495878468366018, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_5CV_iterative_es_if.json b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_5CV_iterative_es_if.json index 7a4cba3e5b..a9a4f641d4 100644 --- a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_5CV_iterative_es_if.json +++ b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_5CV_iterative_es_if.json @@ -23,11 +23,11 @@ "classifier:gradient_boosting:n_iter_no_change": 11, "classifier:gradient_boosting:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "153cd48": { "balancing:strategy": "weighting", @@ -49,11 +49,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.75, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.25870043409765325, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "517b840": { "balancing:strategy": "weighting", @@ -72,11 +72,11 @@ "classifier:sgd:tol": 6.822673172007927e-05, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.002283522288122212, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "251a3fa": { "balancing:strategy": "none", @@ -101,11 +101,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.265488755470474, "classifier:gradient_boosting:n_iter_no_change": 10, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "65b7e1e": { "balancing:strategy": "weighting", @@ -125,11 +125,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0002459755534184245, "classifier:sgd:eta0": 0.0050360748910538156, 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}, "9a87fb9": { "balancing:strategy": "weighting", @@ -651,11 +651,11 @@ "classifier:gradient_boosting:n_iter_no_change": 1, "classifier:gradient_boosting:validation_fraction": 0.22017588944163444, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "2c33a1b": { 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "7c8dfce": { "balancing:strategy": "weighting", @@ -811,11 +811,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.8687568474210459, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.14957346857371134, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "9aa60c4": { "balancing:strategy": "weighting", @@ -837,11 +837,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "classifier:sgd:eta0": 1.4478521162990933e-05, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_holdout_iterative_es_if.json b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_holdout_iterative_es_if.json index b2964e9f37..2f5a0b4a78 100644 --- a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_holdout_iterative_es_if.json +++ b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_holdout_iterative_es_if.json @@ -20,11 +20,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7586354047062515, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.2909702584195659, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "d38f297": { "balancing:strategy": "weighting", @@ -44,11 +44,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.017077961357419263, "classifier:sgd:l1_ratio": 0.1498449443375854, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "099d657": { "balancing:strategy": "none", @@ -125,11 +125,11 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "58e7fea": { "balancing:strategy": "weighting", @@ -181,11 +181,11 @@ "classifier:gradient_boosting:n_iter_no_change": 3, "classifier:gradient_boosting:validation_fraction": 0.1810774322857213, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "35e418d": { "balancing:strategy": "none", @@ -231,11 +231,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.8617966077274979, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.22153286208031922, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "074da43": { "balancing:strategy": "weighting", @@ -256,11 +256,11 @@ "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.005314900629177162, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "7cab112": { "balancing:strategy": "weighting", @@ -285,11 +285,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "classifier:gradient_boosting:n_iter_no_change": 15, "data_preprocessor:__choice__": "feature_type", - 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"data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "05327ff": { "balancing:strategy": "weighting", @@ -370,11 +370,11 @@ "classifier:gradient_boosting:n_iter_no_change": 7, "classifier:gradient_boosting:validation_fraction": 0.07231375899575396, "data_preprocessor:__choice__": 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"data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "a3ba205": { "balancing:strategy": "weighting", @@ -417,11 +417,11 @@ 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"data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "2f6c8d6": { "balancing:strategy": "none", @@ -755,11 +755,11 @@ 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "564103f": { "balancing:strategy": "weighting", @@ -844,11 +844,11 @@ "classifier:gradient_boosting:n_iter_no_change": 4, "classifier:gradient_boosting:validation_fraction": 0.2551421881660974, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "9ce49a5": { "balancing:strategy": "none", @@ -870,11 +870,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.8700451777827737, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.24811629410747338, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json index 23f127343d..59c8b2a50b 100644 --- a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json +++ b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json @@ -21,11 +21,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7668727846319956, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 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"classifier:gradient_boosting:n_iter_no_change": 13, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "3e1a332": { "balancing:strategy": "weighting", @@ -73,11 +73,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1062, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "7500838": { "balancing:strategy": "weighting", @@ -101,11 +101,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "classifier:gradient_boosting:n_iter_no_change": 11, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "dd3a0e8": { "balancing:strategy": "weighting", @@ 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "eac6b8e": { "balancing:strategy": "weighting", @@ -711,11 +711,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1987, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - 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"min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "da90184": { "balancing:strategy": "weighting", @@ -767,11 +767,11 @@ "classifier:gradient_boosting:tol": 1e-07, "classifier:gradient_boosting:n_iter_no_change": 11, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "85bfdfa": { "balancing:strategy": "weighting", @@ -792,11 +792,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1194, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "d4abc34": { "balancing:strategy": "weighting", @@ -823,11 +823,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.748076585259295, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.23337355967199702, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "acb3733": { "balancing:strategy": "weighting", @@ -851,11 +851,11 @@ "classifier:gradient_boosting:n_iter_no_change": 1, "classifier:gradient_boosting:validation_fraction": 0.2938721928434415, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json index 606926fdeb..8a7e0e8263 100644 --- a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json +++ b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json @@ -20,11 +20,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1025, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "08d784a": { "balancing:strategy": "weighting", @@ -48,11 +48,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "classifier:gradient_boosting:n_iter_no_change": 1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "d91f995": { "balancing:strategy": "weighting", @@ -73,11 +73,11 @@ "classifier:sgd:eta0": 0.0008683421715267357, "classifier:sgd:l1_ratio": 0.0037742853051687883, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "9b1161c": { "balancing:strategy": "weighting", @@ -99,11 +99,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.9371966597485295, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.2181009648699602, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "e6b6325": { "balancing:strategy": "weighting", @@ -126,11 +126,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.010402618316459437, "classifier:gradient_boosting:n_iter_no_change": 17, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "53634b7": { "balancing:strategy": "none", @@ -155,11 +155,11 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "ba079f7": { "balancing:strategy": "weighting", @@ -183,11 +183,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7279628505501666, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.24610303193556213, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "eb4397b": { "balancing:strategy": "weighting", @@ -209,11 +209,11 @@ "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.22276171190430583, "data_preprocessor:__choice__": "feature_type", - 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"feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "56a06e6": { "balancing:strategy": "weighting", @@ -259,11 +259,11 @@ "classifier:extra_trees:min_samples_split": 9, "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "1a2077f": { "balancing:strategy": "weighting", @@ -284,11 +284,11 @@ "classifier:sgd:eta0": 0.004594829994128768, "classifier:sgd:power_t": 0.5032138663090429, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "762f55d": { "balancing:strategy": "weighting", @@ -311,11 +311,11 @@ "classifier:sgd:eta0": 8.74381130399994e-05, "classifier:sgd:power_t": 0.040470778304526875, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "3eada54": { "balancing:strategy": "weighting", @@ -331,11 +331,11 @@ "classifier:passive_aggressive:loss": "hinge", "classifier:passive_aggressive:tol": 0.004257493012053348, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "42ab0ad": { "balancing:strategy": "weighting", @@ -358,11 +358,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 839, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "6bdd0f7": { "balancing:strategy": "weighting", @@ -383,11 +383,11 @@ "classifier:sgd:eta0": 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"data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.011827601847252461, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "fcd1bda": { "balancing:strategy": "none", @@ -446,11 +446,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1281, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "418cb3d": { "balancing:strategy": "weighting", @@ -473,11 +473,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.11023361689757208, "classifier:gradient_boosting:n_iter_no_change": 1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "6f053cf": { "balancing:strategy": "none", @@ -503,11 +503,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.012107086584047114, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "237eb9d": { "balancing:strategy": "weighting", @@ -558,11 +558,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.9024015752738876, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.014225351110017392, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "1a6eed2": { "balancing:strategy": "weighting", @@ -584,11 +584,11 @@ "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.04101667890187473, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": 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"data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "2036c61": { "balancing:strategy": "weighting", @@ -642,11 +642,11 @@ "classifier:gradient_boosting:n_iter_no_change": 10, "classifier:gradient_boosting:validation_fraction": 0.2850324540065884, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, 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"data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "61b0988": { "balancing:strategy": "none", @@ -689,11 +689,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1742, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "2190cfc": { "balancing:strategy": "weighting", @@ -714,11 +714,11 @@ "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0015359113788476297, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "15debe5": { "balancing:strategy": "weighting", @@ -744,11 +744,11 @@ "classifier:mlp:tol": 0.0001, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "45b9541": { "balancing:strategy": "none", @@ -775,11 +775,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.006209804535789879, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "48cd11d": { "balancing:strategy": "none", @@ -804,11 +804,11 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "95e43e6": { "balancing:strategy": "none", @@ -836,11 +836,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1093, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "f8aba41": { "balancing:strategy": "weighting", @@ -856,11 +856,11 @@ "classifier:passive_aggressive:loss": "hinge", "classifier:passive_aggressive:tol": 0.010384183550709794, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json index 5f7be31cd6..3f45fe26e9 100644 --- a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json +++ b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json @@ -23,11 +23,11 @@ "classifier:gradient_boosting:n_iter_no_change": 11, "classifier:gradient_boosting:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "153cd48": { "balancing:strategy": "weighting", @@ -49,11 +49,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.75, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.25870043409765325, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "65b7e1e": { "balancing:strategy": "weighting", @@ -73,11 +73,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0002459755534184245, "classifier:sgd:eta0": 0.0050360748910538156, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "7c8dfce": { "balancing:strategy": "weighting", @@ -101,11 +101,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.8687568474210459, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.14957346857371134, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "64ee1f9": { "balancing:strategy": "none", @@ -132,11 +132,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.009442778909715678, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": 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"data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "97dbfc8": { "balancing:strategy": "none", @@ -188,11 +188,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.011240853770638584, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 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"ddebe02": { "balancing:strategy": "weighting", @@ -805,11 +805,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 781, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "af700cf": { "balancing:strategy": "weighting", @@ -826,11 +826,11 @@ "classifier:passive_aggressive:tol": 0.0038046183268173064, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.00016898186356742833, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json index 235f4e7682..93ca58b6b5 100644 --- a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json +++ b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json @@ -20,11 +20,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7586354047062515, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.2909702584195659, "data_preprocessor:__choice__": "feature_type", - 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"data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.017077961357419263, "classifier:sgd:l1_ratio": 0.1498449443375854, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "e249036": { "balancing:strategy": 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"balancing:strategy": "weighting", @@ -257,11 +257,11 @@ "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.012073097426292019, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "68e6606": { "balancing:strategy": "weighting", @@ -361,11 +361,11 @@ "classifier:extra_trees:min_samples_split": 11, "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "f187687": { "balancing:strategy": "weighting", @@ -387,11 +387,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7136760024277188, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.2452308844548974, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", 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"data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "77764f9": { "balancing:strategy": "weighting", @@ -490,11 +490,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1555, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "data_preprocessor:__choice__": "feature_type", - 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"data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "d618703": { "balancing:strategy": "weighting", @@ -577,11 +577,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.007482244990304125, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "c437bea": { "balancing:strategy": "none", @@ -603,11 +603,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7450275104929482, 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"data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.009786276322275262, "classifier:sgd:epsilon": 0.0008711023589705295, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "811cba1": { "balancing:strategy": 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"balancing:strategy": "weighting", @@ -680,11 +680,11 @@ "classifier:gradient_boosting:n_iter_no_change": 16, "classifier:gradient_boosting:validation_fraction": 0.2596215430064656, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "d54bc21": { "balancing:strategy": "weighting", @@ -712,11 +712,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7334232077430226, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.242115842769868, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "b8184c0": { "balancing:strategy": "weighting", @@ -737,11 +737,11 @@ "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0021020326930237176, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "7cab112": { "balancing:strategy": "weighting", @@ -766,11 +766,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "classifier:gradient_boosting:n_iter_no_change": 15, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "b86e0b2": { "balancing:strategy": "weighting", @@ -787,11 +787,11 @@ "classifier:passive_aggressive:tol": 0.0007201456859918386, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.00011374720541793248, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "a2734c9": { "balancing:strategy": "weighting", @@ -816,11 +816,11 @@ "classifier:gradient_boosting:n_iter_no_change": 15, "classifier:gradient_boosting:validation_fraction": 0.373905213665471, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "564103f": { "balancing:strategy": "weighting", @@ -846,11 +846,11 @@ "classifier:gradient_boosting:n_iter_no_change": 4, "classifier:gradient_boosting:validation_fraction": 0.2551421881660974, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_10CV_iterative_es_if.json b/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_10CV_iterative_es_if.json index ef78997fe9..769ec64959 100644 --- a/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_10CV_iterative_es_if.json +++ b/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_10CV_iterative_es_if.json @@ -22,11 +22,11 @@ "classifier:gradient_boosting:n_iter_no_change": 17, "classifier:gradient_boosting:validation_fraction": 0.12820455722720236, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "69a5aed": { "balancing:strategy": "weighting", @@ -52,11 +52,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.020507323983341024, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "278a3b7": { "balancing:strategy": "weighting", @@ -75,11 +75,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7565211582936167, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.2427714099781957, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "2b21643": { "balancing:strategy": "weighting", @@ -106,11 +106,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.010000000000000004, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "186f4ea": { "balancing:strategy": "none", @@ -134,11 +134,11 @@ "classifier:gradient_boosting:n_iter_no_change": 19, "classifier:gradient_boosting:validation_fraction": 0.08286587058642826, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "bad65bc": { "balancing:strategy": "none", @@ -161,11 +161,11 @@ 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"data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "7e1cc2b": { "balancing:strategy": "none", @@ -700,11 +700,11 @@ "classifier:gradient_boosting:scoring": "loss", "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "5ac8b4c": { "balancing:strategy": "weighting", @@ -730,11 +730,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.009080589517127771, "data_preprocessor:__choice__": "feature_type", - 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"data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "db042ae": { "balancing:strategy": "none", @@ -820,11 +820,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.003132312313645055, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "46148ef": { "balancing:strategy": "none", @@ -849,11 +849,11 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "b96cfb3": { "balancing:strategy": "none", @@ -879,11 +879,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0032925248120968527, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "a69fc2a": { "balancing:strategy": "none", @@ -904,11 +904,11 @@ "classifier:gradient_boosting:scoring": "loss", "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_3CV_iterative_es_if.json b/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_3CV_iterative_es_if.json index 336ae5522c..0564fd890a 100644 --- a/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_3CV_iterative_es_if.json +++ b/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_3CV_iterative_es_if.json @@ -22,11 +22,11 @@ "classifier:gradient_boosting:n_iter_no_change": 4, "classifier:gradient_boosting:validation_fraction": 0.23374473517320618, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "a4cc318": { "balancing:strategy": "weighting", @@ -51,11 +51,11 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "5d80c27": { "balancing:strategy": "weighting", @@ -72,11 +72,11 @@ "classifier:passive_aggressive:tol": 0.0005787736999064962, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.012192559003185645, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "772b3a8": { "balancing:strategy": "none", @@ -103,11 +103,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1000, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "aa1d8b0": { "balancing:strategy": "none", @@ -130,11 +130,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.00016137881434812206, "classifier:gradient_boosting:n_iter_no_change": 14, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "9650698": { "balancing:strategy": "weighting", @@ -156,11 +156,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.8072961431686058, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.06513185741134703, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "f203d83": { "balancing:strategy": "weighting", @@ -181,11 +181,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7645599467770883, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.0011091488435678166, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "128ee5e": { "balancing:strategy": "weighting", @@ -208,11 +208,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.1696500193573621, "classifier:gradient_boosting:n_iter_no_change": 20, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "6859bdd": { "balancing:strategy": "none", @@ -233,11 +233,11 @@ "classifier:gradient_boosting:scoring": "loss", "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "7cd518a": { "balancing:strategy": "none", @@ -258,11 +258,11 @@ "classifier:gradient_boosting:scoring": "loss", "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "94dbc0f": { "balancing:strategy": "weighting", @@ -288,11 +288,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.03612642945550235, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "e623b07": { "balancing:strategy": "none", @@ -317,11 +317,11 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "05b30ee": { "balancing:strategy": "weighting", @@ -347,11 +347,11 @@ "classifier:gradient_boosting:n_iter_no_change": 20, "classifier:gradient_boosting:validation_fraction": 0.06937115181143882, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "ab05de1": { "balancing:strategy": "weighting", @@ -373,11 +373,11 @@ "classifier:gradient_boosting:tol": 1e-07, "classifier:gradient_boosting:n_iter_no_change": 7, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "fb3d3cb": { "balancing:strategy": "none", @@ -402,11 +402,11 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "b351885": { "balancing:strategy": "weighting", @@ -428,11 +428,11 @@ "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.000146347913583504, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "e5e92a5": { "balancing:strategy": "none", @@ -454,11 +454,11 @@ "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.08571475959664131, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "80918df": { "balancing:strategy": "none", @@ -485,11 +485,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0001412449266478571, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "789a57a": { "balancing:strategy": "none", @@ -505,11 +505,11 @@ "classifier:passive_aggressive:loss": "squared_hinge", "classifier:passive_aggressive:tol": 1.0363812529580006e-05, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "09b2b34": { "balancing:strategy": "none", @@ -531,11 +531,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1224, 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"balancing:strategy": "none", @@ -590,11 +590,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.8467720098829357, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.047087352576090154, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "0a12676": { "balancing:strategy": "weighting", @@ -620,11 +620,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0922294472020022, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "7cbf2e6": { "balancing:strategy": "none", @@ -647,11 +647,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.9759927921712355, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.20606466507293525, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "6c51fe7": { "balancing:strategy": "none", @@ -729,11 +729,11 @@ "classifier:mlp:tol": 0.0001, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "255fc3f": { "balancing:strategy": "weighting", @@ -759,11 +759,11 @@ "classifier:mlp:tol": 0.0001, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "5c39af4": { "balancing:strategy": "none", @@ -788,11 +788,11 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "3b71f29": { "balancing:strategy": "none", @@ -813,11 +813,11 @@ "classifier:gradient_boosting:scoring": "loss", "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "1726d64": { "balancing:strategy": "weighting", @@ -839,11 +839,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "classifier:sgd:l1_ratio": 0.02078042178977767, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "ddf8743": { "balancing:strategy": "none", @@ -866,11 +866,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.05399187079844309, "classifier:gradient_boosting:n_iter_no_change": 7, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_5CV_iterative_es_if.json b/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_5CV_iterative_es_if.json index 96bcad5987..f4cf97ce11 100644 --- a/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_5CV_iterative_es_if.json +++ b/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_5CV_iterative_es_if.json @@ -21,11 +21,11 @@ "classifier:gradient_boosting:n_iter_no_change": 20, "classifier:gradient_boosting:validation_fraction": 0.021337414482578282, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "04ec8ba": { "balancing:strategy": "weighting", @@ -51,11 +51,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.00010526559119801658, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "e26c1a0": { "balancing:strategy": "weighting", @@ -73,11 +73,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7108230489029451, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.06748030619538817, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "d0b9bfd": { "balancing:strategy": "none", @@ -103,11 +103,11 @@ "classifier:gradient_boosting:n_iter_no_change": 12, "classifier:gradient_boosting:validation_fraction": 0.24423608491087115, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "f697bc3": { "balancing:strategy": "weighting", @@ -123,11 +123,11 @@ "classifier:passive_aggressive:loss": "squared_hinge", "classifier:passive_aggressive:tol": 0.051629399473293695, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "91ff063": { "balancing:strategy": "weighting", @@ -153,11 +153,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.005591829098246694, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "5b541c8": { "balancing:strategy": "weighting", @@ -178,11 +178,11 @@ "classifier:gradient_boosting:scoring": "loss", "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "6e089e5": { "balancing:strategy": "weighting", @@ -205,11 +205,11 @@ 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"cf5bab1": { "balancing:strategy": "weighting", @@ -287,11 +287,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0665604479729369, "classifier:gradient_boosting:n_iter_no_change": 7, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + 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"82d949b": { "balancing:strategy": "weighting", @@ -830,11 +830,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.4281393406025153, "classifier:gradient_boosting:n_iter_no_change": 10, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "1892815": { "balancing:strategy": "weighting", @@ -859,11 +859,11 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "26b1b25": { "balancing:strategy": "none", @@ -886,11 +886,11 @@ "classifier:sgd:eta0": 0.07771727761177989, "classifier:sgd:l1_ratio": 0.1318744496830014, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_holdout_iterative_es_if.json b/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_holdout_iterative_es_if.json index 80dd79454e..403d673e07 100644 --- a/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_holdout_iterative_es_if.json +++ b/autosklearn/experimental/log_loss/askl2_portfolios/RF_None_holdout_iterative_es_if.json @@ -23,11 +23,11 @@ "classifier:gradient_boosting:n_iter_no_change": 19, "classifier:gradient_boosting:validation_fraction": 0.15148736566968157, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "fac7a35": { "balancing:strategy": "weighting", @@ -44,11 +44,11 @@ "classifier:passive_aggressive:tol": 3.994632621445496e-05, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.01158501039849096, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "73f216b": { "balancing:strategy": "none", @@ -73,11 +73,11 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "b73823e": { "balancing:strategy": "weighting", @@ -99,11 +99,11 @@ "classifier:gradient_boosting:tol": 1e-07, "classifier:gradient_boosting:n_iter_no_change": 10, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "bdbd472": { "balancing:strategy": "none", @@ -126,11 +126,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1909, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "e447ff7": { "balancing:strategy": "none", @@ -158,11 +158,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1243, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "2224037": { "balancing:strategy": "weighting", @@ -188,11 +188,11 @@ "classifier:gradient_boosting:n_iter_no_change": 19, "classifier:gradient_boosting:validation_fraction": 0.10022515354287, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "c7e9f6c": { "balancing:strategy": "weighting", @@ -212,11 +212,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7964677178110968, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.06005060035154881, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "6931b43": { "balancing:strategy": "weighting", @@ -241,11 +241,11 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "281561b": { "balancing:strategy": "none", @@ -268,11 +268,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1089, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "a1fef2a": { "balancing:strategy": "none", @@ -294,11 +294,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7455371733579619, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.24115086053573728, "data_preprocessor:__choice__": "feature_type", - 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"feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "440cf58": { "balancing:strategy": "weighting", @@ -356,11 +356,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1323, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "2656bee": { "balancing:strategy": "none", @@ -384,11 +384,11 @@ 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"balancing:strategy": "weighting", @@ -411,11 +411,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.9604530553396105, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.06974330376119121, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "8e0506b": { "balancing:strategy": "none", @@ -442,11 +442,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.9664568034228809, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.05941132539521904, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "857fdf6": { "balancing:strategy": "none", @@ -468,11 +468,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.8441739960764526, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.23854700600096612, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - 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"data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "e2da229": { "balancing:strategy": "none", @@ -782,11 +782,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 440, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "data_preprocessor:__choice__": "feature_type", - 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"data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "2dca877": { "balancing:strategy": "weighting", @@ -836,11 +836,11 @@ 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"data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "981e948": { "balancing:strategy": "none", @@ -887,11 +887,11 @@ "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.026426948053594874, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json b/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json index 5cafbcf25d..2f4886e3ca 100644 --- a/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json +++ b/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json @@ -22,11 +22,11 @@ "classifier:gradient_boosting:n_iter_no_change": 17, "classifier:gradient_boosting:validation_fraction": 0.12820455722720236, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": 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"data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "278a3b7": { "balancing:strategy": "weighting", @@ -75,11 +75,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7565211582936167, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.2427714099781957, "data_preprocessor:__choice__": "feature_type", - 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"classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "11b4871": { "balancing:strategy": "weighting", @@ -160,11 +160,11 @@ "classifier:gradient_boosting:tol": 1e-07, 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"data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "bad65bc": { "balancing:strategy": "none", @@ -242,11 +242,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.72347247098442, 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "e5c9543": { "balancing:strategy": "weighting", @@ -845,11 +845,11 @@ "classifier:sgd:penalty": "l1", "classifier:sgd:tol": 0.0004953514665899356, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "231dfac": { "balancing:strategy": "none", @@ -873,11 +873,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "classifier:gradient_boosting:n_iter_no_change": 2, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json b/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json index 286b843ff4..26070cd64f 100644 --- a/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json +++ b/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json @@ -22,11 +22,11 @@ "classifier:gradient_boosting:n_iter_no_change": 4, "classifier:gradient_boosting:validation_fraction": 0.23374473517320618, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "772b3a8": { "balancing:strategy": "none", @@ -53,11 +53,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1000, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "5d80c27": { "balancing:strategy": "weighting", @@ -74,11 +74,11 @@ "classifier:passive_aggressive:tol": 0.0005787736999064962, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.012192559003185645, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "6ddc39b": { "balancing:strategy": "weighting", @@ -103,11 +103,11 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "e5c81ce": { "balancing:strategy": "weighting", @@ -131,11 +131,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "classifier:gradient_boosting:n_iter_no_change": 8, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "e623b07": { "balancing:strategy": "none", @@ -160,11 +160,11 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "05b30ee": { "balancing:strategy": "weighting", @@ -190,11 +190,11 @@ "classifier:gradient_boosting:n_iter_no_change": 20, "classifier:gradient_boosting:validation_fraction": 0.06937115181143882, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "f203d83": { "balancing:strategy": "weighting", @@ -215,11 +215,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7645599467770883, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.0011091488435678166, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "6554e90": { "balancing:strategy": "none", @@ -241,11 +241,11 @@ "classifier:gradient_boosting:tol": 1e-07, "classifier:gradient_boosting:n_iter_no_change": 11, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "0e85550": { "balancing:strategy": "weighting", @@ -266,11 +266,11 @@ "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0007621274530118648, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "50aa7ad": { "balancing:strategy": "weighting", @@ -296,11 +296,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.006044015369290746, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "7d4aa2b": { "balancing:strategy": "none", @@ -323,11 +323,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.8571845899461281, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.19501334084607963, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "1d66cc6": { "balancing:strategy": "none", @@ -352,11 +352,11 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "ef2bd55": { "balancing:strategy": "weighting", @@ -381,11 +381,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.22744859003175397, "classifier:gradient_boosting:n_iter_no_change": 19, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "6973c5f": { "balancing:strategy": "none", @@ -407,11 +407,11 @@ "classifier:gradient_boosting:tol": 1e-07, "classifier:gradient_boosting:n_iter_no_change": 3, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "789a57a": { "balancing:strategy": "none", @@ -427,11 +427,11 @@ "classifier:passive_aggressive:loss": "squared_hinge", "classifier:passive_aggressive:tol": 1.0363812529580006e-05, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "919a721": { "balancing:strategy": "none", @@ -454,11 +454,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.009771641527306337, "classifier:gradient_boosting:n_iter_no_change": 9, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "3b71f29": { "balancing:strategy": "none", @@ -479,11 +479,11 @@ "classifier:gradient_boosting:scoring": "loss", "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "a43e999": { "balancing:strategy": "weighting", @@ -509,11 +509,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0006140067400027361, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "6859bdd": { "balancing:strategy": "none", @@ -534,11 +534,11 @@ "classifier:gradient_boosting:scoring": "loss", "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "a03ce20": { "balancing:strategy": "none", @@ -566,11 +566,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7665670083302197, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.25, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "172a64f": { "balancing:strategy": "none", @@ -593,11 +593,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0014143330565857936, "classifier:gradient_boosting:n_iter_no_change": 16, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "80918df": { "balancing:strategy": "none", @@ -624,11 +624,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0001412449266478571, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "255fc3f": { "balancing:strategy": "weighting", @@ -765,11 +765,11 @@ "classifier:mlp:tol": 0.0001, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "e5e92a5": { "balancing:strategy": "none", @@ -791,11 +791,11 @@ "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.08571475959664131, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "6d8d3a8": { "balancing:strategy": "weighting", @@ -823,11 +823,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1762, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "84f6f17": { "balancing:strategy": "weighting", @@ -849,11 +849,11 @@ "classifier:sgd:eta0": 3.091511250116596e-05, "classifier:sgd:l1_ratio": 0.3302336331591988, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "6c51fe7": { "balancing:strategy": "none", @@ -879,11 +879,11 @@ "classifier:mlp:tol": 0.0001, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json b/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json index 8b4afb13d8..c3ea7b6241 100644 --- a/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json +++ b/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json @@ -21,11 +21,11 @@ "classifier:gradient_boosting:n_iter_no_change": 20, "classifier:gradient_boosting:validation_fraction": 0.021337414482578282, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "0ba913b": { "balancing:strategy": "weighting", @@ -50,11 +50,11 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "e26c1a0": { "balancing:strategy": "weighting", @@ -72,11 +72,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7108230489029451, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.06748030619538817, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "1bbdcf7": { "balancing:strategy": "weighting", @@ -99,11 +99,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1994, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "f937be6": { "balancing:strategy": "none", @@ -129,11 +129,11 @@ "classifier:mlp:tol": 0.0001, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "b174099": { "balancing:strategy": "weighting", @@ -149,11 +149,11 @@ "classifier:passive_aggressive:loss": "squared_hinge", "classifier:passive_aggressive:tol": 0.00016439976946376104, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "04ec8ba": { "balancing:strategy": "weighting", @@ -179,11 +179,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.00010526559119801658, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "403e74e": { "balancing:strategy": "weighting", @@ -202,11 +202,11 @@ "classifier:sgd:tol": 2.7281817942839784e-05, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.00930364133754329, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "83d9459": { "balancing:strategy": "weighting", @@ -229,11 +229,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0004231632120121218, "classifier:gradient_boosting:n_iter_no_change": 10, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "6ab4d4e": { "balancing:strategy": "none", @@ -255,11 +255,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.9922490300053561, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.1829734948438291, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "6e089e5": { "balancing:strategy": "weighting", @@ -282,11 +282,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.007472986296445738, "classifier:gradient_boosting:n_iter_no_change": 13, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "2ff9871": { "balancing:strategy": "weighting", @@ -313,11 +313,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0042422461274778025, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "8933744": { "balancing:strategy": "weighting", @@ -338,11 +338,11 @@ "classifier:gradient_boosting:scoring": "loss", "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "9eaeaab": { "balancing:strategy": "weighting", @@ -426,11 +426,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.8926817285376502, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.08139907756830209, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "ace7e83": { "balancing:strategy": "none", @@ -456,11 +456,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.010000000000000004, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": 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"data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "7e9bd96": { "balancing:strategy": "none", @@ -517,11 +517,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, 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"data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "ca6d42d": { "balancing:strategy": "none", @@ -563,11 +563,11 @@ "classifier:passive_aggressive:loss": "hinge", "classifier:passive_aggressive:tol": 1.874391988200189e-05, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "27587a2": { "balancing:strategy": "none", @@ -587,11 +587,11 @@ "classifier:extra_trees:min_samples_split": 8, "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "5367b82": { "balancing:strategy": "none", @@ -614,11 +614,11 @@ "classifier:gradient_boosting:n_iter_no_change": 6, "classifier:gradient_boosting:validation_fraction": 0.11360221557156701, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "050fe7a": { "balancing:strategy": "none", @@ -640,11 +640,11 @@ "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.008032087955934802, "data_preprocessor:__choice__": "feature_type", - 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"data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "4f414ab": { "balancing:strategy": "weighting", @@ -704,11 +704,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.25, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "26b1b25": { "balancing:strategy": "none", @@ -731,11 +731,11 @@ "classifier:sgd:eta0": 0.07771727761177989, "classifier:sgd:l1_ratio": 0.1318744496830014, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "8f7d99e": { "balancing:strategy": "weighting", @@ -761,11 +761,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0030354192387363837, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "cca9a17": { "balancing:strategy": "weighting", @@ -785,11 +785,11 @@ "classifier:extra_trees:min_samples_split": 2, "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "28afd70": { "balancing:strategy": "weighting", @@ -815,11 +815,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.010271514866804008, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "2373433": { "balancing:strategy": "weighting", @@ -845,11 +845,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.004960032952598242, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "0a2cd4a": { "balancing:strategy": "none", @@ -873,11 +873,11 @@ "classifier:sgd:eta0": 0.008049468921629188, "classifier:sgd:l1_ratio": 0.5783321148432391, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json b/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json index fe0edab8da..2cfd4df384 100644 --- a/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json +++ b/autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json @@ -23,11 +23,11 @@ "classifier:gradient_boosting:n_iter_no_change": 19, "classifier:gradient_boosting:validation_fraction": 0.15148736566968157, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "fac7a35": { "balancing:strategy": "weighting", @@ -44,11 +44,11 @@ "classifier:passive_aggressive:tol": 3.994632621445496e-05, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.01158501039849096, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "73f216b": { "balancing:strategy": "none", @@ -73,11 +73,11 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - 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"data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "bdbd472": { "balancing:strategy": "none", @@ -239,11 +239,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1909, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - 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"data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "947b3e7": { "balancing:strategy": "none", @@ -654,11 +654,11 @@ "classifier:sgd:eta0": 0.09955932870902368, "classifier:sgd:power_t": 0.0367818634524381, "data_preprocessor:__choice__": "feature_type", - 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"data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "92a2b3e": { "balancing:strategy": "none", @@ -803,11 +803,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.043137864752964175, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "d4b1080": { "balancing:strategy": "weighting", @@ -830,11 +830,11 @@ "classifier:sgd:eta0": 2.7253528919863838e-05, "classifier:sgd:l1_ratio": 0.09043420706418341, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "6933af5": { "balancing:strategy": "weighting", @@ -862,11 +862,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.9921300116071107, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.006717144431080089, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "c2bfd40": { "balancing:strategy": "none", @@ -883,11 +883,11 @@ "classifier:passive_aggressive:tol": 1.197123139199107e-05, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.40377267259792243, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_10CV_iterative_es_if.json b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_10CV_iterative_es_if.json index 650fe8b4a7..71eaa43a07 100644 --- a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_10CV_iterative_es_if.json +++ b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_10CV_iterative_es_if.json @@ -21,11 +21,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.8406370831514932, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.20389889461388738, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "2e26e3e": { "balancing:strategy": "weighting", @@ -53,11 +53,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": 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"data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "ef55f97": { "balancing:strategy": "weighting", @@ -107,11 +107,11 @@ "classifier:gradient_boosting:n_iter_no_change": 1, "classifier:gradient_boosting:validation_fraction": 0.17760188229708512, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "1e0cf43": { "balancing:strategy": "none", @@ -136,11 +136,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.189634987062873, "classifier:gradient_boosting:n_iter_no_change": 17, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "40266af": { "balancing:strategy": "weighting", @@ -162,11 +162,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7569526888248843, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.2412613619390905, "data_preprocessor:__choice__": "feature_type", - 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"data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "5aab6e4": { "balancing:strategy": "weighting", @@ -216,11 +216,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.021809618585192588, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "cb9ea50": { "balancing:strategy": "none", @@ -243,11 +243,11 @@ "classifier:gradient_boosting:n_iter_no_change": 11, "classifier:gradient_boosting:validation_fraction": 0.2767824989716728, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "4a934ee": { "balancing:strategy": "weighting", @@ -268,11 +268,11 @@ "classifier:gradient_boosting:scoring": "loss", "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "c0e6259": { "balancing:strategy": "weighting", @@ -300,11 +300,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.050481917951293094, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "14994aa": { "balancing:strategy": "weighting", @@ -320,11 +320,11 @@ "classifier:passive_aggressive:loss": "hinge", "classifier:passive_aggressive:tol": 0.0002511675542745637, "data_preprocessor:__choice__": "feature_type", - 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"data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "604e370": { "balancing:strategy": "none", @@ -371,11 +371,11 @@ "classifier:extra_trees:min_samples_split": 6, "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:__choice__": "feature_type", - 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"f37dd59": { "balancing:strategy": "none", @@ -544,11 +544,11 @@ "classifier:mlp:tol": 0.0001, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "7c0aca6": { "balancing:strategy": "weighting", @@ -573,11 +573,11 @@ 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"balancing:strategy": "weighting", @@ -708,11 +708,11 @@ "classifier:gradient_boosting:n_iter_no_change": 9, "classifier:gradient_boosting:validation_fraction": 0.21013865145204977, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "b03f092": { "balancing:strategy": "none", @@ -735,11 +735,11 @@ "classifier:sgd:eta0": 4.881378632007281e-07, "classifier:sgd:l1_ratio": 0.20434166925375527, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "27509eb": { "balancing:strategy": "none", @@ -762,11 +762,11 @@ "classifier:gradient_boosting:n_iter_no_change": 2, "classifier:gradient_boosting:validation_fraction": 0.24884856514753817, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "f208775": { "balancing:strategy": "none", @@ -788,11 +788,11 @@ "classifier:gradient_boosting:tol": 1e-07, "classifier:gradient_boosting:n_iter_no_change": 18, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "e3b07fe": { "balancing:strategy": "none", @@ -819,11 +819,11 @@ 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-846,11 +846,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1000, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "952e617": { "balancing:strategy": "none", @@ -870,11 +870,11 @@ "classifier:random_forest:min_samples_split": 19, "classifier:random_forest:min_weight_fraction_leaf": 0.0, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_3CV_iterative_es_if.json b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_3CV_iterative_es_if.json index a270e86217..d8b44f3c4c 100644 --- a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_3CV_iterative_es_if.json +++ b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_3CV_iterative_es_if.json @@ -20,11 +20,11 @@ "classifier:gradient_boosting:tol": 1e-07, "classifier:gradient_boosting:n_iter_no_change": 1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "057a961": { "balancing:strategy": "none", @@ -50,11 +50,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.019274771652010168, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "32b95ed": { "balancing:strategy": "weighting", @@ -104,11 +104,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.3101738987949935, "classifier:gradient_boosting:n_iter_no_change": 9, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, 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"data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "09c23ea": { "balancing:strategy": "none", @@ -299,11 +299,11 @@ "classifier:gradient_boosting:n_iter_no_change": 3, "classifier:gradient_boosting:validation_fraction": 0.3906906166499882, "data_preprocessor:__choice__": 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"670fc33": { "balancing:strategy": "weighting", @@ -470,11 +470,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7080966958283763, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.19286444496554173, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "ccb5d37": { "balancing:strategy": "none", @@ -497,11 +497,11 @@ "classifier:gradient_boosting:n_iter_no_change": 1, "classifier:gradient_boosting:validation_fraction": 0.03367077228049312, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "0d34b7c": { "balancing:strategy": "weighting", @@ -529,11 +529,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 629, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "ea94610": { "balancing:strategy": "none", @@ -554,11 +554,11 @@ "classifier:random_forest:min_weight_fraction_leaf": 0.0, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.2641107295539412, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "70d7673": { "balancing:strategy": "weighting", @@ -639,11 +639,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 124, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "b4eb798": { "balancing:strategy": "none", @@ -672,11 +672,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.17229915174281954, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "0a5c673": { "balancing:strategy": "weighting", @@ -703,11 +703,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.008616859943826969, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "3c13b23": { "balancing:strategy": "none", @@ -733,11 +733,11 @@ "classifier:mlp:tol": 0.0001, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "7a1979d": { "balancing:strategy": "none", @@ -758,11 +758,11 @@ "classifier:gradient_boosting:scoring": "loss", "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "db47f52": { "balancing:strategy": "none", @@ -785,11 +785,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.07919351726301219, "classifier:gradient_boosting:n_iter_no_change": 18, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "57c1d3d": { "balancing:strategy": "weighting", @@ -809,11 +809,11 @@ "classifier:extra_trees:min_samples_split": 7, "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "330533a": { "balancing:strategy": "weighting", @@ -835,11 +835,11 @@ "classifier:gradient_boosting:tol": 1e-07, "classifier:gradient_boosting:n_iter_no_change": 9, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "31f9d24": { "balancing:strategy": "weighting", @@ -860,11 +860,11 @@ "classifier:gradient_boosting:scoring": "loss", "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "b7a81a4": { "balancing:strategy": "none", @@ -888,11 +888,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.8360632014850663, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.2880796632700646, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_5CV_iterative_es_if.json b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_5CV_iterative_es_if.json index db7cd35f5c..2f41ca5ffb 100644 --- a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_5CV_iterative_es_if.json +++ b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_5CV_iterative_es_if.json @@ -24,11 +24,11 @@ "classifier:gradient_boosting:n_iter_no_change": 14, "classifier:gradient_boosting:validation_fraction": 0.12446922535601551, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "48e7d72": { "balancing:strategy": "none", @@ -57,11 +57,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "3b5725c": { "balancing:strategy": "none", @@ -81,11 +81,11 @@ "classifier:random_forest:min_samples_split": 6, "classifier:random_forest:min_weight_fraction_leaf": 0.0, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "e8567fc": { "balancing:strategy": "weighting", @@ -104,11 +104,11 @@ "classifier:sgd:tol": 0.0007858702630144828, "classifier:sgd:eta0": 1.3077397138182272e-06, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "8aac02c": { "balancing:strategy": "weighting", @@ -128,11 +128,11 @@ "classifier:extra_trees:min_samples_split": 2, "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "0b0f56b": { "balancing:strategy": "none", @@ -158,11 +158,11 @@ "classifier:gradient_boosting:n_iter_no_change": 2, "classifier:gradient_boosting:validation_fraction": 0.015980615501050732, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "766431d": { "balancing:strategy": "none", @@ -184,11 +184,11 @@ "classifier:sgd:l1_ratio": 1.2408983312626301e-05, "classifier:sgd:power_t": 0.15473248271262038, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "9454294": { "balancing:strategy": "weighting", @@ -216,11 +216,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.27443311984781915, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "b85628c": { "balancing:strategy": "none", @@ -246,11 +246,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.014783697119079647, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "a8f7d08": { "balancing:strategy": "weighting", @@ -273,11 +273,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.03763157344543798, "classifier:gradient_boosting:n_iter_no_change": 11, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "c5fe8c1": { "balancing:strategy": "weighting", @@ -301,11 +301,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.25, "classifier:gradient_boosting:n_iter_no_change": 14, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "7eb153b": { "balancing:strategy": "weighting", @@ -330,11 +330,11 @@ "classifier:gradient_boosting:n_iter_no_change": 3, "classifier:gradient_boosting:validation_fraction": 0.20375603224425706, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "ed48b1e": { "balancing:strategy": "weighting", @@ -361,11 +361,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.19593845967764903, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "fd4841b": { "balancing:strategy": "weighting", @@ -390,11 +390,11 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "c3c5a39": { "balancing:strategy": "none", @@ -417,11 +417,11 @@ "classifier:gradient_boosting:n_iter_no_change": 13, "classifier:gradient_boosting:validation_fraction": 0.09536739955550967, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "aeea657": { "balancing:strategy": "weighting", @@ -450,11 +450,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "546748f": { "balancing:strategy": "none", @@ -514,11 +514,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7431608138789764, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.22854594976143439, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "ca57fd6": { "balancing:strategy": "none", @@ -540,11 +540,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1805, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "d2a3f8c": { "balancing:strategy": "none", @@ -572,11 +572,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1074, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - 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100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "09555d8": { "balancing:strategy": "none", @@ -629,11 +629,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0016129570857335443, "classifier:gradient_boosting:n_iter_no_change": 12, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "8ffaacd": { "balancing:strategy": "none", @@ -654,11 +654,11 @@ "classifier:random_forest:min_weight_fraction_leaf": 0.0, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0013280091773466545, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "4396ea3": { "balancing:strategy": "weighting", @@ -686,11 +686,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 499, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "1d4a4c2": { "balancing:strategy": "weighting", @@ -712,11 +712,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "classifier:sgd:eta0": 0.08292033673052085, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "038e453": { "balancing:strategy": "weighting", @@ -739,11 +739,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.010000000000000004, "classifier:gradient_boosting:n_iter_no_change": 9, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "d2b0a20": { "balancing:strategy": "none", @@ -772,11 +772,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "7308c48": { "balancing:strategy": "none", @@ -802,11 +802,11 @@ "classifier:gradient_boosting:n_iter_no_change": 17, "classifier:gradient_boosting:validation_fraction": 0.19824476185559053, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "4e08230": { "balancing:strategy": "weighting", @@ -829,11 +829,11 @@ "classifier:gradient_boosting:n_iter_no_change": 11, "classifier:gradient_boosting:validation_fraction": 0.010048821300335692, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "a9c54cc": { "balancing:strategy": "none", @@ -856,11 +856,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0025534629798769164, "classifier:gradient_boosting:n_iter_no_change": 19, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "c9ed268": { "balancing:strategy": "none", @@ -881,11 +881,11 @@ "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.28925175872648934, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "3995b86": { "balancing:strategy": "none", @@ -908,11 +908,11 @@ "classifier:sgd:l1_ratio": 0.5188568617869495, "classifier:sgd:power_t": 0.040071332883664504, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_holdout_iterative_es_if.json b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_holdout_iterative_es_if.json index 882b8c6c2c..29e15fc327 100644 --- a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_holdout_iterative_es_if.json +++ b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_holdout_iterative_es_if.json @@ -21,11 +21,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 467, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "c85132e": { "balancing:strategy": "weighting", @@ -51,11 +51,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.006864495761122583, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "3ffac3d": { "balancing:strategy": "weighting", @@ -76,11 +76,11 @@ "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.011646685244486026, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "3861869": { "balancing:strategy": "weighting", @@ -105,11 +105,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.2689588813833072, "classifier:gradient_boosting:n_iter_no_change": 19, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 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"data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "93a94fc": { "balancing:strategy": "none", @@ -163,11 +163,11 @@ "classifier:mlp:tol": 0.0001, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": 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"data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "e72a0d0": { "balancing:strategy": "none", @@ -414,11 +414,11 @@ "classifier:gradient_boosting:n_iter_no_change": 1, "classifier:gradient_boosting:validation_fraction": 0.058283078146253935, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, 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100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "3230065": { "balancing:strategy": "none", @@ -621,11 +621,11 @@ "classifier:mlp:tol": 0.0001, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": 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"classifier:gradient_boosting:n_iter_no_change": 11, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "87bd8ad": { "balancing:strategy": "weighting", @@ -793,11 +793,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 88, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "33ce15c": { "balancing:strategy": "none", @@ -820,11 +820,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 817, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "7166357": { "balancing:strategy": "weighting", @@ -844,11 +844,11 @@ "classifier:extra_trees:min_samples_split": 3, "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "fcfb9a5": { "balancing:strategy": "none", @@ -873,11 +873,11 @@ "classifier:gradient_boosting:n_iter_no_change": 5, "classifier:gradient_boosting:validation_fraction": 0.03541393128244466, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json index 12c6cf2e01..41cbdbe805 100644 --- a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json +++ b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json @@ -21,11 +21,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.8406370831514932, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.20389889461388738, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "078caa9": { "balancing:strategy": "none", @@ -50,11 +50,11 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "e457d9c": { "balancing:strategy": "weighting", @@ -77,11 +77,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.10560266211666122, "classifier:gradient_boosting:n_iter_no_change": 1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "ef55f97": { "balancing:strategy": "weighting", @@ -104,11 +104,11 @@ "classifier:gradient_boosting:n_iter_no_change": 1, "classifier:gradient_boosting:validation_fraction": 0.17760188229708512, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "3aa9e49": { "balancing:strategy": "weighting", @@ -130,11 +130,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7260837314772796, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.2525954332120069, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "2e26e3e": { "balancing:strategy": "weighting", @@ -162,11 +162,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": 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"data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "f3d6f6e": { "balancing:strategy": "weighting", @@ -218,11 +218,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.20784580770691768, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - 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"data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "0c52278": { "balancing:strategy": "none", @@ -706,11 +706,11 @@ "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.02066959974142489, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "9c231f7": { "balancing:strategy": "none", @@ -728,11 +728,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1282, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "bdb1c5c": { "balancing:strategy": "weighting", @@ -820,11 +820,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "4a934ee": { "balancing:strategy": "weighting", @@ -845,11 +845,11 @@ "classifier:gradient_boosting:scoring": "loss", "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "32ab8ad": { "balancing:strategy": "none", @@ -872,11 +872,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1000, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json index 75f1a2aabd..c88e7485a4 100644 --- a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json +++ b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json @@ -20,11 +20,11 @@ "classifier:gradient_boosting:tol": 1e-07, "classifier:gradient_boosting:n_iter_no_change": 1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "4e2d8ae": { "balancing:strategy": "none", @@ -47,11 +47,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.00683041403778669, "classifier:gradient_boosting:n_iter_no_change": 6, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "a507a1e": { "balancing:strategy": "weighting", @@ -75,11 +75,11 @@ "classifier:gradient_boosting:n_iter_no_change": 1, "classifier:gradient_boosting:validation_fraction": 0.3636102017236529, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "3b9d012": { "balancing:strategy": "weighting", @@ -101,11 +101,11 @@ "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.02647964603290406, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "37ed3d0": { "balancing:strategy": "none", @@ -131,11 +131,11 @@ "classifier:mlp:tol": 0.0001, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "09c23ea": { "balancing:strategy": "none", @@ -158,11 +158,11 @@ "classifier:gradient_boosting:n_iter_no_change": 3, "classifier:gradient_boosting:validation_fraction": 0.3906906166499882, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "70fe9d8": { "balancing:strategy": "weighting", @@ -184,11 +184,11 @@ "classifier:gradient_boosting:tol": 1e-07, "classifier:gradient_boosting:n_iter_no_change": 1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "da2661d": { "balancing:strategy": "none", @@ -216,11 +216,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 453, 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"data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "d13d06f": { "balancing:strategy": "none", @@ -834,11 +834,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.01896761231951133, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "d95f74b": { "balancing:strategy": "none", @@ -860,11 +860,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1456, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "7167f0c": { "balancing:strategy": "none", @@ -890,11 +890,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.05789857129625832, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json index d99c235d16..0136a3bd2b 100644 --- a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json +++ b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json @@ -24,11 +24,11 @@ "classifier:gradient_boosting:n_iter_no_change": 14, "classifier:gradient_boosting:validation_fraction": 0.12446922535601551, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "48e7d72": { "balancing:strategy": "none", @@ -57,11 +57,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "3b5725c": { "balancing:strategy": "none", @@ -81,11 +81,11 @@ "classifier:random_forest:min_samples_split": 6, "classifier:random_forest:min_weight_fraction_leaf": 0.0, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "e8567fc": { "balancing:strategy": "weighting", @@ -104,11 +104,11 @@ "classifier:sgd:tol": 0.0007858702630144828, "classifier:sgd:eta0": 1.3077397138182272e-06, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "d3b1c98": { "balancing:strategy": "none", @@ -131,11 +131,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.8079255643542401, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.2778427452756853, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "766431d": { "balancing:strategy": "none", @@ -184,11 +184,11 @@ "classifier:sgd:l1_ratio": 1.2408983312626301e-05, "classifier:sgd:power_t": 0.15473248271262038, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "9454294": { "balancing:strategy": "weighting", @@ -246,11 +246,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.27443311984781915, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "ed48b1e": { "balancing:strategy": "weighting", @@ -277,11 +277,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.19593845967764903, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "d41cebb": { "balancing:strategy": "none", @@ -338,11 +338,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1006, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "c3c5a39": { "balancing:strategy": "none", @@ -365,11 +365,11 @@ "classifier:gradient_boosting:n_iter_no_change": 13, "classifier:gradient_boosting:validation_fraction": 0.09536739955550967, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, 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"data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "07b7a65": { "balancing:strategy": "weighting", @@ -443,11 +443,11 @@ "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.09025942637402559, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "fd4841b": { "balancing:strategy": "weighting", @@ -472,11 +472,11 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "a9c54cc": { "balancing:strategy": "none", @@ -499,11 +499,11 @@ 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"data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "53af782": { "balancing:strategy": "none", @@ -881,11 +881,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7158581239252029, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.23727106534304226, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "04ee8cd": { "balancing:strategy": "weighting", @@ -908,11 +908,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.010000000000000004, "classifier:gradient_boosting:n_iter_no_change": 14, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 } }, "cutoffs": [ diff --git a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json index 6bcc253cfd..727752ba57 100644 --- a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json +++ b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json @@ -21,11 +21,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 467, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "c85132e": { "balancing:strategy": "weighting", @@ -51,11 +51,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.006864495761122583, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "3ffac3d": { "balancing:strategy": "weighting", @@ -76,11 +76,11 @@ "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.011646685244486026, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "3861869": { "balancing:strategy": "weighting", @@ -105,11 +105,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.2689588813833072, "classifier:gradient_boosting:n_iter_no_change": 19, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "1d8ad41": { "balancing:strategy": "none", @@ -133,11 +133,11 @@ "classifier:gradient_boosting:n_iter_no_change": 2, "classifier:gradient_boosting:validation_fraction": 0.35512720315660784, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "e8c14b9": { "balancing:strategy": "weighting", @@ -160,11 +160,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.012653276795134658, "classifier:gradient_boosting:n_iter_no_change": 7, "data_preprocessor:__choice__": "feature_type", - 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"data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "4522c5c": { "balancing:strategy": "none", @@ -299,11 +299,11 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0031947270899377388, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "cbcd40f": { "balancing:strategy": "none", @@ -328,11 +328,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", 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"data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "3a1cf69": { "balancing:strategy": "weighting", @@ -382,11 +382,11 @@ "classifier:mlp:tol": 0.0001, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - 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"data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "59fbdef": { "balancing:strategy": "none", @@ -464,11 +464,11 @@ "classifier:sgd:tol": 8.27706111772171e-05, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.007965405034296472, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "33ce15c": { "balancing:strategy": "none", @@ -491,11 +491,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 817, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "e72a0d0": { "balancing:strategy": "none", @@ -519,11 +519,11 @@ "classifier:gradient_boosting:n_iter_no_change": 1, "classifier:gradient_boosting:validation_fraction": 0.058283078146253935, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "5ec319d": { "balancing:strategy": "none", @@ -546,11 +546,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7122245303069001, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.26765391722955484, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "3230065": { "balancing:strategy": "none", @@ -576,11 +576,11 @@ "classifier:mlp:tol": 0.0001, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "4a96439": { "balancing:strategy": "none", @@ -600,11 +600,11 @@ "classifier:extra_trees:min_samples_split": 5, "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "970f3cb": { "balancing:strategy": "none", @@ -631,11 +631,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 70, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "3e86168": { "balancing:strategy": "none", @@ -660,11 +660,11 @@ "classifier:gradient_boosting:n_iter_no_change": 14, "classifier:gradient_boosting:validation_fraction": 0.3430240407684142, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "6e537da": { "balancing:strategy": "none", @@ -690,11 +690,11 @@ "classifier:mlp:tol": 0.0001, "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "e5d8521": { "balancing:strategy": "weighting", @@ -722,11 +722,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "classifier:mlp:validation_fraction": 0.1, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "ac7f6c4": { "balancing:strategy": "weighting", @@ -750,11 +750,11 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1000, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "f1d04fb": { "balancing:strategy": "none", @@ -780,11 +780,11 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.008845358970001308, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "191b6ed": { "balancing:strategy": "none", @@ -807,11 +807,11 @@ "classifier:gradient_boosting:n_iter_no_change": 10, "classifier:gradient_boosting:validation_fraction": 0.17784647507533755, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "9c8ea9e": { "balancing:strategy": "weighting", @@ -831,11 +831,11 @@ "classifier:extra_trees:min_samples_split": 16, "classifier:extra_trees:min_weight_fraction_leaf": 0.0, "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:feature_reduction:n_components": 100, "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1 + "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, + "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 }, "842e6d9": { "balancing:strategy": "none", @@ -860,11 +860,11 @@ 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2,no_encoding,no_coalescense,,most_frequent,standardize,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,f_regression,gaussian_process,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.019645201003537352,1.1170068633409441e-07,130.632960269528,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type,100,bag_of_word_encoding,1,min_df_absolute,,,,,,,,,1 6,one_hot_encoding,minority_coalescer,0.000400759453878678,mean,minmax,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09988990258830989,fpr,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,3.0002886744200357e-06,0.015673535665067875,least_squares,255,None,7,23,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type,100,bag_of_word_encoding,1,min_df_absolute,,,,,,,,,1 7,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,809,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.01806079811541296,False,,0.015540128437738566,True,0.9326390049807726,constant,squared_loss,elasticnet,,0.00010576654619459039,feature_type,100,bag_of_word_encoding,1,min_df_absolute,,,,,,,,,1 diff --git a/autosklearn/pipeline/components/data_preprocessing/feature_type_text.py b/autosklearn/pipeline/components/data_preprocessing/feature_type_text.py index 6030460ee1..8924d568a6 100644 --- a/autosklearn/pipeline/components/data_preprocessing/feature_type_text.py +++ b/autosklearn/pipeline/components/data_preprocessing/feature_type_text.py @@ -5,12 +5,12 @@ from sklearn.base import BaseEstimator from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, BasePipeline -from autosklearn.pipeline.components.data_preprocessing.feature_reduction.truncated_svd import ( # noqa: 501 - FeatureReduction, -) from autosklearn.pipeline.components.data_preprocessing.text_encoding import ( BagOfWordChoice, ) +from autosklearn.pipeline.components.data_preprocessing.text_feature_reduction.truncated_svd import ( # noqa: 501 + TextFeatureReduction, +) from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA @@ -126,7 +126,10 @@ def _get_pipeline_steps( default_dataset_properties, random_state=self.random_state ), ), - ("feature_reduction", FeatureReduction(random_state=self.random_state)), + ( + "text_feature_reduction", + TextFeatureReduction(random_state=self.random_state), + ), ] ) return steps diff --git a/autosklearn/pipeline/components/data_preprocessing/feature_reduction/__init__.py b/autosklearn/pipeline/components/data_preprocessing/text_feature_reduction/__init__.py similarity index 100% rename from autosklearn/pipeline/components/data_preprocessing/feature_reduction/__init__.py rename to autosklearn/pipeline/components/data_preprocessing/text_feature_reduction/__init__.py diff --git a/autosklearn/pipeline/components/data_preprocessing/feature_reduction/truncated_svd.py b/autosklearn/pipeline/components/data_preprocessing/text_feature_reduction/truncated_svd.py similarity index 96% rename from autosklearn/pipeline/components/data_preprocessing/feature_reduction/truncated_svd.py rename to autosklearn/pipeline/components/data_preprocessing/text_feature_reduction/truncated_svd.py index c104a18fd7..beecefb028 100644 --- a/autosklearn/pipeline/components/data_preprocessing/feature_reduction/truncated_svd.py +++ b/autosklearn/pipeline/components/data_preprocessing/text_feature_reduction/truncated_svd.py @@ -10,7 +10,7 @@ from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA -class FeatureReduction(AutoSklearnPreprocessingAlgorithm): +class TextFeatureReduction(AutoSklearnPreprocessingAlgorithm): """ Reduces the features created by a bag of words encoding """ @@ -25,7 +25,7 @@ def __init__( def fit( self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None - ) -> "FeatureReduction": + ) -> "TextFeatureReduction": if X.shape[1] > self.n_components: self.preprocessor = TruncatedSVD( n_components=self.n_components, random_state=self.random_state diff --git a/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv b/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv index e36bd31d4a..36263d3333 100755 --- a/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv +++ b/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv @@ -1,129 +1,129 @@ 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+127,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.5396632902741157e-05,0.18584665494894462,auto,255,None,6,48,13,loss,1E-07,0.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,379,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type,100,bag_of_word_encoding,1,min_df_absolute,,,,,,,,,1 +128,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,921.894056649221,,,0.4428468266254544,rbf,-1,False,1.0295003364004332e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7588611364765459,0.25,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type,100,bag_of_word_encoding,1,min_df_absolute,,,,,,,,,1 From 1a28632c8234b8bed4944d19109e029d2017fb73 Mon Sep 17 00:00:00 2001 From: Lukas Strack <73999018+Louquinze@users.noreply.github.com> Date: Tue, 15 Mar 2022 15:13:36 +0100 Subject: [PATCH 039/118] Change treatment of generic column type `object` (#1415) * rename `auto-sklearn/autosklearn/pipeline/components/data_preprocessing/feature_reduction` to `auto-sklearn/autosklearn/pipeline/components/data_preprocessing/text_feature_reduction`. also rename corresponding feature reduction class FeatureReduction to TextFeatureReduction. `auto-sklearn/autosklearn/pipeline/components/data_preprocessing/text_feature_reduction/truncated_svd.py:TextFeatureReduction` This includes adapting all *csv and *json participating in metalearning The "real" changes are limited to 1. truncated_svd.py 2. feature_type_text.py * rename `auto-sklearn/autosklearn/pipeline/components/data_preprocessing/feature_reduction` to `auto-sklearn/autosklearn/pipeline/components/data_preprocessing/text_feature_reduction`. also rename corresponding feature reduction class FeatureReduction to TextFeatureReduction. `auto-sklearn/autosklearn/pipeline/components/data_preprocessing/text_feature_reduction/truncated_svd.py:TextFeatureReduction` This includes adapting all *csv and *json participating in metalearning The "real" changes are limited to 1. truncated_svd.py 2. feature_type_text.py * change treatment of generic column dtype `object` for pandas dataframes. The `object` type will be treated as `string` in the future. * change treatment of generic column dtype `object` for pandas dataframes. The `object` type will be treated as `string` in the future. * change treatment of generic column dtype `object` for pandas dataframes. The `object` type will be treated as `string` in the future. * change treatment of generic column dtype `object` for pandas dataframes. The `object` type will be treated as `string` in the future. add new test case to `test_feature_validator.py` * change treatment of generic column dtype `object` for pandas dataframes. The `object` type will be treated as `string` in the future. add new test case to `test_feature_validator.py` * change treatment of generic column dtype `object` for pandas dataframes. The `object` type will be treated as `string` in the future. add new test case to `test_feature_validator.py` --- autosklearn/data/feature_validator.py | 16 +++---- test/test_data/test_feature_validator.py | 55 +++++++++++++++++++++++- 2 files changed, 60 insertions(+), 11 deletions(-) diff --git a/autosklearn/data/feature_validator.py b/autosklearn/data/feature_validator.py index 0b7ae8e8b1..85bb3a900c 100644 --- a/autosklearn/data/feature_validator.py +++ b/autosklearn/data/feature_validator.py @@ -1,6 +1,7 @@ from typing import Dict, List, Optional, Tuple, Union, cast import logging +import warnings import numpy as np import pandas as pd @@ -304,16 +305,13 @@ def get_feat_type_from_columns( # TypeError: data type not understood in certain pandas types elif not is_numeric_dtype(X[column]): if X[column].dtype.name == "object": - raise ValueError( - f"Input Column {column} has invalid type object. " - "Cast it to a valid dtype before using it in Auto-Sklearn. " - "Valid types are numerical, categorical or boolean. " - "You can cast it to a valid dtype using " - "pandas.Series.astype ." - "If working with string objects, the following " - "tutorial illustrates how to work with text data: " - "https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html" # noqa: E501 + warnings.warn( + f"Input Column {column} has generic type object. " + f"Autosklearn will treat this column as string. " + f"Please ensure that this setting is suitable for your task.", + UserWarning, ) + feat_type[column] = "string" elif pd.core.dtypes.common.is_datetime_or_timedelta_dtype( X[column].dtype ): diff --git a/test/test_data/test_feature_validator.py b/test/test_data/test_feature_validator.py index 0414cd31b4..216f286936 100644 --- a/test/test_data/test_feature_validator.py +++ b/test/test_data/test_feature_validator.py @@ -383,7 +383,6 @@ def test_no_new_category_after_fit(): def test_featurevalidator_new_data_after_fit( openml_id, train_data_type, test_data_type ): - # List is currently not supported as infer_objects # cast list objects to type objects if train_data_type == "list" or test_data_type == "list": @@ -436,7 +435,6 @@ def test_featurevalidator_new_data_after_fit( ), ) def test_list_to_dataframe(openml_id): - X_pandas, y_pandas = sklearn.datasets.fetch_openml( data_id=openml_id, return_X_y=True, as_frame=True ) @@ -509,3 +507,56 @@ def test_unsupported_dataframe_sparse(): ValueError, match=r"Auto-sklearn does not yet support sparse pandas" ): validator.fit(df) + + +def test_object_columns(): + class Dummy: + def __init__(self, x): + self.x = x + + def __call__(self): + print(self.x) + + def dummy_func(self): + for i in range(100): + print("do something 100 times") + + dummy_object = Dummy(1) + lst = [1, 2, 3] + array = np.array([1, 2, 3]) + dummy_stirng = "dummy string" + + df = pd.DataFrame( + { + "dummy_object": [dummy_object] * 4, + "dummy_lst": [lst] * 4, + "dummy_array": [array] * 4, + "dummy_string": [dummy_stirng] * 4, + "type_mix_column": [dummy_stirng, dummy_object, array, lst], + "cat_column": ["a", "b", "a", "b"], + } + ) + df["cat_column"] = df["cat_column"].astype("category") + + with pytest.warns( + UserWarning, + match=r"Input Column dummy_object has " + r"generic type object. " + r"Autosklearn will treat " + r"this column as string. " + r"Please ensure that this setting " + r"is suitable for your task.", + ): + validator = FeatureValidator() + feat_type = validator.get_feat_type_from_columns(df) + + column_types = { + "dummy_object": "string", + "dummy_lst": "string", + "dummy_array": "string", + "dummy_string": "string", + "type_mix_column": "string", + "cat_column": "categorical", + } + + assert feat_type == column_types From 37a89e5cfed13b5c1e861060b886e0e62ba02522 Mon Sep 17 00:00:00 2001 From: Lukas Strack <73999018+Louquinze@users.noreply.github.com> Date: Wed, 16 Mar 2022 14:04:39 +0100 Subject: [PATCH 040/118] implement a new attribute `allow_string_features` (#1420) --- autosklearn/automl.py | 3 ++ autosklearn/data/feature_validator.py | 25 ++++++++++++++--- autosklearn/data/validation.py | 6 +++- autosklearn/estimators.py | 7 +++++ autosklearn/experimental/askl2.py | 2 ++ doc/manual.rst | 35 ++++++++++++++---------- test/test_data/test_feature_validator.py | 32 ++++++++++++++++++++-- 7 files changed, 88 insertions(+), 22 deletions(-) diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 222f9de727..e0aae596e1 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -210,6 +210,7 @@ def __init__( scoring_functions=None, get_trials_callback=None, dataset_compression: Union[bool, Mapping[str, Any]] = True, + allow_string_features: bool = True, ): super(AutoML, self).__init__() self.configuration_space = None @@ -281,6 +282,7 @@ def __init__( self._dataset_compression = validate_dataset_compression_arg( dataset_compression, memory_limit=self._memory_limit ) + self.allow_string_features = allow_string_features self._datamanager = None self._dataset_name = None @@ -687,6 +689,7 @@ def fit( is_classification=is_classification, feat_type=feat_type, logger_port=self._logger_port, + allow_string_features=self.allow_string_features, ) self.InputValidator.fit(X_train=X, y_train=y, X_test=X_test, y_test=y_test) X, y = self.InputValidator.transform(X, y) diff --git a/autosklearn/data/feature_validator.py b/autosklearn/data/feature_validator.py index 85bb3a900c..07c108390c 100644 --- a/autosklearn/data/feature_validator.py +++ b/autosklearn/data/feature_validator.py @@ -42,9 +42,11 @@ def __init__( self, feat_type: Optional[List[str]] = None, logger: Optional[PickableLoggerAdapter] = None, + allow_string_features: bool = True, ) -> None: # If a dataframe was provided, we populate - # this attribute with a mapping from column to {numerical | categorical} + # this attribute with a mapping from column to + # {numerical | categorical | string} self.feat_type: Optional[Dict[Union[str, int], str]] = None if feat_type is not None: if isinstance(feat_type, dict): @@ -52,7 +54,7 @@ def __init__( elif not isinstance(feat_type, List): raise ValueError( "Auto-Sklearn expects a list of categorical/" - "numerical feature types, yet a" + "numerical/string feature types, yet a" " {} was provided".format(type(feat_type)) ) else: @@ -68,6 +70,7 @@ def __init__( self.logger = logger if logger is not None else logging.getLogger(__name__) self._is_fitted = False + self.allow_string_features = allow_string_features def fit( self, @@ -300,7 +303,14 @@ def get_feat_type_from_columns( elif X[column].dtype.name in ["category", "bool"]: feat_type[column] = "categorical" elif X[column].dtype.name == "string": - feat_type[column] = "string" + if self.allow_string_features: + feat_type[column] = "string" + else: + feat_type[column] = "categorical" + warnings.warn( + f"you disabled text encoding column {column} will be " + f"encoded as category" + ) # Move away from np.issubdtype as it causes # TypeError: data type not understood in certain pandas types elif not is_numeric_dtype(X[column]): @@ -311,7 +321,14 @@ def get_feat_type_from_columns( f"Please ensure that this setting is suitable for your task.", UserWarning, ) - feat_type[column] = "string" + if self.allow_string_features: + feat_type[column] = "string" + else: + feat_type[column] = "categorical" + warnings.warn( + f"you disabled text encoding column {column} will be" + f"encoded as category" + ) elif pd.core.dtypes.common.is_datetime_or_timedelta_dtype( X[column].dtype ): diff --git a/autosklearn/data/validation.py b/autosklearn/data/validation.py index 89aaca85c0..324bab4895 100644 --- a/autosklearn/data/validation.py +++ b/autosklearn/data/validation.py @@ -80,6 +80,7 @@ def __init__( feat_type: Optional[List[str]] = None, is_classification: bool = False, logger_port: Optional[int] = None, + allow_string_features: bool = True, ) -> None: self.feat_type = feat_type self.is_classification = is_classification @@ -92,8 +93,11 @@ def __init__( else: self.logger = logging.getLogger("Validation") + self.allow_string_features = allow_string_features self.feature_validator = FeatureValidator( - feat_type=self.feat_type, logger=self.logger + feat_type=self.feat_type, + logger=self.logger, + allow_string_features=self.allow_string_features, ) self.target_validator = TargetValidator( is_classification=self.is_classification, logger=self.logger diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 491309a7b8..eb98e54673 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -51,6 +51,7 @@ def __init__( load_models: bool = True, get_trials_callback=None, dataset_compression: Union[bool, Mapping[str, Any]] = True, + allow_string_features: bool = True, ): """ Parameters @@ -322,6 +323,10 @@ def __init__( accordingly. We guarantee that at least one occurrence of each label is included in the sampled set. + allow_string_features: bool = True + Whether autosklearn should process string features. By default the + textpreprocessing is enabled. + Attributes ---------- cv_results_ : dict of numpy (masked) ndarrays @@ -367,6 +372,7 @@ def __init__( self.load_models = load_models self.get_trials_callback = get_trials_callback self.dataset_compression = dataset_compression + self.allow_string_features = allow_string_features self.automl_ = None # type: Optional[AutoML] @@ -415,6 +421,7 @@ def build_automl(self): scoring_functions=self.scoring_functions, get_trials_callback=self.get_trials_callback, dataset_compression=self.dataset_compression, + allow_string_features=self.allow_string_features, ) return automl diff --git a/autosklearn/experimental/askl2.py b/autosklearn/experimental/askl2.py index 7068270a8e..65ef9b2def 100644 --- a/autosklearn/experimental/askl2.py +++ b/autosklearn/experimental/askl2.py @@ -206,6 +206,7 @@ def __init__( scoring_functions: Optional[List[Scorer]] = None, load_models: bool = True, dataset_compression: Union[bool, Mapping[str, Any]] = True, + allow_string_features: bool = True, ): """ @@ -363,6 +364,7 @@ def __init__( metric=metric, scoring_functions=scoring_functions, load_models=load_models, + allow_string_features=allow_string_features, ) def fit( diff --git a/doc/manual.rst b/doc/manual.rst index b1a4d9353a..cdfcd3cbe0 100644 --- a/doc/manual.rst +++ b/doc/manual.rst @@ -301,23 +301,29 @@ Other Supported formats for these training and testing pairs are: np.ndarray, pd.DataFrame, scipy.sparse.csr_matrix and python lists. - If your data contains categorical values (in the features or targets), autosklearn will automatically encode your - data using a `sklearn.preprocessing.LabelEncoder `_ - for unidimensional data and a `sklearn.preprocessing.OrdinalEncoder `_ - for multidimensional data. - - Regarding the features, there are two methods to guide *auto-sklearn* to properly encode categorical columns: + Regarding the features, there are multiple things to consider: * Providing a X_train/X_test numpy array with the optional flag feat_type. For further details, you can check the Example :ref:`sphx_glr_examples_40_advanced_example_feature_types.py`. * You can provide a pandas DataFrame, with properly formatted columns. If a column has numerical - dtype, *auto-sklearn* will not encode it and it will be passed directly to scikit-learn. If the - column has a categorical/boolean class, it will be encoded. If the column is of any other type - (Object or Timeseries), an error will be raised. For further details on how to properly encode - your data, you can check the Pandas Example - `Working with categorical data `_). - If you are working with time series, it is recommended that you follow this approach + dtype, *auto-sklearn* will not encode it and it will be passed directly to scikit-learn. *auto-sklearn* + supports both categorical or string as column type. Please ensure that you are using the correct + dtype for your task. By default *auto-sklearn* treats object and string columns as strings and + encodes the data using `sklearn.feature_extraction.text.CountVectorizer `_ + * If your data contains categorical values (in the features or targets), ensure that you explicitly label them as categorical. + data labeled as categorical is encoded by using a `sklearn.preprocessing.LabelEncoder `_ + for unidimensional data and a `sklearn.preprodcessing.OrdinalEncoder `_ for multidimensional data. + * For further details on how to properly encode your data, you can check the Pandas Example + `Working with categorical data `_). If you are working with time series, it is recommended that you follow this approach `Working with time data `_. + * If you prefer not using the string option at all you can disable this option. In this case + objects, strings and categorical columns are encoded as categorical. + + .. code:: python + + import autosklearn.classification + automl = autosklearn.classification.AutoSklearnClassifier(allow_string_features=False) + automl.fit(X_train, y_train) Regarding the targets (y_train/y_test), if the task involves a classification problem, such features will be automatically encoded. It is recommended to provide both y_train and y_test during fit, so that a common encoding @@ -336,14 +342,15 @@ Other In order to obtain *vanilla auto-sklearn* as used in `Efficient and Robust Automated Machine Learning `_ - set ``ensemble_size=1`` and ``initial_configurations_via_metalearning=0``: + set ``ensemble_size=1``, ``initial_configurations_via_metalearning=0`` and ``allow_string_features=False``: .. code:: python import autosklearn.classification automl = autosklearn.classification.AutoSklearnClassifier( ensemble_size=1, - initial_configurations_via_metalearning=0 + initial_configurations_via_metalearning=0, + allow_string_features=False, ) An ensemble of size one will result in always choosing the current best model diff --git a/test/test_data/test_feature_validator.py b/test/test_data/test_feature_validator.py index 216f286936..a06b91b0f3 100644 --- a/test/test_data/test_feature_validator.py +++ b/test/test_data/test_feature_validator.py @@ -524,15 +524,15 @@ def dummy_func(self): dummy_object = Dummy(1) lst = [1, 2, 3] array = np.array([1, 2, 3]) - dummy_stirng = "dummy string" + dummy_string = "dummy string" df = pd.DataFrame( { "dummy_object": [dummy_object] * 4, "dummy_lst": [lst] * 4, "dummy_array": [array] * 4, - "dummy_string": [dummy_stirng] * 4, - "type_mix_column": [dummy_stirng, dummy_object, array, lst], + "dummy_string": [dummy_string] * 4, + "type_mix_column": [dummy_string, dummy_object, array, lst], "cat_column": ["a", "b", "a", "b"], } ) @@ -560,3 +560,29 @@ def dummy_func(self): } assert feat_type == column_types + + +def test_allow_string_feature(): + df = pd.DataFrame({"Text": ["Hello", "how are you?"]}) + with pytest.warns( + UserWarning, + match=r"Input Column Text has generic type object. " + r"Autosklearn will treat this column as string. " + r"Please ensure that this setting is suitable for your task.", + ): + validator = FeatureValidator(allow_string_features=False) + feat_type = validator.get_feat_type_from_columns(df) + + column_types = {"Text": "categorical"} + assert feat_type == column_types + + df["Text"] = df["Text"].astype("string") + with pytest.warns( + UserWarning, + match=r"you disabled text encoding column Text will be " r"encoded as category", + ): + validator = FeatureValidator(allow_string_features=False) + feat_type = validator.get_feat_type_from_columns(df) + + column_types = {"Text": "categorical"} + assert feat_type == column_types From d4dc78038fd0b94526bf197df3b15eb175c3499d Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Wed, 23 Mar 2022 13:09:54 +0100 Subject: [PATCH 041/118] Update tests: 1. `automl.py` (#1417) * Add: Dedicated fixtures location * Add: Typing to conftest * Fix: More typing * Move: Dask fixtures to own location * Move: backend to it's own fixture location * Move: Automlstub thing to it's own fixture * Fix: docstring warning in conftest * Fix: Remove dead code * Add: Functionality to skip marked `slow` tests * Add: Fixture for getting datasets * Update: Tests for automl.py w/ fixtures, cases, mocks This commit (too much) focues on extending our pytest capabilities to make things more modular. Notably, making a fixtures and mocks folders for testing so there is a unified source of mocks and fixture locations. Split the tests for automl.py into seperate sub files to allow for an easier time discerning what is being tested where. Introduces `pytest_cases` to allow for easier future testing of properties. Introduces cached automl models to allow for a quicker time testing attributes while keeping tests seperate and distinct. This also rearragnes `unittest` import unfortunatly which causes many files to be touched. * Doc: Add documentation about our testing to conftest.py * Doc: Add a bit more on documenting of tests * Fix: Add pytest-cases to test dependancies * Fix: Broken tests * Fix: Failing test * Fix: Ordering of check in `_includes` * Remove `_includes` and use sets instead * Fix: Black * Update: test workflow knows about `.pytest_cache` --- .flake8 | 1 + .github/workflows/pytest.yml | 2 + autosklearn/automl.py | 713 +++++----- autosklearn/evaluation/__init__.py | 2 + autosklearn/evaluation/abstract_evaluator.py | 56 +- autosklearn/pipeline/util.py | 3 +- autosklearn/util/functional.py | 54 + autosklearn/util/logging_.py | 7 +- pyproject.toml | 16 +- setup.py | 1 + test/conftest.py | 314 +++-- test/fixtures/__init__.py | 0 test/fixtures/automl.py | 91 ++ test/fixtures/backend.py | 76 + test/fixtures/caching.py | 158 +++ test/fixtures/dask.py | 100 ++ test/fixtures/datasets.py | 278 ++++ test/fixtures/ensembles.py | 94 ++ test/fixtures/logging.py | 9 + test/mocks/__init__.py | 0 test/mocks/logging.py | 38 + test/test_automl/automl_utils.py | 21 +- test/test_automl/cases.py | 196 +++ test/test_automl/test_automl.py | 1232 ----------------- test/test_automl/test_construction.py | 95 ++ test/test_automl/test_dataset_compression.py | 135 ++ test/test_automl/test_dummy_predictions.py | 185 +++ test/test_automl/test_fit.py | 82 ++ test/test_automl/test_fit_ensemble.py | 35 + test/test_automl/test_fit_pipeline.py | 1 + test/test_automl/test_model_predict.py | 151 ++ test/test_automl/test_outputs.py | 107 ++ test/test_automl/test_performance.py | 0 .../test_automl/test_performance_over_time.py | 38 + test/test_automl/test_post_fit.py | 62 + test/test_automl/test_predict.py | 1 + test/test_automl/test_refit.py | 61 + test/test_automl/test_show_models.py | 20 + test/test_automl/test_sklearn_compliance.py | 76 + test/test_data/test_feature_validator.py | 3 +- test/test_data/test_target_validator.py | 3 +- test/test_data/test_validation.py | 3 +- test/test_ensemble_builder/ensemble_utils.py | 5 +- test/test_ensemble_builder/test_ensemble.py | 5 +- .../test_ensemble_selection.py | 3 +- test/test_estimators/__init__.py | 0 test/test_estimators/cases.py | 0 .../test_estimators.py | 67 +- test/test_evaluation/evaluation_util.py | 3 +- .../test_abstract_evaluator.py | 5 +- test/test_evaluation/test_custom_splitters.py | 3 +- test/test_evaluation/test_dummy_pipelines.py | 5 +- test/test_evaluation/test_evaluation.py | 5 +- test/test_evaluation/test_test_evaluator.py | 5 +- test/test_evaluation/test_train_evaluator.py | 5 +- .../pyMetaLearn/metalearning/test_kND.py | 3 +- .../pyMetaLearn/test_meta_base.py | 3 +- .../pyMetaLearn/test_meta_features.py | 5 +- .../pyMetaLearn/test_meta_features_sparse.py | 3 +- .../pyMetaLearn/test_metalearner.py | 3 +- .../pyMetaLearn/test_optimizer_base.py | 3 +- test/test_metalearning/test_metalearning.py | 4 +- test/test_metric/test_metrics.py | 5 +- test/test_optimizer/test_smbo.py | 3 +- .../components/classification/test_base.py | 4 +- .../data_preprocessing/test_balancing.py | 3 +- .../test_categorical_imputation.py | 3 +- .../data_preprocessing/test_category_shift.py | 4 +- .../test_data_preprocessing.py | 4 +- .../test_data_preprocessing_categorical.py | 6 +- .../test_data_preprocessing_numerical.py | 4 +- .../test_data_preprocessing_text.py | 4 +- .../test_minority_coalescence.py | 4 +- .../test_one_hot_encoding.py | 4 +- .../data_preprocessing/test_scaling.py | 4 +- .../feature_preprocessing/test_choice.py | 4 +- .../feature_preprocessing/test_fast_ica.py | 4 +- .../feature_preprocessing/test_kernel_pca.py | 4 +- .../test_kitchen_sinks.py | 4 +- .../test_nystroem_sampler.py | 4 +- .../test_random_trees_embedding.py | 4 +- .../test_select_percentile_classification.py | 4 +- .../test_select_percentile_regression.py | 4 +- .../test_select_rates_classification.py | 4 +- .../test_select_rates_regression.py | 4 +- .../test_truncatedSVD.py | 4 +- .../components/regression/test_base.py | 6 +- test/test_pipeline/components/test_base.py | 3 +- .../implementations/test_CategoryShift.py | 4 +- .../implementations/test_MinorityCoalescer.py | 4 +- .../test_SparseOneHotEncoder.py | 4 +- .../implementations/test_util.py | 4 +- test/test_pipeline/test_base.py | 6 +- test/test_pipeline/test_classification.py | 5 +- ..._create_searchspace_util_classification.py | 3 +- test/test_pipeline/test_regression.py | 5 +- test/test_scripts/test_metadata_generation.py | 3 +- test/test_util/test_StopWatch.py | 5 +- test/test_util/test_backend.py | 5 +- test/test_util/test_common.py | 3 +- test/test_util/test_data.py | 3 +- test/test_util/test_dependencies.py | 5 +- test/test_util/test_logging.py | 3 +- test/test_util/test_single_thread_client.py | 3 +- test/test_util/test_trials_callback.py | 3 +- test/util.py | 49 + 106 files changed, 2968 insertions(+), 1909 deletions(-) create mode 100644 autosklearn/util/functional.py create mode 100644 test/fixtures/__init__.py create mode 100644 test/fixtures/automl.py create mode 100644 test/fixtures/backend.py create mode 100644 test/fixtures/caching.py create mode 100644 test/fixtures/dask.py create mode 100644 test/fixtures/datasets.py create mode 100644 test/fixtures/ensembles.py create mode 100644 test/fixtures/logging.py create mode 100644 test/mocks/__init__.py create mode 100644 test/mocks/logging.py create mode 100644 test/test_automl/cases.py delete mode 100644 test/test_automl/test_automl.py create mode 100644 test/test_automl/test_construction.py create mode 100644 test/test_automl/test_dataset_compression.py create mode 100644 test/test_automl/test_dummy_predictions.py create mode 100644 test/test_automl/test_fit.py create mode 100644 test/test_automl/test_fit_ensemble.py create mode 100644 test/test_automl/test_fit_pipeline.py create mode 100644 test/test_automl/test_model_predict.py create mode 100644 test/test_automl/test_outputs.py create mode 100644 test/test_automl/test_performance.py create mode 100644 test/test_automl/test_performance_over_time.py create mode 100644 test/test_automl/test_post_fit.py create mode 100644 test/test_automl/test_predict.py create mode 100644 test/test_automl/test_refit.py create mode 100644 test/test_automl/test_show_models.py create mode 100644 test/test_automl/test_sklearn_compliance.py create mode 100644 test/test_estimators/__init__.py create mode 100644 test/test_estimators/cases.py rename test/{test_automl => test_estimators}/test_estimators.py (97%) create mode 100644 test/util.py diff --git a/.flake8 b/.flake8 index f3a26a3b56..d01cdb93b6 100644 --- a/.flake8 +++ b/.flake8 @@ -5,6 +5,7 @@ extend-exclude = venv .venv build + autosklearn/automl_common extend-ignore = # No whitespace before ':' in [x : y] E203 diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index be2cb7481c..735305307d 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -137,6 +137,8 @@ jobs: - name: Check for files left behind by test if: ${{ always() }} run: | + # Deleting `.pytest_chache` as it's used during testing and not deleted + rm -rf ".pytest_cache" before="${{ steps.status-before.outputs.BEFORE }}" after="$(git status --porcelain -b)" if [[ "$before" != "$after" ]]; then diff --git a/autosklearn/automl.py b/autosklearn/automl.py index e0aae596e1..1496aa2224 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -1,5 +1,6 @@ -# -*- encoding: utf-8 -*- -from typing import Any, Dict, List, Mapping, Optional, Tuple, Union, cast +from __future__ import annotations + +from typing import Any, Callable, Iterable, Mapping, Optional, Tuple import copy import io @@ -11,12 +12,9 @@ import sys import tempfile import time -import unittest.mock import uuid import warnings -import dask -import dask.distributed import distro import joblib import numpy as np @@ -25,8 +23,9 @@ import pkg_resources import scipy.stats import sklearn.utils -from ConfigSpace.configuration_space import Configuration +from ConfigSpace.configuration_space import Configuration, ConfigurationSpace from ConfigSpace.read_and_write import json as cs_json +from dask.distributed import Client, LocalCluster from scipy.sparse import spmatrix from sklearn.base import BaseEstimator from sklearn.dummy import DummyClassifier, DummyRegressor @@ -39,9 +38,11 @@ ) from sklearn.utils import check_random_state from sklearn.utils.validation import check_is_fitted +from smac.callbacks import IncorporateRunResultCallback from smac.runhistory.runhistory import RunInfo, RunValue from smac.stats.stats import Stats from smac.tae import StatusType +from typing_extensions import Literal from autosklearn.automl_common.common.utils.backend import Backend, create from autosklearn.constants import ( @@ -99,6 +100,8 @@ from autosklearn.util.single_thread_client import SingleThreadedClient from autosklearn.util.stopwatch import StopWatch +import unittest.mock + def _model_predict( model: Any, @@ -138,7 +141,7 @@ def _model_predict( The predictions produced by the model """ # Copy the array and ensure is has the attr 'shape' - X_ = np.asarray(X) if isinstance(X, List) else X.copy() + X_ = np.asarray(X) if isinstance(X, list) else X.copy() assert X_.shape[0] >= 1, f"X must have more than 1 sample but has {X_.shape[0]}" @@ -181,173 +184,139 @@ def _model_predict( class AutoML(BaseEstimator): + """Base class for handling the AutoML procedure""" + def __init__( self, - time_left_for_this_task, - per_run_time_limit, + time_left_for_this_task: int, + per_run_time_limit: int, temporary_directory: Optional[str] = None, delete_tmp_folder_after_terminate: bool = True, - initial_configurations_via_metalearning=25, - ensemble_size=1, - ensemble_nbest=1, - max_models_on_disc=1, - seed=1, - memory_limit=3072, - metadata_directory=None, - debug_mode=False, - include=None, - exclude=None, - resampling_strategy="holdout-iterative-fit", - resampling_strategy_arguments=None, - n_jobs=None, - dask_client: Optional[dask.distributed.Client] = None, - precision=32, - disable_evaluator_output=False, - get_smac_object_callback=None, - smac_scenario_args=None, - logging_config=None, - metric=None, - scoring_functions=None, - get_trials_callback=None, - dataset_compression: Union[bool, Mapping[str, Any]] = True, + initial_configurations_via_metalearning: int = 25, + ensemble_size: int = 1, + ensemble_nbest: int = 1, + max_models_on_disc: int = 1, + seed: int = 1, + memory_limit: int = 3072, + metadata_directory: Optional[str] = None, + include: Optional[dict[str, list[str]]] = None, + exclude: Optional[dict[str, list[str]]] = None, + resampling_strategy: str | Any = "holdout-iterative-fit", + resampling_strategy_arguments: Mapping[str, Any] = None, + n_jobs: Optional[int] = None, + dask_client: Optional[Client] = None, + precision: Literal[16, 32, 64] = 32, + disable_evaluator_output: bool | Iterable[str] = False, + get_smac_object_callback: Optional[Callable] = None, + smac_scenario_args: Optional[Mapping] = None, + logging_config: Optional[Mapping] = None, + metric: Optional[Scorer] = None, + scoring_functions: Optional[list[Scorer]] = None, + get_trials_callback: Optional[IncorporateRunResultCallback] = None, + dataset_compression: bool | Mapping[str, Any] = True, allow_string_features: bool = True, ): - super(AutoML, self).__init__() - self.configuration_space = None - self._backend: Optional[Backend] = None - self._temporary_directory = temporary_directory + super().__init__() + + if isinstance(disable_evaluator_output, Iterable): + disable_evaluator_output = list(disable_evaluator_output) # Incase iterator + allowed = set(["model", "cv_model", "y_optimization", "y_test", "y_valid"]) + unknown = allowed - set(disable_evaluator_output) + if any(unknown): + raise ValueError( + f"Unknown arg {unknown} for '_disable_evaluator_output'," + f" must be one of {allowed}" + ) + + # Validate dataset_compression and set its values + self._dataset_compression: Optional[DatasetCompressionSpec] + if isinstance(dataset_compression, bool): + if dataset_compression is True: + self._dataset_compression = default_dataset_compression_arg + else: + self._dataset_compression = None + else: + self._dataset_compression = validate_dataset_compression_arg( + dataset_compression, + memory_limit=memory_limit, + ) + self._delete_tmp_folder_after_terminate = delete_tmp_folder_after_terminate - # self._tmp_dir = tmp_dir self._time_for_task = time_left_for_this_task self._per_run_time_limit = per_run_time_limit - self._initial_configurations_via_metalearning = ( - initial_configurations_via_metalearning - ) + self._metric = metric self._ensemble_size = ensemble_size self._ensemble_nbest = ensemble_nbest self._max_models_on_disc = max_models_on_disc self._seed = seed self._memory_limit = memory_limit - self._data_memory_limit = None self._metadata_directory = metadata_directory self._include = include self._exclude = exclude self._resampling_strategy = resampling_strategy - self._scoring_functions = ( - scoring_functions if scoring_functions is not None else [] - ) - self._resampling_strategy_arguments = ( - resampling_strategy_arguments - if resampling_strategy_arguments is not None - else {} - ) - self._n_jobs = n_jobs - self._dask_client = dask_client - - self.precision = precision self._disable_evaluator_output = disable_evaluator_output - # Check arguments prior to doing anything! - if not isinstance(self._disable_evaluator_output, (bool, List)): - raise ValueError( - "disable_evaluator_output must be of type bool " "or list." - ) - if isinstance(self._disable_evaluator_output, List): - allowed_elements = [ - "model", - "cv_model", - "y_optimization", - "y_test", - "y_valid", - ] - for element in self._disable_evaluator_output: - if element not in allowed_elements: - raise ValueError( - "List member '%s' for argument " - "'disable_evaluator_output' must be one " - "of " + str(allowed_elements) - ) self._get_smac_object_callback = get_smac_object_callback self._get_trials_callback = get_trials_callback self._smac_scenario_args = smac_scenario_args self.logging_config = logging_config + self.precision = precision + self.allow_string_features = allow_string_features + self._initial_configurations_via_metalearning = ( + initial_configurations_via_metalearning + ) - # Validate dataset_compression and set its values - self._dataset_compression: Optional[DatasetCompressionSpec] - if isinstance(dataset_compression, bool): - if dataset_compression is True: - self._dataset_compression = default_dataset_compression_arg - else: - self._dataset_compression = None + self._scoring_functions = scoring_functions or {} + self._resampling_strategy_arguments = resampling_strategy_arguments or {} + + # Single core, local runs should use fork to prevent the __main__ requirements + # in examples. Nevertheless, multi-process runs have spawn as requirement to + # reduce the possibility of a deadlock + if n_jobs == 1 and dask_client is None: + self._multiprocessing_context = "fork" + self._dask_client = SingleThreadedClient() + self._n_jobs = 1 else: - self._dataset_compression = validate_dataset_compression_arg( - dataset_compression, memory_limit=self._memory_limit - ) - self.allow_string_features = allow_string_features + self._multiprocessing_context = "forkserver" + self._dask_client = dask_client + self._n_jobs = n_jobs + # Create the backend + self._backend: Backend = create( + temporary_directory=temporary_directory, + output_directory=None, + prefix="auto-sklearn", + delete_output_folder_after_terminate=delete_tmp_folder_after_terminate, + ) + + self._data_memory_limit = None # TODO: dead variable? Always None self._datamanager = None self._dataset_name = None self._feat_type = None - self._stopwatch = StopWatch() - self._logger = None + self._logger: Optional[PicklableClientLogger] = None self._task = None - - self._metric = metric - self._label_num = None self._parser = None - self.models_ = None - self.cv_models_ = None - self.ensemble_ = None self._can_predict = False - self._debug_mode = debug_mode - - self.InputValidator = None # type: Optional[InputValidator] + self.models_: Optional[dict] = None + self.cv_models_: Optional[dict] = None + self.ensemble_ = None + self.InputValidator: Optional[InputValidator] = None + self.configuration_space = None # The ensemble performance history through time - self.ensemble_performance_history = [] - self.fitted = False - - # Single core, local runs should use fork - # to prevent the __main__ requirements in - # examples. Nevertheless, multi-process runs - # have spawn as requirement to reduce the - # possibility of a deadlock - self._multiprocessing_context = "forkserver" - if self._n_jobs == 1 and self._dask_client is None: - self._multiprocessing_context = "fork" - self._dask_client = SingleThreadedClient() - - if not isinstance(self._time_for_task, int): - raise ValueError( - "time_left_for_this_task not of type integer, " - "but %s" % str(type(self._time_for_task)) - ) - if not isinstance(self._per_run_time_limit, int): - raise ValueError( - "per_run_time_limit not of type integer, but %s" - % str(type(self._per_run_time_limit)) - ) - - # By default try to use the TCP logging port or get a new port + self._stopwatch = StopWatch() self._logger_port = logging.handlers.DEFAULT_TCP_LOGGING_PORT + self.ensemble_performance_history = [] - # Num_run tell us how many runs have been launched - # It can be seen as an identifier for each configuration - # saved to disk + # Num_run tell us how many runs have been launched. It can be seen as an + # identifier for each configuration saved to disk self.num_run = 0 + self.fitted = False - def _create_backend(self) -> Backend: - return create( - temporary_directory=self._temporary_directory, - output_directory=None, - prefix="auto-sklearn", - delete_tmp_folder_after_terminate=self._delete_tmp_folder_after_terminate, - ) - - def _create_dask_client(self): + def _create_dask_client(self) -> None: self._is_dask_client_internally_created = True - self._dask_client = dask.distributed.Client( - dask.distributed.LocalCluster( + self._dask_client = Client( + LocalCluster( n_workers=self._n_jobs, processes=False, threads_per_worker=1, @@ -365,11 +334,9 @@ def _create_dask_client(self): heartbeat_interval=10000, ) - def _close_dask_client(self): - if ( - hasattr(self, "_is_dask_client_internally_created") - and self._is_dask_client_internally_created - and self._dask_client + def _close_dask_client(self, force: bool = False) -> None: + if getattr(self, "_dask_client", None) is not None and ( + force or getattr(self, "_is_dask_client_internally_created", False) ): self._dask_client.shutdown() self._dask_client.close() @@ -378,7 +345,7 @@ def _close_dask_client(self): self._is_dask_client_internally_created = False del self._is_dask_client_internally_created - def _get_logger(self, name): + def _get_logger(self, name: str) -> PicklableClientLogger: logger_name = "AutoML(%d):%s" % (self._seed, name) # Setup the configuration for the logger @@ -432,7 +399,7 @@ def _get_logger(self, name): port=self._logger_port, ) - def _clean_logger(self): + def _clean_logger(self) -> None: if not hasattr(self, "stop_logging_server") or self.stop_logging_server is None: return @@ -451,28 +418,34 @@ def _clean_logger(self): del self.stop_logging_server @staticmethod - def _start_task(watcher, task_name): + def _start_task(watcher: StopWatch, task_name: str) -> None: watcher.start_task(task_name) @staticmethod - def _stop_task(watcher, task_name): + def _stop_task(watcher: StopWatch, task_name: str) -> None: watcher.stop_task(task_name) @staticmethod - def _print_load_time(basename, time_left_for_this_task, time_for_load_data, logger): - - time_left_after_reading = max(0, time_left_for_this_task - time_for_load_data) - logger.info( - "Remaining time after reading %s %5.2f sec" - % (basename, time_left_after_reading) - ) + def _print_load_time( + basename: str, + time_left_for_this_task: float, + time_for_load_data: float, + logger: PicklableClientLogger, + ) -> float: + time_left = max(0, time_left_for_this_task - time_for_load_data) + logger.info(f"Remaining time after reading {basename} {time_left:5.2f} sec") return time_for_load_data - def _do_dummy_prediction(self, datamanager: XYDataManager, num_run: int) -> int: - + def _do_dummy_prediction(self) -> None: # When using partial-cv it makes no sense to do dummy predictions if self._resampling_strategy in ["partial-cv", "partial-cv-iterative-fit"]: - return num_run + return + + if self._metric is None: + raise ValueError("Metric was not set") + + # Dummy prediction always have num_run set to 1 + dummy_run_num = 1 self._logger.info("Starting to create dummy predictions.") @@ -491,7 +464,7 @@ def _do_dummy_prediction(self, datamanager: XYDataManager, num_run: int) -> int: autosklearn_seed=self._seed, multi_objectives=["cost"], resampling_strategy=self._resampling_strategy, - initial_num_run=num_run, + initial_num_run=dummy_run_num, stats=stats, metric=self._metric, memory_limit=memory_limit, @@ -504,7 +477,8 @@ def _do_dummy_prediction(self, datamanager: XYDataManager, num_run: int) -> int: ) status, cost, runtime, additional_info = ta.run( - num_run, cutoff=self._time_for_task + config=dummy_run_num, + cutoff=self._time_for_task, ) if status == StatusType.SUCCESS: self._logger.info("Finished creating dummy predictions.") @@ -528,7 +502,7 @@ def _do_dummy_prediction(self, datamanager: XYDataManager, num_run: int) -> int: self._logger.error(msg) raise ValueError(msg) - return num_run + return @classmethod def _task_type_id(cls, task_type: str) -> int: @@ -545,7 +519,7 @@ def fit( task: Optional[int] = None, X_test: Optional[SUPPORTED_FEAT_TYPES] = None, y_test: Optional[SUPPORTED_TARGET_TYPES] = None, - feat_type: Optional[List[str]] = None, + feat_type: Optional[list[str]] = None, dataset_name: Optional[str] = None, only_return_configuration_space: bool = False, load_models: bool = True, @@ -590,7 +564,6 @@ def fit( Parameters ---------- - X : {array-like, sparse matrix}, shape (n_samples, n_features) The training input samples. @@ -610,7 +583,7 @@ def fit( of all models. This allows to evaluate the performance of Auto-sklearn over time. - feat_type : Optional[List], + feat_type : Optional[list], List of str of `len(X.shape[1])` describing the attribute type. Possible types are `Categorical` and `Numerical`. `Categorical` attributes will be automatically One-Hot encoded. The values @@ -637,7 +610,6 @@ def fit( Returns ------- self - """ if (X_test is not None) ^ (y_test is not None): raise ValueError("Must provide both X_test and y_test together") @@ -664,9 +636,6 @@ def fit( if dataset_name is None: dataset_name = str(uuid.uuid1(clock_seq=os.getpid())) - # Create the backend - self._backend = self._create_backend() - # By default try to use the TCP logging port or get a new port self._logger_port = logging.handlers.DEFAULT_TCP_LOGGING_PORT self._logger = self._get_logger(dataset_name) @@ -707,9 +676,7 @@ def fit( memory_allocation = self._dataset_compression["memory_allocation"] # Remove precision reduction if we can't perform it - if X.dtype not in supported_precision_reductions and "precision" in cast( - List[str], methods - ): # Removable with TypedDict + if "precision" in methods and X.dtype not in supported_precision_reductions: methods = [method for method in methods if method != "precision"] with warnings_to(self._logger): @@ -764,96 +731,11 @@ def fit( # Take the feature types from the validator self._feat_type = self.InputValidator.feature_validator.feat_type - # Produce debug information to the logfile - self._logger.debug("Starting to print environment information") - self._logger.debug(" Python version: %s", sys.version.split("\n")) - try: - self._logger.debug( - f"\tDistribution: {distro.id()}-{distro.version()}-{distro.name()}" - ) - except AttributeError: - pass - - self._logger.debug(" System: %s", platform.system()) - self._logger.debug(" Machine: %s", platform.machine()) - self._logger.debug(" Platform: %s", platform.platform()) - # UNAME appears to leak sensible information - # self._logger.debug(' uname: %s', platform.uname()) - self._logger.debug(" Version: %s", platform.version()) - self._logger.debug(" Mac version: %s", platform.mac_ver()) - requirements = pkg_resources.resource_string("autosklearn", "requirements.txt") - requirements = requirements.decode("utf-8") - requirements = [requirement for requirement in requirements.split("\n")] - for requirement in requirements: - if not requirement: - continue - match = RE_PATTERN.match(requirement) - if match: - name = match.group("name") - module_dist = pkg_resources.get_distribution(name) - self._logger.debug(" %s", module_dist) - else: - raise ValueError("Unable to read requirement: %s" % requirement) - self._logger.debug("Done printing environment information") - self._logger.debug("Starting to print arguments to auto-sklearn") - self._logger.debug( - " tmp_folder: %s", self._backend.context._temporary_directory - ) - self._logger.debug(" time_left_for_this_task: %f", self._time_for_task) - self._logger.debug(" per_run_time_limit: %f", self._per_run_time_limit) - self._logger.debug( - " initial_configurations_via_metalearning: %d", - self._initial_configurations_via_metalearning, - ) - self._logger.debug(" ensemble_size: %d", self._ensemble_size) - self._logger.debug(" ensemble_nbest: %f", self._ensemble_nbest) - self._logger.debug(" max_models_on_disc: %s", str(self._max_models_on_disc)) - self._logger.debug(" seed: %d", self._seed) - self._logger.debug(" memory_limit: %s", str(self._memory_limit)) - self._logger.debug(" metadata_directory: %s", self._metadata_directory) - self._logger.debug(" debug_mode: %s", self._debug_mode) - self._logger.debug(" include: %s", str(self._include)) - self._logger.debug(" exclude: %s", str(self._exclude)) - self._logger.debug(" resampling_strategy: %s", str(self._resampling_strategy)) - self._logger.debug( - " resampling_strategy_arguments: %s", - str(self._resampling_strategy_arguments), - ) - self._logger.debug(" n_jobs: %s", str(self._n_jobs)) - self._logger.debug( - " multiprocessing_context: %s", str(self._multiprocessing_context) - ) - self._logger.debug(" dask_client: %s", str(self._dask_client)) - self._logger.debug(" precision: %s", str(self.precision)) - self._logger.debug( - " disable_evaluator_output: %s", str(self._disable_evaluator_output) - ) - self._logger.debug( - " get_smac_objective_callback: %s", str(self._get_smac_object_callback) - ) - self._logger.debug(" smac_scenario_args: %s", str(self._smac_scenario_args)) - self._logger.debug(" logging_config: %s", str(self.logging_config)) - self._logger.debug(" metric: %s", str(self._metric)) - self._logger.debug("Done printing arguments to auto-sklearn") - self._logger.debug("Starting to print available components") - for choice in ( - ClassifierChoice, - RegressorChoice, - FeaturePreprocessorChoice, - OHEChoice, - RescalingChoice, - CoalescenseChoice, - ): - self._logger.debug( - "%s: %s", - choice.__name__, - choice.get_components(), - ) - self._logger.debug("Done printing available components") + self._log_fit_setup() datamanager = XYDataManager( - X, - y, + X=X, + y=y, X_test=X_test, y_test=y_test, task=self._task, @@ -867,16 +749,6 @@ def fit( # == Pickle the data manager to speed up loading self._backend.save_datamanager(datamanager) - time_for_load_data = self._stopwatch.wall_elapsed(self._dataset_name) - - if self._debug_mode: - self._print_load_time( - self._dataset_name, - self._time_for_task, - time_for_load_data, - self._logger, - ) - # = Create a searchspace # Do this before One Hot Encoding to make sure that it creates a # search space for a dense classifier even if one hot encoding would @@ -896,8 +768,8 @@ def fit( return self.configuration_space # == Perform dummy predictions - # Dummy prediction always have num_run set to 1 - self.num_run += self._do_dummy_prediction(datamanager, num_run=1) + self.num_run += 1 + self._do_dummy_prediction() # == RUN ensemble builder # Do this before calculating the meta-features to make sure that the @@ -1036,9 +908,12 @@ def fit( list(entry[:2]) + [entry[2].get_dictionary()] + list(entry[3:]) for entry in self.trajectory_ ] + with open(trajectory_filename, "w") as fh: json.dump(saveable_trajectory, fh) + except Exception as e: + self._fit_cleanup() self._logger.exception(e) raise @@ -1078,10 +953,96 @@ def fit( return self + def _log_fit_setup(self) -> None: + # Produce debug information to the logfile + self._logger.debug("Starting to print environment information") + self._logger.debug(" Python version: %s", sys.version.split("\n")) + try: + self._logger.debug( + f"\tDistribution: {distro.id()}-{distro.version()}-{distro.name()}" + ) + except AttributeError: + pass + + self._logger.debug(" System: %s", platform.system()) + self._logger.debug(" Machine: %s", platform.machine()) + self._logger.debug(" Platform: %s", platform.platform()) + # UNAME appears to leak sensible information + # self._logger.debug(' uname: %s', platform.uname()) + self._logger.debug(" Version: %s", platform.version()) + self._logger.debug(" Mac version: %s", platform.mac_ver()) + requirements = pkg_resources.resource_string("autosklearn", "requirements.txt") + requirements = requirements.decode("utf-8") + requirements = [requirement for requirement in requirements.split("\n")] + for requirement in requirements: + if not requirement: + continue + match = RE_PATTERN.match(requirement) + if match: + name = match.group("name") + module_dist = pkg_resources.get_distribution(name) + self._logger.debug(" %s", module_dist) + else: + raise ValueError("Unable to read requirement: %s" % requirement) + + self._logger.debug("Done printing environment information") + self._logger.debug("Starting to print arguments to auto-sklearn") + self._logger.debug(" tmp_folder: %s", self._backend.temporary_directory) + self._logger.debug(" time_left_for_this_task: %f", self._time_for_task) + self._logger.debug(" per_run_time_limit: %f", self._per_run_time_limit) + self._logger.debug( + " initial_configurations_via_metalearning: %d", + self._initial_configurations_via_metalearning, + ) + self._logger.debug(" ensemble_size: %d", self._ensemble_size) + self._logger.debug(" ensemble_nbest: %f", self._ensemble_nbest) + self._logger.debug(" max_models_on_disc: %s", str(self._max_models_on_disc)) + self._logger.debug(" seed: %d", self._seed) + self._logger.debug(" memory_limit: %s", str(self._memory_limit)) + self._logger.debug(" metadata_directory: %s", self._metadata_directory) + self._logger.debug(" include: %s", str(self._include)) + self._logger.debug(" exclude: %s", str(self._exclude)) + self._logger.debug(" resampling_strategy: %s", str(self._resampling_strategy)) + self._logger.debug( + " resampling_strategy_arguments: %s", + str(self._resampling_strategy_arguments), + ) + self._logger.debug(" n_jobs: %s", str(self._n_jobs)) + self._logger.debug( + " multiprocessing_context: %s", str(self._multiprocessing_context) + ) + self._logger.debug(" dask_client: %s", str(self._dask_client)) + self._logger.debug(" precision: %s", str(self.precision)) + self._logger.debug( + " disable_evaluator_output: %s", str(self._disable_evaluator_output) + ) + self._logger.debug( + " get_smac_objective_callback: %s", str(self._get_smac_object_callback) + ) + self._logger.debug(" smac_scenario_args: %s", str(self._smac_scenario_args)) + self._logger.debug(" logging_config: %s", str(self.logging_config)) + self._logger.debug(" metric: %s", str(self._metric)) + self._logger.debug("Done printing arguments to auto-sklearn") + self._logger.debug("Starting to print available components") + for choice in ( + ClassifierChoice, + RegressorChoice, + FeaturePreprocessorChoice, + OHEChoice, + RescalingChoice, + CoalescenseChoice, + ): + self._logger.debug( + "%s: %s", + choice.__name__, + choice.get_components(), + ) + self._logger.debug("Done printing available components") + def __sklearn_is_fitted__(self) -> bool: return self.fitted - def _fit_cleanup(self): + def _fit_cleanup(self) -> None: self._logger.info("Closing the dask infrastructure") self._close_dask_client() self._logger.info("Finished closing the dask infrastructure") @@ -1170,16 +1131,36 @@ def _check_resampling_strategy( return - def refit(self, X, y): - # AutoSklearn does not handle sparse y for now - y = convert_if_sparse(y) + def refit( + self, + X: SUPPORTED_FEAT_TYPES, + y: SUPPORTED_TARGET_TYPES, + max_reshuffles: int = 10, + ) -> AutoML: + """Refit the models to a new given set of data + + Parameters + ---------- + X : SUPPORTED_FEAT_TYPES + The data to dit to + + y : SUPPORTED_TARGET_TYPES + The targets to fit to + + max_reshuffles : int = 10 + How many times to try reshuffle the data. If fitting fails, shuffle the + data. This can alleviate the problem in algorithms that depend on the + ordering of the data. + + Returns + ------- + AutoML + Self + """ + check_is_fitted(self) + y = convert_if_sparse(y) # AutoSklearn does not handle sparse y for now # Make sure input data is valid - if self.InputValidator is None or not self.InputValidator._is_fitted: - raise ValueError( - "refit() is only supported after calling fit. Kindly call first " - "the estimator fit() method." - ) X, y = self.InputValidator.transform(X, y) if self.models_ is None or len(self.models_) == 0 or self.ensemble_ is None: @@ -1190,15 +1171,9 @@ def refit(self, X, y): raise ValueError("Refit can only be called if 'ensemble_size != 0'") random_state = check_random_state(self._seed) - for identifier in self.models_: - model = self.models_[identifier] - # this updates the model inplace, it can then later be used in - # predict method - - # try to fit the model. If it fails, shuffle the data. This - # could alleviate the problem in algorithms that depend on - # the ordering of the data. - for i in range(10): + + for identifier, model in self.models_.items(): + for i in range(max_reshuffles): try: if self._budget_type is None: _fit_and_suppress_warnings(self._logger, model, X, y) @@ -1220,7 +1195,7 @@ def refit(self, X, y): X = X[indices] y = y[indices] - if i == 9: + if i == (max_reshuffles - 1): raise e self._can_predict = True @@ -1229,15 +1204,15 @@ def refit(self, X, y): def fit_pipeline( self, X: SUPPORTED_FEAT_TYPES, - y: Union[SUPPORTED_TARGET_TYPES, spmatrix], + y: SUPPORTED_TARGET_TYPES | spmatrix, is_classification: bool, - config: Union[Configuration, Dict[str, Union[str, float, int]]], + config: Configuration | dict[str, str | float | int], task: Optional[int] = None, dataset_name: Optional[str] = None, X_test: Optional[SUPPORTED_FEAT_TYPES] = None, - y_test: Optional[Union[SUPPORTED_TARGET_TYPES, spmatrix]] = None, - feat_type: Optional[List[str]] = None, - **kwargs: Dict, + y_test: Optional[SUPPORTED_TARGET_TYPES | spmatrix] = None, + feat_type: Optional[list[str]] = None, + **kwargs: dict, ) -> Tuple[Optional[BasePipeline], RunInfo, RunValue]: """Fits and individual pipeline configuration and returns the result to the user. @@ -1258,7 +1233,7 @@ def fit_pipeline( If provided, the testing performance will be tracked on this features. y_test: array-like If provided, the testing performance will be tracked on this labels - config: Union[Configuration, Dict[str, Union[str, float, int]]] + config: Configuration | dict[str, str | float | int] A configuration object used to define the pipeline steps. If a dict is passed, a configuration is created based on this dict. dataset_name: Optional[str] @@ -1403,6 +1378,8 @@ def predict(self, X, batch_size=None, n_jobs=1): Parallelize the predictions across the models with n_jobs processes. """ + check_is_fitted(self) + if ( self._resampling_strategy not in ("holdout", "holdout-iterative-fit", "cv", "cv-iterative-fit") @@ -1410,7 +1387,7 @@ def predict(self, X, batch_size=None, n_jobs=1): ): raise NotImplementedError( "Predict is currently not implemented for resampling " - "strategy %s, please call refit()." % self._resampling_strategy + f"strategy {self._resampling_strategy}, please call refit()." ) if self.models_ is None or len(self.models_) == 0 or self.ensemble_ is None: @@ -1485,16 +1462,25 @@ def predict(self, X, batch_size=None, n_jobs=1): def fit_ensemble( self, - y, - task=None, - precision=32, - dataset_name=None, - ensemble_nbest=None, - ensemble_size=None, + y: SUPPORTED_TARGET_TYPES, + task: Optional[int] = None, + precision: Literal[16, 32, 64] = 32, + dataset_name: Optional[str] = None, + ensemble_nbest: Optional[int] = None, + ensemble_size: Optional[int] = None, ): + check_is_fitted(self) + # check for the case when ensemble_size is less than 0 - if not ensemble_size > 0: - raise ValueError("ensemble_size must be greater than 0 for fit_ensemble") + if ensemble_size is not None and ensemble_size <= 0: + raise ValueError("`ensemble_size` must be >= 0 for `fit_ensemble`") + + if ensemble_size is None and ( + self._ensemble_size is None or self._ensemble_size <= 0 + ): + raise ValueError( + "Please pass `ensemble_size` to `fit_ensemble` if not setting in init" + ) # AutoSklearn does not handle sparse y for now y = convert_if_sparse(y) @@ -1509,11 +1495,6 @@ def fit_ensemble( self._logger = self._get_logger(dataset_name) # Make sure that input is valid - if self.InputValidator is None or not self.InputValidator._is_fitted: - raise ValueError( - "fit_ensemble() can only be called after fit. Please call the " - "estimator fit() method prior to fit_ensemble()." - ) y = self.InputValidator.target_validator.transform(y) # Create a client if needed @@ -1553,13 +1534,17 @@ def fit_ensemble( "line output for error messages." ) self.ensemble_performance_history, _, _, _, _ = result + self._ensemble_size = ensemble_size self._load_models() self._close_dask_client() return self def _load_models(self): - self.ensemble_ = self._backend.load_ensemble(self._seed) + if self._ensemble_size > 0: + self.ensemble_ = self._backend.load_ensemble(self._seed) + else: + self.ensemble_ = None # If no ensemble is loaded, try to get the best performing model if not self.ensemble_: @@ -1568,17 +1553,20 @@ def _load_models(self): if self.ensemble_: identifiers = self.ensemble_.get_selected_model_identifiers() self.models_ = self._backend.load_models_by_identifiers(identifiers) + if self._resampling_strategy in ("cv", "cv-iterative-fit"): self.cv_models_ = self._backend.load_cv_models_by_identifiers( identifiers ) else: self.cv_models_ = None + if len(self.models_) == 0 and self._resampling_strategy not in [ "partial-cv", "partial-cv-iterative-fit", ]: raise ValueError("No models fitted!") + if ( self._resampling_strategy in ["cv", "cv-iterative-fit"] and len(self.cv_models_) == 0 @@ -1586,7 +1574,7 @@ def _load_models(self): raise ValueError("No models fitted!") elif self._disable_evaluator_output is False or ( - isinstance(self._disable_evaluator_output, List) + isinstance(self._disable_evaluator_output, list) and "model" not in self._disable_evaluator_output ): model_names = self._backend.list_all_models(self._seed) @@ -1597,10 +1585,14 @@ def _load_models(self): ]: raise ValueError("No models fitted!") + self.ensemble_ = None self.models_ = [] + self.cv_models_ = None else: + self.ensemble_ = None self.models_ = [] + self.cv_models_ = None def _load_best_individual_model(self): """ @@ -1610,7 +1602,6 @@ def _load_best_individual_model(self): This is a robust mechanism to be able to predict, even though no ensemble was found by ensemble builder. """ - # We also require that the model is fit and a task is defined # The ensemble size must also be greater than 1, else it means # that the user intentionally does not want an ensemble @@ -1641,14 +1632,8 @@ def score(self, X, y): # The reason is we do not want to trigger the # check for changing input types on successive # input validator calls + check_is_fitted(self) prediction = self.predict(X) - - # Make sure that input is valid - if self.InputValidator is None or not self.InputValidator._is_fitted: - raise ValueError( - "score() is only supported after calling fit. Kindly call first " - "the estimator fit() method." - ) y = self.InputValidator.target_validator.transform(y) # Encode the prediction using the input validator @@ -1701,6 +1686,7 @@ def _get_runhistory_models_performance(self): @property def performance_over_time_(self): + check_is_fitted(self) individual_performance_frame = self._get_runhistory_models_performance() best_values = pd.Series( { @@ -1747,6 +1733,7 @@ def performance_over_time_(self): @property def cv_results_(self): + check_is_fitted(self) results = dict() # Missing in contrast to scikit-learn @@ -1868,7 +1855,8 @@ def cv_results_(self): return results - def sprint_statistics(self): + def sprint_statistics(self) -> str: + check_is_fitted(self) cv_results = self.cv_results_ sio = io.StringIO() sio.write("auto-sklearn results:\n") @@ -1913,13 +1901,14 @@ def sprint_statistics(self): ) return sio.getvalue() - def get_models_with_weights(self): + def get_models_with_weights(self) -> list[Tuple[float, BasePipeline]]: + check_is_fitted(self) if self.models_ is None or len(self.models_) == 0 or self.ensemble_ is None: self._load_models() return self.ensemble_.get_models_with_weights(self.models_) - def show_models(self) -> Dict[int, Any]: + def show_models(self) -> dict[int, Any]: """Returns a dictionary containing dictionaries of ensemble models. Each model in the ensemble can be accessed by giving its ``model_id`` as key. @@ -1984,13 +1973,12 @@ def show_models(self) -> Dict[int, Any]: Returns ------- - Dict(int, Any) : dictionary of length = number of models in the ensemble + dict[int, Any] : dictionary of length = number of models in the ensemble A dictionary of models in the ensemble, where ``model_id`` is the key. """ # noqa: E501 + check_is_fitted(self) + ensemble_dict = {} - # check for condition whether autosklearn is fitted if not raise runtime error - if not self.__sklearn_is_fitted__(): - raise RuntimeError("AutoSklearn has not been fitted") # check for ensemble_size == 0 if self._ensemble_size == 0: @@ -2082,12 +2070,12 @@ def has_key(rv, key): def _create_search_space( self, - tmp_dir, - backend, - datamanager, - include: Optional[Dict[str, List[str]]] = None, - exclude: Optional[Dict[str, List[str]]] = None, - ): + tmp_dir: str, + backend: Backend, + datamanager: XYDataManager, + include: Optional[Mapping[str, list[str]]] = None, + exclude: Optional[Mapping[str, list[str]]] = None, + ) -> Tuple[ConfigurationSpace, str]: task_name = "CreateConfigSpace" self._stopwatch.start_task(task_name) @@ -2097,27 +2085,23 @@ def _create_search_space( include=include, exclude=exclude, ) - configuration_space = self.configuration_space_created_hook( - datamanager, configuration_space - ) backend.write_txt_file( - configspace_path, cs_json.write(configuration_space), "Configuration space" + configspace_path, + cs_json.write(configuration_space), + "Configuration space", ) self._stopwatch.stop_task(task_name) return configuration_space, configspace_path - def configuration_space_created_hook(self, datamanager, configuration_space): - return configuration_space - - def __getstate__(self) -> Dict[str, Any]: + def __getstate__(self) -> dict[str, Any]: # Cannot serialize a client! self._dask_client = None self.logging_server = None self.stop_logging_server = None return self.__dict__ - def __del__(self): + def __del__(self) -> None: # Clean up the logger self._clean_logger() @@ -2143,14 +2127,14 @@ def _supports_task_type(cls, task_type: str) -> bool: def fit( self, X: SUPPORTED_FEAT_TYPES, - y: Union[SUPPORTED_TARGET_TYPES, spmatrix], + y: SUPPORTED_TARGET_TYPES | spmatrix, X_test: Optional[SUPPORTED_FEAT_TYPES] = None, - y_test: Optional[Union[SUPPORTED_TARGET_TYPES, spmatrix]] = None, - feat_type: Optional[List[bool]] = None, + y_test: Optional[SUPPORTED_TARGET_TYPES | spmatrix] = None, + feat_type: Optional[list[bool]] = None, dataset_name: Optional[str] = None, only_return_configuration_space: bool = False, load_models: bool = True, - ): + ) -> AutoMLClassifier: return super().fit( X, y, @@ -2166,12 +2150,12 @@ def fit( def fit_pipeline( self, X: SUPPORTED_FEAT_TYPES, - y: Union[SUPPORTED_TARGET_TYPES, spmatrix], - config: Union[Configuration, Dict[str, Union[str, float, int]]], + y: SUPPORTED_TARGET_TYPES | spmatrix, + config: Configuration | dict[str, str | float | int], dataset_name: Optional[str] = None, X_test: Optional[SUPPORTED_FEAT_TYPES] = None, - y_test: Optional[Union[SUPPORTED_TARGET_TYPES, spmatrix]] = None, - feat_type: Optional[List[str]] = None, + y_test: Optional[SUPPORTED_TARGET_TYPES | spmatrix] = None, + feat_type: Optional[list[str]] = None, **kwargs, ) -> Tuple[Optional[BasePipeline], RunInfo, RunValue]: return super().fit_pipeline( @@ -2186,16 +2170,18 @@ def fit_pipeline( **kwargs, ) - def predict(self, X, batch_size=None, n_jobs=1): + def predict( + self, + X: SUPPORTED_FEAT_TYPES, + batch_size: Optional[int] = None, + n_jobs: int = 1, + ) -> np.ndarray: + check_is_fitted(self) + predicted_probabilities = super().predict( X, batch_size=batch_size, n_jobs=n_jobs ) - if self.InputValidator is None or not self.InputValidator._is_fitted: - raise ValueError( - "predict() is only supported after calling fit. Kindly call first " - "the estimator fit() method." - ) if self.InputValidator.target_validator.is_single_column_target(): predicted_indexes = np.argmax(predicted_probabilities, axis=1) else: @@ -2203,7 +2189,12 @@ def predict(self, X, batch_size=None, n_jobs=1): return self.InputValidator.target_validator.inverse_transform(predicted_indexes) - def predict_proba(self, X, batch_size=None, n_jobs=1): + def predict_proba( + self, + X: SUPPORTED_FEAT_TYPES, + batch_size: Optional[int] = None, + n_jobs: int = 1, + ) -> np.ndarray: return super().predict(X, batch_size=batch_size, n_jobs=n_jobs) @@ -2226,14 +2217,14 @@ def _supports_task_type(cls, task_type: str) -> bool: def fit( self, X: SUPPORTED_FEAT_TYPES, - y: Union[SUPPORTED_TARGET_TYPES, spmatrix], + y: SUPPORTED_TARGET_TYPES | spmatrix, X_test: Optional[SUPPORTED_FEAT_TYPES] = None, - y_test: Optional[Union[SUPPORTED_TARGET_TYPES, spmatrix]] = None, - feat_type: Optional[List[bool]] = None, + y_test: Optional[SUPPORTED_TARGET_TYPES | spmatrix] = None, + feat_type: Optional[list[bool]] = None, dataset_name: Optional[str] = None, only_return_configuration_space: bool = False, load_models: bool = True, - ): + ) -> AutoMLRegressor: return super().fit( X, y, @@ -2249,13 +2240,13 @@ def fit( def fit_pipeline( self, X: SUPPORTED_FEAT_TYPES, - y: Union[SUPPORTED_TARGET_TYPES, spmatrix], - config: Union[Configuration, Dict[str, Union[str, float, int]]], + y: SUPPORTED_TARGET_TYPES | spmatrix, + config: Configuration | dict[str, str | float | int], dataset_name: Optional[str] = None, X_test: Optional[SUPPORTED_FEAT_TYPES] = None, - y_test: Optional[Union[SUPPORTED_TARGET_TYPES, spmatrix]] = None, - feat_type: Optional[List[str]] = None, - **kwargs: Dict, + y_test: Optional[SUPPORTED_TARGET_TYPES | spmatrix] = None, + feat_type: Optional[list[str]] = None, + **kwargs: dict, ) -> Tuple[Optional[BasePipeline], RunInfo, RunValue]: return super().fit_pipeline( X=X, diff --git a/autosklearn/evaluation/__init__.py b/autosklearn/evaluation/__init__.py index 8eb997c571..89c61d144d 100644 --- a/autosklearn/evaluation/__init__.py +++ b/autosklearn/evaluation/__init__.py @@ -324,6 +324,7 @@ def run( if not (instance_specific is None or instance_specific == "0"): raise ValueError(instance_specific) + init_params = {"instance": instance} if self.init_params is not None: init_params.update(self.init_params) @@ -542,6 +543,7 @@ def run( else: origin = getattr(config, "origin", "UNKNOWN") config_id = config.config_id + additional_run_info["configuration_origin"] = origin runtime = float(obj.wall_clock_time) diff --git a/autosklearn/evaluation/abstract_evaluator.py b/autosklearn/evaluation/abstract_evaluator.py index bc0be0e8d8..7843de6a8a 100644 --- a/autosklearn/evaluation/abstract_evaluator.py +++ b/autosklearn/evaluation/abstract_evaluator.py @@ -30,9 +30,6 @@ ) from autosklearn.util.logging_ import PicklableClientLogger, get_named_client_logger -__all__ = ["AbstractEvaluator"] - - # General TYPE definitions for numpy TYPE_ADDITIONAL_INFO = Dict[str, Union[int, float, str, Dict, List, Tuple]] @@ -49,9 +46,10 @@ def __init__( ): self.config = config if config == 1: - super(MyDummyClassifier, self).__init__(strategy="uniform") + super().__init__(strategy="uniform") else: - super(MyDummyClassifier, self).__init__(strategy="most_frequent") + super().__init__(strategy="most_frequent") + self.random_state = random_state self.init_params = init_params self.dataset_properties = dataset_properties @@ -59,8 +57,11 @@ def __init__( self.exclude = exclude def pre_transform( - self, X: np.ndarray, y: np.ndarray, fit_params: Optional[Dict[str, Any]] = None - ) -> Tuple[np.ndarray, Dict[str, Any]]: # pylint: disable=R0201 + self, + X: np.ndarray, + y: np.ndarray, + fit_params: Optional[Dict[str, Any]] = None, + ) -> Tuple[np.ndarray, Dict[str, Any]]: if fit_params is None: fit_params = {} return X, fit_params @@ -76,22 +77,23 @@ def fit( ) def fit_estimator( - self, X: np.ndarray, y: np.ndarray, fit_params: Optional[Dict[str, Any]] = None + self, + X: np.ndarray, + y: np.ndarray, + fit_params: Optional[Dict[str, Any]] = None, ) -> DummyClassifier: return self.fit(X, y) def predict_proba(self, X: np.ndarray, batch_size: int = 1000) -> np.ndarray: new_X = np.ones((X.shape[0], 1)) - probas = super(MyDummyClassifier, self).predict_proba(new_X) + probas = super().predict_proba(new_X) probas = convert_multioutput_multiclass_to_multilabel(probas).astype(np.float32) return probas - def estimator_supports_iterative_fit(self) -> bool: # pylint: disable=R0201 + def estimator_supports_iterative_fit(self) -> bool: return False - def get_additional_run_info( - self, - ) -> Optional[TYPE_ADDITIONAL_INFO]: # pylint: disable=R0201 + def get_additional_run_info(self) -> Optional[TYPE_ADDITIONAL_INFO]: return None @@ -107,9 +109,9 @@ def __init__( ): self.config = config if config == 1: - super(MyDummyRegressor, self).__init__(strategy="mean") + super().__init__(strategy="mean") else: - super(MyDummyRegressor, self).__init__(strategy="median") + super().__init__(strategy="median") self.random_state = random_state self.init_params = init_params self.dataset_properties = dataset_properties @@ -117,8 +119,11 @@ def __init__( self.exclude = exclude def pre_transform( - self, X: np.ndarray, y: np.ndarray, fit_params: Optional[Dict[str, Any]] = None - ) -> Tuple[np.ndarray, Dict[str, Any]]: # pylint: disable=R0201 + self, + X: np.ndarray, + y: np.ndarray, + fit_params: Optional[Dict[str, Any]] = None, + ) -> Tuple[np.ndarray, Dict[str, Any]]: if fit_params is None: fit_params = {} return X, fit_params @@ -129,25 +134,24 @@ def fit( y: np.ndarray, sample_weight: Optional[Union[np.ndarray, List]] = None, ) -> DummyRegressor: - return super(MyDummyRegressor, self).fit( - np.ones((X.shape[0], 1)), y, sample_weight=sample_weight - ) + return super().fit(np.ones((X.shape[0], 1)), y, sample_weight=sample_weight) def fit_estimator( - self, X: np.ndarray, y: np.ndarray, fit_params: Optional[Dict[str, Any]] = None + self, + X: np.ndarray, + y: np.ndarray, + fit_params: Optional[Dict[str, Any]] = None, ) -> DummyRegressor: return self.fit(X, y) def predict(self, X: np.ndarray, batch_size: int = 1000) -> np.ndarray: new_X = np.ones((X.shape[0], 1)) - return super(MyDummyRegressor, self).predict(new_X).astype(np.float32) + return super().predict(new_X).astype(np.float32) - def estimator_supports_iterative_fit(self) -> bool: # pylint: disable=R0201 + def estimator_supports_iterative_fit(self) -> bool: return False - def get_additional_run_info( - self, - ) -> Optional[TYPE_ADDITIONAL_INFO]: # pylint: disable=R0201 + def get_additional_run_info(self) -> Optional[TYPE_ADDITIONAL_INFO]: return None diff --git a/autosklearn/pipeline/util.py b/autosklearn/pipeline/util.py index 228c31357d..38cf1aa344 100644 --- a/autosklearn/pipeline/util.py +++ b/autosklearn/pipeline/util.py @@ -2,7 +2,6 @@ import inspect import os import pkgutil -import unittest import numpy as np import scipy.sparse @@ -10,6 +9,8 @@ import sklearn.base import sklearn.datasets +import unittest + def find_sklearn_classes(class_): classifiers = set() diff --git a/autosklearn/util/functional.py b/autosklearn/util/functional.py new file mode 100644 index 0000000000..55f38ddf5d --- /dev/null +++ b/autosklearn/util/functional.py @@ -0,0 +1,54 @@ +from typing import Optional + +import numpy as np + + +def normalize(x: np.ndarray, axis: Optional[int] = None) -> np.ndarray: + """Normalizes an array along an axis + + Note + ---- + TODO: Only works for positive numbers + + ..code:: python + + x = np.ndarray([ + [1, 1, 1], + [2, 2, 2], + [7, 7, 7], + ]) + + print(normalize(x, axis=0)) + + np.ndarray([ + [.1, .1, .1] + [.2, .2, .2] + [.7, .7, .7] + ]) + + print(normalize(x, axis=1)) + + np.ndarray([ + [.333, .333, .333] + [.333, .333, .333] + [.333, .333, .333] + ]) + + Note + ---- + Does not account for 0 sums along an axis + + Parameters + ---------- + x : np.ndarray + The array to normalize + + axis : Optional[int] = None + The axis to normalize across + + Returns + ------- + np.ndarray + The normalized array + """ + return x / x.sum(axis=axis, keepdims=True) diff --git a/autosklearn/util/logging_.py b/autosklearn/util/logging_.py index a85e4a80d6..e9ee676b79 100644 --- a/autosklearn/util/logging_.py +++ b/autosklearn/util/logging_.py @@ -1,4 +1,7 @@ -# -*- encoding: utf-8 -*- +""" +For accessing a logger, please default to using +`get_named_client_logger(name, host, port)` +""" from typing import Any, Dict, Iterator, Optional, TextIO, Type, cast import logging @@ -78,7 +81,7 @@ def get_logger(name: str) -> "PickableLoggerAdapter": return logger -class PickableLoggerAdapter(object): +class PickableLoggerAdapter: def __init__(self, name: str): self.name = name self.logger = _create_logger(name) diff --git a/pyproject.toml b/pyproject.toml index 0e48e3fc5f..3e3aafb1f6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,9 +28,19 @@ py_version = "37" profile = "black" # Play nicely with black src_paths = ["autosklearn", "test"] known_types = ["typing", "abc"] # We put these in their own section TYPES +known_testlibs = ["unittest", "pytest", "pytest_cases"] # Put test libs in their own section known_first_party = ["autosklearn"] # Say that autosklearn is FIRSTPARTY known_test = ["test"] # Say that test.* is TEST -sections = ["FUTURE", "TYPES", "STDLIB", "THIRDPARTY", "FIRSTPARTY", "TEST", "LOCALFOLDER"] # section ordering +sections = [ + "FUTURE", + "TYPES", + "STDLIB", + "THIRDPARTY", + "FIRSTPARTY", + "TESTLIBS", + "TEST", + "LOCALFOLDER" +] # section ordering multi_line_output = 3 # https://pycqa.github.io/isort/docs/configuration/multi_line_output_modes.html [tool.pydocstyle] @@ -147,6 +157,10 @@ module = [ "setuptools.*", "pkg_resources.*", "yaml.*", + "psutil.*" ] ignore_missing_imports = true +[[tool.mypy.overrides]] +module = ["test.*"] +disallow_untyped_decorators = false # Test decorators are not properly typed diff --git a/setup.py b/setup.py index 003b573bd4..e182cd716b 100644 --- a/setup.py +++ b/setup.py @@ -35,6 +35,7 @@ "pytest-cov", "pytest-xdist", "pytest-timeout", + "pytest-cases", "mypy", "isort", "black", diff --git a/test/conftest.py b/test/conftest.py index 16a285b9df..36cc77b3ad 100644 --- a/test/conftest.py +++ b/test/conftest.py @@ -1,167 +1,227 @@ -import os -import shutil -import time -import unittest.mock +""" +Testing +======= +The following are some features, guidelines and functionality for testing which makes +updating, adding and refactoring tests easier, especially as features and functionality +changes. + +**Marks** +* todo - ``pytest.mark.todo``` to mark a test which xfails as it's todo +* slow - ``pytest.mark.slow``` to mark a test which is skipped if `pytest --fast` + +**Documenting Tests** +To ease in understanding of tests, what is being tested and what's expected of the test, +each test should be documented with what it's parameters/fixtures are as well as what +the test expects to happen, regardless of the tests implemntation. + + Parameters + ---------- + param1: Type + ... + + param2: Type + ... + + Fixtures + -------- + make_something: Callable[..., Something] + Factory to make Something + + Expects + ------- + * Something should raise a ValueError when called with X as X is not handled by the + validator Y. + +**Test same module across files** +When a module has many complicated avenues to be tested, create a folder and split the +tests according to each avenue. See `test/test_automl` for example as the `automl.py` +module is quite complicated to test and all tests in a single file become difficult to +follow and change. + +**pytest_cases** +Using pytest_cases, we seperate a `case`, something that defines the state of the +object, from the actual `test`, which tests properties of these cases. + +A complicated example can be seen at `test/test_automl/cases.py` where we have +autoML instances that are classifier/regressor, fitted or not, with cv or holdout, +or fitted with no ensemble. TODO: Easier example. + +Docs: https://smarie.github.io/python-pytest-cases/ + +**Caching** +Uses pytest's cache functionality for long training models so they can be shared between +tests and between different test runs. This is primarly used with `cases` so that tests +requiring the same kind of expensive case and used cached values. + +Use `pytest --cache-clear` to clear the cahce + +See `test/test_automl/cases.py` for example of how the fixtures from +`test/fixtures/caching.py` can be used to cache objects between tests. + +**Fixtures** +All fixtures in "test/fixtures" are known in every test file. We try to make use +of fixture `factories` which can be used to construct objects in complicated ways, +removing these complications from the tests themselves, importantly, keeping tests +short. A convention we use is to prefix them with `make`, for example, +`make_something`. This is useful for making data, e.g. `test/fixtures/data::make_data` + +..code:: python + + # Example of fixture factory + @fixture + def make_something(): + def _make(...args): + # ... complicated setup + # ... more complications + # ... make some sub objects which are complicated + return something + + return _make + + @parametrize("arg1", ['a', 'b', 'c']) + def test_something_does_x(arg1, make_something): + something = make_something(arg1, ...) + result = something.run() + assert something == expected +""" +from typing import Any, Iterator, List, Optional + +import re +import signal +from pathlib import Path import psutil -import pytest -from dask.distributed import Client, get_client - -from autosklearn.automl import AutoML -from autosklearn.automl_common.common.utils.backend import Backend, create - -class AutoMLStub(AutoML): - def __init__(self): - self.__class__ = AutoML - self._task = None - self._dask_client = None - self._is_dask_client_internally_created = False +import pytest +from pytest import ExitCode, Item, Session - def __del__(self): - pass +DEFAULT_SEED = 0 -@pytest.fixture(scope="function") -def automl_stub(request): - automl = AutoMLStub() - automl._seed = 42 - automl._backend = unittest.mock.Mock(spec=Backend) - automl._backend.context = unittest.mock.Mock() - automl._delete_output_directories = lambda: 0 - return automl +HERE = Path(__file__) +AUTOSKLEARN_CACHE_NAME = "autosklearn" -@pytest.fixture(scope="function") -def backend(request): +def walk(path: Path, include: Optional[str] = None) -> Iterator[Path]: + """Yeilds all files, iterating over directory - test_dir = os.path.dirname(__file__) - tmp = os.path.join( - test_dir, ".tmp__%s__%s" % (request.module.__name__, request.node.name) - ) + Parameters + ---------- + path: Path + The root path to walk from - for dir in (tmp,): - for i in range(10): - if os.path.exists(dir): - try: - shutil.rmtree(dir) - break - except OSError: - time.sleep(1) - - # Make sure the folders we wanna create do not already exist. - backend = create( - temporary_directory=tmp, output_directory=None, prefix="auto-sklearn" - ) + include: Optional[str] = None + Include only directories which match this string - def get_finalizer(tmp_dir): - def session_run_at_end(): - for dir in (tmp_dir,): - for i in range(10): - if os.path.exists(dir): - try: - shutil.rmtree(dir) - break - except OSError: - time.sleep(1) - - return session_run_at_end - - request.addfinalizer(get_finalizer(tmp)) + Returns + ------- + Iterator[Path] + All file paths that could be found from this walk + """ + for p in path.iterdir(): + if p.is_dir(): + if include is None or re.match(include, p.name): + yield from walk(p, include) + else: + yield p.resolve() - return backend +def is_fixture(path: Path) -> bool: + """Whether a path is a fixture""" + return path.name.endswith("fixtures.py") -@pytest.fixture(scope="function") -def tmp_dir(request): - return _dir_fixture("tmp", request) +def as_module(path: Path) -> str: + """Convert a path to a module as seen from here""" + root = HERE.parent.parent + parts = path.relative_to(root).parts + return ".".join(parts).replace(".py", "") -def _dir_fixture(dir_type, request): - test_dir = os.path.dirname(__file__) - dirname = f".{dir_type}__{request.module.__name__}__{request.node.name}" - dir = os.path.join(test_dir, dirname) +def fixture_modules() -> List[str]: + """Get all fixture modules""" + fixtures_folder = HERE.parent / "fixtures" + return [ + as_module(path) for path in walk(fixtures_folder) if path.name.endswith(".py") + ] - for i in range(10): - if os.path.exists(dir): - try: - shutil.rmtree(dir) - break - except OSError: - pass - def get_finalizer(dir): - def session_run_at_end(): - for i in range(10): - if os.path.exists(dir): - try: - shutil.rmtree(dir) - break - except OSError: - time.sleep(1) +def pytest_runtest_setup(item: Item) -> None: + """Run before each test""" + todos = [marker for marker in item.iter_markers(name="todo")] + if todos: + pytest.xfail(f"Test needs to be implemented, {item.location}") - return session_run_at_end - request.addfinalizer(get_finalizer(dir)) +def pytest_sessionstart(session: Session) -> None: + """Called after the ``Session`` object has been created and before performing collection + and entering the run test loop. - return dir + Parameters + ---------- + session : Session + The pytest session object + """ + return -@pytest.fixture(scope="function") -def dask_client(request): - """ - Create a dask client with two workers. +def pytest_sessionfinish(session: Session, exitstatus: ExitCode) -> None: + """Clean up any child processes""" + proc = psutil.Process() + kill_signal = signal.SIGTERM + for child in proc.children(recursive=True): - Workers are in subprocesses to not create deadlocks with the pynisher and logging. - """ + # https://stackoverflow.com/questions/57336095/access-verbosity-level-in-a-pytest-helper-function + if session.config.getoption("verbose") > 0: + print(child, child.cmdline()) - client = Client(n_workers=2, threads_per_worker=1, processes=False) - print("Started Dask client={}\n".format(client)) + # https://psutil.readthedocs.io/en/latest/#kill-process-tree + try: + child.send_signal(kill_signal) + except psutil.NoSuchProcess: + pass - def get_finalizer(address): - def session_run_at_end(): - client = get_client(address) - print("Closed Dask client={}\n".format(client)) - client.shutdown() - client.close() - del client - return session_run_at_end +Config = Any # Can't find import? - request.addfinalizer(get_finalizer(client.scheduler_info()["address"])) - return client +def pytest_collection_modifyitems( + session: Session, + config: Config, + items: List[Item], +) -> None: + """Modifys the colelction of tests that are captured""" + if config.getoption("--fast"): + skip = pytest.mark.skip(reason="Test marked `slow` and `--fast` arg used") + slow_items = [item for item in items if "slow" in item.keywords] + for item in slow_items: + item.add_marker(skip) -@pytest.fixture(scope="function") -def dask_client_single_worker(request): - """ - Same as above, but only with a single worker. - Using this might cause deadlocks with the pynisher and the logging module. However, - it is used very rarely to avoid this issue as much as possible. - """ +def pytest_configure(config: Config) -> None: + """Used to register marks""" + config.addinivalue_line("markers", "todo: Mark test as todo") + config.addinivalue_line("markers", "slow: Mark test as slow") - client = Client(n_workers=1, threads_per_worker=1, processes=False) - print("Started Dask client={}\n".format(client)) - def get_finalizer(address): - def session_run_at_end(): - client = get_client(address) - print("Closed Dask client={}\n".format(client)) - client.shutdown() - client.close() - del client +pytest_plugins = fixture_modules() - return session_run_at_end - request.addfinalizer(get_finalizer(client.scheduler_info()["address"])) +Parser = Any # Can't find import? - return client +def pytest_addoption(parser: Parser) -> None: + """ -def pytest_sessionfinish(session, exitstatus): - proc = psutil.Process() - for child in proc.children(recursive=True): - print(child, child.cmdline()) + Parameters + ---------- + parser : Parser + The parser to add options to + """ + parser.addoption( + "--fast", + action="store_true", + default=False, + help="Disable tests marked as slow", + ) diff --git a/test/fixtures/__init__.py b/test/fixtures/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/fixtures/automl.py b/test/fixtures/automl.py new file mode 100644 index 0000000000..abf31d304d --- /dev/null +++ b/test/fixtures/automl.py @@ -0,0 +1,91 @@ +from __future__ import annotations + +from typing import Any, Callable, Dict, Tuple, Type + +from functools import partial + +from autosklearn.automl import AutoML, AutoMLClassifier, AutoMLRegressor +from autosklearn.automl_common.common.utils.backend import Backend + +from pytest import fixture +from unittest.mock import Mock + +from test.conftest import DEFAULT_SEED +from test.fixtures.dask import create_test_dask_client + + +def _create_automl( + automl_type: Type[AutoML] = AutoML, + **kwargs: Any, +) -> AutoML: + """ + + Parameters + ---------- + automl_type : Type[AutoML] = AutoML + The type of AutoML object to use + + **kwargs: Any + Options to pass on to the AutoML type for construction + + Returns + ------- + AutoML + The constructed class and a close method for dask, if it exists + """ + test_defaults = { + "n_jobs": 2, + "time_left_for_this_task": 30, + "per_run_time_limit": 5, + "seed": DEFAULT_SEED, + "n_jobs": 2, + } + + opts: Dict[str, Any] = {**test_defaults, **kwargs} + + if "dask_client" not in opts: + client = create_test_dask_client(n_workers=opts["n_jobs"]) + opts["dask_client"] = client + + auto = automl_type(**opts) + return auto + + +@fixture +def make_automl() -> Callable[..., Tuple[AutoML, Callable]]: + """See `_create_automl`""" + yield partial(_create_automl, automl_type=AutoML) + + +@fixture +def make_automl_classifier() -> Callable[..., AutoMLClassifier]: + """See `_create_automl`""" + yield partial(_create_automl, automl_type=AutoMLClassifier) + + +@fixture +def make_automl_regressor() -> Callable[..., AutoMLRegressor]: + """See `_create_automl`""" + yield partial(_create_automl, automl_type=AutoMLRegressor) + + +class AutoMLStub(AutoML): + def __init__(self) -> None: + self.__class__ = AutoML + self._task = None + self._dask_client = None # type: ignore + self._is_dask_client_internally_created = False + + def __del__(self) -> None: + pass + + +@fixture(scope="function") +def automl_stub() -> AutoMLStub: + """TODO remove""" + automl = AutoMLStub() + automl._seed = 42 + automl._backend = Mock(spec=Backend) + automl._backend.context = Mock() + automl._delete_output_directories = lambda: 0 + return automl diff --git a/test/fixtures/backend.py b/test/fixtures/backend.py new file mode 100644 index 0000000000..3ee4626199 --- /dev/null +++ b/test/fixtures/backend.py @@ -0,0 +1,76 @@ +from typing import Callable, Union + +import os +from pathlib import Path + +from autosklearn.automl_common.common.utils.backend import Backend, create + +from pytest import fixture + + +# TODO Update to return path once everything can use a path +@fixture +def tmp_dir(tmp_path: Path) -> str: + """ + Fixtures + -------- + tmp_path : Path + Built in pytest fixture + + Returns + ------- + str + The directory as a str + """ + return str(tmp_path) + + +@fixture +def make_backend() -> Callable[..., Backend]: + """Make a backend + + Parameters + ---------- + path: Union[str, Path] + The path to place the backend at + + Returns + ------- + Backend + The created backend object + """ + # TODO redo once things use paths + def _make(path: Union[str, Path]) -> Backend: + _path = Path(path) if not isinstance(path, Path) else path + assert not _path.exists() + + backend = create( + temporary_directory=str(_path), + output_directory=None, + prefix="auto-sklearn", + ) + + return backend + + return _make + + +@fixture(scope="function") +def backend(tmp_dir: str, make_backend: Callable) -> Backend: + """A backend object + + Fixtures + -------- + tmp_dir : str + A directory to place the backend at + + make_backend : Callable + Factory to make a backend + + Returns + ------- + Backend + A backend object + """ + backend_path = os.path.join(tmp_dir, "backend") + return make_backend(path=backend_path) diff --git a/test/fixtures/caching.py b/test/fixtures/caching.py new file mode 100644 index 0000000000..b6a4ed2fdc --- /dev/null +++ b/test/fixtures/caching.py @@ -0,0 +1,158 @@ +from __future__ import annotations + +from typing import Any, Callable, Optional + +import pickle +import shutil +import traceback +from functools import partial +from pathlib import Path + +from autosklearn.automl import AutoML + +from pytest import FixtureRequest +from pytest_cases import fixture + + +class Cache: + """Used for the below fixtures. + + Mainly used with cases so they don' need to be retrained at every invocation. + The cache can be cleared using `pytest`'s built in mechanism: + + pytest --clear-cache + + To view cached items use: + + pytest --cache-show + + ..code:: python + + def case_fitted_model(cache, ...): + key = "some key unique to this test" + cache = cache(key) + if "model" in cache: + return cache.load("model") + + # ... fit model + + cache.save(model, "model") + return model + + If multiple items are required, they can be access in much the same ways + + ..code:: python + + def case_requires_multiple_things(cache, ...): + + cache1 = cache("key1") + cache2 = cache("key2") + + If multiple things need to be stored in one location, you can access a given path + for a given named thing inside a cache. + + ..code:: python + + def case_fitted_model_and_populated_backend(cache, ...): + cache = cache("some key") + + """ + + def __init__(self, key: str, cache_dir: Path, verbose: int = 0): + """ + Parameters + ---------- + key : str + The key of the item stored + + cache_dir : Path + The dir where the cache should be located + + verbose : int = 0 + Whether to be verbose or not. Currently only has one level (> 0) + """ + self.dir = cache_dir / key + self.verbose = verbose > 0 + + def items(self) -> list[Path]: + """Get any paths associated to items in this dir""" + return list(self.dir.iterdir()) + + def __contains__(self, name: str) -> bool: + return self.path(name).exists() + + def path(self, name: str) -> Path: + """Path to an item for this cache""" + return self.dir / name + + def _load(self, name: str) -> Any: + """Load an item from the cache with a given name""" + if self.verbose: + print(f"Loading cached item {self.path(name)}") + + with self.path(name).open("rb") as f: + return pickle.load(f) + + def _save(self, item: Any, name: str) -> None: + """Dump an item to cache with a name""" + if self.verbose: + print(f"Saving cached item {self.path(name)}") + + with self.path(name).open("wb") as f: + pickle.dump(item, f) + + def reset(self) -> None: + """Delete this caches items""" + shutil.rmtree(self.dir) + self.dir.mkdir() + + +class AutoMLCache(Cache): + def save(self, model: AutoML) -> None: + """Save the model""" + self._save(model, "model") + + def model(self) -> Optional[AutoML]: + """Returns the saved model if it can. + + In the case of an issue loading an existing model file, it will delete + this cache item. + """ + if "model" not in self: + return None + + # Try to load the model, if there was an issue, delete all cached items + # for the model and return None + try: + model = self._load("model") + except Exception: + model = None + print(traceback.format_exc()) + self.reset() + finally: + return model + + def backend_path(self) -> Path: + """The path for the backend of the automl model""" + return self.path("backend") + + +@fixture +def cache(request: FixtureRequest) -> Callable[[str], Cache]: + """Gives the access to a cache.""" + pytest_cache = request.config.cache + assert pytest_cache is not None + + cache_dir = pytest_cache.mkdir("autosklearn-cache") + return partial(Cache, cache_dir=cache_dir) + + +@fixture +def automl_cache(request: FixtureRequest) -> Callable[[str], AutoMLCache]: + """Gives access to an automl cache""" + pytest_cache = request.config.cache + assert pytest_cache is not None + + cache_dir = pytest_cache.mkdir("autosklearn-cache") + verbosity = request.config.getoption("verbose") + return partial(AutoMLCache, cache_dir=cache_dir, verbose=verbosity) diff --git a/test/fixtures/dask.py b/test/fixtures/dask.py new file mode 100644 index 0000000000..0c1f112800 --- /dev/null +++ b/test/fixtures/dask.py @@ -0,0 +1,100 @@ +from __future__ import annotations + +from typing import Callable + +from dask.distributed import Client, get_client + +from pytest import FixtureRequest, fixture + +# Terrible practice but we need to close dask clients somehow +active_clients: dict[str, Callable] = {} + + +@fixture(autouse=True) +def clean_up_any_dask_clients(request: FixtureRequest) -> None: + """Auto injected fixture to close dask clients after each test""" + yield + if any(active_clients): + for adr in list(active_clients.keys()): + if request.config.getoption("verbose") > 1: + print(f"\nFixture closing dask_client at {adr}") + + close = active_clients[adr] + close() + del active_clients[adr] + + +def create_test_dask_client(n_workers: int = 2) -> Client: + """Factory to make a Dask client and a function to close it + them + + Parameters + ---------- + n_workers: int = 2 + inside asklea + inside AutoML. + + Returns + ------- + Client, Callable + The client and a function to call to close that client + """ + # Workers are in subprocesses to not create deadlocks with the pynisher + # and logging. + client = Client( + n_workers=n_workers, + threads_per_worker=1, + processes=False, + scheduler_port=0, # Set to 0 so it chooses a random one + dashboard_address=None, # Disable dashboarding + ) + adr = client.scheduler_info()["address"] + + def close() -> None: + try: + client = get_client(adr, timeout=1) + client.shutdown() + except Exception: + pass + + active_clients[adr] = close + + return client + + +@fixture +def make_dask_client() -> Callable[[int], Client]: + """Factory to make a Dask client and a function to close it + + Parameters + ---------- + n_workers: int = 1 + How many workers to have in the dask client + + Returns + ------- + Client, Callable + The client and a function to call to close that client + """ + return create_test_dask_client + + +# TODO remove in favour of make_dask_client +@fixture(scope="function") +def dask_client(make_dask_client: Callable) -> Client: + """Create a dask client with two workers.""" + client = make_dask_client(n_workers=2) + yield client + + +# TODO remove in favour of make_dask_client +@fixture(scope="function") +def dask_client_single_worker(make_dask_client: Callable) -> Client: + """Dask client with only 1 worker + + Note + ---- + May create deadlocks with logging and pynisher + """ + client = make_dask_client(n_workers=1) + yield client diff --git a/test/fixtures/datasets.py b/test/fixtures/datasets.py new file mode 100644 index 0000000000..39d948e5a9 --- /dev/null +++ b/test/fixtures/datasets.py @@ -0,0 +1,278 @@ +from __future__ import annotations + +from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple + +import numpy as np +import pandas as pd +from scipy.sparse import csr_matrix +from sklearn.utils import check_random_state + +from autosklearn.constants import ( + BINARY_CLASSIFICATION, + MULTICLASS_CLASSIFICATION, + MULTILABEL_CLASSIFICATION, + MULTIOUTPUT_REGRESSION, + REGRESSION, +) +from autosklearn.data.validation import SUPPORTED_FEAT_TYPES, SUPPORTED_TARGET_TYPES +from autosklearn.data.xy_data_manager import XYDataManager +from autosklearn.pipeline.util import get_dataset +from autosklearn.util.functional import normalize + +from pytest import fixture + +from test.conftest import DEFAULT_SEED + +Data = Tuple[SUPPORTED_FEAT_TYPES, SUPPORTED_TARGET_TYPES] + + +def astype( + t: np.ndarray | list | csr_matrix | pd.DataFrame | pd.Series, + x: Any, +) -> Any: + """Convert data to allowed types""" + if t == np.ndarray: + return np.asarray(x) + else: + return t(x) # type: ignore + + +# TODO Remove the implementation in autosklearn.pipeline.util and just put here +@fixture +def make_sklearn_dataset() -> Callable: + """ + + Parameters + ---------- + name : str = "iris" + Name of the dataset to get + + make_sparse : bool = False + Wehther to make the data sparse + + add_NaNs : bool = False + Whether to add NaNs to the data + + train_size_maximum : int = 150 + THe maximum size of training data + + make_multilabel : bool = False + Whether to force the data into being multilabel + + make_binary : bool = False + Whether to force the data into being binary + + as_datamanager: bool = False + Wether to return the information as an XYDataManager + + Returns + ------- + (X_train, Y_train, X_test, Y_Test) | XYDataManager + """ + + def _make( + name: str = "iris", + make_sparse: bool = False, + add_NaNs: bool = False, + train_size_maximum: int = 150, + make_multilabel: bool = False, + make_binary: bool = False, + as_datamanager: bool = False, + task: Optional[int] = None, + feat_type: Optional[Dict | str] = None, + ) -> Any: + X, y, Xt, yt = get_dataset( + dataset=name, + make_sparse=make_sparse, + add_NaNs=add_NaNs, + train_size_maximum=train_size_maximum, + make_multilabel=make_multilabel, + make_binary=make_binary, + ) + + if not as_datamanager: + return (X, y, Xt, yt) + else: + assert task is not None and feat_type is not None + if isinstance(feat_type, str): + feat_type = {i: feat_type for i in range(X.shape[1])} + + return XYDataManager( + X, + y, + Xt, + yt, + task=task, + dataset_name=name, + feat_type=feat_type, + ) + + return _make + + +def _make_binary_data( + dims: Tuple[int, ...] = (100, 3), + weights: Optional[Sequence[float] | np.ndarray] = None, + types: Tuple[ + np.ndarray | csr_matrix | pd.DataFrame | list, + np.ndarray | csr_matrix | pd.DataFrame | list | pd.Series, + ] = (np.ndarray, np.ndarray), + random_state: int | np.random.RandomState = DEFAULT_SEED, +) -> Data: + X_type, y_type = types + rs = check_random_state(random_state) + + classes = [0, 1] + + if not weights: + weights = np.ones_like(classes) / len(classes) + + assert len(weights) == len(classes) + weights = normalize(np.asarray(weights)) + + X = rs.rand(*dims) + y = rs.choice([0, 1], dims[0], p=weights) + + return astype(X_type, X), astype(y_type, y) + + +def _make_multiclass_data( + dims: Tuple[int, ...] = (100, 3), + classes: int | np.ndarray | List = 3, + weights: Optional[np.ndarray | List[float]] = None, + types: Tuple[ + np.ndarray | csr_matrix | pd.DataFrame | list, + np.ndarray | csr_matrix | pd.DataFrame | list | pd.Series, + ] = (np.ndarray, np.ndarray), + random_state: int | np.random.RandomState = DEFAULT_SEED, +) -> Data: + X_type, y_type = types + + if isinstance(classes, int): + classes = np.asarray(list(range(classes))) + + rs = check_random_state(random_state) + + if not weights: + weights = np.ones_like(classes) / len(classes) + + assert len(weights) == len(classes) + weights = normalize(np.asarray(weights)) + + X = rs.rand(*dims) + y = rs.choice(classes, dims[0], p=weights) + + return astype(X_type, X), astype(y_type, y) + + +def _make_multilabel_data( + dims: Tuple[int, ...] = (100, 3), + classes: np.ndarray | List = [[0, 0], [0, 1], [1, 0], [1, 1]], + weights: Optional[np.ndarray | List[float]] = None, + types: Tuple[ + np.ndarray | csr_matrix | pd.DataFrame | list, + np.ndarray | csr_matrix | pd.DataFrame | list | pd.Series, + ] = (np.ndarray, np.ndarray), + random_state: int | np.random.RandomState = DEFAULT_SEED, +) -> Data: + X_type, y_type = types + + classes = np.asarray(classes) + assert classes.ndim > 1 and classes.shape[1] > 1 + + rs = check_random_state(random_state) + + # Weights indicate each label tuple, and not the weights of individual labels + # in that tuple + if not weights: + weights = np.ones(classes.shape[0]) / len(classes) + + assert len(weights) == len(classes) + weights = normalize(np.asarray(weights)) + + X = rs.rand(*dims) + + class_indices = rs.choice(len(classes), dims[0], p=weights) + y = classes[class_indices] + + return astype(X_type, X), astype(y_type, y) + + +def _make_regression_data( + dims: Tuple[int, ...] = (100, 3), + types: Tuple[ + np.ndarray | csr_matrix | pd.DataFrame | list, + np.ndarray | csr_matrix | pd.DataFrame | list | pd.Series, + ] = (np.ndarray, np.ndarray), + random_state: int | np.random.RandomState = DEFAULT_SEED, +) -> Data: + X_type, y_type = types + rs = check_random_state(random_state) + + if X_type == csr_matrix: + X = rs.choice([0, 1], dims) + else: + X = rs.rand(*dims) + + y = rs.rand(dims[0]) + + return astype(X_type, X), astype(y_type, y) + + +def _make_multioutput_regression_data( + dims: Tuple[int, ...] = (100, 3), + targets: int = 2, + types: Tuple[ + np.ndarray | csr_matrix | pd.DataFrame | list, + np.ndarray | csr_matrix | pd.DataFrame | list | pd.Series, + ] = (np.ndarray, np.ndarray), + random_state: int | np.random.RandomState = DEFAULT_SEED, +) -> Data: + X_type, y_type = types + + rs = check_random_state(random_state) + + if X_type == csr_matrix: + X = rs.choice([0, 1], dims) + else: + X = rs.rand(*dims) + + y = rs.rand(dims[0], targets) + + return astype(X_type, X), astype(y_type, y) + + +@fixture +def make_data() -> Callable[..., Data]: + """Generate some arbitrary x,y data + + Parameters + ---------- + kind: int = BINARY_CLASSIFICATION + The task type, one of BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, ... + + **kwargs: Any + See the corresponding `_make_` + + Returns + ------- + Tuple[np.ndarray, np.ndarray] + The generated data + """ + + def _make( + kind: int = BINARY_CLASSIFICATION, + **kwargs: Any, + ) -> Data: + dispatches = { + BINARY_CLASSIFICATION: _make_binary_data, + MULTICLASS_CLASSIFICATION: _make_multiclass_data, + MULTILABEL_CLASSIFICATION: _make_multilabel_data, + REGRESSION: _make_regression_data, + MULTIOUTPUT_REGRESSION: _make_multioutput_regression_data, + } + + f = dispatches[kind] + return f(**kwargs) # type: ignore + + return _make diff --git a/test/fixtures/ensembles.py b/test/fixtures/ensembles.py new file mode 100644 index 0000000000..467c53822f --- /dev/null +++ b/test/fixtures/ensembles.py @@ -0,0 +1,94 @@ +from __future__ import annotations + +from typing import Callable, Collection, Optional, Union + +import numpy as np +from sklearn.ensemble import VotingClassifier, VotingRegressor + +from autosklearn.data.validation import SUPPORTED_FEAT_TYPES, SUPPORTED_TARGET_TYPES +from autosklearn.evaluation.abstract_evaluator import ( + MyDummyClassifier, + MyDummyRegressor, +) +from autosklearn.pipeline.components.base import ( + AutoSklearnClassificationAlgorithm, + AutoSklearnRegressionAlgorithm, +) + +from pytest_cases import fixture + +from test.conftest import DEFAULT_SEED + + +@fixture +def make_voting_classifier() -> Callable[..., VotingClassifier]: + """ + Parameters + ---------- + X: Optional[SUPPORTED_FEAT_TYPES] = None + The X data to fit models on, if None, no fitting occurs + + y: Optional[SUPPORTED_FEAT_TYPES] = None + The y data to fit models on, if None, no fitting occurs + + models: Optional[Collection[AutoSklearnClassificationAlgorithm]] = None + Any collection of algorithms to use, if None, DummyClassifiers are used + """ + + def _make( + X: Optional[SUPPORTED_FEAT_TYPES] = None, + y: Optional[SUPPORTED_TARGET_TYPES] = None, + models: Optional[Collection[AutoSklearnClassificationAlgorithm]] = None, + seed: Union[int, None, np.random.RandomState] = DEFAULT_SEED, + ) -> VotingClassifier: + assert not (X is None) ^ (y is None) + + if not models: + models = [MyDummyClassifier(config=1, random_state=seed) for _ in range(5)] + + if X is not None: + for model in models: + model.fit(X, y) + + voter = VotingClassifier(estimators=None, voting="soft") + voter.estimators_ = models + return voter + + return _make + + +@fixture +def make_voting_regressor() -> Callable[..., VotingRegressor]: + """ + Parameters + ---------- + X: Optional[SUPPORTED_FEAT_TYPES] = None + The X data to fit models on, if None, no fitting occurs + + y: Optional[SUPPORTED_FEAT_TYPES] = None + The y data to fit models on, if None, no fitting occurs + + models: Optional[Collection[AutoSklearnRegressionAlgorithm]] = None + Any collection of algorithms to use, if None, DummyRegressors are used + """ + + def _make( + X: Optional[SUPPORTED_FEAT_TYPES] = None, + y: Optional[SUPPORTED_TARGET_TYPES] = None, + models: Optional[Collection[AutoSklearnRegressionAlgorithm]] = None, + seed: Union[int, None, np.random.RandomState] = DEFAULT_SEED, + ) -> VotingRegressor: + assert not (X is None) ^ (y is None) + + if not models: + models = [MyDummyRegressor(config=1, random_state=seed) for _ in range(5)] + + if X is not None: + for model in models: + model.fit(X, y) + + voter = VotingRegressor(estimators=None) + voter.estimators_ = models + return voter + + return _make diff --git a/test/fixtures/logging.py b/test/fixtures/logging.py new file mode 100644 index 0000000000..778738087b --- /dev/null +++ b/test/fixtures/logging.py @@ -0,0 +1,9 @@ +from pytest_cases import fixture + +from test.mocks.logging import MockLogger + + +@fixture +def mock_logger() -> MockLogger: + """A mock logger with some mock defaults""" + return MockLogger() diff --git a/test/mocks/__init__.py b/test/mocks/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/mocks/logging.py b/test/mocks/logging.py new file mode 100644 index 0000000000..e61ca2c870 --- /dev/null +++ b/test/mocks/logging.py @@ -0,0 +1,38 @@ +from typing import Optional + +from autosklearn.util.logging_ import PicklableClientLogger + +from unittest.mock import Mock + +MOCKNAME = "mock" +MOCKHOST = "mockhost" +MOCKPORT = 9020 + + +class MockLogger(PicklableClientLogger): + """Should not be used for testing the actual loggers functionality + + Overwrites all methods with mock objects that can be queries + * All logging methods do nothing + * isEnabledFor returns True for everything as it's part of the logging config we + don't have access to + * __setstate__ and __getstate__ remain the same and are not mocked + """ + + def __init__( + self, + name: Optional[str] = None, + host: Optional[str] = None, + port: Optional[int] = None, + ): + self.name = name or MOCKNAME + self.host = host or MOCKHOST + self.port = port or MOCKPORT + + # Overwrite the logging implementations with mocks + attrs = ["debug", "info", "warning", "error", "exception", "critical", "log"] + for attr in attrs: + setattr(self, attr, Mock(return_value=None)) + + # This mock logger is enabled for all levels + setattr(self, "isEnabledFor", Mock(return_value=True)) diff --git a/test/test_automl/automl_utils.py b/test/test_automl/automl_utils.py index 577ea97359..7246b26fe5 100644 --- a/test/test_automl/automl_utils.py +++ b/test/test_automl/automl_utils.py @@ -16,7 +16,6 @@ def print_debug_information(automl): - # In case it is called with estimator, # Get the automl object if hasattr(automl, "automl_"): @@ -63,12 +62,6 @@ def print_debug_information(automl): return os.linesep.join(content) -def _includes(scores, all_scores): - return all(score in all_scores for score in scores) and len(scores) == len( - all_scores - ) - - def count_succeses(cv_results): return np.sum( [ @@ -78,7 +71,7 @@ def count_succeses(cv_results): ) -def includes_all_scores(scores): +def includes_all_scores(scores) -> bool: all_scores = ( scores_dict["train_single"] + scores_dict["test_single"] @@ -86,24 +79,24 @@ def includes_all_scores(scores): + scores_dict["test_ensamble"] + ["Timestamp"] ) - return _includes(scores, all_scores) + return set(scores) == set(all_scores) -def include_single_scores(scores): +def include_single_scores(scores) -> bool: all_scores = ( scores_dict["train_single"] + scores_dict["test_single"] + ["Timestamp"] ) - return _includes(scores, all_scores) + return set(scores) == set(all_scores) -def includes_train_scores(scores): +def includes_train_scores(scores) -> bool: all_scores = ( scores_dict["train_single"] + scores_dict["train_ensamble"] + ["Timestamp"] ) - return _includes(scores, all_scores) + return set(scores) == set(all_scores) -def performance_over_time_is_plausible(poT): +def performance_over_time_is_plausible(poT) -> bool: if len(poT) < 1: return False if len(poT.drop(columns=["Timestamp"]).dropna()) < 1: diff --git a/test/test_automl/cases.py b/test/test_automl/cases.py new file mode 100644 index 0000000000..70d68c4b73 --- /dev/null +++ b/test/test_automl/cases.py @@ -0,0 +1,196 @@ +""" +Here we define the several different setups and cache them to allow for easier unit +testing. The caching mechanism is only per session and does not persist over sessions. +There's only really a point for caching fitted models that will be tested multiple times +for different properties. + +Anything using a cached model must not destroy any backend resources, although it can +mock if required. + +Tags: + {classifier, regressor} - The type of AutoML object + classifier - will be fit on "iris" + regressor - will be fit on "boston" + {fitted} - If the automl case has been fitted + {cv, holdout} - Whether explicitly cv or holdout was used + {no_ensemble} - Fit with no ensemble size +""" +from typing import Callable, Tuple + +from pathlib import Path + +import numpy as np + +from autosklearn.automl import AutoMLClassifier, AutoMLRegressor + +from pytest_cases import case, parametrize + +from test.fixtures.caching import AutoMLCache + + +@case(tags=["classifier"]) +def case_classifier( + tmp_dir: str, + make_automl_classifier: Callable[..., AutoMLClassifier], +) -> AutoMLClassifier: + """Case basic unfitted AutoMLClassifier""" + dir = Path(tmp_dir) / "backend" + model = make_automl_classifier(temporary_directory=str(dir)) + return model + + +@case(tags=["classifier"]) +def case_regressor( + tmp_dir: str, + make_automl_regressor: Callable[..., AutoMLRegressor], +) -> AutoMLRegressor: + """Case basic unfitted AutoMLClassifier""" + dir = Path(tmp_dir) / "backend" + model = make_automl_regressor(temporary_directory=str(dir)) + return model + + +# ################################### +# The following are fitted and cached +# ################################### +@case(tags=["classifier", "fitted", "holdout"]) +@parametrize("dataset", ["iris"]) +def case_classifier_fitted_holdout( + automl_cache: Callable[[str], AutoMLCache], + dataset: str, + make_automl_classifier: Callable[..., AutoMLClassifier], + make_sklearn_dataset: Callable[..., Tuple[np.ndarray, ...]], +) -> AutoMLClassifier: + """Case of a holdout fitted classifier""" + resampling_strategy = "holdout-iterative-fit" + + cache = automl_cache(f"case_classifier_{resampling_strategy}_{dataset}") + + model = cache.model() + if model is not None: + return model + + X, y, Xt, yt = make_sklearn_dataset(name=dataset) + + model = make_automl_classifier( + temporary_directory=cache.path("backend"), + delete_tmp_folder_after_terminate=False, + resampling_strategy=resampling_strategy, + ) + model.fit(X, y, dataset_name=dataset) + + cache.save(model) + return model + + +@case(tags=["classifier", "fitted", "cv"]) +@parametrize("dataset", ["iris"]) +def case_classifier_fitted_cv( + automl_cache: Callable[[str], AutoMLCache], + dataset: str, + make_automl_classifier: Callable[..., AutoMLClassifier], + make_sklearn_dataset: Callable[..., Tuple[np.ndarray, ...]], +) -> AutoMLClassifier: + """Case of a fitted cv AutoMLClassifier""" + resampling_strategy = "cv" + cache = automl_cache(f"case_classifier_{resampling_strategy}_{dataset}") + + model = cache.model() + if model is not None: + return model + + X, y, Xt, yt = make_sklearn_dataset(name=dataset) + model = make_automl_classifier( + resampling_strategy=resampling_strategy, + temporary_directory=cache.path("backend"), + delete_tmp_folder_after_terminate=False, + ) + model.fit(X, y, dataset_name=dataset) + + cache.save(model) + return model + + +@case(tags=["regressor", "fitted", "holdout"]) +@parametrize("dataset", ["boston"]) +def case_regressor_fitted_holdout( + automl_cache: Callable[[str], AutoMLCache], + dataset: str, + make_automl_regressor: Callable[..., AutoMLRegressor], + make_sklearn_dataset: Callable[..., Tuple[np.ndarray, ...]], +) -> AutoMLRegressor: + """Case of fitted regressor with cv resampling""" + resampling_strategy = "holdout" + cache = automl_cache(f"case_regressor_{resampling_strategy}_{dataset}") + + model = cache.model() + if model is not None: + return model + + X, y, Xt, yt = make_sklearn_dataset(name=dataset) + model = make_automl_regressor( + resampling_strategy=resampling_strategy, + temporary_directory=cache.path("backend"), + delete_tmp_folder_after_terminate=False, + ) + model.fit(X, y, dataset_name=dataset) + + cache.save(model) + return model + + +@case(tags=["regressor", "fitted", "cv"]) +@parametrize("dataset", ["boston"]) +def case_regressor_fitted_cv( + automl_cache: Callable[[str], AutoMLCache], + dataset: str, + make_automl_regressor: Callable[..., AutoMLRegressor], + make_sklearn_dataset: Callable[..., Tuple[np.ndarray, ...]], +) -> AutoMLRegressor: + """Case of fitted regressor with cv resampling""" + resampling_strategy = "cv" + + cache = automl_cache(f"case_regressor_{resampling_strategy}_{dataset}") + model = cache.model() + if model is not None: + return model + + X, y, Xt, yt = make_sklearn_dataset(name=dataset) + + model = make_automl_regressor( + temporary_directory=cache.path("backend"), + delete_tmp_folder_after_terminate=False, + resampling_strategy=resampling_strategy, + ) + model.fit(X, y, dataset_name=dataset) + + cache.save(model) + return model + + +@case(tags=["classifier", "fitted", "no_ensemble"]) +@parametrize("dataset", ["iris"]) +def case_classifier_fitted_no_ensemble( + automl_cache: Callable[[str], AutoMLCache], + dataset: str, + make_automl_classifier: Callable[..., AutoMLClassifier], + make_sklearn_dataset: Callable[..., Tuple[np.ndarray, ...]], +) -> AutoMLClassifier: + """Case of a fitted classifier but enemble_size was set to 0""" + cache = automl_cache(f"case_classifier_fitted_no_ensemble_{dataset}") + + model = cache.model() + if model is not None: + return model + + X, y, Xt, yt = make_sklearn_dataset(name=dataset) + + model = make_automl_classifier( + temporary_directory=cache.path("backend"), + delete_tmp_folder_after_terminate=False, + ensemble_size=0, + ) + model.fit(X, y, dataset_name=dataset) + + cache.save(model) + return model diff --git a/test/test_automl/test_automl.py b/test/test_automl/test_automl.py deleted file mode 100644 index 3c62f9a4da..0000000000 --- a/test/test_automl/test_automl.py +++ /dev/null @@ -1,1232 +0,0 @@ -# -*- encoding: utf-8 -*- -from typing import Dict, List, Union - -import glob -import itertools -import os -import pickle -import time -import unittest -import unittest.mock -import warnings - -import numpy as np -import pandas as pd -import pytest -import sklearn.datasets -from scipy.sparse import csr_matrix, spmatrix -from sklearn.ensemble import VotingClassifier, VotingRegressor -from smac.facade.roar_facade import ROAR -from smac.scenario.scenario import Scenario -from smac.tae import StatusType - -import autosklearn.automl -import autosklearn.pipeline.util as putil -from autosklearn.automl import AutoML, AutoMLClassifier, AutoMLRegressor, _model_predict -from autosklearn.constants import ( - BINARY_CLASSIFICATION, - CLASSIFICATION_TASKS, - MULTICLASS_CLASSIFICATION, - MULTILABEL_CLASSIFICATION, - MULTIOUTPUT_REGRESSION, - REGRESSION, -) -from autosklearn.data.validation import InputValidator -from autosklearn.data.xy_data_manager import XYDataManager -from autosklearn.evaluation.abstract_evaluator import ( - MyDummyClassifier, - MyDummyRegressor, -) -from autosklearn.metrics import ( - accuracy, - balanced_accuracy, - default_metric_for_task, - log_loss, -) -from autosklearn.util.data import default_dataset_compression_arg -from autosklearn.util.logging_ import PickableLoggerAdapter - -from test.test_automl.automl_utils import ( - AutoMLLogParser, - count_succeses, - includes_train_scores, - performance_over_time_is_plausible, - print_debug_information, -) - - -class AutoMLStub(AutoML): - def __init__(self, classifier: bool = False): - self._task = None - self._dask_client = None - self._is_dask_client_internally_created = False - self._classifier = classifier - - def __del__(self): - pass - - -def test_fit(dask_client): - X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") - automl = autosklearn.automl.AutoML( - seed=0, - time_left_for_this_task=30, - per_run_time_limit=5, - metric=accuracy, - dask_client=dask_client, - ) - - automl.fit(X_train, Y_train, task=MULTICLASS_CLASSIFICATION) - - score = automl.score(X_test, Y_test) - assert score > 0.8 - assert count_succeses(automl.cv_results_) > 0 - assert includes_train_scores(automl.performance_over_time_.columns) is True - assert performance_over_time_is_plausible(automl.performance_over_time_) is True - assert automl._task == MULTICLASS_CLASSIFICATION - - del automl - - -def test_ensemble_size_zero(): - """Test if automl.fit_ensemble raises error when ensemble_size == 0""" - X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") - automl = autosklearn.automl.AutoML( - seed=0, - time_left_for_this_task=30, - per_run_time_limit=5, - metric=accuracy, - ensemble_size=0, - ) - automl.fit(X_train, Y_train, task=MULTICLASS_CLASSIFICATION) - with pytest.raises(ValueError): - automl.fit_ensemble(Y_test, ensemble_size=0) - - -def test_empty_dict_in_show_models(): - """Test if show_models() returns empty dictionary when ensemble_size == 0""" - X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") - automl = autosklearn.automl.AutoMLClassifier( - seed=0, - time_left_for_this_task=30, - per_run_time_limit=5, - metric=accuracy, - ensemble_size=0, - ) - automl.fit(X_train, Y_train) - assert automl.show_models() == {} - - -def test_fitted_models_in_show_models(): - X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") - automl = autosklearn.automl.AutoMLClassifier( - seed=0, - time_left_for_this_task=30, - per_run_time_limit=5, - metric=accuracy, - ensemble_size=0, - ) - with pytest.raises(RuntimeError, match="AutoSklearn has not been fitted"): - automl.show_models() - - -def test_fit_roar(dask_client_single_worker): - def get_roar_object_callback( - scenario_dict, seed, ta, ta_kwargs, dask_client, n_jobs, **kwargs - ): - """Random online adaptive racing. - - http://ml.informatik.uni-freiburg.de/papers/11-LION5-SMAC.pdf""" - scenario = Scenario(scenario_dict) - return ROAR( - scenario=scenario, - rng=seed, - tae_runner=ta, - tae_runner_kwargs=ta_kwargs, - dask_client=dask_client, - n_jobs=n_jobs, - ) - - X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") - automl = autosklearn.automl.AutoML( - time_left_for_this_task=30, - per_run_time_limit=5, - initial_configurations_via_metalearning=0, - get_smac_object_callback=get_roar_object_callback, - metric=accuracy, - dask_client=dask_client_single_worker, - ) - - automl.fit(X_train, Y_train, task=MULTICLASS_CLASSIFICATION) - - score = automl.score(X_test, Y_test) - assert score > 0.8 - assert count_succeses(automl.cv_results_) > 0 - assert includes_train_scores(automl.performance_over_time_.columns) is True - assert automl._task == MULTICLASS_CLASSIFICATION - - del automl - - -def test_refit_shuffle_on_fail(dask_client): - - failing_model = unittest.mock.Mock() - failing_model.fit.side_effect = [ValueError(), ValueError(), None] - failing_model.fit_transformer.side_effect = [ValueError(), ValueError(), (None, {})] - failing_model.get_max_iter.return_value = 100 - - auto = AutoML(30, 5, dask_client=dask_client) - ensemble_mock = unittest.mock.Mock() - ensemble_mock.get_selected_model_identifiers.return_value = [(1, 1, 50.0)] - auto.ensemble_ = ensemble_mock - auto.InputValidator = InputValidator() - for budget_type in [None, "iterations"]: - auto._budget_type = budget_type - - auto.models_ = {(1, 1, 50.0): failing_model} - - # Make sure a valid 2D array is given to automl - X = np.array([1, 2, 3]).reshape(-1, 1) - y = np.array([1, 2, 3]) - auto.InputValidator.fit(X, y) - auto.refit(X, y) - - assert failing_model.fit.call_count == 3 - assert failing_model.fit_transformer.call_count == 3 - - del auto - - -def test_only_loads_ensemble_models(automl_stub): - def side_effect(ids, *args, **kwargs): - return models if ids is identifiers else {} - - # Add a resampling strategy as this is required by load_models - automl_stub._resampling_strategy = "holdout" - identifiers = [(1, 2), (3, 4)] - - models = [42] - load_ensemble_mock = unittest.mock.Mock() - load_ensemble_mock.get_selected_model_identifiers.return_value = identifiers - automl_stub._backend.load_ensemble.return_value = load_ensemble_mock - automl_stub._backend.load_models_by_identifiers.side_effect = side_effect - - automl_stub._load_models() - assert models == automl_stub.models_ - assert automl_stub.cv_models_ is None - - automl_stub._resampling_strategy = "cv" - - models = [42] - automl_stub._backend.load_cv_models_by_identifiers.side_effect = side_effect - - automl_stub._load_models() - assert models == automl_stub.cv_models_ - - -def test_check_for_models_if_no_ensemble(automl_stub): - models = [42] - automl_stub._backend.load_ensemble.return_value = None - automl_stub._backend.list_all_models.return_value = models - automl_stub._disable_evaluator_output = False - - automl_stub._load_models() - - -def test_raises_if_no_models(automl_stub): - automl_stub._backend.load_ensemble.return_value = None - automl_stub._backend.list_all_models.return_value = [] - automl_stub._resampling_strategy = "holdout" - - automl_stub._disable_evaluator_output = False - with pytest.raises(ValueError): - automl_stub._load_models() - - automl_stub._disable_evaluator_output = True - automl_stub._load_models() - - -def test_delete_non_candidate_models(dask_client): - - seed = 555 - X, Y, _, _ = putil.get_dataset("iris") - automl = autosklearn.automl.AutoML( - delete_tmp_folder_after_terminate=False, - time_left_for_this_task=60, - per_run_time_limit=5, - ensemble_nbest=3, - seed=seed, - initial_configurations_via_metalearning=0, - resampling_strategy="holdout", - include={"classifier": ["sgd"], "feature_preprocessor": ["no_preprocessing"]}, - metric=accuracy, - dask_client=dask_client, - # Force model to be deleted. That is, from 50 which is the - # default to 3 to make sure we delete models. - max_models_on_disc=3, - ) - - automl.fit(X, Y, task=MULTICLASS_CLASSIFICATION, X_test=X, y_test=Y) - - # Assert at least one model file has been deleted and that there were no - # deletion errors - log_file_path = glob.glob( - os.path.join( - automl._backend.temporary_directory, "AutoML(" + str(seed) + "):*.log" - ) - ) - with open(log_file_path[0]) as log_file: - log_content = log_file.read() - assert "Deleted files of non-candidate model" in log_content, log_content - assert ( - "Failed to delete files of non-candidate model" not in log_content - ), log_content - assert "Failed to lock model" not in log_content, log_content - - # Assert that the files of the models used by the ensemble weren't deleted - model_files = automl._backend.list_all_models(seed=seed) - model_files_idx = set() - for m_file in model_files: - # Extract the model identifiers from the filename - m_file = os.path.split(m_file)[1].replace(".model", "").split(".", 2) - model_files_idx.add((int(m_file[0]), int(m_file[1]), float(m_file[2]))) - ensemble_members_idx = set(automl.ensemble_.identifiers_) - assert ensemble_members_idx.issubset(model_files_idx), ( - ensemble_members_idx, - model_files_idx, - ) - - del automl - - -def test_binary_score_and_include(dask_client): - """ - Test fix for binary classification prediction - taking the index 1 of second dimension in prediction matrix - """ - - data = sklearn.datasets.make_classification( - n_samples=400, - n_features=10, - n_redundant=1, - n_informative=3, - n_repeated=1, - n_clusters_per_class=2, - random_state=1, - ) - X_train = data[0][:200] - Y_train = data[1][:200] - X_test = data[0][200:] - Y_test = data[1][200:] - - automl = autosklearn.automl.AutoML( - 20, - 5, - include={"classifier": ["sgd"], "feature_preprocessor": ["no_preprocessing"]}, - metric=accuracy, - dask_client=dask_client, - ) - - automl.fit(X_train, Y_train, task=BINARY_CLASSIFICATION) - - assert automl._task == BINARY_CLASSIFICATION - - # TODO, the assumption from above is not really tested here - # Also, the score method should be removed, it only makes little sense - score = automl.score(X_test, Y_test) - assert score >= 0.4 - - del automl - - -def test_automl_outputs(dask_client): - - X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") - name = "iris" - auto = autosklearn.automl.AutoML( - 30, - 5, - initial_configurations_via_metalearning=0, - seed=100, - metric=accuracy, - dask_client=dask_client, - delete_tmp_folder_after_terminate=False, - ) - - auto.fit( - X=X_train, - y=Y_train, - X_test=X_test, - y_test=Y_test, - dataset_name=name, - task=MULTICLASS_CLASSIFICATION, - ) - - data_manager_file = os.path.join( - auto._backend.temporary_directory, ".auto-sklearn", "datamanager.pkl" - ) - - # pickled data manager (without one hot encoding!) - with open(data_manager_file, "rb") as fh: - D = pickle.load(fh) - assert np.allclose(D.data["X_train"], X_train) - - # Check that all directories are there - fixture = [ - "true_targets_ensemble.npy", - "start_time_100", - "datamanager.pkl", - "ensemble_read_preds.pkl", - "ensemble_read_losses.pkl", - "runs", - "ensembles", - "ensemble_history.json", - ] - assert sorted( - os.listdir(os.path.join(auto._backend.temporary_directory, ".auto-sklearn")) - ) == sorted(fixture) - - # At least one ensemble, one validation, one test prediction and one - # model and one ensemble - fixture = glob.glob( - os.path.join( - auto._backend.temporary_directory, - ".auto-sklearn", - "runs", - "*", - "predictions_ensemble*npy", - ) - ) - assert len(fixture) > 0 - - fixture = glob.glob( - os.path.join( - auto._backend.temporary_directory, - ".auto-sklearn", - "runs", - "*", - "100.*.model", - ) - ) - assert len(fixture) > 0 - - fixture = os.listdir( - os.path.join(auto._backend.temporary_directory, ".auto-sklearn", "ensembles") - ) - assert "100.0000000000.ensemble" in fixture - - # Start time - start_time_file_path = os.path.join( - auto._backend.temporary_directory, ".auto-sklearn", "start_time_100" - ) - with open(start_time_file_path, "r") as fh: - start_time = float(fh.read()) - assert time.time() - start_time >= 10, print_debug_information(auto) - - # Then check that the logger matches the run expectation - logfile = glob.glob(os.path.join(auto._backend.temporary_directory, "AutoML*.log"))[ - 0 - ] - parser = AutoMLLogParser(logfile) - - # The number of ensemble trajectories properly in log file - success_ensemble_iters_auto = len(auto.ensemble_performance_history) - success_ensemble_iters_log = parser.count_ensembler_success_pynisher_calls() - assert success_ensemble_iters_auto == success_ensemble_iters_log, "{} != {}".format( - auto.ensemble_performance_history, - print_debug_information(auto), - ) - - # We also care that no iteration got lost - # This is important because it counts for pynisher calls - # and whether a pynisher call actually called the ensemble - total_ensemble_iterations = parser.count_ensembler_iterations() - assert len(total_ensemble_iterations) > 1 # At least 1 iteration - assert range(1, max(total_ensemble_iterations) + 1), total_ensemble_iterations - - # a point where pynisher is called before budget exhaustion - # Dummy not in run history - total_calls_to_pynisher_log = parser.count_tae_pynisher_calls() - 1 - total_returns_from_pynisher_log = parser.count_tae_pynisher_returns() - 1 - total_elements_rh = len( - [ - run_value - for run_value in auto.runhistory_.data.values() - if run_value.status == StatusType.RUNNING - ] - ) - - # Make sure we register all calls to pynisher - # The less than or equal here is added as a WA as - # https://github.com/automl/SMAC3/pull/712 is not yet integrated - assert total_elements_rh <= total_calls_to_pynisher_log, print_debug_information( - auto - ) - - # Make sure we register all returns from pynisher - assert ( - total_elements_rh <= total_returns_from_pynisher_log - ), print_debug_information(auto) - - # Lastly check that settings are print to logfile - ensemble_size = parser.get_automl_setting_from_log( - auto._dataset_name, "ensemble_size" - ) - assert auto._ensemble_size == int(ensemble_size) - - del auto - - -@pytest.mark.parametrize( - "datasets", - [ - ("breast_cancer", BINARY_CLASSIFICATION), - ("wine", MULTICLASS_CLASSIFICATION), - ("diabetes", REGRESSION), - ], -) -def test_do_dummy_prediction(dask_client, datasets): - - name, task = datasets - - X_train, Y_train, X_test, Y_test = putil.get_dataset(name) - datamanager = XYDataManager( - X_train, - Y_train, - X_test, - Y_test, - task=task, - dataset_name=name, - feat_type={i: "numerical" for i in range(X_train.shape[1])}, - ) - - auto = autosklearn.automl.AutoML( - 20, - 5, - initial_configurations_via_metalearning=25, - metric=accuracy, - dask_client=dask_client, - delete_tmp_folder_after_terminate=False, - ) - auto._backend = auto._create_backend() - - # Make a dummy logger - auto._logger_port = 9020 - auto._logger = unittest.mock.Mock() - auto._logger.info.return_value = None - - auto._backend.save_datamanager(datamanager) - D = auto._backend.load_datamanager() - - # Check if data manager is correcly loaded - assert D.info["task"] == datamanager.info["task"] - auto._do_dummy_prediction(D, 1) - - # Ensure that the dummy predictions are not in the current working - # directory, but in the temporary directory. - unexpected_directory = os.path.join(os.getcwd(), ".auto-sklearn") - expected_directory = os.path.join( - auto._backend.temporary_directory, - ".auto-sklearn", - "runs", - "1_1_0.0", - "predictions_ensemble_1_1_0.0.npy", - ) - assert not os.path.exists(unexpected_directory) - assert os.path.exists(expected_directory) - - auto._clean_logger() - - del auto - - -@unittest.mock.patch("autosklearn.evaluation.ExecuteTaFuncWithQueue.run") -def test_fail_if_dummy_prediction_fails(ta_run_mock, dask_client): - - X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") - datamanager = XYDataManager( - X_train, - Y_train, - X_test, - Y_test, - task=2, - feat_type={i: "Numerical" for i in range(X_train.shape[1])}, - dataset_name="iris", - ) - - time_for_this_task = 30 - per_run_time = 10 - auto = autosklearn.automl.AutoML( - time_for_this_task, - per_run_time, - initial_configurations_via_metalearning=25, - metric=accuracy, - dask_client=dask_client, - delete_tmp_folder_after_terminate=False, - ) - auto._backend = auto._create_backend() - auto._backend._make_internals_directory() - auto._backend.save_datamanager(datamanager) - - # Make a dummy logger - auto._logger_port = 9020 - auto._logger = unittest.mock.Mock() - auto._logger.info.return_value = None - - # First of all, check that ta.run() is actually called. - ta_run_mock.return_value = StatusType.SUCCESS, None, None, {} - auto._do_dummy_prediction(datamanager, 1) - ta_run_mock.assert_called_once_with(1, cutoff=time_for_this_task) - - # Case 1. Check that function raises no error when statustype == success. - # ta.run() returns status, cost, runtime, and additional info. - ta_run_mock.return_value = StatusType.SUCCESS, None, None, {} - raised = False - try: - auto._do_dummy_prediction(datamanager, 1) - except ValueError: - raised = True - assert not raised, "Exception raised" - - # Case 2. Check that if statustype returned by ta.run() != success, - # the function raises error. - ta_run_mock.return_value = StatusType.CRASHED, None, None, {} - with pytest.raises( - ValueError, - match="Dummy prediction failed with run state StatusType.CRASHED and additional output: {}.", # noqa - ): - auto._do_dummy_prediction(datamanager, 1) - - ta_run_mock.return_value = StatusType.ABORT, None, None, {} - with pytest.raises( - ValueError, - match="Dummy prediction failed with run state StatusType.ABORT " - "and additional output: {}.", - ): - auto._do_dummy_prediction(datamanager, 1) - ta_run_mock.return_value = StatusType.TIMEOUT, None, None, {} - with pytest.raises( - ValueError, - match="Dummy prediction failed with run state StatusType.TIMEOUT " - "and additional output: {}.", - ): - auto._do_dummy_prediction(datamanager, 1) - ta_run_mock.return_value = StatusType.MEMOUT, None, None, {} - with pytest.raises( - ValueError, - match="Dummy prediction failed with run state StatusType.MEMOUT " - "and additional output: {}.", - ): - auto._do_dummy_prediction(datamanager, 1) - ta_run_mock.return_value = StatusType.CAPPED, None, None, {} - with pytest.raises( - ValueError, - match="Dummy prediction failed with run state StatusType.CAPPED " - "and additional output: {}.", - ): - auto._do_dummy_prediction(datamanager, 1) - - ta_run_mock.return_value = StatusType.CRASHED, None, None, {"exitcode": -6} - with pytest.raises( - ValueError, - match="The error suggests that the provided memory limits are too tight.", - ): - auto._do_dummy_prediction(datamanager, 1) - - -@unittest.mock.patch("autosklearn.smbo.AutoMLSMBO.run_smbo") -def test_exceptions_inside_log_in_smbo(smbo_run_mock, dask_client): - - # Below importing and shutdown is a workaround, to make sure - # we reset the port to collect messages. Randomly, when running - # this test with multiple other test at the same time causes this - # test to fail. This resets the singletons of the logging class - import logging - - logging.shutdown() - - automl = autosklearn.automl.AutoML( - 20, - 5, - metric=accuracy, - dask_client=dask_client, - delete_tmp_folder_after_terminate=False, - ) - - dataset_name = "test_exceptions_inside_log" - - # Create a custom exception to prevent other errors to slip in - class MyException(Exception): - pass - - X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") - # The first call is on dummy predictor failure - message = str(np.random.randint(100)) + "_run_smbo" - smbo_run_mock.side_effect = MyException(message) - - with pytest.raises(MyException): - automl.fit( - X_train, - Y_train, - task=MULTICLASS_CLASSIFICATION, - dataset_name=dataset_name, - ) - - # make sure that the logfile was created - logger_name = "AutoML(%d):%s" % (1, dataset_name) - logger = logging.getLogger(logger_name) - logfile = os.path.join(automl._backend.temporary_directory, logger_name + ".log") - assert os.path.exists(logfile), print_debug_information(automl) + str( - automl._clean_logger() - ) - - # Give some time for the error message to be printed in the - # log file - found_message = False - for incr_tolerance in range(5): - with open(logfile) as f: - lines = f.readlines() - if any(message in line for line in lines): - found_message = True - break - else: - time.sleep(incr_tolerance) - - # Speed up the closing after forced crash - automl._clean_logger() - - if not found_message: - pytest.fail( - "Did not find {} in the log file {} for logger {}/{}/{}".format( - message, - print_debug_information(automl), - vars(automl._logger.logger), - vars(logger), - vars(logging.getLogger()), - ) - ) - - -@pytest.mark.parametrize("metric", [log_loss, balanced_accuracy]) -def test_load_best_individual_model(metric, dask_client): - - X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") - automl = autosklearn.automl.AutoML( - time_left_for_this_task=30, - per_run_time_limit=5, - metric=metric, - dask_client=dask_client, - delete_tmp_folder_after_terminate=False, - ) - - # We cannot easily mock a function sent to dask - # so for this test we create the whole set of models/ensembles - # but prevent it to be loaded - automl.fit(X_train, Y_train, task=MULTICLASS_CLASSIFICATION) - - automl._backend.load_ensemble = unittest.mock.MagicMock(return_value=None) - - # A memory error occurs in the ensemble construction - assert automl._backend.load_ensemble(automl._seed) is None - - # The load model is robust to this and loads the best model - automl._load_models() - assert automl.ensemble_ is not None - - # Just 1 model is there for ensemble and all weight must be on it - get_models_with_weights = automl.get_models_with_weights() - assert len(get_models_with_weights) == 1 - assert get_models_with_weights[0][0] == 1.0 - - # Match a toy dataset - if metric.name == "balanced_accuracy": - assert automl.score(X_test, Y_test) > 0.9 - elif metric.name == "log_loss": - # Seen values in github actions of 0.6978304740364537 - assert automl.score(X_test, Y_test) < 0.7 - else: - raise ValueError(metric.name) - - del automl - - -def test_fail_if_feat_type_on_pandas_input(dask_client): - """We do not support feat type when pandas - is provided as an input - """ - automl = autosklearn.automl.AutoML( - time_left_for_this_task=30, - per_run_time_limit=5, - metric=accuracy, - dask_client=dask_client, - ) - - X_train = pd.DataFrame({"a": [1, 1], "c": [1, 2]}) - y_train = [1, 0] - msg = ( - "providing the option feat_type to the fit method is not supported" - " when using a Dataframe." - ) - with pytest.raises(ValueError, match=msg): - automl.fit( - X_train, - y_train, - task=BINARY_CLASSIFICATION, - feat_type={1: "Categorical", 2: "Numerical"}, - ) - - -def data_input_and_target_types(): - n_rows = 100 - - # Create valid inputs - X_ndarray = np.random.random(size=(n_rows, 5)) - X_ndarray[X_ndarray < 0.9] = 0 - - # Binary Classificaiton - y_binary_ndarray = np.random.random(size=n_rows) - y_binary_ndarray[y_binary_ndarray >= 0.5] = 1 - y_binary_ndarray[y_binary_ndarray < 0.5] = 0 - - # Multiclass classification - y_multiclass_ndarray = np.random.random(size=n_rows) - y_multiclass_ndarray[y_multiclass_ndarray > 0.66] = 2 - y_multiclass_ndarray[ - (y_multiclass_ndarray <= 0.66) & (y_multiclass_ndarray >= 0.33) - ] = 1 - y_multiclass_ndarray[y_multiclass_ndarray < 0.33] = 0 - - # Multilabel classificaiton - y_multilabel_ndarray = np.random.random(size=(n_rows, 3)) - y_multilabel_ndarray[y_multilabel_ndarray > 0.5] = 1 - y_multilabel_ndarray[y_multilabel_ndarray <= 0.5] = 0 - - # Regression - y_regression_ndarray = np.random.random(size=n_rows) - - # Multioutput Regression - y_multioutput_regression_ndarray = np.random.random(size=(n_rows, 3)) - - xs = [ - X_ndarray, - list(X_ndarray), - csr_matrix(X_ndarray), - pd.DataFrame(data=X_ndarray), - ] - - ys_binary = [ - y_binary_ndarray, - list(y_binary_ndarray), - csr_matrix(y_binary_ndarray), - pd.Series(y_binary_ndarray), - pd.DataFrame(data=y_binary_ndarray), - ] - - ys_multiclass = [ - y_multiclass_ndarray, - list(y_multiclass_ndarray), - csr_matrix(y_multiclass_ndarray), - pd.Series(y_multiclass_ndarray), - pd.DataFrame(data=y_multiclass_ndarray), - ] - - ys_multilabel = [ - y_multilabel_ndarray, - list(y_multilabel_ndarray), - csr_matrix(y_multilabel_ndarray), - # pd.Series(y_multilabel_ndarray) - pd.DataFrame(data=y_multilabel_ndarray), - ] - - ys_regression = [ - y_regression_ndarray, - list(y_regression_ndarray), - csr_matrix(y_regression_ndarray), - pd.Series(y_regression_ndarray), - pd.DataFrame(data=y_regression_ndarray), - ] - - ys_multioutput_regression = [ - y_multioutput_regression_ndarray, - list(y_multioutput_regression_ndarray), - csr_matrix(y_multioutput_regression_ndarray), - # pd.Series(y_multioutput_regression_ndarray), - pd.DataFrame(data=y_multioutput_regression_ndarray), - ] - - # [ (X, y, X_test, y_test, task), ... ] - return ( - (X, y, X, y, task) - for X in xs - for y, task in itertools.chain( - itertools.product(ys_binary, [BINARY_CLASSIFICATION]), - itertools.product(ys_multiclass, [MULTICLASS_CLASSIFICATION]), - itertools.product(ys_multilabel, [MULTILABEL_CLASSIFICATION]), - itertools.product(ys_regression, [REGRESSION]), - itertools.product(ys_multioutput_regression, [MULTIOUTPUT_REGRESSION]), - ) - ) - - -@pytest.mark.parametrize("X, y, X_test, y_test, task", data_input_and_target_types()) -def test_input_and_target_types(dask_client, X, y, X_test, y_test, task): - - if task in CLASSIFICATION_TASKS: - automl = AutoMLClassifier( - time_left_for_this_task=15, - per_run_time_limit=5, - dask_client=dask_client, - ) - else: - automl = AutoMLRegressor( - time_left_for_this_task=15, - per_run_time_limit=5, - dask_client=dask_client, - ) - # To save time fitting and only validate the inputs we only return - # the configuration space - automl.fit( - X=X, y=y, X_test=X_test, y_test=y_test, only_return_configuration_space=True - ) - assert automl._task == task - assert automl._metric.name == default_metric_for_task[task].name - - -def data_test_model_predict_outsputs_correct_shapes(): - datasets = sklearn.datasets - binary = datasets.make_classification(n_samples=5, n_classes=2, random_state=0) - multiclass = datasets.make_classification( - n_samples=5, n_informative=3, n_classes=3, random_state=0 - ) - multilabel = datasets.make_multilabel_classification( - n_samples=5, n_classes=3, random_state=0 - ) - regression = datasets.make_regression(n_samples=5, random_state=0) - multioutput = datasets.make_regression(n_samples=5, n_targets=3, random_state=0) - - # TODO issue 1169 - # While testing output shapes, realised all models are wrapped to provide - # a special predict_proba that outputs a different shape than usual. - # This includes DummyClassifier and DummyRegressor which are wrapped as - # `MyDummyClassifier/Regressor` and require a config object. - # config == 1 : Classifier uses 'uniform', Regressor uses 'mean' - # else : Classifier uses 'most_frequent', Regressor uses 'median' - # - # This wrapping of probabilities with - # `convert_multioutput_multiclass_to_multilabel` - # can probably be just put into a base class which queries subclasses - # as to whether it's needed. - # - # tldr; thats why we use MyDummyX here instead of the default models - # from sklearn - def classifier(X, y): - return MyDummyClassifier(config=1, random_state=0).fit(X, y) - - def regressor(X, y): - return MyDummyRegressor(config=1, random_state=0).fit(X, y) - - # How cross validation models are currently grouped together - def voting_classifier(X, y): - classifiers = [ - MyDummyClassifier(config=1, random_state=0).fit(X, y) for _ in range(5) - ] - vc = VotingClassifier(estimators=None, voting="soft") - vc.estimators_ = classifiers - return vc - - def voting_regressor(X, y): - regressors = [ - MyDummyRegressor(config=1, random_state=0).fit(X, y) for _ in range(5) - ] - vr = VotingRegressor(estimators=None) - vr.estimators_ = regressors - return vr - - test_data = { - BINARY_CLASSIFICATION: { - "models": [classifier(*binary), voting_classifier(*binary)], - "data": binary, - # prob of false/true for the one class - "expected_output_shape": (len(binary[0]), 2), - }, - MULTICLASS_CLASSIFICATION: { - "models": [classifier(*multiclass), voting_classifier(*multiclass)], - "data": multiclass, - # prob of true for each possible class - "expected_output_shape": (len(multiclass[0]), 3), - }, - MULTILABEL_CLASSIFICATION: { - "models": [classifier(*multilabel), voting_classifier(*multilabel)], - "data": multilabel, - # probability of true for each binary label - "expected_output_shape": (len(multilabel[0]), 3), # type: ignore - }, - REGRESSION: { - "models": [regressor(*regression), voting_regressor(*regression)], - "data": regression, - # array of single outputs - "expected_output_shape": (len(regression[0]),), - }, - MULTIOUTPUT_REGRESSION: { - "models": [regressor(*multioutput), voting_regressor(*multioutput)], - "data": multioutput, - # array of vector otuputs - "expected_output_shape": (len(multioutput[0]), 3), - }, - } - - return itertools.chain.from_iterable( - [ - (model, cfg["data"], task, cfg["expected_output_shape"]) - for model in cfg["models"] - ] - for task, cfg in test_data.items() - ) - - -@pytest.mark.parametrize( - "model, data, task, expected_output_shape", - data_test_model_predict_outsputs_correct_shapes(), -) -def test_model_predict_outputs_correct_shapes(model, data, task, expected_output_shape): - X, y = data - prediction = _model_predict(model=model, X=X, task=task) - assert prediction.shape == expected_output_shape - - -def test_model_predict_outputs_warnings_to_logs(): - X = list(range(20)) - task = REGRESSION - logger = PickableLoggerAdapter("test_model_predict_correctly_outputs_warnings") - logger.warning = unittest.mock.Mock() - - class DummyModel: - def predict(self, x): - warnings.warn("test warning", Warning) - return x - - model = DummyModel() - - _model_predict(model, X, task, logger=logger) - - assert logger.warning.call_count == 1, "Logger should have had warning called" - - -def test_model_predict_outputs_to_stdout_if_no_logger(): - X = list(range(20)) - task = REGRESSION - - class DummyModel: - def predict(self, x): - warnings.warn("test warning", Warning) - return x - - model = DummyModel() - - with warnings.catch_warnings(record=True) as w: - _model_predict(model, X, task, logger=None) - - assert len(w) == 1, "One warning sould have been emmited" - - -@pytest.mark.parametrize("dataset_compression", [False]) -def test_param_dataset_compression_false(dataset_compression: bool) -> None: - """ - Parameters - ---------- - dataset_compression: bool - The dataset_compression arg set as False - - Expects - ------- - * Should set the private attribute to None - """ - auto = AutoMLRegressor( - time_left_for_this_task=30, - per_run_time_limit=5, - dataset_compression=dataset_compression, - ) - - assert auto._dataset_compression is None - - -@pytest.mark.parametrize("dataset_compression", [True]) -def test_construction_param_dataset_compression_true(dataset_compression: bool) -> None: - """ - Parameters - ---------- - dataset_compression: bool - The dataset_compression arg set as True - - Expects - ------- - * Should set the private attribute to the defaults - """ - auto = AutoMLRegressor( - time_left_for_this_task=30, - per_run_time_limit=5, - dataset_compression=dataset_compression, - ) - - assert auto._dataset_compression == default_dataset_compression_arg - - -@pytest.mark.parametrize("dataset_compression", [{"memory_allocation": 0.2}]) -def test_construction_param_dataset_compression_valid_dict( - dataset_compression: Dict, -) -> None: - """ - Parameters - ---------- - dataset_compression: Dict - The dataset_compression arg set partially specified - - Expects - ------- - * Should set the private attribute to the passed dataset_compression arg + defaults - """ - auto = AutoMLRegressor( - time_left_for_this_task=30, - per_run_time_limit=5, - dataset_compression=dataset_compression, - ) - - expected_memory_allocation = dataset_compression["memory_allocation"] - expected_methods = default_dataset_compression_arg["methods"] - - assert auto._dataset_compression is not None - assert auto._dataset_compression["memory_allocation"] == expected_memory_allocation - assert auto._dataset_compression["methods"] == expected_methods - - -@pytest.mark.parametrize( - "dataset_compression", [{"methods": ["precision", "subsample"]}] -) -@pytest.mark.parametrize("X", [np.ones((100, 10), dtype=int)]) -@pytest.mark.parametrize("y", [np.random.random((100,))]) -@unittest.mock.patch("autosklearn.automl.reduce_dataset_size_if_too_large") -def test_fit_performs_dataset_compression_without_precision_with_int( - mock_reduce_dataset: unittest.mock.MagicMock, - dataset_compression: Dict, - X: np.ndarray, - y: np.ndarray, -) -> None: - """We can't reduce the precision of ints as we do with floats. Suppose someone - was to pass a column with `max_int64` and `min_int64`, any reduction of bits will - cause this information to be lost and not simply reduce precision as it does with - floats. - - Parameters - ---------- - mock_reduce_dataset: MagicMock - A mock function to check the parameters that were passed in - - dataset_compression: Dict - The dataset_compression arg with "precision" set in it - - X: np.ndarray - An array of ints which we can't reduce precision of - - y: np.ndarray - An array of floats as the regression target - - Expects - ------- - * Should call reduce_dataset_size_if_too_large - * "precision" should have been removed from the "methods" passed to the keyword - argument "operations" of `reduce_dataset_size_if_too_large` - """ - # We just return the data - mock_reduce_dataset.return_value = X, y - - auto = AutoMLRegressor( - time_left_for_this_task=30, # not used but required - per_run_time_limit=5, # not used but required - dataset_compression=dataset_compression, - ) - - # To prevent fitting anything we use `only_return_configuration_space` - auto.fit(X, y, only_return_configuration_space=True) - - assert mock_reduce_dataset.call_count == 1 - - args, kwargs = mock_reduce_dataset.call_args - assert kwargs["operations"] == ["subsample"] - - -@pytest.mark.parametrize("dataset_compression", [True]) -@pytest.mark.parametrize( - "X", - [ - np.empty((10, 10)), - csr_matrix(np.identity(10)), - pytest.param( - np.empty((10, 10)).tolist(), - marks=pytest.mark.xfail(reason="Converted to dataframe by InputValidator"), - ), - pytest.param( - pd.DataFrame(np.empty((10, 10))), - marks=pytest.mark.xfail( - reason="No pandas support yet for dataset compression" - ), - ), - ], -) -@pytest.mark.parametrize( - "y", - [ - np.random.random((10, 1)), - np.random.random((10, 1)).tolist(), - pytest.param( - pd.Series(np.random.random((10,))), - marks=pytest.mark.xfail( - reason="No pandas support yet for dataset compression" - ), - ), - pytest.param( - pd.DataFrame(np.random.random((10, 10))), - marks=pytest.mark.xfail( - reason="No pandas support yet for dataset compression" - ), - ), - ], -) -@unittest.mock.patch("autosklearn.automl.reduce_dataset_size_if_too_large") -def test_fit_performs_dataset_compression( - mock_reduce_dataset: unittest.mock.MagicMock, - dataset_compression: bool, - X: Union[np.ndarray, spmatrix, List, pd.DataFrame], - y: Union[np.ndarray, List, pd.Series, pd.DataFrame], -) -> None: - """ - Parameters - ---------- - mock_reduce_dataset: MagicMock - A mock function to view call count - - dataset_compression: bool - Dataset compression set to True - - X: Union[np.ndarray, spmatrix, List, pd.Dataframe] - Feature to reduce - - y: Union[np.ndarray, List, pd.Series, pd.Dataframe] - Target to reduce (regression values) - - Expects - ------- - * Should call reduce_dataset_size_if_too_large - """ - # We just return the data - mock_reduce_dataset.return_value = X, y - - auto = AutoMLRegressor( - time_left_for_this_task=30, # not used but required - per_run_time_limit=5, # not used but required - dataset_compression=dataset_compression, - ) - - # To prevent fitting anything we use `only_return_configuration_space` - auto.fit(X, y, only_return_configuration_space=True) - - assert mock_reduce_dataset.called diff --git a/test/test_automl/test_construction.py b/test/test_automl/test_construction.py new file mode 100644 index 0000000000..5b15812acd --- /dev/null +++ b/test/test_automl/test_construction.py @@ -0,0 +1,95 @@ +"""Property based Tests + +These test are for checking properties of already fitted models. Any test that does +tests using cases should not modify the state as these models are cached between tests +to reduce training time. +""" +from typing import Any, Dict, Optional, Union + +from autosklearn.automl import AutoML +from autosklearn.util.data import default_dataset_compression_arg +from autosklearn.util.single_thread_client import SingleThreadedClient + +import pytest +from pytest_cases import parametrize + + +@parametrize("disable_evaluator_output", [("hello", "world"), ("model", "other")]) +def test_invalid_disable_eval_output_options(disable_evaluator_output: Any) -> None: + """ + Parameters + ---------- + disable_evaluator_output : Iterable[str] + An iterator of invalid options + + Expects + ------- + * Should raise an error about invalid options + """ + with pytest.raises(ValueError, match="Unknown arg"): + AutoML( + time_left_for_this_task=30, + per_run_time_limit=5, + disable_evaluator_output=disable_evaluator_output, + ) + + +@parametrize( + "dataset_compression, expected", + [ + (False, None), + (True, default_dataset_compression_arg), + ( + {"memory_allocation": 0.2}, + {**default_dataset_compression_arg, **{"memory_allocation": 0.2}}, + ), + ], +) +def test_param_dataset_compression_args( + dataset_compression: Union[bool, Dict], + expected: Optional[Dict], +) -> None: + """ + Parameters + ---------- + dataset_compression: Union[bool, Dict] + The dataset_compression arg used + + expected: Optional[Dict] + The expected internal variable setting + + Expects + ------- + * Setting the compression arg should result in the expected value + * False -> None, No dataset compression + * True -> default, The default settings + * dict -> default updated, The default should be updated with the args used + """ + auto = AutoML( + time_left_for_this_task=30, + per_run_time_limit=5, + dataset_compression=dataset_compression, + ) + assert auto._dataset_compression == expected + + +def test_single_job_and_no_dask_client_sets_correct_multiprocessing_context() -> None: + """ + Expects + ------- + * With n_jobs set to 1 and no dask client, we default to a SingleThreadedClient + with a "fork" _multiprocessing_context + """ + n_jobs = 1 + dask_client = None + + automl = AutoML( + time_left_for_this_task=30, + per_run_time_limit=5, + n_jobs=n_jobs, + dask_client=dask_client, + ) + + assert automl._multiprocessing_context == "fork" + assert automl._n_jobs == 1 + assert isinstance(automl._dask_client, SingleThreadedClient) diff --git a/test/test_automl/test_dataset_compression.py b/test/test_automl/test_dataset_compression.py new file mode 100644 index 0000000000..d50869ebbf --- /dev/null +++ b/test/test_automl/test_dataset_compression.py @@ -0,0 +1,135 @@ +from typing import Any, Callable, Dict + +import numpy as np +import pandas as pd +from scipy.sparse import csr_matrix + +from autosklearn.automl import AutoML +from autosklearn.constants import BINARY_CLASSIFICATION + +from pytest_cases import parametrize +from unittest.mock import patch + +from test.util import skip + + +@parametrize("dataset_compression", [{"methods": ["precision", "subsample"]}]) +def test_fit_performs_dataset_compression_without_precision_when_int( + dataset_compression: Dict, + make_automl: Callable[..., AutoML], +) -> None: + """ + Parameters + ---------- + dataset_compression: Dict + The dataset_compression arg with "precision" set in it + + Fixtures + -------- + make_automl: Callable[..., AutoML] + Makes an automl instance + + + Expects + ------- + * Should call reduce_dataset_size_if_too_large + * "precision" should have been removed from the "methods" passed to the keyword + argument "operations" of `reduce_dataset_size_if_too_large`. + + Note + ---- + * Only done with int's as we can't reduce precision of ints in a meaningful way + """ + X = np.ones((100, 10), dtype=int) + y = np.random.random((100,)) + + auto = make_automl(dataset_compression=dataset_compression) + + with patch( + "autosklearn.automl.reduce_dataset_size_if_too_large", return_value=(X, y) + ) as mck: + # To prevent fitting anything we use `only_return_configuration_space` + auto.fit(X, y, only_return_configuration_space=True, task=BINARY_CLASSIFICATION) + + assert mck.call_count == 1 + + args, kwargs = mck.call_args + assert kwargs["operations"] == ["subsample"] + + +@parametrize( + "X_type", + [ + np.ndarray, + csr_matrix, + skip( + list, + "dataset_compression does not support pandas types yet and list gets" + " converted in InputValidator", + ), + skip(pd.DataFrame, "dataset_compression does not support pandas types yet"), + ], +) +@parametrize( + "y_type", + [ + np.ndarray, + skip(csr_matrix, "See TODO note in `test_fit_performs_dataset_compression`"), + list, + skip(pd.DataFrame, "dataset_compression does not support pandas types yet"), + skip(pd.Series, "dataset_compression does not support pandas types yet"), + ], +) +def test_fit_performs_dataset_compression( + X_type: Any, + y_type: Any, + make_automl: Callable[..., AutoML], + make_data: Callable[..., Any], +) -> None: + """ + Parameters + ---------- + mock_reduce_dataset: MagicMock + A mock function to view call + + X_type: Union[np.ndarray, csr_matrix, list, pd.Dataframe] + Feature to reduce + + y_type: Union[np.ndarray, csr_matrix, list, pd.Series, pd.Dataframe] + Target to reduce (regression values) + + Fixtures + -------- + make_automl: Callable[..., AutoML] + Factory to make automl instance + + make_data: Callable + Factory to make data + + Expects + ------- + * Should call reduce_dataset_size_if_too_large + + # TODO not sure how to keep function behaviour and just use the mock object so + # that we can assert it was called. + # + # * `fit` will convert sparse `y` + # * This gets passed to `reduce_dataset_size_if_too_large` + # * The de-sparsified `y` is required for the datamanager later on + # + # Mocking away the functionality and just returning the X, y we see here will means + # that the datamanager will get the sparse y and crash, hence we manually convert + # here + """ + X, y = make_data(types=(X_type, y_type)) + + auto = make_automl(dataset_compression=True) + + with patch( + "autosklearn.automl.reduce_dataset_size_if_too_large", return_value=(X, y) + ) as mck: + # To prevent fitting anything we use `only_return_configuration_space` + auto.fit(X, y, only_return_configuration_space=True, task=BINARY_CLASSIFICATION) + + assert mck.called + del auto diff --git a/test/test_automl/test_dummy_predictions.py b/test/test_automl/test_dummy_predictions.py new file mode 100644 index 0000000000..9a268d1a2c --- /dev/null +++ b/test/test_automl/test_dummy_predictions.py @@ -0,0 +1,185 @@ +from typing import Callable, Tuple + +from pathlib import Path + +import numpy as np +from smac.tae import StatusType + +from autosklearn.automl import AutoML +from autosklearn.constants import ( + BINARY_CLASSIFICATION, + MULTICLASS_CLASSIFICATION, + REGRESSION, +) +from autosklearn.data.xy_data_manager import XYDataManager +from autosklearn.metrics import Scorer, accuracy, r2 +from autosklearn.util.logging_ import PicklableClientLogger + +import pytest +from pytest_cases import parametrize +from unittest.mock import patch + + +@parametrize( + "dataset, metric, task", + [ + ("breast_cancer", accuracy, BINARY_CLASSIFICATION), + ("wine", accuracy, MULTICLASS_CLASSIFICATION), + ("diabetes", r2, REGRESSION), + ], +) +def test_produces_correct_output( + dataset: str, + task: int, + metric: Scorer, + mock_logger: PicklableClientLogger, + make_automl: Callable[..., AutoML], + make_sklearn_dataset: Callable[..., XYDataManager], +) -> None: + """ + Parameters + ---------- + dataset: str + The name of the dataset + + task : int + The task type of the dataset + + metric: Scorer + Metric to use, required as fit usually determines the metric to use + + Fixtures + -------- + mock_logger: PickleableClientLogger + A mock logger to use + + make_automl : Callable[..., AutoML] + Factory to make an AutoML object + + make_sklearn_dataset : Callable[..., XYDataManager] + Factory to get an sklearn dataset + + Expects + ------- + * There should only be one output created with one dummy predictions + * It should be named "1337_1_0.0" with {seed}_{num_run}_{budget} + * It should produce predictions "predictions_ensemble_1337_1_0.0.npy" + """ + seed = 1337 + automl = make_automl(metric=metric, seed=seed) + automl._logger = mock_logger + + datamanager = make_sklearn_dataset( + dataset, + as_datamanager=True, + task=task, + feat_type="numerical", + ) + automl._backend.save_datamanager(datamanager) + automl._do_dummy_prediction() + + path = Path(automl._backend.get_runs_directory()) + run_paths = list(path.iterdir()) + assert len(run_paths) == 1 + + dummy_run_path = run_paths[0] + assert dummy_run_path.name == f"{seed}_1_0.0" + + predictions_path = dummy_run_path / f"predictions_ensemble_{seed}_1_0.0.npy" + assert predictions_path.exists() + + +def test_runs_with_correct_args( + mock_logger: PicklableClientLogger, + make_sklearn_dataset: Callable[..., Tuple[np.ndarray, ...]], + make_automl: Callable[..., AutoML], +) -> None: + """ + Fixtures + -------- + mock_logger: PickleableClientLogger + A mock logger to use + + make_sklearn_dataset : Callable[..., Tuple[np.ndarray, ...]] + Factory to make dataset + + make_automl : Callable[..., AutoML] + Factory to make automl + + Expects + ------- + * The mock run should be called once with: + * config = 1 (The always given number for the dummy) + * cutoff = `automl._time_for_task` (the fulll time for the task) + """ + dataset = "iris" + task = MULTICLASS_CLASSIFICATION + + automl = make_automl(metric=accuracy) + automl._logger = mock_logger + + datamanager = make_sklearn_dataset( + dataset, + as_datamanager=True, + task=task, + feat_type="numerical", + ) + automl._backend.save_datamanager(datamanager) + + with patch("autosklearn.evaluation.ExecuteTaFuncWithQueue.run") as mck: + mck.return_value = (StatusType.SUCCESS, None, None, {}) + automl._do_dummy_prediction() + + mck.assert_called_once_with(config=1, cutoff=automl._time_for_task) + + +def test_crash_due_to_memory_exception( + mock_logger: PicklableClientLogger, + make_sklearn_dataset: Callable[..., Tuple[np.ndarray, ...]], + make_automl: Callable[..., AutoML], +) -> None: + """ + Fixtures + -------- + mock_logger: PickleableClientLogger + A mock logger to use + + make_sklearn_dataset : Callable[..., Tuple[np.ndarray, ...]] + Factory to make dataset + + make_automl : Callable[..., AutoML] + Factory to make automl + + Expects + ------- + * The dummy prediction should raise when encoutering with StatusType.CRASHED + * The error message should indicate it's a memory issue with `{'exitcode' -6}` + encountered + """ + dataset = "iris" + task = MULTICLASS_CLASSIFICATION + + automl = make_automl(metric=accuracy) + automl._logger = mock_logger + + datamanager = make_sklearn_dataset( + dataset, + as_datamanager=True, + task=task, + feat_type="numerical", + ) + + automl._backend.save_datamanager(datamanager) + + with patch("autosklearn.evaluation.ExecuteTaFuncWithQueue.run") as mck: + mck.return_value = (StatusType.CRASHED, None, None, {"exitcode": -6}) + msg = "The error suggests that the provided memory limits are too tight." + + with pytest.raises(ValueError, match=msg): + automl._do_dummy_prediction() + + +def test_raises_if_no_metric_set(make_automl: Callable[..., AutoML]) -> None: + automl = make_automl() + with pytest.raises(ValueError, match="Metric was not set"): + automl._do_dummy_prediction() diff --git a/test/test_automl/test_fit.py b/test/test_automl/test_fit.py new file mode 100644 index 0000000000..2defa2518b --- /dev/null +++ b/test/test_automl/test_fit.py @@ -0,0 +1,82 @@ +from typing import Any, Callable, Dict, Optional, Tuple, Union + +import numpy as np +from dask.distributed import Client +from smac.facade.roar_facade import ROAR +from smac.scenario.scenario import Scenario + +from autosklearn.automl import AutoML +from autosklearn.constants import MULTICLASS_CLASSIFICATION + +from pytest_cases import parametrize + + +@parametrize("dataset, task, bounds", [("iris", MULTICLASS_CLASSIFICATION, (0.8, 1.0))]) +def test_fit_roar( + dataset: str, + task: int, + bounds: Tuple[float, float], + dask_client_single_worker: Client, + make_automl: Callable[..., AutoML], + make_sklearn_dataset: Callable[..., Tuple[np.ndarray, ...]], +) -> None: + """ + Parameters + ---------- + dataset : str + The name of the dataset + + task : int + The task type of the dataset + + bounds : Tuple[float, float] + The bounds the final score should be in, (lowest, upper) + + Fixtures + -------- + make_automl : Callable[..., AutoML] + Factory for making an AutoML instance + + make_sklearn_dataset : Callable[..., Tuple[np.ndarray, ...]] + Factory for getting a dataset + + Expects + ------- + * Should fit without a problem using a different smac object + """ + + def get_roar_object_callback( + scenario_dict: Dict, + seed: Optional[Union[int, np.random.RandomState]], + ta: Callable, + ta_kwargs: Dict, + dask_client: Client, + n_jobs: int, + **kwargs: Any, + ) -> ROAR: + """Random online adaptive racing. + + http://ml.informatik.uni-freiburg.de/papers/11-LION5-SMAC.pdf + """ + scenario = Scenario(scenario_dict) + return ROAR( + run_id=seed, + scenario=scenario, + rng=seed, + tae_runner=ta, + tae_runner_kwargs=ta_kwargs, + dask_client=dask_client, + n_jobs=n_jobs, + ) + + X_train, Y_train, X_test, Y_test = make_sklearn_dataset(dataset) + automl = make_automl( + initial_configurations_via_metalearning=0, + get_smac_object_callback=get_roar_object_callback, + dask_client=dask_client_single_worker, + ) + + automl.fit(X_train, Y_train, task=task) + + score = automl.score(X_test, Y_test) + assert score > 0.8 diff --git a/test/test_automl/test_fit_ensemble.py b/test/test_automl/test_fit_ensemble.py new file mode 100644 index 0000000000..14bc36192a --- /dev/null +++ b/test/test_automl/test_fit_ensemble.py @@ -0,0 +1,35 @@ +import numpy as np + +from autosklearn.automl import AutoML + +import pytest +from pytest_cases import filters as ft +from pytest_cases import parametrize, parametrize_with_cases + +import test.test_automl.cases as cases + + +@parametrize("ensemble_size", [-10, -1, 0]) +@parametrize_with_cases("automl", cases=cases, filter=~ft.has_tag("fitted")) +def test_non_positive_ensemble_size_raises( + tmp_dir: str, + automl: AutoML, + ensemble_size: int, +) -> None: + """ + Parameters + ---------- + automl: AutoML + The AutoML object to test + + ensemble_size : int + The ensemble size to use + + Expects + ------- + * Can't fit ensemble with non-positive ensemble size + """ + dummy_data = np.array([1, 1, 1]) + + with pytest.raises(ValueError): + automl.fit_ensemble(dummy_data, ensemble_size=ensemble_size) diff --git a/test/test_automl/test_fit_pipeline.py b/test/test_automl/test_fit_pipeline.py new file mode 100644 index 0000000000..137b57a5c3 --- /dev/null +++ b/test/test_automl/test_fit_pipeline.py @@ -0,0 +1 @@ +"""TODO""" diff --git a/test/test_automl/test_model_predict.py b/test/test_automl/test_model_predict.py new file mode 100644 index 0000000000..a301d1a9a5 --- /dev/null +++ b/test/test_automl/test_model_predict.py @@ -0,0 +1,151 @@ +from typing import Callable, Dict, Tuple + +import warnings + +import numpy as np +from sklearn.ensemble import VotingClassifier, VotingRegressor + +from autosklearn.automl import _model_predict +from autosklearn.constants import ( + BINARY_CLASSIFICATION, + MULTICLASS_CLASSIFICATION, + MULTILABEL_CLASSIFICATION, + MULTIOUTPUT_REGRESSION, + REGRESSION, +) +from autosklearn.util.logging_ import PicklableClientLogger + +from pytest_cases import parametrize + + +class WarningModel: + def predict(self, X: np.ndarray) -> np.ndarray: + warnings.warn("shout") + return X + + +@parametrize( + "dataspec, expected_shape", + [ + ({"kind": BINARY_CLASSIFICATION, "dims": (100, 5)}, (100, 2)), + ({"kind": MULTICLASS_CLASSIFICATION, "dims": (100, 5), "classes": 3}, (100, 3)), + ( + { + "kind": MULTILABEL_CLASSIFICATION, + "dims": (100, 5), + "classes": [[0, 0], [0, 1], [1, 0], [1, 1]], + }, + (100, 2), # TODO seems wrong + ), + ], +) +def test_classifier_output_shape( + dataspec: Dict, + expected_shape: Tuple[int, ...], + make_voting_classifier: Callable[..., VotingClassifier], + make_data: Callable[..., Tuple[np.ndarray, np.ndarray]], +) -> None: + """ + Parameters + ---------- + dataspec : Dict + The spec to make data of + + expected_shape : Tuple[int, ...] + The expected shape of the output of _model_predict + + Fixtures + -------- + make_voting_classifier : Callable[..., VotingClassifier] + Factory to make a voting classifier which _model_predict expects + + make_data : Callable[..., Tuple[np.ndarray, np.ndarray]] + Factory to make data according to a spec + + Expects + ------- + * The output shape after predicting should be the expected shape + + Note + ---- + * The output shape for MULTILABEL_CLASSIFICATION seems wrong according to + + """ + task = dataspec["kind"] + X, y = make_data(**dataspec) + + voter = make_voting_classifier(X=X, y=y) + + output = _model_predict(voter, X, task=task) + assert output.shape == expected_shape + + +@parametrize( + "dataspec, expected_shape", + [ + ({"kind": REGRESSION, "dims": (100, 5)}, (100,)), + ({"kind": MULTIOUTPUT_REGRESSION, "dims": (100, 5), "targets": 3}, (100, 3)), + ], +) +def test_regressor_output_shape( + dataspec: Dict, + expected_shape: Tuple[int, ...], + make_voting_regressor: Callable[..., VotingRegressor], + make_data: Callable[..., Tuple[np.ndarray, np.ndarray]], +) -> None: + """ + Parameters + ---------- + dataspec : Dict + The spec to make data of + + expected_shape : Tuple[int, ...] + The expected shape of the output of _model_predict + + Fixtures + -------- + make_voting_regressor: Callable[..., VotingRegressor] + Factory to make a voting classifier which _model_predict expects + + make_data : Callable[..., Tuple[np.ndarray, np.ndarray]] + Factory to make data according to a spec + """ + task = dataspec["kind"] + X, y = make_data(**dataspec) + + voter = make_voting_regressor(X=X, y=y) + + output = _model_predict(voter, X, task=task) + assert output.shape == expected_shape + + +def test_outputs_warnings_to_logs( + mock_logger: PicklableClientLogger, +) -> None: + """ + Fixtures + -------- + mock_logger : PicklableClientLogger + A mock logger that can be queried for call counts + + Expects + ------- + * Any warning emitted by a model should be redirected to the logger + """ + _model_predict( + model=WarningModel(), X=np.eye(5), task=REGRESSION, logger=mock_logger + ) + + assert mock_logger.warning.call_count == 1 # type: ignore + + +def test_outputs_to_stdout_if_no_logger() -> None: + """ + Expects + ------- + * With no logger, any warning emitted by a model goes to standard out + """ + with warnings.catch_warnings(record=True) as w: + _model_predict(model=WarningModel(), X=np.eye(5), task=REGRESSION, logger=None) + + assert len(w) == 1, "One warning sould have been emmited" diff --git a/test/test_automl/test_outputs.py b/test/test_automl/test_outputs.py new file mode 100644 index 0000000000..458347c145 --- /dev/null +++ b/test/test_automl/test_outputs.py @@ -0,0 +1,107 @@ +from pathlib import Path + +from autosklearn.automl import AutoML + +from pytest import mark +from pytest_cases import parametrize_with_cases +from pytest_cases.filters import has_tag + +import test.test_automl.cases as cases +from test.conftest import DEFAULT_SEED + +# Some filters +has_ensemble = has_tag("fitted") & ~has_tag("no_ensemble") +no_ensemble = has_tag("fitted") & has_tag("no_ensemble") + + +@mark.todo +def test_datamanager_stored_contents() -> None: + ... + + +@parametrize_with_cases("automl", cases=cases, has_tag="fitted") +def test_paths_created(automl: AutoML) -> None: + """ + Parameters + ---------- + automl : AutoML + A previously fitted automl + + Expects + ------- + * The given paths should exist after the automl has been run and fitted + """ + assert automl._backend is not None + + partial = Path(automl._backend.internals_directory) + expected = [ + partial / fixture + for fixture in ( + "true_targets_ensemble.npy", + f"start_time_{DEFAULT_SEED}", + "datamanager.pkl", + "runs", + ) + ] + + for path in expected: + assert path.exists() + + +@parametrize_with_cases("automl", cases=cases, filter=has_ensemble) +def test_paths_created_with_ensemble(automl: AutoML) -> None: + """ + Parameters + ---------- + automl : AutoML + A previously fitted automl + + Expects + ------- + * The given paths for an automl with an ensemble should include paths + specific to ensemble building + """ + assert automl._backend is not None + + partial = Path(automl._backend.internals_directory) + expected = [ + partial / fixture + for fixture in ( + "ensemble_read_preds.pkl", + "ensemble_read_losses.pkl", + "ensembles", + "ensemble_history.json", + ) + ] + + for path in expected: + assert path.exists() + + +@parametrize_with_cases("automl", cases=cases, has_tag="fitted") +def test_at_least_one_model_and_predictions(automl: AutoML) -> None: + assert automl._backend is not None + runs_dir = Path(automl._backend.get_runs_directory()) + + runs = list(runs_dir.iterdir()) + assert len(runs) > 0 + + at_least_one = False + for run in runs: + prediction_files = run.glob("predictions_ensemble*.npy") + model_files = run.glob("*.*.model") + + if any(prediction_files): + at_least_one = True + assert any(model_files), "Run produced prediction but no model" + + assert at_least_one, "No runs produced predictions" + + +@parametrize_with_cases("automl", cases=cases, filter=has_ensemble) +def test_at_least_one_ensemble(automl: AutoML) -> None: + assert automl._backend is not None + ens_dir = Path(automl._backend.get_ensemble_dir()) + + # TODO make more generic + assert len(list(ens_dir.glob("*.ensemble"))) > 0 diff --git a/test/test_automl/test_performance.py b/test/test_automl/test_performance.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/test_automl/test_performance_over_time.py b/test/test_automl/test_performance_over_time.py new file mode 100644 index 0000000000..d5cc327a41 --- /dev/null +++ b/test/test_automl/test_performance_over_time.py @@ -0,0 +1,38 @@ +from autosklearn.automl import AutoML + +from pytest_cases import parametrize_with_cases +from pytest_cases.filters import has_tag + +import test.test_automl.cases as cases + + +@parametrize_with_cases( + "automl", + cases=cases, + filter=has_tag("fitted") & ~has_tag("no_ensemble"), +) +def test_performance_over_time_with_ensemble(automl: AutoML) -> None: + """ + Parameters + ---------- + automl: AutoMLClassifier + The fitted automl instance with an ensemble + + Expects + ------- + * Performance over time should include only the given columns + * The performance over time should have at least one entry that isn't NaN + * The timestamps should be monotonic + """ + expected_performance_columns = { + "single_best_train_score", + "single_best_optimization_score", + "ensemble_optimization_score", + "Timestamp", + } + columns = automl.performance_over_time_.columns + assert set(columns) == set(expected_performance_columns) + + perf_over_time = automl.performance_over_time_ + assert len(perf_over_time.drop(columns="Timestamp").dropna()) != 0 + assert perf_over_time["Timestamp"].is_monotonic diff --git a/test/test_automl/test_post_fit.py b/test/test_automl/test_post_fit.py new file mode 100644 index 0000000000..674a452d02 --- /dev/null +++ b/test/test_automl/test_post_fit.py @@ -0,0 +1,62 @@ +from autosklearn.automl import AutoML + +from pytest_cases import parametrize_with_cases + +import test.test_automl.cases as cases + + +@parametrize_with_cases("automl", cases=cases, has_tag=["fitted", "holdout"]) +def test_holdout_loaded_models(automl: AutoML) -> None: + """ + Parameters + ---------- + automl : AutoML + The fitted automl object to test + + Expects + ------- + * The ensemble should not be empty + * The models_ should contain the identifiers of what's in the ensemble + * The cv_models_ attr should remain None + """ + assert automl.ensemble_ is not None + assert set(automl.models_.keys()) == set(automl.ensemble_.identifiers_) + assert automl.cv_models_ is None + + +@parametrize_with_cases("automl", cases=cases, has_tag=["fitted", "cv"]) +def test_cv_loaded_models(automl: AutoML) -> None: + """ + Parameters + ---------- + automl : AutoML + The fitted automl object to test + + Expects + ------- + * The ensemble should not be empty + * The models_ should contain the identifiers of what's in the ensemble + * The cv_models_ should contain the identifiers of what's in the ensemble + """ + assert automl.ensemble_ is not None + assert set(automl.models_.keys()) == set(automl.ensemble_.identifiers_) + assert set(automl.cv_models_.keys()) == set(automl.ensemble_.identifiers_) + + +@parametrize_with_cases("automl", cases=cases, has_tag=["fitted", "no_ensemble"]) +def test_no_ensemble(automl: AutoML) -> None: + """ + Parameters + ---------- + automl : AutoML + A fitted automl object with ensemble size specified as 0 + + Expects + ------- + * The ensemble should remain None + * The models_ should be empty + * The cv_models_ should remain None + """ + assert automl.ensemble_ is None + assert automl.models_ == [] + assert automl.cv_models_ is None diff --git a/test/test_automl/test_predict.py b/test/test_automl/test_predict.py new file mode 100644 index 0000000000..137b57a5c3 --- /dev/null +++ b/test/test_automl/test_predict.py @@ -0,0 +1 @@ +"""TODO""" diff --git a/test/test_automl/test_refit.py b/test/test_automl/test_refit.py new file mode 100644 index 0000000000..341486ab13 --- /dev/null +++ b/test/test_automl/test_refit.py @@ -0,0 +1,61 @@ +from typing import Callable, Union + +from itertools import repeat + +import numpy as np + +from autosklearn.automl import AutoML +from autosklearn.data.validation import InputValidator + +from pytest_cases import parametrize +from unittest.mock import Mock + + +@parametrize("budget_type", [None, "iterations"]) +def test_shuffle_on_fail( + budget_type: Union[None, str], + make_automl: Callable[..., AutoML], +) -> None: + """ + Parameters + ---------- + budget_type : Union[None, str] + The budget type to use + + Fixtures + -------- + make_automl : Callable[..., AutoML] + Factory to make an AutoML instance + + Expects + ------- + * The automl should not be able to fit before `refit` + * The model should be attempted to fitted `n_fails` times before successing once + after + * The automl should be able to fit after `refit` + """ + n_fails = 3 + failing_model = Mock() + failing_model.fit.side_effect = [ValueError()] * n_fails + [None] # type: ignore + failing_model.estimator_supports_iterative_fit.side_effect = repeat(False) + + ensemble_mock = Mock() + ensemble_mock.get_selected_model_identifiers.return_value = [(1, 1, 50.0)] + + X = np.ones((3, 2)) + y = np.ones((3,)) + + input_validator = InputValidator() + input_validator.fit(X, y) + + auto = make_automl() + auto.ensemble_ = ensemble_mock # type: ignore + auto.models_ = {(1, 1, 50.0): failing_model} + auto._budget_type = budget_type + auto.InputValidator = input_validator + + assert not auto._can_predict + auto.refit(X, y) + + assert failing_model.fit.call_count == n_fails + 1 + assert auto._can_predict diff --git a/test/test_automl/test_show_models.py b/test/test_automl/test_show_models.py new file mode 100644 index 0000000000..72b2e4f8d6 --- /dev/null +++ b/test/test_automl/test_show_models.py @@ -0,0 +1,20 @@ +from autosklearn.automl import AutoML + +from pytest_cases import parametrize_with_cases + +import test.test_automl.cases as cases + + +@parametrize_with_cases("automl", cases=cases, has_tag=["fitted", "no_ensemble"]) +def test_no_ensemble_produces_empty_show_models(automl: AutoML) -> None: + """ + Parameters + ---------- + automl : AutoML + The automl object with no ensemble size to test + + Expects + ------- + * Show models should return an empty dict + """ + assert automl.show_models() == {} diff --git a/test/test_automl/test_sklearn_compliance.py b/test/test_automl/test_sklearn_compliance.py new file mode 100644 index 0000000000..ce747e1bb8 --- /dev/null +++ b/test/test_automl/test_sklearn_compliance.py @@ -0,0 +1,76 @@ +""" +Note +---- +This is far from complete at the moment +""" +from typing import List, Union + +from sklearn.exceptions import NotFittedError + +from autosklearn.automl import AutoML + +import pytest +from pytest_cases import parametrize, parametrize_with_cases +from pytest_cases.filters import has_tag + +import test.test_automl.cases as cases + + +@pytest.mark.xfail( + reason="__sklearn_is_fitted__ only supported from sklearn 1.0 onwards" +) +@parametrize_with_cases("automl", cases=cases, filter=~has_tag("fitted")) +@parametrize( + "attr, argnames", + [ + ("refit", ["X", "y"]), + ("predict", ["X"]), + ("fit_ensemble", ["y"]), + ("score", ["X", "y"]), + ("performance_over_time_", None), + ("cv_results_", None), + ("sprint_statistics", []), + ("get_models_with_weights", []), + ("show_models", []), + ], +) +def test_attrs_raise_if_not_fitted( + automl: AutoML, + attr: str, + argnames: Union[List[str], None], +) -> None: + """ + Parameters + ---------- + automl : AutoML + An unfitted automl instance + + attr: str + The attribute to test + + argnames: Union[List[str], None] + The arguments of the the method + * ["arg1", "arg2"] for method with args + * [] for method with no args + * None for property + + Expects + ------- + * Should raise a NotFittedError + + Note + ---- + * This also ensures any validation should be done after the fit check as + NotFittedError should be raised + """ + with pytest.raises(NotFittedError): + + if argnames is None: + property = getattr(automl, attr) # noqa + else: + method = getattr(automl, attr) + args = {name: None for name in argnames} + if len(args) > 0: + method(args) + else: + method() diff --git a/test/test_data/test_feature_validator.py b/test/test_data/test_feature_validator.py index a06b91b0f3..3637b91e62 100644 --- a/test/test_data/test_feature_validator.py +++ b/test/test_data/test_feature_validator.py @@ -1,6 +1,5 @@ import numpy as np import pandas as pd -import pytest import sklearn.datasets import sklearn.model_selection from pandas.api.types import is_categorical_dtype, is_numeric_dtype, is_string_dtype @@ -8,6 +7,8 @@ from autosklearn.data.feature_validator import FeatureValidator +import pytest + # Fixtures to be used in this class. By default all elements have 100 datapoints @pytest.fixture diff --git a/test/test_data/test_target_validator.py b/test/test_data/test_target_validator.py index e57f464c72..7c08dba20a 100644 --- a/test/test_data/test_target_validator.py +++ b/test/test_data/test_target_validator.py @@ -1,6 +1,5 @@ import numpy as np import pandas as pd -import pytest import sklearn.datasets import sklearn.model_selection from pandas.api.types import is_bool_dtype, is_numeric_dtype @@ -9,6 +8,8 @@ from autosklearn.data.target_validator import TargetValidator +import pytest + # Fixtures to be used in this class. By default all elements have 100 datapoints @pytest.fixture diff --git a/test/test_data/test_validation.py b/test/test_data/test_validation.py index 4d09c65075..251a8405f2 100644 --- a/test/test_data/test_validation.py +++ b/test/test_data/test_validation.py @@ -1,12 +1,13 @@ import numpy as np import pandas as pd -import pytest import sklearn.datasets import sklearn.model_selection from scipy import sparse from autosklearn.data.validation import InputValidator +import pytest + @pytest.mark.parametrize("openmlid", [2, 40975, 40984]) @pytest.mark.parametrize("as_frame", [True, False]) diff --git a/test/test_ensemble_builder/ensemble_utils.py b/test/test_ensemble_builder/ensemble_utils.py index fa0f22e9e7..7a3cd7f252 100644 --- a/test/test_ensemble_builder/ensemble_utils.py +++ b/test/test_ensemble_builder/ensemble_utils.py @@ -1,7 +1,5 @@ import os import shutil -import unittest -import unittest.mock import numpy as np @@ -11,6 +9,9 @@ from autosklearn.ensemble_builder import EnsembleBuilder from autosklearn.metrics import make_scorer +import unittest +import unittest.mock + def scorer_function(a, b): return 0.9 diff --git a/test/test_ensemble_builder/test_ensemble.py b/test/test_ensemble_builder/test_ensemble.py index 3533da37cd..469f617fb0 100644 --- a/test/test_ensemble_builder/test_ensemble.py +++ b/test/test_ensemble_builder/test_ensemble.py @@ -3,12 +3,10 @@ import shutil import sys import time -import unittest.mock import dask.distributed import numpy as np import pandas as pd -import pytest from smac.runhistory.runhistory import RunHistory, RunKey, RunValue from autosklearn.constants import BINARY_CLASSIFICATION, MULTILABEL_CLASSIFICATION @@ -22,6 +20,9 @@ from autosklearn.ensembles.singlebest_ensemble import SingleBest from autosklearn.metrics import accuracy, log_loss, roc_auc +import pytest +import unittest.mock + this_directory = os.path.dirname(__file__) sys.path.append(this_directory) from ensemble_utils import ( # noqa (E402: module level import not at top of file) diff --git a/test/test_ensemble_builder/test_ensemble_selection.py b/test/test_ensemble_builder/test_ensemble_selection.py index 44e00229fb..07c972c59f 100644 --- a/test/test_ensemble_builder/test_ensemble_selection.py +++ b/test/test_ensemble_builder/test_ensemble_selection.py @@ -1,10 +1,11 @@ import numpy as np -import pytest from autosklearn.constants import BINARY_CLASSIFICATION, REGRESSION from autosklearn.ensembles.ensemble_selection import EnsembleSelection from autosklearn.metrics import accuracy, root_mean_squared_error +import pytest + def testEnsembleSelection(): """ diff --git a/test/test_estimators/__init__.py b/test/test_estimators/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/test_estimators/cases.py b/test/test_estimators/cases.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/test_automl/test_estimators.py b/test/test_estimators/test_estimators.py similarity index 97% rename from test/test_automl/test_estimators.py rename to test/test_estimators/test_estimators.py index ac60e51472..cd4b0922de 100644 --- a/test/test_automl/test_estimators.py +++ b/test/test_estimators/test_estimators.py @@ -8,16 +8,12 @@ import os import pickle import re -import sys import tempfile -import unittest -import unittest.mock import joblib import numpy as np import numpy.ma as npma import pandas as pd -import pytest import sklearn import sklearn.datasets import sklearn.dummy @@ -42,8 +38,11 @@ from autosklearn.metrics import accuracy, f1_macro, mean_squared_error, r2 from autosklearn.smbo import get_smac_object -sys.path.append(os.path.dirname(__file__)) -from automl_utils import ( # noqa (E402: module level import not at top of file) +import pytest +import unittest +import unittest.mock + +from test.test_automl.automl_utils import ( count_succeses, include_single_scores, includes_all_scores, @@ -54,7 +53,6 @@ def test_fit_n_jobs(tmp_dir): - X_train, Y_train, X_test, Y_test = putil.get_dataset("breast_cancer") # test parallel Classifier to predict classes, not only indices @@ -77,7 +75,7 @@ def __call__(self, *args, **kwargs): delete_tmp_folder_after_terminate=False, time_left_for_this_task=30, per_run_time_limit=5, - tmp_folder=tmp_dir, + tmp_folder=os.path.join(tmp_dir, "backend"), seed=1, initial_configurations_via_metalearning=0, ensemble_size=5, @@ -147,18 +145,18 @@ def test_feat_type_wrong_arguments(): X = np.zeros((100, 100)) y = np.zeros((100,)) - cls = AutoSklearnClassifier(ensemble_size=0) + cls = AutoSklearnClassifier() expected_msg = r".*feat_type does not have same number of " "variables as X has features. 1 vs 100.*" with pytest.raises(ValueError, match=expected_msg): cls.fit(X=X, y=y, feat_type=[True]) - cls = AutoSklearnClassifier(ensemble_size=0) + cls = AutoSklearnClassifier() expected_msg = r".*feat_type must only contain strings.*" with pytest.raises(ValueError, match=expected_msg): cls.fit(X=X, y=y, feat_type=[True] * 100) - cls = AutoSklearnClassifier(ensemble_size=0) + cls = AutoSklearnClassifier() expected_msg = r".*Only `Categorical`, `Numerical` and `String` are" "valid feature types, you passed `Car`.*" with pytest.raises(ValueError, match=expected_msg): @@ -206,7 +204,7 @@ def test_type_of_target(mock_estimator): ] ) - cls = AutoSklearnClassifier(ensemble_size=0) + cls = AutoSklearnClassifier() cls.automl_ = unittest.mock.Mock() cls.automl_.InputValidator = unittest.mock.Mock() cls.automl_.InputValidator.target_validator = unittest.mock.Mock() @@ -248,7 +246,7 @@ def test_type_of_target(mock_estimator): ) # Test that regressor raises error for illegal target types. - reg = AutoSklearnRegressor(ensemble_size=0) + reg = AutoSklearnRegressor() # Illegal target types for regression: multilabel-indicator # multiclass-multioutput expected_msg = r".*Regression with data of type" @@ -300,10 +298,10 @@ def test_performance_over_time_no_ensemble(tmp_dir): cls = AutoSklearnClassifier( time_left_for_this_task=30, per_run_time_limit=5, - tmp_folder=tmp_dir, + tmp_folder=os.path.join(tmp_dir, "backend"), seed=1, - initial_configurations_via_metalearning=0, ensemble_size=0, + initial_configurations_via_metalearning=0, ) cls.fit(X_train, Y_train, X_test, Y_test) @@ -321,10 +319,9 @@ def test_cv_results(tmp_dir): cls = AutoSklearnClassifier( time_left_for_this_task=30, per_run_time_limit=5, - tmp_folder=tmp_dir, + tmp_folder=os.path.join(tmp_dir, "backend"), seed=1, initial_configurations_via_metalearning=0, - ensemble_size=0, scoring_functions=[autosklearn.metrics.precision, autosklearn.metrics.roc_auc], ) @@ -416,7 +413,10 @@ def test_leaderboard( X_train, Y_train, _, _ = putil.get_dataset(dataset_name) model = estimator_type( - time_left_for_this_task=30, per_run_time_limit=5, tmp_folder=tmp_dir, seed=1 + time_left_for_this_task=30, + per_run_time_limit=5, + tmp_folder=os.path.join(tmp_dir, "backend"), + seed=1, ) model.fit(X_train, Y_train) @@ -552,7 +552,7 @@ def test_show_models_with_holdout( automl = estimator( time_left_for_this_task=60, per_run_time_limit=5, - tmp_folder=tmp_dir, + tmp_folder=os.path.join(tmp_dir, "backend"), resampling_strategy=resampling_strategy, dask_client=dask_client, ) @@ -627,7 +627,7 @@ def test_show_models_with_cv( automl = estimator( time_left_for_this_task=120, per_run_time_limit=5, - tmp_folder=tmp_dir, + tmp_folder=os.path.join(tmp_dir, "backend"), resampling_strategy=resampling_strategy, dask_client=dask_client, ) @@ -684,7 +684,7 @@ def test_show_models_with_cv( @unittest.mock.patch("autosklearn.estimators.AutoSklearnEstimator.build_automl") def test_fit_n_jobs_negative(build_automl_patch): n_cores = cpu_count() - cls = AutoSklearnEstimator(n_jobs=-1, ensemble_size=0) + cls = AutoSklearnEstimator(n_jobs=-1) cls.fit() assert cls._n_jobs == n_cores @@ -764,7 +764,7 @@ def test_can_pickle_classifier(tmp_dir, dask_client): time_left_for_this_task=30, delete_tmp_folder_after_terminate=False, per_run_time_limit=5, - tmp_folder=tmp_dir, + tmp_folder=os.path.join(tmp_dir, "backend"), dask_client=dask_client, ) @@ -810,7 +810,7 @@ def test_multilabel(tmp_dir, dask_client): automl = AutoSklearnClassifier( time_left_for_this_task=30, per_run_time_limit=5, - tmp_folder=tmp_dir, + tmp_folder=os.path.join(tmp_dir, "backend"), dask_client=dask_client, ) @@ -836,7 +836,7 @@ def test_binary(tmp_dir, dask_client): time_left_for_this_task=40, delete_tmp_folder_after_terminate=False, per_run_time_limit=10, - tmp_folder=tmp_dir, + tmp_folder=os.path.join(tmp_dir, "backend"), dask_client=dask_client, ) @@ -878,7 +878,7 @@ def test_classification_pandas_support(tmp_dir, dask_client): exclude={"classifier": ["libsvm_svc"]}, dask_client=dask_client, seed=5, - tmp_folder=tmp_dir, + tmp_folder=os.path.join(tmp_dir, "backend"), ) automl.fit(X, y) @@ -905,7 +905,7 @@ def test_regression(tmp_dir, dask_client): automl = AutoSklearnRegressor( time_left_for_this_task=30, per_run_time_limit=5, - tmp_folder=tmp_dir, + tmp_folder=os.path.join(tmp_dir, "backend"), dask_client=dask_client, ) @@ -938,7 +938,7 @@ def test_cv_regression(tmp_dir, dask_client): time_left_for_this_task=60, per_run_time_limit=10, resampling_strategy="cv", - tmp_folder=tmp_dir, + tmp_folder=os.path.join(tmp_dir, "backend"), dask_client=dask_client, ) @@ -967,7 +967,7 @@ def test_regression_pandas_support(tmp_dir, dask_client): time_left_for_this_task=40, per_run_time_limit=5, dask_client=dask_client, - tmp_folder=tmp_dir, + tmp_folder=os.path.join(tmp_dir, "backend"), ) # Make sure we error out because y is not encoded @@ -987,8 +987,7 @@ def test_regression_pandas_support(tmp_dir, dask_client): def test_autosklearn_classification_methods_returns_self(dask_client): - """ - Currently this method only tests that the methods of AutoSklearnClassifier + """Currently this method only tests that the methods of AutoSklearnClassifier is able to fit using fit(), fit_ensemble() and refit() """ X_train, y_train, X_test, y_test = putil.get_dataset("iris") @@ -996,7 +995,6 @@ def test_autosklearn_classification_methods_returns_self(dask_client): time_left_for_this_task=60, delete_tmp_folder_after_terminate=False, per_run_time_limit=10, - ensemble_size=0, dask_client=dask_client, exclude={"feature_preprocessor": ["fast_ica"]}, ) @@ -1022,7 +1020,6 @@ def test_autosklearn_regression_methods_returns_self(dask_client): delete_tmp_folder_after_terminate=False, per_run_time_limit=5, dask_client=dask_client, - ensemble_size=0, ) automl_fitted = automl.fit(X_train, y_train) @@ -1039,7 +1036,6 @@ def test_autosklearn2_classification_methods_returns_self(dask_client): X_train, y_train, X_test, y_test = putil.get_dataset("iris") automl = AutoSklearn2Classifier( time_left_for_this_task=60, - ensemble_size=0, delete_tmp_folder_after_terminate=False, dask_client=dask_client, ) @@ -1069,7 +1065,6 @@ def test_autosklearn2_classification_methods_returns_self_sparse(dask_client): ) automl = AutoSklearn2Classifier( time_left_for_this_task=60, - ensemble_size=0, delete_tmp_folder_after_terminate=False, dask_client=dask_client, ) @@ -1201,7 +1196,6 @@ def test_fit_pipeline(dask_client, task_type, resampling_strategy, disable_file_ # Time left for task plays no role # only per run time limit per_run_time_limit=30, - ensemble_size=0, dask_client=dask_client, include=include, seed=seed, @@ -1283,7 +1277,6 @@ def test_fit_pipeline(dask_client, task_type, resampling_strategy, disable_file_ def test_pass_categorical_and_numeric_columns_to_pipeline( dask_client, data_type, include_categorical ): - # Prepare the training data X, y = sklearn.datasets.make_classification(random_state=0) X = cast(np.ndarray, X) @@ -1319,7 +1312,6 @@ def test_pass_categorical_and_numeric_columns_to_pipeline( delete_tmp_folder_after_terminate=False, time_left_for_this_task=120, per_run_time_limit=30, - ensemble_size=0, seed=0, dask_client=dask_client, include={"classifier": ["random_forest"]}, @@ -1384,7 +1376,6 @@ def test_autosklearn_anneal(as_frame): X, y = sklearn.datasets.fetch_openml(data_id=2, return_X_y=True, as_frame=as_frame) automl = AutoSklearnClassifier( time_left_for_this_task=60, - ensemble_size=0, delete_tmp_folder_after_terminate=False, initial_configurations_via_metalearning=0, smac_scenario_args={"runcount_limit": 6}, diff --git a/test/test_evaluation/evaluation_util.py b/test/test_evaluation/evaluation_util.py index d8bf017c35..62623a50ba 100644 --- a/test/test_evaluation/evaluation_util.py +++ b/test/test_evaluation/evaluation_util.py @@ -1,7 +1,6 @@ import functools import tempfile import traceback -import unittest import numpy as np import sklearn.datasets @@ -34,6 +33,8 @@ from autosklearn.pipeline.util import get_dataset from autosklearn.util.data import convert_to_bin +import unittest + SCORER_LIST = [ accuracy, balanced_accuracy, diff --git a/test/test_evaluation/test_abstract_evaluator.py b/test/test_evaluation/test_abstract_evaluator.py index c668a82ffd..7f88383bcd 100644 --- a/test/test_evaluation/test_abstract_evaluator.py +++ b/test/test_evaluation/test_abstract_evaluator.py @@ -4,8 +4,6 @@ import shutil import sys import tempfile -import unittest -import unittest.mock import numpy as np import sklearn.dummy @@ -16,6 +14,9 @@ from autosklearn.metrics import accuracy from autosklearn.pipeline.components.base import _addons +import unittest +import unittest.mock + this_directory = os.path.dirname(__file__) sys.path.append(this_directory) from evaluation_util import get_multiclass_classification_datamanager # noqa E402 diff --git a/test/test_evaluation/test_custom_splitters.py b/test/test_evaluation/test_custom_splitters.py index 64f9dc2f18..96670923dd 100644 --- a/test/test_evaluation/test_custom_splitters.py +++ b/test/test_evaluation/test_custom_splitters.py @@ -1,5 +1,4 @@ import numpy as np -import pytest from autosklearn.constants import ( BINARY_CLASSIFICATION, @@ -8,6 +7,8 @@ ) from autosklearn.evaluation.splitter import CustomStratifiedShuffleSplit +import pytest + @pytest.mark.parametrize( "task, X, y", diff --git a/test/test_evaluation/test_dummy_pipelines.py b/test/test_evaluation/test_dummy_pipelines.py index 3d5f1d0f59..8d1005e178 100644 --- a/test/test_evaluation/test_dummy_pipelines.py +++ b/test/test_evaluation/test_dummy_pipelines.py @@ -1,5 +1,4 @@ import numpy as np -import pytest from sklearn.base import clone from sklearn.datasets import make_classification, make_regression from sklearn.utils.validation import check_is_fitted @@ -9,9 +8,11 @@ MyDummyRegressor, ) +import pytest + @pytest.mark.parametrize("task_type", ["classification", "regression"]) -def test_dummy_pipeline(task_type): +def test_dummy_pipeline(task_type: str) -> None: if task_type == "classification": estimator_class = MyDummyClassifier data_maker = make_classification diff --git a/test/test_evaluation/test_evaluation.py b/test/test_evaluation/test_evaluation.py index 5df1f5fe50..1723b208f2 100644 --- a/test/test_evaluation/test_evaluation.py +++ b/test/test_evaluation/test_evaluation.py @@ -4,8 +4,6 @@ import shutil import sys import time -import unittest -import unittest.mock import numpy as np import pynisher @@ -17,6 +15,9 @@ from autosklearn.evaluation import ExecuteTaFuncWithQueue, get_cost_of_crash from autosklearn.metrics import accuracy, log_loss +import unittest +import unittest.mock + this_directory = os.path.dirname(__file__) sys.path.append(this_directory) from evaluation_util import ( # noqa E402 diff --git a/test/test_evaluation/test_test_evaluator.py b/test/test_evaluation/test_test_evaluator.py index 0a1b67faa9..8615682ce7 100644 --- a/test/test_evaluation/test_test_evaluator.py +++ b/test/test_evaluation/test_test_evaluator.py @@ -7,8 +7,6 @@ import shutil import sys import tempfile -import unittest -import unittest.mock import numpy as np from smac.tae import StatusType @@ -25,6 +23,9 @@ from autosklearn.metrics import accuracy, f1_macro, r2 from autosklearn.util.pipeline import get_configuration_space +import unittest +import unittest.mock + this_directory = os.path.dirname(__file__) sys.path.append(this_directory) from evaluation_util import ( # noqa (E402: module level import not at top of file) diff --git a/test/test_evaluation/test_train_evaluator.py b/test/test_evaluation/test_train_evaluator.py index 92e3cfcc10..afed8b5ce1 100644 --- a/test/test_evaluation/test_train_evaluator.py +++ b/test/test_evaluation/test_train_evaluator.py @@ -7,8 +7,6 @@ import shutil import sys import tempfile -import unittest -import unittest.mock import numpy as np import sklearn.model_selection @@ -53,6 +51,9 @@ from autosklearn.metrics import accuracy, f1_macro, r2 from autosklearn.util.pipeline import get_configuration_space +import unittest +import unittest.mock + this_directory = os.path.dirname(__file__) sys.path.append(this_directory) from evaluation_util import ( # noqa (E402: module level import not at top of file) diff --git a/test/test_metalearning/pyMetaLearn/metalearning/test_kND.py b/test/test_metalearning/pyMetaLearn/metalearning/test_kND.py index 4877379440..cb9dc80ab0 100644 --- a/test/test_metalearning/pyMetaLearn/metalearning/test_kND.py +++ b/test/test_metalearning/pyMetaLearn/metalearning/test_kND.py @@ -1,5 +1,4 @@ import logging -import unittest import numpy as np import pandas as pd @@ -7,6 +6,8 @@ from autosklearn.metalearning.metalearning.kNearestDatasets.kND import KNearestDatasets from autosklearn.metalearning.metalearning.metrics.misc import get_random_metric +import unittest + class kNDTest(unittest.TestCase): _multiprocess_can_split_ = True diff --git a/test/test_metalearning/pyMetaLearn/test_meta_base.py b/test/test_metalearning/pyMetaLearn/test_meta_base.py index 1c6788e816..3f06ad07be 100644 --- a/test/test_metalearning/pyMetaLearn/test_meta_base.py +++ b/test/test_metalearning/pyMetaLearn/test_meta_base.py @@ -1,12 +1,13 @@ import logging import os -import unittest import pandas as pd import autosklearn.pipeline.classification from autosklearn.metalearning.metalearning.meta_base import MetaBase +import unittest + class MetaBaseTest(unittest.TestCase): _multiprocess_can_split_ = True diff --git a/test/test_metalearning/pyMetaLearn/test_meta_features.py b/test/test_metalearning/pyMetaLearn/test_meta_features.py index 6a9bec4dcf..40048c708a 100644 --- a/test/test_metalearning/pyMetaLearn/test_meta_features.py +++ b/test/test_metalearning/pyMetaLearn/test_meta_features.py @@ -1,12 +1,10 @@ import logging import os import tempfile -import unittest import arff import numpy as np import pandas as pd -import pytest from joblib import Memory from sklearn.datasets import fetch_openml, make_multilabel_classification @@ -16,6 +14,9 @@ FeatTypeSplit, ) +import pytest +import unittest + @pytest.fixture(scope="class", params=("pandas", "numpy")) def multilabel_train_data(request): diff --git a/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py b/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py index 34a2c8e8d1..992032a349 100644 --- a/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py +++ b/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py @@ -3,7 +3,6 @@ import arff import numpy as np -import pytest from scipy import sparse from sklearn.impute import SimpleImputer from sklearn.preprocessing import StandardScaler @@ -13,6 +12,8 @@ FeatTypeSplit, ) +import pytest + @pytest.fixture def sparse_data(): diff --git a/test/test_metalearning/pyMetaLearn/test_metalearner.py b/test/test_metalearning/pyMetaLearn/test_metalearner.py index a8b7d604cb..42d27d49da 100644 --- a/test/test_metalearning/pyMetaLearn/test_metalearner.py +++ b/test/test_metalearning/pyMetaLearn/test_metalearner.py @@ -1,6 +1,5 @@ import logging import os -import unittest import numpy as np import pandas as pd @@ -10,6 +9,8 @@ import autosklearn.pipeline.classification from autosklearn.metalearning.metalearning.meta_base import MetaBase +import unittest + logging.basicConfig() diff --git a/test/test_metalearning/pyMetaLearn/test_optimizer_base.py b/test/test_metalearning/pyMetaLearn/test_optimizer_base.py index 63dc2184da..654f0026e5 100644 --- a/test/test_metalearning/pyMetaLearn/test_optimizer_base.py +++ b/test/test_metalearning/pyMetaLearn/test_optimizer_base.py @@ -1,8 +1,9 @@ -import unittest from collections import OrderedDict from autosklearn.metalearning.optimizers import optimizer_base +import unittest + class OptimizerBaseTest(unittest.TestCase): _multiprocess_can_split_ = True diff --git a/test/test_metalearning/test_metalearning.py b/test/test_metalearning/test_metalearning.py index 3ec847a8f5..e9e6e4ca1a 100644 --- a/test/test_metalearning/test_metalearning.py +++ b/test/test_metalearning/test_metalearning.py @@ -1,6 +1,4 @@ # -*- encoding: utf-8 -*- -import unittest - from sklearn.datasets import load_breast_cancer from autosklearn.classification import AutoSklearnClassifier @@ -10,6 +8,8 @@ from autosklearn.smbo import _calculate_metafeatures, _calculate_metafeatures_encoded from autosklearn.util.pipeline import get_configuration_space +import unittest + class MetafeatureValueDummy(object): def __init__(self, name, value): diff --git a/test/test_metric/test_metrics.py b/test/test_metric/test_metrics.py index 334a485fe3..541b2d6783 100644 --- a/test/test_metric/test_metrics.py +++ b/test/test_metric/test_metrics.py @@ -1,8 +1,6 @@ -import unittest import warnings import numpy as np -import pytest import sklearn.metrics from smac.utils.constants import MAXINT @@ -10,6 +8,9 @@ from autosklearn.constants import BINARY_CLASSIFICATION, REGRESSION from autosklearn.metrics import calculate_loss, calculate_metric, calculate_score +import pytest +import unittest + class TestScorer(unittest.TestCase): def test_predict_scorer_binary(self): diff --git a/test/test_optimizer/test_smbo.py b/test/test_optimizer/test_smbo.py index fafd7b5a42..eb23d3a932 100644 --- a/test/test_optimizer/test_smbo.py +++ b/test/test_optimizer/test_smbo.py @@ -1,6 +1,5 @@ import logging.handlers -import pytest from ConfigSpace.configuration_space import Configuration import autosklearn.metrics @@ -11,6 +10,8 @@ from autosklearn.smbo import AutoMLSMBO from autosklearn.util.stopwatch import StopWatch +import pytest + @pytest.mark.parametrize("context", ["fork", "forkserver"]) def test_smbo_metalearning_configurations(backend, context, dask_client): diff --git a/test/test_pipeline/components/classification/test_base.py b/test/test_pipeline/components/classification/test_base.py index a524759bc5..9fc54f4dba 100644 --- a/test/test_pipeline/components/classification/test_base.py +++ b/test/test_pipeline/components/classification/test_base.py @@ -1,7 +1,5 @@ from typing import Dict, Optional -import unittest - import numpy as np import sklearn.metrics @@ -12,6 +10,8 @@ _test_classifier_predict_proba, ) +import unittest + from test.test_pipeline.ignored_warnings import classifier_warnings, ignore_warnings diff --git a/test/test_pipeline/components/data_preprocessing/test_balancing.py b/test/test_pipeline/components/data_preprocessing/test_balancing.py index cf8dc103b8..6a76ce419c 100644 --- a/test/test_pipeline/components/data_preprocessing/test_balancing.py +++ b/test/test_pipeline/components/data_preprocessing/test_balancing.py @@ -1,7 +1,6 @@ __author__ = "feurerm" import copy -import unittest import numpy as np import sklearn.datasets @@ -33,6 +32,8 @@ LibLinear_Preprocessor, ) +import unittest + class BalancingComponentTest(unittest.TestCase): def test_balancing_get_weights_treed_single_label(self): diff --git a/test/test_pipeline/components/data_preprocessing/test_categorical_imputation.py b/test/test_pipeline/components/data_preprocessing/test_categorical_imputation.py index d50e8cf842..41c383bc09 100644 --- a/test/test_pipeline/components/data_preprocessing/test_categorical_imputation.py +++ b/test/test_pipeline/components/data_preprocessing/test_categorical_imputation.py @@ -1,12 +1,13 @@ import numpy as np import pandas as pd -import pytest from scipy import sparse from autosklearn.pipeline.components.data_preprocessing.imputation.categorical_imputation import ( # noqa: E501 CategoricalImputation, ) +import pytest + @pytest.fixture def input_data_imputation(request): diff --git a/test/test_pipeline/components/data_preprocessing/test_category_shift.py b/test/test_pipeline/components/data_preprocessing/test_category_shift.py index ce637f50d4..d97b510fc4 100644 --- a/test/test_pipeline/components/data_preprocessing/test_category_shift.py +++ b/test/test_pipeline/components/data_preprocessing/test_category_shift.py @@ -1,5 +1,3 @@ -import unittest - import numpy as np import scipy.sparse @@ -7,6 +5,8 @@ CategoryShift, ) +import unittest + class CategoryShiftTest(unittest.TestCase): def test_data_type_consistency(self): diff --git a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing.py b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing.py index ac8e9abbe2..4c7ad8383c 100644 --- a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing.py +++ b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing.py @@ -1,5 +1,3 @@ -import unittest - import numpy as np from scipy import sparse @@ -7,6 +5,8 @@ FeatTypeSplit, ) +import unittest + class PreprocessingPipelineTest(unittest.TestCase): def do_a_fit_transform(self, sparse_input): diff --git a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_categorical.py b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_categorical.py index 1d693eb150..029b72d183 100644 --- a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_categorical.py +++ b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_categorical.py @@ -1,13 +1,13 @@ -import unittest - import numpy as np -import pytest from scipy import sparse from autosklearn.pipeline.components.data_preprocessing.feature_type_categorical import ( # noqa: E501 CategoricalPreprocessingPipeline, ) +import pytest +import unittest + class CategoricalPreprocessingPipelineTest(unittest.TestCase): def test_data_type_consistency(self): diff --git a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_numerical.py b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_numerical.py index 5a0a840501..d25cef2a2b 100644 --- a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_numerical.py +++ b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_numerical.py @@ -1,5 +1,3 @@ -import unittest - import numpy as np from scipy import sparse @@ -7,6 +5,8 @@ NumericalPreprocessingPipeline, ) +import unittest + class NumericalPreprocessingPipelineTest(unittest.TestCase): def test_data_type_consistency(self): diff --git a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_text.py b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_text.py index c0729b1dfc..eed5b01bea 100644 --- a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_text.py +++ b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_text.py @@ -1,5 +1,3 @@ -import unittest - import numpy as np import pandas as pd @@ -10,6 +8,8 @@ BagOfWordEncoder as BOW_distinct, ) +import unittest + class TextPreprocessingPipelineTest(unittest.TestCase): def test_fit_transform(self): diff --git a/test/test_pipeline/components/data_preprocessing/test_minority_coalescence.py b/test/test_pipeline/components/data_preprocessing/test_minority_coalescence.py index 8e73e963ab..7fa4c24720 100644 --- a/test/test_pipeline/components/data_preprocessing/test_minority_coalescence.py +++ b/test/test_pipeline/components/data_preprocessing/test_minority_coalescence.py @@ -1,5 +1,3 @@ -import unittest - import numpy as np import scipy.sparse @@ -10,6 +8,8 @@ NoCoalescence, ) +import unittest + class MinorityCoalescerTest(unittest.TestCase): def test_data_type_consistency(self): diff --git a/test/test_pipeline/components/data_preprocessing/test_one_hot_encoding.py b/test/test_pipeline/components/data_preprocessing/test_one_hot_encoding.py index 08d2cadd9e..989ed6784e 100644 --- a/test/test_pipeline/components/data_preprocessing/test_one_hot_encoding.py +++ b/test/test_pipeline/components/data_preprocessing/test_one_hot_encoding.py @@ -1,5 +1,3 @@ -import unittest - import numpy as np from scipy import sparse @@ -11,6 +9,8 @@ ) from autosklearn.pipeline.util import _test_preprocessing +import unittest + def create_X(instances=1000, n_feats=10, categs_per_feat=5, seed=0): rs = np.random.RandomState(seed) diff --git a/test/test_pipeline/components/data_preprocessing/test_scaling.py b/test/test_pipeline/components/data_preprocessing/test_scaling.py index 7f8249e3f1..b87223d14d 100644 --- a/test/test_pipeline/components/data_preprocessing/test_scaling.py +++ b/test/test_pipeline/components/data_preprocessing/test_scaling.py @@ -1,11 +1,11 @@ -import unittest - import numpy as np import sklearn.datasets from autosklearn.pipeline.components.data_preprocessing.rescaling import RescalingChoice from autosklearn.pipeline.util import get_dataset +import unittest + class ScalingComponentTest(unittest.TestCase): def _test_helper(self, Preprocessor, dataset=None, make_sparse=False): diff --git a/test/test_pipeline/components/feature_preprocessing/test_choice.py b/test/test_pipeline/components/feature_preprocessing/test_choice.py index 516cf318bf..89272aafcc 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_choice.py +++ b/test/test_pipeline/components/feature_preprocessing/test_choice.py @@ -1,7 +1,7 @@ -import unittest - import autosklearn.pipeline.components.feature_preprocessing as fp +import unittest + class FeatureProcessingTest(unittest.TestCase): def test_get_available_components(self): diff --git a/test/test_pipeline/components/feature_preprocessing/test_fast_ica.py b/test/test_pipeline/components/feature_preprocessing/test_fast_ica.py index a38097a60e..717c2cce36 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_fast_ica.py +++ b/test/test_pipeline/components/feature_preprocessing/test_fast_ica.py @@ -1,5 +1,3 @@ -import unittest - import sklearn.metrics from sklearn.linear_model import Ridge @@ -10,6 +8,8 @@ get_dataset, ) +import unittest + class FastICAComponentTest(PreprocessingTestCase): def test_default_configuration(self): diff --git a/test/test_pipeline/components/feature_preprocessing/test_kernel_pca.py b/test/test_pipeline/components/feature_preprocessing/test_kernel_pca.py index 2c5a8c865b..6af5cb88d8 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_kernel_pca.py +++ b/test/test_pipeline/components/feature_preprocessing/test_kernel_pca.py @@ -1,5 +1,3 @@ -import unittest - import sklearn.metrics from sklearn.linear_model import RidgeClassifier @@ -10,6 +8,8 @@ get_dataset, ) +import unittest + class KernelPCAComponentTest(PreprocessingTestCase): def test_default_configuration(self): diff --git a/test/test_pipeline/components/feature_preprocessing/test_kitchen_sinks.py b/test/test_pipeline/components/feature_preprocessing/test_kitchen_sinks.py index 16ef41198d..52b2d0acee 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_kitchen_sinks.py +++ b/test/test_pipeline/components/feature_preprocessing/test_kitchen_sinks.py @@ -1,10 +1,10 @@ -import unittest - from autosklearn.pipeline.components.feature_preprocessing.kitchen_sinks import ( RandomKitchenSinks, ) from autosklearn.pipeline.util import PreprocessingTestCase, _test_preprocessing +import unittest + class KitchenSinkComponent(PreprocessingTestCase): def test_default_configuration(self): diff --git a/test/test_pipeline/components/feature_preprocessing/test_nystroem_sampler.py b/test/test_pipeline/components/feature_preprocessing/test_nystroem_sampler.py index d6244c362f..5fa1269cf5 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_nystroem_sampler.py +++ b/test/test_pipeline/components/feature_preprocessing/test_nystroem_sampler.py @@ -1,5 +1,3 @@ -import unittest - import numpy as np import sklearn.preprocessing @@ -8,6 +6,8 @@ ) from autosklearn.pipeline.util import _test_preprocessing, get_dataset +import unittest + class NystroemComponentTest(unittest.TestCase): def test_default_configuration(self): diff --git a/test/test_pipeline/components/feature_preprocessing/test_random_trees_embedding.py b/test/test_pipeline/components/feature_preprocessing/test_random_trees_embedding.py index f84675dc1a..82feaca5ec 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_random_trees_embedding.py +++ b/test/test_pipeline/components/feature_preprocessing/test_random_trees_embedding.py @@ -1,5 +1,3 @@ -import unittest - import numpy as np import scipy.sparse @@ -8,6 +6,8 @@ ) from autosklearn.pipeline.util import _test_preprocessing, get_dataset +import unittest + class RandomTreesEmbeddingComponentTest(unittest.TestCase): def test_default_configuration(self): diff --git a/test/test_pipeline/components/feature_preprocessing/test_select_percentile_classification.py b/test/test_pipeline/components/feature_preprocessing/test_select_percentile_classification.py index b177e4f4ba..f0dbb3e947 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_select_percentile_classification.py +++ b/test/test_pipeline/components/feature_preprocessing/test_select_percentile_classification.py @@ -1,5 +1,3 @@ -import unittest - import numpy as np import scipy.sparse import sklearn.preprocessing @@ -9,6 +7,8 @@ ) from autosklearn.pipeline.util import _test_preprocessing, get_dataset +import unittest + class SelectPercentileClassificationTest(unittest.TestCase): def test_default_configuration(self): diff --git a/test/test_pipeline/components/feature_preprocessing/test_select_percentile_regression.py b/test/test_pipeline/components/feature_preprocessing/test_select_percentile_regression.py index 0fd335fd83..98bc50a690 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_select_percentile_regression.py +++ b/test/test_pipeline/components/feature_preprocessing/test_select_percentile_regression.py @@ -1,5 +1,3 @@ -import unittest - import numpy as np from autosklearn.pipeline.components.feature_preprocessing.select_percentile_regression import ( # noqa: E501 @@ -7,6 +5,8 @@ ) from autosklearn.pipeline.util import _test_preprocessing, get_dataset +import unittest + class SelectPercentileRegressionTest(unittest.TestCase): def test_default_configuration(self): diff --git a/test/test_pipeline/components/feature_preprocessing/test_select_rates_classification.py b/test/test_pipeline/components/feature_preprocessing/test_select_rates_classification.py index 2d1c2aaf78..03c6a45983 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_select_rates_classification.py +++ b/test/test_pipeline/components/feature_preprocessing/test_select_rates_classification.py @@ -1,5 +1,3 @@ -import unittest - import numpy as np import scipy.sparse import sklearn.preprocessing @@ -9,6 +7,8 @@ ) from autosklearn.pipeline.util import _test_preprocessing, get_dataset +import unittest + class SelectClassificationRatesComponentTest(unittest.TestCase): def test_default_configuration(self): diff --git a/test/test_pipeline/components/feature_preprocessing/test_select_rates_regression.py b/test/test_pipeline/components/feature_preprocessing/test_select_rates_regression.py index 869d7fbee2..826d05e53e 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_select_rates_regression.py +++ b/test/test_pipeline/components/feature_preprocessing/test_select_rates_regression.py @@ -1,5 +1,3 @@ -import unittest - import numpy as np import scipy.sparse import sklearn.preprocessing @@ -9,6 +7,8 @@ ) from autosklearn.pipeline.util import _test_preprocessing, get_dataset +import unittest + class SelectRegressionRatesComponentTest(unittest.TestCase): def test_default_configuration(self): diff --git a/test/test_pipeline/components/feature_preprocessing/test_truncatedSVD.py b/test/test_pipeline/components/feature_preprocessing/test_truncatedSVD.py index 7e16fa7fa5..6f09368b6c 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_truncatedSVD.py +++ b/test/test_pipeline/components/feature_preprocessing/test_truncatedSVD.py @@ -1,5 +1,3 @@ -import unittest - import sklearn.metrics from sklearn.linear_model import RidgeClassifier @@ -12,6 +10,8 @@ get_dataset, ) +import unittest + class TruncatedSVDComponentTest(PreprocessingTestCase): def test_default_configuration(self): diff --git a/test/test_pipeline/components/regression/test_base.py b/test/test_pipeline/components/regression/test_base.py index dcf7770332..7ed4afe79a 100644 --- a/test/test_pipeline/components/regression/test_base.py +++ b/test/test_pipeline/components/regression/test_base.py @@ -1,9 +1,6 @@ from typing import Container, Type -import unittest - import numpy as np -import pytest import sklearn.metrics from autosklearn.pipeline.components.regression import RegressorChoice, _regressors @@ -11,6 +8,9 @@ from autosklearn.pipeline.constants import SPARSE from autosklearn.pipeline.util import _test_regressor, _test_regressor_iterative_fit +import pytest +import unittest + from test.test_pipeline.ignored_warnings import ignore_warnings, regressor_warnings diff --git a/test/test_pipeline/components/test_base.py b/test/test_pipeline/components/test_base.py index 1e6ddbbd14..f8fcf6b398 100644 --- a/test/test_pipeline/components/test_base.py +++ b/test/test_pipeline/components/test_base.py @@ -1,12 +1,13 @@ import os import sys -import unittest from autosklearn.pipeline.components.base import ( AutoSklearnClassificationAlgorithm, find_components, ) +import unittest + this_dir = os.path.dirname(os.path.abspath(__file__)) sys.path.append(this_dir) diff --git a/test/test_pipeline/implementations/test_CategoryShift.py b/test/test_pipeline/implementations/test_CategoryShift.py index 1b5e1451e6..e6cbced71e 100644 --- a/test/test_pipeline/implementations/test_CategoryShift.py +++ b/test/test_pipeline/implementations/test_CategoryShift.py @@ -1,10 +1,10 @@ -import unittest - import numpy as np import scipy.sparse from autosklearn.pipeline.implementations.CategoryShift import CategoryShift +import unittest + class CategoryShiftTest(unittest.TestCase): def test_dense(self): diff --git a/test/test_pipeline/implementations/test_MinorityCoalescer.py b/test/test_pipeline/implementations/test_MinorityCoalescer.py index 7bdca8f1aa..d7058453e5 100644 --- a/test/test_pipeline/implementations/test_MinorityCoalescer.py +++ b/test/test_pipeline/implementations/test_MinorityCoalescer.py @@ -1,10 +1,10 @@ -import unittest - import numpy as np import scipy.sparse from autosklearn.pipeline.implementations.MinorityCoalescer import MinorityCoalescer +import unittest + class MinorityCoalescerTest(unittest.TestCase): @property diff --git a/test/test_pipeline/implementations/test_SparseOneHotEncoder.py b/test/test_pipeline/implementations/test_SparseOneHotEncoder.py index 91f1827c06..ed9bc07a89 100644 --- a/test/test_pipeline/implementations/test_SparseOneHotEncoder.py +++ b/test/test_pipeline/implementations/test_SparseOneHotEncoder.py @@ -1,5 +1,3 @@ -import unittest - import numpy as np import scipy.sparse import sklearn.datasets @@ -12,6 +10,8 @@ from autosklearn.pipeline.implementations.CategoryShift import CategoryShift from autosklearn.pipeline.implementations.SparseOneHotEncoder import SparseOneHotEncoder +import unittest + sparse1 = scipy.sparse.csc_matrix( ([3, 2, 1, 1, 2, 3], ((1, 4, 5, 2, 3, 5), (0, 0, 0, 1, 1, 1))), shape=(6, 2) ) diff --git a/test/test_pipeline/implementations/test_util.py b/test/test_pipeline/implementations/test_util.py index 58412e0b0c..d6b6530569 100644 --- a/test/test_pipeline/implementations/test_util.py +++ b/test/test_pipeline/implementations/test_util.py @@ -1,9 +1,9 @@ -import unittest - import numpy as np from autosklearn.pipeline.implementations.util import softmax +import unittest + class UtilTest(unittest.TestCase): def test_softmax_binary(self): diff --git a/test/test_pipeline/test_base.py b/test/test_pipeline/test_base.py index f1efed23b4..f8cfe26912 100644 --- a/test/test_pipeline/test_base.py +++ b/test/test_pipeline/test_base.py @@ -1,6 +1,3 @@ -import unittest -import unittest.mock - import ConfigSpace.configuration_space import autosklearn.pipeline.base @@ -8,6 +5,9 @@ import autosklearn.pipeline.components.classification as classification import autosklearn.pipeline.components.feature_preprocessing as feature_preprocessing +import unittest +import unittest.mock + class BasePipelineMock(autosklearn.pipeline.base.BasePipeline): def __init__(self): diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index 1ce93ebb0d..7be8038119 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -5,8 +5,6 @@ import os import resource import tempfile -import unittest -import unittest.mock import numpy as np import sklearn.datasets @@ -40,6 +38,9 @@ ) from autosklearn.pipeline.util import get_dataset +import unittest +import unittest.mock + from test.test_pipeline.ignored_warnings import classifier_warnings, ignore_warnings diff --git a/test/test_pipeline/test_create_searchspace_util_classification.py b/test/test_pipeline/test_create_searchspace_util_classification.py index a830430097..1b09de2bb7 100644 --- a/test/test_pipeline/test_create_searchspace_util_classification.py +++ b/test/test_pipeline/test_create_searchspace_util_classification.py @@ -1,4 +1,3 @@ -import unittest from collections import OrderedDict import numpy @@ -19,6 +18,8 @@ TruncatedSVD, ) +import unittest + class TestCreateClassificationSearchspace(unittest.TestCase): _multiprocess_can_split_ = True diff --git a/test/test_pipeline/test_regression.py b/test/test_pipeline/test_regression.py index 501b73ec5d..3a50decb8c 100644 --- a/test/test_pipeline/test_regression.py +++ b/test/test_pipeline/test_regression.py @@ -2,8 +2,6 @@ import itertools import resource import tempfile -import unittest -import unittest.mock import numpy as np import sklearn.datasets @@ -34,6 +32,9 @@ from autosklearn.pipeline.regression import SimpleRegressionPipeline from autosklearn.pipeline.util import get_dataset +import unittest +import unittest.mock + from test.test_pipeline.ignored_warnings import ignore_warnings, regressor_warnings diff --git a/test/test_scripts/test_metadata_generation.py b/test/test_scripts/test_metadata_generation.py index 6c6ba70ef5..929b90e029 100644 --- a/test/test_scripts/test_metadata_generation.py +++ b/test/test_scripts/test_metadata_generation.py @@ -4,13 +4,14 @@ import shutil import socket import subprocess -import unittest import arff import numpy as np from autosklearn.metrics import CLASSIFICATION_METRICS, REGRESSION_METRICS +import unittest + class TestMetadataGeneration(unittest.TestCase): def setUp(self): diff --git a/test/test_util/test_StopWatch.py b/test/test_util/test_StopWatch.py index d45ecbf55d..a59940d0c4 100644 --- a/test/test_util/test_StopWatch.py +++ b/test/test_util/test_StopWatch.py @@ -6,11 +6,12 @@ """ import time -import unittest -import unittest.mock from autosklearn.util.stopwatch import StopWatch +import unittest +import unittest.mock + class Test(unittest.TestCase): _multiprocess_can_split_ = True diff --git a/test/test_util/test_backend.py b/test/test_util/test_backend.py index 0673370b97..719b356e89 100644 --- a/test/test_util/test_backend.py +++ b/test/test_util/test_backend.py @@ -1,10 +1,11 @@ # -*- encoding: utf-8 -*- import builtins -import unittest -import unittest.mock from autosklearn.automl_common.common.utils.backend import Backend +import unittest +import unittest.mock + class BackendModelsTest(unittest.TestCase): class BackendStub(Backend): diff --git a/test/test_util/test_common.py b/test/test_util/test_common.py index 33fa4cee31..af17a5c259 100644 --- a/test/test_util/test_common.py +++ b/test/test_util/test_common.py @@ -1,9 +1,10 @@ # -*- encoding: utf-8 -*- import os -import unittest from autosklearn.util.common import check_pid +import unittest + class TestUtilsCommon(unittest.TestCase): _multiprocess_can_split_ = True diff --git a/test/test_util/test_data.py b/test/test_util/test_data.py index 2bceac804a..14a8ec44e6 100644 --- a/test/test_util/test_data.py +++ b/test/test_util/test_data.py @@ -5,7 +5,6 @@ import numpy as np import pandas as pd -import pytest import sklearn.datasets from scipy.sparse import csr_matrix, spmatrix @@ -28,6 +27,8 @@ validate_dataset_compression_arg, ) +import pytest + parametrize = pytest.mark.parametrize diff --git a/test/test_util/test_dependencies.py b/test/test_util/test_dependencies.py index 1c59dad51b..d51e849e8b 100644 --- a/test/test_util/test_dependencies.py +++ b/test/test_util/test_dependencies.py @@ -1,6 +1,4 @@ import re -import unittest -from unittest.mock import Mock, patch import numpy as np import pkg_resources @@ -11,6 +9,9 @@ verify_packages, ) +import unittest +from unittest.mock import Mock, patch + @patch("pkg_resources.get_distribution") class VerifyPackagesTests(unittest.TestCase): diff --git a/test/test_util/test_logging.py b/test/test_util/test_logging.py index d824aecc02..c046df12f3 100644 --- a/test/test_util/test_logging.py +++ b/test/test_util/test_logging.py @@ -2,12 +2,13 @@ import logging.config import os import tempfile -import unittest import yaml from autosklearn.util import logging_ +import unittest + class LoggingTest(unittest.TestCase): def test_setup_logger(self): diff --git a/test/test_util/test_single_thread_client.py b/test/test_util/test_single_thread_client.py index 770ff9f04a..e7163a36b1 100644 --- a/test/test_util/test_single_thread_client.py +++ b/test/test_util/test_single_thread_client.py @@ -1,9 +1,10 @@ import dask.distributed -import pytest from distributed.utils_test import inc from autosklearn.util.single_thread_client import SingleThreadedClient +import pytest + def test_single_thread_client_like_dask_client(): single_thread_client = SingleThreadedClient() diff --git a/test/test_util/test_trials_callback.py b/test/test_util/test_trials_callback.py index d1bfe6b748..b1328b9489 100644 --- a/test/test_util/test_trials_callback.py +++ b/test/test_util/test_trials_callback.py @@ -1,6 +1,5 @@ import os import tempfile -import unittest import pandas as pd from smac.callbacks import IncorporateRunResultCallback @@ -11,6 +10,8 @@ import autosklearn.pipeline.util as putil from autosklearn.classification import AutoSklearnClassifier +import unittest + class AutoMLTrialsCallBack(IncorporateRunResultCallback): def __init__(self, fname): diff --git a/test/util.py b/test/util.py new file mode 100644 index 0000000000..eaf1a34d19 --- /dev/null +++ b/test/util.py @@ -0,0 +1,49 @@ +from typing import Any + +from pytest import mark, param + + +def fails(arg: Any, reason: str = "No reason given") -> Any: + """Mark a parameter for pytest parametrize that it should fail + + ..code:: python + + @parametrize("number", [2, 3, fails(5, "some reason")]) + + Parameters + ---------- + arg : Any + The arg that should fail + + reason : str = "No reason given" + The reason for the expected fail + + Returns + ------- + Any + The param object + """ + return param(arg, marks=mark.xfail(reason=reason)) + + +def skip(arg: Any, reason: str = "No reason given") -> Any: + """Mark a parameter for pytest parametrize that should be skipped + + ..code:: python + + @parametrize("number", [2, 3, skip(5, "some reason")]) + + Parameters + ---------- + arg : Any + The arg that should be skipped + + reason : str = "No Reason given" + The reason for skipping it + + Returns + ------- + Any + The param object + """ + return param(arg, marks=mark.skip(reason=reason)) From f775292129e53586d70740e9dd6edcb51a387fdc Mon Sep 17 00:00:00 2001 From: Lukas Strack <73999018+Louquinze@users.noreply.github.com> Date: Fri, 25 Mar 2022 12:30:06 +0100 Subject: [PATCH 042/118] change treatment of string features in metalearning (#1426) * change treatment of string features. In file `smbo.py` string columns are marked as categorical. Previously string columns are treated as not categorical which transfers to numerical. We decided that encoded string columns are more similar to OHE as they are to numerical transformations. This change effects the metalearning part of `autosklearn` exclusively. * change treatment of string features. In file `smbo.py` string columns are marked as categorical. Previously string columns are treated as not categorical which transfers to numerical. We decided that encoded string columns are more similar to OHE as they are to numerical transformations. This change effects the metalearning part of `autosklearn` exclusively. --- autosklearn/smbo.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/autosklearn/smbo.py b/autosklearn/smbo.py index b179efa8d0..7a74490081 100644 --- a/autosklearn/smbo.py +++ b/autosklearn/smbo.py @@ -96,7 +96,7 @@ def _calculate_metafeatures( watcher.start_task(task_name) categorical = { - col: True if feat_type.lower() == "categorical" else False + col: True if feat_type.lower() in {"categorical", "string"} else False for col, feat_type in data_feat_type.items() } @@ -152,7 +152,7 @@ def _calculate_metafeatures_encoded( task_name = "CalculateMetafeaturesEncoded" watcher.start_task(task_name) categorical = { - col: True if feat_type.lower() == "categorical" else False + col: True if feat_type.lower() in {"categorical", "string"} else False for col, feat_type in data_feat_type.items() } From 6b3d015064456a6bded9507db20950191c6167ba Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Sat, 26 Mar 2022 20:28:30 +0100 Subject: [PATCH 043/118] Fix: `leaderboard` fills nans (#1432) For models that are in the ensemble but have no corresponding entry in model_runs (built from run_history), we fill in NA values to prevent crashing. Why this occurs, I don't know but this should at least prevent total failure when we can still provide other useful information back to the user. --- autosklearn/estimators.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index eb98e54673..db931a338a 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -937,6 +937,25 @@ def has_key(rv, key): # tied together by ordering, might be better to store as tuple for i, weight in enumerate(self.automl_.ensemble_.weights_): (_, model_id, _) = self.automl_.ensemble_.identifiers_[i] + + # We had issues where the model's in the ensembles are not in the runhistory + # collected. I have no clue why this is but to prevent failures, we fill + # the values with NaN + if model_id not in model_runs: + model_runs[model_id] = { + "model_id": model_id, + "seed": pd.NA, + "budget": pd.NA, + "duration": pd.NA, + "config_id": pd.NA, + "start_time": pd.NA, + "end_time": pd.NA, + "status": pd.NA, + "cost": pd.NA, + "train_loss": pd.NA, + "config_origin": pd.NA, + } + model_runs[model_id]["ensemble_weight"] = weight # Filter out non-ensemble members if needed, else fill in a default From 8da3624912a91f6e879fc112ba5255b566148825 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Sun, 27 Mar 2022 14:18:56 +0200 Subject: [PATCH 044/118] Update `StopWatch` to have a context manager and simplify slightly (#1425) * Cleanup: Stopwatch * Uddate: Stopwatch with context manager and use it * Fix: Don't call property * Add: Ignore automl_common * Fix: wrong variable name `self.time_for_task` * Remove: Old test file * Better error message for stopwatch `time_since` * Update: `time_since` can now optionally raise * Use the new `time_since` stopwatch method * Trigger workflow * Add extra time for cross-val automl cases * Add comment in code to explain why --- autosklearn/automl.py | 320 +++++++++++++-------------- autosklearn/smbo.py | 210 +++++++++--------- autosklearn/util/stopwatch.py | 318 ++++++++++++++++---------- pyproject.toml | 1 + scripts/03_calculate_metafeatures.py | 6 +- test/test_automl/cases.py | 4 + test/test_optimizer/test_smbo.py | 7 +- test/test_util/test_StopWatch.py | 42 ---- test/test_util/test_stopwatch.py | 39 ++++ 9 files changed, 498 insertions(+), 449 deletions(-) delete mode 100644 test/test_util/test_StopWatch.py create mode 100644 test/test_util/test_stopwatch.py diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 1496aa2224..50e4591346 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -417,25 +417,6 @@ def _clean_logger(self) -> None: self.logging_server.terminate() del self.stop_logging_server - @staticmethod - def _start_task(watcher: StopWatch, task_name: str) -> None: - watcher.start_task(task_name) - - @staticmethod - def _stop_task(watcher: StopWatch, task_name: str) -> None: - watcher.stop_task(task_name) - - @staticmethod - def _print_load_time( - basename: str, - time_left_for_this_task: float, - time_for_load_data: float, - logger: PicklableClientLogger, - ) -> float: - time_left = max(0, time_left_for_this_task - time_for_load_data) - logger.info(f"Remaining time after reading {basename} {time_left:5.2f} sec") - return time_for_load_data - def _do_dummy_prediction(self) -> None: # When using partial-cv it makes no sense to do dummy predictions if self._resampling_strategy in ["partial-cv", "partial-cv-iterative-fit"]: @@ -726,28 +707,29 @@ def fit( self._is_dask_client_internally_created = False self._dataset_name = dataset_name - self._stopwatch.start_task(self._dataset_name) + self._stopwatch.start(self._dataset_name) # Take the feature types from the validator self._feat_type = self.InputValidator.feature_validator.feat_type self._log_fit_setup() - datamanager = XYDataManager( - X=X, - y=y, - X_test=X_test, - y_test=y_test, - task=self._task, - feat_type=self._feat_type, - dataset_name=dataset_name, - ) + # == Pickle the data manager to speed up loading + with self._stopwatch.time("Save Datamanager"): + datamanager = XYDataManager( + X, + y, + X_test=X_test, + y_test=y_test, + task=self._task, + feat_type=self._feat_type, + dataset_name=dataset_name, + ) - self._backend._make_internals_directory() - self._label_num = datamanager.info["label_num"] + self._backend._make_internals_directory() + self._label_num = datamanager.info["label_num"] - # == Pickle the data manager to speed up loading - self._backend.save_datamanager(datamanager) + self._backend.save_datamanager(datamanager) # = Create a searchspace # Do this before One Hot Encoding to make sure that it creates a @@ -756,69 +738,71 @@ def fit( # densifier and truncatedSVD would probably lead to a MemoryError, # like this we can't use some of the preprocessing methods in case # the data became sparse) - self.configuration_space, configspace_path = self._create_search_space( - self._backend.temporary_directory, - self._backend, - datamanager, - include=self._include, - exclude=self._exclude, - ) + with self._stopwatch.time("Create Search space"): + self.configuration_space, configspace_path = self._create_search_space( + self._backend.temporary_directory, + self._backend, + datamanager, + include=self._include, + exclude=self._exclude, + ) + if only_return_configuration_space: self._fit_cleanup() return self.configuration_space # == Perform dummy predictions - self.num_run += 1 - self._do_dummy_prediction() + with self._stopwatch.time("Dummy predictions"): + self.num_run += 1 + self._do_dummy_prediction() # == RUN ensemble builder # Do this before calculating the meta-features to make sure that the # dummy predictions are actually included in the ensemble even if # calculating the meta-features takes very long - ensemble_task_name = "runEnsemble" - self._stopwatch.start_task(ensemble_task_name) - elapsed_time = self._stopwatch.wall_elapsed(self._dataset_name) - time_left_for_ensembles = max(0, self._time_for_task - elapsed_time) - proc_ensemble = None - if time_left_for_ensembles <= 0: - # Fit only raises error when ensemble_size is not zero but - # time_left_for_ensembles is zero. - if self._ensemble_size > 0: - raise ValueError( - "Not starting ensemble builder because there " - "is no time left. Try increasing the value " - "of time_left_for_this_task." + with self._stopwatch.time("Run Ensemble Builder"): + + elapsed_time = self._stopwatch.time_since(self._dataset_name, "start") + + time_left_for_ensembles = max(0, self._time_for_task - elapsed_time) + proc_ensemble = None + if time_left_for_ensembles <= 0: + # Fit only raises error when ensemble_size is not zero but + # time_left_for_ensembles is zero. + if self._ensemble_size > 0: + raise ValueError( + "Not starting ensemble builder because there " + "is no time left. Try increasing the value " + "of time_left_for_this_task." + ) + elif self._ensemble_size <= 0: + self._logger.info( + "Not starting ensemble builder because " "ensemble size is <= 0." + ) + else: + self._logger.info( + "Start Ensemble with %5.2fsec time left" % time_left_for_ensembles ) - elif self._ensemble_size <= 0: - self._logger.info( - "Not starting ensemble builder because " "ensemble size is <= 0." - ) - else: - self._logger.info( - "Start Ensemble with %5.2fsec time left" % time_left_for_ensembles - ) - - proc_ensemble = EnsembleBuilderManager( - start_time=time.time(), - time_left_for_ensembles=time_left_for_ensembles, - backend=copy.deepcopy(self._backend), - dataset_name=dataset_name, - task=self._task, - metric=self._metric, - ensemble_size=self._ensemble_size, - ensemble_nbest=self._ensemble_nbest, - max_models_on_disc=self._max_models_on_disc, - seed=self._seed, - precision=self.precision, - max_iterations=None, - read_at_most=np.inf, - ensemble_memory_limit=self._memory_limit, - random_state=self._seed, - logger_port=self._logger_port, - pynisher_context=self._multiprocessing_context, - ) - self._stopwatch.stop_task(ensemble_task_name) + proc_ensemble = EnsembleBuilderManager( + start_time=time.time(), + time_left_for_ensembles=time_left_for_ensembles, + backend=copy.deepcopy(self._backend), + dataset_name=dataset_name, + task=self._task, + metric=self._metric, + ensemble_size=self._ensemble_size, + ensemble_nbest=self._ensemble_nbest, + max_models_on_disc=self._max_models_on_disc, + seed=self._seed, + precision=self.precision, + max_iterations=None, + read_at_most=np.inf, + ensemble_memory_limit=self._memory_limit, + random_state=self._seed, + logger_port=self._logger_port, + pynisher_context=self._multiprocessing_context, + ) # kill the datamanager as it will be re-loaded anyways from sub processes try: @@ -827,95 +811,91 @@ def fit( pass # => RUN SMAC - smac_task_name = "runSMAC" - self._stopwatch.start_task(smac_task_name) - elapsed_time = self._stopwatch.wall_elapsed(self._dataset_name) - time_left_for_smac = max(0, self._time_for_task - elapsed_time) - - if self._logger: - self._logger.info("Start SMAC with %5.2fsec time left" % time_left_for_smac) - if time_left_for_smac <= 0: - self._logger.warning("Not starting SMAC because there is no time " "left.") - _proc_smac = None - self._budget_type = None - else: - if ( - self._per_run_time_limit is None - or self._per_run_time_limit > time_left_for_smac - ): - self._logger.warning( - "Time limit for a single run is higher than total time " - "limit. Capping the limit for a single run to the total " - "time given to SMAC (%f)" % time_left_for_smac - ) - per_run_time_limit = time_left_for_smac + with self._stopwatch.time("Run SMAC"): + elapsed_time = self._stopwatch.time_since(self._dataset_name, "start") + time_left = self._time_for_task - elapsed_time + + if self._logger: + self._logger.info("Start SMAC with %5.2fsec time left" % time_left) + if time_left <= 0: + self._logger.warning("Not starting SMAC because there is no time left.") + _proc_smac = None + self._budget_type = None else: - per_run_time_limit = self._per_run_time_limit - - # Make sure that at least 2 models are created for the ensemble process - num_models = time_left_for_smac // per_run_time_limit - if num_models < 2: - per_run_time_limit = time_left_for_smac // 2 - self._logger.warning( - "Capping the per_run_time_limit to {} to have " - "time for a least 2 models in each process.".format( - per_run_time_limit + if ( + self._per_run_time_limit is None + or self._per_run_time_limit > time_left + ): + self._logger.warning( + "Time limit for a single run is higher than total time " + "limit. Capping the limit for a single run to the total " + "time given to SMAC (%f)" % time_left + ) + per_run_time_limit = time_left + else: + per_run_time_limit = self._per_run_time_limit + + # Make sure that at least 2 models are created for the ensemble process + num_models = time_left // per_run_time_limit + if num_models < 2: + per_run_time_limit = time_left // 2 + self._logger.warning( + "Capping the per_run_time_limit to {} to have " + "time for a least 2 models in each process.".format( + per_run_time_limit + ) ) - ) - - _proc_smac = AutoMLSMBO( - config_space=self.configuration_space, - dataset_name=self._dataset_name, - backend=self._backend, - total_walltime_limit=time_left_for_smac, - func_eval_time_limit=per_run_time_limit, - memory_limit=self._memory_limit, - data_memory_limit=self._data_memory_limit, - watcher=self._stopwatch, - n_jobs=self._n_jobs, - dask_client=self._dask_client, - start_num_run=self.num_run, - num_metalearning_cfgs=self._initial_configurations_via_metalearning, - config_file=configspace_path, - seed=self._seed, - metadata_directory=self._metadata_directory, - metric=self._metric, - resampling_strategy=self._resampling_strategy, - resampling_strategy_args=self._resampling_strategy_arguments, - include=self._include, - exclude=self._exclude, - disable_file_output=self._disable_evaluator_output, - get_smac_object_callback=self._get_smac_object_callback, - smac_scenario_args=self._smac_scenario_args, - scoring_functions=self._scoring_functions, - port=self._logger_port, - pynisher_context=self._multiprocessing_context, - ensemble_callback=proc_ensemble, - trials_callback=self._get_trials_callback, - ) - try: - ( - self.runhistory_, - self.trajectory_, - self._budget_type, - ) = _proc_smac.run_smbo() - trajectory_filename = os.path.join( - self._backend.get_smac_output_directory_for_run(self._seed), - "trajectory.json", + _proc_smac = AutoMLSMBO( + config_space=self.configuration_space, + dataset_name=self._dataset_name, + backend=self._backend, + total_walltime_limit=time_left, + func_eval_time_limit=per_run_time_limit, + memory_limit=self._memory_limit, + data_memory_limit=self._data_memory_limit, + stopwatch=self._stopwatch, + n_jobs=self._n_jobs, + dask_client=self._dask_client, + start_num_run=self.num_run, + num_metalearning_cfgs=self._initial_configurations_via_metalearning, + config_file=configspace_path, + seed=self._seed, + metadata_directory=self._metadata_directory, + metric=self._metric, + resampling_strategy=self._resampling_strategy, + resampling_strategy_args=self._resampling_strategy_arguments, + include=self._include, + exclude=self._exclude, + disable_file_output=self._disable_evaluator_output, + get_smac_object_callback=self._get_smac_object_callback, + smac_scenario_args=self._smac_scenario_args, + scoring_functions=self._scoring_functions, + port=self._logger_port, + pynisher_context=self._multiprocessing_context, + ensemble_callback=proc_ensemble, + trials_callback=self._get_trials_callback, ) - saveable_trajectory = [ - list(entry[:2]) + [entry[2].get_dictionary()] + list(entry[3:]) - for entry in self.trajectory_ - ] - - with open(trajectory_filename, "w") as fh: - json.dump(saveable_trajectory, fh) - except Exception as e: - self._fit_cleanup() - self._logger.exception(e) - raise + try: + ( + self.runhistory_, + self.trajectory_, + self._budget_type, + ) = _proc_smac.run_smbo() + trajectory_filename = os.path.join( + self._backend.get_smac_output_directory_for_run(self._seed), + "trajectory.json", + ) + saveable_trajectory = [ + list(entry[:2]) + [entry[2].get_dictionary()] + list(entry[3:]) + for entry in self.trajectory_ + ] + with open(trajectory_filename, "w") as fh: + json.dump(saveable_trajectory, fh) + except Exception as e: + self._logger.exception(e) + raise self._logger.info("Starting shutdown...") # Wait until the ensemble process is finished to avoid shutting down @@ -2076,9 +2056,6 @@ def _create_search_space( include: Optional[Mapping[str, list[str]]] = None, exclude: Optional[Mapping[str, list[str]]] = None, ) -> Tuple[ConfigurationSpace, str]: - task_name = "CreateConfigSpace" - - self._stopwatch.start_task(task_name) configspace_path = os.path.join(tmp_dir, "space.json") configuration_space = pipeline.get_configuration_space( datamanager.info, @@ -2090,7 +2067,6 @@ def _create_search_space( cs_json.write(configuration_space), "Configuration space", ) - self._stopwatch.stop_task(task_name) return configuration_space, configspace_path diff --git a/autosklearn/smbo.py b/autosklearn/smbo.py index 7a74490081..608c58921d 100644 --- a/autosklearn/smbo.py +++ b/autosklearn/smbo.py @@ -42,6 +42,7 @@ from autosklearn.metalearning.mismbo import suggest_via_metalearning from autosklearn.util.logging_ import get_named_client_logger from autosklearn.util.parallel import preload_modules +from autosklearn.util.stopwatch import StopWatch EXCLUDE_META_FEATURES_CLASSIFICATION = { "Landmark1NN", @@ -86,59 +87,67 @@ def _send_warnings_to_log(message, category, filename, lineno, file, line): # metalearning helpers def _calculate_metafeatures( - data_feat_type, data_info_task, basename, x_train, y_train, watcher, logger_ + data_feat_type, + data_info_task, + basename, + x_train, + y_train, + stopwatch: StopWatch, + logger_, ): with warnings.catch_warnings(): warnings.showwarning = get_send_warnings_to_logger(logger_) # == Calculate metafeatures - task_name = "CalculateMetafeatures" - watcher.start_task(task_name) - - categorical = { - col: True if feat_type.lower() in {"categorical", "string"} else False - for col, feat_type in data_feat_type.items() - } + with stopwatch.time("Calculate meta-features") as task_timer: - EXCLUDE_META_FEATURES = ( - EXCLUDE_META_FEATURES_CLASSIFICATION - if data_info_task in CLASSIFICATION_TASKS - else EXCLUDE_META_FEATURES_REGRESSION - ) + categorical = { + col: True if feat_type.lower() in {"categorical", "string"} else False + for col, feat_type in data_feat_type.items() + } - if data_info_task in [ - MULTICLASS_CLASSIFICATION, - BINARY_CLASSIFICATION, - MULTILABEL_CLASSIFICATION, - REGRESSION, - MULTIOUTPUT_REGRESSION, - ]: - logger_.info("Start calculating metafeatures for %s", basename) - result = calculate_all_metafeatures_with_labels( - x_train, - y_train, - categorical=categorical, - dataset_name=basename, - dont_calculate=EXCLUDE_META_FEATURES, - logger=logger_, + EXCLUDE_META_FEATURES = ( + EXCLUDE_META_FEATURES_CLASSIFICATION + if data_info_task in CLASSIFICATION_TASKS + else EXCLUDE_META_FEATURES_REGRESSION ) - for key in list(result.metafeature_values.keys()): - if result.metafeature_values[key].type_ != "METAFEATURE": - del result.metafeature_values[key] - else: - result = None - logger_.info("Metafeatures not calculated") - watcher.stop_task(task_name) - logger_.info( - "Calculating Metafeatures (categorical attributes) took %5.2f", - watcher.wall_elapsed(task_name), - ) + if data_info_task in [ + MULTICLASS_CLASSIFICATION, + BINARY_CLASSIFICATION, + MULTILABEL_CLASSIFICATION, + REGRESSION, + MULTIOUTPUT_REGRESSION, + ]: + logger_.info("Start calculating metafeatures for %s", basename) + result = calculate_all_metafeatures_with_labels( + x_train, + y_train, + categorical=categorical, + dataset_name=basename, + dont_calculate=EXCLUDE_META_FEATURES, + logger=logger_, + ) + for key in list(result.metafeature_values.keys()): + if result.metafeature_values[key].type_ != "METAFEATURE": + del result.metafeature_values[key] + + else: + result = None + logger_.info("Metafeatures not calculated") + + logger_.info(f"{task_timer.name} took {task_timer.wall_duration:5.2f}"), return result def _calculate_metafeatures_encoded( - data_feat_type, basename, x_train, y_train, watcher, task, logger_ + data_feat_type, + basename, + x_train, + y_train, + stopwatch: StopWatch, + task, + logger_, ): with warnings.catch_warnings(): warnings.showwarning = get_send_warnings_to_logger(logger_) @@ -149,29 +158,25 @@ def _calculate_metafeatures_encoded( else EXCLUDE_META_FEATURES_REGRESSION ) - task_name = "CalculateMetafeaturesEncoded" - watcher.start_task(task_name) - categorical = { - col: True if feat_type.lower() in {"categorical", "string"} else False - for col, feat_type in data_feat_type.items() - } + with stopwatch.time("Calculate meta-features encoded") as task_timer: + categorical = { + col: True if feat_type.lower() in {"categorical", "string"} else False + for col, feat_type in data_feat_type.items() + } - result = calculate_all_metafeatures_encoded_labels( - x_train, - y_train, - categorical=categorical, - dataset_name=basename, - dont_calculate=EXCLUDE_META_FEATURES, - logger=logger_, - ) - for key in list(result.metafeature_values.keys()): - if result.metafeature_values[key].type_ != "METAFEATURE": - del result.metafeature_values[key] - watcher.stop_task(task_name) - logger_.info( - "Calculating Metafeatures (encoded attributes) took %5.2fsec", - watcher.wall_elapsed(task_name), - ) + result = calculate_all_metafeatures_encoded_labels( + x_train, + y_train, + categorical=categorical, + dataset_name=basename, + dont_calculate=EXCLUDE_META_FEATURES, + logger=logger_, + ) + for key in list(result.metafeature_values.keys()): + if result.metafeature_values[key].type_ != "METAFEATURE": + del result.metafeature_values[key] + + logger_.info(f"{task_timer.name} took {task_timer.wall_duration:5.2f}sec") return result @@ -182,12 +187,10 @@ def _get_metalearning_configurations( configuration_space, task, initial_configurations_via_metalearning, + stopwatch: StopWatch, is_sparse, - watcher, logger, ): - task_name = "InitialConfigurations" - watcher.start_task(task_name) try: metalearning_configurations = suggest_via_metalearning( meta_base, @@ -203,25 +206,8 @@ def _get_metalearning_configurations( logger.error(str(e)) logger.error(traceback.format_exc()) metalearning_configurations = [] - watcher.stop_task(task_name) - return metalearning_configurations - -def _print_debug_info_of_init_configuration( - initial_configurations, basename, time_for_task, logger, watcher -): - logger.debug("Initial Configurations: (%d)" % len(initial_configurations)) - for initial_configuration in initial_configurations: - logger.debug(initial_configuration) - logger.debug( - "Looking for initial configurations took %5.2fsec", - watcher.wall_elapsed("InitialConfigurations"), - ) - logger.info( - "Time left for %s after finding initial configurations: %5.2fsec", - basename, - time_for_task - watcher.wall_elapsed(basename), - ) + return metalearning_configurations def get_smac_object( @@ -259,7 +245,7 @@ def get_smac_object( ) -class AutoMLSMBO(object): +class AutoMLSMBO: def __init__( self, config_space, @@ -269,7 +255,7 @@ def __init__( func_eval_time_limit, memory_limit, metric, - watcher, + stopwatch: StopWatch, n_jobs, dask_client: dask.distributed.Client, port: int, @@ -319,7 +305,7 @@ def __init__( self.func_eval_time_limit = int(func_eval_time_limit) self.memory_limit = memory_limit self.data_memory_limit = data_memory_limit - self.watcher = watcher + self.stopwatch = stopwatch self.num_metalearning_cfgs = num_metalearning_cfgs self.config_file = config_file self.seed = seed @@ -364,24 +350,29 @@ def reset_data_manager(self, max_mem=None): self.task = self.datamanager.info["task"] def collect_metalearning_suggestions(self, meta_base): - metalearning_configurations = _get_metalearning_configurations( - meta_base=meta_base, - basename=self.dataset_name, - metric=self.metric, - configuration_space=self.config_space, - task=self.task, - is_sparse=self.datamanager.info["is_sparse"], - initial_configurations_via_metalearning=self.num_metalearning_cfgs, - watcher=self.watcher, - logger=self.logger, - ) - _print_debug_info_of_init_configuration( - metalearning_configurations, - self.dataset_name, - self.total_walltime_limit, - self.logger, - self.watcher, - ) + + with self.stopwatch.time("Initial Configurations") as task: + metalearning_configurations = _get_metalearning_configurations( + meta_base=meta_base, + basename=self.dataset_name, + metric=self.metric, + configuration_space=self.config_space, + task=self.task, + is_sparse=self.datamanager.info["is_sparse"], + initial_configurations_via_metalearning=self.num_metalearning_cfgs, + stopwatch=self.stopwatch, + logger=self.logger, + ) + + self.logger.debug(f"Initial Configurations: {len(metalearning_configurations)}") + for config in metalearning_configurations: + self.logger.debug(config) + + self.logger.debug(f"{task.name} took {task.wall_duration:5.2f}sec") + + time_since_start = self.stopwatch.time_since(self.dataset_name, "start") + time_left = self.total_walltime_limit - time_since_start + self.logger.info(f"Time left for {task.name}: {time_left:5.2f}s") return metalearning_configurations @@ -404,7 +395,7 @@ def _calculate_metafeatures_with_limits(self, time_limit): x_train=self.datamanager.data["X_train"], y_train=self.datamanager.data["Y_train"], basename=self.dataset_name, - watcher=self.watcher, + stopwatch=self.stopwatch, logger_=self.logger, ) except Exception as e: @@ -431,7 +422,7 @@ def _calculate_metafeatures_encoded_with_limits(self, time_limit): x_train=self.datamanager.data["X_train"], y_train=self.datamanager.data["Y_train"], basename=self.dataset_name, - watcher=self.watcher, + stopwatch=self.stopwatch, logger_=self.logger, ) except Exception as e: @@ -441,7 +432,7 @@ def _calculate_metafeatures_encoded_with_limits(self, time_limit): def run_smbo(self): - self.watcher.start_task("SMBO") + self.stopwatch.start("SMBO") # == first things first: load the datamanager self.reset_data_manager() @@ -488,8 +479,9 @@ def run_smbo(self): ) ta = ExecuteTaFuncWithQueue - startup_time = self.watcher.wall_elapsed(self.dataset_name) + startup_time = self.stopwatch.time_since(self.dataset_name, "start") total_walltime_limit = self.total_walltime_limit - startup_time - 5 + scenario_dict = { "abort_on_first_run_crash": False, "save-results-instantly": True, @@ -564,6 +556,8 @@ def run_smbo(self): else: raise NotImplementedError(type(smac.solver.tae_runner)) + self.stopwatch.stop("SMBO") + return self.runhistory, self.trajectory, self._budget_type def get_metalearning_suggestions(self): diff --git a/autosklearn/util/stopwatch.py b/autosklearn/util/stopwatch.py index 9ced028cd0..5d2bb1904f 100644 --- a/autosklearn/util/stopwatch.py +++ b/autosklearn/util/stopwatch.py @@ -1,141 +1,219 @@ -# -*- encoding: utf-8 -*- -"""Created on Dec 17, 2014. +from __future__ import annotations -@author: Katharina Eggensperger -@project: AutoML2015 - -""" -from typing import Tuple +from typing import Iterator, Mapping, Optional, Tuple import sys import time -from collections import OrderedDict +from contextlib import contextmanager +from itertools import repeat + +from typing_extensions import Literal -class TimingTask(object): - _cpu_tic = 0.0 - _cpu_tac = 0.0 - _cpu_dur = 0.0 - _wall_tic = 0.0 - _wall_tac = 0.0 - _wall_dur = 0.0 +class TimingTask: + """A task to start""" def __init__(self, name: str): - self._name = name - self._cpu_tic = time.process_time() - self._wall_tic = time.time() + """ + Parameters + ---------- + name : str + The name of the task + """ + self.name = name + self.cpu_start: Optional[float] = None + self.cpu_end: Optional[float] = None + self.wall_start: Optional[float] = None + self.wall_end: Optional[float] = None + + def start(self) -> None: + """Start the task""" + if not self.started(): + self.cpu_start = time.process_time() + self.wall_start = time.time() def stop(self) -> None: - if not self._cpu_tac: - self._cpu_tac = time.process_time() - self._wall_tac = time.time() - self._cpu_dur = self._cpu_tac - self._cpu_tic - self._wall_dur = self._wall_tac - self._wall_tic - else: - sys.stdout.write("Task has already stopped\n") + """Stop the task""" + if not self.finished(): + self.cpu_end = time.process_time() + self.wall_end = time.time() - @property - def name(self) -> str: - return self._name + def finished(self) -> bool: + """Whether this task has been finished""" + return self.cpu_end is not None - @property - def cpu_tic(self) -> float: - return self._cpu_tic + def started(self) -> bool: + """Whether this task has been started""" + return self.cpu_start is not None @property - def cpu_tac(self) -> float: - return self._cpu_tac + def cpu_duration(self) -> Optional[float]: + """The duration according to cpu clock time""" + if self.cpu_start and self.cpu_end: + return self.cpu_end - self.cpu_start + else: + return None @property - def cpu_dur(self) -> float: - return self._cpu_dur + def wall_duration(self) -> Optional[float]: + """The duration according to wall clock time""" + if self.wall_end and self.wall_start: + return self.wall_end - self.wall_start + else: + return None @property - def wall_tic(self) -> float: - return self._wall_tic + def durations(self) -> Tuple[Optional[float], Optional[float]]: + """The durations (cpu, wall)""" + return self.cpu_duration, self.wall_duration - @property - def wall_tac(self) -> float: - return self._wall_tac - @property - def wall_dur(self) -> float: - return self._wall_dur +class StopWatch(Mapping[str, TimingTask]): + """Class to collect timing tasks.""" - @property - def dur(self) -> Tuple[float, float]: - return self._cpu_dur, self._wall_dur + def __init__(self) -> None: + self.tasks = {"_stopwatch_": TimingTask("_stopwatch_")} + self.tasks["_stopwatch_"].start() + def __contains__(self, name: object) -> bool: + return name in self.tasks -class StopWatch: + def __getitem__(self, name: str) -> TimingTask: + return self.tasks[name] - """Class to collect all timing tasks.""" + def __len__(self) -> int: + return len(self.tasks) - def __init__(self) -> None: - self._tasks = OrderedDict() - self._tasks["stopwatch_time"] = TimingTask("stopwatch_time") + def __iter__(self) -> Iterator[str]: + return iter(self.tasks) - def insert_task(self, name: str, cpu_dur: float, wall_dur: float) -> None: - if name not in self._tasks: - self._tasks[name] = TimingTask(name) - self._tasks[name].stop() - self._tasks[name]._wall_dur = wall_dur - self._tasks[name]._cpu_dur = cpu_dur + def start(self, name: str) -> None: + """Start a given task with a name""" + task = TimingTask(name) + self.tasks[name] = task + task.start() - def start_task(self, name: str) -> None: - if name not in self._tasks: - self._tasks[name] = TimingTask(name) + def stop(self, name: str) -> None: + """Stop a given task""" + if name in self.tasks: + self.tasks[name].stop() + else: + sys.stderr.write(f"No task with name {name}") - def wall_elapsed(self, name: str) -> float: - tmp = time.time() - if name in self._tasks: - if not self._tasks[name].wall_dur: - tsk_start = self._tasks[name].wall_tic - return tmp - tsk_start + def total_cpu(self) -> float: + """Return sum of CPU time for all so far finished tasks.""" + tasks = self.tasks.values() + return sum([t.cpu_duration for t in tasks if t.cpu_duration is not None]) + + def total_wall(self) -> float: + """Return sum of wall clock time for all so far finished tasks.""" + tasks = self.tasks.values() + return sum([t.wall_duration for t in tasks if t.wall_duration is not None]) + + def time_since( + self, + name: str, + phase: Literal["start", "end"] = "start", + default: float = 0.0, + raises: bool = False, + ) -> float: + """The wall clock time since a task either began or ended + + Parameters + ---------- + name : str + The name of the task + + phase : Literal["start", "end"] = "start" + From which phase you want to know the time elapsed since + + default: float = 0.0 + If None (default) then an error is raised if an answer can't be given. + + raises: bool = False + Whether the method should raise if it can't find a valid time + + Returns + ------- + float + The time elapsed + + Raises + ------ + ValueError + If no default is specified and + * the task has not been registered + * the "start" and the task never started + * the "end" and the task never started + """ + task = self.tasks.get(name, None) + + if task is None: + if raises: + raise ValueError(f"Task not listed in {list(self.tasks.keys())}") else: - return self._tasks[name].wall_dur - return 0.0 + return default - def cpu_elapsed(self, name: str) -> float: - tmp = time.process_time() - if name in self._tasks: - if not self._tasks[name].cpu_dur: - tsk_start = self._tasks[name].cpu_tic - return tmp - tsk_start + if phase == "start": + event_time = task.wall_start + elif phase == "end": + event_time = task.wall_end + else: + raise NotImplementedError() + + if event_time is None: + if raises: + raise ValueError(f"Task {task} has no time for {phase}") else: - return self._tasks[name].cpu_dur - return 0.0 - - def stop_task(self, name: str) -> None: - try: - self._tasks[name].stop() - except KeyError: - sys.stderr.write("There is no such task: %s\n" % name) - - def get_cpu_dur(self, name: str) -> float: - try: - return self._tasks[name].cpu_dur - except KeyError: - sys.stderr.write("There is no such task: %s\n" % name) - return 0.0 - - def get_wall_dur(self, name: str) -> float: - try: - return self._tasks[name].wall_dur - except KeyError: - sys.stderr.write("There is no such task: %s\n" % name) - return 0.0 - - def cpu_sum(self) -> float: - """Return sum of CPU time for all so far finished tasks.""" - return sum([max(0, self._tasks[tsk].cpu_dur) for tsk in self._tasks]) + return default - def wall_sum(self) -> float: - """Return sum of CPU time for all so far finished tasks.""" - return sum([max(0, self._tasks[tsk].wall_dur) for tsk in self._tasks]) + return time.time() - event_time + + def wall_elapsed(self, name: str) -> float: + """Get the currently elapsed wall time for a task""" + if name not in self.tasks: + return 0.0 + + task = self.tasks[name] + if task.wall_start is None: + return 0.0 - def __repr__(self) -> str: - ret_str = "| %10s | %10s | %10s | %10s | %10s | %10s | %10s |\n" % ( + if task.wall_duration is not None: + return task.wall_duration + else: + return time.time() - task.wall_start + + def cpu_elapsed(self, name: str) -> float: + """Get the currently elapsed cpu time for a task""" + if name not in self.tasks: + return 0.0 + + task = self.tasks[name] + if task.cpu_start is None: + return 0.0 + + if task.cpu_duration is not None: + return task.cpu_duration + else: + return time.time() - task.cpu_start + + @contextmanager + def time(self, name: str) -> Iterator[TimingTask]: + """Start timing a task + + Parameters + ---------- + name : str + The name of the task to measure + """ + task = TimingTask(name) + self.tasks[name] = task + task.start() + yield task + task.stop() + + def __str__(self) -> str: + headers = [ "Name", "CPUStart", "CPUEnd", @@ -143,19 +221,19 @@ def __repr__(self) -> str: "WallStart", "WallEnd", "WallDur", - ) - ret_str += "+" + "------------+" * 7 + "\n" - offset = self._tasks["stopwatch_time"].wall_tic - for tsk in self._tasks: - if self._tasks[tsk].wall_tac: - wall_tac = self._tasks[tsk].wall_tac - offset - ret_str += "| %10s | %10.5f | %10.5f | %10.5f | %10s | %10s | %10s |\n" % ( - tsk, - self._tasks[tsk].cpu_tic, - self._tasks[tsk].cpu_tac, - self.cpu_elapsed(tsk), - self._tasks[tsk].wall_tic - offset, - wall_tac if self._tasks[tsk].wall_tac else False, - self.wall_elapsed(tsk), + ] + header = "|".join([f"{h:10s}" for h in headers]) + + sep = "-" * 12 + seperator = "+" + "+".join(repeat(sep, len(headers))) + "+" + + entries = [] + for name, task in self.tasks.items(): + entry = ( + f"{task.name:10s} | {task.cpu_start:10.5f} | {task.cpu_end: 10.5f} | " + f"{task.cpu_duration:10.5f} | {task.wall_start: 10s} | " + f"{task.wall_end:10s} | {task.wall_duration:10s} |" ) - return ret_str + entries.append(entry) + + return "\n".join([header, seperator] + entries) diff --git a/pyproject.toml b/pyproject.toml index 3e3aafb1f6..7fa3bc4111 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,6 +42,7 @@ sections = [ "LOCALFOLDER" ] # section ordering multi_line_output = 3 # https://pycqa.github.io/isort/docs/configuration/multi_line_output_modes.html +skip = ["autosklearn/automl_common"] # Don't validate automl_common [tool.pydocstyle] convention = "numpy" diff --git a/scripts/03_calculate_metafeatures.py b/scripts/03_calculate_metafeatures.py index 3b32dde8e3..95d857145a 100644 --- a/scripts/03_calculate_metafeatures.py +++ b/scripts/03_calculate_metafeatures.py @@ -33,7 +33,7 @@ def calculate_metafeatures(task_id): X_train, y_train, X_test, y_test, cat, task_type, dataset_name = load_task(task_id) - watch = StopWatch() + stopwatch = StopWatch() if task_type == "classification": if len(np.unique(y_train)) == 2: @@ -50,7 +50,7 @@ def calculate_metafeatures(task_id): data_info_task=task_type, basename=dataset_name, logger_=logger, - watcher=watch, + stopwatch=stopwatch, ) _metafeatures_encoded_labels = _calculate_metafeatures_encoded( @@ -60,7 +60,7 @@ def calculate_metafeatures(task_id): task=task_type, basename=dataset_name, logger_=logger, - watcher=watch, + stopwatch=stopwatch, ) mf = _metafeatures_labels diff --git a/test/test_automl/cases.py b/test/test_automl/cases.py index 70d68c4b73..a3dfa00dba 100644 --- a/test/test_automl/cases.py +++ b/test/test_automl/cases.py @@ -101,6 +101,8 @@ def case_classifier_fitted_cv( X, y, Xt, yt = make_sklearn_dataset(name=dataset) model = make_automl_classifier( + time_left_for_this_task=60, # We include some extra time for cv + per_run_time_limit=10, resampling_strategy=resampling_strategy, temporary_directory=cache.path("backend"), delete_tmp_folder_after_terminate=False, @@ -158,6 +160,8 @@ def case_regressor_fitted_cv( X, y, Xt, yt = make_sklearn_dataset(name=dataset) model = make_automl_regressor( + time_left_for_this_task=60, # Give some extra time for cv + per_run_time_limit=10, temporary_directory=cache.path("backend"), delete_tmp_folder_after_terminate=False, resampling_strategy=resampling_strategy, diff --git a/test/test_optimizer/test_smbo.py b/test/test_optimizer/test_smbo.py index eb23d3a932..0b14f4a722 100644 --- a/test/test_optimizer/test_smbo.py +++ b/test/test_optimizer/test_smbo.py @@ -14,8 +14,7 @@ @pytest.mark.parametrize("context", ["fork", "forkserver"]) -def test_smbo_metalearning_configurations(backend, context, dask_client): - +def test_smbo_metalearning_configurations(backend, context, dask_client) -> None: # Get the inputs to the optimizer X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") config_space = AutoML( @@ -29,7 +28,7 @@ def test_smbo_metalearning_configurations(backend, context, dask_client): task=BINARY_CLASSIFICATION, only_return_configuration_space=True, ) - watcher = StopWatch() + stopwatch = StopWatch() # Create an optimizer smbo = AutoMLSMBO( @@ -40,7 +39,7 @@ def test_smbo_metalearning_configurations(backend, context, dask_client): func_eval_time_limit=5, memory_limit=4096, metric=autosklearn.metrics.accuracy, - watcher=watcher, + stopwatch=stopwatch, n_jobs=1, dask_client=dask_client, port=logging.handlers.DEFAULT_TCP_LOGGING_PORT, diff --git a/test/test_util/test_StopWatch.py b/test/test_util/test_StopWatch.py deleted file mode 100644 index a59940d0c4..0000000000 --- a/test/test_util/test_StopWatch.py +++ /dev/null @@ -1,42 +0,0 @@ -# -*- encoding: utf-8 -*- -"""Created on Dec 16, 2014. - -@author: Katharina Eggensperger -@projekt: AutoML2015 - -""" -import time - -from autosklearn.util.stopwatch import StopWatch - -import unittest -import unittest.mock - - -class Test(unittest.TestCase): - _multiprocess_can_split_ = True - - def test_stopwatch_overhead(self): - - # Wall Overhead - start = time.time() - cpu_start = time.process_time() - watch = StopWatch() - for i in range(1, 1000): - watch.start_task("task_%d" % i) - watch.stop_task("task_%d" % i) - cpu_stop = time.process_time() - stop = time.time() - dur = stop - start - cpu_dur = cpu_stop - cpu_start - cpu_overhead = cpu_dur - watch.cpu_sum() - wall_overhead = dur - watch.wall_sum() - - self.assertLess(cpu_overhead, 1) - self.assertLess(wall_overhead, 1) - self.assertLess(watch.cpu_sum(), 2 * watch.wall_sum()) - - -if __name__ == "__main__": - # import sys;sys.argv = ['', 'Test.testName'] - unittest.main() diff --git a/test/test_util/test_stopwatch.py b/test/test_util/test_stopwatch.py new file mode 100644 index 0000000000..75afd537f2 --- /dev/null +++ b/test/test_util/test_stopwatch.py @@ -0,0 +1,39 @@ +import time + +from autosklearn.util.stopwatch import StopWatch + + +def test_stopwatch_overhead() -> None: + wall_start = time.time() + cpu_start = time.process_time() + + watch = StopWatch() + for i in range(1, 1000): + watch.start("task_%d" % i) + watch.stop("task_%d" % i) + + cpu_end = time.process_time() + wall_end = time.time() + + wall_duration = wall_end - wall_start + cpu_duration = cpu_end - cpu_start + + cpu_overhead = cpu_duration - watch.total_cpu() + wall_overhead = wall_duration - watch.total_wall() + + assert cpu_overhead < 1 + assert wall_overhead < 1 + assert watch.total_cpu() < 2 * watch.total_wall() + + +def test_contextmanager() -> None: + watch = StopWatch() + + with watch.time("task"): + assert watch["task"].started() + + assert "task" in watch + assert watch["task"].finished() + + assert watch["task"].cpu_duration is not None + assert watch["task"].wall_duration is not None From f0ba7de76657df54e76c7b15c91cf8bf01c17f36 Mon Sep 17 00:00:00 2001 From: eddiebergman Date: Thu, 21 Apr 2022 19:21:51 +0200 Subject: [PATCH 045/118] Fix citations Co-authored-by: Rattko --- README.md | 12 ++++++------ doc/index.rst | 2 +- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 2a7908a4af..b506960d70 100644 --- a/README.md +++ b/README.md @@ -26,15 +26,15 @@ predictions = cls.predict(X_test) If you use auto-sklearn in scientific publications, we would appreciate citations. -**Efficient and Robust Automated Machine Learning** -*Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum and Frank Hutter* -Advances in Neural Information Processing Systems 28 (2015) +**Efficient and Robust Automated Machine Learning** +*Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum and Frank Hutter* +Advances in Neural Information Processing Systems 28 (2015) [Link](https://papers.neurips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf) to publication. ``` @inproceedings{feurer-neurips15a, title = {Efficient and Robust Automated Machine Learning}, - author = {Feurer, Matthias and Klein, Aaron and Eggensperger, Katharina Springenberg, Jost and Blum, Manuel and Hutter, Frank}, + author = {Feurer, Matthias and Klein, Aaron and Eggensperger, Katharina and Springenberg, Jost and Blum, Manuel and Hutter, Frank}, booktitle = {Advances in Neural Information Processing Systems 28 (2015)}, pages = {2962--2970}, year = {2015} @@ -43,8 +43,8 @@ Advances in Neural Information Processing Systems 28 (2015) ---------------------------------------- -**Auto-Sklearn 2.0: The Next Generation** -*Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer and Frank Hutter** +**Auto-Sklearn 2.0: The Next Generation** +*Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer and Frank Hutter** arXiv:2007.04074 [cs.LG], 2020 [Link](https://arxiv.org/abs/2007.04074) to publication. diff --git a/doc/index.rst b/doc/index.rst index e0690ac8e7..24c1931f54 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -95,7 +95,7 @@ reference to the following paper: @inproceedings{feurer-neurips15a, title = {Efficient and Robust Automated Machine Learning}, - author = {Feurer, Matthias and Klein, Aaron and Eggensperger, Katharina Springenberg, Jost and Blum, Manuel and Hutter, Frank}, + author = {Feurer, Matthias and Klein, Aaron and Eggensperger, Katharina and Springenberg, Jost and Blum, Manuel and Hutter, Frank}, booktitle = {Advances in Neural Information Processing Systems 28 (2015)}, pages = {2962--2970}, year = {2015} From 85f1de2e3a503cc0e1fcd493dd4a036452b3be4a Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Thu, 21 Apr 2022 20:12:04 +0200 Subject: [PATCH 046/118] Fix github actions `sdist.yaml` and `precommit` (#1451) * Use newer --strict arg for twine * Update precommit config --- .github/workflows/dist.yml | 3 +-- .pre-commit-config.yaml | 6 +++--- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/.github/workflows/dist.yml b/.github/workflows/dist.yml index 29eb0850dc..d9cb142135 100644 --- a/.github/workflows/dist.yml +++ b/.github/workflows/dist.yml @@ -39,8 +39,7 @@ jobs: run: | pip install twine last_dist=$(ls -t dist/auto-sklearn-*.tar.gz | head -n 1) - twine_output=`twine check "$last_dist"` - if [[ "$twine_output" != "Checking $last_dist: PASSED" ]]; then echo $twine_output && exit 1;fi + twine check "$last_dist" --strict - name: Install dist run: | diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index f20c303b2f..ab3472ddc6 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -14,8 +14,8 @@ repos: name: isort imports test files: test/.* - - repo: https://github.com/ambv/black - rev: 22.1.0 + - repo: https://github.com/psf/black + rev: 22.3.0 hooks: - id: black name: black formatter autosklearn @@ -39,7 +39,7 @@ repos: additional_dependencies: ["toml"] # Needed to parse pyproject.toml - repo: https://github.com/pre-commit/mirrors-mypy - rev: v0.930 + rev: v0.942 hooks: - id: mypy name: mypy auto-sklearn From a5610d527f158c7c305d43d819ffea6efbe9e08e Mon Sep 17 00:00:00 2001 From: Lukas Strack <73999018+Louquinze@users.noreply.github.com> Date: Mon, 9 May 2022 12:43:03 +0200 Subject: [PATCH 047/118] Made categorical dictionary more cohesive to overall structure (#1454) * change treatment of string features. In file `smbo.py` string columns are marked as categorical. Previously string columns are treated as not categorical which transfers to numerical. We decided that encoded string columns are more similar to OHE as they are to numerical transformations. This change effects the metalearning part of `autosklearn` exclusively. * change treatment of string features. In file `smbo.py` string columns are marked as categorical. Previously string columns are treated as not categorical which transfers to numerical. We decided that encoded string columns are more similar to OHE as they are to numerical transformations. This change effects the metalearning part of `autosklearn` exclusively. * made categorical dictionary more cohesive. Use the previously create feat_type dictionary which stores more information and reduce it to categorical where it is really needed. * change test files since they now get the feat type dictionary and no longer the categorical one * change test files since they now get the feat type dictionary and no longer the categorical one * moved feat_type to categorical conv. to the helper functions * fixed minor issues * fixed minor issues * fixed minor issues * fixed bug in metalearning tests --- .../metalearning/metafeatures/metafeature.py | 13 +- .../metalearning/metafeatures/metafeatures.py | 201 +++++++------ .../data_preprocessing/feature_type.py | 2 +- autosklearn/smbo.py | 13 +- .../pyMetaLearn/test_meta_features.py | 284 +++++++++--------- .../pyMetaLearn/test_meta_features_sparse.py | 47 ++- 6 files changed, 281 insertions(+), 279 deletions(-) diff --git a/autosklearn/metalearning/metafeatures/metafeature.py b/autosklearn/metalearning/metafeatures/metafeature.py index 033b76116b..ec97eafb22 100644 --- a/autosklearn/metalearning/metafeatures/metafeature.py +++ b/autosklearn/metalearning/metafeatures/metafeature.py @@ -15,19 +15,18 @@ def __init__(self): pass @abstractmethod - def _calculate(cls, X, y, logger, categorical): + def _calculate(cls, X, y, logger, feat_type): pass - def __call__(self, X, y, logger, categorical=None): - if categorical is None: - categorical = [False for i in range(X.shape[1])] + def __call__(self, X, y, logger, feat_type=None): + if feat_type is None: + feat_type = {i: "numerical" for i in range(X.shape[1])} starttime = time.time() - try: if scipy.sparse.issparse(X) and hasattr(self, "_calculate_sparse"): - value = self._calculate_sparse(X, y, logger, categorical) + value = self._calculate_sparse(X, y, logger, feat_type) else: - value = self._calculate(X, y, logger, categorical) + value = self._calculate(X, y, logger, feat_type) comment = "" except MemoryError: value = None diff --git a/autosklearn/metalearning/metafeatures/metafeatures.py b/autosklearn/metalearning/metafeatures/metafeatures.py index 3c95fbf22f..2c4d9cf4bd 100644 --- a/autosklearn/metalearning/metafeatures/metafeatures.py +++ b/autosklearn/metalearning/metafeatures/metafeatures.py @@ -135,13 +135,13 @@ def wrapper(metafeature_class): ################################################################################ @metafeatures.define("NumberOfInstances") class NumberOfInstances(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): return float(X.shape[0]) @metafeatures.define("LogNumberOfInstances", dependency="NumberOfInstances") class LogNumberOfInstances(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): return np.log(metafeatures.get_value("NumberOfInstances")) @@ -154,7 +154,7 @@ class NumberOfClasses(MetaFeature): does this for each label seperately and returns the mean. """ - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): if type_of_target(y) == "multilabel-indicator": # We have a label binary indicator array: # each sample is one row of a 2d array of shape (n_samples, n_classes) @@ -167,23 +167,23 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("NumberOfFeatures") class NumberOfFeatures(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): return float(X.shape[1]) @metafeatures.define("LogNumberOfFeatures", dependency="NumberOfFeatures") class LogNumberOfFeatures(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): return np.log(metafeatures.get_value("NumberOfFeatures")) @helper_functions.define("MissingValues") class MissingValues(HelperFunction): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): missing = pd.isna(X) return missing - def _calculate_sparse(self, X, y, logger, categorical): + def _calculate_sparse(self, X, y, logger, feat_type): data = [True if not np.isfinite(x) else False for x in X.data] missing = X.__class__((data, X.indices, X.indptr), shape=X.shape, dtype=bool) return missing @@ -191,12 +191,12 @@ def _calculate_sparse(self, X, y, logger, categorical): @metafeatures.define("NumberOfInstancesWithMissingValues", dependency="MissingValues") class NumberOfInstancesWithMissingValues(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): missing = helper_functions.get_value("MissingValues") num_missing = missing.sum(axis=1) return float(np.sum([1 if num > 0 else 0 for num in num_missing])) - def _calculate_sparse(self, X, y, logger, categorical): + def _calculate_sparse(self, X, y, logger, feat_type): missing = helper_functions.get_value("MissingValues") new_missing = missing.tocsr() num_missing = [ @@ -212,7 +212,7 @@ def _calculate_sparse(self, X, y, logger, categorical): dependency="NumberOfInstancesWithMissingValues", ) class PercentageOfInstancesWithMissingValues(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): n_missing = metafeatures.get_value("NumberOfInstancesWithMissingValues") n_total = float(metafeatures["NumberOfInstances"](X, y, logger).value) return float(n_missing / n_total) @@ -220,12 +220,12 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("NumberOfFeaturesWithMissingValues", dependency="MissingValues") class NumberOfFeaturesWithMissingValues(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): missing = helper_functions.get_value("MissingValues") num_missing = missing.sum(axis=0) return float(np.sum([1 if num > 0 else 0 for num in num_missing])) - def _calculate_sparse(self, X, y, logger, categorical): + def _calculate_sparse(self, X, y, logger, feat_type): missing = helper_functions.get_value("MissingValues") new_missing = missing.tocsc() num_missing = [ @@ -241,7 +241,7 @@ def _calculate_sparse(self, X, y, logger, categorical): dependency="NumberOfFeaturesWithMissingValues", ) class PercentageOfFeaturesWithMissingValues(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): n_missing = metafeatures.get_value("NumberOfFeaturesWithMissingValues") n_total = float(metafeatures["NumberOfFeatures"](X, y, logger).value) return float(n_missing / n_total) @@ -249,7 +249,7 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("NumberOfMissingValues", dependency="MissingValues") class NumberOfMissingValues(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): if scipy.sparse.issparse(X): return float(helper_functions.get_value("MissingValues").sum()) else: @@ -258,7 +258,7 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("PercentageOfMissingValues", dependency="NumberOfMissingValues") class PercentageOfMissingValues(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): return float(metafeatures.get_value("NumberOfMissingValues")) / float( X.shape[0] * X.shape[1] ) @@ -267,24 +267,24 @@ def _calculate(self, X, y, logger, categorical): # TODO: generalize this! @metafeatures.define("NumberOfNumericFeatures") class NumberOfNumericFeatures(MetaFeature): - def _calculate(self, X, y, logger, categorical): - return len(categorical) - np.sum(list(categorical.values())) + def _calculate(self, X, y, logger, feat_type): + return np.sum([value == "numerical" for value in feat_type.values()]) @metafeatures.define("NumberOfCategoricalFeatures") class NumberOfCategoricalFeatures(MetaFeature): - def _calculate(self, X, y, logger, categorical): - return np.sum(list(categorical.values())) + def _calculate(self, X, y, logger, feat_type): + return np.sum([value == "categorical" for value in feat_type.values()]) @metafeatures.define("RatioNumericalToNominal") class RatioNumericalToNominal(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): num_categorical = float( - metafeatures["NumberOfCategoricalFeatures"](X, y, logger, categorical).value + metafeatures["NumberOfCategoricalFeatures"](X, y, logger, feat_type).value ) num_numerical = float( - metafeatures["NumberOfNumericFeatures"](X, y, logger, categorical).value + metafeatures["NumberOfNumericFeatures"](X, y, logger, feat_type).value ) if num_categorical == 0.0: return 0.0 @@ -293,12 +293,12 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("RatioNominalToNumerical") class RatioNominalToNumerical(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): num_categorical = float( - metafeatures["NumberOfCategoricalFeatures"](X, y, logger, categorical).value + metafeatures["NumberOfCategoricalFeatures"](X, y, logger, feat_type).value ) num_numerical = float( - metafeatures["NumberOfNumericFeatures"](X, y, logger, categorical).value + metafeatures["NumberOfNumericFeatures"](X, y, logger, feat_type).value ) if num_numerical == 0.0: return 0.0 @@ -309,7 +309,7 @@ def _calculate(self, X, y, logger, categorical): # Number of attributes divided by number of samples @metafeatures.define("DatasetRatio") class DatasetRatio(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): return float(metafeatures["NumberOfFeatures"](X, y, logger).value) / float( metafeatures["NumberOfInstances"](X, y, logger).value ) @@ -317,13 +317,13 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("LogDatasetRatio", dependency="DatasetRatio") class LogDatasetRatio(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): return np.log(metafeatures.get_value("DatasetRatio")) @metafeatures.define("InverseDatasetRatio") class InverseDatasetRatio(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): return float(metafeatures["NumberOfInstances"](X, y, logger).value) / float( metafeatures["NumberOfFeatures"](X, y, logger).value ) @@ -331,17 +331,17 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("LogInverseDatasetRatio", dependency="InverseDatasetRatio") class LogInverseDatasetRatio(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): return np.log(metafeatures.get_value("InverseDatasetRatio")) @helper_functions.define("ClassOccurences") class ClassOccurences(HelperFunction): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): if len(y.shape) == 2: occurences = [] for i in range(y.shape[1]): - occurences.append(self._calculate(X, y[:, i], logger, categorical)) + occurences.append(self._calculate(X, y[:, i], logger, feat_type)) return occurences else: occurence_dict = defaultdict(float) @@ -352,7 +352,7 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("ClassProbabilityMin", dependency="ClassOccurences") class ClassProbabilityMin(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): occurences = helper_functions.get_value("ClassOccurences") min_value = np.iinfo(np.int64).max @@ -371,7 +371,7 @@ def _calculate(self, X, y, logger, categorical): # aka default accuracy @metafeatures.define("ClassProbabilityMax", dependency="ClassOccurences") class ClassProbabilityMax(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): occurences = helper_functions.get_value("ClassOccurences") max_value = -1 @@ -389,7 +389,7 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("ClassProbabilityMean", dependency="ClassOccurences") class ClassProbabilityMean(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): occurence_dict = helper_functions.get_value("ClassOccurences") if len(y.shape) == 2: @@ -408,7 +408,7 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("ClassProbabilitySTD", dependency="ClassOccurences") class ClassProbabilitySTD(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): occurence_dict = helper_functions.get_value("ClassOccurences") if len(y.shape) == 2: @@ -434,7 +434,11 @@ def _calculate(self, X, y, logger, categorical): # be the counterpart for the skewness and kurtosis of the numerical features @helper_functions.define("NumSymbols") class NumSymbols(HelperFunction): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): + categorical = { + key: True if value.lower() == "categorical" else False + for key, value in feat_type.items() + } symbols_per_column = [] for i in range(X.shape[1]): if categorical[X.columns[i] if hasattr(X, "columns") else i]: @@ -446,7 +450,11 @@ def _calculate(self, X, y, logger, categorical): symbols_per_column.append(num_unique) return symbols_per_column - def _calculate_sparse(self, X, y, logger, categorical): + def _calculate_sparse(self, X, y, logger, feat_type): + categorical = { + key: True if value.lower() == "categorical" else False + for key, value in feat_type.items() + } symbols_per_column = [] new_X = X.tocsc() for i in range(new_X.shape[1]): @@ -459,7 +467,7 @@ def _calculate_sparse(self, X, y, logger, categorical): @metafeatures.define("SymbolsMin", dependency="NumSymbols") class SymbolsMin(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): # The minimum can only be zero if there are no nominal features, # otherwise it is at least one # TODO: shouldn't this rather be two? @@ -472,7 +480,7 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("SymbolsMax", dependency="NumSymbols") class SymbolsMax(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): values = helper_functions.get_value("NumSymbols") if len(values) == 0: return 0 @@ -481,7 +489,7 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("SymbolsMean", dependency="NumSymbols") class SymbolsMean(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): # TODO: categorical attributes without a symbol don't count towards this # measure values = [val for val in helper_functions.get_value("NumSymbols") if val > 0] @@ -491,7 +499,7 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("SymbolsSTD", dependency="NumSymbols") class SymbolsSTD(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): values = [val for val in helper_functions.get_value("NumSymbols") if val > 0] std = np.nanstd(values) return std if np.isfinite(std) else 0 @@ -499,7 +507,7 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("SymbolsSum", dependency="NumSymbols") class SymbolsSum(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): sum = np.nansum(helper_functions.get_value("NumSymbols")) return sum if np.isfinite(sum) else 0 @@ -514,10 +522,14 @@ def _calculate(self, X, y, logger, categorical): @helper_functions.define("Kurtosisses") class Kurtosisses(HelperFunction): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): + numerical = { + key: True if value.lower() == "numerical" else False + for key, value in feat_type.items() + } kurts = [] for i in range(X.shape[1]): - if not categorical[X.columns[i] if hasattr(X, "columns") else i]: + if numerical[X.columns[i] if hasattr(X, "columns") else i]: kurts.append( scipy.stats.kurtosis( X.iloc[:, i] if hasattr(X, "iloc") else X[:, i] @@ -525,11 +537,15 @@ def _calculate(self, X, y, logger, categorical): ) return kurts - def _calculate_sparse(self, X, y, logger, categorical): + def _calculate_sparse(self, X, y, logger, feat_type): + numerical = { + key: True if value.lower() == "numerical" else False + for key, value in feat_type.items() + } kurts = [] X_new = X.tocsc() for i in range(X_new.shape[1]): - if not categorical[X.columns[i] if hasattr(X, "columns") else i]: + if numerical[X.columns[i] if hasattr(X, "columns") else i]: start = X_new.indptr[i] stop = X_new.indptr[i + 1] kurts.append(scipy.stats.kurtosis(X_new.data[start:stop])) @@ -538,7 +554,7 @@ def _calculate_sparse(self, X, y, logger, categorical): @metafeatures.define("KurtosisMin", dependency="Kurtosisses") class KurtosisMin(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): kurts = helper_functions.get_value("Kurtosisses") minimum = np.nanmin(kurts) if len(kurts) > 0 else 0 return minimum if np.isfinite(minimum) else 0 @@ -546,7 +562,7 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("KurtosisMax", dependency="Kurtosisses") class KurtosisMax(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): kurts = helper_functions.get_value("Kurtosisses") maximum = np.nanmax(kurts) if len(kurts) > 0 else 0 return maximum if np.isfinite(maximum) else 0 @@ -554,7 +570,7 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("KurtosisMean", dependency="Kurtosisses") class KurtosisMean(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): kurts = helper_functions.get_value("Kurtosisses") mean = np.nanmean(kurts) if len(kurts) > 0 else 0 return mean if np.isfinite(mean) else 0 @@ -562,7 +578,7 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("KurtosisSTD", dependency="Kurtosisses") class KurtosisSTD(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): kurts = helper_functions.get_value("Kurtosisses") std = np.nanstd(kurts) if len(kurts) > 0 else 0 return std if np.isfinite(std) else 0 @@ -570,20 +586,28 @@ def _calculate(self, X, y, logger, categorical): @helper_functions.define("Skewnesses") class Skewnesses(HelperFunction): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): + numerical = { + key: True if value.lower() == "numerical" else False + for key, value in feat_type.items() + } skews = [] for i in range(X.shape[1]): - if not categorical[X.columns[i] if hasattr(X, "columns") else i]: + if numerical[X.columns[i] if hasattr(X, "columns") else i]: skews.append( scipy.stats.skew(X.iloc[:, i] if hasattr(X, "iloc") else X[:, i]) ) return skews - def _calculate_sparse(self, X, y, logger, categorical): + def _calculate_sparse(self, X, y, logger, feat_type): + numerical = { + key: True if value.lower() == "numerical" else False + for key, value in feat_type.items() + } skews = [] X_new = X.tocsc() for i in range(X_new.shape[1]): - if not categorical[X.columns[i] if hasattr(X, "columns") else i]: + if numerical[X.columns[i] if hasattr(X, "columns") else i]: start = X_new.indptr[i] stop = X_new.indptr[i + 1] skews.append(scipy.stats.skew(X_new.data[start:stop])) @@ -592,7 +616,7 @@ def _calculate_sparse(self, X, y, logger, categorical): @metafeatures.define("SkewnessMin", dependency="Skewnesses") class SkewnessMin(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): skews = helper_functions.get_value("Skewnesses") minimum = np.nanmin(skews) if len(skews) > 0 else 0 return minimum if np.isfinite(minimum) else 0 @@ -600,7 +624,7 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("SkewnessMax", dependency="Skewnesses") class SkewnessMax(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): skews = helper_functions.get_value("Skewnesses") maximum = np.nanmax(skews) if len(skews) > 0 else 0 return maximum if np.isfinite(maximum) else 0 @@ -608,7 +632,7 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("SkewnessMean", dependency="Skewnesses") class SkewnessMean(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): skews = helper_functions.get_value("Skewnesses") mean = np.nanmean(skews) if len(skews) > 0 else 0 return mean if np.isfinite(mean) else 0 @@ -616,7 +640,7 @@ def _calculate(self, X, y, logger, categorical): @metafeatures.define("SkewnessSTD", dependency="Skewnesses") class SkewnessSTD(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): skews = helper_functions.get_value("Skewnesses") std = np.nanstd(skews) if len(skews) > 0 else 0 return std if np.isfinite(std) else 0 @@ -637,7 +661,7 @@ def cancor2(X, y): # Information-theoretic metafeatures @metafeatures.define("ClassEntropy") class ClassEntropy(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): labels = 1 if len(y.shape) == 1 else y.shape[1] entropies = [] @@ -687,7 +711,7 @@ def _calculate(self, X, y, logger, categorical): # Linear discriminant learner @metafeatures.define("LandmarkLDA") class LandmarkLDA(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): import sklearn.discriminant_analysis if type(y) in ("binary", "multiclass"): @@ -727,14 +751,14 @@ def _calculate(self, X, y, logger, categorical): self.logger.warning("LDA failed: %s Returned 0 instead!" % e) return np.NaN - def _calculate_sparse(self, X, y, logger, categorical): + def _calculate_sparse(self, X, y, logger, feat_type): return np.NaN # Naive Bayes @metafeatures.define("LandmarkNaiveBayes") class LandmarkNaiveBayes(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): import sklearn.naive_bayes if type(y) in ("binary", "multiclass"): @@ -767,14 +791,14 @@ def _calculate(self, X, y, logger, categorical): ) return accuracy / 5 - def _calculate_sparse(self, X, y, logger, categorical): + def _calculate_sparse(self, X, y, logger, feat_type): return np.NaN # Cart learner instead of C5.0 @metafeatures.define("LandmarkDecisionTree") class LandmarkDecisionTree(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): import sklearn.tree if type(y) in ("binary", "multiclass"): @@ -808,7 +832,7 @@ def _calculate(self, X, y, logger, categorical): ) return accuracy / 5 - def _calculate_sparse(self, X, y, logger, categorical): + def _calculate_sparse(self, X, y, logger, feat_type): return np.NaN @@ -821,7 +845,7 @@ def _calculate_sparse(self, X, y, logger, categorical): # saves a lot of time... @metafeatures.define("LandmarkDecisionNodeLearner") class LandmarkDecisionNodeLearner(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): import sklearn.tree if type(y) in ("binary", "multiclass"): @@ -860,13 +884,13 @@ def _calculate(self, X, y, logger, categorical): ) return accuracy / 5 - def _calculate_sparse(self, X, y, logger, categorical): + def _calculate_sparse(self, X, y, logger, feat_type): return np.NaN @metafeatures.define("LandmarkRandomNodeLearner") class LandmarkRandomNodeLearner(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): import sklearn.tree if type(y) in ("binary", "multiclass"): @@ -898,7 +922,7 @@ def _calculate(self, X, y, logger, categorical): ) return accuracy / 5 - def _calculate_sparse(self, X, y, logger, categorical): + def _calculate_sparse(self, X, y, logger, feat_type): return np.NaN @@ -931,7 +955,7 @@ def landmark_worst_node_learner(X, y): # intuition behind this landmark, but Elite 1NN is used nowhere else... @metafeatures.define("Landmark1NN") class Landmark1NN(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): import sklearn.neighbors if type(y) in ("binary", "multiclass"): @@ -974,7 +998,7 @@ def _calculate(self, X, y, logger, categorical): # kurtosis of a dataset projected onto one principal component @helper_functions.define("PCA") class PCA(HelperFunction): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): import sklearn.decomposition pca = sklearn.decomposition.PCA(copy=True) @@ -992,7 +1016,7 @@ def _calculate(self, X, y, logger, categorical): self.logger.warning("Failed to compute a Principle Component Analysis") return None - def _calculate_sparse(self, X, y, logger, categorical): + def _calculate_sparse(self, X, y, logger, feat_type): import sklearn.decomposition rs = np.random.RandomState(42) @@ -1016,7 +1040,7 @@ def _calculate_sparse(self, X, y, logger, categorical): # Maybe define some more... @metafeatures.define("PCAFractionOfComponentsFor95PercentVariance", dependency="PCA") class PCAFractionOfComponentsFor95PercentVariance(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): pca_ = helper_functions.get_value("PCA") if pca_ is None: return np.NaN @@ -1031,7 +1055,7 @@ def _calculate(self, X, y, logger, categorical): # Kurtosis of first PC @metafeatures.define("PCAKurtosisFirstPC", dependency="PCA") class PCAKurtosisFirstPC(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): pca_ = helper_functions.get_value("PCA") if pca_ is None: return np.NaN @@ -1047,7 +1071,7 @@ def _calculate(self, X, y, logger, categorical): # Skewness of first PC @metafeatures.define("PCASkewnessFirstPC", dependency="PCA") class PCASkewnessFirstPC(MetaFeature): - def _calculate(self, X, y, logger, categorical): + def _calculate(self, X, y, logger, feat_type): pca_ = helper_functions.get_value("PCA") if pca_ is None: return np.NaN @@ -1061,7 +1085,7 @@ def _calculate(self, X, y, logger, categorical): def calculate_all_metafeatures_encoded_labels( - X, y, categorical, dataset_name, logger, calculate=None, dont_calculate=None + X, y, feat_type, dataset_name, logger, calculate=None, dont_calculate=None ): """ Calculate only metafeatures for which a 1HotEncoded feature matrix is necessery. @@ -1073,7 +1097,7 @@ def calculate_all_metafeatures_encoded_labels( return calculate_all_metafeatures( X, y, - categorical, + feat_type, dataset_name, calculate=calculate, dont_calculate=dont_calculate, @@ -1082,7 +1106,7 @@ def calculate_all_metafeatures_encoded_labels( def calculate_all_metafeatures_with_labels( - X, y, categorical, dataset_name, logger, calculate=None, dont_calculate=None + X, y, feat_type, dataset_name, logger, calculate=None, dont_calculate=None ): if dont_calculate is None: dont_calculate = set() @@ -1092,7 +1116,7 @@ def calculate_all_metafeatures_with_labels( return calculate_all_metafeatures( X, y, - categorical, + feat_type, dataset_name, calculate=calculate, dont_calculate=dont_calculate, @@ -1103,7 +1127,7 @@ def calculate_all_metafeatures_with_labels( def calculate_all_metafeatures( X, y, - categorical, + feat_type, dataset_name, logger, calculate=None, @@ -1138,11 +1162,6 @@ def calculate_all_metafeatures( # sparse matrices because of wrong sparse format) sparse = scipy.sparse.issparse(X) - feat_type = { - key: "categorical" if value else "numerical" - for key, value in categorical.items() - } - # TODO make this more cohesive to the overall structure (quick bug fix) if isinstance(X, pd.DataFrame): for key in X.select_dtypes(include="string").columns: @@ -1155,8 +1174,8 @@ def calculate_all_metafeatures( force_sparse_output=True, ) X_transformed = DPP.fit_transform(X) - categorical_transformed = { - i: False for i in range(X_transformed.shape[1]) + feat_type_transformed = { + i: "numerical" for i in range(X_transformed.shape[1]) } # Densify the transformed matrix @@ -1185,11 +1204,11 @@ def calculate_all_metafeatures( X_ = X_transformed y_ = y_transformed - categorical_ = categorical_transformed + feat_type_ = feat_type_transformed else: X_ = X y_ = y - categorical_ = categorical + feat_type_ = feat_type dependency = metafeatures.get_dependency(name) if dependency is not None: @@ -1206,14 +1225,14 @@ def calculate_all_metafeatures( elif is_helper_function and not helper_functions.is_calculated(dependency): logger.info("%s: Going to calculate: %s", dataset_name, dependency) value = helper_functions[dependency]( - X_, y_, categorical=categorical_, logger=logger + X_, y_, feat_type=feat_type_, logger=logger ) helper_functions.set_value(dependency, value) mf_[dependency] = value logger.info("%s: Going to calculate: %s", dataset_name, name) - value = metafeatures[name](X_, y_, logger, categorical_) + value = metafeatures[name](X_, y_, logger, feat_type_) metafeatures.set_value(name, value) mf_[name] = value visited.add(name) diff --git a/autosklearn/pipeline/components/data_preprocessing/feature_type.py b/autosklearn/pipeline/components/data_preprocessing/feature_type.py index 5c37e4cb98..bd42d8a67a 100644 --- a/autosklearn/pipeline/components/data_preprocessing/feature_type.py +++ b/autosklearn/pipeline/components/data_preprocessing/feature_type.py @@ -162,9 +162,9 @@ def fit( sklearn_transf_spec = [ (name, transformer, feature_columns) for name, transformer, feature_columns in [ - ("text_transformer", self.txt_ppl, text_features), ("categorical_transformer", self.categ_ppl, categorical_features), ("numerical_transformer", self.numer_ppl, numerical_features), + ("text_transformer", self.txt_ppl, text_features), ] if len(feature_columns) > 0 ] diff --git a/autosklearn/smbo.py b/autosklearn/smbo.py index 608c58921d..c463badb1d 100644 --- a/autosklearn/smbo.py +++ b/autosklearn/smbo.py @@ -101,11 +101,6 @@ def _calculate_metafeatures( # == Calculate metafeatures with stopwatch.time("Calculate meta-features") as task_timer: - categorical = { - col: True if feat_type.lower() in {"categorical", "string"} else False - for col, feat_type in data_feat_type.items() - } - EXCLUDE_META_FEATURES = ( EXCLUDE_META_FEATURES_CLASSIFICATION if data_info_task in CLASSIFICATION_TASKS @@ -123,7 +118,7 @@ def _calculate_metafeatures( result = calculate_all_metafeatures_with_labels( x_train, y_train, - categorical=categorical, + feat_type=data_feat_type, dataset_name=basename, dont_calculate=EXCLUDE_META_FEATURES, logger=logger_, @@ -159,15 +154,11 @@ def _calculate_metafeatures_encoded( ) with stopwatch.time("Calculate meta-features encoded") as task_timer: - categorical = { - col: True if feat_type.lower() in {"categorical", "string"} else False - for col, feat_type in data_feat_type.items() - } result = calculate_all_metafeatures_encoded_labels( x_train, y_train, - categorical=categorical, + feat_type=data_feat_type, dataset_name=basename, dont_calculate=EXCLUDE_META_FEATURES, logger=logger_, diff --git a/test/test_metalearning/pyMetaLearn/test_meta_features.py b/test/test_metalearning/pyMetaLearn/test_meta_features.py index 40048c708a..3dea2c762b 100644 --- a/test/test_metalearning/pyMetaLearn/test_meta_features.py +++ b/test/test_metalearning/pyMetaLearn/test_meta_features.py @@ -48,14 +48,9 @@ def meta_train_data(request): dataset = decoder.decode(fh, encode_nominal=True) # -1 because the last attribute is the class - attribute_types = [ - "numeric" if type(type_) != list else "nominal" - for name, type_ in dataset["attributes"][:-1] - ] - - categorical = { - i: True if attribute == "nominal" else False - for i, attribute in enumerate(attribute_types) + feat_type = { + idx: "numerical" if type(type_) != list else "categorical" + for idx, (name, type_) in enumerate(dataset["attributes"][:-1]) } data = np.array(dataset["data"], dtype=np.float64) @@ -65,20 +60,32 @@ def meta_train_data(request): logger = logging.getLogger("Meta") meta_features.helper_functions.set_value( "MissingValues", - meta_features.helper_functions["MissingValues"](X, y, logger, categorical), + meta_features.helper_functions["MissingValues"](X, y, logger, feat_type), ) meta_features.helper_functions.set_value( "NumSymbols", - meta_features.helper_functions["NumSymbols"](X, y, logger, categorical), + meta_features.helper_functions["NumSymbols"](X, y, logger, feat_type), ) meta_features.helper_functions.set_value( "ClassOccurences", meta_features.helper_functions["ClassOccurences"](X, y, logger), ) if request.param == "numpy": - return X, y, categorical + return X, y, feat_type elif request.param == "pandas": - return pd.DataFrame(X), y, categorical + dtypes = {} + for key, value in feat_type.items(): + if value == "categorical": + dtypes[key] = "category" + elif value == "numerical": + dtypes[key] = "float64" + elif value == "string": + dtypes[key] = "string" + else: + raise KeyError + + X = pd.DataFrame(X).astype(dtypes) + return X, y, feat_type else: raise ValueError(request.param) @@ -93,13 +100,9 @@ def meta_train_data_transformed(request): dataset = decoder.decode(fh, encode_nominal=True) # -1 because the last attribute is the class - attribute_types = [ - "numeric" if type(type_) != list else "nominal" - for name, type_ in dataset["attributes"][:-1] - ] - categorical = { - i: True if attribute == "nominal" else False - for i, attribute in enumerate(attribute_types) + feat_type = { + idx: "numerical" if type(type_) != list else "categorical" + for idx, (name, type_) in enumerate(dataset["attributes"][:-1]) } data = np.array(dataset["data"], dtype=np.float64) @@ -109,30 +112,20 @@ def meta_train_data_transformed(request): logger = logging.getLogger("Meta") meta_features.helper_functions.set_value( "MissingValues", - meta_features.helper_functions["MissingValues"](X, y, logger, categorical), + meta_features.helper_functions["MissingValues"](X, y, logger, feat_type), ) meta_features.helper_functions.set_value( "NumSymbols", - meta_features.helper_functions["NumSymbols"](X, y, logger, categorical), + meta_features.helper_functions["NumSymbols"](X, y, logger, feat_type), ) meta_features.helper_functions.set_value( "ClassOccurences", meta_features.helper_functions["ClassOccurences"](X, y, logger), ) - DPP = FeatTypeSplit( - feat_type={ - col: "categorical" if category else "numerical" - for col, category in categorical.items() - } - ) + DPP = FeatTypeSplit(feat_type=feat_type) X_transformed = DPP.fit_transform(X) - - number_numerical = np.sum(~np.array(list(categorical.values()))) - categorical_transformed = { - i: True if i < (X_transformed.shape[1] - number_numerical) else False - for i in range(X_transformed.shape[1]) - } + feat_type_transformed = {i: "numerical" for i in range(X_transformed.shape[1])} # pre-compute values for transformed inputs meta_features.helper_functions.set_value( @@ -142,55 +135,66 @@ def meta_train_data_transformed(request): meta_features.helper_functions.set_value( "Skewnesses", meta_features.helper_functions["Skewnesses"]( - X_transformed, y, logger, categorical_transformed + X_transformed, y, logger, feat_type_transformed ), ) meta_features.helper_functions.set_value( "Kurtosisses", meta_features.helper_functions["Kurtosisses"]( - X_transformed, y, logger, categorical_transformed + X_transformed, y, logger, feat_type_transformed ), ) if request.param == "numpy": - return X_transformed, y, categorical_transformed + return X_transformed, y, feat_type_transformed elif request.param == "pandas": - return pd.DataFrame(X_transformed), y, categorical_transformed + dtypes = {} + for key, value in feat_type.items(): + if value == "categorical": + dtypes[key] = "category" + elif value == "numerical": + dtypes[key] = "float64" + elif value == "string": + dtypes[key] = "string" + else: + raise KeyError + X_transformed = pd.DataFrame(X_transformed).astype(dtypes) + return X_transformed, y, feat_type_transformed else: raise ValueError(request.param) def test_number_of_instance(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.metafeatures["NumberOfInstances"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) assert mf.value == 898 assert isinstance(mf, MetaFeatureValue) def test_number_of_classes(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.metafeatures["NumberOfClasses"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) assert mf.value == 5 assert isinstance(mf, MetaFeatureValue) def test_number_of_features(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.metafeatures["NumberOfFeatures"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) assert mf.value == 38 assert isinstance(mf, MetaFeatureValue) def test_missing_values(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.helper_functions["MissingValues"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) assert isinstance(mf.value, pd.DataFrame if hasattr(X, "iloc") else np.ndarray) assert mf.value.shape == X.shape @@ -198,162 +202,162 @@ def test_missing_values(meta_train_data): def test_number_of_Instances_with_missing_values(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.metafeatures["NumberOfInstancesWithMissingValues"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) assert mf.value == 898 assert isinstance(mf, MetaFeatureValue) def test_percentage_of_Instances_with_missing_values(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data meta_features.metafeatures.set_value( "NumberOfInstancesWithMissingValues", meta_features.metafeatures["NumberOfInstancesWithMissingValues"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ), ) mf = meta_features.metafeatures["PercentageOfInstancesWithMissingValues"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) assert pytest.approx(mf.value) == 1.0 assert isinstance(mf, MetaFeatureValue) def test_number_of_features_with_missing_values(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.metafeatures["NumberOfFeaturesWithMissingValues"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) assert mf.value == 29 assert isinstance(mf, MetaFeatureValue) def test_percentage_of_features_with_missing_values(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data meta_features.metafeatures.set_value( "NumberOfFeaturesWithMissingValues", meta_features.metafeatures["NumberOfFeaturesWithMissingValues"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ), ) mf = meta_features.metafeatures["PercentageOfFeaturesWithMissingValues"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) assert pytest.approx(mf.value) == float(29) / float(38) assert isinstance(mf, MetaFeatureValue) def test_number_of_missing_values(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data np.save("/tmp/debug", X) mf = meta_features.metafeatures["NumberOfMissingValues"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) assert mf.value == 22175 assert isinstance(mf, MetaFeatureValue) def test_percentage_missing_values(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data meta_features.metafeatures.set_value( "NumberOfMissingValues", meta_features.metafeatures["NumberOfMissingValues"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ), ) mf = meta_features.metafeatures["PercentageOfMissingValues"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) assert pytest.approx(mf.value) == (float(22175) / float(38 * 898)) assert isinstance(mf, MetaFeatureValue) def test_number_of_numeric_features(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.metafeatures["NumberOfNumericFeatures"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) assert mf.value == 6 assert isinstance(mf, MetaFeatureValue) def test_number_of_categorical_features(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.metafeatures["NumberOfCategoricalFeatures"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) assert mf.value == 32 assert isinstance(mf, MetaFeatureValue) def test_ratio_numerical_to_categorical(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.metafeatures["RatioNumericalToNominal"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) assert pytest.approx(mf.value) == (float(6) / float(32)) assert isinstance(mf, MetaFeatureValue) def test_ratio_categorical_to_numerical(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.metafeatures["RatioNominalToNumerical"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) assert pytest.approx(mf.value) == (float(32) / float(6)) assert isinstance(mf, MetaFeatureValue) def test_dataset_ratio(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.metafeatures["DatasetRatio"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) assert pytest.approx(mf.value) == (float(38) / float(898)) assert isinstance(mf, MetaFeatureValue) def test_inverse_dataset_ratio(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.metafeatures["InverseDatasetRatio"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) assert pytest.approx(mf.value) == (float(898) / float(38)) assert isinstance(mf, MetaFeatureValue) def test_class_occurences(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.helper_functions["ClassOccurences"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) assert mf.value == {0.0: 8.0, 1.0: 99.0, 2.0: 684.0, 4.0: 67.0, 5.0: 40.0} def test_class_probability_min(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.metafeatures["ClassProbabilityMin"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) assert pytest.approx(mf.value) == (float(8) / float(898)) assert isinstance(mf, MetaFeatureValue) def test_class_probability_max(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.metafeatures["ClassProbabilityMax"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) assert pytest.approx(mf.value) == (float(684) / float(898)) assert isinstance(mf, MetaFeatureValue) def test_class_probability_mean(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.metafeatures["ClassProbabilityMean"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) classes = np.array((8, 99, 684, 67, 40), dtype=np.float64) prob_mean = (classes / float(898)).mean() @@ -362,9 +366,9 @@ def test_class_probability_mean(meta_train_data): def test_class_probability_std(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.metafeatures["ClassProbabilitySTD"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) classes = np.array((8, 99, 684, 67, 40), dtype=np.float64) prob_std = (classes / float(898)).std() @@ -373,9 +377,9 @@ def test_class_probability_std(meta_train_data): def test_num_symbols(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.helper_functions["NumSymbols"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) symbol_frequency = [ 2, @@ -415,26 +419,26 @@ def test_num_symbols(meta_train_data): def test_symbols_min(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.metafeatures["SymbolsMin"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) assert mf.value == 1 def test_symbols_max(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data # this is attribute steel mf = meta_features.metafeatures["SymbolsMax"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) assert mf.value == 7 def test_symbols_mean(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.metafeatures["SymbolsMean"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) # Empty looking spaces denote empty attributes symbol_frequency = [ @@ -469,9 +473,9 @@ def test_symbols_mean(meta_train_data): def test_symbols_std(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.metafeatures["SymbolsSTD"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) symbol_frequency = [ 2, @@ -505,17 +509,17 @@ def test_symbols_std(meta_train_data): def test_symbols_sum(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.metafeatures["SymbolsSum"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) assert mf.value == 49 def test_class_entropy(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.metafeatures["ClassEntropy"]( - X, y, logging.getLogger("Meta"), categorical + X, y, logging.getLogger("Meta"), feat_type ) classes = np.array((8, 99, 684, 67, 40), dtype=np.float64) classes = classes / sum(classes) @@ -525,96 +529,96 @@ def test_class_entropy(meta_train_data): def test_calculate_all_metafeatures(meta_train_data): - X, y, categorical = meta_train_data + X, y, feat_type = meta_train_data mf = meta_features.calculate_all_metafeatures( - X, y, categorical, "2", logger=logging.getLogger("Meta") + X, y, feat_type, "2", logger=logging.getLogger("Meta") ) assert 52 == len(mf.metafeature_values) assert mf.metafeature_values["NumberOfCategoricalFeatures"].value == 32 def test_kurtosisses(meta_train_data_transformed): - X_transformed, y, categorical_transformed = meta_train_data_transformed + X_transformed, y, feat_type_transformed = meta_train_data_transformed mf = meta_features.helper_functions["Kurtosisses"]( - X_transformed, y, logging.getLogger("Meta"), categorical_transformed + X_transformed, y, logging.getLogger("Meta"), feat_type_transformed ) - assert 6 == len(mf.value) + assert 81 == len(mf.value) def test_kurtosis_min(meta_train_data_transformed): - X_transformed, y, categorical_transformed = meta_train_data_transformed + X_transformed, y, feat_type_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["KurtosisMin"]( - X_transformed, y, logging.getLogger("Meta"), categorical_transformed + X_transformed, y, logging.getLogger("Meta"), feat_type_transformed ) def test_kurtosis_max(meta_train_data_transformed): - X_transformed, y, categorical_transformed = meta_train_data_transformed + X_transformed, y, feat_type_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["KurtosisMax"]( - X_transformed, y, logging.getLogger("Meta"), categorical_transformed + X_transformed, y, logging.getLogger("Meta"), feat_type_transformed ) def test_kurtosis_mean(meta_train_data_transformed): - X_transformed, y, categorical_transformed = meta_train_data_transformed + X_transformed, y, feat_type_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["KurtosisMean"]( - X_transformed, y, logging.getLogger("Meta"), categorical_transformed + X_transformed, y, logging.getLogger("Meta"), feat_type_transformed ) def test_kurtosis_std(meta_train_data_transformed): - X_transformed, y, categorical_transformed = meta_train_data_transformed + X_transformed, y, feat_type_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["KurtosisSTD"]( - X_transformed, y, logging.getLogger("Meta"), categorical_transformed + X_transformed, y, logging.getLogger("Meta"), feat_type_transformed ) def test_skewnesses(meta_train_data_transformed): - X_transformed, y, categorical_transformed = meta_train_data_transformed + X_transformed, y, feat_type_transformed = meta_train_data_transformed mf = meta_features.helper_functions["Skewnesses"]( - X_transformed, y, logging.getLogger("Meta"), categorical_transformed + X_transformed, y, logging.getLogger("Meta"), feat_type_transformed ) - assert 6 == len(mf.value) + assert 81 == len(mf.value) def test_skewness_min(meta_train_data_transformed): - X_transformed, y, categorical_transformed = meta_train_data_transformed + X_transformed, y, feat_type_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["SkewnessMin"]( - X_transformed, y, logging.getLogger("Meta"), categorical_transformed + X_transformed, y, logging.getLogger("Meta"), feat_type_transformed ) def test_skewness_max(meta_train_data_transformed): - X_transformed, y, categorical_transformed = meta_train_data_transformed + X_transformed, y, feat_type_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["SkewnessMax"]( - X_transformed, y, logging.getLogger("Meta"), categorical_transformed + X_transformed, y, logging.getLogger("Meta"), feat_type_transformed ) def test_skewness_mean(meta_train_data_transformed): - X_transformed, y, categorical_transformed = meta_train_data_transformed + X_transformed, y, feat_type_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["SkewnessMean"]( - X_transformed, y, logging.getLogger("Meta"), categorical_transformed + X_transformed, y, logging.getLogger("Meta"), feat_type_transformed ) def test_skewness_std(meta_train_data_transformed): - X_transformed, y, categorical_transformed = meta_train_data_transformed + X_transformed, y, feat_type_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["SkewnessSTD"]( - X_transformed, y, logging.getLogger("Meta"), categorical_transformed + X_transformed, y, logging.getLogger("Meta"), feat_type_transformed ) def test_landmark_lda(meta_train_data_transformed): - X_transformed, y, categorical_transformed = meta_train_data_transformed + X_transformed, y, feat_type_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["LandmarkLDA"]( X_transformed, y, logging.getLogger("Meta") @@ -622,7 +626,7 @@ def test_landmark_lda(meta_train_data_transformed): def test_landmark_naive_bayes(meta_train_data_transformed): - X_transformed, y, categorical_transformed = meta_train_data_transformed + X_transformed, y, feat_type_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["LandmarkNaiveBayes"]( X_transformed, y, logging.getLogger("Meta") @@ -630,7 +634,7 @@ def test_landmark_naive_bayes(meta_train_data_transformed): def test_landmark_decision_tree(meta_train_data_transformed): - X_transformed, y, categorical_transformed = meta_train_data_transformed + X_transformed, y, feat_type_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["LandmarkDecisionTree"]( X_transformed, y, logging.getLogger("Meta") @@ -638,7 +642,7 @@ def test_landmark_decision_tree(meta_train_data_transformed): def test_decision_node(meta_train_data_transformed): - X_transformed, y, categorical_transformed = meta_train_data_transformed + X_transformed, y, feat_type_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["LandmarkDecisionNodeLearner"]( X_transformed, y, logging.getLogger("Meta") @@ -646,7 +650,7 @@ def test_decision_node(meta_train_data_transformed): def test_random_node(meta_train_data_transformed): - X_transformed, y, categorical_transformed = meta_train_data_transformed + X_transformed, y, feat_type_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["LandmarkRandomNodeLearner"]( X_transformed, y, logging.getLogger("Meta") @@ -655,7 +659,7 @@ def test_random_node(meta_train_data_transformed): @unittest.skip("Currently not implemented!") def test_worst_node(meta_train_data_transformed): - X_transformed, y, categorical_transformed = meta_train_data_transformed + X_transformed, y, feat_type_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["LandmarkWorstNodeLearner"]( X_transformed, y, logging.getLogger("Meta") @@ -663,7 +667,7 @@ def test_worst_node(meta_train_data_transformed): def test_1NN(meta_train_data_transformed): - X_transformed, y, categorical_transformed = meta_train_data_transformed + X_transformed, y, feat_type_transformed = meta_train_data_transformed # TODO: somehow compute the expected output? meta_features.metafeatures["Landmark1NN"]( X_transformed, y, logging.getLogger("Meta") @@ -671,12 +675,12 @@ def test_1NN(meta_train_data_transformed): def test_pca(meta_train_data_transformed): - X_transformed, y, categorical_transformed = meta_train_data_transformed + X_transformed, y, feat_type_transformed = meta_train_data_transformed meta_features.helper_functions["PCA"](X_transformed, y, logging.getLogger("Meta")) def test_pca_95percent(meta_train_data_transformed): - X_transformed, y, categorical_transformed = meta_train_data_transformed + X_transformed, y, feat_type_transformed = meta_train_data_transformed mf = meta_features.metafeatures["PCAFractionOfComponentsFor95PercentVariance"]( X_transformed, y, logging.getLogger("Meta") ) @@ -684,7 +688,7 @@ def test_pca_95percent(meta_train_data_transformed): def test_pca_kurtosis_first_pc(meta_train_data_transformed): - X_transformed, y, categorical_transformed = meta_train_data_transformed + X_transformed, y, feat_type_transformed = meta_train_data_transformed mf = meta_features.metafeatures["PCAKurtosisFirstPC"]( X_transformed, y, logging.getLogger("Meta") ) @@ -692,7 +696,7 @@ def test_pca_kurtosis_first_pc(meta_train_data_transformed): def test_pca_skewness_first_pc(meta_train_data_transformed): - X_transformed, y, categorical_transformed = meta_train_data_transformed + X_transformed, y, feat_type_transformed = meta_train_data_transformed mf = meta_features.metafeatures["PCASkewnessFirstPC"]( X_transformed, y, logging.getLogger("Meta") ) @@ -846,9 +850,9 @@ def test_1NN_multilabel(multilabel_train_data): def test_calculate_all_metafeatures_multilabel(multilabel_train_data): meta_features.helper_functions.clear() X, y = multilabel_train_data - categorical = {i: False for i in range(10)} + feat_type = {i: "numerical" for i in range(10)} mf = meta_features.calculate_all_metafeatures( - X, y, categorical, "Generated", logger=logging.getLogger("TestMeta") + X, y, feat_type, "Generated", logger=logging.getLogger("TestMeta") ) assert 52 == len(mf.metafeature_values) @@ -860,11 +864,12 @@ def test_calculate_all_metafeatures_same_results_across_datatypes(): all metafeatures work in this complex dataset """ X, y = fetch_openml(data_id=2, return_X_y=True, as_frame=True) - categorical = { - col: True if X[col].dtype.name == "category" else False for col in X.columns + feat_type = { + col: "categorical" if X[col].dtype.name == "category" else "numerical" + for col in X.columns } mf = meta_features.calculate_all_metafeatures( - X, y, categorical, "2", logger=logging.getLogger("Meta") + X, y, feat_type, "2", logger=logging.getLogger("Meta") ) assert 52 == len(mf.metafeature_values) expected = { @@ -925,12 +930,9 @@ def test_calculate_all_metafeatures_same_results_across_datatypes(): # Then do numpy! X, y = fetch_openml(data_id=2, return_X_y=True, as_frame=False) - categorical = { - i: True if category else False - for i, category in enumerate(categorical.values()) - } + feat_type = {i: value for i, value in enumerate(feat_type.values())} mf = meta_features.calculate_all_metafeatures( - X, y, categorical, "2", logger=logging.getLogger("Meta") + X, y, feat_type, "2", logger=logging.getLogger("Meta") ) assert {k: mf[k].value for k in expected.keys()} == pytest.approx(expected) diff --git a/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py b/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py index 992032a349..66eb2072e1 100644 --- a/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py +++ b/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py @@ -29,8 +29,8 @@ def sparse_data(): "numeric" if type(type_) != list else "nominal" for name, type_ in dataset["attributes"][:-1] ] - categorical = { - i: True if attribute == "nominal" else False + feat_type = { + i: "categorical" if attribute == "nominal" else "numerical" for i, attribute in enumerate(attribute_types) } @@ -53,21 +53,21 @@ def sparse_data(): # Precompute some helper functions helpers.set_value( "MissingValues", - helpers["MissingValues"](X, y, logger, categorical), + helpers["MissingValues"](X, y, logger, feat_type), ) mf.set_value( "NumberOfMissingValues", - mf["NumberOfMissingValues"](X, y, logger, categorical), + mf["NumberOfMissingValues"](X, y, logger, feat_type), ) helpers.set_value( "NumSymbols", - helpers["NumSymbols"](X, y, logger, categorical), + helpers["NumSymbols"](X, y, logger, feat_type), ) helpers.set_value( "ClassOccurences", helpers["ClassOccurences"](X, y, logger), ) - return X, y, categorical + return X, y, feat_type @pytest.fixture @@ -84,8 +84,8 @@ def sparse_data_transformed(): "numeric" if type(type_) != list else "nominal" for name, type_ in dataset["attributes"][:-1] ] - categorical = { - i: True if attribute == "nominal" else False + feat_type = { + i: "categorical" if attribute == "nominal" else "numerical" for i, attribute in enumerate(attribute_types) } @@ -100,12 +100,7 @@ def sparse_data_transformed(): X_sparse[NaNs] = 0 X_sparse = sparse.csr_matrix(X_sparse) - ohe = FeatTypeSplit( - feat_type={ - col: "categorical" if category else "numerical" - for col, category in categorical.items() - } - ) + ohe = FeatTypeSplit(feat_type=feat_type) X_transformed = X_sparse.copy() X_transformed = ohe.fit_transform(X_transformed) imp = SimpleImputer(copy=False) @@ -113,12 +108,8 @@ def sparse_data_transformed(): standard_scaler = StandardScaler(with_mean=False) X_transformed = standard_scaler.fit_transform(X_transformed) - # Transform the array which indicates the categorical metafeatures - number_numerical = np.sum(~np.array(list(categorical.values()))) - categorical_transformed = { - i: True if i < (X_transformed.shape[1] - number_numerical) else False - for i in range(X_transformed.shape[1]) - } + # Transform the array which indicates the numerical metafeatures + feat_type_transformed = {i: "numerical" for i in range(X_transformed.shape[1])} X = X_sparse X_transformed = X_transformed @@ -134,15 +125,15 @@ def sparse_data_transformed(): ) helpers.set_value( "MissingValues", - helpers["MissingValues"](X, y, logger, categorical), + helpers["MissingValues"](X, y, logger, feat_type), ) mf.set_value( "NumberOfMissingValues", - mf["NumberOfMissingValues"](X, y, logger, categorical), + mf["NumberOfMissingValues"](X, y, logger, feat_type), ) helpers.set_value( "NumSymbols", - helpers["NumSymbols"](X, y, logger, categorical), + helpers["NumSymbols"](X, y, logger, feat_type), ) helpers.set_value( "ClassOccurences", @@ -150,13 +141,13 @@ def sparse_data_transformed(): ) helpers.set_value( "Skewnesses", - helpers["Skewnesses"](X_transformed, y, logger, categorical_transformed), + helpers["Skewnesses"](X_transformed, y, logger, feat_type_transformed), ) helpers.set_value( "Kurtosisses", - helpers["Kurtosisses"](X_transformed, y, logger, categorical_transformed), + helpers["Kurtosisses"](X_transformed, y, logger, feat_type_transformed), ) - return X_transformed, y, categorical_transformed + return X_transformed, y, feat_type_transformed def test_missing_values(sparse_data): @@ -419,8 +410,8 @@ def test_pca_skewness_first_pc(sparse_data_transformed): def test_calculate_all_metafeatures(sparse_data): - X, y, categorical = sparse_data + X, y, feat_type = sparse_data mf = meta_features.calculate_all_metafeatures( - X, y, categorical, "2", logger=logging.getLogger("Meta") + X, y, feat_type, "2", logger=logging.getLogger("Meta") ) assert 52 == len(mf.metafeature_values) From 9597c228da34c49bd83f46fef14479a4c0122358 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Thu, 12 May 2022 21:44:21 +0200 Subject: [PATCH 048/118] First draft of multi-objective optimization (#1455) * First draft of multi-objective optimization Co-authored-by: Katharina Eggensperger * Feedback from Eddie * Make metric internally always a list * Fix most examples * Take further feedback into account * Fix unit tests * Fix one more example * Add multi-objective example * Simplify internal interface * Act on further feedback * Fix bug * Update cv_results_ for multi-objective sklearn compliance * Update leaderboard for multi-objective optimization * Include Feedback from Katharina * Take offline feedback into account * Take offline feedback into account * Eddie's feedback * Fix metadata generation unit test * Test for metrics with the same name * Fix? * Test CV results * Test leaderboard for multi-objective optimization * Last batch of unit tests added * Include Eddie's feedback Co-authored-by: Katharina Eggensperger --- autosklearn/automl.py | 144 ++++-- autosklearn/ensemble_builder.py | 26 +- autosklearn/ensembles/ensemble_selection.py | 42 +- autosklearn/estimators.py | 213 +++++--- autosklearn/evaluation/__init__.py | 92 +++- autosklearn/evaluation/abstract_evaluator.py | 78 +-- autosklearn/evaluation/test_evaluator.py | 20 +- autosklearn/evaluation/train_evaluator.py | 103 ++-- autosklearn/experimental/askl2.py | 8 + autosklearn/metrics/__init__.py | 198 ++++---- autosklearn/smbo.py | 29 +- examples/40_advanced/example_metrics.py | 20 +- .../40_advanced/example_multi_objective.py | 65 +++ examples/60_search/example_random_search.py | 12 +- .../60_search/example_successive_halving.py | 2 + test/test_automl/test_dummy_predictions.py | 8 +- test/test_estimators/test_estimators.py | 227 ++++++++- .../test_abstract_evaluator.py | 12 +- test/test_evaluation/test_evaluation.py | 44 +- test/test_evaluation/test_test_evaluator.py | 42 +- test/test_evaluation/test_train_evaluator.py | 469 +++++++++++++----- test/test_metric/test_metrics.py | 144 +++++- test/test_optimizer/test_smbo.py | 6 +- 23 files changed, 1421 insertions(+), 583 deletions(-) create mode 100644 examples/40_advanced/example_multi_objective.py diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 50e4591346..8c0f4717dc 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -1,9 +1,10 @@ from __future__ import annotations -from typing import Any, Callable, Iterable, Mapping, Optional, Tuple +from typing import Any, Callable, Iterable, Mapping, Optional, Sequence, Tuple import copy import io +import itertools import json import logging.handlers import multiprocessing @@ -66,7 +67,12 @@ from autosklearn.evaluation import ExecuteTaFuncWithQueue, get_cost_of_crash from autosklearn.evaluation.abstract_evaluator import _fit_and_suppress_warnings from autosklearn.evaluation.train_evaluator import TrainEvaluator, _fit_with_budget -from autosklearn.metrics import Scorer, calculate_metric, default_metric_for_task +from autosklearn.metrics import ( + Scorer, + _validate_metrics, + compute_single_metric, + default_metric_for_task, +) from autosklearn.pipeline.base import BasePipeline from autosklearn.pipeline.components.classification import ClassifierChoice from autosklearn.pipeline.components.data_preprocessing.categorical_encoding import ( @@ -210,7 +216,7 @@ def __init__( get_smac_object_callback: Optional[Callable] = None, smac_scenario_args: Optional[Mapping] = None, logging_config: Optional[Mapping] = None, - metric: Optional[Scorer] = None, + metrics: Sequence[Scorer] | None = None, scoring_functions: Optional[list[Scorer]] = None, get_trials_callback: Optional[IncorporateRunResultCallback] = None, dataset_compression: bool | Mapping[str, Any] = True, @@ -244,7 +250,7 @@ def __init__( self._delete_tmp_folder_after_terminate = delete_tmp_folder_after_terminate self._time_for_task = time_left_for_this_task self._per_run_time_limit = per_run_time_limit - self._metric = metric + self._metrics = metrics self._ensemble_size = ensemble_size self._ensemble_nbest = ensemble_nbest self._max_models_on_disc = max_models_on_disc @@ -265,7 +271,7 @@ def __init__( initial_configurations_via_metalearning ) - self._scoring_functions = scoring_functions or {} + self._scoring_functions = scoring_functions or [] self._resampling_strategy_arguments = resampling_strategy_arguments or {} # Single core, local runs should use fork to prevent the __main__ requirements @@ -422,8 +428,8 @@ def _do_dummy_prediction(self) -> None: if self._resampling_strategy in ["partial-cv", "partial-cv-iterative-fit"]: return - if self._metric is None: - raise ValueError("Metric was not set") + if self._metrics is None: + raise ValueError("Metric/Metrics was/were not set") # Dummy prediction always have num_run set to 1 dummy_run_num = 1 @@ -447,11 +453,11 @@ def _do_dummy_prediction(self) -> None: resampling_strategy=self._resampling_strategy, initial_num_run=dummy_run_num, stats=stats, - metric=self._metric, + metrics=self._metrics, memory_limit=memory_limit, disable_file_output=self._disable_evaluator_output, abort_on_first_run_crash=False, - cost_for_crash=get_cost_of_crash(self._metric), + cost_for_crash=get_cost_of_crash(self._metrics), port=self._logger_port, pynisher_context=self._multiprocessing_context, **self._resampling_strategy_arguments, @@ -611,8 +617,9 @@ def fit( self._task = task # Assign a metric if it doesnt exist - if self._metric is None: - self._metric = default_metric_for_task[self._task] + if self._metrics is None: + self._metrics = [default_metric_for_task[self._task]] + _validate_metrics(self._metrics, self._scoring_functions) if dataset_name is None: dataset_name = str(uuid.uuid1(clock_seq=os.getpid())) @@ -690,11 +697,16 @@ def fit( # The metric must exist as of this point # It can be provided in the constructor, or automatically # defined in the estimator fit call - if self._metric is None: - raise ValueError("No metric given.") - if not isinstance(self._metric, Scorer): + if isinstance(self._metrics, Sequence): + for entry in self._metrics: + if not isinstance(entry, Scorer): + raise ValueError( + "Metric {entry} must be instance of autosklearn.metrics.Scorer." + ) + else: raise ValueError( - "Metric must be instance of " "autosklearn.metrics.Scorer." + "Metric must be a sequence of instances of " + "autosklearn.metrics.Scorer." ) # If no dask client was provided, we create one, so that we can @@ -790,7 +802,7 @@ def fit( backend=copy.deepcopy(self._backend), dataset_name=dataset_name, task=self._task, - metric=self._metric, + metric=self._metrics[0], ensemble_size=self._ensemble_size, ensemble_nbest=self._ensemble_nbest, max_models_on_disc=self._max_models_on_disc, @@ -862,7 +874,7 @@ def fit( config_file=configspace_path, seed=self._seed, metadata_directory=self._metadata_directory, - metric=self._metric, + metrics=self._metrics, resampling_strategy=self._resampling_strategy, resampling_strategy_args=self._resampling_strategy_arguments, include=self._include, @@ -1001,7 +1013,10 @@ def _log_fit_setup(self) -> None: ) self._logger.debug(" smac_scenario_args: %s", str(self._smac_scenario_args)) self._logger.debug(" logging_config: %s", str(self.logging_config)) - self._logger.debug(" metric: %s", str(self._metric)) + if len(self._metrics) == 1: + self._logger.debug(" metric: %s", str(self._metrics[0])) + else: + self._logger.debug(" metrics: %s", str(self._metrics)) self._logger.debug("Done printing arguments to auto-sklearn") self._logger.debug("Starting to print available components") for choice in ( @@ -1254,8 +1269,8 @@ def fit_pipeline( self._task = task # Assign a metric if it doesnt exist - if self._metric is None: - self._metric = default_metric_for_task[self._task] + if self._metrics is None: + self._metrics = [default_metric_for_task[self._task]] # Get the configuration space # This also ensures that the Backend has processed the @@ -1288,8 +1303,16 @@ def fit_pipeline( kwargs["memory_limit"] = self._memory_limit if "resampling_strategy" not in kwargs: kwargs["resampling_strategy"] = self._resampling_strategy - if "metric" not in kwargs: - kwargs["metric"] = self._metric + if "metrics" not in kwargs: + if "metric" in kwargs: + kwargs["metrics"] = kwargs["metric"] + del kwargs["metric"] + else: + kwargs["metrics"] = self._metrics + if not isinstance(kwargs["metrics"], Sequence): + kwargs["metrics"] = [kwargs["metrics"]] + if "scoring_functions" not in kwargs: + kwargs["scoring_functions"] = self._scoring_functions if "disable_file_output" not in kwargs: kwargs["disable_file_output"] = self._disable_evaluator_output if "pynisher_context" not in kwargs: @@ -1300,6 +1323,8 @@ def fit_pipeline( kwargs["stats"] = Stats(scenario_mock) kwargs["stats"].start_timing() + _validate_metrics(kwargs["metrics"], kwargs["scoring_functions"]) + # Fit a pipeline, which will be stored on disk # which we can later load via the backend ta = ExecuteTaFuncWithQueue( @@ -1307,7 +1332,7 @@ def fit_pipeline( autosklearn_seed=self._seed, abort_on_first_run_crash=False, multi_objectives=["cost"], - cost_for_crash=get_cost_of_crash(kwargs["metric"]), + cost_for_crash=get_cost_of_crash(kwargs["metrics"]), port=self._logger_port, **kwargs, **self._resampling_strategy_arguments, @@ -1492,7 +1517,7 @@ def fit_ensemble( backend=copy.deepcopy(self._backend), dataset_name=dataset_name if dataset_name else self._dataset_name, task=task if task else self._task, - metric=self._metric, + metric=self._metrics[0], ensemble_size=ensemble_size if ensemble_size else self._ensemble_size, ensemble_nbest=ensemble_nbest if ensemble_nbest else self._ensemble_nbest, max_models_on_disc=self._max_models_on_disc, @@ -1590,7 +1615,7 @@ def _load_best_individual_model(self): # SingleBest contains the best model found by AutoML ensemble = SingleBest( - metric=self._metric, + metric=self._metrics[0], seed=self._seed, run_history=self.runhistory_, backend=self._backend, @@ -1624,15 +1649,15 @@ def score(self, X, y): # same representation domain prediction = self.InputValidator.target_validator.transform(prediction) - return calculate_metric( + return compute_single_metric( solution=y, prediction=prediction, task_type=self._task, - metric=self._metric, + metric=self._metrics[0], ) def _get_runhistory_models_performance(self): - metric = self._metric + metric = self._metrics[0] data = self.runhistory_.data performance_list = [] for run_key, run_value in data.items(): @@ -1644,7 +1669,10 @@ def _get_runhistory_models_performance(self): endtime = pd.Timestamp( time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(run_value.endtime)) ) - val_score = metric._optimum - (metric._sign * run_value.cost) + cost = run_value.cost + if len(self._metrics) > 1: + cost = cost[0] + val_score = metric._optimum - (metric._sign * cost) train_score = metric._optimum - ( metric._sign * run_value.additional_info["train_loss"] ) @@ -1656,9 +1684,10 @@ def _get_runhistory_models_performance(self): # Append test-scores, if data for test_loss are available. # This is the case, if X_test and y_test where provided. if "test_loss" in run_value.additional_info: - test_score = metric._optimum - ( - metric._sign * run_value.additional_info["test_loss"] - ) + test_loss = run_value.additional_info["test_loss"] + if len(self._metrics) > 1: + test_loss = test_loss[0] + test_score = metric._optimum - (metric._sign * test_loss) scores["single_best_test_score"] = test_score performance_list.append(scores) @@ -1747,14 +1776,11 @@ def cv_results_(self): metric_mask = dict() metric_dict = dict() - metric_name = [] - for metric in self._scoring_functions: - metric_name.append(metric.name) + for metric in itertools.chain(self._metrics, self._scoring_functions): metric_dict[metric.name] = [] metric_mask[metric.name] = [] - mean_test_score = [] mean_fit_time = [] params = [] status = [] @@ -1787,9 +1813,7 @@ def cv_results_(self): param_dict = config.get_dictionary() params.append(param_dict) - mean_test_score.append( - self._metric._optimum - (self._metric._sign * run_value.cost) - ) + mean_fit_time.append(run_value.time) budgets.append(run_key.budget) @@ -1804,7 +1828,18 @@ def cv_results_(self): parameter_dictionaries[hp_name].append(hp_value) masks[hp_name].append(mask_value) + cost = [run_value.cost] if len(self._metrics) == 1 else run_value.cost + for metric_idx, metric in enumerate(self._metrics): + metric_cost = cost[metric_idx] + metric_value = metric._optimum - (metric._sign * metric_cost) + mask_value = False + metric_dict[metric.name].append(metric_value) + metric_mask[metric.name].append(mask_value) + + optimization_metric_names = set(m.name for m in self._metrics) for metric in self._scoring_functions: + if metric.name in optimization_metric_names: + continue if metric.name in run_value.additional_info.keys(): metric_cost = run_value.additional_info[metric.name] metric_value = metric._optimum - (metric._sign * metric_cost) @@ -1815,15 +1850,26 @@ def cv_results_(self): metric_dict[metric.name].append(metric_value) metric_mask[metric.name].append(mask_value) - results["mean_test_score"] = np.array(mean_test_score) - for name in metric_name: - masked_array = ma.MaskedArray(metric_dict[name], metric_mask[name]) - results["metric_%s" % name] = masked_array + if len(self._metrics) == 1: + results["mean_test_score"] = np.array(metric_dict[self._metrics[0].name]) + rank_order = -1 * self._metrics[0]._sign * results["mean_test_score"] + results["rank_test_scores"] = scipy.stats.rankdata(rank_order, method="min") + else: + for metric in self._metrics: + key = f"mean_test_{metric.name}" + results[key] = np.array(metric_dict[metric.name]) + rank_order = -1 * metric._sign * results[key] + results[f"rank_test_{metric.name}"] = scipy.stats.rankdata( + rank_order, method="min" + ) + for metric in self._scoring_functions: + masked_array = ma.MaskedArray( + metric_dict[metric.name], metric_mask[metric.name] + ) + results[f"metric_{metric.name}"] = masked_array results["mean_fit_time"] = np.array(mean_fit_time) results["params"] = params - rank_order = -1 * self._metric._sign * results["mean_test_score"] - results["rank_test_scores"] = scipy.stats.rankdata(rank_order, method="min") results["status"] = status results["budgets"] = budgets @@ -1841,7 +1887,10 @@ def sprint_statistics(self) -> str: sio = io.StringIO() sio.write("auto-sklearn results:\n") sio.write(" Dataset name: %s\n" % self._dataset_name) - sio.write(" Metric: %s\n" % self._metric) + if len(self._metrics) == 1: + sio.write(" Metric: %s\n" % self._metrics[0]) + else: + sio.write(" Metrics: %s\n" % self._metrics) idx_success = np.where( np.array( [ @@ -1852,7 +1901,7 @@ def sprint_statistics(self) -> str: ) )[0] if len(idx_success) > 0: - if not self._metric._optimum: + if not self._metrics[0]._optimum: idx_best_run = np.argmin(cv_results["mean_test_score"][idx_success]) else: idx_best_run = np.argmax(cv_results["mean_test_score"][idx_success]) @@ -1912,7 +1961,6 @@ def show_models(self) -> dict[int, Any]: .. code-block:: python import sklearn.datasets - import sklearn.metrics import autosklearn.regression X, y = sklearn.datasets.load_diabetes(return_X_y=True) diff --git a/autosklearn/ensemble_builder.py b/autosklearn/ensemble_builder.py index 3707ce84c9..3dec9828ef 100644 --- a/autosklearn/ensemble_builder.py +++ b/autosklearn/ensemble_builder.py @@ -30,7 +30,7 @@ from autosklearn.automl_common.common.utils.backend import Backend from autosklearn.constants import BINARY_CLASSIFICATION from autosklearn.ensembles.ensemble_selection import EnsembleSelection -from autosklearn.metrics import Scorer, calculate_loss, calculate_score +from autosklearn.metrics import Scorer, calculate_losses, calculate_scores from autosklearn.util.logging_ import get_named_client_logger from autosklearn.util.parallel import preload_modules @@ -999,13 +999,13 @@ def compute_loss_per_model(self): # actually read the predictions and compute their respective loss try: y_ensemble = self._read_np_fn(y_ens_fn) - loss = calculate_loss( + loss = calculate_losses( solution=self.y_true_ensemble, prediction=y_ensemble, task_type=self.task_type, - metric=self.metric, + metrics=[self.metric], scoring_functions=None, - ) + )[self.metric.name] if np.isfinite(self.read_losses[y_ens_fn]["ens_loss"]): self.logger.debug( @@ -1511,34 +1511,34 @@ def _add_ensemble_trajectory(self, train_pred, valid_pred, test_pred): performance_stamp = { "Timestamp": pd.Timestamp.now(), - "ensemble_optimization_score": calculate_score( + "ensemble_optimization_score": calculate_scores( solution=self.y_true_ensemble, prediction=train_pred, task_type=self.task_type, - metric=self.metric, + metrics=[self.metric], scoring_functions=None, - ), + )[self.metric.name], } if valid_pred is not None: # TODO: valid_pred are a legacy from competition manager # and this if never happens. Re-evaluate Y_valid support - performance_stamp["ensemble_val_score"] = calculate_score( + performance_stamp["ensemble_val_score"] = calculate_scores( solution=self.y_valid, prediction=valid_pred, task_type=self.task_type, - metric=self.metric, + metrics=[self.metric], scoring_functions=None, - ) + )[self.metric.name] # In case test_pred was provided if test_pred is not None: - performance_stamp["ensemble_test_score"] = calculate_score( + performance_stamp["ensemble_test_score"] = calculate_scores( solution=self.y_test, prediction=test_pred, task_type=self.task_type, - metric=self.metric, + metrics=[self.metric], scoring_functions=None, - ) + )[self.metric.name] self.ensemble_history.append(performance_stamp) diff --git a/autosklearn/ensembles/ensemble_selection.py b/autosklearn/ensembles/ensemble_selection.py index 3ae216da01..0c99db64c1 100644 --- a/autosklearn/ensembles/ensemble_selection.py +++ b/autosklearn/ensembles/ensemble_selection.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, List, Optional, Tuple, Union, cast +from typing import Any, Dict, List, Optional, Tuple, Union import random from collections import Counter @@ -8,7 +8,7 @@ from autosklearn.constants import TASK_TYPES from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble -from autosklearn.metrics import Scorer, calculate_loss +from autosklearn.metrics import Scorer, calculate_losses from autosklearn.pipeline.base import BasePipeline @@ -164,18 +164,13 @@ def _fast( out=fant_ensemble_prediction, ) - # calculate_loss is versatile and can return a dict of losses - # when scoring_functions=None, we know it will be a float - losses[j] = cast( - float, - calculate_loss( - solution=labels, - prediction=fant_ensemble_prediction, - task_type=self.task_type, - metric=self.metric, - scoring_functions=None, - ), - ) + losses[j] = calculate_losses( + solution=labels, + prediction=fant_ensemble_prediction, + task_type=self.task_type, + metrics=[self.metric], + scoring_functions=None, + )[self.metric.name] all_best = np.argwhere(losses == np.nanmin(losses)).flatten() @@ -211,18 +206,13 @@ def _slow(self, predictions: List[np.ndarray], labels: np.ndarray) -> None: for j, pred in enumerate(predictions): ensemble.append(pred) ensemble_prediction = np.mean(np.array(ensemble), axis=0) - # calculate_loss is versatile and can return a dict of losses - # when scoring_functions=None, we know it will be a float - losses[j] = cast( - float, - calculate_loss( - solution=labels, - prediction=ensemble_prediction, - task_type=self.task_type, - metric=self.metric, - scoring_functions=None, - ), - ) + losses[j] = calculate_losses( + solution=labels, + prediction=ensemble_prediction, + task_type=self.task_type, + metrics=[self.metric], + scoring_functions=None, + )[self.metric.name] ensemble.pop() best = np.nanargmin(losses) ensemble.append(predictions[best]) diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index db931a338a..1c283e06e6 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -1,5 +1,7 @@ # -*- encoding: utf-8 -*- -from typing import Any, Dict, Iterable, List, Mapping, Optional, Tuple, Union +from __future__ import annotations + +from typing import Any, Dict, Iterable, List, Mapping, Optional, Sequence, Tuple, Union import dask.distributed import joblib @@ -46,7 +48,7 @@ def __init__( smac_scenario_args=None, logging_config=None, metadata_directory=None, - metric=None, + metric: Scorer | Sequence[Scorer] | None = None, scoring_functions: Optional[List[Scorer]] = None, load_models: bool = True, get_trials_callback=None, @@ -417,7 +419,7 @@ def build_automl(self): smac_scenario_args=self.smac_scenario_args, logging_config=self.logging_config, metadata_directory=self.metadata_directory, - metric=self.metric, + metrics=[self.metric] if isinstance(self.metric, Scorer) else self.metric, scoring_functions=self.scoring_functions, get_trials_callback=self.get_trials_callback, dataset_compression=self.dataset_compression, @@ -792,6 +794,9 @@ def leaderboard( What column to sort by. If that column is not present, the sorting defaults to the ``"model_id"`` index column. + Defaults to the metric optimized. Sort by the first objective + in case of a multi-objective optimization problem + sort_order: "auto" or "ascending" or "descending" = "auto" Which sort order to apply to the ``sort_by`` column. If left as ``"auto"``, it will sort by a sensible default where "better" is @@ -816,7 +821,16 @@ def leaderboard( # TODO validate that `self` is fitted. This is required for # self.ensemble_ to get the identifiers of models it will generate # weights for. - column_types = AutoSklearnEstimator._leaderboard_columns() + num_metrics = ( + 1 + if self.metric is None or isinstance(self.metric, Scorer) + else len(self.metric) + ) + column_types = AutoSklearnEstimator._leaderboard_columns(num_metrics) + if num_metrics == 1: + multi_objective_cost_names = [] + else: + multi_objective_cost_names = [f"cost_{i}" for i in range(num_metrics)] # Validation of top_k if ( @@ -857,11 +871,26 @@ def leaderboard( columns = column_types["simple"] # Validation of sorting - if sort_by not in column_types["all"]: - raise ValueError( - f"sort_by='{sort_by}' must be one of included " - f"columns {set(column_types['all'])}" - ) + if sort_by == "cost": + sort_by_cost = True + if num_metrics == 1: + sort_by = ["cost", "model_id"] + else: + sort_by = multi_objective_cost_names + ["model_id"] + else: + sort_by_cost = False + if isinstance(sort_by, str): + if sort_by not in column_types["all"]: + raise ValueError( + f"sort_by='{sort_by}' must be one of included " + f"columns {set(column_types['all'])}" + ) + elif len(set(sort_by) - set(column_types["all"])) > 0: + too_much = set(sort_by) - set(column_types["all"]) + raise ValueError( + f"sort_by='{too_much}' must be in the included columns " + f"{set(column_types['all'])}" + ) valid_sort_orders = ["auto", "ascending", "descending"] if not (isinstance(sort_order, str) and sort_order in valid_sort_orders): @@ -871,30 +900,37 @@ def leaderboard( # To get all the models that were optmized, we collect what we can from # runhistory first. - def has_key(rv, key): + def additional_info_has_key(rv, key): return rv.additional_info and key in rv.additional_info - model_runs = { - rval.additional_info["num_run"]: { - "model_id": rval.additional_info["num_run"], - "seed": rkey.seed, - "budget": rkey.budget, - "duration": rval.time, - "config_id": rkey.config_id, - "start_time": rval.starttime, - "end_time": rval.endtime, - "status": str(rval.status), - "cost": rval.cost, - "train_loss": rval.additional_info["train_loss"] - if has_key(rval, "train_loss") - else None, - "config_origin": rval.additional_info["configuration_origin"] - if has_key(rval, "configuration_origin") - else None, - } - for rkey, rval in self.automl_.runhistory_.data.items() - if has_key(rval, "num_run") - } + model_runs = {} + for rkey, rval in self.automl_.runhistory_.data.items(): + if not additional_info_has_key(rval, "num_run"): + continue + else: + model_key = rval.additional_info["num_run"] + model_run = { + "model_id": rval.additional_info["num_run"], + "seed": rkey.seed, + "budget": rkey.budget, + "duration": rval.time, + "config_id": rkey.config_id, + "start_time": rval.starttime, + "end_time": rval.endtime, + "status": str(rval.status), + "train_loss": rval.additional_info["train_loss"] + if additional_info_has_key(rval, "train_loss") + else None, + "config_origin": rval.additional_info["configuration_origin"] + if additional_info_has_key(rval, "configuration_origin") + else None, + } + if num_metrics == 1: + model_run["cost"] = rval.cost + else: + for cost_idx, cost in enumerate(rval.cost): + model_run[f"cost_{cost_idx}"] = cost + model_runs[model_key] = model_run # Next we get some info about the model itself model_class_strings = { @@ -942,7 +978,7 @@ def has_key(rv, key): # collected. I have no clue why this is but to prevent failures, we fill # the values with NaN if model_id not in model_runs: - model_runs[model_id] = { + model_run = { "model_id": model_id, "seed": pd.NA, "budget": pd.NA, @@ -951,10 +987,16 @@ def has_key(rv, key): "start_time": pd.NA, "end_time": pd.NA, "status": pd.NA, - "cost": pd.NA, "train_loss": pd.NA, "config_origin": pd.NA, + "type": pd.NA, } + if num_metrics == 1: + model_run["cost"] = pd.NA + else: + for cost_idx in range(num_metrics): + model_run[f"cost_{cost_idx}"] = pd.NA + model_runs[model_id] = model_run model_runs[model_id]["ensemble_weight"] = weight @@ -973,8 +1015,13 @@ def has_key(rv, key): # `rank` relies on `cost` so we include `cost` # We drop it later if it's not requested - if "rank" in columns and "cost" not in columns: - columns = [*columns, "cost"] + if "rank" in columns: + if num_metrics == 1 and "cost" not in columns: + columns = [*columns, "cost"] + elif num_metrics > 1 and any( + cost_name not in columns for cost_name in multi_objective_cost_names + ): + columns = columns + list(multi_objective_cost_names) # Finally, convert into a tabular format by converting the dict into # column wise orientation. @@ -989,43 +1036,65 @@ def has_key(rv, key): # Give it an index, even if not in the `include` dataframe.set_index("model_id", inplace=True) - # Add the `rank` column if needed, dropping `cost` if it's not + # Add the `rank` column if needed # requested by the user if "rank" in columns: - dataframe.sort_values(by="cost", ascending=True, inplace=True) + if num_metrics == 1: + dataframe.sort_values(by="cost", ascending=True, inplace=True) + else: + dataframe.sort_values(by="cost_0", ascending=True, inplace=True) dataframe.insert( column="rank", value=range(1, len(dataframe) + 1), loc=list(columns).index("rank") - 1, ) # account for `model_id` - if "cost" not in columns: - dataframe.drop("cost", inplace=True) - # Decide on the sort order depending on what it gets sorted by descending_columns = ["ensemble_weight", "duration"] if sort_order == "auto": - ascending_param = False if sort_by in descending_columns else True + ascending_param = [ + False if sby in descending_columns else True for sby in sort_by + ] else: ascending_param = False if sort_order == "descending" else True # Sort by the given column name, defaulting to 'model_id' if not present - if sort_by not in dataframe.columns: + if ( + (not sort_by_cost and len(set(sort_by) - set(dataframe.columns)) > 0) + or (sort_by_cost and "cost" not in dataframe.columns) + or ( + sort_by_cost + and any( + cost_name not in dataframe.columns + for cost_name in multi_objective_cost_names + ) + ) + ): self.automl_._logger.warning( f"sort_by = '{sort_by}' was not present" ", defaulting to sort on the index " "'model_id'" ) sort_by = "model_id" + sort_by_cost = False + ascending_param = True - # Cost can be the same but leave rank all over the place - if "rank" in columns and sort_by == "cost": + # Single objective + if sort_by_cost: dataframe.sort_values( - by=[sort_by, "rank"], ascending=[ascending_param, True], inplace=True + by=sort_by, ascending=[True] * len(sort_by), inplace=True ) else: dataframe.sort_values(by=sort_by, ascending=ascending_param, inplace=True) + if num_metrics == 1: + if "cost" not in columns and "cost" in dataframe.columns: + dataframe.drop("cost", inplace=True) + else: + for cost_name in multi_objective_cost_names: + if cost_name not in columns and cost_name in dataframe.columns: + dataframe.drop(cost_name, inplace=True) + # Lastly, just grab the top_k if top_k == "all" or top_k >= len(dataframe): top_k = len(dataframe) @@ -1035,27 +1104,39 @@ def has_key(rv, key): return dataframe @staticmethod - def _leaderboard_columns() -> Dict[Literal["all", "simple", "detailed"], List[str]]: - all = [ - "model_id", - "rank", - "ensemble_weight", - "type", - "cost", - "duration", - "config_id", - "train_loss", - "seed", - "start_time", - "end_time", - "budget", - "status", - "data_preprocessors", - "feature_preprocessors", - "balancing_strategy", - "config_origin", - ] - simple = ["model_id", "rank", "ensemble_weight", "type", "cost", "duration"] + def _leaderboard_columns( + num_metrics: int, + ) -> Dict[Literal["all", "simple", "detailed"], List[str]]: + if num_metrics == 1: + cost_list = ["cost"] + else: + cost_list = [f"cost_{i}" for i in range(num_metrics)] + all = ( + [ + "model_id", + "rank", + "ensemble_weight", + "type", + ] + + cost_list + + [ + "duration", + "config_id", + "train_loss", + "seed", + "start_time", + "end_time", + "budget", + "status", + "data_preprocessors", + "feature_preprocessors", + "balancing_strategy", + "config_origin", + ] + ) + simple = ( + ["model_id", "rank", "ensemble_weight", "type"] + cost_list + ["duration"] + ) detailed = all return {"all": all, "detailed": detailed, "simple": simple} diff --git a/autosklearn/evaluation/__init__.py b/autosklearn/evaluation/__init__.py index 89c61d144d..52794fec03 100644 --- a/autosklearn/evaluation/__init__.py +++ b/autosklearn/evaluation/__init__.py @@ -1,5 +1,18 @@ # -*- encoding: utf-8 -*- -from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union, cast +from __future__ import annotations + +from typing import ( + Any, + Callable, + Dict, + List, + Optional, + Sequence, + Tuple, + Type, + Union, + cast, +) import functools import json @@ -85,23 +98,30 @@ def fit_predict_try_except_decorator( queue.close() -def get_cost_of_crash(metric: Scorer) -> float: - - # The metric must always be defined to extract optimum/worst - if not isinstance(metric, Scorer): - raise ValueError("The metric must be stricly be an instance of Scorer") - - # Autosklearn optimizes the err. This function translates - # worst_possible_result to be a minimization problem. - # For metrics like accuracy that are bounded to [0,1] - # metric.optimum==1 is the worst cost. - # A simple guide is to use greater_is_better embedded as sign - if metric._sign < 0: - worst_possible_result = metric._worst_possible_result - else: - worst_possible_result = metric._optimum - metric._worst_possible_result +def get_cost_of_crash(metrics: Sequence[Scorer]) -> List[float] | float: + """Return the cost of crash. + + Return value can be either a list (multi-objective optimization) or a + raw float (single objective) because SMAC assumes different types in the + two different cases. + """ + costs = [] + for metric in metrics: + if not isinstance(metric, Scorer): + raise ValueError("The metric {metric} must be an instance of Scorer") + + # Autosklearn optimizes the err. This function translates + # worst_possible_result to be a minimization problem. + # For metrics like accuracy that are bounded to [0,1] + # metric.optimum==1 is the worst cost. + # A simple guide is to use greater_is_better embedded as sign + if metric._sign < 0: + worst_possible_result = metric._worst_possible_result + else: + worst_possible_result = metric._optimum - metric._worst_possible_result + costs.append(worst_possible_result) - return worst_possible_result + return costs if len(costs) > 1 else costs[0] def _encode_exit_status( @@ -126,7 +146,7 @@ def __init__( resampling_strategy: Union[ str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit ], - metric: Scorer, + metrics: Sequence[Scorer], cost_for_crash: float, abort_on_first_run_crash: bool, port: int, @@ -144,7 +164,7 @@ def __init__( disable_file_output: bool = False, init_params: Optional[Dict[str, Any]] = None, budget_type: Optional[str] = None, - ta: Optional[Callable] = None, + ta: Optional[Callable] = None, # Required by SMAC's parent class **resampling_strategy_args: Any, ): if resampling_strategy == "holdout": @@ -186,13 +206,14 @@ def __init__( par_factor=par_factor, cost_for_crash=self.worst_possible_result, abort_on_first_run_crash=abort_on_first_run_crash, + multi_objectives=multi_objectives, ) self.backend = backend self.autosklearn_seed = autosklearn_seed self.resampling_strategy = resampling_strategy self.initial_num_run = initial_num_run - self.metric = metric + self.metrics = metrics self.resampling_strategy = resampling_strategy self.resampling_strategy_args = resampling_strategy_args self.scoring_functions = scoring_functions @@ -356,7 +377,7 @@ def run( config=config, backend=self.backend, port=self.port, - metric=self.metric, + metrics=self.metrics, seed=self.autosklearn_seed, num_run=num_run, scoring_functions=self.scoring_functions, @@ -550,4 +571,33 @@ def run( autosklearn.evaluation.util.empty_queue(queue) self.logger.info("Finished evaluating configuration %d" % config_id) + + # Do some sanity checking (for multi objective) + if len(self.multi_objectives) > 1: + error = ( + f"Returned costs {cost} does not match the number of objectives" + f" {len(self.multi_objectives)}." + ) + + # If dict convert to array + # Make sure the ordering is correct + if isinstance(cost, dict): + ordered_cost = [] + for name in self.multi_objectives: + if name not in cost: + raise RuntimeError( + f"Objective {name} was not found " + f"in the returned costs ({cost})" + ) + + ordered_cost.append(cost[name]) + cost = ordered_cost + + if isinstance(cost, list): + if len(cost) != len(self.multi_objectives): + raise RuntimeError(error) + + if isinstance(cost, float): + raise RuntimeError(error) + return status, cost, runtime, additional_run_info diff --git a/autosklearn/evaluation/abstract_evaluator.py b/autosklearn/evaluation/abstract_evaluator.py index 7843de6a8a..ab9e961128 100644 --- a/autosklearn/evaluation/abstract_evaluator.py +++ b/autosklearn/evaluation/abstract_evaluator.py @@ -1,4 +1,6 @@ -from typing import Any, Dict, List, Optional, TextIO, Tuple, Type, Union, cast +from __future__ import annotations + +from typing import Any, Dict, List, Optional, Sequence, TextIO, Tuple, Type, Union, cast import logging import multiprocessing @@ -23,7 +25,7 @@ MULTIOUTPUT_REGRESSION, REGRESSION_TASKS, ) -from autosklearn.metrics import Scorer, calculate_loss +from autosklearn.metrics import Scorer, calculate_losses from autosklearn.pipeline.components.base import ThirdPartyComponents, _addons from autosklearn.pipeline.implementations.util import ( convert_multioutput_multiclass_to_multilabel, @@ -184,7 +186,7 @@ def __init__( self, backend: Backend, queue: multiprocessing.Queue, - metric: Scorer, + metrics: Sequence[Scorer], additional_components: Dict[str, ThirdPartyComponents], port: Optional[int], configuration: Optional[Union[int, Configuration]] = None, @@ -219,12 +221,12 @@ def __init__( self.X_test = self.datamanager.data.get("X_test") self.y_test = self.datamanager.data.get("Y_test") - self.metric = metric + self.metrics = metrics self.task_type = self.datamanager.info["task"] self.seed = seed self.output_y_hat_optimization = output_y_hat_optimization - self.scoring_functions = scoring_functions + self.scoring_functions = scoring_functions if scoring_functions else [] if isinstance(disable_file_output, (bool, list)): self.disable_file_output: Union[bool, List[str]] = disable_file_output @@ -326,8 +328,7 @@ def _loss( self, y_true: np.ndarray, y_hat: np.ndarray, - scoring_functions: Optional[List[Scorer]] = None, - ) -> Union[float, Dict[str, float]]: + ) -> Dict[str, float]: """Auto-sklearn follows a minimization goal. The calculate_loss internally translate a score function to a minimization problem. @@ -338,27 +339,28 @@ def _loss( ---------- y_true """ - scoring_functions = ( - self.scoring_functions if scoring_functions is None else scoring_functions - ) if not isinstance(self.configuration, Configuration): - if scoring_functions: - return {self.metric.name: self.metric._worst_possible_result} - else: - return self.metric._worst_possible_result - - return calculate_loss( - y_true, - y_hat, - self.task_type, - self.metric, - scoring_functions=scoring_functions, - ) + # Dummy prediction + rval = {} + for metric in self.scoring_functions if self.scoring_functions else []: + rval[metric.name] = metric._worst_possible_result + for metric in self.metrics: + rval[metric.name] = metric._worst_possible_result + return rval + + else: + return calculate_losses( + y_true, + y_hat, + self.task_type, + self.metrics, + scoring_functions=self.scoring_functions, + ) def finish_up( self, loss: Union[Dict[str, float], float], - train_loss: Optional[Union[float, Dict[str, float]]], + train_loss: Optional[Dict[str, float]], opt_pred: np.ndarray, valid_pred: np.ndarray, test_pred: np.ndarray, @@ -400,19 +402,25 @@ def finish_up( if file_out_loss is not None: return self.duration, file_out_loss, self.seed, additional_run_info_ - if isinstance(loss, dict): - loss_ = loss - loss = loss_[self.metric.name] + loss_ = loss + if len(self.metrics) == 1: + loss = loss_[self.metrics[0].name] else: - loss_ = {} + loss = {metric.name: loss_[metric.name] for metric in self.metrics} additional_run_info = {} if additional_run_info is None else additional_run_info - for metric_name, value in loss_.items(): - additional_run_info[metric_name] = value + for metric in self.scoring_functions: + if metric.name in loss_: + additional_run_info[metric.name] = loss_[metric.name] additional_run_info["duration"] = self.duration additional_run_info["num_run"] = self.num_run if train_loss is not None: - additional_run_info["train_loss"] = train_loss + if len(self.metrics) == 1: + additional_run_info["train_loss"] = train_loss[self.metrics[0].name] + else: + additional_run_info["train_loss"] = [ + train_loss[metric.name] for metric in self.metrics + ] if validation_loss is not None: additional_run_info["validation_loss"] = validation_loss if test_loss is not None: @@ -433,14 +441,14 @@ def calculate_auxiliary_losses( self, Y_valid_pred: np.ndarray, Y_test_pred: np.ndarray, - ) -> Tuple[Optional[float], Optional[float]]: + ) -> Tuple[Optional[float | Sequence[float]], Optional[float | Sequence[float]]]: if Y_valid_pred is not None: if self.y_valid is not None: validation_loss: Optional[Union[float, Dict[str, float]]] = self._loss( self.y_valid, Y_valid_pred ) - if isinstance(validation_loss, dict): - validation_loss = validation_loss[self.metric.name] + if len(self.metrics) == 1: + validation_loss = validation_loss[self.metrics[0].name] else: validation_loss = None else: @@ -451,8 +459,8 @@ def calculate_auxiliary_losses( test_loss: Optional[Union[float, Dict[str, float]]] = self._loss( self.y_test, Y_test_pred ) - if isinstance(test_loss, dict): - test_loss = test_loss[self.metric.name] + if len(self.metrics) == 1: + test_loss = test_loss[self.metrics[0].name] else: test_loss = None else: diff --git a/autosklearn/evaluation/test_evaluator.py b/autosklearn/evaluation/test_evaluator.py index 4b6cf8452c..e76186aa06 100644 --- a/autosklearn/evaluation/test_evaluator.py +++ b/autosklearn/evaluation/test_evaluator.py @@ -1,5 +1,5 @@ # -*- encoding: utf-8 -*- -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Any, Dict, List, Optional, Sequence, Tuple, Union import multiprocessing @@ -12,7 +12,7 @@ AbstractEvaluator, _fit_and_suppress_warnings, ) -from autosklearn.metrics import Scorer, calculate_loss +from autosklearn.metrics import Scorer, calculate_losses from autosklearn.pipeline.components.base import ThirdPartyComponents __all__ = ["eval_t", "TestEvaluator"] @@ -23,7 +23,7 @@ def __init__( self, backend: Backend, queue: multiprocessing.Queue, - metric: Scorer, + metrics: Sequence[Scorer], additional_components: Dict[str, ThirdPartyComponents], port: Optional[int], configuration: Optional[Union[int, Configuration]] = None, @@ -39,7 +39,7 @@ def __init__( queue=queue, port=port, configuration=configuration, - metric=metric, + metrics=metrics, additional_components=additional_components, scoring_functions=scoring_functions, seed=seed, @@ -83,22 +83,22 @@ def predict_and_loss( Y_pred = self.predict_function( self.X_train, self.model, self.task_type, self.Y_train ) - err = calculate_loss( + err = calculate_losses( solution=self.Y_train, prediction=Y_pred, task_type=self.task_type, - metric=self.metric, + metrics=self.metrics, scoring_functions=self.scoring_functions, ) else: Y_pred = self.predict_function( self.X_test, self.model, self.task_type, self.Y_train ) - err = calculate_loss( + err = calculate_losses( solution=self.Y_test, prediction=Y_pred, task_type=self.task_type, - metric=self.metric, + metrics=self.metrics, scoring_functions=self.scoring_functions, ) @@ -111,7 +111,7 @@ def eval_t( queue: multiprocessing.Queue, config: Union[int, Configuration], backend: Backend, - metric: Scorer, + metrics: Sequence[Scorer], seed: int, num_run: int, instance: Dict[str, Any], @@ -129,7 +129,7 @@ def eval_t( evaluator = TestEvaluator( configuration=config, backend=backend, - metric=metric, + metrics=metrics, seed=seed, port=port, queue=queue, diff --git a/autosklearn/evaluation/train_evaluator.py b/autosklearn/evaluation/train_evaluator.py index 7a047d3e10..f6317ca94e 100644 --- a/autosklearn/evaluation/train_evaluator.py +++ b/autosklearn/evaluation/train_evaluator.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, List, Optional, Tuple, Union, cast +from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast import copy import json @@ -182,7 +182,7 @@ def __init__( self, backend: Backend, queue: multiprocessing.Queue, - metric: Scorer, + metrics: Sequence[Scorer], additional_components: Dict[str, ThirdPartyComponents], port: Optional[int], configuration: Optional[Union[int, Configuration]] = None, @@ -210,7 +210,7 @@ def __init__( queue=queue, port=port, configuration=configuration, - metric=metric, + metrics=metrics, additional_components=additional_components, scoring_functions=scoring_functions, seed=seed, @@ -328,8 +328,8 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: y = _get_y_array(self.Y_train, self.task_type) - # stores train loss of each fold. - train_losses = [np.NaN] * self.num_cv_folds + # stores train loss(es) of each fold. + train_losses = [dict()] * self.num_cv_folds # used as weights when averaging train losses. train_fold_weights = [np.NaN] * self.num_cv_folds # stores opt (validation) loss of each fold. @@ -395,8 +395,6 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: Y_test_pred[i] = test_pred train_splits[i] = train_indices - # Compute train loss of this fold and store it. train_loss could - # either be a scalar or a dict of scalars with metrics as keys. train_loss = self._loss( self.Y_train.iloc[train_indices] if hasattr(self.Y_train, "iloc") @@ -437,37 +435,24 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: w / sum(opt_fold_weights) for w in opt_fold_weights ] - # train_losses is a list of either scalars or dicts. If it contains - # dicts, then train_loss is computed using the target metric - # (self.metric). - if all(isinstance(elem, dict) for elem in train_losses): - train_loss = np.average( + train_loss = { + metric.name: np.average( [ - train_losses[i][str(self.metric)] + train_losses[i][str(metric)] for i in range(self.num_cv_folds) ], weights=train_fold_weights_percentage, ) - else: - train_loss = np.average( - train_losses, weights=train_fold_weights_percentage - ) + for metric in self.metrics + } # if all_scoring_function is true, return a dict of opt_loss. # Otherwise, return a scalar. - if self.scoring_functions: - opt_loss = {} - for metric in opt_losses[0].keys(): - opt_loss[metric] = np.average( - [ - opt_losses[i][metric] - for i in range(self.num_cv_folds) - ], - weights=opt_fold_weights_percentage, - ) - else: - opt_loss = np.average( - opt_losses, weights=opt_fold_weights_percentage + opt_loss = {} + for metric in opt_losses[0].keys(): + opt_loss[metric] = np.average( + [opt_losses[i][metric] for i in range(self.num_cv_folds)], + weights=opt_fold_weights_percentage, ) Y_targets = self.Y_targets @@ -614,8 +599,6 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: Y_test_pred[i] = test_pred train_splits[i] = train_split - # Compute train loss of this fold and store it. train_loss could - # either be a scalar or a dict of scalars with metrics as keys. train_loss = self._loss( self.Y_train_targets[train_split], train_pred, @@ -642,30 +625,24 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: ] opt_fold_weights = [w / sum(opt_fold_weights) for w in opt_fold_weights] - # train_losses is a list of either scalars or dicts. If it contains dicts, - # then train_loss is computed using the target metric (self.metric). - if all(isinstance(elem, dict) for elem in train_losses): - train_loss = np.average( - [ - train_losses[i][str(self.metric)] - for i in range(self.num_cv_folds) - ], + train_loss = { + metric.name: np.average( + [train_losses[i][str(metric)] for i in range(self.num_cv_folds)], weights=train_fold_weights, ) - else: - train_loss = np.average(train_losses, weights=train_fold_weights) + for metric in self.metrics + } # if all_scoring_function is true, return a dict of opt_loss. Otherwise, # return a scalar. - if self.scoring_functions: - opt_loss = {} - for metric in opt_losses[0].keys(): - opt_loss[metric] = np.average( - [opt_losses[i][metric] for i in range(self.num_cv_folds)], - weights=opt_fold_weights, - ) - else: - opt_loss = np.average(opt_losses, weights=opt_fold_weights) + opt_loss = {} + for metric_name in list(opt_losses[0].keys()) + [ + metric.name for metric in self.metrics + ]: + opt_loss[metric_name] = np.average( + [opt_losses[i][metric_name] for i in range(self.num_cv_folds)], + weights=opt_fold_weights, + ) Y_targets = self.Y_targets Y_train_targets = self.Y_train_targets @@ -1316,7 +1293,7 @@ def eval_holdout( str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit ], resampling_strategy_args: Dict[str, Optional[Union[float, int, str]]], - metric: Scorer, + metrics: Sequence[Scorer], seed: int, num_run: int, instance: str, @@ -1338,7 +1315,7 @@ def eval_holdout( queue=queue, resampling_strategy=resampling_strategy, resampling_strategy_args=resampling_strategy_args, - metric=metric, + metrics=metrics, configuration=config, seed=seed, num_run=num_run, @@ -1363,7 +1340,7 @@ def eval_iterative_holdout( str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit ], resampling_strategy_args: Dict[str, Optional[Union[float, int, str]]], - metric: Scorer, + metrics: Sequence[Scorer], seed: int, num_run: int, instance: str, @@ -1383,7 +1360,7 @@ def eval_iterative_holdout( port=port, config=config, backend=backend, - metric=metric, + metrics=metrics, resampling_strategy=resampling_strategy, resampling_strategy_args=resampling_strategy_args, seed=seed, @@ -1410,7 +1387,7 @@ def eval_partial_cv( str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit ], resampling_strategy_args: Dict[str, Optional[Union[float, int, str]]], - metric: Scorer, + metrics: Sequence[Scorer], seed: int, num_run: int, instance: str, @@ -1435,7 +1412,7 @@ def eval_partial_cv( backend=backend, port=port, queue=queue, - metric=metric, + metrics=metrics, configuration=config, resampling_strategy=resampling_strategy, resampling_strategy_args=resampling_strategy_args, @@ -1463,7 +1440,7 @@ def eval_partial_cv_iterative( str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit ], resampling_strategy_args: Dict[str, Optional[Union[float, int, str]]], - metric: Scorer, + metrics: Sequence[Scorer], seed: int, num_run: int, instance: str, @@ -1484,7 +1461,7 @@ def eval_partial_cv_iterative( queue=queue, config=config, backend=backend, - metric=metric, + metrics=metrics, resampling_strategy=resampling_strategy, resampling_strategy_args=resampling_strategy_args, seed=seed, @@ -1511,7 +1488,7 @@ def eval_cv( str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit ], resampling_strategy_args: Dict[str, Optional[Union[float, int, str]]], - metric: Scorer, + metrics: Sequence[Scorer], seed: int, num_run: int, instance: str, @@ -1531,7 +1508,7 @@ def eval_cv( backend=backend, port=port, queue=queue, - metric=metric, + metrics=metrics, configuration=config, seed=seed, num_run=num_run, @@ -1559,7 +1536,7 @@ def eval_iterative_cv( str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit ], resampling_strategy_args: Dict[str, Optional[Union[float, int, str]]], - metric: Scorer, + metrics: Sequence[Scorer], seed: int, num_run: int, instance: str, @@ -1578,7 +1555,7 @@ def eval_iterative_cv( eval_cv( backend=backend, queue=queue, - metric=metric, + metrics=metrics, config=config, seed=seed, num_run=num_run, diff --git a/autosklearn/experimental/askl2.py b/autosklearn/experimental/askl2.py index 65ef9b2def..8e9112e48f 100644 --- a/autosklearn/experimental/askl2.py +++ b/autosklearn/experimental/askl2.py @@ -93,6 +93,8 @@ def __call__( metalearning_configurations, n_jobs, dask_client, + multi_objective_algorithm, + multi_objective_kwargs, ): from smac.facade.smac_ac_facade import SMAC4AC from smac.intensification.simple_intensifier import SimpleIntensifier @@ -122,6 +124,8 @@ def __call__( run_id=seed, n_jobs=n_jobs, dask_client=dask_client, + multi_objective_algorithm=multi_objective_algorithm, + multi_objective_kwargs=multi_objective_kwargs, ) @@ -141,6 +145,8 @@ def __call__( metalearning_configurations, n_jobs, dask_client, + multi_objective_algorithm, + multi_objective_kwargs, ): from smac.facade.smac_ac_facade import SMAC4AC from smac.intensification.successive_halving import SuccessiveHalving @@ -178,6 +184,8 @@ def __call__( }, dask_client=dask_client, n_jobs=n_jobs, + multi_objective_algorithm=multi_objective_algorithm, + multi_objective_kwargs=multi_objective_kwargs, ) smac4ac.solver.epm_chooser.min_samples_model = int( len(scenario.cs.get_hyperparameters()) / 2 diff --git a/autosklearn/metrics/__init__.py b/autosklearn/metrics/__init__.py index 3234329658..3104716da3 100644 --- a/autosklearn/metrics/__init__.py +++ b/autosklearn/metrics/__init__.py @@ -1,6 +1,7 @@ from abc import ABCMeta, abstractmethod -from typing import Any, Callable, Dict, List, Optional, Union, cast +from typing import Any, Callable, Dict, List, Optional, Sequence +import collections from functools import partial from itertools import product @@ -384,17 +385,47 @@ def make_scorer( CLASSIFICATION_METRICS[name] = scorer -def calculate_score( +def _validate_metrics( + metrics: Sequence[Scorer], + scoring_functions: Optional[List[Scorer]] = None, +) -> None: + """ + Validate metrics given to Auto-sklearn. Raises an Exception in case of a problem. + + metrics: Sequence[Scorer] + A list of objects that hosts a function to calculate how good the + prediction is according to the solution. + scoring_functions: Optional[List[Scorer]] + A list of metrics to calculate multiple losses + """ + + to_score = list(metrics) + if scoring_functions: + to_score.extend(scoring_functions) + + if len(metrics) == 0: + raise ValueError("Number of metrics to compute must be greater than zero.") + + metric_counter = collections.Counter(to_score) + metric_names_counter = collections.Counter(metric.name for metric in to_score) + if len(metric_counter) != len(metric_names_counter): + raise ValueError( + "Error in metrics passed to Auto-sklearn. A metric name was used " + "multiple times for different metrics!" + ) + + +def calculate_scores( solution: np.ndarray, prediction: np.ndarray, task_type: int, - metric: Scorer, + metrics: Sequence[Scorer], scoring_functions: Optional[List[Scorer]] = None, -) -> Union[float, Dict[str, float]]: +) -> Dict[str, float]: """ - Returns a score (a magnitude that allows casting the + Returns the scores (a magnitude that allows casting the optimization problem as a maximization one) for the - given Auto-Sklearn Scorer object + given Auto-Sklearn Scorer objects. Parameters ---------- @@ -405,82 +436,83 @@ def calculate_score( task_type: int To understand if the problem task is classification or regression - metric: Scorer - Object that host a function to calculate how good the + metrics: Sequence[Scorer] + A list of objects that hosts a function to calculate how good the prediction is according to the solution. scoring_functions: List[Scorer] A list of metrics to calculate multiple losses Returns ------- - float or Dict[str, float] + Dict[str, float] """ if task_type not in TASK_TYPES: raise NotImplementedError(task_type) - if scoring_functions: - score_dict = dict() - if task_type in REGRESSION_TASKS: - for metric_ in scoring_functions + [metric]: - - try: - score_dict[metric_.name] = _compute_scorer( - metric_, prediction, solution, task_type - ) - except ValueError as e: - print(e, e.args[0]) - if ( - e.args[0] - == "Mean Squared Logarithmic Error cannot be used when " - "targets contain negative values." - ): - continue - else: - raise e - - else: - for metric_ in scoring_functions + [metric]: + _validate_metrics(metrics=metrics, scoring_functions=scoring_functions) - # TODO maybe annotate metrics to define which cases they can - # handle? + to_score = list(metrics) + if scoring_functions: + to_score.extend(scoring_functions) - try: - score_dict[metric_.name] = _compute_scorer( - metric_, prediction, solution, task_type - ) - except ValueError as e: - if e.args[0] == "multiclass format is not supported": - continue - elif ( - e.args[0] == "Samplewise metrics are not available " - "outside of multilabel classification." - ): - continue - elif ( - e.args[0] == "Target is multiclass but " - "average='binary'. Please choose another average " - "setting, one of [None, 'micro', 'macro', 'weighted']." - ): - continue - else: - raise e - - return score_dict + score_dict = dict() + if task_type in REGRESSION_TASKS: + for metric_ in to_score: + + try: + score_dict[metric_.name] = _compute_single_scorer( + metric_, prediction, solution, task_type + ) + except ValueError as e: + print(e, e.args[0]) + if ( + e.args[0] == "Mean Squared Logarithmic Error cannot be used when " + "targets contain negative values." + ): + continue + else: + raise e else: - return _compute_scorer(metric, prediction, solution, task_type) + for metric_ in to_score: + + # TODO maybe annotate metrics to define which cases they can + # handle? + + try: + score_dict[metric_.name] = _compute_single_scorer( + metric_, prediction, solution, task_type + ) + except ValueError as e: + if e.args[0] == "multiclass format is not supported": + continue + elif ( + e.args[0] == "Samplewise metrics are not available " + "outside of multilabel classification." + ): + continue + elif ( + e.args[0] == "Target is multiclass but " + "average='binary'. Please choose another average " + "setting, one of [None, 'micro', 'macro', 'weighted']." + ): + continue + else: + raise e + + return score_dict -def calculate_loss( +def calculate_losses( solution: np.ndarray, prediction: np.ndarray, task_type: int, - metric: Scorer, + metrics: Sequence[Scorer], scoring_functions: Optional[List[Scorer]] = None, -) -> Union[float, Dict[str, float]]: +) -> Dict[str, float]: """ - Returns a loss (a magnitude that allows casting the + Returns the losses (a magnitude that allows casting the optimization problem as a minimization one) for the - given Auto-Sklearn Scorer object + given Auto-Sklearn Scorer objects. Parameters ---------- @@ -491,45 +523,39 @@ def calculate_loss( task_type: int To understand if the problem task is classification or regression - metric: Scorer - Object that host a function to calculate how good the + metrics: Sequence[Scorer] + A list of objects that hosts a function to calculate how good the prediction is according to the solution. scoring_functions: List[Scorer] A list of metrics to calculate multiple losses Returns ------- - float or Dict[str, float] + Dict[str, float] A loss function for each of the provided scorer objects """ - score = calculate_score( + score = calculate_scores( solution=solution, prediction=prediction, task_type=task_type, - metric=metric, + metrics=metrics, scoring_functions=scoring_functions, ) + scoring_functions = scoring_functions if scoring_functions else [] - if scoring_functions: - score = cast(Dict, score) - # we expect a dict() object for which we should calculate the loss - loss_dict = dict() - for metric_ in scoring_functions + [metric]: - # TODO: When metrics are annotated with type_of_target support - # we can remove this check - if metric_.name not in score: - continue - # maybe metric argument is not in scoring_functions - # so append it to the list. Rather than check if such - # is the case, redefining loss_dict[metric] is less expensive - loss_dict[metric_.name] = metric_._optimum - score[metric_.name] - return loss_dict - else: - rval = metric._optimum - cast(float, score) - return rval + # we expect a dict() object for which we should calculate the loss + loss_dict = dict() + for metric_ in scoring_functions + list(metrics): + # maybe metric argument is not in scoring_functions + # TODO: When metrics are annotated with type_of_target support + # we can remove this check + if metric_.name not in score: + continue + loss_dict[metric_.name] = metric_._optimum - score[metric_.name] + return loss_dict -def calculate_metric( +def compute_single_metric( metric: Scorer, prediction: np.ndarray, solution: np.ndarray, task_type: int ) -> float: """ @@ -553,7 +579,7 @@ def calculate_metric( ------- float """ - score = _compute_scorer( + score = _compute_single_scorer( solution=solution, prediction=prediction, metric=metric, @@ -562,7 +588,7 @@ def calculate_metric( return metric._sign * score -def _compute_scorer( +def _compute_single_scorer( metric: Scorer, prediction: np.ndarray, solution: np.ndarray, task_type: int ) -> float: """ diff --git a/autosklearn/smbo.py b/autosklearn/smbo.py index c463badb1d..6326d3a36f 100644 --- a/autosklearn/smbo.py +++ b/autosklearn/smbo.py @@ -1,5 +1,5 @@ import typing -from typing import Dict, List, Optional +from typing import Dict, List, Optional, Sequence import copy import json @@ -16,6 +16,7 @@ from smac.facade.smac_ac_facade import SMAC4AC from smac.intensification.intensification import Intensifier from smac.intensification.simple_intensifier import SimpleIntensifier +from smac.optimizer.multi_objective.parego import ParEGO from smac.runhistory.runhistory2epm import RunHistory2EPM4LogCost from smac.scenario.scenario import Scenario from smac.tae.dask_runner import DaskParallelRunner @@ -40,6 +41,7 @@ ) from autosklearn.metalearning.metalearning.meta_base import MetaBase from autosklearn.metalearning.mismbo import suggest_via_metalearning +from autosklearn.metrics import Scorer from autosklearn.util.logging_ import get_named_client_logger from autosklearn.util.parallel import preload_modules from autosklearn.util.stopwatch import StopWatch @@ -209,6 +211,8 @@ def get_smac_object( metalearning_configurations, n_jobs, dask_client, + multi_objective_algorithm, + multi_objective_kwargs, ): if len(scenario_dict["instances"]) > 1: intensifier = Intensifier @@ -233,6 +237,8 @@ def get_smac_object( intensifier=intensifier, dask_client=dask_client, n_jobs=n_jobs, + multi_objective_algorithm=multi_objective_algorithm, + multi_objective_kwargs=multi_objective_kwargs, ) @@ -245,7 +251,7 @@ def __init__( total_walltime_limit, func_eval_time_limit, memory_limit, - metric, + metrics: Sequence[Scorer], stopwatch: StopWatch, n_jobs, dask_client: dask.distributed.Client, @@ -272,7 +278,7 @@ def __init__( # data related self.dataset_name = dataset_name self.datamanager = None - self.metric = metric + self.metrics = metrics self.task = None self.backend = backend self.port = port @@ -291,7 +297,7 @@ def __init__( self.resampling_strategy_args = resampling_strategy_args # and a bunch of useful limits - self.worst_possible_result = get_cost_of_crash(self.metric) + self.worst_possible_result = get_cost_of_crash(self.metrics) self.total_walltime_limit = int(total_walltime_limit) self.func_eval_time_limit = int(func_eval_time_limit) self.memory_limit = memory_limit @@ -346,7 +352,7 @@ def collect_metalearning_suggestions(self, meta_base): metalearning_configurations = _get_metalearning_configurations( meta_base=meta_base, basename=self.dataset_name, - metric=self.metric, + metric=self.metrics[0], configuration_space=self.config_space, task=self.task, is_sparse=self.datamanager.info["is_sparse"], @@ -460,7 +466,7 @@ def run_smbo(self): initial_num_run=num_run, include=self.include, exclude=self.exclude, - metric=self.metric, + metrics=self.metrics, memory_limit=self.memory_limit, disable_file_output=self.disable_file_output, scoring_functions=self.scoring_functions, @@ -526,6 +532,13 @@ def run_smbo(self): "n_jobs": self.n_jobs, "dask_client": self.dask_client, } + if len(self.metrics) > 1: + smac_args["multi_objective_algorithm"] = ParEGO + smac_args["multi_objective_kwargs"] = {"rho": 0.05} + scenario_dict["multi_objectives"] = [metric.name for metric in self.metrics] + else: + smac_args["multi_objective_algorithm"] = None + smac_args["multi_objective_kwargs"] = {} if self.get_smac_object_callback is not None: smac = self.get_smac_object_callback(**smac_args) else: @@ -571,7 +584,7 @@ def get_metalearning_suggestions(self): "files", "%s_%s_%s" % ( - self.metric, + self.metrics[0], TASK_TYPES_TO_STRING[meta_task], "sparse" if self.datamanager.info["is_sparse"] else "dense", ), @@ -598,7 +611,7 @@ def get_metalearning_suggestions(self): self.metadata_directory, "%s_%s_%s" % ( - self.metric, + self.metrics[0], TASK_TYPES_TO_STRING[meta_task], "sparse" if self.datamanager.info["is_sparse"] else "dense", ), diff --git a/examples/40_advanced/example_metrics.py b/examples/40_advanced/example_metrics.py index 33d0f678fd..7784491746 100644 --- a/examples/40_advanced/example_metrics.py +++ b/examples/40_advanced/example_metrics.py @@ -71,18 +71,18 @@ def error_wk(solution, prediction, extra_argument): print("#" * 80) print("Use predefined accuracy metric") +scorer = autosklearn.metrics.accuracy cls = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=60, per_run_time_limit=30, seed=1, - metric=autosklearn.metrics.accuracy, + metric=scorer, ) cls.fit(X_train, y_train) predictions = cls.predict(X_test) -score = sklearn.metrics.accuracy_score(y_test, predictions) -metric_name = cls.automl_._metric.name -print(f"Accuracy score {score:.3f} using {metric_name}") +score = scorer(y_test, predictions) +print(f"Accuracy score {score:.3f} using {scorer.name}") ############################################################################ # Second example: Use own accuracy metric @@ -108,8 +108,7 @@ def error_wk(solution, prediction, extra_argument): predictions = cls.predict(X_test) score = accuracy_scorer(y_test, predictions) -metric_name = cls.automl_._metric.name -print(f"Accuracy score {score:.3f} using {metric_name:s}") +print(f"Accuracy score {score:.3f} using {accuracy_scorer.name:s}") ############################################################################ # Third example: Use own error metric @@ -135,8 +134,7 @@ def error_wk(solution, prediction, extra_argument): cls.predictions = cls.predict(X_test) score = error_rate(y_test, predictions) -metric_name = cls.automl_._metric.name -print(f"Error score {score:.3f} using {metric_name:s}") +print(f"Error score {score:.3f} using {error_rate.name:s}") ############################################################################ # Fourth example: Use own accuracy metric with additional argument @@ -160,8 +158,7 @@ def error_wk(solution, prediction, extra_argument): predictions = cls.predict(X_test) score = accuracy_scorer(y_test, predictions) -metric_name = cls.automl_._metric.name -print(f"Accuracy score {score:.3f} using {metric_name:s}") +print(f"Accuracy score {score:.3f} using {accuracy_scorer.name:s}") ############################################################################ # Fifth example: Use own accuracy metric with additional argument @@ -188,5 +185,4 @@ def error_wk(solution, prediction, extra_argument): predictions = cls.predict(X_test) score = error_rate(y_test, predictions) -metric_name = cls.automl_._metric.name -print(f"Error score {score:.3f} using {metric_name:s}") +print(f"Error score {score:.3f} using {error_rate.name:s}") diff --git a/examples/40_advanced/example_multi_objective.py b/examples/40_advanced/example_multi_objective.py new file mode 100644 index 0000000000..f81f0d4709 --- /dev/null +++ b/examples/40_advanced/example_multi_objective.py @@ -0,0 +1,65 @@ +# -*- encoding: utf-8 -*- +""" +============== +Classification +============== + +The following example shows how to fit *auto-sklearn* to optimize for two +competing metrics: `precision` and `recall` (read more on this tradeoff +in the `scikit-learn docs `_. + +Auto-sklearn uses `SMAC3's implementation of ParEGO `_. +Multi-objective ensembling and proper access to the full Pareto front will be added in the near +future. +""" +from pprint import pprint + +import sklearn.datasets +import sklearn.metrics + +import autosklearn.classification +import autosklearn.metrics + + +############################################################################ +# Data Loading +# ============ + +X, y = sklearn.datasets.load_breast_cancer(return_X_y=True) +X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1 +) + +############################################################################ +# Build and fit a classifier +# ========================== + +automl = autosklearn.classification.AutoSklearnClassifier( + time_left_for_this_task=30, + tmp_folder="/tmp/autosklearn_multi_objective_example_tmp", + metric=[autosklearn.metrics.precision, autosklearn.metrics.recall], +) +automl.fit(X_train, y_train, dataset_name="breast_cancer") + +############################################################################ +# Compute the two competing metrics +# ================================= + +predictions = automl.predict(X_test) +print("Precision", sklearn.metrics.precision_score(y_test, predictions)) +print("Recall", sklearn.metrics.recall_score(y_test, predictions)) + +############################################################################ +# View the models found by auto-sklearn +# ===================================== +# They are by default sorted by the first metric given to *auto-sklearn*. + +print(automl.leaderboard()) + +############################################################################ +# ``cv_results`` also contains both metrics +# ========================================= +# Similarly to the leaderboard, they are sorted by the first metric given +# to *auto-sklearn*. + +pprint(automl.cv_results_) diff --git a/examples/60_search/example_random_search.py b/examples/60_search/example_random_search.py index 520c8c18b0..908fe44ffe 100644 --- a/examples/60_search/example_random_search.py +++ b/examples/60_search/example_random_search.py @@ -45,6 +45,8 @@ def get_roar_object_callback( metalearning_configurations, n_jobs, dask_client, + multi_objective_algorithm, # This argument will be ignored as ROAR does not yet support multi-objective optimization + multi_objective_kwargs, ): """Random online adaptive racing.""" @@ -91,7 +93,15 @@ def get_roar_object_callback( # Fit a classifier using Random Search # ==================================== def get_random_search_object_callback( - scenario_dict, seed, ta, ta_kwargs, metalearning_configurations, n_jobs, dask_client + scenario_dict, + seed, + ta, + ta_kwargs, + metalearning_configurations, + n_jobs, + dask_client, + multi_objective_algorithm, # This argument will be ignored as ROAR does not yet support multi-objective optimization + multi_objective_kwargs, ): """Random search""" diff --git a/examples/60_search/example_successive_halving.py b/examples/60_search/example_successive_halving.py index e57be7f157..71749f5668 100644 --- a/examples/60_search/example_successive_halving.py +++ b/examples/60_search/example_successive_halving.py @@ -37,6 +37,8 @@ def get_smac_object( metalearning_configurations, n_jobs, dask_client, + multi_objective_algorithm, # This argument will be ignored as SH does not yet support multi-objective optimization + multi_objective_kwargs, ): from smac.facade.smac_ac_facade import SMAC4AC from smac.intensification.successive_halving import SuccessiveHalving diff --git a/test/test_automl/test_dummy_predictions.py b/test/test_automl/test_dummy_predictions.py index 9a268d1a2c..3b9350ce8b 100644 --- a/test/test_automl/test_dummy_predictions.py +++ b/test/test_automl/test_dummy_predictions.py @@ -66,7 +66,7 @@ def test_produces_correct_output( * It should produce predictions "predictions_ensemble_1337_1_0.0.npy" """ seed = 1337 - automl = make_automl(metric=metric, seed=seed) + automl = make_automl(metrics=[metric], seed=seed) automl._logger = mock_logger datamanager = make_sklearn_dataset( @@ -115,7 +115,7 @@ def test_runs_with_correct_args( dataset = "iris" task = MULTICLASS_CLASSIFICATION - automl = make_automl(metric=accuracy) + automl = make_automl(metrics=[accuracy]) automl._logger = mock_logger datamanager = make_sklearn_dataset( @@ -159,7 +159,7 @@ def test_crash_due_to_memory_exception( dataset = "iris" task = MULTICLASS_CLASSIFICATION - automl = make_automl(metric=accuracy) + automl = make_automl(metrics=[accuracy]) automl._logger = mock_logger datamanager = make_sklearn_dataset( @@ -181,5 +181,5 @@ def test_crash_due_to_memory_exception( def test_raises_if_no_metric_set(make_automl: Callable[..., AutoML]) -> None: automl = make_automl() - with pytest.raises(ValueError, match="Metric was not set"): + with pytest.raises(ValueError, match="Metric/Metrics was/were not set"): automl._do_dummy_prediction() diff --git a/test/test_estimators/test_estimators.py b/test/test_estimators/test_estimators.py index cd4b0922de..4dd13d4c17 100644 --- a/test/test_estimators/test_estimators.py +++ b/test/test_estimators/test_estimators.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, Type, Union, cast +from typing import Any, Dict, Sequence, Type, Union, cast import copy import glob @@ -42,6 +42,7 @@ import unittest import unittest.mock +import test.conftest from test.test_automl.automl_utils import ( count_succeses, include_single_scores, @@ -378,6 +379,74 @@ def test_cv_results(tmp_dir): assert hasattr(cls, "classes_") +def test_cv_results_multi_objective(tmp_dir): + # TODO restructure and actually use real SMAC output from a long run + # to do this unittest! + X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") + + cls = AutoSklearnClassifier( + time_left_for_this_task=30, + per_run_time_limit=5, + tmp_folder=os.path.join(tmp_dir, "backend"), + seed=1, + initial_configurations_via_metalearning=0, + metric=[autosklearn.metrics.precision_macro, autosklearn.metrics.roc_auc], + scoring_functions=[autosklearn.metrics.accuracy, autosklearn.metrics.roc_auc], + ) + + params = cls.get_params() + original_params = copy.deepcopy(params) + + cls.fit(X_train, Y_train) + + cv_results = cls.cv_results_ + assert isinstance(cv_results, dict), type(cv_results) + assert "mean_test_score" not in cv_results + assert "rank_test_scores" not in cv_results + for expected_column in ( + "mean_test_precision_macro", + "mean_test_roc_auc", + "mean_fit_time", + "rank_test_precision_macro", + "rank_test_roc_auc", + "metric_roc_auc", + "metric_accuracy", + ): + assert isinstance(cv_results[expected_column], np.ndarray), type( + cv_results[expected_column] + ) + + assert isinstance(cv_results["params"], list), type(cv_results["params"]) + cv_result_items = [ + isinstance(val, npma.MaskedArray) + for key, val in cv_results.items() + if key.startswith("param_") + ] + assert all(cv_result_items), cv_results.items() + + # Compare the state of the model parameters with the original parameters + new_params = clone(cls).get_params() + for param_name, original_value in original_params.items(): + new_value = new_params[param_name] + + # Taken from Sklearn code: + # We should never change or mutate the internal state of input + # parameters by default. To check this we use the joblib.hash function + # that introspects recursively any subobjects to compute a checksum. + # The only exception to this rule of immutable constructor parameters + # is possible RandomState instance but in this check we explicitly + # fixed the random_state params recursively to be integer seeds. + assert joblib.hash(new_value) == joblib.hash(original_value), ( + "Estimator %s should not change or mutate " + " the parameter %s from %s to %s during fit." + % (cls, param_name, original_value, new_value) + ) + + # Comply with https://scikit-learn.org/dev/glossary.html#term-classes + is_classifier(cls) + assert hasattr(cls, "classes_") + + @pytest.mark.parametrize( "estimator_type,dataset_name", [(AutoSklearnClassifier, "iris"), (AutoSklearnRegressor, "boston")], @@ -388,7 +457,7 @@ def test_leaderboard( # Comprehensive test tasks a substantial amount of time, manually set if # required. MAX_COMBO_SIZE_FOR_INCLUDE_PARAM = 3 # [0, len(valid_columns) + 1] - column_types = AutoSklearnEstimator._leaderboard_columns() + column_types = AutoSklearnEstimator._leaderboard_columns(num_metrics=1) # Create a dict of all possible param values for each param # with some invalid one's of the incorrect type @@ -496,6 +565,160 @@ def exclude(lst, s): assert all(leaderboard["ensemble_weight"] > 0) +@pytest.mark.parametrize( + "estimator_type,dataset_name,metrics", + [ + ( + AutoSklearnClassifier, + "iris", + (autosklearn.metrics.accuracy, autosklearn.metrics.balanced_accuracy), + ), + ( + AutoSklearnRegressor, + "boston", + (autosklearn.metrics.r2, autosklearn.metrics.root_mean_squared_error), + ), + ], +) +def test_leaderboard_multi_objective( + tmp_dir: str, + estimator_type: Type[AutoSklearnEstimator], + dataset_name: str, + metrics: Sequence[autosklearn.metrics.Scorer], +): + # Comprehensive test tasks a substantial amount of time, manually set if + # required. + MAX_COMBO_SIZE_FOR_INCLUDE_PARAM = 3 # [0, len(valid_columns) + 1] + column_types = AutoSklearnEstimator._leaderboard_columns(num_metrics=2) + + # Create a dict of all possible param values for each param + # with some invalid one's of the incorrect type + include_combinations = itertools.chain( + itertools.combinations(column_types["all"], item_count) + for item_count in range(1, MAX_COMBO_SIZE_FOR_INCLUDE_PARAM) + ) + valid_params = { + "detailed": [True, False], + "ensemble_only": [True, False], + "top_k": [-10, 0, 1, 10, "all"], + "sort_by": [ + "cost", + "cost_0", + "cost_1", + ["cost_1", "cost_0"], + *column_types["all"], + "invalid", + ], + "sort_order": ["ascending", "descending", "auto", "invalid", None], + "include": itertools.chain([None, "invalid", "type"], include_combinations), + } + + # Create a generator of all possible combinations of valid_params + params_generator = iter( + dict(zip(valid_params.keys(), param_values)) + for param_values in itertools.product(*valid_params.values()) + ) + + X_train, Y_train, _, _ = putil.get_dataset(dataset_name) + model = estimator_type( + time_left_for_this_task=30, + per_run_time_limit=5, + tmp_folder=os.path.join(tmp_dir, "backend"), + seed=test.conftest.DEFAULT_SEED, + metric=metrics, + ) + + model.fit(X_train, Y_train) + + for params in params_generator: + # Convert from iterator to solid list + if params["include"] is not None and not isinstance(params["include"], str): + params["include"] = list(params["include"]) + + # Invalid top_k should raise an error, is a positive int or 'all' + if not (params["top_k"] == "all" or params["top_k"] > 0): + with pytest.raises(ValueError): + model.leaderboard(**params) + + # Invalid sort_by column + elif ( + params["sort_by"] not in column_types["all"] + and params["sort_by"] != "cost" + and params["sort_by"] != ["cost_1", "cost_0"] + and params["sort_by"] not in ["cost_0", "cost_1"] + ): + with pytest.raises(ValueError): + model.leaderboard(**params) + + # Shouldn't accept an invalid sort order + elif params["sort_order"] not in ["ascending", "descending", "auto"]: + with pytest.raises(ValueError): + model.leaderboard(**params) + + # include is single str but not valid + elif ( + isinstance(params["include"], str) + and params["include"] not in column_types["all"] + ): + with pytest.raises(ValueError): + model.leaderboard(**params) + + # Crash if include is list but contains invalid column + elif ( + isinstance(params["include"], list) + and len(set(params["include"]) - set(column_types["all"])) != 0 + ): + with pytest.raises(ValueError): + model.leaderboard(**params) + + # Can't have just model_id, in both single str and list case + elif params["include"] == "model_id" or params["include"] == ["model_id"]: + with pytest.raises(ValueError): + model.leaderboard(**params) + + # Else all valid combinations should be validated + else: + leaderboard = model.leaderboard(**params) + assert "cost" not in leaderboard.columns + + if params["include"] is None: + assert "cost_0" in leaderboard.columns + assert "cost_1" in leaderboard.columns + else: + for cost_name in ["cost_0", "cost_1"]: + if cost_name in params["include"]: + assert cost_name in leaderboard.columns + + # top_k should never be less than the rows given back + # It can however be larger + if isinstance(params["top_k"], int): + assert params["top_k"] >= len(leaderboard) + + # Check the right columns are present and in the right order + # The model_id is set as the index, not included in pandas columns + columns = list(leaderboard.columns) + + def exclude(lst, s): + return [x for x in lst if x != s] + + if params["include"] is not None: + # Include with only single str should be the only column + if isinstance(params["include"], str): + assert params["include"] in columns and len(columns) == 1 + # Include as a list should have all the columns without model_id + else: + assert columns == exclude(params["include"], "model_id") + elif params["detailed"]: + assert columns == exclude(column_types["detailed"], "model_id") + else: + assert columns == exclude(column_types["simple"], "model_id") + + # Ensure that if it's ensemble only + # Can only check if 'ensemble_weight' is present + if params["ensemble_only"] and "ensemble_weight" in columns: + assert all(leaderboard["ensemble_weight"] > 0) + + @pytest.mark.parametrize("estimator", [AutoSklearnRegressor]) @pytest.mark.parametrize("resampling_strategy", ["holdout"]) @pytest.mark.parametrize( diff --git a/test/test_evaluation/test_abstract_evaluator.py b/test/test_evaluation/test_abstract_evaluator.py index 7f88383bcd..7bd52c0f76 100644 --- a/test/test_evaluation/test_abstract_evaluator.py +++ b/test/test_evaluation/test_abstract_evaluator.py @@ -65,7 +65,7 @@ def test_finish_up_model_predicts_NaN(self): port=self.port, output_y_hat_optimization=False, queue=queue_mock, - metric=accuracy, + metrics=[accuracy], additional_components=dict(), ) ae.Y_optimization = rs.rand(33, 3) @@ -143,7 +143,7 @@ def test_disable_file_output(self): backend=self.backend_mock, queue=queue_mock, disable_file_output=True, - metric=accuracy, + metrics=[accuracy], port=self.port, additional_components=dict(), ) @@ -169,7 +169,7 @@ def test_disable_file_output(self): output_y_hat_optimization=False, queue=queue_mock, disable_file_output=[disable], - metric=accuracy, + metrics=[accuracy], port=self.port, additional_components=dict(), ) @@ -226,7 +226,7 @@ def test_disable_file_output(self): backend=self.backend_mock, output_y_hat_optimization=False, queue=queue_mock, - metric=accuracy, + metrics=[accuracy], disable_file_output=["y_optimization"], port=self.port, additional_components=dict(), @@ -286,7 +286,7 @@ def test_file_output(self): backend=backend, output_y_hat_optimization=False, queue=queue_mock, - metric=accuracy, + metrics=[accuracy], port=self.port, additional_components=dict(), ) @@ -357,7 +357,7 @@ def test_add_additional_components(self): backend=backend, output_y_hat_optimization=False, queue=queue_mock, - metric=accuracy, + metrics=[accuracy], port=self.port, additional_components=additional_components, ) diff --git a/test/test_evaluation/test_evaluation.py b/test/test_evaluation/test_evaluation.py index 1723b208f2..f5292060a6 100644 --- a/test/test_evaluation/test_evaluation.py +++ b/test/test_evaluation/test_evaluation.py @@ -95,8 +95,8 @@ def test_eval_with_limits_holdout(self, pynisher_mock): multi_objectives=["cost"], stats=self.stats, memory_limit=3072, - metric=accuracy, - cost_for_crash=get_cost_of_crash(accuracy), + metrics=[accuracy], + cost_for_crash=get_cost_of_crash([accuracy]), abort_on_first_run_crash=False, pynisher_context="fork", ) @@ -126,8 +126,8 @@ def test_zero_or_negative_cutoff(self, pynisher_mock): multi_objectives=["cost"], resampling_strategy="holdout", stats=self.stats, - metric=accuracy, - cost_for_crash=get_cost_of_crash(accuracy), + metrics=[accuracy], + cost_for_crash=get_cost_of_crash([accuracy]), abort_on_first_run_crash=False, pynisher_context="forkserver", ) @@ -156,8 +156,8 @@ def test_cutoff_lower_than_remaining_time(self, pynisher_mock): multi_objectives=["cost"], resampling_strategy="holdout", stats=self.stats, - metric=accuracy, - cost_for_crash=get_cost_of_crash(accuracy), + metrics=[accuracy], + cost_for_crash=get_cost_of_crash([accuracy]), abort_on_first_run_crash=False, pynisher_context="forkserver", ) @@ -189,8 +189,8 @@ def test_eval_with_limits_holdout_fail_silent(self, pynisher_mock): resampling_strategy="holdout", stats=self.stats, memory_limit=3072, - metric=accuracy, - cost_for_crash=get_cost_of_crash(accuracy), + metrics=[accuracy], + cost_for_crash=get_cost_of_crash([accuracy]), abort_on_first_run_crash=False, pynisher_context="fork", ) @@ -260,8 +260,8 @@ def test_eval_with_limits_holdout_fail_memory_error(self, pynisher_mock): resampling_strategy="holdout", stats=self.stats, memory_limit=3072, - metric=log_loss, - cost_for_crash=get_cost_of_crash(log_loss), + metrics=[log_loss], + cost_for_crash=get_cost_of_crash([log_loss]), abort_on_first_run_crash=False, pynisher_context="fork", ) @@ -302,8 +302,8 @@ def test_eval_with_limits_holdout_fail_timeout(self, pynisher_mock): resampling_strategy="holdout", stats=self.stats, memory_limit=3072, - metric=accuracy, - cost_for_crash=get_cost_of_crash(accuracy), + metrics=[accuracy], + cost_for_crash=get_cost_of_crash([accuracy]), abort_on_first_run_crash=False, pynisher_context="forkserver", ) @@ -352,8 +352,8 @@ def side_effect(**kwargs): resampling_strategy="holdout", stats=self.stats, memory_limit=3072, - metric=accuracy, - cost_for_crash=get_cost_of_crash(accuracy), + metrics=[accuracy], + cost_for_crash=get_cost_of_crash([accuracy]), abort_on_first_run_crash=False, pynisher_context="forkserver", ) @@ -388,8 +388,8 @@ def side_effect(**kwargs): resampling_strategy="holdout", stats=self.stats, memory_limit=3072, - metric=accuracy, - cost_for_crash=get_cost_of_crash(accuracy), + metrics=[accuracy], + cost_for_crash=get_cost_of_crash([accuracy]), abort_on_first_run_crash=False, pynisher_context="forkserver", ) @@ -432,8 +432,8 @@ def side_effect(*args, **kwargs): resampling_strategy="holdout", stats=self.stats, memory_limit=3072, - metric=accuracy, - cost_for_crash=get_cost_of_crash(accuracy), + metrics=[accuracy], + cost_for_crash=get_cost_of_crash([accuracy]), abort_on_first_run_crash=False, pynisher_context="fork", ) @@ -468,8 +468,8 @@ def test_exception_in_target_function(self, eval_holdout_mock): resampling_strategy="holdout", stats=self.stats, memory_limit=3072, - metric=accuracy, - cost_for_crash=get_cost_of_crash(accuracy), + metrics=[accuracy], + cost_for_crash=get_cost_of_crash([accuracy]), abort_on_first_run_crash=False, pynisher_context="fork", ) @@ -504,8 +504,8 @@ def test_silent_exception_in_target_function(self): multi_objectives=["cost"], stats=self.stats, memory_limit=3072, - metric=accuracy, - cost_for_crash=get_cost_of_crash(accuracy), + metrics=[accuracy], + cost_for_crash=get_cost_of_crash([accuracy]), abort_on_first_run_crash=False, iterative=False, pynisher_context="fork", diff --git a/test/test_evaluation/test_test_evaluator.py b/test/test_evaluation/test_test_evaluator.py index 8615682ce7..457661df03 100644 --- a/test/test_evaluation/test_test_evaluator.py +++ b/test/test_evaluation/test_test_evaluator.py @@ -20,12 +20,14 @@ ) from autosklearn.evaluation.test_evaluator import TestEvaluator, eval_t from autosklearn.evaluation.util import read_queue -from autosklearn.metrics import accuracy, f1_macro, r2 +from autosklearn.metrics import accuracy, balanced_accuracy, f1_macro, r2 from autosklearn.util.pipeline import get_configuration_space import unittest import unittest.mock +import test.conftest + this_directory = os.path.dirname(__file__) sys.path.append(this_directory) from evaluation_util import ( # noqa (E402: module level import not at top of file) @@ -72,7 +74,7 @@ def test_datasets(self): evaluator = TestEvaluator( backend_mock, queue_, - metric=metric_lookup[D.info["task"]], + metrics=[metric_lookup[D.info["task"]]], port=logging.handlers.DEFAULT_TCP_LOGGING_PORT, additional_components=dict(), ) @@ -110,8 +112,8 @@ def test_eval_test(self): queue=self.queue, backend=self.backend, config=self.configuration, - metric=accuracy, - seed=1, + metrics=[accuracy], + seed=test.conftest.DEFAULT_SEED, num_run=1, scoring_functions=None, output_y_hat_optimization=False, @@ -124,7 +126,35 @@ def test_eval_test(self): ) rval = read_queue(self.queue) self.assertEqual(len(rval), 1) - self.assertAlmostEqual(rval[0]["loss"], 0.040000000000000036) + self.assertAlmostEqual(rval[0]["loss"], 0.07999999999999996) + self.assertEqual(rval[0]["status"], StatusType.SUCCESS) + self.assertNotIn("bac_metric", rval[0]["additional_run_info"]) + + def test_eval_test_multi_objective(self): + metrics = { + accuracy: 0.07999999999999996, + balanced_accuracy: 0.05555555555555547, + } + eval_t( + queue=self.queue, + backend=self.backend, + config=self.configuration, + metrics=list(metrics.keys()), + seed=test.conftest.DEFAULT_SEED, + num_run=1, + scoring_functions=None, + output_y_hat_optimization=False, + include=None, + exclude=None, + disable_file_output=False, + instance=self.dataset_name, + port=self.port, + additional_components=dict(), + ) + rval = read_queue(self.queue) + self.assertEqual(len(rval), 1) + for metric, loss in metrics.items(): + self.assertAlmostEqual(rval[0]["loss"][metric.name], loss) self.assertEqual(rval[0]["status"], StatusType.SUCCESS) self.assertNotIn("bac_metric", rval[0]["additional_run_info"]) @@ -133,7 +163,7 @@ def test_eval_test_all_loss_functions(self): queue=self.queue, backend=self.backend, config=self.configuration, - metric=accuracy, + metrics=[accuracy], seed=1, num_run=1, scoring_functions=SCORER_LIST, diff --git a/test/test_evaluation/test_train_evaluator.py b/test/test_evaluation/test_train_evaluator.py index afed8b5ce1..23607b8e4d 100644 --- a/test/test_evaluation/test_train_evaluator.py +++ b/test/test_evaluation/test_train_evaluator.py @@ -48,12 +48,14 @@ subsample_indices, ) from autosklearn.evaluation.util import read_queue -from autosklearn.metrics import accuracy, f1_macro, r2 +from autosklearn.metrics import accuracy, balanced_accuracy, f1_macro, r2 from autosklearn.util.pipeline import get_configuration_space import unittest import unittest.mock +import test.conftest + this_directory = os.path.dirname(__file__) sys.path.append(this_directory) from evaluation_util import ( # noqa (E402: module level import not at top of file) @@ -139,7 +141,7 @@ def test_holdout(self, pipeline_mock): resampling_strategy_args={"train_size": 0.66}, scoring_functions=None, output_y_hat_optimization=True, - metric=accuracy, + metrics=[accuracy], port=self.port, additional_components=dict(), ) @@ -229,7 +231,7 @@ def configuration_fully_fitted(self): resampling_strategy="holdout", scoring_functions=None, output_y_hat_optimization=True, - metric=accuracy, + metrics=[accuracy], budget=0.0, additional_components=dict(), ) @@ -239,42 +241,45 @@ def configuration_fully_fitted(self): class LossSideEffect(object): def __init__(self): self.losses = [ - 1.0, - 1.0, - 1.0, - 1.0, - 0.9, - 0.9, - 0.9, - 0.9, - 0.8, - 0.8, - 0.8, - 0.8, - 0.7, - 0.7, - 0.7, - 0.7, - 0.6, - 0.6, - 0.6, - 0.6, - 0.5, - 0.5, - 0.5, - 0.5, - 0.4, - 0.4, - 0.4, - 0.4, - 0.3, - 0.3, - 0.3, - 0.3, - 0.2, - 0.2, - 0.2, - 0.2, + {"accuracy": value} + for value in [ + 1.0, + 1.0, + 1.0, + 1.0, + 0.9, + 0.9, + 0.9, + 0.9, + 0.8, + 0.8, + 0.8, + 0.8, + 0.7, + 0.7, + 0.7, + 0.7, + 0.6, + 0.6, + 0.6, + 0.6, + 0.5, + 0.5, + 0.5, + 0.5, + 0.4, + 0.4, + 0.4, + 0.4, + 0.3, + 0.3, + 0.3, + 0.3, + 0.2, + 0.2, + 0.2, + 0.2, + ] ] self.iteration = 0 @@ -381,7 +386,7 @@ def configuration_fully_fitted(self): resampling_strategy="holdout-iterative-fit", scoring_functions=None, output_y_hat_optimization=True, - metric=accuracy, + metrics=[accuracy], budget=0.0, additional_components=dict(), ) @@ -391,26 +396,29 @@ def configuration_fully_fitted(self): class LossSideEffect(object): def __init__(self): self.losses = [ - 0.8, - 0.8, - 0.8, - 0.8, - 0.6, - 0.6, - 0.6, - 0.6, - 0.4, - 0.4, - 0.4, - 0.4, - 0.2, - 0.2, - 0.2, - 0.2, - 0.0, - 0.0, - 0.0, - 0.0, + {"accuracy": value} + for value in [ + 0.8, + 0.8, + 0.8, + 0.8, + 0.6, + 0.6, + 0.6, + 0.6, + 0.4, + 0.4, + 0.4, + 0.4, + 0.2, + 0.2, + 0.2, + 0.2, + 0.0, + 0.0, + 0.0, + 0.0, + ] ] self.iteration = 0 @@ -482,7 +490,7 @@ def test_iterative_holdout_not_iterative(self, pipeline_mock): resampling_strategy="holdout-iterative-fit", scoring_functions=None, output_y_hat_optimization=True, - metric=accuracy, + metrics=[accuracy], additional_components=dict(), ) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) @@ -538,7 +546,7 @@ def test_cv(self, pipeline_mock): resampling_strategy_args={"folds": 5}, scoring_functions=None, output_y_hat_optimization=True, - metric=accuracy, + metrics=[accuracy], additional_components=dict(), ) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) @@ -606,7 +614,7 @@ def test_partial_cv(self, pipeline_mock): resampling_strategy_args={"folds": 5}, scoring_functions=None, output_y_hat_optimization=True, - metric=accuracy, + metrics=[accuracy], additional_components=dict(), ) @@ -688,7 +696,7 @@ def configuration_fully_fitted(self): resampling_strategy_args={"folds": 5}, scoring_functions=None, output_y_hat_optimization=True, - metric=accuracy, + metrics=[accuracy], budget=0.0, additional_components=dict(), ) @@ -698,42 +706,45 @@ def configuration_fully_fitted(self): class LossSideEffect(object): def __init__(self): self.losses = [ - 1.0, - 1.0, - 1.0, - 1.0, - 0.9, - 0.9, - 0.9, - 0.9, - 0.8, - 0.8, - 0.8, - 0.8, - 0.7, - 0.7, - 0.7, - 0.7, - 0.6, - 0.6, - 0.6, - 0.6, - 0.5, - 0.5, - 0.5, - 0.5, - 0.4, - 0.4, - 0.4, - 0.4, - 0.3, - 0.3, - 0.3, - 0.3, - 0.2, - 0.2, - 0.2, - 0.2, + {"accuracy": value} + for value in [ + 1.0, + 1.0, + 1.0, + 1.0, + 0.9, + 0.9, + 0.9, + 0.9, + 0.8, + 0.8, + 0.8, + 0.8, + 0.7, + 0.7, + 0.7, + 0.7, + 0.6, + 0.6, + 0.6, + 0.6, + 0.5, + 0.5, + 0.5, + 0.5, + 0.4, + 0.4, + 0.4, + 0.4, + 0.3, + 0.3, + 0.3, + 0.3, + 0.2, + 0.2, + 0.2, + 0.2, + ] ] self.iteration = 0 @@ -791,7 +802,7 @@ def test_file_output(self, loss_mock, model_mock): resampling_strategy_args={"folds": 5}, scoring_functions=SCORER_LIST, output_y_hat_optimization=True, - metric=accuracy, + metrics=[accuracy], additional_components=dict(), ) @@ -903,7 +914,7 @@ def test_subsample_indices_classification(self, mock, backend_mock): configuration=configuration, resampling_strategy="cv", resampling_strategy_args={"folds": 10}, - metric=accuracy, + metrics=[accuracy], additional_components=dict(), ) train_indices = np.arange(69, dtype=int) @@ -974,7 +985,7 @@ def test_subsample_indices_regression(self, mock, backend_mock): configuration=configuration, resampling_strategy="cv", resampling_strategy_args={"folds": 10}, - metric=accuracy, + metrics=[accuracy], additional_components=dict(), ) train_indices = np.arange(69, dtype=int) @@ -1043,7 +1054,7 @@ def test_predict_proba_binary_classification(self, mock): resampling_strategy="cv", resampling_strategy_args={"folds": 10}, output_y_hat_optimization=False, - metric=accuracy, + metrics=[accuracy], additional_components=dict(), ) @@ -1091,7 +1102,7 @@ def test_fit_predict_and_loss_standard_additional_run_info( configuration=configuration, resampling_strategy="holdout", output_y_hat_optimization=False, - metric=accuracy, + metrics=[accuracy], additional_components=dict(), ) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) @@ -1139,7 +1150,7 @@ def __call__(self, *args, **kwargs): resampling_strategy="cv", resampling_strategy_args={"folds": 2}, output_y_hat_optimization=False, - metric=accuracy, + metrics=[accuracy], additional_components=dict(), ) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) @@ -1182,7 +1193,7 @@ def __call__(self): mock.get_current_iter.side_effect = Counter() mock.get_max_iter.return_value = 1 mock.get_additional_run_info.return_value = 14678 - loss_mock.return_value = 0.5 + loss_mock.return_value = {"accuracy": 0.5} D = get_binary_classification_datamanager() backend_mock.load_datamanager.return_value = D @@ -1199,7 +1210,7 @@ def __call__(self): configuration=configuration, resampling_strategy="holdout", output_y_hat_optimization=False, - metric=accuracy, + metrics=[accuracy], budget=0.0, additional_components=dict(), ) @@ -1228,7 +1239,7 @@ def test_fit_predict_and_loss_iterative_noniterativemodel_additional_run_info( mock.estimator_supports_iterative_fit.return_value = False mock.fit_transformer.return_value = ("Xt", {}) mock.get_additional_run_info.return_value = 14678 - loss_mock.return_value = 0.5 + loss_mock.return_value = {"accuracy": 0.5} D = get_binary_classification_datamanager() backend_mock.load_datamanager.return_value = D @@ -1245,7 +1256,7 @@ def test_fit_predict_and_loss_iterative_noniterativemodel_additional_run_info( configuration=configuration, resampling_strategy="holdout", output_y_hat_optimization=False, - metric=accuracy, + metrics=[accuracy], additional_components=dict(), ) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) @@ -1285,7 +1296,7 @@ def __call__(self): mock.fit_transformer.return_value = ("Xt", {}) mock.get_additional_run_info.return_value = {"val": 14678} mock.get_max_iter.return_value = 512 - loss_mock.return_value = 0.5 + loss_mock.return_value = {"accuracy": 0.5} D = get_binary_classification_datamanager() backend_mock.load_datamanager.return_value = D @@ -1302,7 +1313,7 @@ def __call__(self): configuration=configuration, resampling_strategy="holdout", output_y_hat_optimization=False, - metric=accuracy, + metrics=[accuracy], budget_type="iterations", budget=50, additional_components=dict(), @@ -1335,7 +1346,7 @@ def test_fit_predict_and_loss_budget_2_additional_run_info( mock.estimator_supports_iterative_fit.return_value = False mock.fit_transformer.return_value = ("Xt", {}) mock.get_additional_run_info.return_value = {"val": 14678} - loss_mock.return_value = 0.5 + loss_mock.return_value = {"accuracy": 0.5} D = get_binary_classification_datamanager() backend_mock.load_datamanager.return_value = D @@ -1352,7 +1363,7 @@ def test_fit_predict_and_loss_budget_2_additional_run_info( configuration=configuration, resampling_strategy="holdout", output_y_hat_optimization=False, - metric=accuracy, + metrics=[accuracy], budget_type="subsample", budget=50, additional_components=dict(), @@ -1406,7 +1417,7 @@ def test_datasets(self): resampling_strategy="cv", resampling_strategy_args={"folds": 2}, output_y_hat_optimization=False, - metric=metric_lookup[D.info["task"]], + metrics=[metric_lookup[D.info["task"]]], additional_components=dict(), ) @@ -2984,7 +2995,7 @@ def test_eval_holdout(self): exclude=None, disable_file_output=False, instance=self.dataset_name, - metric=accuracy, + metrics=[accuracy], additional_components=dict(), ) info = read_queue(self.queue) @@ -2993,6 +3004,36 @@ def test_eval_holdout(self): self.assertEqual(info[0]["status"], StatusType.SUCCESS) self.assertNotIn("bac_metric", info[0]["additional_run_info"]) + def test_eval_holdout_multi_objective(self): + metrics = { + accuracy: 0.030303030303030276, + balanced_accuracy: 0.033333333333333326, + } + eval_holdout( + queue=self.queue, + port=self.port, + config=self.configuration, + backend=self.backend, + resampling_strategy="holdout", + resampling_strategy_args=None, + seed=test.conftest.DEFAULT_SEED, + num_run=1, + scoring_functions=None, + output_y_hat_optimization=True, + include=None, + exclude=None, + disable_file_output=False, + instance=self.dataset_name, + metrics=list(metrics.keys()), + additional_components=dict(), + ) + info = read_queue(self.queue) + self.assertEqual(len(info), 1) + for metric, loss in metrics.items(): + self.assertAlmostEqual(info[0]["loss"][metric.name], loss) + self.assertEqual(info[0]["status"], StatusType.SUCCESS) + self.assertNotIn("bac_metric", info[0]["additional_run_info"]) + def test_eval_holdout_all_loss_functions(self): eval_holdout( queue=self.queue, @@ -3009,7 +3050,7 @@ def test_eval_holdout_all_loss_functions(self): exclude=None, disable_file_output=False, instance=self.dataset_name, - metric=accuracy, + metrics=[accuracy], additional_components=dict(), ) rval = read_queue(self.queue) @@ -3063,7 +3104,7 @@ def test_eval_holdout_iterative_fit_no_timeout(self): exclude=None, disable_file_output=False, instance=self.dataset_name, - metric=accuracy, + metrics=[accuracy], additional_components=dict(), ) rval = read_queue(self.queue) @@ -3072,6 +3113,36 @@ def test_eval_holdout_iterative_fit_no_timeout(self): self.assertEqual(rval[0]["status"], StatusType.DONOTADVANCE) self.assertEqual(rval[-1]["status"], StatusType.SUCCESS) + def test_eval_holdout_iterative_fit_no_timeout_multi_objective(self): + metrics = { + accuracy: 0.030303030303030276, + balanced_accuracy: 0.033333333333333326, + } + eval_iterative_holdout( + queue=self.queue, + port=self.port, + config=self.configuration, + backend=self.backend, + resampling_strategy="holdout", + resampling_strategy_args=None, + seed=1, + num_run=1, + scoring_functions=None, + output_y_hat_optimization=True, + include=None, + exclude=None, + disable_file_output=False, + instance=self.dataset_name, + metrics=list(metrics.keys()), + additional_components=dict(), + ) + rval = read_queue(self.queue) + self.assertEqual(len(rval), 9) + for metric, loss in metrics.items(): + self.assertAlmostEqual(rval[-1]["loss"][metric.name], loss) + self.assertEqual(rval[0]["status"], StatusType.DONOTADVANCE) + self.assertEqual(rval[-1]["status"], StatusType.SUCCESS) + def test_eval_holdout_budget_iterations(self): eval_holdout( queue=self.queue, @@ -3088,7 +3159,7 @@ def test_eval_holdout_budget_iterations(self): exclude=None, disable_file_output=False, instance=self.dataset_name, - metric=accuracy, + metrics=[accuracy], budget=1, budget_type="iterations", additional_components=dict(), @@ -3099,7 +3170,39 @@ def test_eval_holdout_budget_iterations(self): self.assertEqual(info[0]["status"], StatusType.SUCCESS) self.assertNotIn("bac_metric", info[0]["additional_run_info"]) - def test_eval_holdout_budget_iterations_converged(self): + def test_eval_holdout_budget_iterations_multi_objective(self): + metrics = { + accuracy: 0.06060606060606055, + balanced_accuracy: 0.06666666666666676, + } + eval_holdout( + queue=self.queue, + port=self.port, + config=self.configuration, + backend=self.backend, + resampling_strategy="holdout", + resampling_strategy_args=None, + seed=1, + num_run=1, + scoring_functions=None, + output_y_hat_optimization=True, + include=None, + exclude=None, + disable_file_output=False, + instance=self.dataset_name, + metrics=list(metrics.keys()), + budget=1, # Not iterative, but only for 1% of the budget + budget_type="iterations", + additional_components=dict(), + ) + info = read_queue(self.queue) + self.assertEqual(len(info), 1) + for metric, loss in metrics.items(): + self.assertAlmostEqual(info[0]["loss"][metric.name], loss) + self.assertEqual(info[0]["status"], StatusType.SUCCESS) + self.assertNotIn("bac_metric", info[0]["additional_run_info"]) + + def test_eval_holdout_budget_iterations_converged_multi_objective(self): configuration = get_configuration_space( exclude={"classifier": ["random_forest", "liblinear_svc"]}, info={"task": MULTICLASS_CLASSIFICATION, "is_sparse": False}, @@ -3119,7 +3222,7 @@ def test_eval_holdout_budget_iterations_converged(self): exclude={"classifier": ["random_forest", "liblinear_svc"]}, disable_file_output=False, instance=self.dataset_name, - metric=accuracy, + metrics=[accuracy], budget=80, budget_type="iterations", additional_components=dict(), @@ -3130,6 +3233,42 @@ def test_eval_holdout_budget_iterations_converged(self): self.assertEqual(info[0]["status"], StatusType.DONOTADVANCE) self.assertNotIn("bac_metric", info[0]["additional_run_info"]) + def test_eval_holdout_budget_iterations_converged(self): + metrics = { + accuracy: 0.18181818181818177, + balanced_accuracy: 0.18787878787878787, + } + configuration = get_configuration_space( + exclude={"classifier": ["random_forest", "liblinear_svc"]}, + info={"task": MULTICLASS_CLASSIFICATION, "is_sparse": False}, + ).get_default_configuration() + eval_holdout( + queue=self.queue, + port=self.port, + config=configuration, + backend=self.backend, + resampling_strategy="holdout", + resampling_strategy_args=None, + seed=1, + num_run=1, + scoring_functions=None, + output_y_hat_optimization=True, + include=None, + exclude={"classifier": ["random_forest", "liblinear_svc"]}, + disable_file_output=False, + instance=self.dataset_name, + metrics=list(metrics.keys()), + budget=80, + budget_type="iterations", + additional_components=dict(), + ) + info = read_queue(self.queue) + self.assertEqual(len(info), 1) + for metric, loss in metrics.items(): + self.assertAlmostEqual(info[0]["loss"][metric.name], loss) + self.assertEqual(info[0]["status"], StatusType.DONOTADVANCE) + self.assertNotIn("bac_metric", info[0]["additional_run_info"]) + def test_eval_holdout_budget_subsample(self): eval_holdout( queue=self.queue, @@ -3146,7 +3285,7 @@ def test_eval_holdout_budget_subsample(self): exclude=None, disable_file_output=False, instance=self.dataset_name, - metric=accuracy, + metrics=[accuracy], budget=30, budget_type="subsample", additional_components=dict(), @@ -3157,6 +3296,38 @@ def test_eval_holdout_budget_subsample(self): self.assertEqual(info[0]["status"], StatusType.SUCCESS) self.assertNotIn("bac_metric", info[0]["additional_run_info"]) + def test_eval_holdout_budget_subsample_multi_objective(self): + metrics = { + accuracy: 0.0, + f1_macro: 0.0, + } + eval_holdout( + queue=self.queue, + port=self.port, + config=self.configuration, + backend=self.backend, + resampling_strategy="holdout", + resampling_strategy_args=None, + seed=1, + num_run=1, + scoring_functions=None, + output_y_hat_optimization=True, + include=None, + exclude=None, + disable_file_output=False, + instance=self.dataset_name, + metrics=list(metrics.keys()), + budget=30, + budget_type="subsample", + additional_components=dict(), + ) + info = read_queue(self.queue) + self.assertEqual(len(info), 1) + for metric, loss in metrics.items(): + self.assertAlmostEqual(info[0]["loss"][metric.name], loss) + self.assertEqual(info[0]["status"], StatusType.SUCCESS) + self.assertNotIn("bac_metric", info[0]["additional_run_info"]) + def test_eval_holdout_budget_mixed_iterations(self): print(self.configuration) eval_holdout( @@ -3174,7 +3345,7 @@ def test_eval_holdout_budget_mixed_iterations(self): exclude=None, disable_file_output=False, instance=self.dataset_name, - metric=accuracy, + metrics=[accuracy], budget=1, budget_type="mixed", additional_components=dict(), @@ -3204,7 +3375,7 @@ def test_eval_holdout_budget_mixed_subsample(self): exclude={"classifier": ["random_forest"]}, disable_file_output=False, instance=self.dataset_name, - metric=accuracy, + metrics=[accuracy], budget=40, budget_type="mixed", additional_components=dict(), @@ -3231,7 +3402,7 @@ def test_eval_cv(self): exclude=None, disable_file_output=False, instance=self.dataset_name, - metric=accuracy, + metrics=[accuracy], additional_components=dict(), ) rval = read_queue(self.queue) @@ -3256,7 +3427,7 @@ def test_eval_cv_all_loss_functions(self): exclude=None, disable_file_output=False, instance=self.dataset_name, - metric=accuracy, + metrics=[accuracy], additional_components=dict(), ) rval = read_queue(self.queue) @@ -3330,10 +3501,54 @@ def test_eval_partial_cv(self): include=None, exclude=None, disable_file_output=False, - metric=accuracy, + metrics=[accuracy], additional_components=dict(), ) rval = read_queue(self.queue) self.assertEqual(len(rval), 1) self.assertAlmostEqual(rval[0]["loss"], results[fold]) self.assertEqual(rval[0]["status"], StatusType.SUCCESS) + + def test_eval_partial_cv_multi_objective(self): + metrics = { + accuracy: [ + 0.050000000000000044, + 0.0, + 0.09999999999999998, + 0.09999999999999998, + 0.050000000000000044, + ], + balanced_accuracy: [ + 0.04761904761904756, + 0.0, + 0.10317460317460314, + 0.11111111111111116, + 0.05555555555555547, + ], + } + + for fold in range(5): + instance = json.dumps({"task_id": "data", "fold": fold}) + eval_partial_cv( + port=self.port, + queue=self.queue, + config=self.configuration, + backend=self.backend, + seed=1, + num_run=1, + instance=instance, + resampling_strategy="partial-cv", + resampling_strategy_args={"folds": 5}, + scoring_functions=None, + output_y_hat_optimization=True, + include=None, + exclude=None, + disable_file_output=False, + metrics=list(metrics.keys()), + additional_components=dict(), + ) + rval = read_queue(self.queue) + self.assertEqual(len(rval), 1) + for metric, loss in metrics.items(): + self.assertAlmostEqual(rval[0]["loss"][metric.name], loss[fold]) + self.assertEqual(rval[0]["status"], StatusType.SUCCESS) diff --git a/test/test_metric/test_metrics.py b/test/test_metric/test_metrics.py index 541b2d6783..2cb7dc2158 100644 --- a/test/test_metric/test_metrics.py +++ b/test/test_metric/test_metrics.py @@ -6,7 +6,11 @@ import autosklearn.metrics from autosklearn.constants import BINARY_CLASSIFICATION, REGRESSION -from autosklearn.metrics import calculate_loss, calculate_metric, calculate_score +from autosklearn.metrics import ( + calculate_losses, + calculate_scores, + compute_single_metric, +) import pytest import unittest @@ -543,7 +547,7 @@ def test_unsupported_task_type(self): raised = False try: - calculate_score(y_true, y_pred, 6, scorer) + calculate_scores(y_true, y_pred, 6, scorer) except NotImplementedError: raised = True self.assertTrue(raised) @@ -561,11 +565,11 @@ def test_classification_scoring_functions(self): y_pred = np.array( [[0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0]] ) - score_dict = calculate_score( + score_dict = calculate_scores( y_true, y_pred, BINARY_CLASSIFICATION, - autosklearn.metrics.accuracy, + [autosklearn.metrics.accuracy], scoring_functions, ) @@ -591,11 +595,11 @@ def test_regression_scoring_functions(self): y_true = np.array([1, 2, 3, -4]) y_pred = y_true.copy() - score_dict = calculate_score( + score_dict = calculate_scores( y_true, y_pred, REGRESSION, - autosklearn.metrics.root_mean_squared_error, + [autosklearn.metrics.root_mean_squared_error], scoring_functions, ) @@ -615,7 +619,9 @@ def test_classification_only_metric(self): ) scorer = autosklearn.metrics.accuracy - score = calculate_score(y_true, y_pred, BINARY_CLASSIFICATION, scorer) + score = calculate_scores(y_true, y_pred, BINARY_CLASSIFICATION, [scorer])[ + "accuracy" + ] previous_score = scorer._optimum self.assertAlmostEqual(score, previous_score) @@ -625,36 +631,126 @@ def test_regression_only_metric(self): y_pred = y_true.copy() scorer = autosklearn.metrics.root_mean_squared_error - score = calculate_score(y_true, y_pred, REGRESSION, scorer) + score = calculate_scores(y_true, y_pred, REGRESSION, [scorer])[ + "root_mean_squared_error" + ] previous_score = scorer._optimum self.assertAlmostEqual(score, previous_score) -def test_calculate_loss(): +def test_calculate_losses(): # In a 0-1 ranged scorer, make sure that the loss # has an expected positive value y_pred = np.array([0, 1, 0, 1, 1, 1, 0, 0, 0, 0]) y_true = np.array([0, 1, 0, 1, 1, 0, 0, 0, 0, 0]) score = sklearn.metrics.accuracy_score(y_true, y_pred) - assert pytest.approx(score) == calculate_score( + assert {"accuracy": pytest.approx(score)} == calculate_scores( solution=y_true, prediction=y_pred, task_type=BINARY_CLASSIFICATION, - metric=autosklearn.metrics.accuracy, + metrics=[autosklearn.metrics.accuracy], ) - assert pytest.approx(1.0 - score) == calculate_loss( + assert {"accuracy": pytest.approx(1.0 - score)} == calculate_losses( solution=y_true, prediction=y_pred, task_type=BINARY_CLASSIFICATION, - metric=autosklearn.metrics.accuracy, + metrics=[autosklearn.metrics.accuracy], ) - # Test the dictionary case - score_dict = calculate_score( + # Test two metrics + score_dict = calculate_scores( solution=y_true, prediction=y_pred, task_type=BINARY_CLASSIFICATION, - metric=autosklearn.metrics.accuracy, + metrics=[ + autosklearn.metrics.accuracy, + autosklearn.metrics.balanced_accuracy, + ], + ) + expected_score_dict = { + "accuracy": 0.9, + "balanced_accuracy": 0.9285714285714286, + } + loss_dict = calculate_losses( + solution=y_true, + prediction=y_pred, + task_type=BINARY_CLASSIFICATION, + metrics=[ + autosklearn.metrics.accuracy, + autosklearn.metrics.balanced_accuracy, + ], + ) + for expected_metric, expected_score in expected_score_dict.items(): + assert pytest.approx(expected_score) == score_dict[expected_metric] + assert pytest.approx(1 - expected_score) == loss_dict[expected_metric] + + # Test no metric + with pytest.raises( + ValueError, match="Number of metrics to compute must be greater than zero." + ): + calculate_scores( + solution=y_true, + prediction=y_pred, + task_type=BINARY_CLASSIFICATION, + metrics=[], + ) + + with pytest.raises( + ValueError, match="Number of metrics to compute must be greater than zero." + ): + calculate_scores( + solution=y_true, + prediction=y_pred, + task_type=BINARY_CLASSIFICATION, + metrics=[], + scoring_functions=[ + autosklearn.metrics.accuracy, + autosklearn.metrics.balanced_accuracy, + ], + ) + + # Test the same metric twice + accuracy_fixture = {"accuracy": pytest.approx(0.9)} + assert accuracy_fixture == calculate_scores( + solution=y_true, + prediction=y_pred, + task_type=BINARY_CLASSIFICATION, + metrics=[autosklearn.metrics.accuracy, autosklearn.metrics.accuracy], + ) + assert accuracy_fixture == calculate_scores( + solution=y_true, + prediction=y_pred, + task_type=BINARY_CLASSIFICATION, + metrics=[autosklearn.metrics.accuracy], + scoring_functions=[autosklearn.metrics.accuracy], + ) + assert accuracy_fixture == calculate_scores( + solution=y_true, + prediction=y_pred, + task_type=BINARY_CLASSIFICATION, + metrics=[autosklearn.metrics.accuracy], + scoring_functions=[autosklearn.metrics.accuracy, autosklearn.metrics.accuracy], + ) + + # Test the same name for multiple metrics! + bogus_accuracy = autosklearn.metrics.make_scorer( + "accuracy", + score_func=sklearn.metrics.roc_auc_score, + ) + with pytest.raises(ValueError, match="used multiple times"): + calculate_scores( + solution=y_true, + prediction=y_pred, + task_type=BINARY_CLASSIFICATION, + metrics=[autosklearn.metrics.accuracy, bogus_accuracy], + ) + + # Test additional scoring functions + score_dict = calculate_scores( + solution=y_true, + prediction=y_pred, + task_type=BINARY_CLASSIFICATION, + metrics=[autosklearn.metrics.accuracy], scoring_functions=[ autosklearn.metrics.accuracy, autosklearn.metrics.balanced_accuracy, @@ -664,11 +760,11 @@ def test_calculate_loss(): "accuracy": 0.9, "balanced_accuracy": 0.9285714285714286, } - loss_dict = calculate_loss( + loss_dict = calculate_losses( solution=y_true, prediction=y_pred, task_type=BINARY_CLASSIFICATION, - metric=autosklearn.metrics.accuracy, + metrics=[autosklearn.metrics.accuracy], scoring_functions=[ autosklearn.metrics.accuracy, autosklearn.metrics.balanced_accuracy, @@ -683,17 +779,17 @@ def test_calculate_loss(): y_true = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6]) y_pred = np.array([0.11, 0.22, 0.33, 0.44, 0.55, 0.66]) score = sklearn.metrics.mean_squared_error(y_true, y_pred) - assert pytest.approx(0 - score) == calculate_score( + assert {"mean_squared_error": pytest.approx(0 - score)} == calculate_scores( solution=y_true, prediction=y_pred, task_type=REGRESSION, - metric=autosklearn.metrics.mean_squared_error, + metrics=[autosklearn.metrics.mean_squared_error], ) - assert pytest.approx(score) == calculate_loss( + assert {"mean_squared_error": pytest.approx(score)} == calculate_losses( solution=y_true, prediction=y_pred, task_type=REGRESSION, - metric=autosklearn.metrics.mean_squared_error, + metrics=[autosklearn.metrics.mean_squared_error], ) @@ -702,7 +798,7 @@ def test_calculate_metric(): y_pred = np.array([0, 1, 0, 1, 1, 1, 0, 0, 0, 0]) y_true = np.array([0, 1, 0, 1, 1, 0, 0, 0, 0, 0]) score = sklearn.metrics.accuracy_score(y_true, y_pred) - assert pytest.approx(score) == calculate_metric( + assert pytest.approx(score) == compute_single_metric( solution=y_true, prediction=y_pred, task_type=BINARY_CLASSIFICATION, @@ -713,7 +809,7 @@ def test_calculate_metric(): y_true = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6]) y_pred = np.array([0.11, 0.22, 0.33, 0.44, 0.55, 0.66]) score = sklearn.metrics.mean_squared_error(y_true, y_pred) - assert pytest.approx(score) == calculate_metric( + assert pytest.approx(score) == compute_single_metric( solution=y_true, prediction=y_pred, task_type=REGRESSION, diff --git a/test/test_optimizer/test_smbo.py b/test/test_optimizer/test_smbo.py index 0b14f4a722..8462c67baf 100644 --- a/test/test_optimizer/test_smbo.py +++ b/test/test_optimizer/test_smbo.py @@ -13,13 +13,13 @@ import pytest -@pytest.mark.parametrize("context", ["fork", "forkserver"]) +@pytest.mark.parametrize("context", ["fork", "forkserver", "spawn"]) def test_smbo_metalearning_configurations(backend, context, dask_client) -> None: # Get the inputs to the optimizer X_train, Y_train, X_test, Y_test = putil.get_dataset("iris") config_space = AutoML( delete_tmp_folder_after_terminate=False, - metric=autosklearn.metrics.accuracy, + metrics=[autosklearn.metrics.accuracy], time_left_for_this_task=20, per_run_time_limit=5, ).fit( @@ -38,7 +38,7 @@ def test_smbo_metalearning_configurations(backend, context, dask_client) -> None total_walltime_limit=10, func_eval_time_limit=5, memory_limit=4096, - metric=autosklearn.metrics.accuracy, + metrics=[autosklearn.metrics.accuracy], stopwatch=stopwatch, n_jobs=1, dask_client=dask_client, From c26cbe97ed9db8532b8f2fce75d37cfb71b6f368 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Thu, 12 May 2022 21:44:54 +0200 Subject: [PATCH 049/118] Add radom-state to feature-type categorical/numerical (#1465) --- .../feature_type_categorical.py | 28 +++++++++++++------ .../feature_type_numerical.py | 12 ++++++-- 2 files changed, 28 insertions(+), 12 deletions(-) diff --git a/autosklearn/pipeline/components/data_preprocessing/feature_type_categorical.py b/autosklearn/pipeline/components/data_preprocessing/feature_type_categorical.py index 0a46887799..dfdaf7af62 100644 --- a/autosklearn/pipeline/components/data_preprocessing/feature_type_categorical.py +++ b/autosklearn/pipeline/components/data_preprocessing/feature_type_categorical.py @@ -128,15 +128,25 @@ def _get_pipeline_steps( if dataset_properties is not None and isinstance(dataset_properties, dict): default_dataset_properties.update(dataset_properties) - steps.extend( - [ - ("imputation", CategoricalImputation()), - ("encoding", OrdinalEncoding()), - ("category_shift", CategoryShift()), - ("category_coalescence", CoalescenseChoice(default_dataset_properties)), - ("categorical_encoding", OHEChoice(default_dataset_properties)), - ] - ) + steps = [ + ("imputation", CategoricalImputation(random_state=self.random_state)), + ("encoding", OrdinalEncoding(random_state=self.random_state)), + ("category_shift", CategoryShift(random_state=self.random_state)), + ( + "category_coalescence", + CoalescenseChoice( + dataset_properties=default_dataset_properties, + random_state=self.random_state, + ), + ), + ( + "categorical_encoding", + OHEChoice( + dataset_properties=default_dataset_properties, + random_state=self.random_state, + ), + ), + ] return steps diff --git a/autosklearn/pipeline/components/data_preprocessing/feature_type_numerical.py b/autosklearn/pipeline/components/data_preprocessing/feature_type_numerical.py index 18008378ab..b50bf0d357 100644 --- a/autosklearn/pipeline/components/data_preprocessing/feature_type_numerical.py +++ b/autosklearn/pipeline/components/data_preprocessing/feature_type_numerical.py @@ -126,11 +126,17 @@ def _get_pipeline_steps( steps.extend( [ - ("imputation", NumericalImputation()), - ("variance_threshold", VarianceThreshold()), + ("imputation", NumericalImputation(random_state=self.random_state)), + ( + "variance_threshold", + VarianceThreshold(random_state=self.random_state), + ), ( "rescaling", - rescaling_components.RescalingChoice(default_dataset_properties), + rescaling_components.RescalingChoice( + dataset_properties=default_dataset_properties, + random_state=self.random_state, + ), ), ] ) From 2f9b66b14ea1fb856ff50ca851bcb242bdb031b2 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Thu, 12 May 2022 21:45:30 +0200 Subject: [PATCH 050/118] Make test caching opt-in with `--cached` (#1464) * Make test caching opt-in `--cached` * Remove unused param --- test/conftest.py | 41 ++++++++++++++++++++++------------------ test/fixtures/caching.py | 6 ++++-- 2 files changed, 27 insertions(+), 20 deletions(-) diff --git a/test/conftest.py b/test/conftest.py index 36cc77b3ad..f2d9e3a6f3 100644 --- a/test/conftest.py +++ b/test/conftest.py @@ -11,21 +11,8 @@ **Documenting Tests** To ease in understanding of tests, what is being tested and what's expected of the test, -each test should be documented with what it's parameters/fixtures are as well as what -the test expects to happen, regardless of the tests implemntation. - - Parameters - ---------- - param1: Type - ... - - param2: Type - ... - - Fixtures - -------- - make_something: Callable[..., Something] - Factory to make Something +each test should doc expected bhaviour, not describe the steps of the test. +Commenst relating to how a test does things can be left in the tests and not the doc. Expects ------- @@ -53,7 +40,7 @@ tests and between different test runs. This is primarly used with `cases` so that tests requiring the same kind of expensive case and used cached values. -Use `pytest --cache-clear` to clear the cahce +Use `pytest --cached` to use this feature. See `test/test_automl/cases.py` for example of how the fixtures from `test/fixtures/caching.py` can be used to cache objects between tests. @@ -87,6 +74,7 @@ def test_something_does_x(arg1, make_something): from typing import Any, Iterator, List, Optional import re +import shutil import signal from pathlib import Path @@ -99,7 +87,7 @@ def test_something_does_x(arg1, make_something): HERE = Path(__file__) -AUTOSKLEARN_CACHE_NAME = "autosklearn" +AUTOSKLEARN_CACHE_NAME = "autosklearn-cache" def walk(path: Path, include: Optional[str] = None) -> Iterator[Path]: @@ -162,6 +150,18 @@ def pytest_sessionstart(session: Session) -> None: session : Session The pytest session object """ + config = session.config + cache = config.cache + + if cache is None: + return + + # We specifically only remove the cached items dir, not any information + # about previous tests which also exist in `.pytest_cache` + if not config.getoption("--cached"): + dir = cache.mkdir(AUTOSKLEARN_CACHE_NAME) + shutil.rmtree(dir) + return @@ -202,7 +202,6 @@ def pytest_collection_modifyitems( def pytest_configure(config: Config) -> None: """Used to register marks""" config.addinivalue_line("markers", "todo: Mark test as todo") - config.addinivalue_line("markers", "slow: Mark test as slow") pytest_plugins = fixture_modules() @@ -225,3 +224,9 @@ def pytest_addoption(parser: Parser) -> None: default=False, help="Disable tests marked as slow", ) + parser.addoption( + "--cached", + action="store_true", + default=False, + help="Cache everything between invocations of pytest", + ) diff --git a/test/fixtures/caching.py b/test/fixtures/caching.py index b6a4ed2fdc..936d7c35a9 100644 --- a/test/fixtures/caching.py +++ b/test/fixtures/caching.py @@ -13,6 +13,8 @@ from pytest import FixtureRequest from pytest_cases import fixture +from test.conftest import AUTOSKLEARN_CACHE_NAME + class Cache: """Used for the below fixtures. @@ -143,7 +145,7 @@ def cache(request: FixtureRequest) -> Callable[[str], Cache]: pytest_cache = request.config.cache assert pytest_cache is not None - cache_dir = pytest_cache.mkdir("autosklearn-cache") + cache_dir = pytest_cache.mkdir(AUTOSKLEARN_CACHE_NAME) return partial(Cache, cache_dir=cache_dir) @@ -153,6 +155,6 @@ def automl_cache(request: FixtureRequest) -> Callable[[str], AutoMLCache]: pytest_cache = request.config.cache assert pytest_cache is not None - cache_dir = pytest_cache.mkdir("autosklearn-cache") + cache_dir = pytest_cache.mkdir(AUTOSKLEARN_CACHE_NAME) verbosity = request.config.getoption("verbose") return partial(AutoMLCache, cache_dir=cache_dir, verbose=verbosity) From 894789086c18ca6754bd70bd2bb7655956391401 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Thu, 12 May 2022 21:45:47 +0200 Subject: [PATCH 051/118] Re-organize docs to advertise new text features (#1468) --- doc/index.rst | 24 ++++++++---------------- doc/manual.rst | 19 +++++++++++++++++++ 2 files changed, 27 insertions(+), 16 deletions(-) diff --git a/doc/index.rst b/doc/index.rst index 24c1931f54..8b8a2b5bb4 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -25,22 +25,10 @@ the technology behind *auto-sklearn* by reading our paper published at `NeurIPS 2015 `_ . -.. topic:: NEW: Auto-sklearn 2.0 +.. topic:: NEW: Text feature support - Auto-sklearn 2.0 includes latest research on automatically configuring the AutoML system itself - and contains a multitude of improvements which speed up the fitting the AutoML system. - -*auto-sklearn 2.0* works the same way as regular *auto-sklearn* and you can use it via - -.. code:: python - - from autosklearn.experimental.askl2 import AutoSklearn2Classifier - -A paper describing our advances is available on `arXiv `_. - -.. topic:: NEW: Material from tutorials and presentations - - We provide slides and notebooks from talks and tutorials here: `auto-sklearn-talks `_ + Auto-sklearn now supports text features, check our new example: + :ref:`sphx_glr_examples_40_advanced_example_text_preprocessing.py` Example @@ -64,7 +52,6 @@ Example This will run for one hour and should result in an accuracy above 0.98. - Manual ****** @@ -74,6 +61,11 @@ Manual * :ref:`extending` * :ref:`faq` +Additional Material +******************* + +We provide slides and notebooks from talks and tutorials here: +`auto-sklearn-talks `_ License ******* diff --git a/doc/manual.rst b/doc/manual.rst index cdfcd3cbe0..c3a37e19e3 100644 --- a/doc/manual.rst +++ b/doc/manual.rst @@ -19,6 +19,25 @@ give short explanations (click the title to expand text), e.g. We provide resources for talks, tutorials and presentations on *auto-sklearn* under `auto-sklearn-talks `_ +.. _askl2: + +Auto-sklearn 2.0 +================ + +Auto-sklearn 2.0 includes latest research on automatically configuring the AutoML system itself +and contains a multitude of improvements which speed up the fitting the AutoML system. +Concretely, Auto-sklearn 2.0 automatically sets the :ref:`bestmodel`, decides whether it can use +the efficient bandit strategy *Successive Halving* and uses meta-feature free *Portfolios* for +efficient meta-learning. + +*auto-sklearn 2.0* has the same interface as regular *auto-sklearn* and you can use it via + +.. code:: python + + from autosklearn.experimental.askl2 import AutoSklearn2Classifier + +A paper describing our advances is available on `arXiv `_. + .. _limits: Resource limits From 71008601d4ff62bbda897726605ce3edade12dcf Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Fri, 13 May 2022 18:30:33 +0200 Subject: [PATCH 052/118] Update ens builder (#1434) * Move ensemble_bulder test data to named folder * Update backend to take a temlate to copy from * Update tests to use new cases system * Update tests to be documented and cleaned up * Switch to using cached automl backends * Readd missing file which failed test for `case_3_models` * Seperate out tests that rely on old toy data and those that don't * Setup test framework for ensemble builder on real situations * Formatting * Remove `unit_test` arg * Remove SAVE2DISC * Split builder and manager into seperate files * Tidy up init of EnsembleBuilder * Moved to cached properties * Change List to list * Move to solely using cached properties * Add disk util file with `sizeof` * Update tests to use cached mechanism * Switch `sizeof` for disk consumption * Remove disk consumption * Remove unneeded function * Add type hints and documenation * Simplyify _read_np_fn * Update get_valid_test_preds to use Pathlib * Add intersection to functional * Make functional take *args * Further simplifications * Add a dataclass to represent run information for builder * Rename to Run * Change to Run objects * Formatting * Reduce side effects of `compute_loss_per_model` To make testing easier and changes easier, the targets are now passed to the method. This also reduces it's complexity by removing the checking from the method as we can assume the parameters coming in are correct. * Change Tuple to tuple * Forcibly add data files for tests * Fix: Can now load pickled numpy arrays w/ test * Add test for checking ensemble builder output * Fix bug with using list instead of set * Making deubgging message a little clearer * Fix typing and case name * Rename test file to reflect what it tests * Make pynisher context optional * Fix loaded models test * Updates to Run dataclass * Add method to `Run` to allow recording of last modified * Change Run mtimes to dictionary * Change `compute_loss_per_model` to use new Run dataclass * Factor out run loss into main loop * Simplyify get_nbest and compute_losses * Major rewrite of ensemble builder main loop * Change to simpler hashing * Start value split * Add `value_split` * Reworked Builder * Add some docstring * Formatting * Fix type signature * Fix typing for `loss` * Removed Literal * Mypy fixes for ensemble builder * Mypy fixes * Tests for `Runs` * Move `make_run` to fixtures * Fix run deletion * Test candidates * Made delete it's own function * Further simplifications * Fixup test with simplification * Test: `max_models` for `requires_deletion` * Test: `memory_limit` for `requires_deletion` * Test: Loss of runs * Test: Delete runs * Test: `fit_ensemble` of ensemble builder * Add test for run time parameter * Remove parameter `return_predictions` * Add note about pickled arrays should not be supported * Make cached automl instances copy backend * Add valid static method to run * Remove old test data * Add filter for bad run dirs * Made `main` args optional * Fix check for updated runs * Make `main` raise errors * Fix default value for ensemble builder `main` * Test valid ensemble with real runs * Rename parameter for manager * Add defaults and reorder parameters for EnsembleBuilderManager * Fixup parameters in `fit_and_return_ensemble` * Typing fixes * Make `fit_and_return_ensemble` a staticmethod * Add: `make_ensemble_builder_manager` * Add: Test files for manager * Add atomic rmtree * Add: atomic rmtree now accepts where mv should go * Make builder use atomic rmtree * Fix import bugs, remove valid preds in builder * Remove `np.inf` as valid arg for `read_at_most` * Possible reproducible num_run, no predictions error * Make automl caching robust to `pytest-xdist` * Test fixes * Extend interval for test on run caching * Use pickle for reseting cache * Fix test for caching mechanism to not rely on `stat` * Move run deletion to the end of the builder `main` * Remove `getattr` version of tae.client * Remove `normalize` * Extend not for `Run` * Fix `__init__` of `Run` * Parameter and comment fixes from feedback * Change to `min(...)` instead of `sorted(...)[0]` * Make default time `np.inf` * Add test for safe deletion in builder * Update docstring of `loss` for a run * Remove stray print * Minor feedback fixes * Fix `_metric` to `_metrics` * Fix `make_ensemble_builder` * One more fix for multiple metrics --- autosklearn/automl.py | 31 +- autosklearn/ensemble_builder.py | 1637 ----------------- autosklearn/ensemble_building/__init__.py | 5 + autosklearn/ensemble_building/builder.py | 926 ++++++++++ autosklearn/ensemble_building/manager.py | 370 ++++ autosklearn/ensemble_building/run.py | 181 ++ autosklearn/smbo.py | 2 +- autosklearn/util/disk.py | 103 ++ autosklearn/util/functional.py | 198 +- pyproject.toml | 3 +- setup.py | 2 +- test/fixtures/automl.py | 31 +- test/fixtures/backend.py | 96 +- test/fixtures/caching.py | 68 +- test/fixtures/dask.py | 41 +- test/fixtures/datasets.py | 17 +- test/fixtures/ensemble_building.py | 223 +++ test/mocks/logging.py | 14 +- test/test_automl/cases.py | 197 +- test/test_automl/test_outputs.py | 15 +- test/test_automl/test_post_fit.py | 12 +- .../predictions_ensemble_true.npy | Bin 160 -> 0 bytes .../.auto-sklearn/runs/0_1_0.0/0.1.0.0.model | 0 .../0_1_0.0/predictions_ensemble_0_1_0.0.npy | Bin 160 -> 0 bytes .../runs/0_1_0.0/predictions_test_0_1_0.0.npy | Bin 160 -> 0 bytes .../0_1_0.0/predictions_valid_0_1_0.0.npy | Bin 160 -> 0 bytes .../.auto-sklearn/runs/0_2_0.0/0.2.0.0.model | 0 .../0_2_0.0/predictions_ensemble_0_2_0.0.npy | Bin 160 -> 0 bytes .../runs/0_2_0.0/predictions_test_0_2_0.0.np | Bin 160 -> 0 bytes .../runs/0_2_0.0/predictions_test_0_2_0.0.npy | Bin 160 -> 0 bytes .../0_2_0.0/predictions_valid_0_2_0.0.npy | Bin 160 -> 0 bytes .../runs/0_3_100.0/0.3.0.0.model | 0 .../runs/0_3_100.0/0.3.100.0.model | 0 .../predictions_ensemble_0_3_100.0.npy | Bin 160 -> 0 bytes .../0_3_100.0/predictions_test_0_3_100.0.npy | Bin 160 -> 0 bytes .../0_3_100.0/predictions_valid_0_3_100.0.npy | Bin 160 -> 0 bytes test/test_ensemble_builder/ensemble_utils.py | 116 -- test/test_ensemble_builder/test_ensemble.py | 780 -------- .../test_ensemble_builder.py | 638 +++++++ .../test_ensemble_builder_real.py | 98 + test/test_ensemble_builder/test_manager.py | 29 + test/test_ensemble_builder/test_run.py | 169 ++ test/test_estimators/test_estimators.py | 51 +- test/test_util/test_trials_callback.py | 1 - 44 files changed, 3274 insertions(+), 2780 deletions(-) delete mode 100644 autosklearn/ensemble_builder.py create mode 100644 autosklearn/ensemble_building/__init__.py create mode 100644 autosklearn/ensemble_building/builder.py create mode 100644 autosklearn/ensemble_building/manager.py create mode 100644 autosklearn/ensemble_building/run.py create mode 100644 autosklearn/util/disk.py create mode 100644 test/fixtures/ensemble_building.py delete mode 100644 test/test_ensemble_builder/data/.auto-sklearn/predictions_ensemble_true.npy delete mode 100644 test/test_ensemble_builder/data/.auto-sklearn/runs/0_1_0.0/0.1.0.0.model delete mode 100644 test/test_ensemble_builder/data/.auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy delete mode 100644 test/test_ensemble_builder/data/.auto-sklearn/runs/0_1_0.0/predictions_test_0_1_0.0.npy delete mode 100644 test/test_ensemble_builder/data/.auto-sklearn/runs/0_1_0.0/predictions_valid_0_1_0.0.npy delete mode 100644 test/test_ensemble_builder/data/.auto-sklearn/runs/0_2_0.0/0.2.0.0.model delete mode 100644 test/test_ensemble_builder/data/.auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy delete mode 100644 test/test_ensemble_builder/data/.auto-sklearn/runs/0_2_0.0/predictions_test_0_2_0.0.np delete mode 100644 test/test_ensemble_builder/data/.auto-sklearn/runs/0_2_0.0/predictions_test_0_2_0.0.npy delete mode 100644 test/test_ensemble_builder/data/.auto-sklearn/runs/0_2_0.0/predictions_valid_0_2_0.0.npy delete mode 100644 test/test_ensemble_builder/data/.auto-sklearn/runs/0_3_100.0/0.3.0.0.model delete mode 100644 test/test_ensemble_builder/data/.auto-sklearn/runs/0_3_100.0/0.3.100.0.model delete mode 100644 test/test_ensemble_builder/data/.auto-sklearn/runs/0_3_100.0/predictions_ensemble_0_3_100.0.npy delete mode 100644 test/test_ensemble_builder/data/.auto-sklearn/runs/0_3_100.0/predictions_test_0_3_100.0.npy delete mode 100644 test/test_ensemble_builder/data/.auto-sklearn/runs/0_3_100.0/predictions_valid_0_3_100.0.npy delete mode 100644 test/test_ensemble_builder/ensemble_utils.py delete mode 100644 test/test_ensemble_builder/test_ensemble.py create mode 100644 test/test_ensemble_builder/test_ensemble_builder.py create mode 100644 test/test_ensemble_builder/test_ensemble_builder_real.py create mode 100644 test/test_ensemble_builder/test_manager.py create mode 100644 test/test_ensemble_builder/test_run.py diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 8c0f4717dc..d8c624d06a 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -62,7 +62,7 @@ convert_if_sparse, ) from autosklearn.data.xy_data_manager import XYDataManager -from autosklearn.ensemble_builder import EnsembleBuilderManager +from autosklearn.ensemble_building import EnsembleBuilderManager from autosklearn.ensembles.singlebest_ensemble import SingleBest from autosklearn.evaluation import ExecuteTaFuncWithQueue, get_cost_of_crash from autosklearn.evaluation.abstract_evaluator import _fit_and_suppress_warnings @@ -303,6 +303,8 @@ def __init__( self._label_num = None self._parser = None self._can_predict = False + self._read_at_most = None + self._max_ensemble_build_iterations = None self.models_: Optional[dict] = None self.cv_models_: Optional[dict] = None self.ensemble_ = None @@ -808,9 +810,9 @@ def fit( max_models_on_disc=self._max_models_on_disc, seed=self._seed, precision=self.precision, - max_iterations=None, - read_at_most=np.inf, - ensemble_memory_limit=self._memory_limit, + max_iterations=self._max_ensemble_build_iterations, + read_at_most=self._read_at_most, + memory_limit=self._memory_limit, random_state=self._seed, logger_port=self._logger_port, pynisher_context=self._multiprocessing_context, @@ -923,7 +925,7 @@ def fit( ) result = proc_ensemble.futures.pop().result() if result: - ensemble_history, _, _, _, _ = result + ensemble_history, _ = result self.ensemble_performance_history.extend(ensemble_history) self._logger.info("Ensemble script finished, continue shutdown.") @@ -1524,8 +1526,8 @@ def fit_ensemble( seed=self._seed, precision=precision if precision else self.precision, max_iterations=1, - read_at_most=np.inf, - ensemble_memory_limit=self._memory_limit, + read_at_most=None, + memory_limit=self._memory_limit, random_state=self._seed, logger_port=self._logger_port, pynisher_context=self._multiprocessing_context, @@ -1538,7 +1540,7 @@ def fit_ensemble( "Error building the ensemble - please check the log file and command " "line output for error messages." ) - self.ensemble_performance_history, _, _, _, _ = result + self.ensemble_performance_history, _ = result self._ensemble_size = ensemble_size self._load_models() @@ -2096,6 +2098,15 @@ def has_key(rv, key): return ensemble_dict + def has_ensemble(self) -> bool: + """ + Returns + ------- + bool + Whether this AutoML instance has an ensemble + """ + return self.ensemble_ is not None + def _create_search_space( self, tmp_dir: str, @@ -2154,7 +2165,7 @@ def fit( y: SUPPORTED_TARGET_TYPES | spmatrix, X_test: Optional[SUPPORTED_FEAT_TYPES] = None, y_test: Optional[SUPPORTED_TARGET_TYPES | spmatrix] = None, - feat_type: Optional[list[bool]] = None, + feat_type: Optional[list[str]] = None, dataset_name: Optional[str] = None, only_return_configuration_space: bool = False, load_models: bool = True, @@ -2244,7 +2255,7 @@ def fit( y: SUPPORTED_TARGET_TYPES | spmatrix, X_test: Optional[SUPPORTED_FEAT_TYPES] = None, y_test: Optional[SUPPORTED_TARGET_TYPES | spmatrix] = None, - feat_type: Optional[list[bool]] = None, + feat_type: Optional[list[str]] = None, dataset_name: Optional[str] = None, only_return_configuration_space: bool = False, load_models: bool = True, diff --git a/autosklearn/ensemble_builder.py b/autosklearn/ensemble_builder.py deleted file mode 100644 index 3dec9828ef..0000000000 --- a/autosklearn/ensemble_builder.py +++ /dev/null @@ -1,1637 +0,0 @@ -# -*- encoding: utf-8 -*- -from typing import List, Optional, Tuple, Union - -import glob -import gzip -import logging.handlers -import math -import multiprocessing -import numbers -import os -import pickle -import re -import shutil -import time -import traceback -import zlib - -import dask.distributed -import numpy as np -import pandas as pd -import pynisher -from smac.callbacks import IncorporateRunResultCallback -from smac.optimizer.smbo import SMBO -from smac.runhistory.runhistory import RunInfo, RunValue -from smac.tae.base import StatusType - -from autosklearn.automl_common.common.ensemble_building.abstract_ensemble import ( # noqa: E501 - AbstractEnsemble, -) -from autosklearn.automl_common.common.utils.backend import Backend -from autosklearn.constants import BINARY_CLASSIFICATION -from autosklearn.ensembles.ensemble_selection import EnsembleSelection -from autosklearn.metrics import Scorer, calculate_losses, calculate_scores -from autosklearn.util.logging_ import get_named_client_logger -from autosklearn.util.parallel import preload_modules - -Y_ENSEMBLE = 0 -Y_VALID = 1 -Y_TEST = 2 - -MODEL_FN_RE = r"_([0-9]*)_([0-9]*)_([0-9]{1,3}\.[0-9]*)\.npy" - - -class EnsembleBuilderManager(IncorporateRunResultCallback): - def __init__( - self, - start_time: float, - time_left_for_ensembles: float, - backend: Backend, - dataset_name: str, - task: int, - metric: Scorer, - ensemble_size: int, - ensemble_nbest: int, - max_models_on_disc: Union[float, int], - seed: int, - precision: int, - max_iterations: Optional[int], - read_at_most: int, - ensemble_memory_limit: Optional[int], - random_state: Union[int, np.random.RandomState], - logger_port: int = logging.handlers.DEFAULT_TCP_LOGGING_PORT, - pynisher_context: str = "fork", - ): - """SMAC callback to handle ensemble building - - Parameters - ---------- - start_time: int - the time when this job was started, to account for any latency in job - allocation. - - time_left_for_ensemble: int - How much time is left for the task. Job should finish within this - allocated time - - backend: util.backend.Backend - backend to write and read files - - dataset_name: str - name of dataset - - task_type: int - type of ML task - - metric: str - name of metric to compute the loss of the given predictions - - ensemble_size: int - maximal size of ensemble - - ensemble_nbest: int/float - if int: consider only the n best prediction - if float: consider only this fraction of the best models - Both wrt to validation predictions - If performance_range_threshold > 0, might return less models - - max_models_on_disc: int - Defines the maximum number of models that are kept in the disc. - - If int, it must be greater or equal than 1, and dictates the max - number of models to keep. - - If float, it will be interpreted as the max megabytes allowed of - disc space. That is, if the number of ensemble candidates require more - disc space than this float value, the worst models will be deleted to - keep within this budget. Models and predictions of the worst-performing - models will be deleted then. - - If None, the feature is disabled. It defines an upper bound on the - models that can be used in the ensemble. - - seed: int - random seed - - max_iterations: int - maximal number of iterations to run this script - (default None --> deactivated) - - precision: [16,32,64,128] - precision of floats to read the predictions - - memory_limit: Optional[int] - memory limit in mb. If ``None``, no memory limit is enforced. - - read_at_most: int - read at most n new prediction files in each iteration - - logger_port: int - port that receives logging records - - pynisher_context: str - The multiprocessing context for pynisher. One of spawn/fork/forkserver. - - """ - self.start_time = start_time - self.time_left_for_ensembles = time_left_for_ensembles - self.backend = backend - self.dataset_name = dataset_name - self.task = task - self.metric = metric - self.ensemble_size = ensemble_size - self.ensemble_nbest = ensemble_nbest - self.max_models_on_disc = max_models_on_disc - self.seed = seed - self.precision = precision - self.max_iterations = max_iterations - self.read_at_most = read_at_most - self.ensemble_memory_limit = ensemble_memory_limit - self.random_state = random_state - self.logger_port = logger_port - self.pynisher_context = pynisher_context - - # Store something similar to SMAC's runhistory - self.history = [] - - # We only submit new ensembles when there is not an active ensemble job - self.futures = [] - - # The last criteria is the number of iterations - self.iteration = 0 - - # Keep track of when we started to know when we need to finish! - self.start_time = time.time() - - def __call__( - self, - smbo: "SMBO", - run_info: RunInfo, - result: RunValue, - time_left: float, - ): - """ - Returns - ------- - List[Tuple[int, float, float, float]]: - A list with the performance history of this ensemble, of the form - [(pandas_timestamp, train_performance, val_performance, test_performance)] - """ - if result.status in (StatusType.STOP, StatusType.ABORT) or smbo._stop: - return - self.build_ensemble(smbo.tae_runner.client) - - def build_ensemble( - self, dask_client: dask.distributed.Client, unit_test: bool = False - ) -> None: - - # The second criteria is elapsed time - elapsed_time = time.time() - self.start_time - - logger = get_named_client_logger( - name="EnsembleBuilder", - port=self.logger_port, - ) - - # First test for termination conditions - if self.time_left_for_ensembles < elapsed_time: - logger.info( - "Terminate ensemble building as not time is left (run for {}s)".format( - elapsed_time - ), - ) - return - if self.max_iterations is not None and self.max_iterations <= self.iteration: - logger.info( - "Terminate ensemble building because of max iterations:" - f" {self.max_iterations} of {self.iteration}" - ) - return - - if len(self.futures) != 0: - if self.futures[0].done(): - result = self.futures.pop().result() - if result: - ensemble_history, self.ensemble_nbest, _, _, _ = result - logger.debug( - "iteration={} @ elapsed_time={} has history={}".format( - self.iteration, - elapsed_time, - ensemble_history, - ) - ) - self.history.extend(ensemble_history) - - # Only submit new jobs if the previous ensemble job finished - if len(self.futures) == 0: - - # Add the result of the run - # On the next while iteration, no references to - # ensemble builder object, so it should be garbage collected to - # save memory while waiting for resources - # Also, notice how ensemble nbest is returned, so we don't waste - # iterations testing if the deterministic predictions size can - # be fitted in memory - try: - # Submit a Dask job from this job, to properly - # see it in the dask diagnostic dashboard - # Notice that the forked ensemble_builder_process will - # wait for the below function to be done - self.futures.append( - dask_client.submit( - fit_and_return_ensemble, - backend=self.backend, - dataset_name=self.dataset_name, - task_type=self.task, - metric=self.metric, - ensemble_size=self.ensemble_size, - ensemble_nbest=self.ensemble_nbest, - max_models_on_disc=self.max_models_on_disc, - seed=self.seed, - precision=self.precision, - memory_limit=self.ensemble_memory_limit, - read_at_most=self.read_at_most, - random_state=self.random_state, - end_at=self.start_time + self.time_left_for_ensembles, - iteration=self.iteration, - return_predictions=False, - priority=100, - pynisher_context=self.pynisher_context, - logger_port=self.logger_port, - unit_test=unit_test, - ) - ) - - logger.info( - "{}/{} Started Ensemble builder job at {} for iteration {}.".format( - # Log the client to make sure we - # remain connected to the scheduler - self.futures[0], - dask_client, - time.strftime("%Y.%m.%d-%H.%M.%S"), - self.iteration, - ), - ) - self.iteration += 1 - except Exception as e: - exception_traceback = traceback.format_exc() - error_message = repr(e) - logger.critical(exception_traceback) - logger.critical(error_message) - - -def fit_and_return_ensemble( - backend: Backend, - dataset_name: str, - task_type: str, - metric: Scorer, - ensemble_size: int, - ensemble_nbest: int, - max_models_on_disc: Union[float, int], - seed: int, - precision: int, - read_at_most: int, - end_at: float, - iteration: int, - return_predictions: bool, - pynisher_context: str, - logger_port: int = logging.handlers.DEFAULT_TCP_LOGGING_PORT, - unit_test: bool = False, - memory_limit: Optional[int] = None, - random_state: Optional[Union[int, np.random.RandomState]] = None, -) -> Tuple[ - List[Tuple[int, float, float, float]], - int, - Optional[np.ndarray], - Optional[np.ndarray], - Optional[np.ndarray], -]: - """ - - A short function to fit and create an ensemble. It is just a wrapper to easily send - a request to dask to create an ensemble and clean the memory when finished - - Parameters - ---------- - backend: util.backend.Backend - backend to write and read files - - dataset_name: str - name of dataset - - metric: str - name of metric to compute the loss of the given predictions - - task_type: int - type of ML task - - ensemble_size: int - maximal size of ensemble (passed to autosklearn.ensemble.ensemble_selection) - - ensemble_nbest: int/float - if int: consider only the n best prediction - if float: consider only this fraction of the best models - Both wrt to validation predictions - If performance_range_threshold > 0, might return less models - - max_models_on_disc: int - Defines the maximum number of models that are kept in the disc. - - If int, it must be greater or equal than 1, and dictates the max number of - models to keep. - - If float, it will be interpreted as the max megabytes allowed of disc space. - That is, if the number of ensemble candidates require more disc space than - this float value, the worst models will be deleted to keep within this - budget. Models and predictions of the worst-performing models will be - deleted then. - - If None, the feature is disabled. - It defines an upper bound on the models that can be used in the ensemble. - - seed: int - random seed - - precision: [16,32,64,128] - precision of floats to read the predictions - - read_at_most: int - read at most n new prediction files in each iteration - - end_at: float - At what time the job must finish. Needs to be the endtime and not the - time left because we do not know when dask schedules the job. - - iteration: int - The current iteration - - pynisher_context: str - Context to use for multiprocessing, can be either fork, spawn or forkserver. - - logger_port: int = DEFAULT_TCP_LOGGING_PORT - The port where the logging server is listening to. - - unit_test: bool = False - Turn on unit testing mode. This currently makes fit_ensemble raise a - MemoryError. Having this is very bad coding style, but I did not find a way - to make unittest.mock work through the pynisher with all spawn contexts. - If you know a better solution, please let us know by opening an issue. - - memory_limit: Optional[int] = None - memory limit in mb. If ``None``, no memory limit is enforced. - - random_state: Optional[int | RandomState] = None - A random state used for the ensemble selection process. - - Returns - ------- - List[Tuple[int, float, float, float]] - A list with the performance history of this ensemble, of the form - [(pandas_timestamp, train_performance, val_performance, test_performance)] - """ - result = EnsembleBuilder( - backend=backend, - dataset_name=dataset_name, - task_type=task_type, - metric=metric, - ensemble_size=ensemble_size, - ensemble_nbest=ensemble_nbest, - max_models_on_disc=max_models_on_disc, - seed=seed, - precision=precision, - memory_limit=memory_limit, - read_at_most=read_at_most, - random_state=random_state, - logger_port=logger_port, - unit_test=unit_test, - ).run( - end_at=end_at, - iteration=iteration, - return_predictions=return_predictions, - pynisher_context=pynisher_context, - ) - return result - - -class EnsembleBuilder(object): - def __init__( - self, - backend: Backend, - dataset_name: str, - task_type: int, - metric: Scorer, - ensemble_size: int = 10, - ensemble_nbest: Union[int, float] = 100, - max_models_on_disc: int = 100, - performance_range_threshold: float = 0, - seed: int = 1, - precision: int = 32, - memory_limit: Optional[int] = 1024, - read_at_most: int = 5, - logger_port: int = logging.handlers.DEFAULT_TCP_LOGGING_PORT, - random_state: Optional[Union[int, np.random.RandomState]] = None, - unit_test: bool = False, - ): - """ - Constructor - - Parameters - ---------- - backend: util.backend.Backend - backend to write and read files - dataset_name: str - name of dataset - task_type: int - type of ML task - metric: str - name of metric to compute the loss of the given predictions - ensemble_size: int = 10 - maximal size of ensemble (passed to autosklearn.ensemble.ensemble_selection) - ensemble_nbest: int | float = 100 - if int: consider only the n best prediction - if float: consider only this fraction of the best models - Both with respect to the validation predictions - If performance_range_threshold > 0, might return less models - max_models_on_disc: int = 100 - Defines the maximum number of models that are kept in the disc. - If int, it must be greater or equal than 1, and dictates the max number of - models to keep. - If float, it will be interpreted as the max megabytes allowed of disc space. - That is, if the number of ensemble candidates require more disc space than - this float value, the worst models are deleted to keep within this budget. - Models and predictions of the worst-performing models will be deleted then. - If None, the feature is disabled. - It defines an upper bound on the models that can be used in the ensemble. - performance_range_threshold: float = 0 - Keep only models that are better than: - dummy + (best - dummy)*performance_range_threshold - E.g dummy=2, best=4, thresh=0.5 --> only consider models with loss > 3 - Will at most return the minimum between ensemble_nbest models, - and max_models_on_disc. Might return less - seed: int = 1 - random seed that is used as part of the filename - precision: int in [16,32,64,128] = 32 - precision of floats to read the predictions - memory_limit: Optional[int] = 1024 - memory limit in mb. If ``None``, no memory limit is enforced. - read_at_most: int = 5 - read at most n new prediction files in each iteration - logger_port: int = DEFAULT_TCP_LOGGING_PORT - port that receives logging records - random_state: Optional[int | RandomState] = None - An int or RandomState object used for generating the ensemble. - unit_test: bool = False - Turn on unit testing mode. This currently makes fit_ensemble raise - a MemoryError. Having this is very bad coding style, but I did not find a - way to make unittest.mock work through the pynisher with all spawn contexts. - If you know a better solution, please let us know by opening an issue. - """ - - super(EnsembleBuilder, self).__init__() - - self.backend = backend # communication with filesystem - self.dataset_name = dataset_name - self.task_type = task_type - self.metric = metric - self.ensemble_size = ensemble_size - self.performance_range_threshold = performance_range_threshold - - if isinstance(ensemble_nbest, numbers.Integral) and ensemble_nbest < 1: - raise ValueError( - "Integer ensemble_nbest has to be larger 1: %s" % ensemble_nbest - ) - elif not isinstance(ensemble_nbest, numbers.Integral): - if ensemble_nbest < 0 or ensemble_nbest > 1: - raise ValueError( - "Float ensemble_nbest best has to be >= 0 and <= 1: %s" - % ensemble_nbest - ) - - self.ensemble_nbest = ensemble_nbest - - # max_models_on_disc can be a float, in such case we need to - # remember the user specified Megabytes and translate this to - # max number of ensemble models. max_resident_models keeps the - # maximum number of models in disc - if max_models_on_disc is not None and max_models_on_disc < 0: - raise ValueError("max_models_on_disc has to be a positive number or None") - self.max_models_on_disc = max_models_on_disc - self.max_resident_models = None - - self.seed = seed - self.precision = precision - self.memory_limit = memory_limit - self.read_at_most = read_at_most - self.random_state = random_state - self.unit_test = unit_test - - # Setup the logger - self.logger_port = logger_port - self.logger = get_named_client_logger( - name="EnsembleBuilder", - port=self.logger_port, - ) - - if ensemble_nbest == 1: - self.logger.debug( - "Behaviour depends on int/float: %s, %s (ensemble_nbest, type)" - % (ensemble_nbest, type(ensemble_nbest)) - ) - - self.start_time = 0 - self.model_fn_re = re.compile(MODEL_FN_RE) - - self.last_hash = None # hash of ensemble training data - self.y_true_ensemble = None - self.SAVE2DISC = True - - # already read prediction files - # {"file name": { - # "ens_loss": float - # "mtime_ens": str, - # "mtime_valid": str, - # "mtime_test": str, - # "seed": int, - # "num_run": int, - # }} - self.read_losses = {} - # {"file_name": { - # Y_ENSEMBLE: np.ndarray - # Y_VALID: np.ndarray - # Y_TEST: np.ndarray - # } - # } - self.read_preds = {} - - # Depending on the dataset dimensions, - # regenerating every iteration, the predictions - # losses for self.read_preds - # is too computationally expensive - # As the ensemble builder is stateless - # (every time the ensemble builder gets resources - # from dask, it builds this object from scratch) - # we save the state of this dictionary to memory - # and read it if available - self.ensemble_memory_file = os.path.join( - self.backend.internals_directory, "ensemble_read_preds.pkl" - ) - if os.path.exists(self.ensemble_memory_file): - try: - with (open(self.ensemble_memory_file, "rb")) as memory: - self.read_preds, self.last_hash = pickle.load(memory) - except Exception as e: - self.logger.warning( - "Could not load the previous iterations of ensemble_builder" - " predictions. This might impact the quality of the run." - f" Exception={e} {traceback.format_exc()}" - ) - self.ensemble_loss_file = os.path.join( - self.backend.internals_directory, "ensemble_read_losses.pkl" - ) - if os.path.exists(self.ensemble_loss_file): - try: - with (open(self.ensemble_loss_file, "rb")) as memory: - self.read_losses = pickle.load(memory) - except Exception as e: - self.logger.warning( - "Could not load the previous iterations of ensemble_builder losses." - "This might impact the quality of the run. Exception={} {}".format( - e, - traceback.format_exc(), - ) - ) - - # hidden feature which can be activated via an environment variable. - # This keeps all models and predictions which have ever been a candidate. - # This is necessary to post-hoc compute the whole ensemble building trajectory. - self._has_been_candidate = set() - - self.validation_performance_ = np.inf - - # Track the ensemble performance - datamanager = self.backend.load_datamanager() - self.y_valid = datamanager.data.get("Y_valid") - self.y_test = datamanager.data.get("Y_test") - del datamanager - self.ensemble_history = [] - - def run( - self, - iteration: int, - pynisher_context: str, - time_left: Optional[float] = None, - end_at: Optional[float] = None, - time_buffer=5, - return_predictions: bool = False, - ): - - if time_left is None and end_at is None: - raise ValueError("Must provide either time_left or end_at.") - elif time_left is not None and end_at is not None: - raise ValueError("Cannot provide both time_left and end_at.") - - self.logger = get_named_client_logger( - name="EnsembleBuilder", - port=self.logger_port, - ) - - process_start_time = time.time() - while True: - - if time_left is not None: - time_elapsed = time.time() - process_start_time - time_left -= time_elapsed - else: - current_time = time.time() - if current_time > end_at: - break - else: - time_left = end_at - current_time - - wall_time_in_s = int(time_left - time_buffer) - if wall_time_in_s < 1: - break - context = multiprocessing.get_context(pynisher_context) - preload_modules(context) - - safe_ensemble_script = pynisher.enforce_limits( - wall_time_in_s=wall_time_in_s, - mem_in_mb=self.memory_limit, - logger=self.logger, - context=context, - )(self.main) - safe_ensemble_script(time_left, iteration, return_predictions) - if safe_ensemble_script.exit_status is pynisher.MemorylimitException: - # if ensemble script died because of memory error, - # reduce nbest to reduce memory consumption and try it again - - # ATTENTION: main will start from scratch; - # all data structures are empty again - try: - os.remove(self.ensemble_memory_file) - except: # noqa E722 - pass - - if ( - isinstance(self.ensemble_nbest, numbers.Integral) - and self.ensemble_nbest <= 1 - ): - if self.read_at_most == 1: - self.logger.error( - "Memory Exception -- Unable to further reduce the number" - " of ensemble members and can no further limit the number" - " of ensemble members loaded per iteration, please restart" - " Auto-sklearn with a higher value for the argument" - f" `memory_limit` (current limit is {self.memory_limit}MB)." - " The ensemble builder will keep running to delete files" - " from disk in case this was enabled.", - ) - self.ensemble_nbest = 0 - else: - self.read_at_most = 1 - self.logger.warning( - "Memory Exception -- Unable to further reduce the number of" - " ensemble members. Now reducing the number of predictions" - " per call to read at most to 1." - ) - else: - if isinstance(self.ensemble_nbest, numbers.Integral): - self.ensemble_nbest = max(1, int(self.ensemble_nbest / 2)) - else: - self.ensemble_nbest = self.ensemble_nbest / 2 - self.logger.warning( - "Memory Exception -- restart with " - "less ensemble_nbest: %d" % self.ensemble_nbest - ) - return [], self.ensemble_nbest, None, None, None - else: - return safe_ensemble_script.result - - return [], self.ensemble_nbest, None, None, None - - def main(self, time_left, iteration, return_predictions): - - # Pynisher jobs inside dask 'forget' - # the logger configuration. So we have to set it up - # accordingly - self.logger = get_named_client_logger( - name="EnsembleBuilder", - port=self.logger_port, - ) - - self.start_time = time.time() - train_pred, valid_pred, test_pred = None, None, None - - used_time = time.time() - self.start_time - self.logger.debug( - "Starting iteration %d, time left: %f", - iteration, - time_left - used_time, - ) - - # populates self.read_preds and self.read_losses - if not self.compute_loss_per_model(): - if return_predictions: - return ( - self.ensemble_history, - self.ensemble_nbest, - train_pred, - valid_pred, - test_pred, - ) - else: - return self.ensemble_history, self.ensemble_nbest, None, None, None - - # Only the models with the n_best predictions are candidates - # to be in the ensemble - candidate_models = self.get_n_best_preds() - if not candidate_models: # no candidates yet - if return_predictions: - return ( - self.ensemble_history, - self.ensemble_nbest, - train_pred, - valid_pred, - test_pred, - ) - else: - return self.ensemble_history, self.ensemble_nbest, None, None, None - - # populates predictions in self.read_preds - # reduces selected models if file reading failed - n_sel_valid, n_sel_test = self.get_valid_test_preds( - selected_keys=candidate_models - ) - - # If valid/test predictions loaded, then reduce candidate models to this set - if ( - len(n_sel_test) != 0 - and len(n_sel_valid) != 0 - and len(set(n_sel_valid).intersection(set(n_sel_test))) == 0 - ): - # Both n_sel_* have entries, but there is no overlap, this is critical - self.logger.error( - "n_sel_valid and n_sel_test are not empty, but do not overlap" - ) - if return_predictions: - return ( - self.ensemble_history, - self.ensemble_nbest, - train_pred, - valid_pred, - test_pred, - ) - else: - return self.ensemble_history, self.ensemble_nbest, None, None, None - - # If any of n_sel_* is not empty and overlaps with candidate_models, - # then ensure candidate_models AND n_sel_test are sorted the same - candidate_models_set = set(candidate_models) - if candidate_models_set.intersection(n_sel_valid).intersection(n_sel_test): - candidate_models = sorted( - list( - candidate_models_set.intersection(n_sel_valid).intersection( - n_sel_test - ) - ) - ) - n_sel_test = candidate_models - n_sel_valid = candidate_models - elif candidate_models_set.intersection(n_sel_valid): - candidate_models = sorted( - list(candidate_models_set.intersection(n_sel_valid)) - ) - n_sel_valid = candidate_models - elif candidate_models_set.intersection(n_sel_test): - candidate_models = sorted( - list(candidate_models_set.intersection(n_sel_test)) - ) - n_sel_test = candidate_models - else: - # This has to be the case - n_sel_test = [] - n_sel_valid = [] - - if os.environ.get("ENSEMBLE_KEEP_ALL_CANDIDATES"): - for candidate in candidate_models: - self._has_been_candidate.add(candidate) - - # train ensemble - ensemble = self.fit_ensemble(selected_keys=candidate_models) - - # Save the ensemble for later use in the main auto-sklearn module! - if ensemble is not None and self.SAVE2DISC: - self.backend.save_ensemble(ensemble, iteration, self.seed) - - # Delete files of non-candidate models - can only be done after fitting the - # ensemble and saving it to disc so we do not accidentally delete models in - # the previous ensemble - if self.max_resident_models is not None: - self._delete_excess_models(selected_keys=candidate_models) - - # Save the read losses status for the next iteration - with open(self.ensemble_loss_file, "wb") as memory: - pickle.dump(self.read_losses, memory) - - if ensemble is not None: - train_pred = self.predict( - set_="train", - ensemble=ensemble, - selected_keys=candidate_models, - n_preds=len(candidate_models), - index_run=iteration, - ) - # We can't use candidate_models here, as n_sel_* might be empty - valid_pred = self.predict( - set_="valid", - ensemble=ensemble, - selected_keys=n_sel_valid, - n_preds=len(candidate_models), - index_run=iteration, - ) - # TODO if predictions fails, build the model again during the - # next iteration! - test_pred = self.predict( - set_="test", - ensemble=ensemble, - selected_keys=n_sel_test, - n_preds=len(candidate_models), - index_run=iteration, - ) - - # Add a score to run history to see ensemble progress - self._add_ensemble_trajectory(train_pred, valid_pred, test_pred) - - # The loaded predictions and hash can only be saved after the ensemble has been - # built, because the hash is computed during the construction of the ensemble - with open(self.ensemble_memory_file, "wb") as memory: - pickle.dump((self.read_preds, self.last_hash), memory) - - if return_predictions: - return ( - self.ensemble_history, - self.ensemble_nbest, - train_pred, - valid_pred, - test_pred, - ) - else: - return self.ensemble_history, self.ensemble_nbest, None, None, None - - def get_disk_consumption(self, pred_path): - """ - gets the cost of a model being on disc - """ - - match = self.model_fn_re.search(pred_path) - if not match: - raise ValueError("Invalid path format %s" % pred_path) - _seed = int(match.group(1)) - _num_run = int(match.group(2)) - _budget = float(match.group(3)) - - stored_files_for_run = os.listdir( - self.backend.get_numrun_directory(_seed, _num_run, _budget) - ) - stored_files_for_run = [ - os.path.join( - self.backend.get_numrun_directory(_seed, _num_run, _budget), file_name - ) - for file_name in stored_files_for_run - ] - this_model_cost = sum([os.path.getsize(path) for path in stored_files_for_run]) - - # get the megabytes - return round(this_model_cost / math.pow(1024, 2), 2) - - def compute_loss_per_model(self): - """ - Compute the loss of the predictions on ensemble building data set; - populates self.read_preds and self.read_losses - """ - - self.logger.debug("Read ensemble data set predictions") - - if self.y_true_ensemble is None: - try: - self.y_true_ensemble = self.backend.load_targets_ensemble() - except FileNotFoundError: - self.logger.debug( - "Could not find true targets on ensemble data set: %s", - traceback.format_exc(), - ) - return False - - pred_path = os.path.join( - glob.escape(self.backend.get_runs_directory()), - "%d_*_*" % self.seed, - "predictions_ensemble_%s_*_*.npy*" % self.seed, - ) - y_ens_files = glob.glob(pred_path) - y_ens_files = [ - y_ens_file - for y_ens_file in y_ens_files - if y_ens_file.endswith(".npy") or y_ens_file.endswith(".npy.gz") - ] - self.y_ens_files = y_ens_files - # no validation predictions so far -- no files - if len(self.y_ens_files) == 0: - self.logger.debug( - "Found no prediction files on ensemble data set:" " %s" % pred_path - ) - return False - - # First sort files chronologically - to_read = [] - for y_ens_fn in self.y_ens_files: - match = self.model_fn_re.search(y_ens_fn) - _seed = int(match.group(1)) - _num_run = int(match.group(2)) - _budget = float(match.group(3)) - mtime = os.path.getmtime(y_ens_fn) - - to_read.append([y_ens_fn, match, _seed, _num_run, _budget, mtime]) - - n_read_files = 0 - # Now read file wrt to num_run - for y_ens_fn, match, _seed, _num_run, _budget, mtime in sorted( - to_read, key=lambda x: x[5] - ): - if self.read_at_most and n_read_files >= self.read_at_most: - # limit the number of files that will be read - # to limit memory consumption - break - - if not y_ens_fn.endswith(".npy") and not y_ens_fn.endswith(".npy.gz"): - self.logger.info( - "Error loading file (not .npy or .npy.gz): %s", y_ens_fn - ) - continue - - if not self.read_losses.get(y_ens_fn): - self.read_losses[y_ens_fn] = { - "ens_loss": np.inf, - "mtime_ens": 0, - "mtime_valid": 0, - "mtime_test": 0, - "seed": _seed, - "num_run": _num_run, - "budget": _budget, - "disc_space_cost_mb": None, - # Lazy keys so far: - # 0 - not loaded - # 1 - loaded and in memory - # 2 - loaded but dropped again - # 3 - deleted from disk due to space constraints - "loaded": 0, - } - if not self.read_preds.get(y_ens_fn): - self.read_preds[y_ens_fn] = { - Y_ENSEMBLE: None, - Y_VALID: None, - Y_TEST: None, - } - - if self.read_losses[y_ens_fn]["mtime_ens"] == mtime: - # same time stamp; nothing changed; - continue - - # actually read the predictions and compute their respective loss - try: - y_ensemble = self._read_np_fn(y_ens_fn) - loss = calculate_losses( - solution=self.y_true_ensemble, - prediction=y_ensemble, - task_type=self.task_type, - metrics=[self.metric], - scoring_functions=None, - )[self.metric.name] - - if np.isfinite(self.read_losses[y_ens_fn]["ens_loss"]): - self.logger.debug( - "Changing ensemble loss for file %s from %f to %f " - "because file modification time changed? %f - %f", - y_ens_fn, - self.read_losses[y_ens_fn]["ens_loss"], - loss, - self.read_losses[y_ens_fn]["mtime_ens"], - os.path.getmtime(y_ens_fn), - ) - - self.read_losses[y_ens_fn]["ens_loss"] = loss - - # It is not needed to create the object here - # To save memory, we just compute the loss. - self.read_losses[y_ens_fn]["mtime_ens"] = os.path.getmtime(y_ens_fn) - self.read_losses[y_ens_fn]["loaded"] = 2 - self.read_losses[y_ens_fn][ - "disc_space_cost_mb" - ] = self.get_disk_consumption(y_ens_fn) - - n_read_files += 1 - - except Exception: - self.logger.warning( - "Error loading %s: %s", - y_ens_fn, - traceback.format_exc(), - ) - self.read_losses[y_ens_fn]["ens_loss"] = np.inf - - self.logger.debug( - "Done reading %d new prediction files. Loaded %d predictions in " "total.", - n_read_files, - np.sum([pred["loaded"] > 0 for pred in self.read_losses.values()]), - ) - return True - - def get_n_best_preds(self): - """ - get best n predictions (i.e., keys of self.read_losses) - according to the loss on the "ensemble set" - n: self.ensemble_nbest - - Side effects: - ->Define the n-best models to use in ensemble - ->Only the best models are loaded - ->Any model that is not best is candidate to deletion - if max models in disc is exceeded. - """ - - sorted_keys = self._get_list_of_sorted_preds() - - # number of models available - num_keys = len(sorted_keys) - # remove all that are at most as good as random - # note: dummy model must have run_id=1 (there is no run_id=0) - dummy_losses = list(filter(lambda x: x[2] == 1, sorted_keys)) - # number of dummy models - num_dummy = len(dummy_losses) - dummy_loss = dummy_losses[0] - self.logger.debug("Use %f as dummy loss" % dummy_loss[1]) - - # sorted_keys looks like: (k, v["ens_loss"], v["num_run"]) - # On position 1 we have the loss of a minimization problem. - # keep only the predictions with a loss smaller than the dummy - # prediction - sorted_keys = filter(lambda x: x[1] < dummy_loss[1], sorted_keys) - - # remove Dummy Classifier - sorted_keys = list(filter(lambda x: x[2] > 1, sorted_keys)) - if not sorted_keys: - # no model left; try to use dummy loss (num_run==0) - # log warning when there are other models but not better than dummy model - if num_keys > num_dummy: - self.logger.warning( - "No models better than random - using Dummy loss!" - "Number of models besides current dummy model: %d. " - "Number of dummy models: %d", - num_keys - 1, - num_dummy, - ) - sorted_keys = [ - (k, v["ens_loss"], v["num_run"]) - for k, v in self.read_losses.items() - if v["seed"] == self.seed and v["num_run"] == 1 - ] - # reload predictions if losses changed over time and a model is - # considered to be in the top models again! - if not isinstance(self.ensemble_nbest, numbers.Integral): - # Transform to number of models to keep. Keep at least one - keep_nbest = max( - 1, min(len(sorted_keys), int(len(sorted_keys) * self.ensemble_nbest)) - ) - self.logger.debug( - "Library pruning: using only top %f percent of the models for ensemble " - "(%d out of %d)", - self.ensemble_nbest * 100, - keep_nbest, - len(sorted_keys), - ) - else: - # Keep only at most ensemble_nbest - keep_nbest = min(self.ensemble_nbest, len(sorted_keys)) - self.logger.debug( - "Library Pruning: using for ensemble only " - " %d (out of %d) models" % (keep_nbest, len(sorted_keys)) - ) - - # If max_models_on_disc is None, do nothing - # One can only read at most max_models_on_disc models - if self.max_models_on_disc is not None: - if not isinstance(self.max_models_on_disc, numbers.Integral): - consumption = [ - [ - v["ens_loss"], - v["disc_space_cost_mb"], - ] - for v in self.read_losses.values() - if v["disc_space_cost_mb"] is not None - ] - max_consumption = max(c[1] for c in consumption) - - # We are pessimistic with the consumption limit indicated by - # max_models_on_disc by 1 model. Such model is assumed to spend - # max_consumption megabytes - if ( - sum(c[1] for c in consumption) + max_consumption - ) > self.max_models_on_disc: - - # just leave the best -- smaller is better! - # This list is in descending order, to preserve the best models - sorted_cum_consumption = ( - np.cumsum([c[1] for c in list(sorted(consumption))]) - + max_consumption - ) - max_models = np.argmax( - sorted_cum_consumption > self.max_models_on_disc - ) - - # Make sure that at least 1 model survives - self.max_resident_models = max(1, max_models) - self.logger.warning( - "Limiting num of models via float max_models_on_disc={}" - " as accumulated={} worst={} num_models={}".format( - self.max_models_on_disc, - (sum(c[1] for c in consumption) + max_consumption), - max_consumption, - self.max_resident_models, - ) - ) - else: - self.max_resident_models = None - else: - self.max_resident_models = self.max_models_on_disc - - if ( - self.max_resident_models is not None - and keep_nbest > self.max_resident_models - ): - self.logger.debug( - "Restricting the number of models to %d instead of %d due to argument " - "max_models_on_disc", - self.max_resident_models, - keep_nbest, - ) - keep_nbest = self.max_resident_models - - # consider performance_range_threshold - if self.performance_range_threshold > 0: - best_loss = sorted_keys[0][1] - worst_loss = dummy_loss[1] - worst_loss -= (worst_loss - best_loss) * self.performance_range_threshold - if sorted_keys[keep_nbest - 1][1] > worst_loss: - # We can further reduce number of models - # since worst model is worse than thresh - for i in range(0, keep_nbest): - # Look at most at keep_nbest models, - # but always keep at least one model - current_loss = sorted_keys[i][1] - if current_loss >= worst_loss: - self.logger.debug( - "Dynamic Performance range: " - "Further reduce from %d to %d models", - keep_nbest, - max(1, i), - ) - keep_nbest = max(1, i) - break - ensemble_n_best = keep_nbest - - # reduce to keys - sorted_keys = list(map(lambda x: x[0], sorted_keys)) - - # remove loaded predictions for non-winning models - for k in sorted_keys[ensemble_n_best:]: - if k in self.read_preds: - self.read_preds[k][Y_ENSEMBLE] = None - self.read_preds[k][Y_VALID] = None - self.read_preds[k][Y_TEST] = None - if self.read_losses[k]["loaded"] == 1: - self.logger.debug( - "Dropping model %s (%d,%d) with loss %f.", - k, - self.read_losses[k]["seed"], - self.read_losses[k]["num_run"], - self.read_losses[k]["ens_loss"], - ) - self.read_losses[k]["loaded"] = 2 - - # Load the predictions for the winning - for k in sorted_keys[:ensemble_n_best]: - if ( - k not in self.read_preds or self.read_preds[k][Y_ENSEMBLE] is None - ) and self.read_losses[k]["loaded"] != 3: - self.read_preds[k][Y_ENSEMBLE] = self._read_np_fn(k) - # No need to load valid and test here because they are loaded - # only if the model ends up in the ensemble - self.read_losses[k]["loaded"] = 1 - - # return keys of self.read_losses with lowest losses - return sorted_keys[:ensemble_n_best] - - def get_valid_test_preds( - self, selected_keys: List[str] - ) -> Tuple[List[str], List[str]]: - """Get valid and test predictions from disc and store them in self.read_preds - Parameters - --------- - selected_keys: list - list of selected keys of self.read_preds - - Return - ------ - success_keys: - all keys in selected keys for which we could read the valid and - test predictions - """ - success_keys_valid = [] - success_keys_test = [] - - for k in selected_keys: - valid_fn = glob.glob( - os.path.join( - glob.escape(self.backend.get_runs_directory()), - "%d_%d_%s" - % ( - self.read_losses[k]["seed"], - self.read_losses[k]["num_run"], - self.read_losses[k]["budget"], - ), - "predictions_valid_%d_%d_%s.npy*" - % ( - self.read_losses[k]["seed"], - self.read_losses[k]["num_run"], - self.read_losses[k]["budget"], - ), - ) - ) - valid_fn = [ - vfn - for vfn in valid_fn - if vfn.endswith(".npy") or vfn.endswith(".npy.gz") - ] - test_fn = glob.glob( - os.path.join( - glob.escape(self.backend.get_runs_directory()), - "%d_%d_%s" - % ( - self.read_losses[k]["seed"], - self.read_losses[k]["num_run"], - self.read_losses[k]["budget"], - ), - "predictions_test_%d_%d_%s.npy*" - % ( - self.read_losses[k]["seed"], - self.read_losses[k]["num_run"], - self.read_losses[k]["budget"], - ), - ) - ) - test_fn = [ - tfn - for tfn in test_fn - if tfn.endswith(".npy") or tfn.endswith(".npy.gz") - ] - - if len(valid_fn) == 0: - # self.logger.debug("Not found validation prediction file " - # "(although ensemble predictions available): " - # "%s" % valid_fn) - pass - else: - valid_fn = valid_fn[0] - if ( - self.read_losses[k]["mtime_valid"] == os.path.getmtime(valid_fn) - and k in self.read_preds - and self.read_preds[k][Y_VALID] is not None - ): - success_keys_valid.append(k) - continue - try: - y_valid = self._read_np_fn(valid_fn) - self.read_preds[k][Y_VALID] = y_valid - success_keys_valid.append(k) - self.read_losses[k]["mtime_valid"] = os.path.getmtime(valid_fn) - except Exception: - self.logger.warning( - "Error loading %s: %s", valid_fn, traceback.format_exc() - ) - - if len(test_fn) == 0: - # self.logger.debug("Not found test prediction file (although " - # "ensemble predictions available):%s" % - # test_fn) - pass - else: - test_fn = test_fn[0] - if ( - self.read_losses[k]["mtime_test"] == os.path.getmtime(test_fn) - and k in self.read_preds - and self.read_preds[k][Y_TEST] is not None - ): - success_keys_test.append(k) - continue - try: - y_test = self._read_np_fn(test_fn) - self.read_preds[k][Y_TEST] = y_test - success_keys_test.append(k) - self.read_losses[k]["mtime_test"] = os.path.getmtime(test_fn) - except Exception: - self.logger.warning( - "Error loading %s: %s", test_fn, traceback.format_exc() - ) - - return success_keys_valid, success_keys_test - - def fit_ensemble(self, selected_keys: list): - """ - Parameters - --------- - selected_keys: list - list of selected keys of self.read_losses - - Returns - ------- - ensemble: EnsembleSelection - trained Ensemble - """ - if self.unit_test: - raise MemoryError() - - predictions_train = [self.read_preds[k][Y_ENSEMBLE] for k in selected_keys] - include_num_runs = [ - ( - self.read_losses[k]["seed"], - self.read_losses[k]["num_run"], - self.read_losses[k]["budget"], - ) - for k in selected_keys - ] - - # check hash if ensemble training data changed - current_hash = "".join( - [ - str(zlib.adler32(predictions_train[i].data.tobytes())) - for i in range(len(predictions_train)) - ] - ) - if self.last_hash == current_hash: - self.logger.debug( - "No new model predictions selected -- skip ensemble building " - "-- current performance: %f", - self.validation_performance_, - ) - - return None - self.last_hash = current_hash - - ensemble = EnsembleSelection( - ensemble_size=self.ensemble_size, - task_type=self.task_type, - metric=self.metric, - random_state=self.random_state, - ) - - try: - self.logger.debug( - "Fitting the ensemble on %d models.", - len(predictions_train), - ) - start_time = time.time() - ensemble.fit(predictions_train, self.y_true_ensemble, include_num_runs) - end_time = time.time() - self.logger.debug( - "Fitting the ensemble took %.2f seconds.", - end_time - start_time, - ) - self.logger.info(ensemble) - self.validation_performance_ = min( - self.validation_performance_, - ensemble.get_validation_performance(), - ) - - except ValueError: - self.logger.error("Caught ValueError: %s", traceback.format_exc()) - return None - except IndexError: - self.logger.error("Caught IndexError: %s" + traceback.format_exc()) - return None - finally: - # Explicitly free memory - del predictions_train - - return ensemble - - def predict( - self, - set_: str, - ensemble: AbstractEnsemble, - selected_keys: list, - n_preds: int, - index_run: int, - ): - """Save preditions on ensemble, validation and test data on disc - - Parameters - ---------- - set_: ["valid","test"] - data split name - ensemble: EnsembleSelection - trained Ensemble - selected_keys: list - list of selected keys of self.read_losses - n_preds: int - number of prediction models used for ensemble building - same number of predictions on valid and test are necessary - index_run: int - n-th time that ensemble predictions are written to disc - - Return - ------ - y: np.ndarray - """ - self.logger.debug("Predicting the %s set with the ensemble!", set_) - - if set_ == "valid": - pred_set = Y_VALID - elif set_ == "test": - pred_set = Y_TEST - else: - pred_set = Y_ENSEMBLE - predictions = [self.read_preds[k][pred_set] for k in selected_keys] - - if n_preds == len(predictions): - y = ensemble.predict(predictions) - if self.task_type == BINARY_CLASSIFICATION: - y = y[:, 1] - return y - else: - self.logger.info( - "Found inconsistent number of predictions and models (%d vs " - "%d) for subset %s", - len(predictions), - n_preds, - set_, - ) - return None - - def _add_ensemble_trajectory(self, train_pred, valid_pred, test_pred): - """ - Records a snapshot of how the performance look at a given training - time. - - Parameters - ---------- - ensemble: EnsembleSelection - The ensemble selection object to record - valid_pred: np.ndarray - The predictions on the validation set using ensemble - test_pred: np.ndarray - The predictions on the test set using ensemble - - """ - if self.task_type == BINARY_CLASSIFICATION: - if len(train_pred.shape) == 1 or train_pred.shape[1] == 1: - train_pred = np.vstack( - ((1 - train_pred).reshape((1, -1)), train_pred.reshape((1, -1))) - ).transpose() - if valid_pred is not None and ( - len(valid_pred.shape) == 1 or valid_pred.shape[1] == 1 - ): - valid_pred = np.vstack( - ((1 - valid_pred).reshape((1, -1)), valid_pred.reshape((1, -1))) - ).transpose() - if test_pred is not None and ( - len(test_pred.shape) == 1 or test_pred.shape[1] == 1 - ): - test_pred = np.vstack( - ((1 - test_pred).reshape((1, -1)), test_pred.reshape((1, -1))) - ).transpose() - - performance_stamp = { - "Timestamp": pd.Timestamp.now(), - "ensemble_optimization_score": calculate_scores( - solution=self.y_true_ensemble, - prediction=train_pred, - task_type=self.task_type, - metrics=[self.metric], - scoring_functions=None, - )[self.metric.name], - } - if valid_pred is not None: - # TODO: valid_pred are a legacy from competition manager - # and this if never happens. Re-evaluate Y_valid support - performance_stamp["ensemble_val_score"] = calculate_scores( - solution=self.y_valid, - prediction=valid_pred, - task_type=self.task_type, - metrics=[self.metric], - scoring_functions=None, - )[self.metric.name] - - # In case test_pred was provided - if test_pred is not None: - performance_stamp["ensemble_test_score"] = calculate_scores( - solution=self.y_test, - prediction=test_pred, - task_type=self.task_type, - metrics=[self.metric], - scoring_functions=None, - )[self.metric.name] - - self.ensemble_history.append(performance_stamp) - - def _get_list_of_sorted_preds(self): - """ - Returns a list of sorted predictions in descending order - Losses are taken from self.read_losses. - - Parameters - ---------- - None - - Return - ------ - sorted_keys: list - """ - # Sort by loss - smaller is better! - sorted_keys = list( - sorted( - [(k, v["ens_loss"], v["num_run"]) for k, v in self.read_losses.items()], - # Sort by loss as priority 1 and then by num_run on a ascending order - # We want small num_run first - key=lambda x: (x[1], x[2]), - ) - ) - return sorted_keys - - def _delete_excess_models(self, selected_keys: List[str]): - """ - Deletes models excess models on disc. self.max_models_on_disc - defines the upper limit on how many models to keep. - Any additional model with a worst loss than the top - self.max_models_on_disc is deleted. - - """ - - # Loop through the files currently in the directory - for pred_path in self.y_ens_files: - - # Do not delete candidates - if pred_path in selected_keys: - continue - - if pred_path in self._has_been_candidate: - continue - - match = self.model_fn_re.search(pred_path) - _seed = int(match.group(1)) - _num_run = int(match.group(2)) - _budget = float(match.group(3)) - - # Do not delete the dummy prediction - if _num_run == 1: - continue - - numrun_dir = self.backend.get_numrun_directory(_seed, _num_run, _budget) - try: - os.rename(numrun_dir, numrun_dir + ".old") - shutil.rmtree(numrun_dir + ".old") - self.logger.info("Deleted files of non-candidate model %s", pred_path) - self.read_losses[pred_path]["disc_space_cost_mb"] = None - self.read_losses[pred_path]["loaded"] = 3 - self.read_losses[pred_path]["ens_loss"] = np.inf - except Exception as e: - self.logger.error( - "Failed to delete files of non-candidate model %s due" - " to error %s", - pred_path, - e, - ) - - def _read_np_fn(self, path): - - # Support for string precision - if isinstance(self.precision, str): - precision = int(self.precision) - self.logger.warning("Interpreted str-precision as {}".format(precision)) - else: - precision = self.precision - - if path.endswith("gz"): - open_method = gzip.open - elif path.endswith("npy"): - open_method = open - else: - raise ValueError("Unknown filetype %s" % path) - with open_method(path, "rb") as fp: - if precision == 16: - predictions = np.load(fp, allow_pickle=True).astype(dtype=np.float16) - elif precision == 32: - predictions = np.load(fp, allow_pickle=True).astype(dtype=np.float32) - elif precision == 64: - predictions = np.load(fp, allow_pickle=True).astype(dtype=np.float64) - else: - predictions = np.load(fp, allow_pickle=True) - return predictions diff --git a/autosklearn/ensemble_building/__init__.py b/autosklearn/ensemble_building/__init__.py new file mode 100644 index 0000000000..4c63165e1b --- /dev/null +++ b/autosklearn/ensemble_building/__init__.py @@ -0,0 +1,5 @@ +from autosklearn.ensemble_building.builder import EnsembleBuilder +from autosklearn.ensemble_building.manager import EnsembleBuilderManager +from autosklearn.ensemble_building.run import Run + +__all__ = ["EnsembleBuilder", "EnsembleBuilderManager", "Run"] diff --git a/autosklearn/ensemble_building/builder.py b/autosklearn/ensemble_building/builder.py new file mode 100644 index 0000000000..fdd9a31b05 --- /dev/null +++ b/autosklearn/ensemble_building/builder.py @@ -0,0 +1,926 @@ +from __future__ import annotations + +from typing import Any, Iterable, Sequence, cast + +import logging.handlers +import multiprocessing +import numbers +import os +import pickle +import time +import traceback +from itertools import accumulate +from pathlib import Path + +import numpy as np +import pandas as pd +import pynisher + +from autosklearn.automl_common.common.utils.backend import Backend +from autosklearn.data.xy_data_manager import XYDataManager +from autosklearn.ensemble_building.run import Run, RunID +from autosklearn.ensembles.ensemble_selection import EnsembleSelection +from autosklearn.metrics import Scorer, calculate_losses, calculate_scores +from autosklearn.util.disk import rmtree +from autosklearn.util.functional import cut, findwhere, split +from autosklearn.util.logging_ import get_named_client_logger +from autosklearn.util.parallel import preload_modules + +CANDIDATES_FILENAME = "previous_ensemble_building_candidates.pkl" + + +class EnsembleBuilder: + """Builds ensembles out of runs that exist in the Backend + + This is used by EnsembleBuilderManager and created in a dask-client + every time a run finishes and there is currently no EnsembleBuilder active. + """ + + def __init__( + self, + backend: Backend, + dataset_name: str, + task_type: int, + metric: Scorer, + ensemble_size: int = 50, + ensemble_nbest: int | float = 50, + max_models_on_disc: int | float | None = 100, + performance_range_threshold: float = 0, + seed: int = 1, + precision: int = 32, + memory_limit: int | None = 1024, + read_at_most: int | None = None, + logger_port: int = logging.handlers.DEFAULT_TCP_LOGGING_PORT, + random_state: int | np.random.RandomState | None = None, + ): + """ + Parameters + ---------- + backend: Backend + backend to write and read files + + dataset_name: str + name of dataset + + task_type: int + type of ML task + + metric: str + name of metric to compute the loss of the given predictions + + ensemble_size: int = 50 + maximal size of ensemble (passed to autosklearn.ensemble.ensemble_selection) + + ensemble_nbest: int | float = 50 + + * int: consider only the n best prediction (> 0) + + * float: consider only this fraction of the best, between (0, 1) + + Both with respect to the validation predictions. + If performance_range_threshold > 0, might return less models + + max_models_on_disc: int | float | None = 100 + Defines the maximum number of models that are kept in the disc. + It defines an upper bound on the models that can be used in the ensemble. + + * int: and dictates the max number of models to keep. (>= 1) + + * float: it will be interpreted as the max megabytes allowed of disc space. + If the number of ensemble candidates require more disc space than + this float value, the worst models are deleted to keep within this budget. + Models and predictions of the worst-performing models will be deleted then. + + * None: the feature is disabled. + + performance_range_threshold: float = 0 + Will at most return the minimum between ensemble_nbest models, + and max_models_on_disc. Might return less + + Keep only models that are better than: + + x = performance_range_threshold + x * dummy + + E.g dummy=2, best=4, thresh=0.5 --> only consider models with loss > 3 + + seed: int = 1 + random seed that is used as part of the filename + + precision: int [16 | 32 | 64 | 128] = 32 + precision of floats to read the predictions + + memory_limit: int | None = 1024 + memory limit in mb. If ``None``, no memory limit is enforced. + + read_at_most: int | None = None + read at most n new prediction files in each iteration. If `None`, will read + the predictions and calculate losses for all runs that require it. + + + logger_port: int = DEFAULT_TCP_LOGGING_PORT + port that receives logging records + + random_state: int | RandomState | None = None + An int or RandomState object used for generating the ensemble. + """ + if isinstance(ensemble_nbest, int) and ensemble_nbest < 1: + raise ValueError(f"int ensemble_nbest ({ensemble_nbest}) must be (>1)") + + if isinstance(ensemble_nbest, float) and not (0 <= ensemble_nbest <= 1): + raise ValueError(f"float ensemble_nbest ({ensemble_nbest}) not in (0,1)") + + if max_models_on_disc is not None and max_models_on_disc < 0: + raise ValueError("max_models_on_disc must be positive or None") + + if read_at_most is not None and (read_at_most < 1 or read_at_most == np.inf): + raise ValueError("Read at most must be int greater than 1 or None") + + # Setup the logger + self.logger = get_named_client_logger(name="EnsembleBuilder", port=logger_port) + self.logger_port = logger_port + + # Log the behaviour + if ensemble_nbest == 1: + t = type(ensemble_nbest) + self.logger.debug(f"Using behaviour when {t} for {ensemble_nbest}:{t}") + + self.seed = seed + self.metric = metric + self.backend = backend + self.precision = precision + self.task_type = task_type + self.memory_limit = memory_limit + self.read_at_most = read_at_most + self.random_state = random_state + self.dataset_name = dataset_name + self.ensemble_size = ensemble_size + self.ensemble_nbest = ensemble_nbest + self.performance_range_threshold = performance_range_threshold + + # Decide if self.max_models_on_disc is a memory limit or model limit + self.max_models_on_disc: int | None = None + self.model_memory_limit: float | None = None + + if isinstance(max_models_on_disc, int): + self.max_models_on_disc = max_models_on_disc + elif isinstance(max_models_on_disc, float): + self.model_memory_limit = max_models_on_disc + + # The starting time of the procedure + self.start_time: float = 0.0 + + # Track the ensemble performance + self.ensemble_history: list[dict[str, Any]] = [] + + # Keep running knowledge of its validation performance + self.validation_performance_ = np.inf + + # Data we may need + datamanager: XYDataManager = self.backend.load_datamanager() + self._y_test: np.ndarray | None = datamanager.data.get("Y_test", None) + self._y_ensemble: np.ndarray | None = None + + @property + def previous_candidates_path(self) -> Path: + """Path to the cached losses we store between runs""" + return Path(self.backend.internals_directory) / CANDIDATES_FILENAME + + def previous_candidates(self) -> dict[RunID, Run]: + """Load any previous candidates that were saved from previous runs + + Returns + ------- + dict[RunID, Run] + A dictionary from RunId's to the previous candidates + """ + if self.previous_candidates_path.exists(): + with self.previous_candidates_path.open("rb") as f: + return pickle.load(f) + else: + return {} + + def available_runs(self) -> dict[RunID, Run]: + """Get a dictionary of all available runs on the filesystem + + Returns + ------- + dict[RunID, Run] + A dictionary from RunId's to the available runs + """ + runs_dir = Path(self.backend.get_runs_directory()) + runs = iter(Run(path=dir) for dir in runs_dir.iterdir() if Run.valid(dir)) + return {run.id: run for run in runs} + + def targets(self, kind: str = "ensemble") -> np.ndarray | None: + """The ensemble targets used for training the ensemble + + It will attempt to load and cache them in memory but + return None if it can't. + + Returns + ------- + np.ndarray | None + The ensemble targets, if they can be loaded + """ + if kind == "ensemble": + if self._y_ensemble is None: + if os.path.exists(self.backend._get_targets_ensemble_filename()): + self._y_ensemble = self.backend.load_targets_ensemble() + return self._y_ensemble + + elif kind == "test": + return self._y_test + + else: + raise NotImplementedError(kind) + + def run( + self, + iteration: int, + pynisher_context: str | None = None, + time_left: float | None = None, + end_at: float | None = None, + time_buffer: int = 5, + ) -> tuple[list[dict[str, Any]], int | float]: + """Run the ensemble building process + + Parameters + ---------- + iteration : int + What iteration to associate with this run + + pynisher_context : str | None = None + The pynisher context to run in. If None, defaults to + multiprocessing.get_context(None) + + time_left : float | None = None + How much time should be left for this run. Either this or `end_at` must + be provided. + + end_at : float | None = None + When this run should end. Either this or `time_left` must be provided. + + time_buffer : int = 5 + How much extra time to add as a buffer to this run. This means there is + always some amount of time to do something useful. + + Returns + ------- + (ensemble_history, nbest) + """ + if time_left is None and end_at is None: + raise ValueError("Must provide either time_left or end_at.") + + elif time_left is not None and end_at is not None: + raise ValueError("Cannot provide both time_left and end_at.") + + if not self.logger: + self.logger = get_named_client_logger( + name="EnsembleBuilder", + port=self.logger_port, + ) + + process_start_time = time.time() + while True: + + if time_left is not None: + time_elapsed = time.time() - process_start_time + time_left -= time_elapsed + else: + assert end_at is not None + current_time = time.time() + if current_time > end_at: + break + else: + time_left = end_at - current_time + + wall_time_in_s = int(time_left - time_buffer) + if wall_time_in_s < 1: + break + + context = multiprocessing.get_context(pynisher_context) + preload_modules(context) + + safe_ensemble_script = pynisher.enforce_limits( + wall_time_in_s=wall_time_in_s, + mem_in_mb=self.memory_limit, + logger=self.logger, + context=context, + )(self.main) + + safe_ensemble_script(time_left, iteration) + + status = safe_ensemble_script.exit_status + if isinstance(status, pynisher.MemorylimitException): + # if ensemble script died because of memory error, + # reduce nbest to reduce memory consumption and try it again + + # ATTENTION: main will start from scratch; + # all data structures are empty again + try: + self.previous_candidates_path.unlink() + except: # noqa E722 + pass + + if ( + isinstance(self.ensemble_nbest, numbers.Integral) + and self.ensemble_nbest <= 1 + ): + if self.read_at_most == 1: + self.logger.error( + "Memory Exception -- Unable to further reduce the number" + " of ensemble members and can no further limit the number" + " of ensemble members loaded per iteration, please restart" + " Auto-sklearn with a higher value for the argument" + f" `memory_limit` (current limit is {self.memory_limit}MB)." + " The ensemble builder will keep running to delete files" + " from disk in case this was enabled.", + ) + self.ensemble_nbest = 0 + else: + self.read_at_most = 1 + self.logger.warning( + "Memory Exception -- Unable to further reduce the number of" + " ensemble members. Now reducing the number of predictions" + " per call to read at most to 1." + ) + else: + if isinstance(self.ensemble_nbest, numbers.Integral): + self.ensemble_nbest = max(1, int(self.ensemble_nbest / 2)) + else: + self.ensemble_nbest = self.ensemble_nbest / 2 + self.logger.warning( + "Memory Exception -- restart with " + "less ensemble_nbest: %d" % self.ensemble_nbest + ) + return [], self.ensemble_nbest + elif isinstance(status, pynisher.AnythingException): + return ([], self.ensemble_nbest) + else: + return safe_ensemble_script.result + + return [], self.ensemble_nbest + + def main( + self, + time_left: float | None = None, + iteration: int = 0, + ) -> tuple[list[dict[str, Any]], int | float]: + """Run the main loop of ensemble building + + The process is: + * Load all available runs + previous candidates (if any) + * Update the loss of those that require + * From these runs, get a list of candidates + * Save candidates + * Delete models that are not candidates + * Build an ensemble from the candidates if there are new candidates + + Parameters + ---------- + time_left : float | None = None + How much time is left for this run + + iteration : int = 0 + The iteration of this run + + Returns + ------- + (ensemble_history: list[dict[str, Any]], nbest: int | float) + """ + # Pynisher jobs inside dask 'forget' the logger configuration. + # So we have to set it up accordingly + self.logger = get_named_client_logger( + name="EnsembleBuilder", + port=self.logger_port, + ) + + if time_left is not None: + self.start_time = time.time() + used_time = time.time() - self.start_time + left_for_iter = time_left - used_time + itr = iteration if str(iteration) is not None else "" + self.logger.debug(f"Starting iteration {itr}, time left: {left_for_iter}") + + # Can't load data, exit early + if not os.path.exists(self.backend._get_targets_ensemble_filename()): + self.logger.debug(f"No targets for ensemble: {traceback.format_exc()}") + raise RuntimeError("No targets for ensemble") + + # We will delete runs once we are complete + deletable_runs: set[Run] = set() + + # Load in information from previous candidates and also runs + available_runs = self.available_runs() + + # Update runs with information of available previous candidates + previous_candidates = self.previous_candidates() + available_runs.update(previous_candidates) + + # We just need the values now, not the key value pairs {run.id: Run} + runs = list(available_runs.values()) + + if len(runs) == 0: + self.logger.debug("Found no runs") + raise RuntimeError("Found no runs") + + # Calculate the loss for those that require it + requires_update = self.requires_loss_update(runs) + if self.read_at_most is not None: + requires_update = requires_update[: self.read_at_most] + + for run in requires_update: + run.record_modified_times() # So we don't count as modified next time + run.loss = self.loss(run, kind="ensemble") + + # Get the dummy and real runs + dummies, candidates = split(runs, by=lambda r: r.is_dummy()) + + # We see if we need to delete any of the real runs before we waste compute + # on evaluating their candidacy for ensemble building + if any(candidates): + candidates, to_delete = self.requires_deletion( + candidates, + max_models=self.max_models_on_disc, + memory_limit=self.model_memory_limit, + ) + + # If there are no candidates left, we just keep the best one + if not any(candidates): + best = min(to_delete, key=lambda r: (r.loss, r.num_run)) + candidates = [best] + to_delete.remove(best) + + if any(to_delete): + self.logger.info( + f"Deleting runs {to_delete} due to" + f" max_models={self.max_models_on_disc} and/or" + f" memory_limit={self.model_memory_limit}" + ) + deletable_runs.update(to_delete) + + # If there are any candidates, perform candidates selection + if any(candidates): + candidates, to_delete = self.candidate_selection( + runs=candidates, + dummies=dummies, + better_than_dummy=True, + nbest=self.ensemble_nbest, + performance_range_threshold=self.performance_range_threshold, + ) + if any(to_delete): + self.logger.info( + f"Deleting runs {to_delete} due to" + f" nbest={self.ensemble_nbest} and/or" + f" performance_range_threshold={self.performance_range_threshold}" + ) + deletable_runs.update(to_delete) + else: + candidates = dummies + self.logger.warning("No runs were available to build an ensemble from") + + # In case we record test predictions and not every model has test predictions, + # only use the subset of models that has predictions for both the test set and + # the ensemble optimization set. + candidates_set = set(candidates) + test_subset = {r for r in candidates if r.pred_path("test").exists()} + + if len(test_subset) > 0: + candidates = sorted(test_subset, key=lambda r: r.id) + test_models = candidates + + to_delete = candidates_set - test_subset + if any(to_delete): + self.logger.info( + f"Deleting runs {to_delete} due to runs not" + ' having "test_predictions" while others do not:' + f"\nHave test_predictions = {test_subset}" + f"\nNo test_predictions = {to_delete}" + ) + deletable_runs.update(to_delete) + + else: + candidates = sorted(candidates_set, key=lambda r: r.id) + test_models = [] + + # Save the candidates for the next round + with self.previous_candidates_path.open("wb") as f: + pickle.dump({run.id: run for run in candidates}, f) + + # If there was any change from the previous run, either in terms of + # runs or one of those runs had its loss updated, then we need to + # fit the ensemble builder + previous_candidate_ids = set(previous_candidates) + current_candidate_ids = set(run.id for run in candidates) + difference = previous_candidate_ids ^ current_candidate_ids + + was_updated_candidates = list(run in candidates for run in requires_update) + + if not any(difference) and not any(was_updated_candidates): + self.logger.info("All ensemble candidates the same, no update required") + return self.ensemble_history, self.ensemble_nbest + + targets = cast(np.ndarray, self.targets("ensemble")) # Sure they exist + + ensemble = self.fit_ensemble( + candidates, + targets=targets, + size=self.ensemble_size, + task=self.task_type, + metric=self.metric, + precision=self.precision, + random_state=self.random_state, + ) + + self.logger.info(str(ensemble)) + ens_perf = ensemble.get_validation_performance() + self.validation_performance_ = min(self.validation_performance_, ens_perf) + self.backend.save_ensemble( + ensemble=ensemble, idx=iteration, seed=self.seed # type: ignore + ) + + performance_stamp = {"Timestamp": pd.Timestamp.now()} + + for kind, score_name, models in [ + ("ensemble", "optimization", candidates), + ("test", "test", test_models), + ]: + if len(models) == 0: + continue + + pred_targets = self.targets(kind) + if pred_targets is None: + self.logger.warning(f"No ensemble targets for {kind}") + continue + + run_preds = [r.predictions(kind, precision=self.precision) for r in models] + pred = ensemble.predict(run_preds) + + score = calculate_scores( + solution=pred_targets, + prediction=pred, + task_type=self.task_type, + metrics=[self.metric], + scoring_functions=None, + )[self.metric.name] + performance_stamp[f"ensemble_{score_name}_score"] = score + self.ensemble_history.append(performance_stamp) + + # Lastly, delete any runs that need to be deleted. We save this as the last step + # so that we have an ensemble saved that is up to date. If we do not do so, + # there could be runs deleted that are in th previous ensemble and we do not + # manage to update the ensemble due to a crash or the process being killed + # before it could be updated + self.delete_runs(deletable_runs) + + return self.ensemble_history, self.ensemble_nbest + + def requires_loss_update( + self, + runs: Sequence[Run], + ) -> list[Run]: + """ + + Parameters + ---------- + runs : Sequence[Run] + The runs to process + + Returns + ------- + list[Run] + The runs that require a loss to be calculated + """ + queue = [] + for run in sorted(runs, key=lambda run: run.recorded_mtimes["ensemble"]): + if run.loss == np.inf: + queue.append(run) + + elif run.was_modified(): + self.logger.debug(f"{run.id} had its predictions modified?") + queue.append(run) + + return queue + + def candidate_selection( + self, + runs: Sequence[Run], + dummies: Run | list[Run], + *, + better_than_dummy: bool = False, + nbest: int | float | None = None, + performance_range_threshold: float | None = None, + ) -> tuple[list[Run], set[Run]]: + """Get a list of candidates from `runs`, garuanteeing at least one + + Applies a set of reductions in order of parameters to reach a set of final + candidates. + + Expects at least one `dummies` run. + + Parameters + ---------- + runs : Sequence[Run] + The runs to evaluate candidates from. + + dummies: Run | Sequence[Run] + The dummy run to base from + + better_than_dummy: bool = False + Whether the run must be better than the best dummy run to be a candidate. + In the case where there are no candidates left, the dummies will then be + used. + + nbest : int | float | None + The nbest models to select. If `int`, acts as an absolute limit. + If `float`, acts as a percentage of available candidates. + + model_memory_limit : float | None + A maximum memory limit in MB for the runs to occupy. If the candidates at + this point exceed this limit, the best n candidates that fit into this limit + will be chosen. + + performance_range_threshold : float | None + A number in (0, 1) to select candidates from. Expects a dummy run for worst + + Returns + ------- + (candidates: list[Run], discarded: set[Run]) + A tuple of runs that are candidates and also those that didn't make it + """ + if isinstance(dummies, Run): + dummies = [dummies] + + assert len(dummies) > 0 and len(runs) > 0, "At least 1 real run and dummy run" + + all_discarded: set[Run] = set() + + # We filter out all runs that don't have any predictions for the ensemble + candidates, discarded = split( + runs, by=lambda run: run.pred_path("ensemble").exists() + ) + all_discarded.update(discarded) + + if len(candidates) == 0: + self.logger.debug("No runs with predictions on ensemble set, using dummies") + return dummies, all_discarded + + for run in discarded: + self.logger.warning(f"Have no ensemble predictions for {run}") + + # Get all the ones that have a tangible loss + candidates, discarded = split( + candidates, + by=lambda r: r.loss < np.inf, + ) + all_discarded.update(discarded) + + if len(candidates) == 0: + self.logger.debug("No runs with a usable loss, using dummies") + return dummies, all_discarded + + if better_than_dummy: + dummies = sorted(dummies, key=lambda r: r.loss) + dummy_cutoff = dummies[0].loss + self.logger.debug(f"Using {dummy_cutoff} to filter candidates") + + candidates, discarded = split( + candidates, + by=lambda r: r.loss < dummy_cutoff, + ) + all_discarded.update(discarded) + + # If there are no real candidates left, use the dummies + if len(candidates) == 0: + self.logger.warning( + "No models better than random - using Dummy loss!" + f"\n\tModels besides current dummy model: {len(candidates)}" + f"\n\tDummy models: {len(dummies)}", + ) + return dummies, all_discarded + + # Sort the candidates so that they ordered by best loss, using num_run for tie + candidates = sorted(candidates, key=lambda r: (r.loss, r.num_run)) + + if nbest is not None: + # Determine how many to keep, always keeping one + if isinstance(nbest, float): + nkeep = int(len(candidates) * nbest) + else: + nkeep = nbest + + candidates, discarded = cut(candidates, nkeep) + self.logger.info(f"Discarding {len(discarded)}/{len(candidates)} runs") + + # Always preserve at least one, the best + if len(candidates) == 0: + candidates, discared = cut(discarded, 1) + self.logger.warning("nbest too aggresive, using single best") + + all_discarded.update(discarded) + + if performance_range_threshold is not None: + x = performance_range_threshold + worst = dummies[0].loss + best = candidates[0].loss + + cutoff = x * best + (1 - x) * worst + + candidates, discarded = cut(candidates, where=lambda r: r.loss >= cutoff) + + # Always preserve at least one, the best + if len(candidates) == 0: + candidates, discared = cut(discarded, 1) + self.logger.warning("No models in performance range, using single best") + + all_discarded.update(discarded) + + return candidates, all_discarded + + def fit_ensemble( + self, + runs: list[Run], + targets: np.ndarray, + *, + size: int | None = None, + task: int | None = None, + metric: Scorer | None = None, + precision: int | None = None, + random_state: int | np.random.RandomState | None = None, + ) -> EnsembleSelection: + """Fit an ensemble from the provided runs. + + Note + ---- + Expects all runs to have the "ensemble" predictions present + + Parameters + ---------- + runs: list[Run] + List of runs to build an ensemble from + + targets: np.ndarray + The targets to build the ensemble with + + size: int | None = None + The size of the ensemble to build + + task: int | None = None + The kind of task performed + + metric: Scorer | None = None + The metric to use when comparing run predictions to the targets + + precision: int | None = None + The precision with which to load run predictions + + random_state: int | RandomState | None = None + The random state to use + + Returns + ------- + ensemble: EnsembleSelection + The trained ensemble + """ + task = task if task is not None else self.task_type + size = size if size is not None else self.ensemble_size + metric = metric if metric is not None else self.metric + rs = random_state if random_state is not None else self.random_state + + ensemble = EnsembleSelection( + ensemble_size=size, + task_type=task, + metric=metric, + random_state=rs, + ) + + self.logger.debug(f"Fitting ensemble on {len(runs)} models") + start_time = time.time() + + precision = precision if precision is not None else self.precision + predictions_train = [ + run.predictions("ensemble", precision=precision) for run in runs + ] + + ensemble.fit( + predictions=predictions_train, + labels=targets, + identifiers=[run.id for run in runs], + ) + + duration = time.time() - start_time + self.logger.debug(f"Fitting the ensemble took {duration} seconds.") + return ensemble + + def requires_deletion( + self, + runs: Sequence[Run], + *, + max_models: int | None = None, + memory_limit: float | None = None, + ) -> tuple[list[Run], set[Run]]: + """Cut a list of runs into those to keep and those to delete + + If neither params are specified, this method should do nothing. + + Parameters + ---------- + runs : Sequence[Run] + The runs to check + + max_models : int | None = None + The maximum amount of models to have on disk. Leave `None` for no effect + + memory_limit : float | None = None + The memory limit in MB, leave `None` for no effect + + Returns + ------- + (keep: list[Run], delete: set[Run]) + The list of runs to keep and those to delete + """ + if memory_limit is None and max_models is None: + return list(runs), set() + + # Start with keep all runs and dummies, deleteing None + keep = sorted(runs, key=lambda r: (r.loss, r.num_run)) + delete: set[Run] = set() + + if max_models is not None and max_models < len(runs): + keep, to_delete = cut(keep, max_models) + + if any(to_delete): + delete.update(to_delete) + + if memory_limit is not None: + largest = max(runs, key=lambda r: r.mem_usage) + cutoff = memory_limit - largest.mem_usage + + accumulated_mem_usage = accumulate(r.mem_usage for r in runs) + + cutpoint = findwhere(accumulated_mem_usage, lambda mem: mem > cutoff) + keep, to_delete = cut(keep, cutpoint) + + if any(to_delete): + self.logger.warning( + "Limiting num of models via `memory_limit`" + f" memory_limit={memory_limit}" + f" cutoff={cutoff}" + f" largest={largest.mem_usage}" + f" remaining={len(keep)}" + f" discarded={len(to_delete)}" + ) + delete.update(to_delete) + + return keep, delete + + def loss(self, run: Run, kind: str = "ensemble") -> float: + """Calculate the loss for a run + + Parameters + ---------- + run: Run + The run to calculate the loss for + + Returns + ------- + float + The loss for the run + """ + targets = self.targets(kind) + if targets is None: + self.logger.error(f"No targets of {kind}") + return np.inf + + try: + predictions = run.predictions(kind, precision=self.precision) + loss: float = calculate_losses( # type: ignore + solution=targets, + prediction=predictions, + task_type=self.task_type, + metrics=[self.metric], + )[self.metric.name] + except Exception as e: + self.logger.error(f"Error getting loss {run}:{e}{traceback.format_exc()}") + loss = np.inf + finally: + return loss + + def delete_runs(self, runs: Iterable[Run]) -> None: + """Delete runs + + Will not delete dummy runs + + Parameters + ---------- + runs : Sequence[Run] + The runs to delete + """ + items = iter(run for run in runs if not run.is_dummy() and run.dir.exists()) + for run in items: + try: + rmtree(run.dir, atomic=True) + self.logger.info(f"Deleted files for {run}") + except Exception as e: + self.logger.error(f"Failed to delete files for {run}: \n{e}") diff --git a/autosklearn/ensemble_building/manager.py b/autosklearn/ensemble_building/manager.py new file mode 100644 index 0000000000..46ab291bc2 --- /dev/null +++ b/autosklearn/ensemble_building/manager.py @@ -0,0 +1,370 @@ +from __future__ import annotations + +from typing import Any + +import logging.handlers +import time +import traceback + +import dask.distributed +import numpy as np +from smac.callbacks import IncorporateRunResultCallback +from smac.optimizer.smbo import SMBO +from smac.runhistory.runhistory import RunInfo, RunValue +from smac.tae.base import StatusType + +from autosklearn.automl_common.common.utils.backend import Backend +from autosklearn.ensemble_building.builder import EnsembleBuilder +from autosklearn.metrics import Scorer +from autosklearn.util.logging_ import get_named_client_logger + + +class EnsembleBuilderManager(IncorporateRunResultCallback): + def __init__( + self, + backend: Backend, + dataset_name: str, + task: int, + metric: Scorer, + time_left_for_ensembles: float = np.inf, + max_iterations: int | None = None, + pynisher_context: str = "fork", + ensemble_size: int = 50, + ensemble_nbest: int | float = 50, + max_models_on_disc: int | float | None = None, + seed: int = 1, + precision: int = 32, + memory_limit: int | None = None, + read_at_most: int | None = None, + logger_port: int = logging.handlers.DEFAULT_TCP_LOGGING_PORT, + random_state: int | np.random.RandomState | None = None, + start_time: float | None = None, + ): + """SMAC callback to handle ensemble building + + Parameters + ---------- + backend: Backend + backend to write and read files + + dataset_name: str + name of dataset + + task: int + Type of ML task + + metric: Scorer + Metric to compute the loss of the given predictions + + time_left_for_ensemble: float = np.inf + How much time is left for the task in seconds. + Job should finish within this allocated time + + max_iterations: int | None = None + maximal number of iterations to run this script. None indicates no limit + on iterations. + + pynisher_context: "spawn" | "fork" | "forkserver" = "fork" + The multiprocessing context for pynisher. + + ensemble_size: int = 50 + maximal size of ensemble + + ensemble_nbest: int | float = 50 + If int: consider only the n best prediction + If float: consider only this fraction of the best models + + max_models_on_disc: int | float | None = None + Defines the maximum number of models that are kept in the disc. + + If int, it must be greater or equal than 1, and dictates the max + number of models to keep. + + If float, it will be interpreted as the max megabytes allowed of + disc space. That is, if the number of ensemble candidates require more + disc space than this float value, the worst models will be deleted to + keep within this budget. Models and predictions of the worst-performing + models will be deleted then. + + If None, the feature is disabled. It defines an upper bound on the + models that can be used in the ensemble. + + seed: int = 1 + Seed used for the inidividual runs + + precision: 16 | 32 | 64 | 128 = 32 + Precision of floats to read the predictions + + memory_limit: int | None = None + Memory limit in mb. If ``None``, no memory limit is enforced. + + read_at_most: int | None = None + read at most n new prediction files in each iteration. If `None`, will read + the predictions and calculate losses for all runs that require it. + + logger_port: int = DEFAULT_TCP_LOGGING_PORT + Port that receives logging records + + start_time: float | None = None + DISABLED: Just using time.time() to set it + The time when this job was started, to account for any latency in job + allocation. + """ + self.time_left_for_ensembles = time_left_for_ensembles + self.backend = backend + self.dataset_name = dataset_name + self.task = task + self.metric = metric + self.ensemble_size = ensemble_size + self.ensemble_nbest = ensemble_nbest + self.max_models_on_disc = max_models_on_disc + self.seed = seed + self.precision = precision + self.max_iterations = max_iterations + self.read_at_most = read_at_most + self.memory_limit = memory_limit + self.random_state = random_state + self.logger_port = logger_port + self.pynisher_context = pynisher_context + + # Store something similar to SMAC's runhistory + self.history: list[dict[str, Any]] = [] + + # We only submit new ensembles when there is not an active ensemble job + self.futures: list[dask.distributed.Future] = [] + + # The last criteria is the number of iterations + self.iteration = 0 + + # Keep track of when we started to know when we need to finish! + self.start_time = time.time() + + def __call__( + self, + smbo: "SMBO", + run_info: RunInfo, + result: RunValue, + time_left: float, + ) -> None: + """ + Returns + ------- + List[Tuple[int, float, float, float]]: + A list with the performance history of this ensemble, of the form + [(pandas_timestamp, train_performance, val_performance, test_performance)] + """ + if result.status in (StatusType.STOP, StatusType.ABORT) or smbo._stop: + return + self.build_ensemble(smbo.tae_runner.client) + + def build_ensemble( + self, + dask_client: dask.distributed.Client, + ) -> None: + """Build the ensemble + + Parameters + ---------- + dask_client: dask.distributed.Client + The dask client to use + """ + # The second criteria is elapsed time + elapsed_time = time.time() - self.start_time + + logger = get_named_client_logger( + name="EnsembleBuilder", + port=self.logger_port, + ) + + # First test for termination conditions + if self.time_left_for_ensembles < elapsed_time: + logger.info( + "Terminate ensemble building as not time is left (run for {}s)".format( + elapsed_time + ), + ) + return + if self.max_iterations is not None and self.max_iterations <= self.iteration: + logger.info( + "Terminate ensemble building because of max iterations:" + f" {self.max_iterations} of {self.iteration}" + ) + return + + if len(self.futures) != 0: + if self.futures[0].done(): + result = self.futures.pop().result() + if result: + ensemble_history, self.ensemble_nbest = result + logger.debug( + f"iteration={self.iteration} @ elapsed_time={elapsed_time}" + f" has history={ensemble_history}" + ) + self.history.extend(ensemble_history) + + # Only submit new jobs if the previous ensemble job finished + if len(self.futures) == 0: + + # Add the result of the run + # On the next while iteration, no references to + # ensemble builder object, so it should be garbage collected to + # save memory while waiting for resources + # Also, notice how ensemble nbest is returned, so we don't waste + # iterations testing if the deterministic predictions size can + # be fitted in memory + try: + # Submit a Dask job from this job, to properly + # see it in the dask diagnostic dashboard + # Notice that the forked ensemble_builder_process will + # wait for the below function to be done + self.futures.append( + dask_client.submit( + EnsembleBuilderManager.fit_and_return_ensemble, + backend=self.backend, + dataset_name=self.dataset_name, + task_type=self.task, + metric=self.metric, + ensemble_size=self.ensemble_size, + ensemble_nbest=self.ensemble_nbest, + max_models_on_disc=self.max_models_on_disc, + seed=self.seed, + precision=self.precision, + memory_limit=self.memory_limit, + read_at_most=self.read_at_most, + random_state=self.random_state, + end_at=self.start_time + self.time_left_for_ensembles, + iteration=self.iteration, + pynisher_context=self.pynisher_context, + logger_port=self.logger_port, + ) + ) + + logger.info( + "{}/{} Started Ensemble builder job at {} for iteration {}.".format( + # Log the client to make sure we + # remain connected to the scheduler + self.futures[0], + dask_client, + time.strftime("%Y.%m.%d-%H.%M.%S"), + self.iteration, + ), + ) + self.iteration += 1 + except Exception as e: + exception_traceback = traceback.format_exc() + error_message = repr(e) + logger.critical(exception_traceback) + logger.critical(error_message) + + @staticmethod + def fit_and_return_ensemble( + iteration: int, + end_at: float, + backend: Backend, + dataset_name: str, + task_type: int, + metric: Scorer, + pynisher_context: str, + ensemble_size: int = 50, + ensemble_nbest: int | float = 50, + max_models_on_disc: int | float | None = None, + seed: int = 1, + precision: int = 32, + memory_limit: int | None = None, + read_at_most: int | None = None, + logger_port: int = logging.handlers.DEFAULT_TCP_LOGGING_PORT, + random_state: int | np.random.RandomState | None = None, + ) -> tuple[list[dict[str, Any]], int | float]: + """ + A short function to fit and create an ensemble. It is just a wrapper to easily + send a request to dask to create an ensemble and clean the memory when finished + + Parameters + ---------- + iteration: int + The current iteration + + end_at: float + At what time the job must finish. Needs to be the endtime and not the + time left because we do not know when dask schedules the job. + + backend: Backend + Backend to write and read files + + dataset_name: str + name of dataset + + task_type: int + type of ML task + + metric: Scorer + Metric to compute the loss of the given predictions + + pynisher_context: "fork" | "spawn" | "forkserver" = "fork" + Context to use for multiprocessing, can be either fork, spawn or forkserver. + + ensemble_size: int = 50 + Maximal size of ensemble + + ensemble_nbest: int | float = 50 + If int: consider only the n best prediction + If float: consider only this fraction of the best models + + max_models_on_disc: int | float | None = 100 + Defines the maximum number of models that are kept in the disc. + + If int, it must be greater or equal than 1, and dictates the max number of + models to keep. + + If float, it will be interpreted as the max megabytes allowed of disc space. + That is, if the number of ensemble candidates require more disc space than + this float value, the worst models will be deleted to keep within this + budget. Models and predictions of the worst-performing models will be + deleted then. + + If None, the feature is disabled. + + seed: int = 1 + Seed used for training the models in the backend + + precision: 16 | 32 | 64 | 128 = 32 + Precision of floats to read the predictions + + memory_limit: int | None = None + Memory limit in mb. If ``None``, no memory limit is enforced. + + read_at_most: int | None = None + read at most n new prediction files in each iteration. If `None`, will read + the predictions and calculate losses for all runs that require it. + + logger_port: int = DEFAULT_TCP_LOGGING_PORT + The port where the logging server is listening to. + + random_state: int | RandomState | None = None + A random state used for the ensemble selection process. + + Returns + ------- + (ensemble_history: list[dict[str, Any]], nbest: int | float) + The ensemble history and the nbest chosen members + """ + result = EnsembleBuilder( + backend=backend, + dataset_name=dataset_name, + task_type=task_type, + metric=metric, + ensemble_size=ensemble_size, + ensemble_nbest=ensemble_nbest, + max_models_on_disc=max_models_on_disc, + seed=seed, + precision=precision, + memory_limit=memory_limit, + read_at_most=read_at_most, + random_state=random_state, + logger_port=logger_port, + ).run( + end_at=end_at, + iteration=iteration, + pynisher_context=pynisher_context, + ) + return result diff --git a/autosklearn/ensemble_building/run.py b/autosklearn/ensemble_building/run.py new file mode 100644 index 0000000000..fa73f91e45 --- /dev/null +++ b/autosklearn/ensemble_building/run.py @@ -0,0 +1,181 @@ +from __future__ import annotations + +from typing import Tuple + +import re +from pathlib import Path + +import numpy as np + +from autosklearn.util.disk import sizeof + +RunID = Tuple[int, int, float] + + +class Run: + """Class for storing information about a run used during ensemble building. + + Note + ---- + This is for internal use by the EnsembleBuilder and not for general usage. + """ + + # For matching prediction files + RE_MODEL_PREDICTION_FILE = ( + r"^predictions_ensemble_([0-9]*)_([0-9]*)_([0-9]{1,3}\.[0-9]*).npy$" + ) + + # For matching run directories + RE_MODEL_DIR = r"^([0-9]*)_([0-9]*)_([0-9]{1,3}\.[0-9]*)$" + + def __init__(self, path: Path) -> None: + """Creates a Run from a path pointing to the directory of a run + + Parameters + ---------- + path: Path + Expects something like /path/to/{seed}_{numrun}_{budget} + """ + name = path.name + seed, num_run, budget = name.split("_") + + self.dir = path + self.seed = int(seed) + self.num_run = int(num_run) + self.budget = float(budget) + + self.loss: float = np.inf + self._mem_usage: float | None = None + + # Items that will be delete when the run is saved back to file + self._cache: dict[str, np.ndarray] = {} + + # The recorded time of ensemble/test predictions modified + self.recorded_mtimes: dict[str, float] = {} + self.record_modified_times() + + @property + def mem_usage(self) -> float: + """The memory usage of this run based on it's directory""" + if self._mem_usage is None: + self._mem_usage = round(sizeof(self.dir, unit="MB"), 2) + + return self._mem_usage + + def is_dummy(self) -> bool: + """Whether this run is a dummy run or not""" + return self.num_run == 1 + + def was_modified(self) -> bool: + """Query for when the ens file was last modified""" + recorded = self.recorded_mtimes.get("ensemble") + last = self.pred_path().stat().st_mtime + return recorded != last + + def pred_path(self, kind: str = "ensemble") -> Path: + """Get the path to certain predictions""" + fname = f"predictions_{kind}_{self.seed}_{self.num_run}_{self.budget}.npy" + return self.dir / fname + + def record_modified_times(self) -> None: + """Records the last time each prediction file type was modified, if it exists""" + self.recorded_mtimes = {} + for kind in ["ensemble", "test"]: + path = self.pred_path(kind) # type: ignore + if path.exists(): + self.recorded_mtimes[kind] = path.stat().st_mtime + + def has_predictions(self, kind: str = "ensemble") -> bool: + """ + Parameters + ---------- + kind: "ensemble" | "test" = "ensemble" + The kind of predictions to query for + + Returns + ------- + bool + Whether this run has the kind of predictions queried for + """ + return self.pred_path(kind).exists() + + def predictions( + self, + kind: str = "ensemble", + precision: int | None = None, + ) -> np.ndarray: + """Load the predictions for this run + + Parameters + ---------- + kind : "ensemble" | "test" + The kind of predictions to load + + precisions : type | None = None + What kind of precision reduction to apply + + Returns + ------- + np.ndarray + The loaded predictions + """ + key = f"predictions_{kind}" + if key in self._cache: + return self._cache[key] + + path = self.pred_path(kind) + + with path.open("rb") as f: + # TODO: We should probably remove this requirement. I'm not sure why model + # predictions are being saved as pickled + predictions = np.load(f, allow_pickle=True) + + if precision: + dtypes: dict[int, type] = {16: np.float16, 32: np.float32, 64: np.float64} + dtype = dtypes.get(precision, None) + + if dtype is not None: + predictions = predictions.astype(dtype=dtype, copy=False) + + self._cache[key] = predictions + return predictions + + def __getstate__(self) -> dict: + """Remove the cache when pickling.""" + state = self.__dict__.copy() + del state["_cache"] + return state + + def __setstate__(self, state: dict) -> None: + """Reset state and instansiate blank cache.""" + self.__dict__.update(state) + self._cache = {} + + @property + def id(self) -> RunID: + """Get the three components of it's id""" + return self.seed, self.num_run, self.budget + + def __hash__(self) -> int: + return hash(self.id) + + def __repr__(self) -> str: + return f"Run(id={self.id}, loss={self.loss})" + + def __eq__(self, other: object) -> bool: + return isinstance(other, Run) and other.id == self.id + + @staticmethod + def valid(path: Path) -> bool: + """ + Parameters + ---------- + path: Path + The path to check + + Returns + ------- + bool + Whether the path is a valid run dir + """ + return re.match(Run.RE_MODEL_DIR, path.name) is not None diff --git a/autosklearn/smbo.py b/autosklearn/smbo.py index 6326d3a36f..583a09e34d 100644 --- a/autosklearn/smbo.py +++ b/autosklearn/smbo.py @@ -33,7 +33,7 @@ TASK_TYPES_TO_STRING, ) from autosklearn.data.abstract_data_manager import AbstractDataManager -from autosklearn.ensemble_builder import EnsembleBuilderManager +from autosklearn.ensemble_building import EnsembleBuilderManager from autosklearn.evaluation import ExecuteTaFuncWithQueue, get_cost_of_crash from autosklearn.metalearning.metafeatures.metafeatures import ( calculate_all_metafeatures_encoded_labels, diff --git a/autosklearn/util/disk.py b/autosklearn/util/disk.py new file mode 100644 index 0000000000..279a88f0ea --- /dev/null +++ b/autosklearn/util/disk.py @@ -0,0 +1,103 @@ +from __future__ import annotations + +from typing import Any + +import math +import shutil +import tempfile +import uuid +from pathlib import Path + +sizes = { + "B": 0, + "KB": 1, + "MB": 2, + "GB": 3, + "TB": 4, +} + + +def sizeof(path: Path | str, unit: str = "B") -> float: + """Get the size of some path object + + Parameters + ---------- + path : Path | str + The path of the file or directory to get the size of + + unit : "B" | "KB" | "MB" | "GB" | "TB" = "B" + What unit to get the answer in + + Returns + ------- + float + The size of the folder/file in the given units + """ + if unit not in sizes: + raise ValueError(f"Not a known unit {unit}") + + if not isinstance(path, Path): + path = Path(path) + + if path.is_file(): + size = path.stat().st_size + else: + size = sum(f.stat().st_size for f in path.glob("**/*") if f.is_file()) + + power = sizes[unit] + return size / math.pow(1024, power) + + +def rmtree( + path: Path | str, + *, + atomic: bool = False, + tmp: bool | Path | str = False, + **kwargs: Any, +) -> None: + """Delete a file or directory + + Parameters + ---------- + path: Path | str + The path to delete + + atomic: bool = False + Whether to delete the file/folder atomically. This is done by first + using a `move` before `rmtree`. + + The `move` is not guaranteed to be atomic if moving between + different file systems which can happen when moving to /tmp, + depending on the OS and setup. + + The deletion part is not atomic. + + * https://docs.python.org/3/library/shutil.html#shutil.move + + tmp: bool | Path | str = False + If bool, this defines whether atomic should use the tmp dir + for it's move. Otherwise, a path can be specified to use + + **kwargs + Forwarded to `rmtree` + * https://docs.python.org/3/library/shutil.html#shutil.rmtree + """ + if isinstance(path, str): + path = Path(path) + + if atomic: + if tmp is True: + dir = Path(tempfile.gettempdir()) + uid = uuid.uuid4() + mvpath = dir / f"autosklearn-{path.name}.old_{uid}" + + elif tmp is False: + uid = uuid.uuid4() + mvpath = path.parent / f"{path.name}.old_{uid}" + else: + mvpath = tmp if isinstance(tmp, Path) else Path(tmp) + + shutil.move(str(path), str(mvpath)) + shutil.rmtree(mvpath, **kwargs) + else: + shutil.rmtree(path, **kwargs) diff --git a/autosklearn/util/functional.py b/autosklearn/util/functional.py index 55f38ddf5d..e48f018305 100644 --- a/autosklearn/util/functional.py +++ b/autosklearn/util/functional.py @@ -1,54 +1,188 @@ -from typing import Optional +from __future__ import annotations -import numpy as np +from typing import Callable, Iterable, TypeVar +from functools import reduce +from itertools import chain, tee -def normalize(x: np.ndarray, axis: Optional[int] = None) -> np.ndarray: - """Normalizes an array along an axis +T = TypeVar("T") - Note - ---- - TODO: Only works for positive numbers + +def intersection(*items: Iterable[T]) -> set[T]: + """Does an intersection over all collection of items ..code:: python - x = np.ndarray([ - [1, 1, 1], - [2, 2, 2], - [7, 7, 7], - ]) + ans = intersection(["a", "b", "c"], "ab", ("b", "c")) + + items = [(1, 2, 3), (2, 3), (4, 5)] + ans = intesection(*items) + + Parameters + ---------- + *items : Iterable[T] + A list of lists + + Returns + ------- + Set[T] + The intersection of all items + """ + if len(items) == 0: + return set() + + return set(reduce(lambda s1, s2: set(s1) & set(s2), items, items[0])) - print(normalize(x, axis=0)) - np.ndarray([ - [.1, .1, .1] - [.2, .2, .2] - [.7, .7, .7] - ]) +def cut( + itr: Iterable[T], + where: int | Callable[[T], bool], +) -> tuple[list[T], list[T]]: + """Cut a list in two at a given index or predicate - print(normalize(x, axis=1)) + Parameters + ---------- + lst : Iterable[T] + An iterable of items - np.ndarray([ - [.333, .333, .333] - [.333, .333, .333] - [.333, .333, .333] - ]) + at : int | Callable[[T], bool] + Where to split at, either an index or a predicate + + Returns + ------- + tuple[list[T], list[T]] + The split items + """ + if isinstance(where, int): + lst = list(itr) + return lst[:where], lst[where:] + + a = [] + itr2 = iter(itr) + broke = False + for x in itr2: + if not where(x): + a.append(x) + else: + broke = True + break + + if broke: + return a, [x] + list(itr2) + else: + return a, [] + + +def split( + lst: Iterable[T], + by: Callable[[T], bool], +) -> tuple[list[T], list[T]]: + """Split a list in two based on a predicate. Note ---- - Does not account for 0 sums along an axis + First element can not contain None + + Parameters + ---------- + lst : Iterable[T] + The iterator to split + + by : Callable[[T], bool] + The predicate to split it on + + Returns + ------- + (a: list[T], b: list[T]) + a is where the func is True and b is where the func was False. + """ + a = [] + b = [] + for x in lst: + if by(x): + a.append(x) + else: + b.append(x) + + return a, b + + +def bound(val: float, bounds: tuple[float, float]) -> float: + """Bounds a value between a low and high + + .. code:: python + + x = bound(14, low=0, high=13.1) + # x == 13.1 + + Parameters + ---------- + val : float + The value to bound + + bounds: tuple[foat, float] + The bounds to bound the value between (low, high) + + Returns + ------- + float + The bounded value + """ + return max(bounds[0], min(val, bounds[1])) + + +def findwhere(itr: Iterable[T], func: Callable[[T], bool], *, default: int = -1) -> int: + """Find the index of the next occurence where func is True. Parameters ---------- - x : np.ndarray - The array to normalize + itr : Iterable[T] + The iterable to search over - axis : Optional[int] = None - The axis to normalize across + func : Callable[[T], bool] + The function to use + + default : int = -1 + The default value to give if no value was found where func was True Returns ------- - np.ndarray - The normalized array + int + The first index where func was True """ - return x / x.sum(axis=axis, keepdims=True) + return next((i for i, t in enumerate(itr) if func(t)), default) + + +def pairs(itr: Iterable[T]) -> Iterable[tuple[T, T]]: + """An iterator over pairs of items in the iterator + + ..code:: python + + # Check if sorted + if all(a < b for a, b in pairs(items)): + ... + + Parameters + ---------- + itr : Iterable[T] + An itr of items + + Returns + ------- + Iterable[tuple[T, T]] + An itr of sequential pairs of the items + """ + itr1, itr2 = tee(itr) + + # Skip first item + _ = next(itr2) + + # Check there is a second element + peek = next(itr2, None) + if peek is None: + raise ValueError("Can't create a pair from iterable with 1 item") + + # Put it back in + itr2 = chain([peek], itr2) + + return iter((a, b) for a, b in zip(itr1, itr2)) diff --git a/pyproject.toml b/pyproject.toml index 7fa3bc4111..5a131b5ec9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ [tool.pytest.ini_options] testpaths = ["test"] minversion = "3.7" -#addopts = "--cov=autosklearn" +addopts = "--forked" [tool.coverage.run] branch = true @@ -96,7 +96,6 @@ module = [ "autosklearn.automl", "autosklearn.smbo", "autosklearn.experimental.askl2", - "autosklearn.ensemble_builder", "autosklearn.ensembles.singlebest_ensemble", "autosklearn.ensembles.ensemble_selection", "autosklearn.evaluation", #__init__ diff --git a/setup.py b/setup.py index e182cd716b..84af24be33 100644 --- a/setup.py +++ b/setup.py @@ -35,7 +35,7 @@ "pytest-cov", "pytest-xdist", "pytest-timeout", - "pytest-cases", + "pytest-cases>=3.6.11", "mypy", "isort", "black", diff --git a/test/fixtures/automl.py b/test/fixtures/automl.py index abf31d304d..726424b9a0 100644 --- a/test/fixtures/automl.py +++ b/test/fixtures/automl.py @@ -7,7 +7,7 @@ from autosklearn.automl import AutoML, AutoMLClassifier, AutoMLRegressor from autosklearn.automl_common.common.utils.backend import Backend -from pytest import fixture +from pytest import FixtureRequest, fixture from unittest.mock import Mock from test.conftest import DEFAULT_SEED @@ -16,6 +16,7 @@ def _create_automl( automl_type: Type[AutoML] = AutoML, + _id: str | None = None, **kwargs: Any, ) -> AutoML: """ @@ -25,6 +26,10 @@ def _create_automl( automl_type : Type[AutoML] = AutoML The type of AutoML object to use + _id: str | None = None + If no dask client is provided, a unique id is required to create one + so that it can be shut down after the test ends + **kwargs: Any Options to pass on to the AutoML type for construction @@ -39,12 +44,22 @@ def _create_automl( "per_run_time_limit": 5, "seed": DEFAULT_SEED, "n_jobs": 2, + "ensemble_size": 10, + "ensemble_nbest": 10, + "max_models_on_disc": 10, + "initial_configurations_via_metalearning": 5, } + # If a temp directory was explicitly passed, don't delete it automatically + # Normally the `tmp_path` fixutre will delete it anyways + if "temporary_directory" in kwargs: + test_defaults["delete_tmp_folder_after_terminate"] = False + opts: Dict[str, Any] = {**test_defaults, **kwargs} if "dask_client" not in opts: - client = create_test_dask_client(n_workers=opts["n_jobs"]) + assert _id is not None + client = create_test_dask_client(id=_id, n_workers=opts["n_jobs"]) opts["dask_client"] = client auto = automl_type(**opts) @@ -52,21 +67,21 @@ def _create_automl( @fixture -def make_automl() -> Callable[..., Tuple[AutoML, Callable]]: +def make_automl(request: FixtureRequest) -> Callable[..., Tuple[AutoML, Callable]]: """See `_create_automl`""" - yield partial(_create_automl, automl_type=AutoML) + yield partial(_create_automl, automl_type=AutoML, _id=request.node.nodeid) @fixture -def make_automl_classifier() -> Callable[..., AutoMLClassifier]: +def make_automl_classifier(request: FixtureRequest) -> Callable[..., AutoMLClassifier]: """See `_create_automl`""" - yield partial(_create_automl, automl_type=AutoMLClassifier) + yield partial(_create_automl, automl_type=AutoMLClassifier, _id=request.node.nodeid) @fixture -def make_automl_regressor() -> Callable[..., AutoMLRegressor]: +def make_automl_regressor(request: FixtureRequest) -> Callable[..., AutoMLRegressor]: """See `_create_automl`""" - yield partial(_create_automl, automl_type=AutoMLRegressor) + yield partial(_create_automl, automl_type=AutoMLRegressor, _id=request.node.nodeid) class AutoMLStub(AutoML): diff --git a/test/fixtures/backend.py b/test/fixtures/backend.py index 3ee4626199..2dfcc4d472 100644 --- a/test/fixtures/backend.py +++ b/test/fixtures/backend.py @@ -1,12 +1,64 @@ -from typing import Callable, Union +from __future__ import annotations + +from typing import Callable import os +from distutils.dir_util import copy_tree from pathlib import Path from autosklearn.automl_common.common.utils.backend import Backend, create +from autosklearn.data.xy_data_manager import XYDataManager from pytest import fixture +HERE = Path(__file__).parent.resolve() +DATAPATH = HERE.parent / "data" + + +def copy_backend(old: Backend | Path | str, new: Backend | Path | str) -> Backend: + """Transfers a backend to a new path + + Parameters + ---------- + old_backend: Backend | Path | str + The backend to transfer from + + new_path: Backend | Path | str + Where to place the new backend + + Returns + ------- + Backend + The new backend with the contents of the old + """ + if isinstance(new, str): + new_backend = create( + temporary_directory=new, + output_directory=None, + prefix="auto-sklearn", + ) + elif isinstance(new, Path): + new_backend = create( + temporary_directory=str(new), + output_directory=None, + prefix="auto-sklearn", + ) + else: + new_backend = new + + dst = new_backend.temporary_directory + + if isinstance(old, str): + src = old + elif isinstance(old, Path): + src = str(old) + else: + src = old.temporary_directory + + copy_tree(src, dst) + + return new_backend + # TODO Update to return path once everything can use a path @fixture @@ -26,7 +78,7 @@ def tmp_dir(tmp_path: Path) -> str: @fixture -def make_backend() -> Callable[..., Backend]: +def make_backend(tmp_path: Path) -> Callable[..., Backend]: """Make a backend Parameters @@ -34,21 +86,43 @@ def make_backend() -> Callable[..., Backend]: path: Union[str, Path] The path to place the backend at + template: Optional[Path] = None + Setup with a pre-existing layout if not None + Returns ------- Backend The created backend object """ # TODO redo once things use paths - def _make(path: Union[str, Path]) -> Backend: - _path = Path(path) if not isinstance(path, Path) else path - assert not _path.exists() - - backend = create( - temporary_directory=str(_path), - output_directory=None, - prefix="auto-sklearn", - ) + def _make( + path: str | Path | None = None, + template: Path | Backend | None = None, + datamanager: XYDataManager | None = None, + ) -> Backend: + if template is not None and datamanager is not None: + raise ValueError("Does not support template and datamanager") + + if path is None: + _path = Path(tmp_path) / "backend" + elif isinstance(path, str): + _path = Path(path) + else: + _path = path + + assert not _path.exists(), "Path exists, Try passing path / 'backend'" + + if template is not None: + backend = copy_backend(old=template, new=_path) + else: + backend = create( + temporary_directory=str(_path), + output_directory=None, + prefix="auto-sklearn", + ) + + if datamanager is not None: + backend.save_datamanager(datamanager) return backend diff --git a/test/fixtures/caching.py b/test/fixtures/caching.py index 936d7c35a9..d8be21aca4 100644 --- a/test/fixtures/caching.py +++ b/test/fixtures/caching.py @@ -1,20 +1,23 @@ from __future__ import annotations -from typing import Any, Callable, Optional +from typing import Any, Callable +import os import pickle import shutil -import traceback +import tempfile from functools import partial from pathlib import Path -from autosklearn.automl import AutoML +from filelock import FileLock from pytest import FixtureRequest from pytest_cases import fixture from test.conftest import AUTOSKLEARN_CACHE_NAME +LOCK_DIR = Path(tempfile.gettempdir()) + class Cache: """Used for the below fixtures. @@ -73,8 +76,10 @@ def __init__(self, key: str, cache_dir: Path, verbose: int = 0): verbose : int = 0 Whether to be verbose or not. Currently only has one level (> 0) """ + self.key = key self.dir = cache_dir / key self.verbose = verbose > 0 + self._lock: FileLock = None def items(self) -> list[Path]: """Get any paths associated to items in this dir""" @@ -87,7 +92,7 @@ def path(self, name: str) -> Path: """Path to an item for this cache""" return self.dir / name - def _load(self, name: str) -> Any: + def load(self, name: str) -> Any: """Load an item from the cache with a given name""" if self.verbose: print(f"Loading cached item {self.path(name)}") @@ -95,7 +100,7 @@ def _load(self, name: str) -> Any: with self.path(name).open("rb") as f: return pickle.load(f) - def _save(self, item: Any, name: str) -> None: + def save(self, item: Any, name: str) -> None: """Dump an item to cache with a name""" if self.verbose: print(f"Saving cached item {self.path(name)}") @@ -108,53 +113,28 @@ def reset(self) -> None: shutil.rmtree(self.dir) self.dir.mkdir() + def __enter__(self): + if int(os.environ.get("PYTEST_XDIST_WORKER_COUNT", 1)) <= 1: + return self + else: + path = LOCK_DIR / f"{self.key}.lock" + self._lock = FileLock(path) + self._lock.acquire(poll_interval=1.0) + if self.verbose: + print(f"locked cache {path}") -class AutoMLCache(Cache): - def save(self, model: AutoML) -> None: - """Save the model""" - self._save(model, "model") - - def model(self) -> Optional[AutoML]: - """Returns the saved model if it can. - - In the case of an issue loading an existing model file, it will delete - this cache item. - """ - if "model" not in self: - return None - - # Try to load the model, if there was an issue, delete all cached items - # for the model and return None - try: - model = self._load("model") - except Exception: - model = None - print(traceback.format_exc()) - self.reset() - finally: - return model + return self - def backend_path(self) -> Path: - """The path for the backend of the automl model""" - return self.path("backend") + def __exit__(self, *args, **kwargs): + if self._lock is not None: + self._lock.release() @fixture -def cache(request: FixtureRequest) -> Callable[[str], Cache]: +def make_cache(request: FixtureRequest) -> Callable[[str], Cache]: """Gives the access to a cache.""" pytest_cache = request.config.cache assert pytest_cache is not None cache_dir = pytest_cache.mkdir(AUTOSKLEARN_CACHE_NAME) return partial(Cache, cache_dir=cache_dir) - - -@fixture -def automl_cache(request: FixtureRequest) -> Callable[[str], AutoMLCache]: - """Gives access to an automl cache""" - pytest_cache = request.config.cache - assert pytest_cache is not None - - cache_dir = pytest_cache.mkdir(AUTOSKLEARN_CACHE_NAME) - verbosity = request.config.getoption("verbose") - return partial(AutoMLCache, cache_dir=cache_dir, verbose=verbosity) diff --git a/test/fixtures/dask.py b/test/fixtures/dask.py index 0c1f112800..40a113e9d6 100644 --- a/test/fixtures/dask.py +++ b/test/fixtures/dask.py @@ -2,6 +2,8 @@ from typing import Callable +from functools import partial + from dask.distributed import Client, get_client from pytest import FixtureRequest, fixture @@ -13,31 +15,36 @@ @fixture(autouse=True) def clean_up_any_dask_clients(request: FixtureRequest) -> None: """Auto injected fixture to close dask clients after each test""" - yield - if any(active_clients): - for adr in list(active_clients.keys()): - if request.config.getoption("verbose") > 1: - print(f"\nFixture closing dask_client at {adr}") + yield # Give control to the function + + # Initiate cleanup + id = request.node.nodeid + if id in active_clients: + if request.config.getoption("verbose") > 1: + print(f"\nFixture closing dask_client for {id}") - close = active_clients[adr] - close() - del active_clients[adr] + close = active_clients[id] + close() -def create_test_dask_client(n_workers: int = 2) -> Client: +def create_test_dask_client( + id: str, + n_workers: int = 2, +) -> Client: """Factory to make a Dask client and a function to close it - them + them. Parameters ---------- + id: str + An id to associate with this dask client + n_workers: int = 2 - inside asklea - inside AutoML. Returns ------- - Client, Callable - The client and a function to call to close that client + Client + The client """ # Workers are in subprocesses to not create deadlocks with the pynisher # and logging. @@ -57,13 +64,13 @@ def close() -> None: except Exception: pass - active_clients[adr] = close + active_clients[id] = close return client @fixture -def make_dask_client() -> Callable[[int], Client]: +def make_dask_client(request: FixtureRequest) -> Callable[[int], Client]: """Factory to make a Dask client and a function to close it Parameters @@ -76,7 +83,7 @@ def make_dask_client() -> Callable[[int], Client]: Client, Callable The client and a function to call to close that client """ - return create_test_dask_client + return partial(create_test_dask_client, id=request.node.nodeid) # TODO remove in favour of make_dask_client diff --git a/test/fixtures/datasets.py b/test/fixtures/datasets.py index 39d948e5a9..569241ac51 100644 --- a/test/fixtures/datasets.py +++ b/test/fixtures/datasets.py @@ -17,7 +17,6 @@ from autosklearn.data.validation import SUPPORTED_FEAT_TYPES, SUPPORTED_TARGET_TYPES from autosklearn.data.xy_data_manager import XYDataManager from autosklearn.pipeline.util import get_dataset -from autosklearn.util.functional import normalize from pytest import fixture @@ -41,7 +40,6 @@ def astype( @fixture def make_sklearn_dataset() -> Callable: """ - Parameters ---------- name : str = "iris" @@ -62,6 +60,12 @@ def make_sklearn_dataset() -> Callable: make_binary : bool = False Whether to force the data into being binary + task: Optional[int] = None + The task of the data, required for the datamanager + + feat_type: Optional[Dict | str] = None + The features types for the data if making a XYDataManager + as_datamanager: bool = False Wether to return the information as an XYDataManager @@ -77,9 +81,9 @@ def _make( train_size_maximum: int = 150, make_multilabel: bool = False, make_binary: bool = False, - as_datamanager: bool = False, task: Optional[int] = None, feat_type: Optional[Dict | str] = None, + as_datamanager: bool = False, ) -> Any: X, y, Xt, yt = get_dataset( dataset=name, @@ -93,6 +97,7 @@ def _make( if not as_datamanager: return (X, y, Xt, yt) else: + assert task is not None and feat_type is not None if isinstance(feat_type, str): feat_type = {i: feat_type for i in range(X.shape[1])} @@ -128,7 +133,7 @@ def _make_binary_data( weights = np.ones_like(classes) / len(classes) assert len(weights) == len(classes) - weights = normalize(np.asarray(weights)) + weights = weights / np.sum(weights, keepdims=True) X = rs.rand(*dims) y = rs.choice([0, 1], dims[0], p=weights) @@ -157,7 +162,7 @@ def _make_multiclass_data( weights = np.ones_like(classes) / len(classes) assert len(weights) == len(classes) - weights = normalize(np.asarray(weights)) + weights = weights / np.sum(weights, keepdims=True) X = rs.rand(*dims) y = rs.choice(classes, dims[0], p=weights) @@ -188,7 +193,7 @@ def _make_multilabel_data( weights = np.ones(classes.shape[0]) / len(classes) assert len(weights) == len(classes) - weights = normalize(np.asarray(weights)) + weights = weights / np.sum(weights, keepdims=True) X = rs.rand(*dims) diff --git a/test/fixtures/ensemble_building.py b/test/fixtures/ensemble_building.py new file mode 100644 index 0000000000..42dd7fbb9a --- /dev/null +++ b/test/fixtures/ensemble_building.py @@ -0,0 +1,223 @@ +from __future__ import annotations + +from typing import Any, Callable + +import math +import pickle +import sys +from pathlib import Path + +import numpy as np + +from autosklearn.automl import AutoML +from autosklearn.automl_common.common.utils.backend import Backend +from autosklearn.constants import BINARY_CLASSIFICATION +from autosklearn.data.xy_data_manager import XYDataManager +from autosklearn.ensemble_building import EnsembleBuilder, EnsembleBuilderManager, Run +from autosklearn.metrics import Scorer, accuracy + +from pytest_cases import fixture + +from test.conftest import DEFAULT_SEED + + +@fixture +def make_run(tmp_path: Path) -> Callable[..., Run]: + def _make( + id: int | None = None, + dummy: bool = False, + backend: Backend | None = None, + seed: int = DEFAULT_SEED, + modified: bool = False, + budget: float = 0.0, + loss: float | None = None, + model_size: int | None = None, + mem_usage: float | None = None, + predictions: str | list[str] | dict[str, np.ndarray] | None = "ensemble", + ) -> Run: + if dummy: + assert id is None + id = 1 + loss = loss if loss is not None else 50_000 + + if id is None: + id = np.random.randint(sys.maxsize) + + model_id = f"{seed}_{id}_{budget}" + + # Use this backend to set things up + if backend is not None: + runsdir = Path(backend.get_runs_directory()) + else: + runsdir = tmp_path + + dir = runsdir / model_id + + if not dir.exists(): + dir.mkdir() + + # Populate if None + if isinstance(predictions, str): + predictions = [predictions] + + # Convert to dict + if isinstance(predictions, list): + preds = np.asarray([[0]]) + predictions = {kind: preds for kind in predictions} + + # Write them + if isinstance(predictions, dict): + for kind, val in predictions.items(): + fname = f"predictions_{kind}_{seed}_{id}_{budget}.npy" + with (dir / fname).open("wb") as f: + np.save(f, val) + + run = Run(dir) + + if modified: + assert predictions is not None, "Can only modify if predictions" + for k, v in run.recorded_mtimes.items(): + run.recorded_mtimes[k] = v + 1e-4 + + if loss is not None: + run.loss = loss + + if mem_usage is not None: + run._mem_usage = mem_usage + + # MB + if model_size is not None: + n_bytes = int(model_size * math.pow(1024, 2)) + model_path = dir / f"{seed}.{id}.{budget}.model" + with model_path.open("wb") as f: + f.write(bytearray(n_bytes)) + + return run + + return _make + + +@fixture +def make_ensemble_builder( + make_backend: Callable[..., Backend], + make_sklearn_dataset: Callable[..., XYDataManager], +) -> Callable[..., EnsembleBuilder]: + def _make( + *, + automl: AutoML | None = None, + previous_candidates: list[Run] | None = None, + backend: Backend | None = None, + dataset_name: str = "TEST", + task_type: int = BINARY_CLASSIFICATION, + metric: Scorer = accuracy, + **kwargs: Any, + ) -> EnsembleBuilder: + + if automl: + backend = automl._backend + dataset_name = automl._dataset_name + task_type = automl._task + metric = automl._metrics[0] + kwargs = { + "ensemble_size": automl._ensemble_size, + "ensemble_nbest": automl._ensemble_nbest, + "max_models_on_disc": automl._max_models_on_disc, + "precision": automl.precision, + "read_at_most": automl._read_at_most, + "memory_limit": automl._memory_limit, + "logger_port": automl._logger_port, + } + + if backend is None: + backend = make_backend() + + # If there's no datamanager, just try populate it with some generic one, + if not Path(backend._get_datamanager_pickle_filename()).exists(): + datamanager = make_sklearn_dataset( + name="breast_cancer", + task=BINARY_CLASSIFICATION, + feat_type="numerical", # They're all numerical + as_datamanager=True, + ) + backend.save_datamanager(datamanager) + + # Annoyingly, some places use datamanger, some places use the file + # Hence, we take the y_train of the datamanager and use that as the + # the targets + if "Y_train" in datamanager.data: + backend.save_targets_ensemble(datamanager.data["Y_train"]) + + builder = EnsembleBuilder( + backend=backend, + dataset_name=dataset_name, + task_type=task_type, + metric=metric, + **kwargs, + ) + + if previous_candidates is not None: + with builder.previous_candidates_path.open("wb") as f: + pickle.dump({run.id: run for run in previous_candidates}, f) + + return builder + + return _make + + +@fixture +def make_ensemble_builder_manager( + make_backend: Callable[..., Backend], + make_sklearn_dataset: Callable[..., XYDataManager], +) -> Callable[..., EnsembleBuilderManager]: + """Use `make_run` to create runs for this manager + + .. code:: python + + def test_x(make_run, make_ensemble_builder_manager): + manager = make_ensemble_builder(...) + + # Will use the backend to place runs correctly + runs = make_run(predictions={"ensemble": ...}, backend=manager.backend) + + # ... test stuff + + + """ + + def _make( + *, + backend: Backend | None = None, + dataset_name: str = "TEST", + task: int = BINARY_CLASSIFICATION, + metric: Scorer = accuracy, + random_state: int | np.random.RandomState | None = DEFAULT_SEED, + **kwargs: Any, + ) -> EnsembleBuilderManager: + if backend is None: + backend = make_backend() + + if not Path(backend._get_datamanager_pickle_filename()).exists(): + datamanager = make_sklearn_dataset( + name="breast_cancer", + task=BINARY_CLASSIFICATION, + feat_type="numerical", # They're all numerical + as_datamanager=True, + ) + backend.save_datamanager(datamanager) + + # Annoyingly, some places use datamanger, some places use the file + # Hence, we take the y_train of the datamanager and use that as the + # the targets + if "Y_train" in datamanager.data: + backend.save_targets_ensemble(datamanager.data["Y_train"]) + + return EnsembleBuilderManager( + backend=backend, + dataset_name=dataset_name, + task=task, + metric=metric, + random_state=random_state, + **kwargs, + ) + + return _make diff --git a/test/mocks/logging.py b/test/mocks/logging.py index e61ca2c870..50e6578489 100644 --- a/test/mocks/logging.py +++ b/test/mocks/logging.py @@ -30,9 +30,11 @@ def __init__( self.port = port or MOCKPORT # Overwrite the logging implementations with mocks - attrs = ["debug", "info", "warning", "error", "exception", "critical", "log"] - for attr in attrs: - setattr(self, attr, Mock(return_value=None)) - - # This mock logger is enabled for all levels - setattr(self, "isEnabledFor", Mock(return_value=True)) + self.debug = Mock(return_value=None) # type: ignore + self.info = Mock(return_value=None) # type: ignore + self.warning = Mock(return_value=None) # type: ignore + self.error = Mock(return_value=None) # type: ignore + self.exception = Mock(return_value=None) # type: ignore + self.critical = Mock(return_value=None) # type: ignore + self.log = Mock(return_value=None) # type: ignore + self.isEnabledFor = Mock(return_value=True) # type: ignore diff --git a/test/test_automl/cases.py b/test/test_automl/cases.py index a3dfa00dba..a6779d1b08 100644 --- a/test/test_automl/cases.py +++ b/test/test_automl/cases.py @@ -14,7 +14,10 @@ {fitted} - If the automl case has been fitted {cv, holdout} - Whether explicitly cv or holdout was used {no_ensemble} - Fit with no ensemble size + {cached} - If the resulting case is then cached """ +from __future__ import annotations + from typing import Callable, Tuple from pathlib import Path @@ -22,10 +25,12 @@ import numpy as np from autosklearn.automl import AutoMLClassifier, AutoMLRegressor +from autosklearn.automl_common.common.utils.backend import Backend from pytest_cases import case, parametrize -from test.fixtures.caching import AutoMLCache +from test.fixtures.backend import copy_backend +from test.fixtures.caching import Cache @case(tags=["classifier"]) @@ -53,148 +58,180 @@ def case_regressor( # ################################### # The following are fitted and cached # ################################### -@case(tags=["classifier", "fitted", "holdout"]) +@case(tags=["classifier", "fitted", "holdout", "cached"]) @parametrize("dataset", ["iris"]) -def case_classifier_fitted_holdout( - automl_cache: Callable[[str], AutoMLCache], +def case_classifier_fitted_holdout_iterative( dataset: str, + make_cache: Callable[[str], Cache], + make_backend: Callable[..., Backend], make_automl_classifier: Callable[..., AutoMLClassifier], make_sklearn_dataset: Callable[..., Tuple[np.ndarray, ...]], ) -> AutoMLClassifier: """Case of a holdout fitted classifier""" resampling_strategy = "holdout-iterative-fit" - cache = automl_cache(f"case_classifier_{resampling_strategy}_{dataset}") + key = f"case_classifier_{resampling_strategy}_{dataset}" + + # This locks the cache for this item while we check, required for pytest-xdist + with make_cache(key) as cache: + if "model" not in cache: + # Make the model in the cache + model = make_automl_classifier( + temporary_directory=cache.path("backend"), + delete_tmp_folder_after_terminate=False, + resampling_strategy=resampling_strategy, + ) - model = cache.model() - if model is not None: - return model + X, y, Xt, yt = make_sklearn_dataset(name=dataset) + model.fit(X, y, dataset_name=dataset) - X, y, Xt, yt = make_sklearn_dataset(name=dataset) + # Save the model + cache.save(model, "model") - model = make_automl_classifier( - temporary_directory=cache.path("backend"), - delete_tmp_folder_after_terminate=False, - resampling_strategy=resampling_strategy, - ) - model.fit(X, y, dataset_name=dataset) + # Try the model from the cache + model = cache.load("model") + model._backend = copy_backend(old=model._backend, new=make_backend()) - cache.save(model) return model -@case(tags=["classifier", "fitted", "cv"]) +@case(tags=["classifier", "fitted", "cv", "cached"]) @parametrize("dataset", ["iris"]) def case_classifier_fitted_cv( - automl_cache: Callable[[str], AutoMLCache], + make_cache: Callable[[str], Cache], dataset: str, + make_backend: Callable[..., Backend], make_automl_classifier: Callable[..., AutoMLClassifier], make_sklearn_dataset: Callable[..., Tuple[np.ndarray, ...]], ) -> AutoMLClassifier: """Case of a fitted cv AutoMLClassifier""" resampling_strategy = "cv" - cache = automl_cache(f"case_classifier_{resampling_strategy}_{dataset}") - - model = cache.model() - if model is not None: - return model - - X, y, Xt, yt = make_sklearn_dataset(name=dataset) - model = make_automl_classifier( - time_left_for_this_task=60, # We include some extra time for cv - per_run_time_limit=10, - resampling_strategy=resampling_strategy, - temporary_directory=cache.path("backend"), - delete_tmp_folder_after_terminate=False, - ) - model.fit(X, y, dataset_name=dataset) - - cache.save(model) + + key = f"case_classifier_{resampling_strategy}_{dataset}" + + # This locks the cache for this item while we check, required for pytest-xdist + with make_cache(key) as cache: + if "model" not in cache: + model = make_automl_classifier( + resampling_strategy=resampling_strategy, + temporary_directory=cache.path("backend"), + delete_tmp_folder_after_terminate=False, + time_left_for_this_task=60, # Give some more for CV + per_run_time_limit=10, + ) + + X, y, Xt, yt = make_sklearn_dataset(name=dataset) + model.fit(X, y, dataset_name=dataset) + + cache.save(model, "model") + + # Try the model from the cache + model = cache.load("model") + model._backend = copy_backend(old=model._backend, new=make_backend()) + return model -@case(tags=["regressor", "fitted", "holdout"]) +@case(tags=["regressor", "fitted", "holdout", "cached"]) @parametrize("dataset", ["boston"]) def case_regressor_fitted_holdout( - automl_cache: Callable[[str], AutoMLCache], + make_cache: Callable[[str], Cache], dataset: str, + make_backend: Callable[..., Backend], make_automl_regressor: Callable[..., AutoMLRegressor], make_sklearn_dataset: Callable[..., Tuple[np.ndarray, ...]], ) -> AutoMLRegressor: - """Case of fitted regressor with cv resampling""" + """Case of fitted regressor with holdout""" resampling_strategy = "holdout" - cache = automl_cache(f"case_regressor_{resampling_strategy}_{dataset}") - model = cache.model() - if model is not None: - return model + key = f"case_regressor_{resampling_strategy}_{dataset}" + + # This locks the cache for this item while we check, required for pytest-xdist + with make_cache(key) as cache: + if "model" not in cache: + model = make_automl_regressor( + temporary_directory=cache.path("backend"), + resampling_strategy=resampling_strategy, + delete_tmp_folder_after_terminate=False, + ) + + X, y, Xt, yt = make_sklearn_dataset(name=dataset) + model.fit(X, y, dataset_name=dataset) + + cache.save(model, "model") - X, y, Xt, yt = make_sklearn_dataset(name=dataset) - model = make_automl_regressor( - resampling_strategy=resampling_strategy, - temporary_directory=cache.path("backend"), - delete_tmp_folder_after_terminate=False, - ) - model.fit(X, y, dataset_name=dataset) + # Try the model from the cache + model = cache.load("model") + model._backend = copy_backend(old=model._backend, new=make_backend()) - cache.save(model) return model -@case(tags=["regressor", "fitted", "cv"]) +@case(tags=["regressor", "fitted", "cv", "cached"]) @parametrize("dataset", ["boston"]) def case_regressor_fitted_cv( - automl_cache: Callable[[str], AutoMLCache], + make_cache: Callable[[str], Cache], dataset: str, + make_backend: Callable[..., Backend], make_automl_regressor: Callable[..., AutoMLRegressor], make_sklearn_dataset: Callable[..., Tuple[np.ndarray, ...]], ) -> AutoMLRegressor: """Case of fitted regressor with cv resampling""" resampling_strategy = "cv" + key = f"case_regressor_{resampling_strategy}_{dataset}" - cache = automl_cache(f"case_regressor_{resampling_strategy}_{dataset}") - model = cache.model() - if model is not None: - return model + # This locks the cache for this item while we check, required for pytest-xdist + with make_cache(key) as cache: - X, y, Xt, yt = make_sklearn_dataset(name=dataset) + if "model" not in cache: + model = make_automl_regressor( + temporary_directory=cache.path("backend"), + resampling_strategy=resampling_strategy, + delete_tmp_folder_after_terminate=False, + time_left_for_this_task=60, # Some extra time for CV + per_run_time_limit=10, + ) - model = make_automl_regressor( - time_left_for_this_task=60, # Give some extra time for cv - per_run_time_limit=10, - temporary_directory=cache.path("backend"), - delete_tmp_folder_after_terminate=False, - resampling_strategy=resampling_strategy, - ) - model.fit(X, y, dataset_name=dataset) + X, y, Xt, yt = make_sklearn_dataset(name=dataset) + model.fit(X, y, dataset_name=dataset) + + cache.save(model, "model") + + # Try the model from the cache + model = cache.load("model") + model._backend = copy_backend(old=model._backend, new=make_backend()) - cache.save(model) return model -@case(tags=["classifier", "fitted", "no_ensemble"]) +@case(tags=["classifier", "fitted", "no_ensemble", "cached"]) @parametrize("dataset", ["iris"]) def case_classifier_fitted_no_ensemble( - automl_cache: Callable[[str], AutoMLCache], + make_cache: Callable[[str], Cache], dataset: str, + make_backend: Callable[..., Backend], make_automl_classifier: Callable[..., AutoMLClassifier], make_sklearn_dataset: Callable[..., Tuple[np.ndarray, ...]], ) -> AutoMLClassifier: """Case of a fitted classifier but enemble_size was set to 0""" - cache = automl_cache(f"case_classifier_fitted_no_ensemble_{dataset}") + key = f"case_classifier_fitted_no_ensemble_{dataset}" + + # This locks the cache for this item while we check, required for pytest-xdist + with make_cache(key) as cache: + + if "model" not in cache: + model = make_automl_classifier( + temporary_directory=cache.path("backend"), + delete_tmp_folder_after_terminate=False, + ensemble_size=0, + ) - model = cache.model() - if model is not None: - return model + X, y, Xt, yt = make_sklearn_dataset(name=dataset) + model.fit(X, y, dataset_name=dataset) - X, y, Xt, yt = make_sklearn_dataset(name=dataset) + cache.save(model, "model") - model = make_automl_classifier( - temporary_directory=cache.path("backend"), - delete_tmp_folder_after_terminate=False, - ensemble_size=0, - ) - model.fit(X, y, dataset_name=dataset) + model = cache.load("model") + model._backend = copy_backend(old=model._backend, new=make_backend()) - cache.save(model) return model diff --git a/test/test_automl/test_outputs.py b/test/test_automl/test_outputs.py index 458347c145..ba005019bd 100644 --- a/test/test_automl/test_outputs.py +++ b/test/test_automl/test_outputs.py @@ -1,6 +1,7 @@ from pathlib import Path from autosklearn.automl import AutoML +from autosklearn.ensemble_building.builder import CANDIDATES_FILENAME from pytest import mark from pytest_cases import parametrize_with_cases @@ -67,10 +68,9 @@ def test_paths_created_with_ensemble(automl: AutoML) -> None: expected = [ partial / fixture for fixture in ( - "ensemble_read_preds.pkl", - "ensemble_read_losses.pkl", "ensembles", "ensemble_history.json", + CANDIDATES_FILENAME, ) ] @@ -80,6 +80,12 @@ def test_paths_created_with_ensemble(automl: AutoML) -> None: @parametrize_with_cases("automl", cases=cases, has_tag="fitted") def test_at_least_one_model_and_predictions(automl: AutoML) -> None: + """ + Expects + ------- + * There should be at least one models saved + * Each model saved should have predictions for the ensemble + """ assert automl._backend is not None runs_dir = Path(automl._backend.get_runs_directory()) @@ -100,6 +106,11 @@ def test_at_least_one_model_and_predictions(automl: AutoML) -> None: @parametrize_with_cases("automl", cases=cases, filter=has_ensemble) def test_at_least_one_ensemble(automl: AutoML) -> None: + """ + Expects + ------- + * There should be at least one ensemble generated + """ assert automl._backend is not None ens_dir = Path(automl._backend.get_ensemble_dir()) diff --git a/test/test_automl/test_post_fit.py b/test/test_automl/test_post_fit.py index 674a452d02..7cc6dafb6e 100644 --- a/test/test_automl/test_post_fit.py +++ b/test/test_automl/test_post_fit.py @@ -20,7 +20,10 @@ def test_holdout_loaded_models(automl: AutoML) -> None: * The cv_models_ attr should remain None """ assert automl.ensemble_ is not None - assert set(automl.models_.keys()) == set(automl.ensemble_.identifiers_) + + ensemble_identifiers = automl.ensemble_.get_selected_model_identifiers() + + assert set(automl.models_.keys()) == set(ensemble_identifiers) assert automl.cv_models_ is None @@ -39,8 +42,11 @@ def test_cv_loaded_models(automl: AutoML) -> None: * The cv_models_ should contain the identifiers of what's in the ensemble """ assert automl.ensemble_ is not None - assert 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numpy as np - -from autosklearn.automl_common.common.ensemble_building.abstract_ensemble import ( - AbstractEnsemble, -) -from autosklearn.ensemble_builder import EnsembleBuilder -from autosklearn.metrics import make_scorer - -import unittest -import unittest.mock - - -def scorer_function(a, b): - return 0.9 - - -MockMetric = make_scorer("mock", scorer_function) - - -class BackendMock(object): - def __init__(self, target_directory): - this_directory = os.path.abspath(os.path.dirname(__file__)) - shutil.copytree( - os.path.join(this_directory, "data"), os.path.join(target_directory) - ) - self.temporary_directory = target_directory - self.internals_directory = os.path.join( - self.temporary_directory, ".auto-sklearn" - ) - - def load_datamanager(self): - manager = unittest.mock.Mock() - manager.__reduce__ = lambda self: (unittest.mock.MagicMock, ()) - array = np.load( - os.path.join( - self.temporary_directory, - ".auto-sklearn", - "runs", - "0_3_100.0", - "predictions_test_0_3_100.0.npy", - ) - ) - manager.data.get.return_value = array - return manager - - def load_targets_ensemble(self): - with open( - os.path.join( - self.temporary_directory, - ".auto-sklearn", - "predictions_ensemble_true.npy", - ), - "rb", - ) as fp: - y = np.load(fp, allow_pickle=True) - return y - - def save_ensemble(self, ensemble, index_run, seed): - return - - def save_predictions_as_txt(self, predictions, subset, idx, prefix, precision): - return - - def get_runs_directory(self) -> str: - return os.path.join(self.temporary_directory, ".auto-sklearn", "runs") - - def get_numrun_directory(self, seed: int, num_run: int, budget: float) -> str: - return os.path.join( - self.get_runs_directory(), "%d_%d_%s" % (seed, num_run, budget) - ) - - def get_model_filename(self, seed: int, idx: int, budget: float) -> str: - return "%s.%s.%s.model" % (seed, idx, budget) - - -def compare_read_preds(read_preds1, read_preds2): - """ - compares read_preds attribute. An alternative to - assert Dict Equal as it contains np arrays, so we have - to use np testing utilities accordingly - """ - - # Both arrays should have the same splits - assert set(read_preds1.keys()) == set(read_preds2.keys()) - - for k, v in read_preds1.items(): - - # Each split should have the same elements - assert set(read_preds1[k].keys()) == set(read_preds2[k].keys()) - - # This level contains the scores/ensmebles/etc - for actual_k, actual_v in read_preds1[k].items(): - - # If it is a numpy array, make sure it is the same - if type(actual_v) is np.ndarray: - np.testing.assert_array_equal(actual_v, read_preds2[k][actual_k]) - else: - assert actual_v == read_preds2[k][actual_k] - - -class EnsembleBuilderMemMock(EnsembleBuilder): - def fit_ensemble(self, selected_keys): - return True - - def predict( - self, - set_: str, - ensemble: AbstractEnsemble, - selected_keys: list, - n_preds: int, - index_run: int, - ): - np.ones([10000000, 1000000]) diff --git a/test/test_ensemble_builder/test_ensemble.py b/test/test_ensemble_builder/test_ensemble.py deleted file mode 100644 index 469f617fb0..0000000000 --- a/test/test_ensemble_builder/test_ensemble.py +++ /dev/null @@ -1,780 +0,0 @@ -import os -import pickle -import shutil -import sys -import time - -import dask.distributed -import numpy as np -import pandas as pd -from smac.runhistory.runhistory import RunHistory, RunKey, RunValue - -from autosklearn.constants import BINARY_CLASSIFICATION, MULTILABEL_CLASSIFICATION -from autosklearn.ensemble_builder import ( - Y_ENSEMBLE, - Y_TEST, - Y_VALID, - EnsembleBuilder, - EnsembleBuilderManager, -) -from autosklearn.ensembles.singlebest_ensemble import SingleBest -from autosklearn.metrics import accuracy, log_loss, roc_auc - -import pytest -import unittest.mock - -this_directory = os.path.dirname(__file__) -sys.path.append(this_directory) -from ensemble_utils import ( # noqa (E402: module level import not at top of file) - BackendMock, - EnsembleBuilderMemMock, - MockMetric, - compare_read_preds, -) - - -@pytest.fixture(scope="function") -def ensemble_backend(request): - test_id = "%s_%s" % (request.module.__name__, request.node.name) - test_dir = os.path.join(this_directory, test_id) - - try: - shutil.rmtree(test_dir) - except: # noqa E722 - pass - - # Make sure the folders we wanna create do not already exist. - backend = BackendMock(test_dir) - - def get_finalizer(ensemble_backend): - def session_run_at_end(): - try: - shutil.rmtree(test_dir) - except: # noqa E722 - pass - - return session_run_at_end - - request.addfinalizer(get_finalizer(backend)) - - return backend - - -@pytest.fixture(scope="function") -def ensemble_run_history(request): - - run_history = RunHistory() - run_history._add( - RunKey( - config_id=3, instance_id='{"task_id": "breast_cancer"}', seed=1, budget=3.0 - ), - RunValue( - cost=0.11347517730496459, - time=0.21858787536621094, - status=None, - starttime=time.time(), - endtime=time.time(), - additional_info={ - "duration": 0.20323538780212402, - "num_run": 3, - "configuration_origin": "Random Search", - }, - ), - status=None, - origin=None, - ) - run_history._add( - RunKey( - config_id=6, instance_id='{"task_id": "breast_cancer"}', seed=1, budget=6.0 - ), - RunValue( - cost=2 * 0.11347517730496459, - time=2 * 0.21858787536621094, - status=None, - starttime=time.time(), - endtime=time.time(), - additional_info={ - "duration": 0.20323538780212402, - "num_run": 6, - "configuration_origin": "Random Search", - }, - ), - status=None, - origin=None, - ) - return run_history - - -def testRead(ensemble_backend): - - ensbuilder = EnsembleBuilder( - backend=ensemble_backend, - dataset_name="TEST", - task_type=BINARY_CLASSIFICATION, - metric=roc_auc, - seed=0, # important to find the test files - ) - - success = ensbuilder.compute_loss_per_model() - assert success, str(ensbuilder.read_preds) - assert len(ensbuilder.read_preds) == 3, ensbuilder.read_preds.keys() - assert len(ensbuilder.read_losses) == 3, ensbuilder.read_losses.keys() - - filename = os.path.join( - ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy", - ) - assert ensbuilder.read_losses[filename]["ens_loss"] == 0.5 - - filename = os.path.join( - ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy", - ) - assert ensbuilder.read_losses[filename]["ens_loss"] == 0.0 - - -@pytest.mark.parametrize( - "ensemble_nbest,max_models_on_disc,exp", - ( - (1, None, 1), - (1.0, None, 2), - (0.1, None, 1), - (0.9, None, 1), - (1, 2, 1), - (2, 1, 1), - ), -) -def testNBest(ensemble_backend, ensemble_nbest, max_models_on_disc, exp): - ensbuilder = EnsembleBuilder( - backend=ensemble_backend, - dataset_name="TEST", - task_type=BINARY_CLASSIFICATION, - metric=roc_auc, - seed=0, # important to find the test files - ensemble_nbest=ensemble_nbest, - max_models_on_disc=max_models_on_disc, - ) - - ensbuilder.compute_loss_per_model() - sel_keys = ensbuilder.get_n_best_preds() - - assert len(sel_keys) == exp - - fixture = os.path.join( - ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy", - ) - assert sel_keys[0] == fixture - - -@pytest.mark.parametrize( - "test_case,exp", - [ - # If None, no reduction - (None, 2), - # If Int, limit only on exceed - (4, 2), - (1, 1), - # If Float, translate float to # models. - # below, mock of each file is 100 Mb and 4 files .model and .npy (test/val/pred) - # per run (except for run3, there they are 5). Now, it takes 500MB for run 3 and - # another 500 MB of slack because we keep as much space as the largest model - # available as slack - (1499.0, 1), - (1500.0, 2), - (9999.0, 2), - ], -) -def testMaxModelsOnDisc(ensemble_backend, test_case, exp): - ensemble_nbest = 4 - ensbuilder = EnsembleBuilder( - backend=ensemble_backend, - dataset_name="TEST", - task_type=BINARY_CLASSIFICATION, - metric=roc_auc, - seed=0, # important to find the test files - ensemble_nbest=ensemble_nbest, - max_models_on_disc=test_case, - ) - - with unittest.mock.patch("os.path.getsize") as mock: - mock.return_value = 100 * 1024 * 1024 - ensbuilder.compute_loss_per_model() - sel_keys = ensbuilder.get_n_best_preds() - assert len(sel_keys) == exp, test_case - - -def testMaxModelsOnDisc2(ensemble_backend): - # Test for Extreme scenarios - # Make sure that the best predictions are kept - ensbuilder = EnsembleBuilder( - backend=ensemble_backend, - dataset_name="TEST", - task_type=BINARY_CLASSIFICATION, - metric=roc_auc, - seed=0, # important to find the test files - ensemble_nbest=50, - max_models_on_disc=10000.0, - ) - ensbuilder.read_preds = {} - for i in range(50): - ensbuilder.read_losses["pred" + str(i)] = { - "ens_loss": -i * 10, - "num_run": i, - "loaded": 1, - "seed": 1, - "disc_space_cost_mb": 50 * i, - } - ensbuilder.read_preds["pred" + str(i)] = {Y_ENSEMBLE: True} - sel_keys = ensbuilder.get_n_best_preds() - assert ["pred49", "pred48", "pred47"] == sel_keys - - # Make sure at least one model is kept alive - ensbuilder.max_models_on_disc = 0.0 - sel_keys = ensbuilder.get_n_best_preds() - assert ["pred49"] == sel_keys - - -@pytest.mark.parametrize( - "performance_range_threshold,exp", - ((0.0, 4), (0.1, 4), (0.3, 3), (0.5, 2), (0.6, 2), (0.8, 1), (1.0, 1), (1, 1)), -) -def testPerformanceRangeThreshold(ensemble_backend, performance_range_threshold, exp): - ensbuilder = EnsembleBuilder( - backend=ensemble_backend, - dataset_name="TEST", - task_type=BINARY_CLASSIFICATION, - metric=roc_auc, - seed=0, # important to find the test files - ensemble_nbest=100, - performance_range_threshold=performance_range_threshold, - ) - ensbuilder.read_losses = { - "A": {"ens_loss": -1, "num_run": 1, "loaded": -1, "seed": 1}, - "B": {"ens_loss": -2, "num_run": 2, "loaded": -1, "seed": 1}, - "C": {"ens_loss": -3, "num_run": 3, "loaded": -1, "seed": 1}, - "D": {"ens_loss": -4, "num_run": 4, "loaded": -1, "seed": 1}, - "E": {"ens_loss": -5, "num_run": 5, "loaded": -1, "seed": 1}, - } - ensbuilder.read_preds = { - key: {key_2: True for key_2 in (Y_ENSEMBLE, Y_VALID, Y_TEST)} - for key in ensbuilder.read_losses - } - sel_keys = ensbuilder.get_n_best_preds() - - assert len(sel_keys) == exp - - -@pytest.mark.parametrize( - "performance_range_threshold,ensemble_nbest,exp", - ( - (0.0, 1, 1), - (0.0, 1.0, 4), - (0.1, 2, 2), - (0.3, 4, 3), - (0.5, 1, 1), - (0.6, 10, 2), - (0.8, 0.5, 1), - (1, 1.0, 1), - ), -) -def testPerformanceRangeThresholdMaxBest( - ensemble_backend, performance_range_threshold, ensemble_nbest, exp -): - ensbuilder = EnsembleBuilder( - backend=ensemble_backend, - dataset_name="TEST", - task_type=BINARY_CLASSIFICATION, - metric=roc_auc, - seed=0, # important to find the test files - ensemble_nbest=ensemble_nbest, - performance_range_threshold=performance_range_threshold, - max_models_on_disc=None, - ) - ensbuilder.read_losses = { - "A": {"ens_loss": -1, "num_run": 1, "loaded": -1, "seed": 1}, - "B": {"ens_loss": -2, "num_run": 2, "loaded": -1, "seed": 1}, - "C": {"ens_loss": -3, "num_run": 3, "loaded": -1, "seed": 1}, - "D": {"ens_loss": -4, "num_run": 4, "loaded": -1, "seed": 1}, - "E": {"ens_loss": -5, "num_run": 5, "loaded": -1, "seed": 1}, - } - ensbuilder.read_preds = { - key: {key_2: True for key_2 in (Y_ENSEMBLE, Y_VALID, Y_TEST)} - for key in ensbuilder.read_losses - } - sel_keys = ensbuilder.get_n_best_preds() - - assert len(sel_keys) == exp - - -def testFallBackNBest(ensemble_backend): - - ensbuilder = EnsembleBuilder( - backend=ensemble_backend, - dataset_name="TEST", - task_type=BINARY_CLASSIFICATION, - metric=roc_auc, - seed=0, # important to find the test files - ensemble_nbest=1, - ) - - ensbuilder.compute_loss_per_model() - print() - print(ensbuilder.read_preds.keys()) - print(ensbuilder.read_losses.keys()) - print(ensemble_backend.temporary_directory) - - filename = os.path.join( - ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy", - ) - ensbuilder.read_losses[filename]["ens_loss"] = -1 - - filename = os.path.join( - ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_3_100.0/predictions_ensemble_0_3_100.0.npy", - ) - ensbuilder.read_losses[filename]["ens_loss"] = -1 - - filename = os.path.join( - ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy", - ) - ensbuilder.read_losses[filename]["ens_loss"] = -1 - - sel_keys = ensbuilder.get_n_best_preds() - - fixture = os.path.join( - ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy", - ) - assert len(sel_keys) == 1 - assert sel_keys[0] == fixture - - -def testGetValidTestPreds(ensemble_backend): - - ensbuilder = EnsembleBuilder( - backend=ensemble_backend, - dataset_name="TEST", - task_type=BINARY_CLASSIFICATION, - metric=roc_auc, - seed=0, # important to find the test files - ensemble_nbest=1, - ) - - ensbuilder.compute_loss_per_model() - - # d1 is a dummt prediction. d2 and d3 have the same prediction with - # different name. num_run=2 is selected when doing sorted() - d1 = os.path.join( - ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy", - ) - d2 = os.path.join( - ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy", - ) - d3 = os.path.join( - ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_3_100.0/predictions_ensemble_0_3_100.0.npy", - ) - - sel_keys = ensbuilder.get_n_best_preds() - assert len(sel_keys) == 1 - ensbuilder.get_valid_test_preds(selected_keys=sel_keys) - - # Number of read files should be three and - # predictions_ensemble_0_4_0.0.npy must not be in there - assert len(ensbuilder.read_preds) == 3 - assert ( - os.path.join( - ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_4_0.0/predictions_ensemble_0_4_0.0.npy", - ) - not in ensbuilder.read_preds - ) - - # not selected --> should still be None - assert ensbuilder.read_preds[d1][Y_VALID] is None - assert ensbuilder.read_preds[d1][Y_TEST] is None - assert ensbuilder.read_preds[d3][Y_VALID] is None - assert ensbuilder.read_preds[d3][Y_TEST] is None - - # selected --> read valid and test predictions - assert ensbuilder.read_preds[d2][Y_VALID] is not None - assert ensbuilder.read_preds[d2][Y_TEST] is not None - - -def testEntireEnsembleBuilder(ensemble_backend): - - ensbuilder = EnsembleBuilder( - backend=ensemble_backend, - dataset_name="TEST", - task_type=BINARY_CLASSIFICATION, - metric=roc_auc, - seed=0, # important to find the test files - ensemble_nbest=2, - ) - ensbuilder.SAVE2DISC = False - - ensbuilder.compute_loss_per_model() - - d2 = os.path.join( - ensemble_backend.temporary_directory, - ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy", - ) - - sel_keys = ensbuilder.get_n_best_preds() - assert len(sel_keys) > 0 - - ensemble = ensbuilder.fit_ensemble(selected_keys=sel_keys) - print(ensemble, sel_keys) - - n_sel_valid, n_sel_test = ensbuilder.get_valid_test_preds(selected_keys=sel_keys) - - # both valid and test prediction files are available - assert len(n_sel_valid) > 0 - assert n_sel_valid == n_sel_test - - y_valid = ensbuilder.predict( - set_="valid", - ensemble=ensemble, - selected_keys=n_sel_valid, - n_preds=len(sel_keys), - index_run=1, - ) - y_test = ensbuilder.predict( - set_="test", - ensemble=ensemble, - selected_keys=n_sel_test, - n_preds=len(sel_keys), - index_run=1, - ) - - # predictions for valid and test are the same - # --> should results in the same predictions - np.testing.assert_array_almost_equal(y_valid, y_test) - - # since d2 provides perfect predictions - # it should get a higher weight - # so that y_valid should be exactly y_valid_d2 - y_valid_d2 = ensbuilder.read_preds[d2][Y_VALID][:, 1] - np.testing.assert_array_almost_equal(y_valid, y_valid_d2) - - -def test_main(ensemble_backend): - - ensbuilder = EnsembleBuilder( - backend=ensemble_backend, - dataset_name="TEST", - task_type=MULTILABEL_CLASSIFICATION, # Multilabel Classification - metric=roc_auc, - seed=0, # important to find the test files - ensemble_nbest=2, - max_models_on_disc=None, - ) - ensbuilder.SAVE2DISC = False - - run_history, ensemble_nbest, _, _, _ = ensbuilder.main( - time_left=np.inf, - iteration=1, - return_predictions=False, - ) - - assert len(ensbuilder.read_preds) == 3 - assert ensbuilder.last_hash is not None - assert ensbuilder.y_true_ensemble is not None - - # Make sure the run history is ok - - # We expect at least 1 element to be in the ensemble - assert len(run_history) > 0 - - # As the data loader loads the same val/train/test - # we expect 1.0 as score and all keys available - expected_performance = { - "ensemble_val_score": 1.0, - "ensemble_test_score": 1.0, - "ensemble_optimization_score": 1.0, - } - - # Make sure that expected performance is a subset of the run history - assert all(item in run_history[0].items() for item in expected_performance.items()) - assert "Timestamp" in run_history[0] - assert isinstance(run_history[0]["Timestamp"], pd.Timestamp) - - assert os.path.exists( - os.path.join(ensemble_backend.internals_directory, "ensemble_read_preds.pkl") - ), os.listdir(ensemble_backend.internals_directory) - assert os.path.exists( - os.path.join(ensemble_backend.internals_directory, "ensemble_read_losses.pkl") - ), os.listdir(ensemble_backend.internals_directory) - - -def test_run_end_at(ensemble_backend): - with unittest.mock.patch("pynisher.enforce_limits") as pynisher_mock: - ensbuilder = EnsembleBuilder( - backend=ensemble_backend, - dataset_name="TEST", - task_type=MULTILABEL_CLASSIFICATION, # Multilabel Classification - metric=roc_auc, - seed=0, # important to find the test files - ensemble_nbest=2, - max_models_on_disc=None, - ) - ensbuilder.SAVE2DISC = False - - current_time = time.time() - - ensbuilder.run( - end_at=current_time + 10, iteration=1, pynisher_context="forkserver" - ) - # 4 seconds left because: 10 seconds - 5 seconds overhead - little overhead - # but then rounded to an integer - assert pynisher_mock.call_args_list[0][1]["wall_time_in_s"], 4 - - -def testLimit(ensemble_backend): - ensbuilder = EnsembleBuilderMemMock( - backend=ensemble_backend, - dataset_name="TEST", - task_type=BINARY_CLASSIFICATION, - metric=roc_auc, - seed=0, # important to find the test files - ensemble_nbest=10, - # small to trigger MemoryException - memory_limit=100, - ) - ensbuilder.SAVE2DISC = False - - read_losses_file = os.path.join( - ensemble_backend.internals_directory, "ensemble_read_losses.pkl" - ) - read_preds_file = os.path.join( - ensemble_backend.internals_directory, "ensemble_read_preds.pkl" - ) - - def mtime_mock(filename): - mtimes = { - "predictions_ensemble_0_1_0.0.npy": 0, - "predictions_valid_0_1_0.0.npy": 0.1, - "predictions_test_0_1_0.0.npy": 0.2, - "predictions_ensemble_0_2_0.0.npy": 1, - "predictions_valid_0_2_0.0.npy": 1.1, - "predictions_test_0_2_0.0.npy": 1.2, - "predictions_ensemble_0_3_100.0.npy": 2, - "predictions_valid_0_3_100.0.npy": 2.1, - "predictions_test_0_3_100.0.npy": 2.2, - } - return mtimes[os.path.split(filename)[1]] - - with unittest.mock.patch( - "logging.getLogger" - ) as get_logger_mock, unittest.mock.patch( - "logging.config.dictConfig" - ) as _, unittest.mock.patch( - "os.path.getmtime" - ) as mtime: - logger_mock = unittest.mock.Mock() - logger_mock.handlers = [] - get_logger_mock.return_value = logger_mock - mtime.side_effect = mtime_mock - - ensbuilder.run(time_left=1000, iteration=0, pynisher_context="fork") - assert os.path.exists(read_losses_file) - assert not os.path.exists(read_preds_file) - assert logger_mock.warning.call_count == 1 - ensbuilder.run(time_left=1000, iteration=0, pynisher_context="fork") - assert os.path.exists(read_losses_file) - assert not os.path.exists(read_preds_file) - assert logger_mock.warning.call_count == 2 - ensbuilder.run(time_left=1000, iteration=0, pynisher_context="fork") - assert os.path.exists(read_losses_file) - assert not os.path.exists(read_preds_file) - assert logger_mock.warning.call_count == 3 - - # it should try to reduce ensemble_nbest until it also failed at 2 - assert ensbuilder.ensemble_nbest == 1 - - ensbuilder.run(time_left=1000, iteration=0, pynisher_context="fork") - assert os.path.exists(read_losses_file) - assert not os.path.exists(read_preds_file) - assert logger_mock.warning.call_count == 4 - - # it should next reduce the number of models to read at most - assert ensbuilder.read_at_most == 1 - - # And then it still runs, but basically won't do anything any more except for - # raising error messages via the logger - ensbuilder.run(time_left=1000, iteration=0, pynisher_context="fork") - assert os.path.exists(read_losses_file) - assert not os.path.exists(read_preds_file) - assert logger_mock.warning.call_count == 4 - - # In the previous assert, reduction is tried until failure - # So that means we should have more than 1 memoryerror message - assert logger_mock.error.call_count >= 1, "{}".format( - logger_mock.error.call_args_list - ) - for i in range(len(logger_mock.error.call_args_list)): - assert "Memory Exception -- Unable to further reduce" in str( - logger_mock.error.call_args_list[i] - ) - - -def test_read_pickle_read_preds(ensemble_backend): - """ - This procedure test that we save the read predictions before - destroying the ensemble builder and that we are able to read - them safely after - """ - ensbuilder = EnsembleBuilder( - backend=ensemble_backend, - dataset_name="TEST", - task_type=MULTILABEL_CLASSIFICATION, # Multilabel Classification - metric=roc_auc, - seed=0, # important to find the test files - ensemble_nbest=2, - max_models_on_disc=None, - ) - ensbuilder.SAVE2DISC = False - - ensbuilder.main(time_left=np.inf, iteration=1, return_predictions=False) - - # Check that the memory was created - ensemble_memory_file = os.path.join( - ensemble_backend.internals_directory, "ensemble_read_preds.pkl" - ) - assert os.path.exists(ensemble_memory_file) - - # Make sure we pickle the correct read preads and hash - with (open(ensemble_memory_file, "rb")) as memory: - read_preds, last_hash = pickle.load(memory) - - compare_read_preds(read_preds, ensbuilder.read_preds) - assert last_hash == ensbuilder.last_hash - - ensemble_memory_file = os.path.join( - ensemble_backend.internals_directory, "ensemble_read_losses.pkl" - ) - assert os.path.exists(ensemble_memory_file) - - # Make sure we pickle the correct read scores - with (open(ensemble_memory_file, "rb")) as memory: - read_losses = pickle.load(memory) - - compare_read_preds(read_losses, ensbuilder.read_losses) - - # Then create a new instance, which should automatically read this file - ensbuilder2 = EnsembleBuilder( - backend=ensemble_backend, - dataset_name="TEST", - task_type=MULTILABEL_CLASSIFICATION, # Multilabel Classification - metric=roc_auc, - seed=0, # important to find the test files - ensemble_nbest=2, - max_models_on_disc=None, - ) - compare_read_preds(ensbuilder2.read_preds, ensbuilder.read_preds) - compare_read_preds(ensbuilder2.read_losses, ensbuilder.read_losses) - assert ensbuilder2.last_hash == ensbuilder.last_hash - - -@pytest.mark.parametrize("metric", [log_loss, accuracy]) -@unittest.mock.patch("os.path.exists") -def test_get_identifiers_from_run_history( - exists, metric, ensemble_run_history, ensemble_backend -): - exists.return_value = True - ensemble = SingleBest( - metric=log_loss, - seed=1, - run_history=ensemble_run_history, - backend=ensemble_backend, - ) - - # Just one model - assert len(ensemble.identifiers_) == 1 - - # That model must be the best - seed, num_run, budget = ensemble.identifiers_[0] - assert num_run == 3 - assert seed == 1 - assert budget == 3.0 - - -def test_ensemble_builder_process_realrun(dask_client_single_worker, ensemble_backend): - manager = EnsembleBuilderManager( - start_time=time.time(), - time_left_for_ensembles=1000, - backend=ensemble_backend, - dataset_name="Test", - task=BINARY_CLASSIFICATION, - metric=MockMetric, - ensemble_size=50, - ensemble_nbest=10, - max_models_on_disc=None, - seed=0, - precision=32, - max_iterations=1, - read_at_most=np.inf, - ensemble_memory_limit=None, - random_state=0, - ) - manager.build_ensemble(dask_client_single_worker) - future = manager.futures.pop() - dask.distributed.wait([future]) # wait for the ensemble process to finish - result = future.result() - history, _, _, _, _ = result - - assert "ensemble_optimization_score" in history[0] - assert history[0]["ensemble_optimization_score"] == 0.9 - assert "ensemble_val_score" in history[0] - assert history[0]["ensemble_val_score"] == 0.9 - assert "ensemble_test_score" in history[0] - assert history[0]["ensemble_test_score"] == 0.9 - - -def test_ensemble_builder_nbest_remembered( - ensemble_backend, - dask_client_single_worker, -): - """ - Makes sure ensemble builder returns the size of the ensemble that pynisher allowed - This way, we can remember it and not waste more time trying big ensemble sizes - """ - - manager = EnsembleBuilderManager( - start_time=time.time(), - time_left_for_ensembles=1000, - backend=ensemble_backend, - dataset_name="Test", - task=MULTILABEL_CLASSIFICATION, - metric=roc_auc, - ensemble_size=50, - ensemble_nbest=10, - max_models_on_disc=None, - seed=0, - precision=32, - read_at_most=np.inf, - ensemble_memory_limit=1000, - random_state=0, - max_iterations=None, - ) - - manager.build_ensemble(dask_client_single_worker, unit_test=True) - future = manager.futures[0] - dask.distributed.wait([future]) # wait for the ensemble process to finish - assert future.result() == ([], 5, None, None, None) - file_path = os.path.join( - ensemble_backend.internals_directory, "ensemble_read_preds.pkl" - ) - assert not os.path.exists(file_path) - - manager.build_ensemble(dask_client_single_worker, unit_test=True) - - future = manager.futures[0] - dask.distributed.wait([future]) # wait for the ensemble process to finish - assert not os.path.exists(file_path) - assert future.result() == ([], 2, None, None, None) diff --git a/test/test_ensemble_builder/test_ensemble_builder.py b/test/test_ensemble_builder/test_ensemble_builder.py new file mode 100644 index 0000000000..7c5e593864 --- /dev/null +++ b/test/test_ensemble_builder/test_ensemble_builder.py @@ -0,0 +1,638 @@ +from __future__ import annotations + +from typing import Callable + +import random +import time +from pathlib import Path + +import numpy as np + +from autosklearn.automl_common.common.utils.backend import Backend +from autosklearn.ensemble_building import EnsembleBuilder, Run +from autosklearn.util.functional import bound, pairs + +import pytest +from pytest_cases import fixture, parametrize +from unittest.mock import patch + +from test.conftest import DEFAULT_SEED + + +@fixture +def builder(make_ensemble_builder: Callable[..., EnsembleBuilder]) -> EnsembleBuilder: + """A default ensemble builder""" + return make_ensemble_builder() + + +@parametrize("kind", ["ensemble", "test"]) +def test_targets(builder: EnsembleBuilder, kind: str) -> None: + """ + Expects + ------- + * Should be able to load each of the targets + """ + assert builder.targets(kind) is not None + + +def test_available_runs(builder: EnsembleBuilder) -> None: + """ + Expects + ------- + * Should be able to read runs from the backends rundir where runs are tagged + {seed}_{numrun}_{budget} + """ + runsdir = Path(builder.backend.get_runs_directory()) + + ids = {(0, i, 0.0) for i in range(1, 10)} + paths = [runsdir / f"{s}_{n}_{b}" for s, n, b in ids] + + for path in paths: + path.mkdir() + + available_runs = builder.available_runs() + + assert len(available_runs) == len(ids) + for run_id in available_runs.keys(): + assert run_id in ids + + +def test_available_runs_with_bad_dir_contained(builder: EnsembleBuilder) -> None: + """ + Expects + ------- + * Should ignore dirs that aren't in format + """ + runsdir = Path(builder.backend.get_runs_directory()) + + ids = {(0, i, 0.0) for i in range(1, 10)} + paths = [runsdir / f"{s}_{n}_{b}" for s, n, b in ids] + + bad_path = runsdir / "Im_a_bad_path" + + for path in paths + [bad_path]: + path.mkdir() + + available_runs = builder.available_runs() + assert len(available_runs) == len(paths) + + +def test_requires_loss_update_with_modified_runs( + builder: EnsembleBuilder, + make_run: Callable[..., Run], +) -> None: + """ + Expects + ------- + * Should include runs that were modified, even if they have a loss + """ + run_okay = [make_run(loss=1) for _ in range(5)] + run_modified = [make_run(loss=1, modified=True) for _ in range(5)] + + runs = run_okay + run_modified + + requires_update = builder.requires_loss_update(runs) + + assert set(run_modified) == set(requires_update) + + +def test_requires_loss_update_with_no_loss( + builder: EnsembleBuilder, + make_run: Callable[..., Run], +) -> None: + """ + Expects + ------- + * Should include runs that have no loss + """ + run_okay = [make_run(loss=10) for _ in range(5)] + run_no_loss = [make_run() for _ in range(5)] + + runs = run_okay + run_no_loss + + requires_update = builder.requires_loss_update(runs) + + assert set(run_no_loss) == set(requires_update) + + +def test_candidates_no_filters( + builder: EnsembleBuilder, make_run: Callable[..., Run] +) -> None: + """ + Expects + ------- + * Should have nothing in common between candidates and discarded + * Should not filter out any viable runs if no filters set. Here a viable run + has a loss and ensemble predictions + """ + dummy = make_run(dummy=True) + runs = [make_run(loss=n) for n in range(10)] + + candidates, discarded = builder.candidate_selection( + runs, + dummy, + better_than_dummy=False, + nbest=None, + performance_range_threshold=None, + ) + + assert len(set(candidates) & discarded) == 0 + assert len(candidates) == len(runs) + assert len(discarded) == 0 + + +def test_candidates_filters_runs_with_no_predictions( + builder: EnsembleBuilder, make_run: Callable[..., Run] +) -> None: + """ + Expects + ------- + * Should have nothing in common between candidates and discarded + * Should filter out runs with no "ensemble" predictions + """ + bad_runs = [make_run(predictions=None) for _ in range(5)] + dummy = make_run(dummy=True, loss=2) + good_run = make_run(predictions="ensemble", loss=1) + + runs = bad_runs + [good_run] + + candidates, discarded = builder.candidate_selection(runs, dummy) + + assert len(set(candidates) & discarded) == 0 + assert len(candidates) == 1 + assert len(discarded) == len(bad_runs) + assert candidates[0].pred_path("ensemble").exists() + + +def test_candidates_filters_runs_with_no_loss( + builder: EnsembleBuilder, make_run: Callable[..., Run] +) -> None: + """ + Expects + ------- + * Should filter out runs with no loss + """ + bad_runs = [make_run(loss=None) for _ in range(5)] + dummy_run = make_run(dummy=True, loss=2) + good_run = make_run(loss=1) + + runs = bad_runs + [good_run] + + candidates, discarded = builder.candidate_selection(runs, dummy_run) + + assert len(candidates) == 1 + assert len(discarded) == len(bad_runs) + assert candidates[0].loss == 1 + + +def test_candidates_filters_out_better_than_dummy( + builder: EnsembleBuilder, + make_run: Callable[..., Run], +) -> None: + """ + Expects + ------- + * Should filter out runs worse than dummy + """ + bad_runs = [make_run(loss=1) for _ in range(2)] + dummy_run = make_run(dummy=True, loss=0) + good_runs = [make_run(loss=-1) for _ in range(3)] + + runs = bad_runs + good_runs + + candidates, discarded = builder.candidate_selection( + runs, dummy_run, better_than_dummy=True + ) + + assert set(candidates) + + assert len(candidates) == 3 + assert all(run.loss < dummy_run.loss for run in candidates) + + assert len(discarded) == 2 + assert all(run.loss >= dummy_run.loss for run in discarded) + + +def test_candidates_uses_dummy_if_no_candidates_better( + builder: EnsembleBuilder, + make_run: Callable[..., Run], +) -> None: + """ + Expects + ------- + * If no run is better than a dummy run, the candidates will then consist + of the dummy runs. + """ + runs = [make_run(loss=10) for _ in range(10)] + dummies = [make_run(dummy=True, loss=0) for _ in range(2)] + + candidates, discarded = builder.candidate_selection( + runs, + dummies, + better_than_dummy=True, + ) + + assert len(candidates) == 2 + assert all(run.is_dummy() for run in candidates) + + +@parametrize("nbest", [0, 1, 5, 1000]) +def test_candidates_nbest_int( + builder: EnsembleBuilder, + make_run: Callable[..., Run], + nbest: int, +) -> None: + """ + Expects + ------- + * Should only select the nbest candidates + * They should be ordered by loss + """ + n = 10 + expected = int(bound(nbest, bounds=(1, n))) + + dummy = make_run(dummy=True) + runs = [make_run(loss=i) for i in range(n)] + candidates, discarded = builder.candidate_selection(runs, dummy, nbest=nbest) + + assert len(candidates) == expected + + if len(candidates) > 1: + assert all(a.loss <= b.loss for a, b in pairs(candidates)) + + if any(discarded): + worst_candidate = candidates[-1] + assert all(worst_candidate.loss <= d.loss for d in discarded) + + +@parametrize("nbest", [0.0, 0.25, 0.5, 1.0]) +def test_candidates_nbest_float( + builder: EnsembleBuilder, + make_run: Callable[..., Run], + nbest: float, +) -> None: + """ + Expects + ------- + * Should select nbest percentage of candidates + * They should be ordered by loss + """ + n = 10 + expected = int(bound(nbest * n, bounds=(1, n))) + + dummy = make_run(dummy=True, loss=0) + runs = [make_run(id=i, loss=i) for i in range(2, n + 2)] + candidates, discarded = builder.candidate_selection(runs, dummy, nbest=nbest) + + assert len(candidates) == expected + + if len(candidates) > 1: + assert all(a.loss <= b.loss for a, b in pairs(candidates)) + + if any(discarded): + worst_candidate = candidates[-1] + assert all(worst_candidate.loss <= d.loss for d in discarded) + + +@parametrize("threshold", [0.0, 0.25, 0.5, 1.0]) +def test_candidates_performance_range_threshold( + builder: EnsembleBuilder, + make_run: Callable[..., Run], + threshold: float, +) -> None: + """ + Expects + ------- + * Should select runs that are `threshold` between the dummy loss and the best loss + This value is captured in `boundary`. + """ + worst_loss = 100 + best_loss = 0 + dummy_loss = 50 + + boundary = threshold * best_loss + (1 - threshold) * dummy_loss + + dummy = make_run(dummy=True, loss=dummy_loss) + runs = [make_run(loss=loss) for loss in np.linspace(best_loss, worst_loss, 101)] + + candidates, discarded = builder.candidate_selection( + runs, + dummy, + performance_range_threshold=threshold, + ) + + assert len(candidates) > 0 + + # When no run is better than threshold, we just get 1 candidate, + # Make sure it's the best + if len(candidates) == 1: + assert all(r.loss >= candidates[0].loss for r in discarded) + + else: + for run in candidates: + assert run.loss < boundary + + for run in discarded: + assert run.loss >= boundary + + +def test_requires_deletion_does_nothing_without_params( + builder: EnsembleBuilder, + make_run: Callable[..., Run], +) -> None: + """ + Expects + ------- + * All runs should be kept + """ + runs = [make_run() for _ in range(10)] + + keep, delete = builder.requires_deletion( + runs, + max_models=None, + memory_limit=None, + ) + + assert set(runs) == set(keep) + assert len(delete) == 0 + + +@parametrize("max_models", [0, 1, 2, 5]) +def test_requires_deletion_max_models( + builder: EnsembleBuilder, + max_models: int, + make_run: Callable[..., Run], +) -> None: + """ + Expects + ------- + * Should keep exactly as many models as `max_models` + * Should not have any in common between keep and delete + """ + runs = [make_run() for _ in range(10)] + keep, delete = builder.requires_deletion(runs=runs, max_models=max_models) + + assert len(keep) == max_models + assert len(delete) == len(runs) - max_models + + assert not any(set(keep) & set(delete)) + + +@parametrize("memory_limit, expected", [(0, 0), (100, 0), (200, 1), (5000, 49)]) +def test_requires_memory_limit( + builder: EnsembleBuilder, + make_run: Callable[..., Run], + memory_limit: int, + expected: int, +) -> None: + """ + Expects + ------- + * Should keep the expected amount of models + * The kept models should be sorted by lowest loss + * Should not have any models in common between keep and delete + * All models kept should be better than those deleted + """ + runs = [make_run(mem_usage=100, loss=-n) for n in range(50)] + random.shuffle(runs) + + keep, delete = builder.requires_deletion(runs=runs, memory_limit=memory_limit) + + # The cutoff for memory is (memory_limit - largest) + # E.g. + # 5 models at 100 ea = 500mb usage + # largest = 100mb + # memory_limit = 400mb + # cutoff = memory_limit - largest (400mb - 100mb) = 300mb + # We can store 300mb which means the 3 best models + assert len(keep) == expected + assert len(delete) == len(runs) - expected + + assert not any(set(keep) & set(delete)) + + if len(keep) > 2: + assert all(a.loss <= b.loss for a, b in pairs(keep)) + + best_deleted = min(r.loss for r in delete) + assert not any(run.loss > best_deleted for run in keep) + + +@parametrize("kind", ["ensemble", "test"]) +def test_loss_with_no_ensemble_targets( + builder: EnsembleBuilder, + make_run: Callable[..., Run], + kind: str, +) -> None: + """ + Expects + ------- + * Should give a loss of np.inf if run has no predictions of a given kind + """ + run = make_run(predictions=None) + + assert builder.loss(run, kind=kind) == np.inf + + +@parametrize("kind", ["ensemble", "test"]) +def test_loss_with_targets( + builder: EnsembleBuilder, + make_run: Callable[..., Run], + kind: str, +) -> None: + """ + Expects + ------- + * Should give a loss < np.inf if the predictions exist + """ + targets = builder.targets(kind) + + run = make_run(predictions={kind: targets}) + + assert builder.loss(run, kind) < np.inf + + +def test_delete_runs(builder: EnsembleBuilder, make_run: Callable[..., Run]) -> None: + """ + Expects + ------- + * Should delete runs so they can not be found again by the ensemble builder + """ + runs = [make_run(backend=builder.backend) for _ in range(5)] + assert all(run.dir.exists() for run in runs) + + builder.delete_runs(runs) + assert not any(run.dir.exists() for run in runs) + + loaded = builder.available_runs() + assert len(loaded) == 0 + + +def test_delete_runs_does_not_delete_dummy( + builder: EnsembleBuilder, + make_run: Callable[..., Run], +) -> None: + """ + Expects + ------- + * Should + """ + backend = builder.backend + normal_runs = [make_run(backend=backend) for _ in range(5)] + dummy_runs = [make_run(dummy=True, seed=i, backend=backend) for i in range(2)] + + runs = normal_runs + dummy_runs + assert all(run.dir.exists() for run in runs) + + builder.delete_runs(runs) + assert not any(run.dir.exists() for run in normal_runs) + assert all(dummy.dir.exists() for dummy in dummy_runs) + + loaded = builder.available_runs() + assert set(loaded.values()) == set(dummy_runs) + + +def test_fit_ensemble_produces_ensemble( + builder: EnsembleBuilder, + make_run: Callable[..., Run], +) -> None: + """ + Expects + ------- + * Should produce an ensemble if all runs have predictions + """ + targets = builder.targets("ensemble") + assert targets is not None + + predictions = targets + runs = [make_run(predictions={"ensemble": predictions}) for _ in range(10)] + + ensemble = builder.fit_ensemble(runs, targets) + + assert ensemble is not None + + +def test_fit_with_error_gives_no_ensemble( + builder: EnsembleBuilder, + make_run: Callable[..., Run], +) -> None: + """ + Expects + ------- + * A run without predictions will raise an error will cause `fit_ensemble` to fail + as it requires all runs to have valid predictions + """ + targets = builder.targets("ensemble") + assert targets is not None + + predictions = targets + + runs = [make_run(predictions={"ensemble": predictions}) for _ in range(10)] + bad_run = make_run(predictions=None) + + runs.append(bad_run) + + with pytest.raises(FileNotFoundError): + builder.fit_ensemble(runs, targets) + + +@parametrize("time_buffer", [1, 5]) +@parametrize("duration", [10, 20]) +def test_run_end_at(builder: EnsembleBuilder, time_buffer: int, duration: int) -> None: + """ + Expects + ------- + * The limits enforced by pynisher should account for the time_buffer and duration + to run for + a little bit of overhead that gets rounded to a second. + """ + with patch("pynisher.enforce_limits") as pynisher_mock: + builder.run( + end_at=time.time() + duration, + iteration=1, + time_buffer=time_buffer, + pynisher_context="forkserver", + ) + # The 1 comes from the small overhead in conjuction with rounding down + expected = duration - time_buffer - 1 + + # The 1 comes from the small overhead in conjuction with rounding down + expected = duration - time_buffer - 1 + assert pynisher_mock.call_args_list[0][1]["wall_time_in_s"] == expected + + +def test_deletion_will_not_break_current_ensemble( + make_backend: Callable[..., Backend], + make_ensemble_builder: Callable[..., EnsembleBuilder], + make_run: Callable[..., Run], +) -> None: + """ + Expects + ------- + * When running the builder, it's previous ensemble should not have it's runs deleted + until a new ensemble is built. + """ + # Make a builder with this backend and limit it to only allow 10 models on disc + builder = make_ensemble_builder( + max_models_on_disc=10, + seed=DEFAULT_SEED, + ) + + # Stick a dummy run and 10 bad runs into the backend + datamanager = builder.backend.load_datamanager() + targets = datamanager.data["Y_train"] + + bad_predictions = {"ensemble": np.zeros_like(targets)} + good_predictions = {"ensemble": targets} + + make_run(dummy=True, loss=10000, backend=builder.backend) + bad_runs = [ + make_run(backend=builder.backend, predictions=bad_predictions) + for _ in range(10) + ] + + ens_dir = Path(builder.backend.get_ensemble_dir()) + + # Make sure there's no ensemble and run with the candidates available + assert not ens_dir.exists() + builder.main(time_left=100) + + # Make sure an ensemble was built + assert ens_dir.exists() + first_builder_contents = set(ens_dir.iterdir()) + + # Create 10 new and better runs and put them in the backend + new_runs = [ + make_run(backend=builder.backend, predictions=good_predictions) + for _ in range(10) + ] + + # Now we make `save_ensemble` crash so that even though we run the builder, it does + # not manage to save the new ensemble + with patch.object(builder.backend, "save_ensemble", side_effect=ValueError): + try: + builder.main(time_left=100) + except Exception: + pass + + # Ensure that no new ensemble was created + second_builder_contents = set(ens_dir.iterdir()) + assert first_builder_contents == second_builder_contents + + # Now we make sure that the ensemble there still has access to all the bad models + # that it contained from the first run, even though the second crashed. + available_runs = builder.available_runs().values() + for run in bad_runs + new_runs: + assert run in available_runs + + # As a sanity check, run the builder one more time without crashing and make + # sure the bad runs are removed with the good ones kept. + # We remove its previous candidates so that it won't remember previous candidates + # and will fit a new ensemble + builder.previous_candidates_path.unlink() + builder.main(time_left=100) + available_runs = builder.available_runs().values() + + for run in bad_runs: + assert run not in available_runs + + for run in new_runs: + assert run in available_runs diff --git a/test/test_ensemble_builder/test_ensemble_builder_real.py b/test/test_ensemble_builder/test_ensemble_builder_real.py new file mode 100644 index 0000000000..fdad0a45c9 --- /dev/null +++ b/test/test_ensemble_builder/test_ensemble_builder_real.py @@ -0,0 +1,98 @@ +""" +This file tests the ensemble builder with real runs generated from running AutoML +""" +from __future__ import annotations + +from typing import Callable + +from autosklearn.automl import AutoML +from autosklearn.ensemble_building.builder import EnsembleBuilder + +from pytest_cases import parametrize_with_cases +from pytest_cases.filters import has_tag +from unittest.mock import MagicMock, patch + +import test.test_automl.cases as cases + + +@parametrize_with_cases( + "automl", + cases=cases, + filter=has_tag("fitted") & ~has_tag("no_ensemble"), +) +def case_real_runs( + automl: AutoML, + make_ensemble_builder: Callable[..., EnsembleBuilder], +) -> EnsembleBuilder: + """Uses real runs from a fitted automl instance which have an ensemble + + This will copy the ensemble builder based on the AutoML instance parameterss. This + includes ensemble_nbest, ensemble_size, etc... + """ + builder = make_ensemble_builder(automl=automl) + return builder + + +@parametrize_with_cases("builder", cases=case_real_runs) +def test_run_builds_valid_ensemble(builder: EnsembleBuilder) -> None: + """ + Expects + ------- + * Using the same builder as used in the real run should result in the same + candidate models for the ensemble. + * Check that there is no overlap between candidate models and those deleted + * The generated ensemble should not be empty + * If any deleted, should be no overlap with those deleted and those in ensemble + * If any deleted, they should all be worse than those in the ensemble + """ + # We need to clear previous candidates so the ensemble builder is presented with + # only "new" runs and has no information of previous candidates + if builder.previous_candidates_path.exists(): + builder.previous_candidates_path.unlink() + + # So we can capture the saved ensemble + mock_save = MagicMock() + builder.backend.save_ensemble = mock_save # type: ignore + + # So we can capture what was deleted + mock_delete = MagicMock() + builder.delete_runs = mock_delete # type: ignore + + # So we can capture the candidate runs used, we still wrap the actual fitting + with patch.object(builder, "fit_ensemble", wraps=builder.fit_ensemble) as mock_fit: + history, nbest = builder.main() + + # Check the ensemble was fitted once + mock_save.assert_called_once() + _, kwargs = mock_save.call_args + ens = kwargs["ensemble"] # `backend.save_ensemble(ens, ...)` + ensemble_ids = set(ens.get_selected_model_identifiers()) + assert len(ensemble_ids) > 0 + + # Check that the ids of runs in the ensemble were all candidates + candidates = mock_fit.call_args[0][0] # `fit_ensemble(candidates, ...)` + candidate_ids = {run.id for run in candidates} + assert ensemble_ids <= candidate_ids + + # Could be the case no run is deleted + if not mock_delete.called: + return + + args, _ = mock_delete.call_args + deleted = args[0] # `delete_runs(runs)` + + # If we deleted runs, we better make sure of a few things + if len(deleted) > 0: + deleted_ids = {run.id for run in deleted} + + # Make sure theres no overlap between candidates/ensemble and those deleted + assert not any(deleted_ids & candidate_ids) + assert not any(deleted_ids & ensemble_ids) + + # Make sure that the best deleted model is better than the worst candidate + best_deleted = min(deleted, key=lambda r: (r.loss, r.num_run)) + worst_candidate = max(candidates, key=lambda r: (r.loss, r.num_run)) + + a = (worst_candidate.loss, worst_candidate.num_run) + b = (best_deleted.loss, best_deleted.num_run) + assert a <= b diff --git a/test/test_ensemble_builder/test_manager.py b/test/test_ensemble_builder/test_manager.py new file mode 100644 index 0000000000..6e4039ca86 --- /dev/null +++ b/test/test_ensemble_builder/test_manager.py @@ -0,0 +1,29 @@ +from __future__ import annotations + +from typing import Callable + +from autosklearn.automl import AutoML +from autosklearn.ensemble_building import EnsembleBuilderManager + +from pytest_cases import parametrize_with_cases + +import test.test_automl.cases as cases +from test.conftest import DEFAULT_SEED + + +@parametrize_with_cases("automl", cases=cases, has_tag="fitted") +def case_real_runs( + automl: AutoML, + make_ensemble_builder_manager: Callable[..., EnsembleBuilderManager], +) -> EnsembleBuilderManager: + """Uses real runs from a fitted automl instance""" + manager = make_ensemble_builder_manager( + backend=automl._backend, + metric=automl._metrics[0], + task=automl._task, + dataset_name=automl._dataset_name, + seed=automl._seed, + logger_port=automl._logger_port, + random_state=DEFAULT_SEED, + ) + return manager diff --git a/test/test_ensemble_builder/test_run.py b/test/test_ensemble_builder/test_run.py new file mode 100644 index 0000000000..5c494208ca --- /dev/null +++ b/test/test_ensemble_builder/test_run.py @@ -0,0 +1,169 @@ +from __future__ import annotations + +from typing import Callable + +import pickle +import time +from pathlib import Path + +import numpy as np + +from autosklearn.ensemble_building import Run + +from pytest_cases import parametrize + + +def test_is_dummy(make_run: Callable[..., Run]) -> None: + """ + Expects + ------- + * We expect runs with an num_run (id) of 1 to be a dummy + """ + run = make_run(id=1) + assert run.is_dummy() + + run = make_run(id=2) + assert not run.is_dummy() + + +def test_was_modified(make_run: Callable[..., Run]) -> None: + """ + Expects + ------- + * Should properly indicate when a file was modified + """ + run = make_run() + assert not run.was_modified() + + time.sleep(0.2) # Just to give some time after creation + path = run.pred_path("ensemble") + path.touch() + + assert run.was_modified() + + +def test_record_modified_times_with_was_modified(make_run: Callable[..., Run]) -> None: + """ + Expects + ------- + * Updating the recorded times should not trigger `was_modified` + * Should update the recorded times so `was_modified` will give False after being + updated + """ + run = make_run() + path = run.pred_path("ensemble") + + time.sleep(0.2) + run.record_modified_times() + assert not run.was_modified() + + time.sleep(0.2) + path.touch() + assert run.was_modified() + + time.sleep(0.2) + run.record_modified_times() + assert not run.was_modified() + + +def test_predictions_pickled(make_run: Callable[..., Run]) -> None: + """ + Expects + ------- + * Should be able to load pickled predictions + + Note + ---- + * Not sure this should be supported + """ + run = make_run(predictions=[]) + x = np.array([0]) + + path = run.pred_path("ensemble") + with path.open("wb") as f: + pickle.dump(x, f) + + assert run.predictions("ensemble") is not None + + +@parametrize( + "precision, expected", [(16, np.float16), (32, np.float32), (64, np.float64)] +) +def test_predictions_precision( + make_run: Callable[..., Run], precision: int, expected: type +) -> None: + """ + Expects + ------- + * Loading predictions with a given precision should load the expected type + """ + run = make_run() + assert run.predictions(precision=precision).dtype == expected + + +def test_caching(make_run: Callable[..., Run]) -> None: + """ + Expects + ------- + * Attempting to load the same predictions again will cause the result to be cached + * Unloading the cache will cause it to reload and reread the predictions + """ + run = make_run() + + assert len(run._cache) == 0 + first_load = run.predictions() # Should cache result + assert len(run._cache) == 1 + + cache_load = run.predictions() # Should use cache result + assert len(run._cache) == 1 + + # The should be the same object + assert id(first_load) == id(cache_load) + + pickled_run = pickle.dumps(run) + unpickled_run = pickle.loads(pickled_run) + + assert len(unpickled_run._cache) == 0 + unpickled_load = unpickled_run.predictions() # Should have reloaded it + assert len(unpickled_run._cache) == 1 + + # Should not be the same object as before once pickled + assert id(unpickled_load) != id(first_load) + + +def test_equality(make_run: Callable[..., Run]) -> None: + """ + Expects + ------- + * Two runs with the same id's should be considered equal + * Otherwise, they should be considered different + """ + r1 = make_run(id=1, budget=49.3, seed=3) + r2 = make_run(id=1, budget=49.3, seed=3) + + assert r1 == r2 + + r3 = make_run(id=1, budget=0.0, seed=3) + + assert r1 != r3 + assert r2 != r3 + + +@parametrize( + "name, expected", + [ + ("0_0_0.0", True), + ("1_152_64.24", True), + ("123412_3462_100.0", True), + ("tmp_sf12198", False), + ("tmp_0_0_0.0", False), + ], +) +def test_valid(name: str, expected: bool) -> None: + """ + Expects + ------- + * Should be able to correctly consider valid run dir names + """ + path = Path(name) + assert Run.valid(path) == expected diff --git a/test/test_estimators/test_estimators.py b/test/test_estimators/test_estimators.py index 4dd13d4c17..8e3134e3a3 100644 --- a/test/test_estimators/test_estimators.py +++ b/test/test_estimators/test_estimators.py @@ -28,7 +28,7 @@ import autosklearn.pipeline.util as putil from autosklearn.automl import AutoMLClassifier from autosklearn.data.validation import InputValidator -from autosklearn.ensemble_builder import MODEL_FN_RE +from autosklearn.ensemble_building.run import Run from autosklearn.estimators import ( AutoSklearnClassifier, AutoSklearnEstimator, @@ -60,6 +60,8 @@ def test_fit_n_jobs(tmp_dir): Y_train += 1 Y_test += 1 + n_jobs = 2 + class get_smac_object_wrapper: def __call__(self, *args, **kwargs): self.n_jobs = kwargs["n_jobs"] @@ -78,49 +80,52 @@ def __call__(self, *args, **kwargs): per_run_time_limit=5, tmp_folder=os.path.join(tmp_dir, "backend"), seed=1, - initial_configurations_via_metalearning=0, - ensemble_size=5, - n_jobs=2, - include={"classifier": ["sgd"], "feature_preprocessor": ["no_preprocessing"]}, + n_jobs=n_jobs, get_smac_object_callback=get_smac_object_wrapper_instance, max_models_on_disc=None, ) - automl.fit(X_train, Y_train) # Test that the argument is correctly passed to SMAC - assert getattr(get_smac_object_wrapper_instance, "n_jobs") == 2 - assert getattr(get_smac_object_wrapper_instance, "dask_n_jobs") == 2 - assert getattr(get_smac_object_wrapper_instance, "dask_client_n_jobs") == 2 + assert get_smac_object_wrapper_instance.n_jobs == n_jobs + assert get_smac_object_wrapper_instance.dask_n_jobs == n_jobs + assert get_smac_object_wrapper_instance.dask_client_n_jobs == n_jobs - available_num_runs = set() - for run_key, run_value in automl.automl_.runhistory_.data.items(): + runhistory_data = automl.automl_.runhistory_.data + + successful_runs = { + run_value.additional_info["num_run"] + for run_value in runhistory_data.values() if ( run_value.additional_info is not None and "num_run" in run_value.additional_info - ): - available_num_runs.add(run_value.additional_info["num_run"]) + and run_value.status == StatusType.SUCCESS + ) + } + available_predictions = set() predictions = glob.glob( os.path.join( - automl.automl_._backend.get_runs_directory(), - "*", - "predictions_ensemble*.npy", + automl.automl_._backend.get_runs_directory(), "*", "predictions_ensemble_*" ) ) seeds = set() for prediction in predictions: prediction = os.path.split(prediction)[1] - match = re.match(MODEL_FN_RE, prediction.replace("predictions_ensemble", "")) + match = re.match(Run.RE_MODEL_PREDICTION_FILE, prediction) if match: - num_run = int(match.group(2)) - available_predictions.add(num_run) - seed = int(match.group(1)) - seeds.add(seed) + print(match) + seed, num_run, _ = match.groups() + available_predictions.add(int(num_run)) + seeds.add(int(seed)) # Remove the dummy prediction, it is not part of the runhistory - available_predictions.remove(1) - assert available_num_runs.issubset(available_predictions) + if 1 in available_predictions: + available_predictions.remove(1) + + # Make sure all predictions available are associated with a successful run + # Don't want a rogue prediction file + assert available_predictions <= successful_runs assert len(seeds) == 1 diff --git a/test/test_util/test_trials_callback.py b/test/test_util/test_trials_callback.py index b1328b9489..e20f0abc40 100644 --- a/test/test_util/test_trials_callback.py +++ b/test/test_util/test_trials_callback.py @@ -62,7 +62,6 @@ def test_trials_callback_execution(self): time_left_for_this_task=30, initial_configurations_via_metalearning=0, per_run_time_limit=10, - memory_limit=1024, delete_tmp_folder_after_terminate=False, n_jobs=1, include={"feature_preprocessor": ["pca"], "classifier": ["sgd"]}, From 510c931f6675307907ef190c351dedfc18925eee Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Wed, 18 May 2022 22:23:25 +0200 Subject: [PATCH 053/118] Load single best model as fallback (#1479) * Load single best model as fallback * Update estimators.py * Improve comment in code. * Fix meta-data generation test, potentially improve model loading --- autosklearn/automl.py | 68 ++++++++++++++----------------- autosklearn/estimators.py | 6 +-- test/test_automl/test_post_fit.py | 9 ++-- 3 files changed, 38 insertions(+), 45 deletions(-) diff --git a/autosklearn/automl.py b/autosklearn/automl.py index d8c624d06a..94d8f489d1 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -1396,6 +1396,10 @@ def predict(self, X, batch_size=None, n_jobs=1): "Predict is currently not implemented for resampling " f"strategy {self._resampling_strategy}, please call refit()." ) + elif self._disable_evaluator_output is not False: + raise NotImplementedError( + "Predict cannot be called when evaluator output is disabled." + ) if self.models_ is None or len(self.models_) == 0 or self.ensemble_ is None: self._load_models() @@ -1553,11 +1557,31 @@ def _load_models(self): else: self.ensemble_ = None - # If no ensemble is loaded, try to get the best performing model - if not self.ensemble_: + # If no ensemble is loaded, try to get the best performing model. + # This is triggered if + # 1. self._ensemble_size == 0 (see if-statement above) + # 2. if the ensemble builder crashed and no ensemble is available + # 3. if the ensemble cannot be built because of arguments passed + # by the user (disable_evaluator_output and + # resampling_strategy) + if ( + not self.ensemble_ + and not ( + self._disable_evaluator_output is True + or ( + isinstance(self._disable_evaluator_output, list) + and "model" in self._disable_evaluator_output + ) + ) + and self._resampling_strategy + not in ( + "partial-cv", + "partial-cv-iterative-fit", + ) + ): self.ensemble_ = self._load_best_individual_model() - if self.ensemble_: + if self.ensemble_ is not None: identifiers = self.ensemble_.get_selected_model_identifiers() self.models_ = self._backend.load_models_by_identifiers(identifiers) @@ -1567,39 +1591,9 @@ def _load_models(self): ) else: self.cv_models_ = None - - if len(self.models_) == 0 and self._resampling_strategy not in [ - "partial-cv", - "partial-cv-iterative-fit", - ]: - raise ValueError("No models fitted!") - - if ( - self._resampling_strategy in ["cv", "cv-iterative-fit"] - and len(self.cv_models_) == 0 - ): - raise ValueError("No models fitted!") - - elif self._disable_evaluator_output is False or ( - isinstance(self._disable_evaluator_output, list) - and "model" not in self._disable_evaluator_output - ): - model_names = self._backend.list_all_models(self._seed) - - if len(model_names) == 0 and self._resampling_strategy not in [ - "partial-cv", - "partial-cv-iterative-fit", - ]: - raise ValueError("No models fitted!") - - self.ensemble_ = None - self.models_ = [] - self.cv_models_ = None - else: - self.ensemble_ = None self.models_ = [] - self.cv_models_ = None + self.cv_models_ = [] def _load_best_individual_model(self): """ @@ -1608,11 +1602,11 @@ def _load_best_individual_model(self): by AutoML. This is a robust mechanism to be able to predict, even though no ensemble was found by ensemble builder. + It is also used to load the single best model in case + the user does not want to build an ensemble. """ # We also require that the model is fit and a task is defined - # The ensemble size must also be greater than 1, else it means - # that the user intentionally does not want an ensemble - if not self._task or self._ensemble_size < 1: + if not self._task: return None # SingleBest contains the best model found by AutoML diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 1c283e06e6..5e891b0a8e 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -79,7 +79,8 @@ def __init__( ensemble_size : int, optional (default=50) Number of models added to the ensemble built by *Ensemble selection from libraries of models*. Models are drawn with - replacement. If set to ``0`` no ensemble is fit. + replacement. If set to ``0`` no ensemble is fit and the single + best model is loaded. ensemble_nbest : int, optional (default=50) Only consider the ``ensemble_nbest`` models when building an @@ -526,9 +527,6 @@ def fit_ensemble( All parameters are ``None`` by default. If no other value is given, the default values which were set in a call to ``fit()`` are used. - Calling this function is only necessary if ``ensemble_size==0``, for - example when executing *auto-sklearn* in parallel. - Parameters ---------- y : array-like diff --git a/test/test_automl/test_post_fit.py b/test/test_automl/test_post_fit.py index 7cc6dafb6e..ed7882d7b4 100644 --- a/test/test_automl/test_post_fit.py +++ b/test/test_automl/test_post_fit.py @@ -1,4 +1,5 @@ from autosklearn.automl import AutoML +from autosklearn.ensembles.singlebest_ensemble import SingleBest from pytest_cases import parametrize_with_cases @@ -59,10 +60,10 @@ def test_no_ensemble(automl: AutoML) -> None: Expects ------- - * The ensemble should remain None - * The models_ should be empty + * Auto-sklearn loads a single best model + * The models_ should be of size 1 * The cv_models_ should remain None """ - assert automl.ensemble_ is None - assert automl.models_ == [] + assert isinstance(automl.ensemble_, SingleBest) + assert len(automl.models_) == 1 assert automl.cv_models_ is None From a8a3cae5e9803a6916ed253feca40778b9ef4509 Mon Sep 17 00:00:00 2001 From: Katharina Eggensperger Date: Tue, 24 May 2022 13:50:09 +0200 Subject: [PATCH 054/118] Extend Scorer (#1475) * FIX minor * update submodule * update submodule * ADD pass xdata to metric * update submodule * Fix tests * update submodule * ADD example * UPDATE example * ADD extra method to concat data * RM note * Fix minor * ADD more types for xdata * FIX unittests * FIX variable naming bug * change varible name; fix docstring * Rename variable * FIX example * Update examples/40_advanced/example_metrics.py Co-authored-by: Matthias Feurer Co-authored-by: Matthias Feurer --- CONTRIBUTING.md | 23 ++- autosklearn/automl_common | 2 +- autosklearn/data/target_validator.py | 1 + autosklearn/evaluation/abstract_evaluator.py | 16 +- autosklearn/evaluation/train_evaluator.py | 93 ++++++++---- autosklearn/metrics/__init__.py | 152 +++++++++++++++---- examples/40_advanced/example_metrics.py | 47 ++++++ test/fixtures/ensemble_building.py | 4 +- test/test_estimators/test_estimators.py | 4 +- test/test_evaluation/test_evaluation.py | 8 +- test/test_evaluation/test_train_evaluator.py | 14 +- test/test_metric/test_metrics.py | 32 +++- 12 files changed, 308 insertions(+), 88 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 19aaca371e..73ce781618 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -61,7 +61,7 @@ Following that we'll tell you about how you can test your changes locally and th # If you missed the --recurse-submodules arg during clone or need to install the # submodule manually, then execute the following line: # - # git submodule udate --init --recursive + # git submodule update --init --recursive ``` The reason to create a new branch is two fold: @@ -207,16 +207,16 @@ Sometimes, the new functionality isn't so clear from a simple parameter descript Lastly, if the feature really is a game changer or you're very proud of it, consider making an `example_*.py` that will be run and rendered in the online docs! ## Testing -* Let's assume you've made some changes, now we have to make sure they work. +* Let's assume you've made some changes, now we have to make sure they work. Begin by simply running all the tests. If there's any errors, they'll pop up once it's complete. ```bash pytest ``` - * Note that these may take a while so check out `pytest --help` to see how you can run tests so that only previous failures run or only certain tests are run. + * Note that these may take a while so check out `pytest --help` to see how you can run tests so that only previous failures run or only certain tests are run. This can help you try changes and get results faster. Do however run one last full `pytest` once you are finished and happy! - * Here are some we find particularly useful + * Here are some we find particularly useful ``` # Run tests in specific file like 'test_estimators.py' pytest "test/test_automl/test_estimators.py" @@ -236,9 +236,18 @@ Lastly, if the feature really is a game changer or you're very proud of it, cons # Exit on the first test failure pytest -x ``` - * More advanced editors like PyCharm may have built in integrations which could be good to check out! + * More advanced editors like PyCharm may have built in integrations which could be good to check out! + * Running all unittests will take a while, here's how you can run them in parallel + ``` + export OPENBLAS_NUM_THREADS=1 + export MKL_NUM_THREADS=1 + export OMP_NUM_THREADS=1 + + pytest -n 4 + ``` + -* Now we are going to use [sphinx](https://www.sphinx-doc.org/en/master/) to generate all the documentation and make sure there are no issues. +* Now we are going to use [sphinx](https://www.sphinx-doc.org/en/master/) to generate all the documentation and make sure there are no issues. ```bash make doc ``` @@ -261,7 +270,7 @@ Lastly, if the feature really is a game changer or you're very proud of it, cons xdg-open ./doc/build/html/index.html ``` -* Once you've made all your changes and all the tests pass successfully, we need to make sure that the code fits a certain format and that the [typing](https://docs.python.org/3/library/typing.html) is correct. +* Once you've made all your changes and all the tests pass successfully, we need to make sure that the code fits a certain format and that the [typing](https://docs.python.org/3/library/typing.html) is correct. * Formatting and import sorting can helps keep things uniform across all coding styles. We use [`black`](https://black.readthedocs.io/en/stable/) and [`isort`](https://isort.readthedocs.io/en/latest/) to do this for us. To automatically run these formatters across the code base, just run the following command: ```bash make format diff --git a/autosklearn/automl_common b/autosklearn/automl_common index 4c8ab915e0..63877b2531 160000 --- a/autosklearn/automl_common +++ b/autosklearn/automl_common @@ -1 +1 @@ -Subproject commit 4c8ab915e007745611b9b7266137497839aba701 +Subproject commit 63877b253124ca7ac805aefaf02b69ebd7837010 diff --git a/autosklearn/data/target_validator.py b/autosklearn/data/target_validator.py index 030a40b9b0..6f1ee1335c 100644 --- a/autosklearn/data/target_validator.py +++ b/autosklearn/data/target_validator.py @@ -17,6 +17,7 @@ from autosklearn.util.logging_ import PickableLoggerAdapter SUPPORTED_TARGET_TYPES = Union[List, pd.Series, pd.DataFrame, np.ndarray, spmatrix] +SUPPORTED_XDATA_TYPES = Union[pd.Series, pd.DataFrame, np.ndarray, spmatrix] class TargetValidator(BaseEstimator): diff --git a/autosklearn/evaluation/abstract_evaluator.py b/autosklearn/evaluation/abstract_evaluator.py index ab9e961128..9f45acea41 100644 --- a/autosklearn/evaluation/abstract_evaluator.py +++ b/autosklearn/evaluation/abstract_evaluator.py @@ -25,6 +25,10 @@ MULTIOUTPUT_REGRESSION, REGRESSION_TASKS, ) +from autosklearn.data.target_validator import ( + SUPPORTED_TARGET_TYPES, + SUPPORTED_XDATA_TYPES, +) from autosklearn.metrics import Scorer, calculate_losses from autosklearn.pipeline.components.base import ThirdPartyComponents, _addons from autosklearn.pipeline.implementations.util import ( @@ -273,7 +277,8 @@ def __init__( port=self.port, ) - self.Y_optimization: Optional[Union[List, np.ndarray]] = None + self.X_optimization: Optional[SUPPORTED_XDATA_TYPES] = None + self.Y_optimization: Optional[SUPPORTED_TARGET_TYPES] = None self.Y_actual_train = None self.budget = budget @@ -328,6 +333,7 @@ def _loss( self, y_true: np.ndarray, y_hat: np.ndarray, + X_data: Optional[SUPPORTED_XDATA_TYPES] = None, ) -> Dict[str, float]: """Auto-sklearn follows a minimization goal. The calculate_loss internally translate a score function to @@ -354,6 +360,7 @@ def _loss( y_hat, self.task_type, self.metrics, + X_data=X_data, scoring_functions=self.scoring_functions, ) @@ -522,7 +529,12 @@ def file_output( # This file can be written independently of the others down bellow if "y_optimization" not in self.disable_file_output: if self.output_y_hat_optimization: - self.backend.save_targets_ensemble(self.Y_optimization) + self.backend.save_additional_data( + self.Y_optimization, what="targets_ensemble" + ) + self.backend.save_additional_data( + self.X_optimization, what="input_ensemble" + ) models: Optional[BaseEstimator] = None if hasattr(self, "models"): diff --git a/autosklearn/evaluation/train_evaluator.py b/autosklearn/evaluation/train_evaluator.py index f6317ca94e..a8433c2136 100644 --- a/autosklearn/evaluation/train_evaluator.py +++ b/autosklearn/evaluation/train_evaluator.py @@ -7,6 +7,9 @@ import warnings import numpy as np +import pandas +import pandas as pd +import scipy.sparse from ConfigSpace import Configuration from sklearn.base import BaseEstimator from sklearn.model_selection import ( @@ -177,6 +180,26 @@ def _fit_with_budget( raise ValueError(budget_type) +def concat_data( + data: List[Any], num_cv_folds: int +) -> Union[np.ndarray, pd.DataFrame, scipy.sparse.csr_matrix]: + if isinstance(data[0], np.ndarray): + return np.concatenate( + [data[i] for i in range(num_cv_folds) if data[i] is not None] + ) + elif isinstance(data[0], scipy.sparse.spmatrix): + return scipy.sparse.vstack( + [data[i] for i in range(num_cv_folds) if data[i] is not None] + ) + elif isinstance(data[0], pd.DataFrame): + return pd.concat( + [data[i] for i in range(num_cv_folds) if data[i] is not None], + axis=0, + ) + else: + raise ValueError(f"Unknown datatype {type(data[0])}") + + class TrainEvaluator(AbstractEvaluator): def __init__( self, @@ -235,7 +258,7 @@ def __init__( ) self.X_train = self.datamanager.data["X_train"] self.Y_train = self.datamanager.data["Y_train"] - self.Y_optimization: Optional[SUPPORTED_TARGET_TYPES] = None + self.X_targets = [None] * self.num_cv_folds self.Y_targets = [None] * self.num_cv_folds self.Y_train_targets = np.ones(self.Y_train.shape) * np.NaN self.models = [None] * self.num_cv_folds @@ -265,6 +288,11 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: self.Y_train, groups=self.resampling_strategy_args.get("groups"), ): + self.X_optimization = ( + self.X_train.iloc[test_split] + if hasattr(self.X_train, "iloc") + else self.X_train[test_split] + ) self.Y_optimization = self.Y_train[test_split] self.Y_actual_train = self.Y_train[train_split] self._partial_fit_and_predict_iterative( @@ -359,6 +387,12 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: if hasattr(self.Y_train, "iloc") else self.Y_train[train_indices] ) + self.X_targets[i] = ( + self.X_train.iloc[test_indices] + if hasattr(self.X_train, "iloc") + else self.X_train[train_indices] + ) + self.Y_targets[i] = self.Y_train[test_indices] Xt, fit_params = model.fit_transformer( @@ -400,6 +434,7 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: if hasattr(self.Y_train, "iloc") else self.Y_train[train_indices], train_pred, + X_data=Xt_array[i], ) train_losses[i] = train_loss # Number of training data points for this fold. @@ -408,8 +443,7 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: # Compute validation loss of this fold and store it. optimization_loss = self._loss( - self.Y_targets[i], - opt_pred, + self.Y_targets[i], opt_pred, X_data=self.X_targets[i] ) opt_losses[i] = optimization_loss # number of optimization data points for this fold. @@ -455,23 +489,15 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: weights=opt_fold_weights_percentage, ) + X_targets = self.X_targets Y_targets = self.Y_targets Y_train_targets = self.Y_train_targets - Y_optimization_preds = np.concatenate( - [ - Y_optimization_pred[i] - for i in range(self.num_cv_folds) - if Y_optimization_pred[i] is not None - ] - ) - Y_targets = np.concatenate( - [ - Y_targets[i] - for i in range(self.num_cv_folds) - if Y_targets[i] is not None - ] + Y_optimization_pred_concat = concat_data( + Y_optimization_pred, num_cv_folds=self.num_cv_folds ) + X_targets = concat_data(X_targets, num_cv_folds=self.num_cv_folds) + Y_targets = concat_data(Y_targets, num_cv_folds=self.num_cv_folds) if self.X_valid is not None: Y_valid_preds = np.array( @@ -501,6 +527,7 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: else: Y_test_preds = None + self.X_optimization = X_targets self.Y_optimization = Y_targets self.Y_actual_train = Y_train_targets @@ -516,7 +543,7 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: self.finish_up( loss=opt_loss, train_loss=train_loss, - opt_pred=Y_optimization_preds, + opt_pred=Y_optimization_pred_concat, valid_pred=Y_valid_preds, test_pred=Y_test_preds, additional_run_info=additional_run_info, @@ -602,6 +629,9 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: train_loss = self._loss( self.Y_train_targets[train_split], train_pred, + X_data=self.X_train.iloc[train_split] + if hasattr(self.X_train, "iloc") + else self.X_train[train_split], ) train_losses.append(train_loss) # number of training data points for this fold. Used for weighting @@ -610,8 +640,7 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: # Compute validation loss of this fold and store it. optimization_loss = self._loss( - self.Y_targets[i], - opt_pred, + self.Y_targets[i], opt_pred, X_data=self.X_targets[i] ) opt_losses.append(optimization_loss) # number of optimization data points for this fold. Used for weighting @@ -644,23 +673,15 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: weights=opt_fold_weights, ) + X_targets = self.X_targets Y_targets = self.Y_targets Y_train_targets = self.Y_train_targets - Y_optimization_pred = np.concatenate( - [ - Y_optimization_pred[i] - for i in range(self.num_cv_folds) - if Y_optimization_pred[i] is not None - ] - ) - Y_targets = np.concatenate( - [ - Y_targets[i] - for i in range(self.num_cv_folds) - if Y_targets[i] is not None - ] + Y_optimization_pred = concat_data( + Y_optimization_pred, num_cv_folds=self.num_cv_folds ) + X_targets = concat_data(X_targets, num_cv_folds=self.num_cv_folds) + Y_targets = concat_data(Y_targets, num_cv_folds=self.num_cv_folds) if self.X_valid is not None: Y_valid_pred = np.array( @@ -686,6 +707,7 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: if len(np.shape(Y_test_pred)) == 3: Y_test_pred = np.nanmean(Y_test_pred, axis=0) + self.X_optimization = X_targets self.Y_optimization = Y_targets self.Y_actual_train = Y_train_targets @@ -754,6 +776,7 @@ def partial_fit_predict_and_loss(self, fold: int, iterative: bool = False) -> No break if self.num_cv_folds > 1: + self.X_optimization = self.X_train[test_split] self.Y_optimization = self.Y_train[test_split] self.Y_actual_train = self.Y_train[train_split] @@ -981,6 +1004,11 @@ def _partial_fit_and_predict_standard( else: self.models[fold] = model + self.X_targets[fold] = ( + self.X_train.iloc[test_indices] + if hasattr(self.X_train, "iloc") + else self.X_train[test_indices] + ) self.Y_targets[fold] = ( self.Y_train.iloc[test_indices] if hasattr(self.Y_train, "iloc") @@ -1026,6 +1054,7 @@ def _partial_fit_and_predict_budget( model = self._get_model() self.indices[fold] = (train_indices, test_indices) + self.X_targets[fold] = self.X_train[test_indices] self.Y_targets[fold] = self.Y_train[test_indices] self.Y_train_targets[train_indices] = ( self.Y_train.iloc[train_indices] diff --git a/autosklearn/metrics/__init__.py b/autosklearn/metrics/__init__.py index 3104716da3..1fa124532a 100644 --- a/autosklearn/metrics/__init__.py +++ b/autosklearn/metrics/__init__.py @@ -1,5 +1,5 @@ from abc import ABCMeta, abstractmethod -from typing import Any, Callable, Dict, List, Optional, Sequence +from typing import Any, Callable, Dict, List, Optional, Sequence, Union import collections from functools import partial @@ -19,6 +19,7 @@ REGRESSION_TASKS, TASK_TYPES, ) +from autosklearn.data.target_validator import SUPPORTED_XDATA_TYPES from .util import sanitize_array @@ -32,11 +33,13 @@ def __init__( worst_possible_result: float, sign: float, kwargs: Any, + needs_X: bool = False, ) -> None: self.name = name self._kwargs = kwargs self._score_func = score_func self._optimum = optimum + self._needs_X = needs_X self._worst_possible_result = worst_possible_result self._sign = sign @@ -45,6 +48,7 @@ def __call__( self, y_true: np.ndarray, y_pred: np.ndarray, + X_data: Optional[np.ndarray] = None, sample_weight: Optional[List[float]] = None, ) -> float: pass @@ -58,6 +62,7 @@ def __call__( self, y_true: np.ndarray, y_pred: np.ndarray, + X_data: Optional[np.ndarray] = None, sample_weight: Optional[List[float]] = None, ) -> float: """Evaluate predicted target values for X relative to y_true. @@ -70,6 +75,10 @@ def __call__( y_pred : array-like, [n_samples x n_classes] Model predictions + X_data : array-like [n_samples x n_features] + X data used to obtain the predictions: each row x_j corresponds to the input + used to obtain predictions y_j + sample_weight : array-like, optional (default=None) Sample weights. @@ -104,12 +113,15 @@ def __call__( else: raise ValueError(type_true) + scorer_kwargs = {} # type: Dict[str, Union[List[float], np.ndarray]] if sample_weight is not None: - return self._sign * self._score_func( - y_true, y_pred, sample_weight=sample_weight, **self._kwargs - ) - else: - return self._sign * self._score_func(y_true, y_pred, **self._kwargs) + scorer_kwargs["sample_weight"] = sample_weight + if self._needs_X is True: + scorer_kwargs["X_data"] = X_data + + return self._sign * self._score_func( + y_true, y_pred, **scorer_kwargs, **self._kwargs + ) class _ProbaScorer(Scorer): @@ -117,6 +129,7 @@ def __call__( self, y_true: np.ndarray, y_pred: np.ndarray, + X_data: Optional[np.ndarray] = None, sample_weight: Optional[List[float]] = None, ) -> float: """Evaluate predicted probabilities for X relative to y_true. @@ -129,6 +142,10 @@ def __call__( y_pred : array-like, [n_samples x n_classes] Model predictions + X_data : array-like [n_samples x n_features] + X data used to obtain the predictions: each row x_j corresponds to the input + used to obtain predictions y_j + sample_weight : array-like, optional (default=None) Sample weights. @@ -156,12 +173,15 @@ def __call__( y_true, y_pred, labels=labels, **self._kwargs ) + scorer_kwargs = {} # type: Dict[str, Union[List[float], np.ndarray]] if sample_weight is not None: - return self._sign * self._score_func( - y_true, y_pred, sample_weight=sample_weight, **self._kwargs - ) - else: - return self._sign * self._score_func(y_true, y_pred, **self._kwargs) + scorer_kwargs["sample_weight"] = sample_weight + if self._needs_X is True: + scorer_kwargs["X_data"] = X_data + + return self._sign * self._score_func( + y_true, y_pred, **scorer_kwargs, **self._kwargs + ) class _ThresholdScorer(Scorer): @@ -169,6 +189,7 @@ def __call__( self, y_true: np.ndarray, y_pred: np.ndarray, + X_data: Optional[np.ndarray] = None, sample_weight: Optional[List[float]] = None, ) -> float: """Evaluate decision function output for X relative to y_true. @@ -181,6 +202,10 @@ def __call__( y_pred : array-like, [n_samples x n_classes] Model predictions + X_data : array-like [n_samples x n_features] + X data used to obtain the predictions: each row x_j corresponds to the input + used to obtain predictions y_j + sample_weight : array-like, optional (default=None) Sample weights. @@ -199,22 +224,27 @@ def __call__( elif isinstance(y_pred, list): y_pred = np.vstack([p[:, -1] for p in y_pred]).T + scorer_kwargs = {} # type: Dict[str, Union[List[float], np.ndarray]] if sample_weight is not None: - return self._sign * self._score_func( - y_true, y_pred, sample_weight=sample_weight, **self._kwargs - ) - else: - return self._sign * self._score_func(y_true, y_pred, **self._kwargs) + scorer_kwargs["sample_weight"] = sample_weight + if self._needs_X is True: + scorer_kwargs["X_data"] = X_data + + return self._sign * self._score_func( + y_true, y_pred, **scorer_kwargs, **self._kwargs + ) def make_scorer( name: str, score_func: Callable, + *, optimum: float = 1.0, worst_possible_result: float = 0.0, greater_is_better: bool = True, needs_proba: bool = False, needs_threshold: bool = False, + needs_X: bool = False, **kwargs: Any, ) -> Scorer: """Make a scorer from a performance metric or loss function. @@ -224,6 +254,9 @@ def make_scorer( Parameters ---------- + name: str + Descriptive name of the metric + score_func : callable Score function (or loss function) with signature ``score_func(y, y_pred, **kwargs)``. @@ -232,6 +265,10 @@ def make_scorer( The best score achievable by the score function, i.e. maximum in case of scorer function and minimum in case of loss function. + worst_possible_result : int of float, default=0 + The worst score achievable by the score function, i.e. minimum in case of + scorer function and maximum in case of loss function. + greater_is_better : boolean, default=True Whether score_func is a score function (default), meaning high is good, or a loss function, meaning low is good. In the latter case, the @@ -245,27 +282,34 @@ def make_scorer( Whether score_func takes a continuous decision certainty. This only works for binary classification. + needs_X : boolean, default=False + Whether score_func requires X in __call__ to compute a metric. + **kwargs : additional arguments Additional parameters to be passed to score_func. Returns ------- scorer : callable - Callable object that returns a scalar score; greater is better. + Callable object that returns a scalar score; greater is better or set + greater_is_better to False. """ sign = 1 if greater_is_better else -1 - if needs_proba: - return _ProbaScorer( - name, score_func, optimum, worst_possible_result, sign, kwargs + if needs_proba and needs_threshold: + raise ValueError( + "Set either needs_proba or needs_threshold to True, but not both." ) + + cls = None # type: Any + if needs_proba: + cls = _ProbaScorer elif needs_threshold: - return _ThresholdScorer( - name, score_func, optimum, worst_possible_result, sign, kwargs - ) + cls = _ThresholdScorer else: - return _PredictScorer( - name, score_func, optimum, worst_possible_result, sign, kwargs - ) + cls = _PredictScorer + return cls( + name, score_func, optimum, worst_possible_result, sign, kwargs, needs_X=needs_X + ) # Standard regression scores @@ -420,6 +464,8 @@ def calculate_scores( prediction: np.ndarray, task_type: int, metrics: Sequence[Scorer], + *, + X_data: Optional[SUPPORTED_XDATA_TYPES] = None, scoring_functions: Optional[List[Scorer]] = None, ) -> Dict[str, float]: """ @@ -439,6 +485,8 @@ def calculate_scores( metrics: Sequence[Scorer] A list of objects that hosts a function to calculate how good the prediction is according to the solution. + X_data : array-like [n_samples x n_features] + X data used to obtain the predictions scoring_functions: List[Scorer] A list of metrics to calculate multiple losses Returns @@ -460,7 +508,11 @@ def calculate_scores( try: score_dict[metric_.name] = _compute_single_scorer( - metric_, prediction, solution, task_type + metric_, + prediction, + solution, + task_type, + X_data, ) except ValueError as e: print(e, e.args[0]) @@ -480,7 +532,11 @@ def calculate_scores( try: score_dict[metric_.name] = _compute_single_scorer( - metric_, prediction, solution, task_type + metric_, + prediction, + solution, + task_type, + X_data, ) except ValueError as e: if e.args[0] == "multiclass format is not supported": @@ -507,6 +563,8 @@ def calculate_losses( prediction: np.ndarray, task_type: int, metrics: Sequence[Scorer], + *, + X_data: Optional[SUPPORTED_XDATA_TYPES] = None, scoring_functions: Optional[List[Scorer]] = None, ) -> Dict[str, float]: """ @@ -526,6 +584,8 @@ def calculate_losses( metrics: Sequence[Scorer] A list of objects that hosts a function to calculate how good the prediction is according to the solution. + X_data: Optional[np.ndarray] + X data used to obtain the predictions scoring_functions: List[Scorer] A list of metrics to calculate multiple losses @@ -537,6 +597,7 @@ def calculate_losses( score = calculate_scores( solution=solution, prediction=prediction, + X_data=X_data, task_type=task_type, metrics=metrics, scoring_functions=scoring_functions, @@ -556,7 +617,11 @@ def calculate_losses( def compute_single_metric( - metric: Scorer, prediction: np.ndarray, solution: np.ndarray, task_type: int + metric: Scorer, + prediction: np.ndarray, + solution: np.ndarray, + task_type: int, + X_data: Optional[np.ndarray] = None, ) -> float: """ Returns a metric for the given Auto-Sklearn Scorer object. @@ -574,6 +639,8 @@ def compute_single_metric( metric: Scorer Object that host a function to calculate how good the prediction is according to the solution. + X_data : array-like [n_samples x n_features] + X data used to obtain the predictions Returns ------- @@ -583,13 +650,18 @@ def compute_single_metric( solution=solution, prediction=prediction, metric=metric, + X_data=X_data, task_type=task_type, ) return metric._sign * score def _compute_single_scorer( - metric: Scorer, prediction: np.ndarray, solution: np.ndarray, task_type: int + metric: Scorer, + prediction: np.ndarray, + solution: np.ndarray, + task_type: int, + X_data: Optional[SUPPORTED_XDATA_TYPES] = None, ) -> float: """ Returns a score (a magnitude that allows casting the @@ -608,10 +680,30 @@ def _compute_single_scorer( metric: Scorer Object that host a function to calculate how good the prediction is according to the solution. + X_data : array-like [n_samples x n_features] + X data used to obtain the predictions Returns ------- float """ + if metric._needs_X: + if X_data is None: + raise ValueError( + f"Metric {metric.name} needs X_data, but X_data is {X_data}" + ) + elif X_data.shape[0] != solution.shape[0]: + raise ValueError( + f"X_data has wrong length. " + f"Should be {solution.shape[0]}, but is {X_data.shape[0]}" + ) + if task_type in REGRESSION_TASKS: + # TODO put this into the regression metric itself + cprediction = sanitize_array(prediction) + score = metric(solution, cprediction, X_data=X_data) + else: + score = metric(solution, prediction, X_data=X_data) + return score + if task_type in REGRESSION_TASKS: # TODO put this into the regression metric itself cprediction = sanitize_array(prediction) diff --git a/examples/40_advanced/example_metrics.py b/examples/40_advanced/example_metrics.py index 7784491746..4da40f3cbb 100644 --- a/examples/40_advanced/example_metrics.py +++ b/examples/40_advanced/example_metrics.py @@ -46,6 +46,13 @@ def error_wk(solution, prediction, extra_argument): return np.mean(solution != prediction) +def metric_which_needs_x(solution, prediction, X_data, consider_col, val_threshold): + # custom function defining accuracy + assert X_data is not None + rel_idx = X_data[:, consider_col] > val_threshold + return np.mean(solution[rel_idx] == prediction[rel_idx]) + + ############################################################################ # Data Loading # ============ @@ -186,3 +193,43 @@ def error_wk(solution, prediction, extra_argument): predictions = cls.predict(X_test) score = error_rate(y_test, predictions) print(f"Error score {score:.3f} using {error_rate.name:s}") + + +############################################################################# +# Sixth example: Use a metric with additional argument which also needs xdata +# =========================================================================== +""" +Finally, *Auto-sklearn* also support metric that require the train data (aka X_data) to +compute a value. This can be useful if one only cares about the score on a subset of the +data. +""" + +accuracy_scorer = autosklearn.metrics.make_scorer( + name="accu_X", + score_func=metric_which_needs_x, + optimum=1, + greater_is_better=True, + needs_proba=False, + needs_X=True, + needs_threshold=False, + consider_col=1, + val_threshold=18.8, +) +cls = autosklearn.classification.AutoSklearnClassifier( + time_left_for_this_task=60, + per_run_time_limit=30, + seed=1, + metric=accuracy_scorer, + ensemble_size=0, +) +cls.fit(X_train, y_train) + +predictions = cls.predict(X_test) +score = metric_which_needs_x( + y_test, + predictions, + X_data=X_test, + consider_col=1, + val_threshold=18.8, +) +print(f"Error score {score:.3f} using {error_rate.name:s}") diff --git a/test/fixtures/ensemble_building.py b/test/fixtures/ensemble_building.py index 42dd7fbb9a..1a6c18073a 100644 --- a/test/fixtures/ensemble_building.py +++ b/test/fixtures/ensemble_building.py @@ -145,7 +145,9 @@ def _make( # Hence, we take the y_train of the datamanager and use that as the # the targets if "Y_train" in datamanager.data: - backend.save_targets_ensemble(datamanager.data["Y_train"]) + backend.save_additional_data( + datamanager.data["Y_train"], what="targets_ensemble" + ) builder = EnsembleBuilder( backend=backend, diff --git a/test/test_estimators/test_estimators.py b/test/test_estimators/test_estimators.py index 8e3134e3a3..dc5c359c8a 100644 --- a/test/test_estimators/test_estimators.py +++ b/test/test_estimators/test_estimators.py @@ -1281,7 +1281,7 @@ def test_autosklearn2_classification_methods_returns_self(dask_client): predictions = automl_fitted.predict(X_test) assert ( - sklearn.metrics.accuracy_score(y_test, predictions) >= 2 / 3 + sklearn.metrics.accuracy_score(y_test, predictions) >= 4 / 5 ), print_debug_information(automl) pickle.dumps(automl_fitted) @@ -1310,7 +1310,7 @@ def test_autosklearn2_classification_methods_returns_self_sparse(dask_client): predictions = automl_fitted.predict(X_test) assert ( - sklearn.metrics.accuracy_score(y_test, predictions) >= 2 / 3 + sklearn.metrics.accuracy_score(y_test, predictions) >= 4 / 5 ), print_debug_information(automl) assert "boosting" not in str(automl.get_configuration_space(X=X_train, y=y_train)) diff --git a/test/test_evaluation/test_evaluation.py b/test/test_evaluation/test_evaluation.py index f5292060a6..32ba71e925 100644 --- a/test/test_evaluation/test_evaluation.py +++ b/test/test_evaluation/test_evaluation.py @@ -495,7 +495,7 @@ def test_silent_exception_in_target_function(self): config = unittest.mock.Mock() config.config_id = 198 - delattr(self.backend, "save_targets_ensemble") + delattr(self.backend, "save_additional_data") ta = ExecuteTaFuncWithQueue( backend=self.backend, port=self.logger_port, @@ -531,10 +531,10 @@ def test_silent_exception_in_target_function(self): info[1].additional_info["error"], ( """AttributeError("'BackendMock' object has no attribute """ - """'save_targets_ensemble'",)""", + """'save_additional_data'",)""", """AttributeError("'BackendMock' object has no attribute """ - """'save_targets_ensemble'")""", - """AttributeError('save_targets_ensemble')""", + """'save_additional_data'")""", + """AttributeError('save_additional_data')""", ), ) self.assertNotIn("exitcode", info[1].additional_info) diff --git a/test/test_evaluation/test_train_evaluator.py b/test/test_evaluation/test_train_evaluator.py index 23607b8e4d..9413af5509 100644 --- a/test/test_evaluation/test_train_evaluator.py +++ b/test/test_evaluation/test_train_evaluator.py @@ -816,7 +816,7 @@ def test_file_output(self, loss_mock, model_mock): ) self.assertEqual(rval, (None, {})) - self.assertEqual(self.backend_mock.save_targets_ensemble.call_count, 1) + self.assertEqual(self.backend_mock.save_additional_data.call_count, 2) self.assertEqual(self.backend_mock.save_numrun_to_dir.call_count, 1) self.assertEqual( self.backend_mock.save_numrun_to_dir.call_args_list[-1][1].keys(), @@ -845,7 +845,7 @@ def test_file_output(self, loss_mock, model_mock): D.data["Y_test"], ) self.assertEqual(rval, (None, {})) - self.assertEqual(self.backend_mock.save_targets_ensemble.call_count, 2) + self.assertEqual(self.backend_mock.save_additional_data.call_count, 4) self.assertEqual(self.backend_mock.save_numrun_to_dir.call_count, 2) self.assertEqual( self.backend_mock.save_numrun_to_dir.call_args_list[-1][1].keys(), @@ -1109,6 +1109,7 @@ def test_fit_predict_and_loss_standard_additional_run_info( evaluator.file_output.return_value = (None, {}) evaluator.model = unittest.mock.Mock() evaluator.model.estimator_supports_iterative_fit.return_value = False + evaluator.X_targets[0] = np.array([1, 0] * 23) evaluator.Y_targets[0] = np.array([1] * 23) evaluator.Y_train_targets = np.array([1] * 69) rval = evaluator.fit_predict_and_loss(iterative=False) @@ -1269,6 +1270,7 @@ def test_fit_predict_and_loss_iterative_noniterativemodel_additional_run_info( self.assertEqual(finish_up_mock.call_count, 1) self.assertEqual(finish_up_mock.call_args[1]["additional_run_info"], 14678) + @unittest.mock.patch("autosklearn.evaluation.train_evaluator.concat_data") @unittest.mock.patch.object(TrainEvaluator, "_loss") @unittest.mock.patch.object(TrainEvaluator, "finish_up") @unittest.mock.patch("autosklearn.automl_common.common.utils.backend.Backend") @@ -1281,6 +1283,7 @@ def test_fit_predict_and_loss_budget_additional_run_info( backend_mock, finish_up_mock, loss_mock, + _, ): class Counter: counter = 0 @@ -1330,6 +1333,7 @@ def __call__(self): finish_up_mock.call_args[1]["additional_run_info"], {"val": 14678} ) + @unittest.mock.patch("autosklearn.evaluation.train_evaluator.concat_data") @unittest.mock.patch.object(TrainEvaluator, "_loss") @unittest.mock.patch.object(TrainEvaluator, "finish_up") @unittest.mock.patch("autosklearn.automl_common.common.utils.backend.Backend") @@ -1337,11 +1341,7 @@ def __call__(self): "autosklearn.pipeline.classification.SimpleClassificationPipeline" ) def test_fit_predict_and_loss_budget_2_additional_run_info( - self, - mock, - backend_mock, - finish_up_mock, - loss_mock, + self, mock, backend_mock, finish_up_mock, loss_mock, _ ): mock.estimator_supports_iterative_fit.return_value = False mock.fit_transformer.return_value = ("Xt", {}) diff --git a/test/test_metric/test_metrics.py b/test/test_metric/test_metrics.py index 2cb7dc2158..4443024c4b 100644 --- a/test/test_metric/test_metrics.py +++ b/test/test_metric/test_metrics.py @@ -17,6 +17,34 @@ class TestScorer(unittest.TestCase): + def test_needs_X(self): + y_true = np.array([0, 0, 1, 1]) + y_pred = np.array([[1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0]]) + + def dummy_metric(y_true, y_pred, X_data=None, **kwargs): + if not np.array_equal(np.array([45]), X_data): + raise ValueError(f"is {X_data}") + return 1 + + scorer = autosklearn.metrics._PredictScorer( + "accuracy", dummy_metric, 1, 0, 1, {}, needs_X=True + ) + scorer(y_true, y_pred, X_data=np.array([45])) + + scorer_nox = autosklearn.metrics._PredictScorer( + "accuracy", dummy_metric, 1, 0, 1, {}, needs_X=False + ) + with self.assertRaises(ValueError) as cm: + scorer_nox(y_true, y_pred, X_data=np.array([32])) + the_exception = cm.exception + # X_data is not forwarded + self.assertEqual(the_exception.args[0], "is None") + + scorer_nox = autosklearn.metrics._PredictScorer( + "accuracy", sklearn.metrics.accuracy_score, 1, 0, 1, {}, needs_X=False + ) + scorer_nox(y_true, y_pred, X_data=np.array([32])) + def test_predict_scorer_binary(self): y_true = np.array([0, 0, 1, 1]) y_pred = np.array([[1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0]]) @@ -570,7 +598,7 @@ def test_classification_scoring_functions(self): y_pred, BINARY_CLASSIFICATION, [autosklearn.metrics.accuracy], - scoring_functions, + scoring_functions=scoring_functions, ) self.assertIsInstance(score_dict, dict) @@ -600,7 +628,7 @@ def test_regression_scoring_functions(self): y_pred, REGRESSION, [autosklearn.metrics.root_mean_squared_error], - scoring_functions, + scoring_functions=scoring_functions, ) self.assertIsInstance(score_dict, dict) From 011fe71bc9aebc577b5846496aade48970871308 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Mon, 30 May 2022 15:39:43 +0200 Subject: [PATCH 055/118] Multi-objective ensemble API (#1485) * Multi-objective ensemble API Co-authored-by: eddiebergman * update for rebase, add loading of X_data in ensemble builder * Add unit tests * Fix unittest?, increase coverage (hopefully) * Rename methods to be Pareto set methods Co-authored-by: eddiebergman --- autosklearn/automl.py | 215 ++++++++--- autosklearn/data/target_validator.py | 6 +- autosklearn/ensemble_building/builder.py | 346 +++++++++++------ autosklearn/ensemble_building/manager.py | 65 ++-- autosklearn/ensemble_building/run.py | 6 +- autosklearn/ensembles/__init__.py | 10 + autosklearn/ensembles/abstract_ensemble.py | 64 +++- autosklearn/ensembles/ensemble_selection.py | 127 +++++-- autosklearn/ensembles/singlebest_ensemble.py | 57 ++- autosklearn/estimators.py | 202 ++++++++-- autosklearn/evaluation/__init__.py | 5 +- autosklearn/evaluation/abstract_evaluator.py | 32 +- autosklearn/experimental/askl2.py | 26 +- autosklearn/metrics/__init__.py | 48 ++- autosklearn/pipeline/util.py | 7 + autosklearn/util/functional.py | 75 +++- doc/api.rst | 16 + doc/faq.rst | 23 +- .../example_interpretable_models.py | 2 +- examples/40_advanced/example_metrics.py | 8 +- .../40_advanced/example_multi_objective.py | 35 +- .../example_parallel_manual_spawning_cli.py | 2 +- ...example_parallel_manual_spawning_python.py | 2 +- examples/60_search/example_sequential.py | 8 +- pyproject.toml | 2 - ...un_auto-sklearn_for_metadata_generation.py | 2 +- test/fixtures/automl.py | 5 +- test/fixtures/datasets.py | 2 + test/fixtures/ensemble_building.py | 28 +- test/test_automl/cases.py | 51 ++- test/test_automl/test_dummy_predictions.py | 49 ++- test/test_automl/test_fit_ensemble.py | 35 -- test/test_automl/test_outputs.py | 4 +- test/test_automl/test_post_fit.py | 49 ++- test/test_data/test_target_validator.py | 4 +- .../test_ensemble_builder.py | 349 ++++++++++++++---- .../test_ensemble_builder_real.py | 37 +- .../test_ensemble_selection.py | 20 +- test/test_estimators/test_estimators.py | 35 +- test/test_metalearning/test_metalearning.py | 2 +- 40 files changed, 1559 insertions(+), 502 deletions(-) delete mode 100644 test/test_automl/test_fit_ensemble.py diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 94d8f489d1..12e80b8e4e 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -1,6 +1,16 @@ from __future__ import annotations -from typing import Any, Callable, Iterable, Mapping, Optional, Sequence, Tuple +from typing import ( + Any, + Callable, + Dict, + Iterable, + Mapping, + Optional, + Sequence, + Tuple, + Type, +) import copy import io @@ -13,6 +23,7 @@ import sys import tempfile import time +import types import uuid import warnings @@ -30,7 +41,7 @@ from scipy.sparse import spmatrix from sklearn.base import BaseEstimator from sklearn.dummy import DummyClassifier, DummyRegressor -from sklearn.ensemble import VotingRegressor +from sklearn.ensemble import VotingClassifier, VotingRegressor from sklearn.metrics._classification import type_of_target from sklearn.model_selection._split import ( BaseCrossValidator, @@ -63,6 +74,11 @@ ) from autosklearn.data.xy_data_manager import XYDataManager from autosklearn.ensemble_building import EnsembleBuilderManager +from autosklearn.ensembles.abstract_ensemble import ( + AbstractEnsemble, + AbstractMultiObjectiveEnsemble, +) +from autosklearn.ensembles.ensemble_selection import EnsembleSelection from autosklearn.ensembles.singlebest_ensemble import SingleBest from autosklearn.evaluation import ExecuteTaFuncWithQueue, get_cost_of_crash from autosklearn.evaluation.abstract_evaluator import _fit_and_suppress_warnings @@ -199,7 +215,8 @@ def __init__( temporary_directory: Optional[str] = None, delete_tmp_folder_after_terminate: bool = True, initial_configurations_via_metalearning: int = 25, - ensemble_size: int = 1, + ensemble_class: Type[AbstractEnsemble] | None = EnsembleSelection, + ensemble_kwargs: Dict[str, Any] | None = None, ensemble_nbest: int = 1, max_models_on_disc: int = 1, seed: int = 1, @@ -251,7 +268,8 @@ def __init__( self._time_for_task = time_left_for_this_task self._per_run_time_limit = per_run_time_limit self._metrics = metrics - self._ensemble_size = ensemble_size + self._ensemble_class = ensemble_class + self._ensemble_kwargs = ensemble_kwargs self._ensemble_nbest = ensemble_nbest self._max_models_on_disc = max_models_on_disc self._seed = seed @@ -451,7 +469,7 @@ def _do_dummy_prediction(self) -> None: ta = ExecuteTaFuncWithQueue( backend=self._backend, autosklearn_seed=self._seed, - multi_objectives=["cost"], + multi_objectives=[metric.name for metric in self._metrics], resampling_strategy=self._resampling_strategy, initial_num_run=dummy_run_num, stats=stats, @@ -517,7 +535,7 @@ def fit( """Fit AutoML to given training set (X, y). Fit both optimizes the machine learning models and builds an ensemble - out of them. To disable ensembling, set ``ensemble_size==0``. + out of them. # TODO PR1213 # @@ -714,7 +732,9 @@ def fit( # If no dask client was provided, we create one, so that we can # start a ensemble process in parallel to smbo optimize if self._dask_client is None and ( - self._ensemble_size > 0 or self._n_jobs is not None and self._n_jobs > 1 + self._ensemble_class is not None + or self._n_jobs is not None + and self._n_jobs > 1 ): self._create_dask_client() else: @@ -781,17 +801,17 @@ def fit( time_left_for_ensembles = max(0, self._time_for_task - elapsed_time) proc_ensemble = None if time_left_for_ensembles <= 0: - # Fit only raises error when ensemble_size is not zero but + # Fit only raises error when an ensemble class is given but # time_left_for_ensembles is zero. - if self._ensemble_size > 0: + if self._ensemble_class is not None: raise ValueError( "Not starting ensemble builder because there " "is no time left. Try increasing the value " "of time_left_for_this_task." ) - elif self._ensemble_size <= 0: + elif self._ensemble_class is None: self._logger.info( - "Not starting ensemble builder because " "ensemble size is <= 0." + "Not starting ensemble builder because no ensemble class is given." ) else: self._logger.info( @@ -804,8 +824,9 @@ def fit( backend=copy.deepcopy(self._backend), dataset_name=dataset_name, task=self._task, - metric=self._metrics[0], - ensemble_size=self._ensemble_size, + metrics=self._metrics, + ensemble_class=self._ensemble_class, + ensemble_kwargs=self._ensemble_kwargs, ensemble_nbest=self._ensemble_nbest, max_models_on_disc=self._max_models_on_disc, seed=self._seed, @@ -988,7 +1009,8 @@ def _log_fit_setup(self) -> None: " initial_configurations_via_metalearning: %d", self._initial_configurations_via_metalearning, ) - self._logger.debug(" ensemble_size: %d", self._ensemble_size) + self._logger.debug(" ensemble_class: %s", self._ensemble_class) + self._logger.debug(" ensemble_kwargs: %s", self._ensemble_kwargs) self._logger.debug(" ensemble_nbest: %f", self._ensemble_nbest) self._logger.debug(" max_models_on_disc: %s", str(self._max_models_on_disc)) self._logger.debug(" seed: %d", self._seed) @@ -1107,7 +1129,7 @@ def _check_resampling_strategy( "partial-cv", "partial-cv-iterative-fit", ] - and self._ensemble_size != 0 + and self._ensemble_class is not None ): raise ValueError( "Resampling strategy %s cannot be used " @@ -1163,9 +1185,9 @@ def refit( if self.models_ is None or len(self.models_) == 0 or self.ensemble_ is None: self._load_models() - # Refit is not applicable when ensemble_size is set to zero. + # Refit is not applicable when no ensemble class is provided if self.ensemble_ is None: - raise ValueError("Refit can only be called if 'ensemble_size != 0'") + raise ValueError("Refit can only be called if an ensemble class is given") random_state = check_random_state(self._seed) @@ -1404,13 +1426,13 @@ def predict(self, X, batch_size=None, n_jobs=1): if self.models_ is None or len(self.models_) == 0 or self.ensemble_ is None: self._load_models() - # If self.ensemble_ is None, it means that ensemble_size is set to zero. + # If self.ensemble_ is None, it means that ensemble_class is None. # In such cases, raise error because predict and predict_proba cannot # be called. if self.ensemble_ is None: raise ValueError( "Predict and predict_proba can only be called " - "if 'ensemble_size != 0'" + "if ensemble class is given." ) # Make sure that input is valid @@ -1478,19 +1500,15 @@ def fit_ensemble( precision: Literal[16, 32, 64] = 32, dataset_name: Optional[str] = None, ensemble_nbest: Optional[int] = None, - ensemble_size: Optional[int] = None, + ensemble_class: Optional[AbstractEnsemble] = EnsembleSelection, + ensemble_kwargs: Optional[Dict[str, Any]] = None, ): check_is_fitted(self) - # check for the case when ensemble_size is less than 0 - if ensemble_size is not None and ensemble_size <= 0: - raise ValueError("`ensemble_size` must be >= 0 for `fit_ensemble`") - - if ensemble_size is None and ( - self._ensemble_size is None or self._ensemble_size <= 0 - ): + if ensemble_class is None and self._ensemble_class is None: raise ValueError( - "Please pass `ensemble_size` to `fit_ensemble` if not setting in init" + "Please pass `ensemble_class` either to `fit_ensemble()` " + "or the constructor." ) # AutoSklearn does not handle sparse y for now @@ -1523,8 +1541,15 @@ def fit_ensemble( backend=copy.deepcopy(self._backend), dataset_name=dataset_name if dataset_name else self._dataset_name, task=task if task else self._task, - metric=self._metrics[0], - ensemble_size=ensemble_size if ensemble_size else self._ensemble_size, + metrics=self._metrics, + ensemble_class=( + ensemble_class if ensemble_class is not None else self._ensemble_class + ), + ensemble_kwargs=( + ensemble_kwargs + if ensemble_kwargs is not None + else self._ensemble_kwargs + ), ensemble_nbest=ensemble_nbest if ensemble_nbest else self._ensemble_nbest, max_models_on_disc=self._max_models_on_disc, seed=self._seed, @@ -1545,21 +1570,21 @@ def fit_ensemble( "line output for error messages." ) self.ensemble_performance_history, _ = result - self._ensemble_size = ensemble_size + self._ensemble_class = ensemble_class self._load_models() self._close_dask_client() return self def _load_models(self): - if self._ensemble_size > 0: + if self._ensemble_class is not None: self.ensemble_ = self._backend.load_ensemble(self._seed) else: self.ensemble_ = None # If no ensemble is loaded, try to get the best performing model. # This is triggered if - # 1. self._ensemble_size == 0 (see if-statement above) + # 1. self._ensemble_class is None (see if-statement above) # 2. if the ensemble builder crashed and no ensemble is available # 3. if the ensemble cannot be built because of arguments passed # by the user (disable_evaluator_output and @@ -1581,7 +1606,7 @@ def _load_models(self): ): self.ensemble_ = self._load_best_individual_model() - if self.ensemble_ is not None: + if self.ensemble_: identifiers = self.ensemble_.get_selected_model_identifiers() self.models_ = self._backend.load_models_by_identifiers(identifiers) @@ -1611,19 +1636,107 @@ def _load_best_individual_model(self): # SingleBest contains the best model found by AutoML ensemble = SingleBest( - metric=self._metrics[0], + metrics=self._metrics, + task_type=self._task, seed=self._seed, run_history=self.runhistory_, backend=self._backend, + random_state=self._seed, ) self._logger.warning( "No valid ensemble was created. Please check the log" "file for errors. Default to the best individual estimator:{}".format( - ensemble.identifiers_ + ensemble.get_identifiers_with_weights()[0][0] ) ) return ensemble + def _load_pareto_set(self) -> Sequence[VotingClassifier | VotingRegressor]: + if len(self._metrics) <= 1: + raise ValueError("Pareto set is only available for two or more metrics.") + + if self._ensemble_class is not None: + self.ensemble_ = self._backend.load_ensemble(self._seed) + else: + self.ensemble_ = None + + # If no ensemble is loaded we cannot do anything + if not self.ensemble_: + + raise ValueError( + "Pareto set can only be accessed if an ensemble is available." + ) + + if isinstance(self.ensemble_, AbstractMultiObjectiveEnsemble): + pareto_set = self.ensemble_.get_pareto_set() + else: + self._logger.warning( + "Pareto set not available for single objective ensemble " + "method. The Pareto set will only include the single ensemble " + "constructed by %s", + type(self.ensemble_), + ) + pareto_set = [self.ensemble_] + + ensembles = [] + for ensemble in pareto_set: + identifiers = ensemble.get_selected_model_identifiers() + weights = { + identifier: weight + for identifier, weight in ensemble.get_identifiers_with_weights() + } + + if self._task in CLASSIFICATION_TASKS: + voter = VotingClassifier( + estimators=None, + voting="soft", + ) + else: + voter = VotingRegressor(estimators=None) + + if self._resampling_strategy in ("cv", "cv-iterative-fit"): + models = self._backend.load_cv_models_by_identifiers(identifiers) + else: + models = self._backend.load_models_by_identifiers(identifiers) + + if len(models) == 0: + raise ValueError("No models fitted!") + + weight_vector = [] + estimators = [] + for identifier in identifiers: + weight_vector.append(weights[identifier]) + estimators.append(models[identifier]) + + voter.estimators = estimators + voter.estimators_ = estimators + voter.weights = weight_vector + + if self._task in CLASSIFICATION_TASKS: + # Scikit-learn would raise a shape error here which we + # have to work around. + + def inverse_transform(self, y): + if len(y.shape) == 1: + y = y.reshape((-1, 1)) + reshaped = True + else: + reshaped = False + y = self.old_inverse_transform(y) + if reshaped: + return y.flatten() + else: + return y + + voter.le_ = copy.deepcopy(self.InputValidator.target_validator.encoder) + functype = types.MethodType + voter.le_.old_inverse_transform = voter.le_.inverse_transform + voter.le_.inverse_transform = functype(inverse_transform, voter.le_) + + ensembles.append(voter) + + return ensembles + def score(self, X, y): # fix: Consider only index 1 of second dimension # Don't know if the reshaping should be done there or in calculate_score @@ -1666,12 +1779,12 @@ def _get_runhistory_models_performance(self): time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(run_value.endtime)) ) cost = run_value.cost + train_loss = run_value.additional_info["train_loss"] if len(self._metrics) > 1: cost = cost[0] + train_loss = train_loss[0] val_score = metric._optimum - (metric._sign * cost) - train_score = metric._optimum - ( - metric._sign * run_value.additional_info["train_loss"] - ) + train_score = metric._optimum - (metric._sign * train_loss) scores = { "Timestamp": endtime, "single_best_optimization_score": val_score, @@ -1710,7 +1823,7 @@ def performance_over_time_(self): performance_over_time = individual_performance_frame - if self._ensemble_size != 0: + if self._ensemble_class is not None: ensemble_performance_frame = pd.DataFrame(self.ensemble_performance_history) best_values = pd.Series( {"ensemble_optimization_score": -np.inf, "ensemble_test_score": -np.inf} @@ -1897,11 +2010,17 @@ def sprint_statistics(self) -> str: ) )[0] if len(idx_success) > 0: + key = ( + "mean_test_score" + if len(self._metrics) == 1 + else f"mean_test_" f"{self._metrics[0].name}" + ) + if not self._metrics[0]._optimum: - idx_best_run = np.argmin(cv_results["mean_test_score"][idx_success]) + idx_best_run = np.argmin(cv_results[key][idx_success]) else: - idx_best_run = np.argmax(cv_results["mean_test_score"][idx_success]) - best_score = cv_results["mean_test_score"][idx_success][idx_best_run] + idx_best_run = np.argmax(cv_results[key][idx_success]) + best_score = cv_results[key][idx_success][idx_best_run] sio.write(" Best validation score: %f\n" % best_score) num_runs = len(cv_results["status"]) sio.write(" Number of target algorithm runs: %d\n" % num_runs) @@ -2004,9 +2123,10 @@ def show_models(self) -> dict[int, Any]: ensemble_dict = {} - # check for ensemble_size == 0 - if self._ensemble_size == 0: - warnings.warn("No models in the ensemble. Kindly check the ensemble size.") + if self._ensemble_class is not None: + warnings.warn( + "No models in the ensemble. Kindly provide an ensemble class." + ) return ensemble_dict # check for condition when ensemble_size > 0 but there is no ensemble to load @@ -2029,8 +2149,7 @@ def has_key(rv, key): "No model found. Try increasing 'time_left_for_this_task'." ) - for i, weight in enumerate(self.ensemble_.weights_): - (_, model_id, _) = self.ensemble_.identifiers_[i] + for (_, model_id, _), weight in self.ensemble_.get_identifiers_with_weights(): table_dict[model_id]["ensemble_weight"] = weight table = pd.DataFrame.from_dict(table_dict, orient="index") diff --git a/autosklearn/data/target_validator.py b/autosklearn/data/target_validator.py index 6f1ee1335c..740fa67cb2 100644 --- a/autosklearn/data/target_validator.py +++ b/autosklearn/data/target_validator.py @@ -171,10 +171,8 @@ def _fit( f" Data involved = {shape}/{self.type_of_target}" ) - # Creat the encoder - self.encoder = OrdinalEncoder( - handle_unknown="use_encoded_value", unknown_value=-1 - ) + # Create the encoder + self.encoder = OrdinalEncoder(handle_unknown="error") # Clear typing to just numpy arrays and pandas if isinstance(y_train, List): diff --git a/autosklearn/ensemble_building/builder.py b/autosklearn/ensemble_building/builder.py index fdd9a31b05..487332cbe1 100644 --- a/autosklearn/ensemble_building/builder.py +++ b/autosklearn/ensemble_building/builder.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import Any, Iterable, Sequence, cast +from typing import Any, Dict, Iterable, Sequence, Type, cast import logging.handlers import multiprocessing @@ -15,14 +15,17 @@ import numpy as np import pandas as pd import pynisher +from sklearn.utils.validation import check_random_state from autosklearn.automl_common.common.utils.backend import Backend +from autosklearn.data.validation import SUPPORTED_FEAT_TYPES from autosklearn.data.xy_data_manager import XYDataManager from autosklearn.ensemble_building.run import Run, RunID +from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble from autosklearn.ensembles.ensemble_selection import EnsembleSelection -from autosklearn.metrics import Scorer, calculate_losses, calculate_scores +from autosklearn.metrics import Scorer, calculate_loss, calculate_scores from autosklearn.util.disk import rmtree -from autosklearn.util.functional import cut, findwhere, split +from autosklearn.util.functional import cut, findwhere, roundrobin, split from autosklearn.util.logging_ import get_named_client_logger from autosklearn.util.parallel import preload_modules @@ -41,11 +44,11 @@ def __init__( backend: Backend, dataset_name: str, task_type: int, - metric: Scorer, - ensemble_size: int = 50, + metrics: Sequence[Scorer], + ensemble_class: Type[AbstractEnsemble] = EnsembleSelection, + ensemble_kwargs: Dict[str, Any] | None = None, ensemble_nbest: int | float = 50, max_models_on_disc: int | float | None = 100, - performance_range_threshold: float = 0, seed: int = 1, precision: int = 32, memory_limit: int | None = 1024, @@ -65,11 +68,12 @@ def __init__( task_type: int type of ML task - metric: str - name of metric to compute the loss of the given predictions + metrics: Sequence[Scorer] + Metrics to optimize the ensemble for. These must be non-duplicated. + + ensemble_class - ensemble_size: int = 50 - maximal size of ensemble (passed to autosklearn.ensemble.ensemble_selection) + ensemble_kwargs ensemble_nbest: int | float = 50 @@ -78,7 +82,6 @@ def __init__( * float: consider only this fraction of the best, between (0, 1) Both with respect to the validation predictions. - If performance_range_threshold > 0, might return less models max_models_on_disc: int | float | None = 100 Defines the maximum number of models that are kept in the disc. @@ -93,17 +96,6 @@ def __init__( * None: the feature is disabled. - performance_range_threshold: float = 0 - Will at most return the minimum between ensemble_nbest models, - and max_models_on_disc. Might return less - - Keep only models that are better than: - - x = performance_range_threshold - x * dummy - - E.g dummy=2, best=4, thresh=0.5 --> only consider models with loss > 3 - seed: int = 1 random seed that is used as part of the filename @@ -146,17 +138,17 @@ def __init__( self.logger.debug(f"Using behaviour when {t} for {ensemble_nbest}:{t}") self.seed = seed - self.metric = metric + self.metrics = metrics self.backend = backend self.precision = precision self.task_type = task_type self.memory_limit = memory_limit self.read_at_most = read_at_most - self.random_state = random_state + self.random_state = check_random_state(random_state) self.dataset_name = dataset_name - self.ensemble_size = ensemble_size + self.ensemble_class = ensemble_class + self.ensemble_kwargs = ensemble_kwargs self.ensemble_nbest = ensemble_nbest - self.performance_range_threshold = performance_range_threshold # Decide if self.max_models_on_disc is a memory limit or model limit self.max_models_on_disc: int | None = None @@ -178,7 +170,9 @@ def __init__( # Data we may need datamanager: XYDataManager = self.backend.load_datamanager() + self._X_test: SUPPORTED_FEAT_TYPES | None = datamanager.data.get("X_test", None) self._y_test: np.ndarray | None = datamanager.data.get("Y_test", None) + self._X_ensemble: SUPPORTED_FEAT_TYPES | None = None self._y_ensemble: np.ndarray | None = None @property @@ -235,6 +229,29 @@ def targets(self, kind: str = "ensemble") -> np.ndarray | None: else: raise NotImplementedError(kind) + def X_data(self, kind: str = "ensemble") -> SUPPORTED_FEAT_TYPES: + """The ensemble targets used for training the ensemble + + It will attempt to load and cache them in memory but + return None if it can't. + + Returns + ------- + np.ndarray | None + The ensemble targets, if they can be loaded + """ + if kind == "ensemble": + if self._X_ensemble is None: + if os.path.exists(self.backend._get_input_ensemble_filename()): + self._X_ensemble = self.backend.load_input_ensemble() + return self._X_ensemble + + elif kind == "test": + return self._X_test + + else: + raise NotImplementedError(kind) + def run( self, iteration: int, @@ -432,7 +449,12 @@ def main( for run in requires_update: run.record_modified_times() # So we don't count as modified next time - run.loss = self.loss(run, kind="ensemble") + run.losses = { + metric.name: self.loss( + run, metric=metric, X_data=self.X_data("ensemble") + ) + for metric in self.metrics + } # Get the dummy and real runs dummies, candidates = split(runs, by=lambda r: r.is_dummy()) @@ -446,9 +468,16 @@ def main( memory_limit=self.model_memory_limit, ) - # If there are no candidates left, we just keep the best one + # If there are no candidates left, we just keep the best one, ordered by + # objectives and finally num_run if not any(candidates): - best = min(to_delete, key=lambda r: (r.loss, r.num_run)) + best = min( + to_delete, + key=lambda r: ( + *[r.losses.get(m.name, np.inf) for m in self.metrics], + r.num_run, + ), + ) candidates = [best] to_delete.remove(best) @@ -467,13 +496,11 @@ def main( dummies=dummies, better_than_dummy=True, nbest=self.ensemble_nbest, - performance_range_threshold=self.performance_range_threshold, ) if any(to_delete): self.logger.info( f"Deleting runs {to_delete} due to" - f" nbest={self.ensemble_nbest} and/or" - f" performance_range_threshold={self.performance_range_threshold}" + f" nbest={self.ensemble_nbest} or worse than dummies" ) deletable_runs.update(to_delete) else: @@ -522,13 +549,17 @@ def main( return self.ensemble_history, self.ensemble_nbest targets = cast(np.ndarray, self.targets("ensemble")) # Sure they exist + X_data = self.X_data("ensemble") ensemble = self.fit_ensemble( - candidates, + candidates=candidates, + X_data=X_data, targets=targets, - size=self.ensemble_size, + runs=runs, + ensemble_class=self.ensemble_class, + ensemble_kwargs=self.ensemble_kwargs, task=self.task_type, - metric=self.metric, + metrics=self.metrics, precision=self.precision, random_state=self.random_state, ) @@ -556,15 +587,19 @@ def main( run_preds = [r.predictions(kind, precision=self.precision) for r in models] pred = ensemble.predict(run_preds) + X_data = self.X_data(kind) - score = calculate_scores( + scores = calculate_scores( solution=pred_targets, prediction=pred, task_type=self.task_type, - metrics=[self.metric], + metrics=self.metrics, + X_data=X_data, scoring_functions=None, - )[self.metric.name] - performance_stamp[f"ensemble_{score_name}_score"] = score + ) + performance_stamp[f"ensemble_{score_name}_score"] = scores[ + self.metrics[0].name + ] self.ensemble_history.append(performance_stamp) # Lastly, delete any runs that need to be deleted. We save this as the last step @@ -579,6 +614,7 @@ def main( def requires_loss_update( self, runs: Sequence[Run], + metrics: Sequence[Scorer] | None = None, ) -> list[Run]: """ @@ -587,14 +623,19 @@ def requires_loss_update( runs : Sequence[Run] The runs to process + metrics: Sequence[Scorer] | None = None + The metrics to check for, defaults to builders + Returns ------- list[Run] The runs that require a loss to be calculated """ + metrics = metrics if metrics is not None else self.metrics + queue = [] for run in sorted(runs, key=lambda run: run.recorded_mtimes["ensemble"]): - if run.loss == np.inf: + if any(metric.name not in run.losses for metric in metrics): queue.append(run) elif run.was_modified(): @@ -610,7 +651,7 @@ def candidate_selection( *, better_than_dummy: bool = False, nbest: int | float | None = None, - performance_range_threshold: float | None = None, + metrics: Sequence[Scorer] | None = None, ) -> tuple[list[Run], set[Run]]: """Get a list of candidates from `runs`, garuanteeing at least one @@ -636,19 +677,16 @@ def candidate_selection( The nbest models to select. If `int`, acts as an absolute limit. If `float`, acts as a percentage of available candidates. - model_memory_limit : float | None - A maximum memory limit in MB for the runs to occupy. If the candidates at - this point exceed this limit, the best n candidates that fit into this limit - will be chosen. - - performance_range_threshold : float | None - A number in (0, 1) to select candidates from. Expects a dummy run for worst + metrics : Sequence[Scorer] | None = None + The metrics to consider, defaults to the builders Returns ------- (candidates: list[Run], discarded: set[Run]) A tuple of runs that are candidates and also those that didn't make it """ + metrics = metrics if metrics else self.metrics + if isinstance(dummies, Run): dummies = [dummies] @@ -658,7 +696,8 @@ def candidate_selection( # We filter out all runs that don't have any predictions for the ensemble candidates, discarded = split( - runs, by=lambda run: run.pred_path("ensemble").exists() + runs, + by=lambda run: run.pred_path("ensemble").exists(), ) all_discarded.update(discarded) @@ -669,10 +708,13 @@ def candidate_selection( for run in discarded: self.logger.warning(f"Have no ensemble predictions for {run}") - # Get all the ones that have a tangible loss + # Get all the ones that have all metrics and tangible losses + has_metrics = lambda r: all(m.name in r.losses for m in metrics) + tangible_losses = lambda r: all(loss < np.inf for loss in r.losses.values()) + candidates, discarded = split( candidates, - by=lambda r: r.loss < np.inf, + by=lambda r: has_metrics(r) and tangible_losses(r), ) all_discarded.update(discarded) @@ -681,29 +723,68 @@ def candidate_selection( return dummies, all_discarded if better_than_dummy: - dummies = sorted(dummies, key=lambda r: r.loss) - dummy_cutoff = dummies[0].loss - self.logger.debug(f"Using {dummy_cutoff} to filter candidates") - candidates, discarded = split( - candidates, - by=lambda r: r.loss < dummy_cutoff, - ) + # In the single objective case, they must explicitly be better than dummy + if len(metrics) == 1: + metric = metrics[0] + + best_dummy = min(dummies, key=lambda d: d.losses[metric.name]) + self.logger.debug(f"Using dummy {best_dummy} to filter candidates") + + candidates, discarded = split( + candidates, + by=lambda r: r.losses[metric.name] < best_dummy.losses[metric.name], + ) + + # In the multiobjective case, they must be better than at least one of the + # best dummies, where there is a best dummy for each metric + else: + best_dummies = { + metric.name: min(dummies, key=lambda d: d.losses[metric.name]) + for metric in metrics + } + self.logger.debug(f"Using dummies {best_dummies} to filter candidates") + + candidates, discarded = split( + candidates, + by=lambda r: any( + r.losses[metric_name] < best_dummy.losses[metric_name] + for metric_name, best_dummy in best_dummies.items() + ), + ) + all_discarded.update(discarded) # If there are no real candidates left, use the dummies if len(candidates) == 0: self.logger.warning( - "No models better than random - using Dummy loss!" + "No models better than random - using Dummy losses!" f"\n\tModels besides current dummy model: {len(candidates)}" f"\n\tDummy models: {len(dummies)}", ) return dummies, all_discarded - # Sort the candidates so that they ordered by best loss, using num_run for tie - candidates = sorted(candidates, key=lambda r: (r.loss, r.num_run)) - if nbest is not None: + # We order the runs according to a roundrobin of the metrics + # i.e. the 1st run will be best in metric[0], + # the 2nd run will be best in metric[1], + # the 3rd run will be second best in metric[0], + # the 4th run will be second best in metric[1], + # ... whoops, what would be the 5th run, third best in metric[0] was + # also the same as the 4th run. This was skipped + # the 5th run will be third best in metric[1] **as we skipped above** + # + # With removing duplicates, this means if a run was already contained, it + # will skip to the next member in the roundrobin fashion. + sorted_candidates = [ + sorted( + candidates, + key=lambda r: (r.losses.get(m.name, np.inf), r.num_run), + ) + for m in metrics + ] + candidates = list(roundrobin(*sorted_candidates, duplicates=False)) + # Determine how many to keep, always keeping one if isinstance(nbest, float): nkeep = int(len(candidates) * nbest) @@ -720,35 +801,22 @@ def candidate_selection( all_discarded.update(discarded) - if performance_range_threshold is not None: - x = performance_range_threshold - worst = dummies[0].loss - best = candidates[0].loss - - cutoff = x * best + (1 - x) * worst - - candidates, discarded = cut(candidates, where=lambda r: r.loss >= cutoff) - - # Always preserve at least one, the best - if len(candidates) == 0: - candidates, discared = cut(discarded, 1) - self.logger.warning("No models in performance range, using single best") - - all_discarded.update(discarded) - return candidates, all_discarded def fit_ensemble( self, - runs: list[Run], + candidates: list[Run], + X_data: SUPPORTED_FEAT_TYPES, targets: np.ndarray, *, - size: int | None = None, + runs: list[Run], + ensemble_class: Type[AbstractEnsemble] = EnsembleSelection, + ensemble_kwargs: Dict[str, Any] | None = None, task: int | None = None, - metric: Scorer | None = None, + metrics: Sequence[Scorer] | None = None, precision: int | None = None, random_state: int | np.random.RandomState | None = None, - ) -> EnsembleSelection: + ) -> AbstractEnsemble: """Fit an ensemble from the provided runs. Note @@ -757,20 +825,29 @@ def fit_ensemble( Parameters ---------- - runs: list[Run] + candidates: list[Run] List of runs to build an ensemble from + X_data: SUPPORTED_FEAT_TYPES + The base level data. + targets: np.ndarray The targets to build the ensemble with - size: int | None = None - The size of the ensemble to build + runs: list[Run] + List of all runs (also pruned ones and dummy runs) + + ensemble_class: AbstractEnsemble + Implementation of the ensemble algorithm. + + ensemble_kwargs: Dict[str, Any] + Arguments passed to the constructor of the ensemble algorithm. task: int | None = None The kind of task performed - metric: Scorer | None = None - The metric to use when comparing run predictions to the targets + metrics: Sequence[Scorer] | None = None + Metrics to optimize the ensemble for. precision: int | None = None The precision with which to load run predictions @@ -780,33 +857,41 @@ def fit_ensemble( Returns ------- - ensemble: EnsembleSelection - The trained ensemble + AbstractEnsemble """ task = task if task is not None else self.task_type - size = size if size is not None else self.ensemble_size - metric = metric if metric is not None else self.metric + ensemble_class = ( + ensemble_class if ensemble_class is not None else self.ensemble_class + ) + ensemble_kwargs = ( + ensemble_kwargs if ensemble_kwargs is not None else self.ensemble_kwargs + ) + ensemble_kwargs = ensemble_kwargs if ensemble_kwargs is not None else {} + metrics = metrics if metrics is not None else self.metrics rs = random_state if random_state is not None else self.random_state - ensemble = EnsembleSelection( - ensemble_size=size, + ensemble = ensemble_class( task_type=task, - metric=metric, + metrics=metrics, random_state=rs, - ) + backend=self.backend, + **ensemble_kwargs, + ) # type: AbstractEnsemble - self.logger.debug(f"Fitting ensemble on {len(runs)} models") + self.logger.debug(f"Fitting ensemble on {len(candidates)} models") start_time = time.time() precision = precision if precision is not None else self.precision predictions_train = [ - run.predictions("ensemble", precision=precision) for run in runs + run.predictions("ensemble", precision=precision) for run in candidates ] ensemble.fit( - predictions=predictions_train, - labels=targets, - identifiers=[run.id for run in runs], + base_models_predictions=predictions_train, + X_data=X_data, + true_targets=targets, + model_identifiers=[run.id for run in candidates], + runs=runs, ) duration = time.time() - start_time @@ -819,11 +904,15 @@ def requires_deletion( *, max_models: int | None = None, memory_limit: float | None = None, + metrics: Sequence[Scorer] | None = None, ) -> tuple[list[Run], set[Run]]: """Cut a list of runs into those to keep and those to delete If neither params are specified, this method should do nothing. + In the case of multiple metrics, we interleave these according to the order of + the provided metrics. + Parameters ---------- runs : Sequence[Run] @@ -835,6 +924,10 @@ def requires_deletion( memory_limit : float | None = None The memory limit in MB, leave `None` for no effect + metrics: Sequence[Scorer] | None = None + The metrics to consider for the runs, defaults to `self.metrics` passed + during construction if `None`. + Returns ------- (keep: list[Run], delete: set[Run]) @@ -843,14 +936,37 @@ def requires_deletion( if memory_limit is None and max_models is None: return list(runs), set() - # Start with keep all runs and dummies, deleteing None - keep = sorted(runs, key=lambda r: (r.loss, r.num_run)) + metrics = metrics if metrics is not None else self.metrics + + # We order the runs according to a roundrobin of the metrics + # i.e. the 1st run will be best in metric[0], + # the 2nd run will be best in metric[1], + # the 3rd run will be second best in metric[0], + # the 4th run will be second best in metric[1], + # ... whoops, what would be the 5th run, third best in metric[0] was also + # ... the same as the 4th run. This was skipped + # the 5th run will be third best in metric[1] **as we skipped above** + # + # With removing duplicates, this means if a run was already contained, it will + # skip to the next member in the roundrobin fashion. + sorted_runs = [ + sorted(runs, key=lambda r: (r.losses.get(metric.name, np.inf), r.num_run)) + for metric in metrics + ] + keep = list(roundrobin(*sorted_runs, duplicates=False)) + delete: set[Run] = set() if max_models is not None and max_models < len(runs): keep, to_delete = cut(keep, max_models) if any(to_delete): + self.logger.info( + "Limiting num of models via `max_models_on_disc`" + f" max_models={max_models}" + f" remaining={len(keep)}" + f" discarded={len(to_delete)}" + ) delete.update(to_delete) if memory_limit is not None: @@ -863,7 +979,7 @@ def requires_deletion( keep, to_delete = cut(keep, cutpoint) if any(to_delete): - self.logger.warning( + self.logger.info( "Limiting num of models via `memory_limit`" f" memory_limit={memory_limit}" f" cutoff={cutoff}" @@ -875,7 +991,13 @@ def requires_deletion( return keep, delete - def loss(self, run: Run, kind: str = "ensemble") -> float: + def loss( + self, + run: Run, + metric: Scorer, + X_data: SUPPORTED_FEAT_TYPES, + kind: str = "ensemble", + ) -> float: """Calculate the loss for a run Parameters @@ -883,6 +1005,12 @@ def loss(self, run: Run, kind: str = "ensemble") -> float: run: Run The run to calculate the loss for + metric: Scorer + The metric to calculate the loss of + + kind: str = "ensemble" + The kind of targets to use for the run + Returns ------- float @@ -895,14 +1023,16 @@ def loss(self, run: Run, kind: str = "ensemble") -> float: try: predictions = run.predictions(kind, precision=self.precision) - loss: float = calculate_losses( # type: ignore + loss: float = calculate_loss( solution=targets, prediction=predictions, task_type=self.task_type, - metrics=[self.metric], - )[self.metric.name] + metric=metric, + X_data=X_data, + ) except Exception as e: - self.logger.error(f"Error getting loss {run}:{e}{traceback.format_exc()}") + tb = traceback.format_exc() + self.logger.error(f"Error getting loss `{metric.name}` for {run}:{e}{tb}") loss = np.inf finally: return loss diff --git a/autosklearn/ensemble_building/manager.py b/autosklearn/ensemble_building/manager.py index 46ab291bc2..31a42b2dbe 100644 --- a/autosklearn/ensemble_building/manager.py +++ b/autosklearn/ensemble_building/manager.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import Any +from typing import Any, Dict, Sequence, Type import logging.handlers import time @@ -8,6 +8,7 @@ import dask.distributed import numpy as np +from sklearn.utils.validation import check_random_state from smac.callbacks import IncorporateRunResultCallback from smac.optimizer.smbo import SMBO from smac.runhistory.runhistory import RunInfo, RunValue @@ -15,6 +16,8 @@ from autosklearn.automl_common.common.utils.backend import Backend from autosklearn.ensemble_building.builder import EnsembleBuilder +from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble +from autosklearn.ensembles.ensemble_selection import EnsembleSelection from autosklearn.metrics import Scorer from autosklearn.util.logging_ import get_named_client_logger @@ -25,11 +28,12 @@ def __init__( backend: Backend, dataset_name: str, task: int, - metric: Scorer, + metrics: Sequence[Scorer], time_left_for_ensembles: float = np.inf, max_iterations: int | None = None, pynisher_context: str = "fork", - ensemble_size: int = 50, + ensemble_class: Type[AbstractEnsemble] = EnsembleSelection, + ensemble_kwargs: Dict[str, Any] | None = None, ensemble_nbest: int | float = 50, max_models_on_disc: int | float | None = None, seed: int = 1, @@ -53,8 +57,8 @@ def __init__( task: int Type of ML task - metric: Scorer - Metric to compute the loss of the given predictions + metrics: Sequence[Scorer] + Metrics to optimize the ensemble for time_left_for_ensemble: float = np.inf How much time is left for the task in seconds. @@ -67,8 +71,15 @@ def __init__( pynisher_context: "spawn" | "fork" | "forkserver" = "fork" The multiprocessing context for pynisher. - ensemble_size: int = 50 - maximal size of ensemble + ensemble_class : Type[AbstractEnsemble] (default=EnsembleSelection) + Class implementing the post-hoc ensemble algorithm. Set to + ``None`` to disable ensemble building or use ``SingleBest`` + to obtain only use the single best model instead of an + ensemble. + + ensemble_kwargs : Dict, optional + Keyword arguments that are passed to the ensemble class upon + initialization. ensemble_nbest: int | float = 50 If int: consider only the n best prediction @@ -114,8 +125,9 @@ def __init__( self.backend = backend self.dataset_name = dataset_name self.task = task - self.metric = metric - self.ensemble_size = ensemble_size + self.metrics = metrics + self.ensemble_class = ensemble_class + self.ensemble_kwargs = ensemble_kwargs self.ensemble_nbest = ensemble_nbest self.max_models_on_disc = max_models_on_disc self.seed = seed @@ -123,7 +135,7 @@ def __init__( self.max_iterations = max_iterations self.read_at_most = read_at_most self.memory_limit = memory_limit - self.random_state = random_state + self.random_state = check_random_state(random_state) self.logger_port = logger_port self.pynisher_context = pynisher_context @@ -223,8 +235,9 @@ def build_ensemble( backend=self.backend, dataset_name=self.dataset_name, task_type=self.task, - metric=self.metric, - ensemble_size=self.ensemble_size, + metrics=self.metrics, + ensemble_class=self.ensemble_class, + ensemble_kwargs=self.ensemble_kwargs, ensemble_nbest=self.ensemble_nbest, max_models_on_disc=self.max_models_on_disc, seed=self.seed, @@ -263,9 +276,10 @@ def fit_and_return_ensemble( backend: Backend, dataset_name: str, task_type: int, - metric: Scorer, + metrics: Sequence[Scorer], pynisher_context: str, - ensemble_size: int = 50, + ensemble_class: Type[AbstractEnsemble] = EnsembleSelection, + ensemble_kwargs: Dict[str, Any] | None = None, ensemble_nbest: int | float = 50, max_models_on_disc: int | float | None = None, seed: int = 1, @@ -297,14 +311,21 @@ def fit_and_return_ensemble( task_type: int type of ML task - metric: Scorer - Metric to compute the loss of the given predictions + metrics: Sequence[Scorer] + Metrics to optimize the ensemble for. pynisher_context: "fork" | "spawn" | "forkserver" = "fork" Context to use for multiprocessing, can be either fork, spawn or forkserver. - ensemble_size: int = 50 - Maximal size of ensemble + ensemble_class : Type[AbstractEnsemble] (default=EnsembleSelection) + Class implementing the post-hoc ensemble algorithm. Set to + ``None`` to disable ensemble building or use ``SingleBest`` + to obtain only use the single best model instead of an + ensemble. + + ensemble_kwargs : Dict, optional + Keyword arguments that are passed to the ensemble class upon + initialization. ensemble_nbest: int | float = 50 If int: consider only the n best prediction @@ -348,19 +369,21 @@ def fit_and_return_ensemble( (ensemble_history: list[dict[str, Any]], nbest: int | float) The ensemble history and the nbest chosen members """ + random_state = check_random_state(random_state) result = EnsembleBuilder( backend=backend, dataset_name=dataset_name, task_type=task_type, - metric=metric, - ensemble_size=ensemble_size, + metrics=metrics, + ensemble_class=ensemble_class, + ensemble_kwargs=ensemble_kwargs, ensemble_nbest=ensemble_nbest, max_models_on_disc=max_models_on_disc, seed=seed, precision=precision, memory_limit=memory_limit, read_at_most=read_at_most, - random_state=random_state, + random_state=random_state.randint(10000000), logger_port=logger_port, ).run( end_at=end_at, diff --git a/autosklearn/ensemble_building/run.py b/autosklearn/ensemble_building/run.py index fa73f91e45..9687169944 100644 --- a/autosklearn/ensemble_building/run.py +++ b/autosklearn/ensemble_building/run.py @@ -44,7 +44,9 @@ def __init__(self, path: Path) -> None: self.num_run = int(num_run) self.budget = float(budget) - self.loss: float = np.inf + # These are ordered based on preference + self.losses: dict[str, float] = {} + self._mem_usage: float | None = None # Items that will be delete when the run is saved back to file @@ -160,7 +162,7 @@ def __hash__(self) -> int: return hash(self.id) def __repr__(self) -> str: - return f"Run(id={self.id}, loss={self.loss})" + return f"Run(id={self.id}, losses={self.losses})" def __eq__(self, other: object) -> bool: return isinstance(other, Run) and other.id == self.id diff --git a/autosklearn/ensembles/__init__.py b/autosklearn/ensembles/__init__.py index e69de29bb2..4e1af926cf 100644 --- a/autosklearn/ensembles/__init__.py +++ b/autosklearn/ensembles/__init__.py @@ -0,0 +1,10 @@ +from .abstract_ensemble import AbstractEnsemble, AbstractMultiObjectiveEnsemble +from .ensemble_selection import EnsembleSelection +from .singlebest_ensemble import SingleBest + +__all__ = [ + "AbstractEnsemble", + "AbstractMultiObjectiveEnsemble", + "EnsembleSelection", + "SingleBest", +] diff --git a/autosklearn/ensembles/abstract_ensemble.py b/autosklearn/ensembles/abstract_ensemble.py index 24d352ab5e..ea14a45e90 100644 --- a/autosklearn/ensembles/abstract_ensemble.py +++ b/autosklearn/ensembles/abstract_ensemble.py @@ -1,20 +1,36 @@ -from abc import ABCMeta, abstractmethod -from typing import Dict, List, Tuple, Union +from __future__ import annotations + +from abc import ABC, abstractmethod +from typing import Dict, List, Sequence, Tuple, Union import numpy as np +from autosklearn.automl_common.common.utils.backend import Backend +from autosklearn.data.validation import SUPPORTED_FEAT_TYPES +from autosklearn.ensemble_building.run import Run +from autosklearn.metrics import Scorer from autosklearn.pipeline.base import BasePipeline -class AbstractEnsemble(object): - __metaclass__ = ABCMeta +class AbstractEnsemble(ABC): + @abstractmethod + def __init__( + self, + task_type: int, + metrics: Sequence[Scorer] | Scorer, + random_state: int | np.random.RandomState | None, + backend: Backend, + ): + pass @abstractmethod def fit( self, - base_models_predictions: np.ndarray, + base_models_predictions: np.ndarray | List[np.ndarray], + X_data: SUPPORTED_FEAT_TYPES, true_targets: np.ndarray, model_identifiers: List[Tuple[int, int, float]], + runs: Sequence[Run], ) -> "AbstractEnsemble": """Fit an ensemble given predictions of base models and targets. @@ -28,11 +44,20 @@ def fit( n_targets is the number of classes in case of classification, n_targets is 0 or 1 in case of regression + Can be a list of 2d numpy arrays as well to prevent copying all + predictions into a single, large numpy array. + + X_data : list-like or sparse data + true_targets : array of shape [n_targets] model_identifiers : identifier for each base model. Can be used for practical text output of the ensemble. + runs: Sequence[Run] + Additional information for each run executed by SMAC that was + considered by the ensemble builder. + Returns ------- self @@ -59,8 +84,10 @@ def predict( pass @abstractmethod - def get_models_with_weights(self, models: Dict) -> List[Tuple[float, BasePipeline]]: - """Return a list of (weight, model) pairs + def get_models_with_weights( + self, models: Dict[Tuple[int, int, float], BasePipeline] + ) -> List[Tuple[float, BasePipeline]]: + """Return a list of (weight, model) pairs for all models included in the ensemble. Parameters ---------- @@ -73,6 +100,24 @@ def get_models_with_weights(self, models: Dict) -> List[Tuple[float, BasePipelin array : [(weight_1, model_1), ..., (weight_n, model_n)] """ + @abstractmethod + def get_identifiers_with_weights( + self, + ) -> List[Tuple[Tuple[int, int, float], float]]: + """Return a (identifier, weight)-pairs for all models that were passed to the + ensemble builder. + + Parameters + ---------- + models : dict {identifier : model object} + The identifiers are the same as the one presented to the fit() + method. Models can be used for nice printing. + + Returns + ------- + array : [(identifier_1, weight_1), ..., (identifier_n, weight_n)] + """ + @abstractmethod def get_selected_model_identifiers(self) -> List[Tuple[int, int, float]]: """Return identifiers of models in the ensemble. @@ -92,3 +137,8 @@ def get_validation_performance(self) -> float: ------ float """ + + +class AbstractMultiObjectiveEnsemble(AbstractEnsemble): + def get_pareto_set(self) -> Sequence[AbstractEnsemble]: + pass diff --git a/autosklearn/ensembles/ensemble_selection.py b/autosklearn/ensembles/ensemble_selection.py index 0c99db64c1..310f2eb6a4 100644 --- a/autosklearn/ensembles/ensemble_selection.py +++ b/autosklearn/ensembles/ensemble_selection.py @@ -1,12 +1,18 @@ -from typing import Any, Dict, List, Optional, Tuple, Union +from __future__ import annotations + +from typing import Any, Dict, List, Optional, Sequence, Tuple, Union import random +import warnings from collections import Counter import numpy as np from sklearn.utils import check_random_state +from autosklearn.automl_common.common.utils.backend import Backend from autosklearn.constants import TASK_TYPES +from autosklearn.data.validation import SUPPORTED_FEAT_TYPES +from autosklearn.ensemble_building.run import Run from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble from autosklearn.metrics import Scorer, calculate_losses from autosklearn.pipeline.base import BasePipeline @@ -15,12 +21,13 @@ class EnsembleSelection(AbstractEnsemble): def __init__( self, - ensemble_size: int, task_type: int, - metric: Scorer, + metrics: Sequence[Scorer] | Scorer, + random_state: Optional[Union[int, np.random.RandomState]], + backend: Backend, + ensemble_size: int = 50, bagging: bool = False, mode: str = "fast", - random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> None: """An ensemble of selected algorithms @@ -31,25 +38,50 @@ def __init__( ---------- task_type: int An identifier indicating which task is being performed. - metric: Scorer - The metric used to evaluate the models + + metrics: Sequence[Scorer] | Scorer + The metric used to evaluate the models. If multiple metrics are passed, + ensemble selection only optimizes for the first + + random_state: Optional[int | RandomState] = None + The random_state used for ensemble selection. + + * None - Uses numpy's default RandomState object + * int - Successive calls to fit will produce the same results + * RandomState - Truly random, each call to fit will produce + different results, even with the same object. + + backend : Backend + Gives access to the backend of Auto-sklearn. Not used by Ensemble Selection. + bagging: bool = False Whether to use bagging in ensemble selection + mode: str in ['fast', 'slow'] = 'fast' Which kind of ensemble generation to use - * 'slow' - The original method used in Rich Caruana's ensemble selection. - * 'fast' - A faster version of Rich Caruanas' ensemble selection. + * 'slow' - The original method used in Rich Caruana's ensemble selection. + * 'fast' - A faster version of Rich Caruanas' ensemble selection. - random_state: Optional[int | RandomState] = None - The random_state used for ensemble selection. - * None - Uses numpy's default RandomState object - * int - Successive calls to fit will produce the same results - * RandomState - Truely random, each call to fit will produce - different results, even with the same object. - """ + References + ---------- + | Ensemble selection from libraries of models + | Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew and Alex Ksikes + | ICML 2004 + | https://dl.acm.org/doi/10.1145/1015330.1015432 + | https://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml04.icdm06long.pdf + """ # noqa: E501 self.ensemble_size = ensemble_size self.task_type = task_type - self.metric = metric + if isinstance(metrics, Sequence): + if len(metrics) > 1: + warnings.warn( + "Ensemble selection can only optimize one metric, " + "but multiple metrics were passed, dropping all " + "except for the first metric." + ) + self.metric = metrics[0] + else: + self.metric = metrics self.bagging = bagging self.mode = mode @@ -68,15 +100,16 @@ def __getstate__(self) -> Dict[str, Any]: # one in __main__. we don't use the metric # in the EnsembleSelection so this should # be fine - self.metric = None # type: ignore - return self.__dict__ + return {key: value for key, value in self.__dict__.items() if key != "metrics"} def fit( self, - predictions: List[np.ndarray], - labels: np.ndarray, - identifiers: List[Tuple[int, int, float]], - ) -> AbstractEnsemble: + base_models_predictions: List[np.ndarray], + X_data: SUPPORTED_FEAT_TYPES, + true_targets: np.ndarray, + model_identifiers: List[Tuple[int, int, float]], + runs: Sequence[Run], + ) -> EnsembleSelection: self.ensemble_size = int(self.ensemble_size) if self.ensemble_size < 1: raise ValueError("Ensemble size cannot be less than one!") @@ -94,27 +127,33 @@ def fit( raise ValueError("Unknown mode %s" % self.mode) if self.bagging: - self._bagging(predictions, labels) + self._bagging(base_models_predictions, true_targets) else: - self._fit(predictions, labels) + self._fit( + predictions=base_models_predictions, + X_data=X_data, + labels=true_targets, + ) self._calculate_weights() - self.identifiers_ = identifiers + self.identifiers_ = model_identifiers return self def _fit( self, predictions: List[np.ndarray], + X_data: SUPPORTED_FEAT_TYPES, labels: np.ndarray, - ) -> AbstractEnsemble: + ) -> EnsembleSelection: if self.mode == "fast": - self._fast(predictions, labels) + self._fast(predictions, X_data, labels) else: - self._slow(predictions, labels) + self._slow(predictions, X_data, labels) return self def _fast( self, predictions: List[np.ndarray], + X_data: SUPPORTED_FEAT_TYPES, labels: np.ndarray, ) -> None: """Fast version of Rich Caruana's ensemble selection method.""" @@ -169,6 +208,7 @@ def _fast( prediction=fant_ensemble_prediction, task_type=self.task_type, metrics=[self.metric], + X_data=X_data, scoring_functions=None, )[self.metric.name] @@ -188,7 +228,12 @@ def _fast( self.trajectory_ = trajectory self.train_loss_ = trajectory[-1] - def _slow(self, predictions: List[np.ndarray], labels: np.ndarray) -> None: + def _slow( + self, + predictions: List[np.ndarray], + X_data: SUPPORTED_FEAT_TYPES, + labels: np.ndarray, + ) -> None: """Rich Caruana's ensemble selection method.""" self.num_input_models_ = len(predictions) @@ -211,6 +256,7 @@ def _slow(self, predictions: List[np.ndarray], labels: np.ndarray) -> None: prediction=ensemble_prediction, task_type=self.task_type, metrics=[self.metric], + X_data=X_data, scoring_functions=None, )[self.metric.name] ensemble.pop() @@ -273,23 +319,25 @@ def _bagging( dtype=np.int64, ) - def predict(self, predictions: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray: + def predict( + self, base_models_predictions: Union[np.ndarray, List[np.ndarray]] + ) -> np.ndarray: - average = np.zeros_like(predictions[0], dtype=np.float64) - tmp_predictions = np.empty_like(predictions[0], dtype=np.float64) + average = np.zeros_like(base_models_predictions[0], dtype=np.float64) + tmp_predictions = np.empty_like(base_models_predictions[0], dtype=np.float64) # if predictions.shape[0] == len(self.weights_), # predictions include those of zero-weight models. - if len(predictions) == len(self.weights_): - for pred, weight in zip(predictions, self.weights_): + if len(base_models_predictions) == len(self.weights_): + for pred, weight in zip(base_models_predictions, self.weights_): np.multiply(pred, weight, out=tmp_predictions) np.add(average, tmp_predictions, out=average) # if prediction model.shape[0] == len(non_null_weights), # predictions do not include those of zero-weight models. - elif len(predictions) == np.count_nonzero(self.weights_): + elif len(base_models_predictions) == np.count_nonzero(self.weights_): non_null_weights = [w for w in self.weights_ if w > 0] - for pred, weight in zip(predictions, non_null_weights): + for pred, weight in zip(base_models_predictions, non_null_weights): np.multiply(pred, weight, out=tmp_predictions) np.add(average, tmp_predictions, out=average) @@ -322,7 +370,7 @@ def __str__(self) -> str: ) def get_models_with_weights( - self, models: BasePipeline + self, models: Dict[Tuple[int, int, float], BasePipeline] ) -> List[Tuple[float, BasePipeline]]: output = [] for i, weight in enumerate(self.weights_): @@ -335,6 +383,11 @@ def get_models_with_weights( return output + def get_identifiers_with_weights( + self, + ) -> List[Tuple[Tuple[int, int, float], float]]: + return list(zip(self.identifiers_, self.weights_)) + def get_selected_model_identifiers(self) -> List[Tuple[int, int, float]]: output = [] diff --git a/autosklearn/ensembles/singlebest_ensemble.py b/autosklearn/ensembles/singlebest_ensemble.py index 58e026dff2..497ee4d495 100644 --- a/autosklearn/ensembles/singlebest_ensemble.py +++ b/autosklearn/ensembles/singlebest_ensemble.py @@ -1,4 +1,6 @@ -from typing import List, Tuple, Union +from __future__ import annotations + +from typing import Dict, List, Sequence, Tuple, Union import os @@ -6,6 +8,8 @@ from smac.runhistory.runhistory import RunHistory from autosklearn.automl_common.common.utils.backend import Backend +from autosklearn.data.validation import SUPPORTED_FEAT_TYPES +from autosklearn.ensemble_building.run import Run from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble from autosklearn.metrics import Scorer from autosklearn.pipeline.base import BasePipeline @@ -23,12 +27,20 @@ class SingleBest(AbstractEnsemble): def __init__( self, - metric: Scorer, + task_type: int, + metrics: Sequence[Scorer] | Scorer, + random_state: int | np.random.RandomState | None, + backend: Backend, run_history: RunHistory, seed: int, - backend: Backend, ): - self.metric = metric + self.task_type = task_type + if isinstance(metrics, Sequence): + self.metrics = metrics + elif isinstance(metrics, Scorer): + self.metrics = [metrics] + else: + raise TypeError(type(metrics)) self.seed = seed self.backend = backend @@ -38,6 +50,16 @@ def __init__( self.run_history = run_history self.identifiers_ = self.get_identifiers_from_run_history() + def fit( + self, + base_models_predictions: np.ndarray | List[np.ndarray], + X_data: SUPPORTED_FEAT_TYPES, + true_targets: np.ndarray, + model_identifiers: List[Tuple[int, int, float]], + runs: Sequence[Run], + ) -> "AbstractEnsemble": + return self + def get_identifiers_from_run_history(self) -> List[Tuple[int, int, float]]: """Parses the run history, to identify the best performing model @@ -45,14 +67,19 @@ def get_identifiers_from_run_history(self) -> List[Tuple[int, int, float]]: the actual model. """ best_model_identifier = [] - best_model_score = self.metric._worst_possible_result + best_model_score = self.metrics[0]._worst_possible_result for run_key in self.run_history.data.keys(): run_value = self.run_history.data[run_key] - score = self.metric._optimum - (self.metric._sign * run_value.cost) - - if (score > best_model_score and self.metric._sign > 0) or ( - score < best_model_score and self.metric._sign < 0 + print(run_key, run_value) + if len(self.metrics) == 1: + cost = run_value.cost + else: + cost = run_value.cost[0] + score = self.metrics[0]._optimum - (self.metrics[0]._sign * cost) + + if (score > best_model_score and self.metrics[0]._sign > 0) or ( + score < best_model_score and self.metrics[0]._sign < 0 ): # Make sure that the individual best model actually exists @@ -85,6 +112,8 @@ def get_identifiers_from_run_history(self) -> List[Tuple[int, int, float]]: " fit a valid model. Please check the log file for errors." ) + self.best_model_score_ = best_model_score + return best_model_identifier def predict(self, predictions: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray: @@ -108,7 +137,7 @@ def __str__(self) -> str: ) def get_models_with_weights( - self, models: BasePipeline + self, models: Dict[Tuple[int, int, float], BasePipeline] ) -> List[Tuple[float, BasePipeline]]: output = [] for i, weight in enumerate(self.weights_): @@ -121,6 +150,11 @@ def get_models_with_weights( return output + def get_identifiers_with_weights( + self, + ) -> List[Tuple[Tuple[int, int, float], float]]: + return list(zip(self.identifiers_, self.weights_)) + def get_selected_model_identifiers(self) -> List[Tuple[int, int, float]]: output = [] @@ -130,3 +164,6 @@ def get_selected_model_identifiers(self) -> List[Tuple[int, int, float]]: output.append(identifier) return output + + def get_validation_performance(self) -> float: + return self.best_model_score_ diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 5e891b0a8e..7144fcc39c 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -1,7 +1,20 @@ # -*- encoding: utf-8 -*- from __future__ import annotations -from typing import Any, Dict, Iterable, List, Mapping, Optional, Sequence, Tuple, Union +from typing import ( + Any, + Dict, + Iterable, + List, + Mapping, + Optional, + Sequence, + Tuple, + Type, + Union, +) + +import warnings import dask.distributed import joblib @@ -10,6 +23,7 @@ from ConfigSpace.configuration_space import Configuration, ConfigurationSpace from scipy.sparse import spmatrix from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin +from sklearn.ensemble import VotingClassifier, VotingRegressor from sklearn.utils.multiclass import type_of_target from smac.runhistory.runhistory import RunInfo, RunValue from typing_extensions import Literal @@ -20,6 +34,8 @@ SUPPORTED_TARGET_TYPES, convert_if_sparse, ) +from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble +from autosklearn.ensembles.ensemble_selection import EnsembleSelection from autosklearn.metrics import Scorer from autosklearn.pipeline.base import BasePipeline @@ -30,7 +46,9 @@ def __init__( time_left_for_this_task=3600, per_run_time_limit=None, initial_configurations_via_metalearning=25, - ensemble_size: int = 50, + ensemble_size: int | None = None, + ensemble_class: Type[AbstractEnsemble] | None = EnsembleSelection, + ensemble_kwargs: Dict[str, Any] | None = None, ensemble_nbest=50, max_models_on_disc=50, seed=1, @@ -76,15 +94,31 @@ def __init__( datasets. Disable if the hyperparameter optimization algorithm should start from scratch. - ensemble_size : int, optional (default=50) + ensemble_size : int, optional Number of models added to the ensemble built by *Ensemble selection from libraries of models*. Models are drawn with - replacement. If set to ``0`` no ensemble is fit and the single - best model is loaded. + replacement. If set to ``0`` no ensemble is fit. + + Deprecated - will be removed in Auto-sklearn 0.16. Please pass + this argument via ``ensemble_kwargs={"ensemble_size": int}`` + if you want to change the ensemble size for ensemble selection. + + ensemble_class : Type[AbstractEnsemble], optional (default=EnsembleSelection) + Class implementing the post-hoc ensemble algorithm. Set to + ``None`` to disable ensemble building or use ``SingleBest`` + to obtain only use the single best model instead of an + ensemble. + + ensemble_kwargs : Dict, optional + Keyword arguments that are passed to the ensemble class upon + initialization. ensemble_nbest : int, optional (default=50) Only consider the ``ensemble_nbest`` models when building an - ensemble. + ensemble. This is inspired by a concept called library pruning + introduced in `Getting Most out of Ensemble Selection`. This + is independent of the ``ensemble_class`` argument and this + pruning step is done prior to constructing an ensemble. max_models_on_disc: int, optional (default=50), Defines the maximum number of models that are kept in the disc. @@ -352,7 +386,50 @@ def __init__( self.initial_configurations_via_metalearning = ( initial_configurations_via_metalearning ) - self.ensemble_size = ensemble_size + self.ensemble_class = ensemble_class + + # User specified `ensemble_size` explicitly, warn them about deprecation + if ensemble_size is not None: + # Keep consistent behaviour + message = ( + "`ensemble_size` has been deprecated, please use `ensemble_kwargs = " + "{'ensemble_size': %d}`. Inserting `ensemble_size` into " + "`ensemble_kwargs` for now. `ensemble_size` will be removed in " + "auto-sklearn 0.16." + ) % ensemble_size + if ensemble_class == EnsembleSelection: + if ensemble_kwargs is None: + ensemble_kwargs = {"ensemble_size": ensemble_size} + warnings.warn(message, DeprecationWarning, stacklevel=2) + elif "ensemble_size" not in ensemble_kwargs: + ensemble_kwargs["ensemble_size"] = ensemble_size + warnings.warn(message, DeprecationWarning, stacklevel=2) + else: + warnings.warn( + "Deprecated argument `ensemble_size` is both provided " + "as an argument to the constructor and passed inside " + "`ensemble_kwargs`. Will ignore the argument and use " + "the value given in `ensemble_kwargs` (%d). `ensemble_size` " + "will be removed in auto-sklearn 0.16." + % ensemble_kwargs["ensemble_size"], + DeprecationWarning, + stacklevel=2, + ) + else: + warnings.warn( + "`ensemble_size` has been deprecated, please use " + "`ensemble_kwargs = {'ensemble_size': %d} if this " + "was intended. Ignoring `ensemble_size` because " + "`ensemble_class` != EnsembleSelection. " + "`ensemble_size` will be removed in auto-sklearn 0.16." + % ensemble_size, + DeprecationWarning, + stacklevel=2, + ) + self.ensemble_size = ( + ensemble_size # Otherwise sklean.base.get_params() will complain + ) + self.ensemble_kwargs = ensemble_kwargs self.ensemble_nbest = ensemble_nbest self.max_models_on_disc = max_models_on_disc self.seed = seed @@ -404,7 +481,8 @@ def build_automl(self): time_left_for_this_task=self.time_left_for_this_task, per_run_time_limit=self.per_run_time_limit, initial_configurations_via_metalearning=initial_configs, - ensemble_size=self.ensemble_size, + ensemble_class=self.ensemble_class, + ensemble_kwargs=self.ensemble_kwargs, ensemble_nbest=self.ensemble_nbest, max_models_on_disc=self.max_models_on_disc, seed=self.seed, @@ -516,11 +594,13 @@ def fit_pipeline( def fit_ensemble( self, y, - task=None, - precision=32, - dataset_name=None, - ensemble_nbest=None, - ensemble_size=None, + task: int = None, + precision: Literal[16, 21, 64] = 32, + dataset_name: Optional[str] = None, + ensemble_size: int | None = None, + ensemble_kwargs: Optional[Dict[str, Any]] = None, + ensemble_nbest: Optional[int] = None, + ensemble_class: Optional[AbstractEnsemble] = EnsembleSelection, ): """Fit an ensemble to models trained during an optimization process. @@ -537,26 +617,84 @@ def fit_ensemble( the task type (binary classification, multiclass classification, multilabel classification or regression). - precision : str + precision : int Numeric precision used when loading ensemble data. Can be either - ``'16'``, ``'32'`` or ``'64'``. + ``16``, ``32`` or ``64``. dataset_name : str Name of the current data set. - ensemble_nbest : int - Determines how many models should be considered from the ensemble - building. This is inspired by a concept called library pruning - introduced in `Getting Most out of Ensemble Selection`. + ensemble_size : int, optional + Number of models added to the ensemble built by *Ensemble + selection from libraries of models*. Models are drawn with + replacement. If set to ``0`` no ensemble is fit. + + Deprecated - will be removed in Auto-sklearn 0.16. Please pass + this argument via ``ensemble_kwargs={"ensemble_size": int}`` + if you want to change the ensemble size for ensemble selection. + + ensemble_kwargs : Dict, optional + Keyword arguments that are passed to the ensemble class upon + initialization. - ensemble_size : int - Size of the ensemble built by `Ensemble Selection`. + ensemble_nbest : int + Only consider the ``ensemble_nbest`` models when building an + ensemble. This is inspired by a concept called library pruning + introduced in `Getting Most out of Ensemble Selection`. This + is independent of the ``ensemble_class`` argument and this + pruning step is done prior to constructing an ensemble. + + ensemble_class : Type[AbstractEnsemble], optional (default=EnsembleSelection) + Class implementing the post-hoc ensemble algorithm. Set to + ``None`` to disable ensemble building or use ``SingleBest`` + to obtain only use the single best model instead of an + ensemble. Returns ------- self """ + + # User specified `ensemble_size` explicitly, warn them about deprecation + if ensemble_size is not None: + # Keep consistent behaviour + message = ( + "`ensemble_size` has been deprecated, please use `ensemble_kwargs = " + "{'ensemble_size': %d}`. Inserting `ensemble_size` into " + "`ensemble_kwargs` for now. `ensemble_size` will be removed in " + "auto-sklearn 0.16." + ) % ensemble_size + if ensemble_class == EnsembleSelection: + if ensemble_kwargs is None: + ensemble_kwargs = {"ensemble_size": ensemble_size} + warnings.warn(message, DeprecationWarning, stacklevel=2) + elif "ensemble_size" not in ensemble_kwargs: + ensemble_kwargs["ensemble_size"] = ensemble_size + warnings.warn(message, DeprecationWarning, stacklevel=2) + else: + warnings.warn( + "Deprecated argument `ensemble_size` is both provided " + "as an argument to the constructor and passed inside " + "`ensemble_kwargs`. Will ignore the argument and use " + "the value given in `ensemble_kwargs` (%d). `ensemble_size` " + "will be removed in auto-sklearn 0.16." + % ensemble_kwargs["ensemble_size"], + DeprecationWarning, + stacklevel=2, + ) + else: + warnings.warn( + "`ensemble_size` has been deprecated, please use " + "`ensemble_kwargs = {'ensemble_size': %d} if this " + "was intended. Ignoring `ensemble_size` because " + "`ensemble_class` != EnsembleSelection. " + "`ensemble_size` will be removed in auto-sklearn 0.16." + % ensemble_size, + DeprecationWarning, + stacklevel=2, + ) + if self.automl_ is None: # Build a dummy automl object to call fit_ensemble # The ensemble size is honored in the .automl_.fit_ensemble @@ -568,7 +706,8 @@ def fit_ensemble( precision=precision, dataset_name=dataset_name, ensemble_nbest=ensemble_nbest, - ensemble_size=ensemble_size, + ensemble_class=ensemble_class, + ensemble_kwargs=ensemble_kwargs, ) return self @@ -964,13 +1103,11 @@ def additional_info_has_key(rv, key): ) # Get the models ensemble weight if it has one - # TODO both implementing classes of AbstractEnsemble have a property - # `identifiers_` and `weights_`, might be good to put it as an - # abstract property - # TODO `ensemble_.identifiers_` and `ensemble_.weights_` are loosely - # tied together by ordering, might be better to store as tuple - for i, weight in enumerate(self.automl_.ensemble_.weights_): - (_, model_id, _) = self.automl_.ensemble_.identifiers_[i] + for ( + _, + model_id, + _, + ), weight in self.automl_.ensemble_.get_identifiers_with_weights(): # We had issues where the model's in the ensembles are not in the runhistory # collected. I have no clue why this is but to prevent failures, we fill @@ -1185,6 +1322,9 @@ def get_configuration_space( else self.automl_.configuration_space ) + def get_pareto_set(self) -> Sequence[VotingClassifier | VotingRegressor]: + return self.automl_._load_pareto_set() + class AutoSklearnClassifier(AutoSklearnEstimator, ClassifierMixin): """This class implements the classification task.""" @@ -1193,7 +1333,7 @@ def fit(self, X, y, X_test=None, y_test=None, feat_type=None, dataset_name=None) """Fit *auto-sklearn* to given training set (X, y). Fit both optimizes the machine learning models and builds an ensemble - out of them. To disable ensembling, set ``ensemble_size==0``. + out of them. Parameters ---------- @@ -1327,7 +1467,7 @@ def fit(self, X, y, X_test=None, y_test=None, feat_type=None, dataset_name=None) """Fit *Auto-sklearn* to given training set (X, y). Fit both optimizes the machine learning models and builds an ensemble - out of them. To disable ensembling, set ``ensemble_size==0``. + out of them. Parameters ---------- diff --git a/autosklearn/evaluation/__init__.py b/autosklearn/evaluation/__init__.py index 52794fec03..aace158c00 100644 --- a/autosklearn/evaluation/__init__.py +++ b/autosklearn/evaluation/__init__.py @@ -332,7 +332,10 @@ def run( budget: float = 0.0, instance_specific: Optional[str] = None, ) -> Tuple[ - StatusType, float, float, Dict[str, Union[int, float, str, Dict, List, Tuple]] + StatusType, + float | list[float], + float, + Dict[str, Union[int, float, str, Dict, List, Tuple]], ]: # Additional information of each of the tae executions diff --git a/autosklearn/evaluation/abstract_evaluator.py b/autosklearn/evaluation/abstract_evaluator.py index 9f45acea41..efd87c6cc3 100644 --- a/autosklearn/evaluation/abstract_evaluator.py +++ b/autosklearn/evaluation/abstract_evaluator.py @@ -345,24 +345,14 @@ def _loss( ---------- y_true """ - if not isinstance(self.configuration, Configuration): - # Dummy prediction - rval = {} - for metric in self.scoring_functions if self.scoring_functions else []: - rval[metric.name] = metric._worst_possible_result - for metric in self.metrics: - rval[metric.name] = metric._worst_possible_result - return rval - - else: - return calculate_losses( - y_true, - y_hat, - self.task_type, - self.metrics, - X_data=X_data, - scoring_functions=self.scoring_functions, - ) + return calculate_losses( + y_true, + y_hat, + self.task_type, + self.metrics, + X_data=X_data, + scoring_functions=self.scoring_functions, + ) def finish_up( self, @@ -410,6 +400,12 @@ def finish_up( return self.duration, file_out_loss, self.seed, additional_run_info_ loss_ = loss + for metric in self.metrics: + if metric.name not in loss_: + raise ValueError( + f"Unable to compute optimization metric {metric.name}. Are you " + f"sure {metric.name} is applicable for the given task type?" + ) if len(self.metrics) == 1: loss = loss_[self.metrics[0].name] else: diff --git a/autosklearn/experimental/askl2.py b/autosklearn/experimental/askl2.py index 8e9112e48f..bc6f78764b 100644 --- a/autosklearn/experimental/askl2.py +++ b/autosklearn/experimental/askl2.py @@ -1,3 +1,5 @@ +from __future__ import annotations + from typing import Any, Dict, List, Mapping, Optional, Union import hashlib @@ -16,6 +18,8 @@ import autosklearn import autosklearn.experimental.selector from autosklearn.classification import AutoSklearnClassifier +from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble +from autosklearn.ensembles.ensemble_selection import EnsembleSelection from autosklearn.metrics import Scorer, accuracy, balanced_accuracy, log_loss, roc_auc metrics = (balanced_accuracy, roc_auc, log_loss) @@ -198,7 +202,9 @@ def __init__( self, time_left_for_this_task: int = 3600, per_run_time_limit=None, - ensemble_size: int = 50, + ensemble_size: int | None = None, + ensemble_class: AbstractEnsemble | None = EnsembleSelection, + ensemble_kwargs: Dict[str, Any] | None = None, ensemble_nbest: Union[float, int] = 50, max_models_on_disc: int = 50, seed: int = 1, @@ -232,15 +238,25 @@ def __init__( that typical machine learning algorithms can be fit on the training data. - ensemble_size : int, optional (default=50) + ensemble_size : int, optional Number of models added to the ensemble built by *Ensemble selection from libraries of models*. Models are drawn with replacement. If set to ``0`` no ensemble is fit. - ensemble_nbest : int, optional (default=50) - Only consider the ``ensemble_nbest`` models when building an + Deprecated - will be removed in Auto-sklearn 0.16. Please pass + this argument via ``ensemble_kwargs={"ensemble_size": int}`` + if you want to change the ensemble size for ensemble selection. + + ensemble_class : Type[AbstractEnsemble], optional (default=EnsembleSelection) + Class implementing the post-hoc ensemble algorithm. Set to + ``None`` to disable ensemble building or use ``SingleBest`` + to obtain only use the single best model instead of an ensemble. + ensemble_kwargs : Dict, optional + Keyword arguments that are passed to the ensemble class upon + initialization. + max_models_on_disc: int, optional (default=50), Defines the maximum number of models that are kept in the disc. The additional number of models are permanently deleted. Due to the @@ -352,6 +368,8 @@ def __init__( per_run_time_limit=per_run_time_limit, initial_configurations_via_metalearning=0, ensemble_size=ensemble_size, + ensemble_class=ensemble_class, + ensemble_kwargs=ensemble_kwargs, ensemble_nbest=ensemble_nbest, max_models_on_disc=max_models_on_disc, seed=seed, diff --git a/autosklearn/metrics/__init__.py b/autosklearn/metrics/__init__.py index 1fa124532a..0485db892c 100644 --- a/autosklearn/metrics/__init__.py +++ b/autosklearn/metrics/__init__.py @@ -48,7 +48,7 @@ def __call__( self, y_true: np.ndarray, y_pred: np.ndarray, - X_data: Optional[np.ndarray] = None, + X_data: Optional[SUPPORTED_XDATA_TYPES] = None, sample_weight: Optional[List[float]] = None, ) -> float: pass @@ -62,7 +62,7 @@ def __call__( self, y_true: np.ndarray, y_pred: np.ndarray, - X_data: Optional[np.ndarray] = None, + X_data: Optional[SUPPORTED_XDATA_TYPES] = None, sample_weight: Optional[List[float]] = None, ) -> float: """Evaluate predicted target values for X relative to y_true. @@ -129,7 +129,7 @@ def __call__( self, y_true: np.ndarray, y_pred: np.ndarray, - X_data: Optional[np.ndarray] = None, + X_data: Optional[SUPPORTED_XDATA_TYPES] = None, sample_weight: Optional[List[float]] = None, ) -> float: """Evaluate predicted probabilities for X relative to y_true. @@ -189,7 +189,7 @@ def __call__( self, y_true: np.ndarray, y_pred: np.ndarray, - X_data: Optional[np.ndarray] = None, + X_data: Optional[SUPPORTED_XDATA_TYPES] = None, sample_weight: Optional[List[float]] = None, ) -> float: """Evaluate decision function output for X relative to y_true. @@ -558,6 +558,42 @@ def calculate_scores( return score_dict +def calculate_loss( + solution: np.ndarray, + prediction: np.ndarray, + task_type: int, + metric: Scorer, + X_data: Optional[SUPPORTED_XDATA_TYPES] = None, +) -> float: + """Calculate the loss with a given metric + + Parameters + ---------- + solution: np.ndarray + The solutions + + prediction: np.ndarray + The predictions generated + + task_type: int + The task type of the problem + + metric: Scorer + The metric to use + + X_data: Optional[SUPPORTED_XDATA_TYPES] + X data used to obtain the predictions + """ + losses = calculate_losses( + solution=solution, + prediction=prediction, + task_type=task_type, + metrics=[metric], + X_data=X_data, + ) + return losses[metric.name] + + def calculate_losses( solution: np.ndarray, prediction: np.ndarray, @@ -584,7 +620,7 @@ def calculate_losses( metrics: Sequence[Scorer] A list of objects that hosts a function to calculate how good the prediction is according to the solution. - X_data: Optional[np.ndarray] + X_data: Optional[SUPPORTED_XDATA_TYPES] X data used to obtain the predictions scoring_functions: List[Scorer] A list of metrics to calculate multiple losses @@ -621,7 +657,7 @@ def compute_single_metric( prediction: np.ndarray, solution: np.ndarray, task_type: int, - X_data: Optional[np.ndarray] = None, + X_data: Optional[SUPPORTED_XDATA_TYPES] = None, ) -> float: """ Returns a metric for the given Auto-Sklearn Scorer object. diff --git a/autosklearn/pipeline/util.py b/autosklearn/pipeline/util.py index 38cf1aa344..90a1ba9c72 100644 --- a/autosklearn/pipeline/util.py +++ b/autosklearn/pipeline/util.py @@ -50,10 +50,17 @@ def get_dataset( train_size_maximum=150, make_multilabel=False, make_binary=False, + return_target_as_string=False, ): iris = getattr(sklearn.datasets, "load_%s" % dataset)() X = iris.data.astype(np.float32) Y = iris.target + + if return_target_as_string: + if make_binary or make_multilabel or (len(Y.shape) > 1 and Y.shape[1] > 1): + raise NotImplementedError() + Y = np.array([iris.target_names[y] for y in Y]) + rs = np.random.RandomState(42) indices = np.arange(X.shape[0]) train_size = min(int(len(indices) / 3.0 * 2.0), train_size_maximum) diff --git a/autosklearn/util/functional.py b/autosklearn/util/functional.py index e48f018305..80e83856bf 100644 --- a/autosklearn/util/functional.py +++ b/autosklearn/util/functional.py @@ -1,9 +1,9 @@ from __future__ import annotations -from typing import Callable, Iterable, TypeVar +from typing import Callable, Hashable, Iterable, Iterator, TypeVar from functools import reduce -from itertools import chain, tee +from itertools import chain, cycle, islice, tee T = TypeVar("T") @@ -153,7 +153,7 @@ def findwhere(itr: Iterable[T], func: Callable[[T], bool], *, default: int = -1) return next((i for i, t in enumerate(itr) if func(t)), default) -def pairs(itr: Iterable[T]) -> Iterable[tuple[T, T]]: +def pairs(itr: Iterable[T]) -> Iterator[tuple[T, T]]: """An iterator over pairs of items in the iterator ..code:: python @@ -186,3 +186,72 @@ def pairs(itr: Iterable[T]) -> Iterable[tuple[T, T]]: itr2 = chain([peek], itr2) return iter((a, b) for a, b in zip(itr1, itr2)) + + +def roundrobin( + *iterables: Iterable[T], + duplicates: bool = True, + key: Callable[[T], Hashable] | None = None, +) -> Iterator[T]: + """Performs a round robin iteration of several iterables + + Adapted from https://docs.python.org/3/library/itertools.html#recipes + + ..code:: python + + colours = ["orange", "red", "green"] + fruits = ["apple", "banana", "orange"] + + list(roundrobin(colors, fruits)) + # ["orange", "apple", "red", "banana", "green", "orange"] + + list(roundrobin(colors, fruits, duplicates=False)) + # ["orange", "apple", "red", "banana", "green"] + + Parameters + ---------- + *iterables: Iterable[T] + Any amount of iterables + + duplicates: bool = True + Whether duplicates are allowed + + key: Callable[[T], Hashable] | None = None + A key to use when checking for duplicates + + Returns + ------- + Iterator[T] + A round robin iterator over the iterables passed + """ + active_iterators = len(iterables) + nexts = cycle(iter(it).__next__ for it in iterables) # roundrobin workhorse + + # We split up the algorithm into removing duplicates and not. This removes the if + # statement from within the loop at the cost of duplication. + if duplicates: + while active_iterators > 0: + try: + for nxt in nexts: + yield nxt() + except StopIteration: + active_iterators -= 1 + nexts = cycle(islice(nexts, active_iterators)) + + else: + seen = set() + key = key if key is not None else lambda x: x # Identity if None + + while active_iterators > 0: + try: + for nxt in nexts: + item = nxt() + identifier = key(item) + + if identifier not in seen: + seen.add(identifier) + yield item + + except StopIteration: + active_iterators -= 1 + nexts = cycle(islice(nexts, active_iterators)) diff --git a/doc/api.rst b/doc/api.rst index 6c5d7bea69..789d2d24e3 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -111,4 +111,20 @@ Extension Interfaces .. autoclass:: autosklearn.pipeline.components.base.AutoSklearnPreprocessingAlgorithm :members: +.. _api_ensemble: +========= +Ensembles +========= + +.. autoclass:: autosklearn.ensembles.AbstractEnsemble + :members: + +.. autoclass:: autosklearn.ensembles.AbstractMultiObjectiveEnsemble + :members: + +.. autoclass:: autosklearn.ensembles.EnsembleSelection + :members: + +.. autoclass:: autosklearn.ensembles.SingleBest + :members: diff --git a/doc/faq.rst b/doc/faq.rst index 000f8ece47..23a0124ce1 100644 --- a/doc/faq.rst +++ b/doc/faq.rst @@ -388,10 +388,18 @@ Ensembling The following hyperparameters control how the ensemble is constructed: - * ``ensemble_size`` determines the maximal size of the ensemble. If it is set to zero, no ensemble will be constructed. + * ``ensemble_class`` class object implementing :class:`autosklearn.ensembles.AbstractEnsemble`, + will be instantiated by *auto-sklearn*'s ensemble builder. + * ``ensemble_kwargs`` are keyword arguments that are passed to the ``ensemble_class`` upon + instantiation. See below for an example argument. * ``ensemble_nbest`` allows the user to directly specify the number of models considered for the ensemble. This hyperparameter can be an integer *n*, such that only the best *n* models are used in the final ensemble. If a float between 0.0 and 1.0 is provided, ``ensemble_nbest`` would be interpreted as a fraction suggesting the percentage of models to use in the ensemble building process (namely, if ensemble_nbest is a float, library pruning is implemented as described in `Caruana et al. (2006) `_). * ``max_models_on_disc`` defines the maximum number of models that are kept on the disc, as a mechanism to control the amount of disc space consumed by *auto-sklearn*. Throughout the automl process, different individual models are optimized, and their predictions (and other metadata) is stored on disc. The user can set the upper bound on how many models are acceptable to keep on disc, yet this variable takes priority in the definition of the number of models used by the ensemble builder (that is, the minimum of ``ensemble_size``, ``ensemble_nbest`` and ``max_models_on_disc`` determines the maximal amount of models used in the ensemble). If set to None, this feature is disabled. + The default method for Auto-sklearn is :class:`autosklearn.ensembles.EnsembleSelection`, + which features the argument ``ensemble_size``. that determines the maximal size of the + ensemble. Models can be added repeatedly, so the number of different models is usually + less than the ``ensemble_size``. + .. collapse:: Which models are in the final ensemble? The results obtained from the final ensemble can be printed by calling ``show_models()`` or ``leaderboard()``. @@ -513,17 +521,18 @@ Other In order to obtain *vanilla auto-sklearn* as used in `Efficient and Robust Automated Machine Learning `_ - set ``ensemble_size=1`` and ``initial_configurations_via_metalearning=0``: + set ``ensemble_class=autosklearn.ensembles.SingleBest`` and ``initial_configurations_via_metalearning=0``: .. code:: python import autosklearn.classification + import autosklearn.ensembles automl = autosklearn.classification.AutoSklearnClassifier( - ensemble_size=1, + ensemble_class=autosklearn.ensembles.SingleBest, initial_configurations_via_metalearning=0 ) - An ensemble of size one will result in always choosing the current best model - according to its performance on the validation set. Setting the initial - configurations found by meta-learning to zero makes *auto-sklearn* use the - regular SMAC algorithm for suggesting new hyperparameter configurations. + This will always choose the best model according to the validation set. + Setting the initial configurations found by meta-learning to zero makes + *auto-sklearn* use the regular SMAC algorithm for suggesting new + hyperparameter configurations. diff --git a/examples/40_advanced/example_interpretable_models.py b/examples/40_advanced/example_interpretable_models.py index 7b551de7b8..5298b22d54 100644 --- a/examples/40_advanced/example_interpretable_models.py +++ b/examples/40_advanced/example_interpretable_models.py @@ -67,7 +67,7 @@ "select_percentile_classification", ], }, - ensemble_size=1, + ensemble_kwargs={"ensemble_size": 1}, ) automl.fit(X_train, y_train, dataset_name="breast_cancer") diff --git a/examples/40_advanced/example_metrics.py b/examples/40_advanced/example_metrics.py index 4da40f3cbb..30ecd8b70a 100644 --- a/examples/40_advanced/example_metrics.py +++ b/examples/40_advanced/example_metrics.py @@ -81,7 +81,6 @@ def metric_which_needs_x(solution, prediction, X_data, consider_col, val_thresho scorer = autosklearn.metrics.accuracy cls = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=60, - per_run_time_limit=30, seed=1, metric=scorer, ) @@ -107,7 +106,6 @@ def metric_which_needs_x(solution, prediction, X_data, consider_col, val_thresho ) cls = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=60, - per_run_time_limit=30, seed=1, metric=accuracy_scorer, ) @@ -133,7 +131,6 @@ def metric_which_needs_x(solution, prediction, X_data, consider_col, val_thresho ) cls = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=60, - per_run_time_limit=30, seed=1, metric=error_rate, ) @@ -184,7 +181,6 @@ def metric_which_needs_x(solution, prediction, X_data, consider_col, val_thresho ) cls = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=60, - per_run_time_limit=30, seed=1, metric=error_rate, ) @@ -217,10 +213,8 @@ def metric_which_needs_x(solution, prediction, X_data, consider_col, val_thresho ) cls = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=60, - per_run_time_limit=30, seed=1, metric=accuracy_scorer, - ensemble_size=0, ) cls.fit(X_train, y_train) @@ -232,4 +226,4 @@ def metric_which_needs_x(solution, prediction, X_data, consider_col, val_thresho consider_col=1, val_threshold=18.8, ) -print(f"Error score {score:.3f} using {error_rate.name:s}") +print(f"Error score {score:.3f} using {accuracy_scorer.name:s}") diff --git a/examples/40_advanced/example_multi_objective.py b/examples/40_advanced/example_multi_objective.py index f81f0d4709..2e4ceb1c7e 100644 --- a/examples/40_advanced/example_multi_objective.py +++ b/examples/40_advanced/example_multi_objective.py @@ -9,11 +9,13 @@ in the `scikit-learn docs `_. Auto-sklearn uses `SMAC3's implementation of ParEGO `_. -Multi-objective ensembling and proper access to the full Pareto front will be added in the near +Multi-objective ensembling and proper access to the full Pareto set will be added in the near future. """ from pprint import pprint +import matplotlib.pyplot as plt +import numpy as np import sklearn.datasets import sklearn.metrics @@ -25,7 +27,11 @@ # Data Loading # ============ -X, y = sklearn.datasets.load_breast_cancer(return_X_y=True) +X, y = sklearn.datasets.fetch_openml(data_id=31, return_X_y=True, as_frame=True) +# Change the target to align with scikit-learn's convention that +# ``1`` is the minority class. In this example it is predicting +# that a credit is "bad", i.e. that it will default. +y = np.array([1 if val == "bad" else 0 for val in y]) X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( X, y, random_state=1 ) @@ -35,11 +41,11 @@ # ========================== automl = autosklearn.classification.AutoSklearnClassifier( - time_left_for_this_task=30, - tmp_folder="/tmp/autosklearn_multi_objective_example_tmp", + time_left_for_this_task=120, metric=[autosklearn.metrics.precision, autosklearn.metrics.recall], + delete_tmp_folder_after_terminate=False, ) -automl.fit(X_train, y_train, dataset_name="breast_cancer") +automl.fit(X_train, y_train, dataset_name="German Credit") ############################################################################ # Compute the two competing metrics @@ -63,3 +69,22 @@ # to *auto-sklearn*. pprint(automl.cv_results_) + +############################################################################ +# Visualize the Pareto set +# ========================== +plot_values = [] +pareto_front = automl.get_pareto_set() +for ensemble in pareto_front: + predictions = ensemble.predict(X_test) + precision = sklearn.metrics.precision_score(y_test, predictions) + recall = sklearn.metrics.recall_score(y_test, predictions) + plot_values.append((precision, recall)) +fig = plt.figure() +ax = fig.add_subplot(111) +for precision, recall in plot_values: + ax.scatter(precision, recall, c="blue") +ax.set_xlabel("Precision") +ax.set_ylabel("Recall") +ax.set_title("Pareto set") +plt.show() diff --git a/examples/60_search/example_parallel_manual_spawning_cli.py b/examples/60_search/example_parallel_manual_spawning_cli.py index fa2bff375b..8a1a9d5a7c 100644 --- a/examples/60_search/example_parallel_manual_spawning_cli.py +++ b/examples/60_search/example_parallel_manual_spawning_cli.py @@ -207,7 +207,7 @@ def cli_start_worker(scheduler_file_name): y_train, task=MULTICLASS_CLASSIFICATION, dataset_name="digits", - ensemble_size=20, + ensemble_kwargs={"ensemble_size": 20}, ensemble_nbest=50, ) diff --git a/examples/60_search/example_parallel_manual_spawning_python.py b/examples/60_search/example_parallel_manual_spawning_python.py index 75c5bcee30..17be3d312d 100644 --- a/examples/60_search/example_parallel_manual_spawning_python.py +++ b/examples/60_search/example_parallel_manual_spawning_python.py @@ -146,7 +146,7 @@ async def do_work(): y_train, task=MULTICLASS_CLASSIFICATION, dataset_name="digits", - ensemble_size=20, + ensemble_kwargs={"ensemble_size": 20}, ensemble_nbest=50, ) diff --git a/examples/60_search/example_sequential.py b/examples/60_search/example_sequential.py index 1ff63649da..8b67f84f7b 100644 --- a/examples/60_search/example_sequential.py +++ b/examples/60_search/example_sequential.py @@ -20,6 +20,7 @@ ############################################################################ # Data Loading # ====================================== +from autosklearn.ensembles.ensemble_selection import EnsembleSelection X, y = sklearn.datasets.load_breast_cancer(return_X_y=True) X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( @@ -31,20 +32,19 @@ # ====================================== automl = autosklearn.classification.AutoSklearnClassifier( - time_left_for_this_task=120, - per_run_time_limit=30, + time_left_for_this_task=60, tmp_folder="/tmp/autosklearn_sequential_example_tmp", # Do not construct ensembles in parallel to avoid using more than one # core at a time. The ensemble will be constructed after auto-sklearn # finished fitting all machine learning models. - ensemble_size=0, + ensemble_class=None, delete_tmp_folder_after_terminate=False, ) automl.fit(X_train, y_train, dataset_name="breast_cancer") # This call to fit_ensemble uses all models trained in the previous call # to fit to build an ensemble which can be used with automl.predict() -automl.fit_ensemble(y_train, ensemble_size=50) +automl.fit_ensemble(y_train, ensemble_class=EnsembleSelection) ############################################################################ # Print the final ensemble constructed by auto-sklearn diff --git a/pyproject.toml b/pyproject.toml index 5a131b5ec9..40ea854030 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -96,8 +96,6 @@ module = [ "autosklearn.automl", "autosklearn.smbo", "autosklearn.experimental.askl2", - "autosklearn.ensembles.singlebest_ensemble", - "autosklearn.ensembles.ensemble_selection", "autosklearn.evaluation", #__init__ "autosklearn.evaluation.abstract_evaluator", "autosklearn.evaluation.test_evaluator", diff --git a/scripts/run_auto-sklearn_for_metadata_generation.py b/scripts/run_auto-sklearn_for_metadata_generation.py index 6b82b233c7..8e045ee47f 100644 --- a/scripts/run_auto-sklearn_for_metadata_generation.py +++ b/scripts/run_auto-sklearn_for_metadata_generation.py @@ -66,7 +66,7 @@ "time_left_for_this_task": time_limit, "per_run_time_limit": per_run_time_limit, "initial_configurations_via_metalearning": 0, - "ensemble_size": 0, + "ensemble_class": None, "ensemble_nbest": 0, "seed": seed, "memory_limit": 3072, diff --git a/test/fixtures/automl.py b/test/fixtures/automl.py index 726424b9a0..853c0e57e0 100644 --- a/test/fixtures/automl.py +++ b/test/fixtures/automl.py @@ -6,6 +6,7 @@ from autosklearn.automl import AutoML, AutoMLClassifier, AutoMLRegressor from autosklearn.automl_common.common.utils.backend import Backend +from autosklearn.ensembles.ensemble_selection import EnsembleSelection from pytest import FixtureRequest, fixture from unittest.mock import Mock @@ -43,8 +44,8 @@ def _create_automl( "time_left_for_this_task": 30, "per_run_time_limit": 5, "seed": DEFAULT_SEED, - "n_jobs": 2, - "ensemble_size": 10, + "ensemble_class": EnsembleSelection, + "ensemble_kwargs": {"ensemble_size": 10}, "ensemble_nbest": 10, "max_models_on_disc": 10, "initial_configurations_via_metalearning": 5, diff --git a/test/fixtures/datasets.py b/test/fixtures/datasets.py index 569241ac51..580f03c64c 100644 --- a/test/fixtures/datasets.py +++ b/test/fixtures/datasets.py @@ -84,6 +84,7 @@ def _make( task: Optional[int] = None, feat_type: Optional[Dict | str] = None, as_datamanager: bool = False, + return_target_as_string: bool = False, ) -> Any: X, y, Xt, yt = get_dataset( dataset=name, @@ -92,6 +93,7 @@ def _make( train_size_maximum=train_size_maximum, make_multilabel=make_multilabel, make_binary=make_binary, + return_target_as_string=return_target_as_string, ) if not as_datamanager: diff --git a/test/fixtures/ensemble_building.py b/test/fixtures/ensemble_building.py index 1a6c18073a..cca68f76d0 100644 --- a/test/fixtures/ensemble_building.py +++ b/test/fixtures/ensemble_building.py @@ -2,6 +2,7 @@ from typing import Any, Callable +import copy import math import pickle import sys @@ -31,14 +32,22 @@ def _make( modified: bool = False, budget: float = 0.0, loss: float | None = None, + losses: dict[str, float] | list[float] | None = None, model_size: int | None = None, mem_usage: float | None = None, predictions: str | list[str] | dict[str, np.ndarray] | None = "ensemble", ) -> Run: + if loss is not None and losses is not None: + raise ValueError("Can only specify either `loss` or `losses`") + + if isinstance(loss, dict): + raise ValueError("Please use `losses` for dict of losses") + if dummy: assert id is None id = 1 - loss = loss if loss is not None else 50_000 + if loss is None and losses is None: + losses = {"metric_0": 50_000} if id is None: id = np.random.randint(sys.maxsize) @@ -80,7 +89,13 @@ def _make( run.recorded_mtimes[k] = v + 1e-4 if loss is not None: - run.loss = loss + losses = [loss] + + if isinstance(losses, list): + losses = {f"metric_{i}": loss for i, loss in enumerate(losses)} + + if isinstance(losses, dict): + run.losses = losses if mem_usage is not None: run._mem_usage = mem_usage @@ -109,7 +124,7 @@ def _make( backend: Backend | None = None, dataset_name: str = "TEST", task_type: int = BINARY_CLASSIFICATION, - metric: Scorer = accuracy, + metrics: Scorer = copy.deepcopy([accuracy]), **kwargs: Any, ) -> EnsembleBuilder: @@ -117,9 +132,10 @@ def _make( backend = automl._backend dataset_name = automl._dataset_name task_type = automl._task - metric = automl._metrics[0] + metrics = automl._metrics kwargs = { - "ensemble_size": automl._ensemble_size, + "ensemble_class": automl._ensemble_class, + "ensemble_kwargs": automl._ensemble_kwargs, "ensemble_nbest": automl._ensemble_nbest, "max_models_on_disc": automl._max_models_on_disc, "precision": automl.precision, @@ -153,7 +169,7 @@ def _make( backend=backend, dataset_name=dataset_name, task_type=task_type, - metric=metric, + metrics=metrics, **kwargs, ) diff --git a/test/test_automl/cases.py b/test/test_automl/cases.py index a6779d1b08..c80d3d3b5f 100644 --- a/test/test_automl/cases.py +++ b/test/test_automl/cases.py @@ -15,6 +15,7 @@ {cv, holdout} - Whether explicitly cv or holdout was used {no_ensemble} - Fit with no ensemble size {cached} - If the resulting case is then cached + {multiobjective} - If the automl instance is multiobjective """ from __future__ import annotations @@ -24,6 +25,7 @@ import numpy as np +import autosklearn.metrics from autosklearn.automl import AutoMLClassifier, AutoMLRegressor from autosklearn.automl_common.common.utils.backend import Backend @@ -132,6 +134,49 @@ def case_classifier_fitted_cv( return model +@case(tags=["classifier", "fitted", "holdout", "cached", "multiobjective"]) +@parametrize("dataset", ["iris"]) +def case_classifier_fitted_holdout_multiobjective( + dataset: str, + make_cache: Callable[[str], Cache], + make_backend: Callable[..., Backend], + make_automl_classifier: Callable[..., AutoMLClassifier], + make_sklearn_dataset: Callable[..., Tuple[np.ndarray, ...]], +) -> AutoMLClassifier: + """Case of a holdout fitted classifier""" + resampling_strategy = "holdout" + + key = f"case_classifier_{resampling_strategy}_{dataset}" + + # This locks the cache for this item while we check, required for pytest-xdist + with make_cache(key) as cache: + if "model" not in cache: + # Make the model in the cache + model = make_automl_classifier( + temporary_directory=cache.path("backend"), + delete_tmp_folder_after_terminate=False, + resampling_strategy=resampling_strategy, + metrics=[ + autosklearn.metrics.balanced_accuracy, + autosklearn.metrics.log_loss, + ], + ) + + X, y, Xt, yt = make_sklearn_dataset( + name=dataset, return_target_as_string=True + ) + model.fit(X, y, dataset_name=dataset) + + # Save the model + cache.save(model, "model") + + # Try the model from the cache + model = cache.load("model") + model._backend = copy_backend(old=model._backend, new=make_backend()) + + return model + + @case(tags=["regressor", "fitted", "holdout", "cached"]) @parametrize("dataset", ["boston"]) def case_regressor_fitted_holdout( @@ -213,7 +258,8 @@ def case_classifier_fitted_no_ensemble( make_automl_classifier: Callable[..., AutoMLClassifier], make_sklearn_dataset: Callable[..., Tuple[np.ndarray, ...]], ) -> AutoMLClassifier: - """Case of a fitted classifier but enemble_size was set to 0""" + """Case of a fitted classifier but ensemble was disabled by + not writing models to disk""" key = f"case_classifier_fitted_no_ensemble_{dataset}" # This locks the cache for this item while we check, required for pytest-xdist @@ -223,7 +269,8 @@ def case_classifier_fitted_no_ensemble( model = make_automl_classifier( temporary_directory=cache.path("backend"), delete_tmp_folder_after_terminate=False, - ensemble_size=0, + ensemble_class=None, + disable_evaluator_output=True, ) X, y, Xt, yt = make_sklearn_dataset(name=dataset) diff --git a/test/test_automl/test_dummy_predictions.py b/test/test_automl/test_dummy_predictions.py index 3b9350ce8b..c4aa560791 100644 --- a/test/test_automl/test_dummy_predictions.py +++ b/test/test_automl/test_dummy_predictions.py @@ -1,4 +1,6 @@ -from typing import Callable, Tuple +from __future__ import annotations + +from typing import Callable, Sequence, Tuple from pathlib import Path @@ -12,7 +14,7 @@ REGRESSION, ) from autosklearn.data.xy_data_manager import XYDataManager -from autosklearn.metrics import Scorer, accuracy, r2 +from autosklearn.metrics import Scorer, accuracy, log_loss, precision, r2 from autosklearn.util.logging_ import PicklableClientLogger import pytest @@ -21,17 +23,18 @@ @parametrize( - "dataset, metric, task", + "dataset, metrics, task", [ - ("breast_cancer", accuracy, BINARY_CLASSIFICATION), - ("wine", accuracy, MULTICLASS_CLASSIFICATION), - ("diabetes", r2, REGRESSION), + ("breast_cancer", [accuracy], BINARY_CLASSIFICATION), + ("breast_cancer", [accuracy, log_loss], BINARY_CLASSIFICATION), + ("wine", [accuracy], MULTICLASS_CLASSIFICATION), + ("diabetes", [r2], REGRESSION), ], ) def test_produces_correct_output( dataset: str, task: int, - metric: Scorer, + metrics: Sequence[Scorer], mock_logger: PicklableClientLogger, make_automl: Callable[..., AutoML], make_sklearn_dataset: Callable[..., XYDataManager], @@ -45,8 +48,8 @@ def test_produces_correct_output( task : int The task type of the dataset - metric: Scorer - Metric to use, required as fit usually determines the metric to use + metrics: Sequence[Scorer] + Metric(s) to use, required as fit usually determines the metric to use Fixtures -------- @@ -66,7 +69,7 @@ def test_produces_correct_output( * It should produce predictions "predictions_ensemble_1337_1_0.0.npy" """ seed = 1337 - automl = make_automl(metrics=[metric], seed=seed) + automl = make_automl(metrics=metrics, seed=seed) automl._logger = mock_logger datamanager = make_sklearn_dataset( @@ -183,3 +186,29 @@ def test_raises_if_no_metric_set(make_automl: Callable[..., AutoML]) -> None: automl = make_automl() with pytest.raises(ValueError, match="Metric/Metrics was/were not set"): automl._do_dummy_prediction() + + +def test_raises_invalid_metric( + mock_logger: PicklableClientLogger, + make_automl: Callable[..., AutoML], + make_sklearn_dataset: Callable[..., XYDataManager], +) -> None: + dataset = "iris" + task = MULTICLASS_CLASSIFICATION + + automl = make_automl(metrics=[accuracy, precision]) + automl._logger = mock_logger + + datamanager = make_sklearn_dataset( + dataset, + as_datamanager=True, + task=task, + feat_type="numerical", + ) + automl._backend.save_datamanager(datamanager) + + with pytest.raises( + ValueError, + match="Are you sure precision is applicable for the given task type", + ): + automl._do_dummy_prediction() diff --git a/test/test_automl/test_fit_ensemble.py b/test/test_automl/test_fit_ensemble.py deleted file mode 100644 index 14bc36192a..0000000000 --- a/test/test_automl/test_fit_ensemble.py +++ /dev/null @@ -1,35 +0,0 @@ -import numpy as np - -from autosklearn.automl import AutoML - -import pytest -from pytest_cases import filters as ft -from pytest_cases import parametrize, parametrize_with_cases - -import test.test_automl.cases as cases - - -@parametrize("ensemble_size", [-10, -1, 0]) -@parametrize_with_cases("automl", cases=cases, filter=~ft.has_tag("fitted")) -def test_non_positive_ensemble_size_raises( - tmp_dir: str, - automl: AutoML, - ensemble_size: int, -) -> None: - """ - Parameters - ---------- - automl: AutoML - The AutoML object to test - - ensemble_size : int - The ensemble size to use - - Expects - ------- - * Can't fit ensemble with non-positive ensemble size - """ - dummy_data = np.array([1, 1, 1]) - - with pytest.raises(ValueError): - automl.fit_ensemble(dummy_data, ensemble_size=ensemble_size) diff --git a/test/test_automl/test_outputs.py b/test/test_automl/test_outputs.py index ba005019bd..5b31e60331 100644 --- a/test/test_automl/test_outputs.py +++ b/test/test_automl/test_outputs.py @@ -20,7 +20,7 @@ def test_datamanager_stored_contents() -> None: ... -@parametrize_with_cases("automl", cases=cases, has_tag="fitted") +@parametrize_with_cases("automl", cases=cases, filter=has_ensemble) def test_paths_created(automl: AutoML) -> None: """ Parameters @@ -78,7 +78,7 @@ def test_paths_created_with_ensemble(automl: AutoML) -> None: assert path.exists() -@parametrize_with_cases("automl", cases=cases, has_tag="fitted") +@parametrize_with_cases("automl", cases=cases, filter=has_ensemble) def test_at_least_one_model_and_predictions(automl: AutoML) -> None: """ Expects diff --git a/test/test_automl/test_post_fit.py b/test/test_automl/test_post_fit.py index ed7882d7b4..ccc5f25b9b 100644 --- a/test/test_automl/test_post_fit.py +++ b/test/test_automl/test_post_fit.py @@ -1,5 +1,7 @@ +import numpy as np +from sklearn.ensemble import VotingClassifier, VotingRegressor + from autosklearn.automl import AutoML -from autosklearn.ensembles.singlebest_ensemble import SingleBest from pytest_cases import parametrize_with_cases @@ -60,10 +62,45 @@ def test_no_ensemble(automl: AutoML) -> None: Expects ------- - * Auto-sklearn loads a single best model - * The models_ should be of size 1 + * Auto-sklearn does not load a model + * The models_ should be of size 0 * The cv_models_ should remain None """ - assert isinstance(automl.ensemble_, SingleBest) - assert len(automl.models_) == 1 - assert automl.cv_models_ is None + assert automl.ensemble_ is None + assert len(automl.models_) == 0 + assert len(automl.cv_models_) == 0 + + +@parametrize_with_cases("automl", cases, has_tag=["multiobjective"]) +def test__load_pareto_front(automl: AutoML) -> None: + """ + Parameters + ---------- + automl : AutoML + An AutoML object fitted with multiple objective metrics + + Expects + ------- + * Auto-sklearn can predict and has a model + * _load_pareto_front returns one scikit-learn ensemble + """ + # Check that the predict function works + X = np.array([[1.0, 1.0, 1.0, 1.0]]) + + assert automl.predict_proba(X).shape == (1, 3) + assert automl.predict(X).shape == (1,) + + pareto_front = automl._load_pareto_set() + assert len(pareto_front) == 1 + for ensemble in pareto_front: + assert isinstance(ensemble, (VotingClassifier, VotingRegressor)) + y_pred = ensemble.predict_proba(X) + assert y_pred.shape == (1, 3) + y_pred = ensemble.predict(X) + assert y_pred in ["setosa", "versicolor", "virginica"] + + statistics = automl.sprint_statistics() + assert "Metrics" in statistics + assert ("Best validation score: 0.9" in statistics) or ( + "Best validation score: 1.0" in statistics + ), statistics diff --git a/test/test_data/test_target_validator.py b/test/test_data/test_target_validator.py index 7c08dba20a..909a3b423f 100644 --- a/test/test_data/test_target_validator.py +++ b/test/test_data/test_target_validator.py @@ -516,8 +516,8 @@ def test_unknown_categories_in_targets(input_data_targettest): elif isinstance(input_data_targettest, np.ndarray): input_data_targettest[-1] = 5000 - x_t = validator.transform(input_data_targettest) - assert x_t[-1].item(0) == -1 + with pytest.raises(ValueError, match="Found unknown categories"): + validator.transform(input_data_targettest) def test_is_single_column_target(): diff --git a/test/test_ensemble_builder/test_ensemble_builder.py b/test/test_ensemble_builder/test_ensemble_builder.py index 7c5e593864..a46da42ef1 100644 --- a/test/test_ensemble_builder/test_ensemble_builder.py +++ b/test/test_ensemble_builder/test_ensemble_builder.py @@ -10,6 +10,7 @@ from autosklearn.automl_common.common.utils.backend import Backend from autosklearn.ensemble_building import EnsembleBuilder, Run +from autosklearn.metrics import make_scorer from autosklearn.util.functional import bound, pairs import pytest @@ -86,8 +87,11 @@ def test_requires_loss_update_with_modified_runs( ------- * Should include runs that were modified, even if they have a loss """ - run_okay = [make_run(loss=1) for _ in range(5)] - run_modified = [make_run(loss=1, modified=True) for _ in range(5)] + metrics = builder.metrics + run_okay = [make_run(losses={m.name: 1 for m in metrics}) for _ in range(5)] + run_modified = [ + make_run(losses={m.name: 1 for m in metrics}, modified=True) for _ in range(5) + ] runs = run_okay + run_modified @@ -105,7 +109,8 @@ def test_requires_loss_update_with_no_loss( ------- * Should include runs that have no loss """ - run_okay = [make_run(loss=10) for _ in range(5)] + metrics = builder.metrics + run_okay = [make_run(losses={m.name: 10 for m in metrics}) for _ in range(5)] run_no_loss = [make_run() for _ in range(5)] runs = run_okay + run_no_loss @@ -125,15 +130,15 @@ def test_candidates_no_filters( * Should not filter out any viable runs if no filters set. Here a viable run has a loss and ensemble predictions """ + metrics = builder.metrics dummy = make_run(dummy=True) - runs = [make_run(loss=n) for n in range(10)] + runs = [make_run(losses={m.name: n for m in metrics}) for n in range(10)] candidates, discarded = builder.candidate_selection( runs, dummy, better_than_dummy=False, nbest=None, - performance_range_threshold=None, ) assert len(set(candidates) & discarded) == 0 @@ -150,9 +155,12 @@ def test_candidates_filters_runs_with_no_predictions( * Should have nothing in common between candidates and discarded * Should filter out runs with no "ensemble" predictions """ + metrics = builder.metrics + dummy = make_run(dummy=True, losses={m.name: 2 for m in metrics}) + bad_runs = [make_run(predictions=None) for _ in range(5)] - dummy = make_run(dummy=True, loss=2) - good_run = make_run(predictions="ensemble", loss=1) + + good_run = make_run(predictions="ensemble", losses={m.name: 1 for m in metrics}) runs = bad_runs + [good_run] @@ -172,9 +180,11 @@ def test_candidates_filters_runs_with_no_loss( ------- * Should filter out runs with no loss """ - bad_runs = [make_run(loss=None) for _ in range(5)] - dummy_run = make_run(dummy=True, loss=2) - good_run = make_run(loss=1) + metrics = builder.metrics + + bad_runs = [make_run(losses=None) for _ in range(5)] + dummy_run = make_run(dummy=True, losses={m.name: 2 for m in metrics}) + good_run = make_run(losses={m.name: 1 for m in metrics}) runs = bad_runs + [good_run] @@ -182,7 +192,9 @@ def test_candidates_filters_runs_with_no_loss( assert len(candidates) == 1 assert len(discarded) == len(bad_runs) - assert candidates[0].loss == 1 + + # Only candidate should be the one with a loss of 1 + assert candidates[0].losses == {m.name: 1 for m in metrics} def test_candidates_filters_out_better_than_dummy( @@ -194,23 +206,31 @@ def test_candidates_filters_out_better_than_dummy( ------- * Should filter out runs worse than dummy """ - bad_runs = [make_run(loss=1) for _ in range(2)] - dummy_run = make_run(dummy=True, loss=0) - good_runs = [make_run(loss=-1) for _ in range(3)] + metrics = builder.metrics + + bad_runs = [make_run(losses={m.name: 1 for m in metrics}) for _ in range(2)] + dummy_run = make_run(dummy=True, losses={m.name: 0 for m in metrics}) + good_runs = [make_run(losses={m.name: -1 for m in metrics}) for _ in range(3)] runs = bad_runs + good_runs candidates, discarded = builder.candidate_selection( - runs, dummy_run, better_than_dummy=True + runs, + dummy_run, + better_than_dummy=True, ) assert set(candidates) assert len(candidates) == 3 - assert all(run.loss < dummy_run.loss for run in candidates) + + for run, metric in zip(candidates, metrics): + assert run.losses[metric.name] < dummy_run.losses[metric.name] assert len(discarded) == 2 - assert all(run.loss >= dummy_run.loss for run in discarded) + + for run, metric in zip(discarded, metrics): + assert run.losses[metric.name] >= dummy_run.losses[metric.name] def test_candidates_uses_dummy_if_no_candidates_better( @@ -223,8 +243,12 @@ def test_candidates_uses_dummy_if_no_candidates_better( * If no run is better than a dummy run, the candidates will then consist of the dummy runs. """ - runs = [make_run(loss=10) for _ in range(10)] - dummies = [make_run(dummy=True, loss=0) for _ in range(2)] + metrics = builder.metrics + + runs = [make_run(losses={m.name: 10 for m in metrics}) for _ in range(10)] + dummies = [ + make_run(dummy=True, losses={m.name: 0 for m in metrics}) for _ in range(2) + ] candidates, discarded = builder.candidate_selection( runs, @@ -236,104 +260,144 @@ def test_candidates_uses_dummy_if_no_candidates_better( assert all(run.is_dummy() for run in candidates) -@parametrize("nbest", [0, 1, 5, 1000]) -def test_candidates_nbest_int( +def test_candidates_better_than_dummy_multiobjective( builder: EnsembleBuilder, make_run: Callable[..., Run], - nbest: int, ) -> None: """ Expects ------- - * Should only select the nbest candidates - * They should be ordered by loss + * For a run to be considered a candidate, it must be better than the best dummy for + any one of the objectives. """ - n = 10 - expected = int(bound(nbest, bounds=(1, n))) + x = make_scorer("x", lambda: None) + y = make_scorer("y", lambda: None) - dummy = make_run(dummy=True) - runs = [make_run(loss=i) for i in range(n)] - candidates, discarded = builder.candidate_selection(runs, dummy, nbest=nbest) + # The will be one best dummy per objective achieving a loss of 1 + # You can think of this as a joint filter [1, 1] over "x", "y" losses + dummies = [ + make_run(dummy=True, losses={"x": 1, "y": 500}), + make_run(dummy=True, losses={"x": 500, "y": 1}), + ] - assert len(candidates) == expected + # Clearly better on both objectives + good_runs = [make_run(losses={"x": 0, "y": 0}) for _ in range(5)] - if len(candidates) > 1: - assert all(a.loss <= b.loss for a, b in pairs(candidates)) + # Worse on both + badd_runs = [make_run(losses={"x": 2, "y": 2}) for _ in range(5)] - if any(discarded): - worst_candidate = candidates[-1] - assert all(worst_candidate.loss <= d.loss for d in discarded) + # Better on 1 objective but worse on the other + may1_runs = [make_run(losses={"x": 2, "y": 0}) for _ in range(5)] + may2_runs = [make_run(losses={"x": 0, "y": 2}) for _ in range(5)] + # Better on 1 objective but equal one the other + may3_runs = [make_run(losses={"x": 1, "y": 0}) for _ in range(5)] + may4_runs = [make_run(losses={"x": 0, "y": 1}) for _ in range(5)] -@parametrize("nbest", [0.0, 0.25, 0.5, 1.0]) -def test_candidates_nbest_float( + # Equal on 1 objective but worse one the other + may5_runs = [make_run(losses={"x": 1, "y": 2}) for _ in range(5)] + may6_runs = [make_run(losses={"x": 2, "y": 1}) for _ in range(5)] + + expected_candidates = [ + *good_runs, + *may1_runs, + *may2_runs, + *may3_runs, + *may4_runs, + ] + + expected_discarded = [ + *badd_runs, + *may5_runs, + *may6_runs, + ] + + runs = expected_candidates + expected_discarded + + candidates, discarded = builder.candidate_selection( + runs, dummies, better_than_dummy=True, metrics=[x, y] + ) + + # No duplicates + assert len(candidates) == len(set(candidates)) + + assert set(candidates) == set(expected_candidates) + assert discarded == set(expected_discarded) + + +@parametrize("nbest", [0, 1, 5, 1000]) +def test_candidates_nbest_int_single_objective( builder: EnsembleBuilder, make_run: Callable[..., Run], - nbest: float, + nbest: int, ) -> None: """ Expects ------- - * Should select nbest percentage of candidates + * Should only select the nbest candidates * They should be ordered by loss """ + # Make sure it's single objective being tested + assert len(builder.metrics) == 1 + metric = builder.metrics[0] + n = 10 - expected = int(bound(nbest * n, bounds=(1, n))) + expected = int(bound(nbest, bounds=(1, n))) - dummy = make_run(dummy=True, loss=0) - runs = [make_run(id=i, loss=i) for i in range(2, n + 2)] + dummy = make_run(dummy=True, losses={metric.name: 50_000}) + runs = [make_run(losses={metric.name: i}) for i in range(n)] candidates, discarded = builder.candidate_selection(runs, dummy, nbest=nbest) assert len(candidates) == expected if len(candidates) > 1: - assert all(a.loss <= b.loss for a, b in pairs(candidates)) + for a, b in pairs(candidates): + assert a.losses[metric.name] <= b.losses[metric.name] + # Make sure all discarded are worse than the worst candidate if any(discarded): - worst_candidate = candidates[-1] - assert all(worst_candidate.loss <= d.loss for d in discarded) + worst_candidate_loss = candidates[-1].losses[metric.name] + for d in discarded: + assert worst_candidate_loss <= d.losses[metric.name] -@parametrize("threshold", [0.0, 0.25, 0.5, 1.0]) -def test_candidates_performance_range_threshold( + +@parametrize("nbest", [0.0, 0.25, 0.5, 1.0]) +def test_candidates_nbest_float_single_objective( builder: EnsembleBuilder, make_run: Callable[..., Run], - threshold: float, + nbest: float, ) -> None: """ Expects ------- - * Should select runs that are `threshold` between the dummy loss and the best loss - This value is captured in `boundary`. + * Should select nbest percentage of candidates + * They should be ordered by loss """ - worst_loss = 100 - best_loss = 0 - dummy_loss = 50 + # Make sure it's single objective being tested + assert len(builder.metrics) == 1 + metric = builder.metrics[0] - boundary = threshold * best_loss + (1 - threshold) * dummy_loss + n = 10 + expected = int(bound(nbest * n, bounds=(1, n))) - dummy = make_run(dummy=True, loss=dummy_loss) - runs = [make_run(loss=loss) for loss in np.linspace(best_loss, worst_loss, 101)] + dummy = make_run(dummy=True, losses={metric.name: 50_000}) + runs = [make_run(id=i, losses={metric.name: i}) for i in range(2, n + 2)] - candidates, discarded = builder.candidate_selection( - runs, - dummy, - performance_range_threshold=threshold, - ) + candidates, discarded = builder.candidate_selection(runs, dummy, nbest=nbest) - assert len(candidates) > 0 + assert len(candidates) == expected - # When no run is better than threshold, we just get 1 candidate, - # Make sure it's the best - if len(candidates) == 1: - assert all(r.loss >= candidates[0].loss for r in discarded) + if len(candidates) > 1: + for a, b in pairs(candidates): + assert a.losses[metric.name] <= b.losses[metric.name] - else: - for run in candidates: - assert run.loss < boundary + # Make sure all discarded are worse than the worst candidate + if any(discarded): + worst_candidate_loss = candidates[-1].losses[metric.name] - for run in discarded: - assert run.loss >= boundary + for d in discarded: + assert worst_candidate_loss <= d.losses[metric.name] def test_requires_deletion_does_nothing_without_params( @@ -378,8 +442,116 @@ def test_requires_deletion_max_models( assert not any(set(keep) & set(delete)) +@parametrize("max_models", [0, 1, 2, 5]) +def test_requires_deletion_max_models_multiobjective_no_overlap( + builder: EnsembleBuilder, + make_run: Callable[..., Run], + max_models: int, +) -> None: + """ + Expects + ------- + * When deleting runs with multiple objectives, they runs kept should be done so in + a roundrobin fashion with respect to each metric. + """ + # In this case, the runs have no overlap as they are round robin'ed, the order they + # are put in is the same as the order they come out where priority shifts between + # the three objectives + runs = [ + make_run(id=2, losses={"x": 0, "y": 100, "z": 100}), + make_run(id=3, losses={"x": 100, "y": 0, "z": 100}), + make_run(id=4, losses={"x": 100, "y": 100, "z": 0}), + # + make_run(id=5, losses={"x": 1, "y": 100, "z": 100}), + make_run(id=6, losses={"x": 100, "y": 1, "z": 100}), + make_run(id=7, losses={"x": 100, "y": 100, "z": 1}), + # + make_run(id=8, losses={"x": 2, "y": 100, "z": 100}), + make_run(id=9, losses={"x": 100, "y": 2, "z": 100}), + make_run(id=10, losses={"x": 100, "y": 100, "z": 2}), + ] + + expected_keep = list(runs[:max_models]) + expected_delete = set(runs[max_models:]) + + # Dummy metrics, only used for their names + x = make_scorer("x", lambda: None) + y = make_scorer("y", lambda: None) + z = make_scorer("z", lambda: None) + + # Shuffle the runs to ensure correct sorting takes place + random.shuffle(runs) + + keep, delete = builder.requires_deletion( + runs, + max_models=max_models, + metrics=[x, y, z], + ) + + assert keep == expected_keep + assert delete == expected_delete + + +@parametrize("max_models", [0, 1, 2, 5, 7]) +def test_requires_deletion_max_models_multiobjective_overlap( + builder: EnsembleBuilder, + make_run: Callable[..., Run], + max_models: int, +) -> None: + """ + Expects + ------- + * When deleting runs with multiple objectives, they runs kept should be done so in + a roundrobin fashion with respect to each metric. With overlap, it should + prioritize the first objective. + """ + # You can read the losses as a binary counter to make sense of it + runs = [ + make_run(id=2, losses={"x": 0, "y": 0, "z": 0}), + make_run(id=3, losses={"x": 0, "y": 0, "z": 1}), + make_run(id=4, losses={"x": 0, "y": 1, "z": 0}), + make_run(id=5, losses={"x": 0, "y": 1, "z": 1}), + make_run(id=6, losses={"x": 1, "y": 0, "z": 0}), + make_run(id=7, losses={"x": 1, "y": 0, "z": 1}), + make_run(id=8, losses={"x": 1, "y": 1, "z": 0}), + make_run(id=9, losses={"x": 1, "y": 1, "z": 1}), + ] + + # The expected order is to prefer the lowest on all objectives + expected_order = [ + # Best at all + make_run(id=2, losses={"x": 0, "y": 0, "z": 0}), + # Best at 2 but prioritizing the first objective + make_run(id=3, losses={"x": 0, "y": 0, "z": 1}), + make_run(id=4, losses={"x": 0, "y": 1, "z": 0}), + make_run(id=6, losses={"x": 1, "y": 0, "z": 0}), + # Best at 1 + make_run(id=5, losses={"x": 0, "y": 1, "z": 1}), + make_run(id=7, losses={"x": 1, "y": 0, "z": 1}), + make_run(id=8, losses={"x": 1, "y": 1, "z": 0}), + # + make_run(id=9, losses={"x": 1, "y": 1, "z": 1}), + ] + + # Dummy metrics, only used for their names + x = make_scorer("x", lambda: None) + y = make_scorer("y", lambda: None) + + # Shuffle the runs to ensure correct sorting takes place + random.shuffle(runs) + + keep, delete = builder.requires_deletion( + runs, + max_models=max_models, + metrics=[x, y], + ) + + assert keep == expected_order[:max_models] + assert delete == set(expected_order[max_models:]) + + @parametrize("memory_limit, expected", [(0, 0), (100, 0), (200, 1), (5000, 49)]) -def test_requires_memory_limit( +def test_requires_memory_limit_single_objective( builder: EnsembleBuilder, make_run: Callable[..., Run], memory_limit: int, @@ -393,7 +565,11 @@ def test_requires_memory_limit( * Should not have any models in common between keep and delete * All models kept should be better than those deleted """ - runs = [make_run(mem_usage=100, loss=-n) for n in range(50)] + # Make sure it's single objective being tested + assert len(builder.metrics) == 1 + metric = builder.metrics[0] + + runs = [make_run(mem_usage=100, losses={metric.name: -n}) for n in range(50)] random.shuffle(runs) keep, delete = builder.requires_deletion(runs=runs, memory_limit=memory_limit) @@ -411,10 +587,12 @@ def test_requires_memory_limit( assert not any(set(keep) & set(delete)) if len(keep) > 2: - assert all(a.loss <= b.loss for a, b in pairs(keep)) + for a, b in pairs(keep): + assert a.losses[metric.name] <= b.losses[metric.name] - best_deleted = min(r.loss for r in delete) - assert not any(run.loss > best_deleted for run in keep) + # Make sure that the best run deleted is still worse than all those kept + best_deleted = min(r.losses[metric.name] for r in delete) + assert not any(run.losses[metric.name] > best_deleted for run in keep) @parametrize("kind", ["ensemble", "test"]) @@ -429,8 +607,10 @@ def test_loss_with_no_ensemble_targets( * Should give a loss of np.inf if run has no predictions of a given kind """ run = make_run(predictions=None) + X_data = builder.X_data() + metric = builder.metrics[0] - assert builder.loss(run, kind=kind) == np.inf + assert builder.loss(run, metric=metric, X_data=X_data, kind=kind) == np.inf @parametrize("kind", ["ensemble", "test"]) @@ -444,11 +624,13 @@ def test_loss_with_targets( ------- * Should give a loss < np.inf if the predictions exist """ + X_data = builder.X_data(kind) targets = builder.targets(kind) + metric = builder.metrics[0] run = make_run(predictions={kind: targets}) - assert builder.loss(run, kind) < np.inf + assert builder.loss(run, metric=metric, X_data=X_data, kind=kind) < np.inf def test_delete_runs(builder: EnsembleBuilder, make_run: Callable[..., Run]) -> None: @@ -500,13 +682,16 @@ def test_fit_ensemble_produces_ensemble( ------- * Should produce an ensemble if all runs have predictions """ + X_data = builder.X_data("ensemble") targets = builder.targets("ensemble") assert targets is not None predictions = targets runs = [make_run(predictions={"ensemble": predictions}) for _ in range(10)] - ensemble = builder.fit_ensemble(runs, targets) + ensemble = builder.fit_ensemble( + candidates=runs, X_data=X_data, targets=targets, runs=runs + ) assert ensemble is not None @@ -521,6 +706,7 @@ def test_fit_with_error_gives_no_ensemble( * A run without predictions will raise an error will cause `fit_ensemble` to fail as it requires all runs to have valid predictions """ + X_data = builder.X_data("ensemble") targets = builder.targets("ensemble") assert targets is not None @@ -532,7 +718,7 @@ def test_fit_with_error_gives_no_ensemble( runs.append(bad_run) with pytest.raises(FileNotFoundError): - builder.fit_ensemble(runs, targets) + builder.fit_ensemble(candidates=runs, X_data=X_data, targets=targets, runs=runs) @parametrize("time_buffer", [1, 5]) @@ -575,6 +761,7 @@ def test_deletion_will_not_break_current_ensemble( max_models_on_disc=10, seed=DEFAULT_SEED, ) + metric = builder.metrics[0] # Stick a dummy run and 10 bad runs into the backend datamanager = builder.backend.load_datamanager() @@ -583,7 +770,7 @@ def test_deletion_will_not_break_current_ensemble( bad_predictions = {"ensemble": np.zeros_like(targets)} good_predictions = {"ensemble": targets} - make_run(dummy=True, loss=10000, backend=builder.backend) + make_run(dummy=True, losses={metric.name: 10000}, backend=builder.backend) bad_runs = [ make_run(backend=builder.backend, predictions=bad_predictions) for _ in range(10) diff --git a/test/test_ensemble_builder/test_ensemble_builder_real.py b/test/test_ensemble_builder/test_ensemble_builder_real.py index fdad0a45c9..f181ff6669 100644 --- a/test/test_ensemble_builder/test_ensemble_builder_real.py +++ b/test/test_ensemble_builder/test_ensemble_builder_real.py @@ -59,8 +59,15 @@ def test_run_builds_valid_ensemble(builder: EnsembleBuilder) -> None: builder.delete_runs = mock_delete # type: ignore # So we can capture the candidate runs used, we still wrap the actual fitting - with patch.object(builder, "fit_ensemble", wraps=builder.fit_ensemble) as mock_fit: - history, nbest = builder.main() + with patch( + "autosklearn.ensembles.ensemble_selection.EnsembleSelection" + ".get_validation_performance" + ) as mock_get_validation_performance: + mock_get_validation_performance.return_value = 0.2 + with patch.object( + builder, "fit_ensemble", wraps=builder.fit_ensemble + ) as mock_fit: + history, nbest = builder.main() # Check the ensemble was fitted once mock_save.assert_called_once() @@ -69,8 +76,9 @@ def test_run_builds_valid_ensemble(builder: EnsembleBuilder) -> None: ensemble_ids = set(ens.get_selected_model_identifiers()) assert len(ensemble_ids) > 0 + assert mock_fit.call_count == 1 # Check that the ids of runs in the ensemble were all candidates - candidates = mock_fit.call_args[0][0] # `fit_ensemble(candidates, ...)` + candidates = mock_fit.call_args[1]["candidates"] candidate_ids = {run.id for run in candidates} assert ensemble_ids <= candidate_ids @@ -89,10 +97,19 @@ def test_run_builds_valid_ensemble(builder: EnsembleBuilder) -> None: assert not any(deleted_ids & candidate_ids) assert not any(deleted_ids & ensemble_ids) - # Make sure that the best deleted model is better than the worst candidate - best_deleted = min(deleted, key=lambda r: (r.loss, r.num_run)) - worst_candidate = max(candidates, key=lambda r: (r.loss, r.num_run)) - - a = (worst_candidate.loss, worst_candidate.num_run) - b = (best_deleted.loss, best_deleted.num_run) - assert a <= b + # Make sure that the best deleted model is still worse than the worst candidate + # This does not necessarily hold with respect to any single metric in the + # multiobjective case as the best deleted may have been deleted with respect to + # some other metric when compared to the worst candidate, we can't know which. + if len(builder.metrics) == 1: + metric = builder.metrics[0] + best_deleted = min( + deleted, key=lambda r: (r.losses[metric.name], r.num_run) + ) + worst_candidate = max( + candidates, key=lambda r: (r.losses[metric.name], r.num_run) + ) + + a = (worst_candidate.losses[metric.name], worst_candidate.num_run) + b = (best_deleted.losses[metric.name], best_deleted.num_run) + assert a <= b diff --git a/test/test_ensemble_builder/test_ensemble_selection.py b/test/test_ensemble_builder/test_ensemble_selection.py index 07c972c59f..291b22bd0a 100644 --- a/test/test_ensemble_builder/test_ensemble_selection.py +++ b/test/test_ensemble_builder/test_ensemble_selection.py @@ -7,7 +7,7 @@ import pytest -def testEnsembleSelection(): +def testEnsembleSelection(backend): """ Makes sure ensemble selection fit method creates an ensemble correctly """ @@ -16,11 +16,14 @@ def testEnsembleSelection(): ensemble_size=10, task_type=REGRESSION, random_state=0, - metric=root_mean_squared_error, + backend=backend, + metrics=[root_mean_squared_error], ) # We create a problem such that we encourage the addition of members to the ensemble # Fundamentally, the average of 10 sequential number is 5.5 + # X_data will be ignored and is therefore random + X_data = np.random.random(size=(100, 2)) y_true = np.full((100), 5.5) predictions = [] for i in range(1, 20): @@ -28,7 +31,13 @@ def testEnsembleSelection(): pred[i * 5 : 5 * (i + 1)] = 5.5 * i predictions.append(pred) - ensemble.fit(predictions, y_true, identifiers=[(i, i, i) for i in range(20)]) + ensemble.fit( + base_models_predictions=predictions, + X_data=X_data, + true_targets=y_true, + model_identifiers=[(i, i, i) for i in range(20)], + runs=[], + ) np.testing.assert_array_equal( ensemble.weights_, @@ -78,7 +87,7 @@ def testEnsembleSelection(): ) -def testPredict(): +def testPredict(backend): # Test that ensemble prediction applies weights correctly to given # predictions. There are two possible cases: # 1) predictions.shape[0] == len(self.weights_). In this case, @@ -93,7 +102,8 @@ def testPredict(): ensemble_size=3, task_type=BINARY_CLASSIFICATION, random_state=0, - metric=accuracy, + backend=backend, + metrics=[accuracy], ) # Test for case 1. Create (3, 2, 2) predictions. per_model_pred = np.array( diff --git a/test/test_estimators/test_estimators.py b/test/test_estimators/test_estimators.py index dc5c359c8a..fabdecb08a 100644 --- a/test/test_estimators/test_estimators.py +++ b/test/test_estimators/test_estimators.py @@ -306,7 +306,7 @@ def test_performance_over_time_no_ensemble(tmp_dir): per_run_time_limit=5, tmp_folder=os.path.join(tmp_dir, "backend"), seed=1, - ensemble_size=0, + ensemble_class=None, initial_configurations_via_metalearning=0, ) @@ -395,8 +395,11 @@ def test_cv_results_multi_objective(tmp_dir): tmp_folder=os.path.join(tmp_dir, "backend"), seed=1, initial_configurations_via_metalearning=0, - metric=[autosklearn.metrics.precision_macro, autosklearn.metrics.roc_auc], - scoring_functions=[autosklearn.metrics.accuracy, autosklearn.metrics.roc_auc], + metric=[autosklearn.metrics.precision_macro, autosklearn.metrics.recall_macro], + scoring_functions=[ + autosklearn.metrics.accuracy, + autosklearn.metrics.balanced_accuracy, + ], ) params = cls.get_params() @@ -410,11 +413,11 @@ def test_cv_results_multi_objective(tmp_dir): assert "rank_test_scores" not in cv_results for expected_column in ( "mean_test_precision_macro", - "mean_test_roc_auc", + "mean_test_recall_macro", "mean_fit_time", "rank_test_precision_macro", - "rank_test_roc_auc", - "metric_roc_auc", + "rank_test_recall_macro", + "metric_balanced_accuracy", "metric_accuracy", ): assert isinstance(cv_results[expected_column], np.ndarray), type( @@ -1231,7 +1234,9 @@ def test_autosklearn_classification_methods_returns_self(dask_client): assert automl is automl_fitted - automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5) + automl_ensemble_fitted = automl.fit_ensemble( + y_train, ensemble_kwargs={"ensemble_size": 5} + ) assert automl is automl_ensemble_fitted automl_refitted = automl.refit(X_train.copy(), y_train.copy()) @@ -1253,7 +1258,9 @@ def test_autosklearn_regression_methods_returns_self(dask_client): automl_fitted = automl.fit(X_train, y_train) assert automl is automl_fitted - automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5) + automl_ensemble_fitted = automl.fit_ensemble( + y_train, ensemble_kwargs={"ensemble_size": 5} + ) assert automl is automl_ensemble_fitted automl_refitted = automl.refit(X_train.copy(), y_train.copy()) @@ -1272,7 +1279,9 @@ def test_autosklearn2_classification_methods_returns_self(dask_client): assert automl is automl_fitted - automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5) + automl_ensemble_fitted = automl.fit_ensemble( + y_train, ensemble_kwargs={"ensemble_size": 5} + ) assert automl is automl_ensemble_fitted automl_refitted = automl.refit(X_train.copy(), y_train.copy()) @@ -1301,7 +1310,9 @@ def test_autosklearn2_classification_methods_returns_self_sparse(dask_client): assert automl is automl_fitted - automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5) + automl_ensemble_fitted = automl.fit_ensemble( + y_train, ensemble_kwargs={"ensemble_size": 5} + ) assert automl is automl_ensemble_fitted automl_refitted = automl.refit(X_train.copy(), y_train.copy()) @@ -1627,7 +1638,9 @@ def test_autosklearn_anneal(as_frame): assert automl is automl_fitted - automl_ensemble_fitted = automl.fit_ensemble(y, ensemble_size=5) + automl_ensemble_fitted = automl.fit_ensemble( + y, ensemble_kwargs={"ensemble_size": 5} + ) assert automl is automl_ensemble_fitted # We want to make sure we can learn from this data. diff --git a/test/test_metalearning/test_metalearning.py b/test/test_metalearning/test_metalearning.py index e9e6e4ca1a..9740313ff8 100644 --- a/test/test_metalearning/test_metalearning.py +++ b/test/test_metalearning/test_metalearning.py @@ -135,7 +135,7 @@ def test_metadata_directory(self): per_run_time_limit=5, metadata_directory=nonexistent_dir, # user specified metadata_dir dask_client=dask_client, - ensemble_size=0, + ensemble_class=None, ) X, y = load_breast_cancer(return_X_y=True) self.assertRaisesRegex( From 734a4d618002f04a23118936145c82485bc92303 Mon Sep 17 00:00:00 2001 From: Eric Pedley Date: Wed, 1 Jun 2022 22:35:56 -0700 Subject: [PATCH 056/118] Changed deprecated DataFrame.append to pd.concat to fix warning (#1487) lso removed a loop that wasn't doing anything. DataFrame.append doesn't operate in place so that second loop wasn't causing any side effects, and it was also just adding empty series anyway. If anyone has any insight about why that was there in the first place that would be nice. --- autosklearn/metalearning/metalearning/meta_base.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/autosklearn/metalearning/metalearning/meta_base.py b/autosklearn/metalearning/metalearning/meta_base.py index f193a61fef..826bfaeda3 100644 --- a/autosklearn/metalearning/metalearning/meta_base.py +++ b/autosklearn/metalearning/metalearning/meta_base.py @@ -73,11 +73,7 @@ def add_dataset(self, name, metafeatures): "Dataset %s already in meta-data. Removing occurence.", name.lower() ) self.metafeatures.drop(name.lower(), inplace=True) - self.metafeatures = self.metafeatures.append(metafeatures) - - runs = pd.Series([], name=name, dtype=float) - for metric in self.algorithm_runs.keys(): - self.algorithm_runs[metric].append(runs) + self.metafeatures = pd.concat([self.metafeatures, pd.DataFrame(metafeatures).T]) def get_runs(self, dataset_name, performance_measure=None): """Return a list of all runs for a dataset.""" From 0d05f4b075f0b3fa7f94fbbf8d53501915ab211c Mon Sep 17 00:00:00 2001 From: Aseem Kannal Date: Thu, 2 Jun 2022 11:13:41 +0530 Subject: [PATCH 057/118] Encapsulated the selector training within a function and called it inside _init_ (#1473) * Encapsulated the selector training within a function and called it inside __init__ * Made changes in the test script that trains selectors before testing * Moved global variables inside the askl2 class, changes in the test script supporting that * Committing after running pre-commit * Fixed issue resulting into failure of case 2 * Changed metrics to selector_metrics * Added additional tests for askl2 * Added a boolean to the class to check for re-training of selectors * Using tmp_path instead of tmpdir * Removed tests for all metrics * Removed test for custom metric --- autosklearn/experimental/askl2.py | 151 ++++++++++-------- .../.auto-sklearn/runs/0_1_0.0/0.1.0.0.model | 0 .../.auto-sklearn/runs/0_2_0.0/0.2.0.0.model | 0 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test/test_ensemble_builder/test.test_ensemble_builder.test_ensemble_test_run_end_at/.auto-sklearn/runs/0_2_0.0/0.2.0.0.model create mode 100644 test/test_ensemble_builder/test.test_ensemble_builder.test_ensemble_test_run_end_at/.auto-sklearn/runs/0_2_0.0/predictions_test_0_2_0.0.np create mode 100644 test/test_ensemble_builder/test.test_ensemble_builder.test_ensemble_test_run_end_at/.auto-sklearn/runs/0_3_100.0/0.3.0.0.model create mode 100644 test/test_ensemble_builder/test.test_ensemble_builder.test_ensemble_test_run_end_at/.auto-sklearn/runs/0_3_100.0/0.3.100.0.model diff --git a/autosklearn/experimental/askl2.py b/autosklearn/experimental/askl2.py index bc6f78764b..4303c953dc 100644 --- a/autosklearn/experimental/askl2.py +++ b/autosklearn/experimental/askl2.py @@ -22,66 +22,7 @@ from autosklearn.ensembles.ensemble_selection import EnsembleSelection from autosklearn.metrics import Scorer, accuracy, balanced_accuracy, log_loss, roc_auc -metrics = (balanced_accuracy, roc_auc, log_loss) -selector_files = {} -this_directory = pathlib.Path(__file__).resolve().parent -for metric in metrics: - training_data_file = this_directory / metric.name / "askl2_training_data.json" - with open(training_data_file) as fh: - training_data = json.load(fh) - fh.seek(0) - m = hashlib.md5() - m.update(fh.read().encode("utf8")) - training_data_hash = m.hexdigest()[:10] - selector_filename = "askl2_selector_%s_%s_%s_%s.pkl" % ( - autosklearn.__version__, - sklearn.__version__, - metric.name, - training_data_hash, - ) - selector_directory = os.environ.get("XDG_CACHE_HOME") - if selector_directory is None: - selector_directory = pathlib.Path.home() - selector_directory = ( - pathlib.Path(selector_directory).joinpath("auto-sklearn").expanduser() - ) - selector_files[metric.name] = selector_directory / selector_filename - metafeatures = pd.DataFrame(training_data["metafeatures"]) - strategies = training_data["strategies"] - y_values = pd.DataFrame( - training_data["y_values"], columns=strategies, index=metafeatures.index - ) - minima_for_methods = training_data["minima_for_methods"] - maxima_for_methods = training_data["maxima_for_methods"] - default_strategies = training_data["tie_break_order"] - if not selector_files[metric.name].exists(): - selector = autosklearn.experimental.selector.OVORF( - configuration=training_data["configuration"], - random_state=np.random.RandomState(1), - n_estimators=500, - tie_break_order=default_strategies, - ) - selector = autosklearn.experimental.selector.FallbackWrapper( - selector, default_strategies - ) - selector.fit( - X=metafeatures, - y=y_values, - minima=minima_for_methods, - maxima=maxima_for_methods, - ) - selector_files[metric.name].parent.mkdir(exist_ok=True, parents=True) - - try: - with open(selector_files[metric.name], "wb") as fh: - pickle.dump(selector, fh) - except Exception as e: - print( - "AutoSklearn2Classifier needs to create a selector file under " - "the user's home directory or XDG_CACHE_HOME. Nevertheless " - "the path {} is not writable.".format(selector_files[metric.name]) - ) - raise e +selector_metrics = (balanced_accuracy, roc_auc, log_loss) class SmacObjectCallback: @@ -349,7 +290,7 @@ def __init__( Not all keys returned by scikit-learn are supported yet. """ # noqa (links are too long) - + self.required_training = False # Boolean to indicate if selectors were trained. include_estimators = [ "extra_trees", "passive_aggressive", @@ -363,6 +304,7 @@ def __init__( "classifier": include_estimators, "feature_preprocessor": include_preprocessors, } + self.train_selectors(selected_metric=metric) super().__init__( time_left_for_this_task=time_left_for_this_task, per_run_time_limit=per_run_time_limit, @@ -393,6 +335,83 @@ def __init__( allow_string_features=allow_string_features, ) + def train_selectors(self, selected_metric=None): + self.selector_metrics = (balanced_accuracy, roc_auc, log_loss) + self.selector_files = {} + self.this_directory = pathlib.Path(__file__).resolve().parent + + if selected_metric is not None: + metric_list = [selected_metric] + else: + metric_list = self.selector_metrics + + for metric in metric_list: + training_data_file = ( + self.this_directory / metric.name / "askl2_training_data.json" + ) + with open(training_data_file) as fh: + training_data = json.load(fh) + fh.seek(0) + m = hashlib.md5() + m.update(fh.read().encode("utf8")) + training_data_hash = m.hexdigest()[:10] + selector_filename = "askl2_selector_%s_%s_%s_%s.pkl" % ( + autosklearn.__version__, + sklearn.__version__, + metric.name, + training_data_hash, + ) + selector_directory = os.environ.get("XDG_CACHE_HOME") + if selector_directory is None: + selector_directory = pathlib.Path.home() + selector_directory = ( + pathlib.Path(selector_directory).joinpath("auto-sklearn").expanduser() + ) + self.selector_files[metric.name] = selector_directory / selector_filename + metafeatures = pd.DataFrame(training_data["metafeatures"]) + self.strategies = training_data["strategies"] + y_values = pd.DataFrame( + training_data["y_values"], + columns=self.strategies, + index=metafeatures.index, + ) + minima_for_methods = training_data["minima_for_methods"] + maxima_for_methods = training_data["maxima_for_methods"] + default_strategies = training_data["tie_break_order"] + if not self.selector_files[metric.name].exists(): + self.required_training = True + selector = autosklearn.experimental.selector.OVORF( + configuration=training_data["configuration"], + random_state=np.random.RandomState(1), + n_estimators=500, + tie_break_order=default_strategies, + ) + selector = autosklearn.experimental.selector.FallbackWrapper( + selector, default_strategies + ) + selector.fit( + X=metafeatures, + y=y_values, + minima=minima_for_methods, + maxima=maxima_for_methods, + ) + self.selector_files[metric.name].parent.mkdir( + exist_ok=True, parents=True + ) + + try: + with open(self.selector_files[metric.name], "wb") as fh: + pickle.dump(selector, fh) + except Exception as e: + print( + "AutoSklearn2Classifier needs to create a selector file under " + "the user's home directory or XDG_CACHE_HOME. Nevertheless " + "the path {} is not writable.".format( + self.selector_files[metric.name] + ) + ) + raise e + def fit( self, X, @@ -434,12 +453,12 @@ def fit( else: self.metric = log_loss - if self.metric in metrics: + if self.metric in self.selector_metrics: metric_name = self.metric.name - selector_file = selector_files[metric_name] + selector_file = self.selector_files[metric_name] else: metric_name = "balanced_accuracy" - selector_file = selector_files[metric_name] + selector_file = self.selector_files[metric_name] with open(selector_file, "rb") as fh: selector = pickle.load(fh) @@ -447,7 +466,7 @@ def fit( {dataset_name: [X.shape[1], X.shape[0]]} ).transpose() selection = np.argmax(selector.predict(metafeatures)) - automl_policy = strategies[selection] + automl_policy = self.strategies[selection] setting = { "RF_None_holdout_iterative_es_if": { @@ -497,7 +516,7 @@ def fit( resampling_strategy_kwargs = None portfolio_file = ( - this_directory + self.this_directory / metric_name / 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fabdecb08a..d0d3f28bdb 100644 --- a/test/test_estimators/test_estimators.py +++ b/test/test_estimators/test_estimators.py @@ -1352,31 +1352,91 @@ def test_check_estimator_signature(class_): ], ) def test_selector_file_askl2_can_be_created(selector_path): + with unittest.mock.patch("os.environ.get") as mock_foo: mock_foo.return_value = selector_path if selector_path is not None and not os.access(selector_path, os.W_OK): with pytest.raises(PermissionError): importlib.reload(autosklearn.experimental.askl2) + automl = AutoSklearn2Classifier( + time_left_for_this_task=60, delete_tmp_folder_after_terminate=False + ) + else: importlib.reload(autosklearn.experimental.askl2) - for metric in autosklearn.experimental.askl2.metrics: - assert os.path.exists( - autosklearn.experimental.askl2.selector_files[metric.name] - ) + automl = AutoSklearn2Classifier( + time_left_for_this_task=60, delete_tmp_folder_after_terminate=False + ) + for metric in automl.selector_metrics: + assert os.path.exists(automl.selector_files[metric.name]) if selector_path is None or not os.access(selector_path, os.W_OK): # We default to home in worst case assert os.path.expanduser("~") in str( - autosklearn.experimental.askl2.selector_files[metric.name] + automl.selector_files[metric.name] ) else: # a dir provided via XDG_CACHE_HOME - assert selector_path in str( - autosklearn.experimental.askl2.selector_files[metric.name] - ) + assert selector_path in str(automl.selector_files[metric.name]) # Re import it at the end so we do not affect other test importlib.reload(autosklearn.experimental.askl2) +@pytest.mark.parametrize( + "metric", + [metric for metric in autosklearn.experimental.askl2.selector_metrics], +) +def test_askl2_fits_selector_for_given_metrics_at_init(tmp_path, metric): + + assert tmp_path.is_dir() + assert len(list(tmp_path.iterdir())) == 0 + temp_dir = str(tmp_path) + + with unittest.mock.patch("os.environ.get") as mock_foo: + mock_foo.return_value = temp_dir + automl = AutoSklearn2Classifier( + time_left_for_this_task=60, + delete_tmp_folder_after_terminate=False, + metric=metric, + ) + assert ( + len(automl.selector_files) == 1 + ) # only one selector file should have been created + assert os.path.exists( + str(automl.selector_files[metric.name]) + ) # check if the path exists + + # check if selector is retrained when + # another object with the same metric is created + automl_1 = AutoSklearn2Classifier( + time_left_for_this_task=60, + delete_tmp_folder_after_terminate=False, + metric=metric, + ) + assert ( + len(automl_1.selector_files) == 1 + ) # only one selector file should have been created + assert os.path.exists( + str(automl_1.selector_files[metric.name]) + ) # check if the path exists + assert not automl_1.required_training + + +def test_askl2_fit_when_no_metric_specified(tmp_path): + + assert tmp_path.is_dir() + assert len(list(tmp_path.iterdir())) == 0 + temp_dir = str(tmp_path) + + with unittest.mock.patch("os.environ.get") as mock_foo: + mock_foo.return_value = temp_dir + automl = AutoSklearn2Classifier( + time_left_for_this_task=60, delete_tmp_folder_after_terminate=False + ) + assert len(automl.selector_files) == 3 + for metric in automl.selector_metrics: + assert os.path.exists(str(automl.selector_files[metric.name])) + + def test_check_askl2_same_arguments_as_askl() -> None: """Check the asklearn2 has the same args as asklearn1 From 7f8f547cd9fcef00ccc25f288e4146e60dc46fdc Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Thu, 2 Jun 2022 08:17:02 +0200 Subject: [PATCH 058/118] Update workflow versions, add dependabot to check them (#1490) * Update workflows * Add dependabot to check workflows --- .github/dependabot.yml | 9 +++++++++ .github/workflows/citation_cff.yml | 2 +- .github/workflows/dist.yml | 4 ++-- .github/workflows/docker-publish.yml | 4 +++- .github/workflows/docs.yml | 4 ++-- .github/workflows/generate-baselines.yml | 5 +---- .github/workflows/pre-commit.yaml | 4 ++-- .github/workflows/pytest.yml | 4 ++-- .github/workflows/regressions.yml | 9 +++++---- .github/workflows/stale.yaml | 2 +- 10 files changed, 28 insertions(+), 19 deletions(-) create mode 100644 .github/dependabot.yml diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000000..e5e5092a20 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,9 @@ +version: 2 + +updates: + # This will check for updates to github actions every day + # https://docs.github.com/en/enterprise-server@3.4/code-security/dependabot/working-with-dependabot/keeping-your-actions-up-to-date-with-dependabot + - package-ecosystem: "github-actions" + directory: "/" + schedule: + interval: "daily" diff --git a/.github/workflows/citation_cff.yml b/.github/workflows/citation_cff.yml index 707a71c4b2..6851c52d38 100644 --- a/.github/workflows/citation_cff.yml +++ b/.github/workflows/citation_cff.yml @@ -11,7 +11,7 @@ jobs: runs-on: ubuntu-latest steps: - name: Check out a copy of the repository - uses: actions/checkout@v2 + uses: actions/checkout@v3 - name: Check whether the citation metadata from CITATION.cff is valid uses: citation-file-format/cffconvert-github-action@2.0.0 diff --git a/.github/workflows/dist.yml b/.github/workflows/dist.yml index d9cb142135..24fc6bbf1d 100644 --- a/.github/workflows/dist.yml +++ b/.github/workflows/dist.yml @@ -22,12 +22,12 @@ jobs: steps: - name: Check out the repo - uses: actions/checkout@v2 + uses: actions/checkout@v3 with: submodules: recursive - name: Setup Python - uses: actions/setup-python@v2 + uses: actions/setup-python@v3 with: python-version: 3.8 diff --git a/.github/workflows/docker-publish.yml b/.github/workflows/docker-publish.yml index 3a9af5bf94..4c4773700e 100644 --- a/.github/workflows/docker-publish.yml +++ b/.github/workflows/docker-publish.yml @@ -17,7 +17,7 @@ jobs: steps: - name: Check out the repo - uses: actions/checkout@v2 + uses: actions/checkout@v3 with: submodules: recursive @@ -26,6 +26,7 @@ jobs: run: echo "##[set-output name=branch;]$(echo ${GITHUB_REF#refs/heads/})" id: extract_branch + # Updating this to @v2 requires a github auth token - name: Push to GitHub Packages uses: docker/build-push-action@v1 with: @@ -36,6 +37,7 @@ jobs: tag_with_ref: true tags: ${{ steps.extract_branch.outputs.branch }} + # Updating this to @v2 requires a github auth token - name: Push to Docker Hub uses: docker/build-push-action@v1 with: diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 3645596c7b..2b5d32a4f4 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -23,12 +23,12 @@ jobs: steps: - name: Checkout - uses: actions/checkout@v2 + uses: actions/checkout@v3 with: submodules: recursive - name: Setup Python - uses: actions/setup-python@v2 + uses: actions/setup-python@v3 with: python-version: 3.8 diff --git a/.github/workflows/generate-baselines.yml b/.github/workflows/generate-baselines.yml index f066d4150c..337fdd269e 100644 --- a/.github/workflows/generate-baselines.yml +++ b/.github/workflows/generate-baselines.yml @@ -1,3 +1,4 @@ +# Not currently in use name: Generate Baselines on: @@ -8,10 +9,6 @@ on: required: false default: 'Dummy argument' - push: - branches: - - development - env: AUTOMLBENCHMARK_REPO: 'openml/automlbenchmark' AUTOMLBENCHMARK_REF: 'master' diff --git a/.github/workflows/pre-commit.yaml b/.github/workflows/pre-commit.yaml index 03ca861dff..2d28dd1eae 100644 --- a/.github/workflows/pre-commit.yaml +++ b/.github/workflows/pre-commit.yaml @@ -20,12 +20,12 @@ jobs: run-all-files: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 with: submodules: recursive - name: Setup Python 3.7 - uses: actions/setup-python@v2 + uses: actions/setup-python@v3 with: python-version: 3.7 diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index 735305307d..de29e860fc 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -74,12 +74,12 @@ jobs: steps: - name: Checkout - uses: actions/checkout@v2 + uses: actions/checkout@v3 with: submodules: recursive - name: Setup Python ${{ matrix.python-version }} - uses: actions/setup-python@v2 + uses: actions/setup-python@v3 with: python-version: ${{ matrix.python-version }} diff --git a/.github/workflows/regressions.yml b/.github/workflows/regressions.yml index 8042afd666..70be44beff 100644 --- a/.github/workflows/regressions.yml +++ b/.github/workflows/regressions.yml @@ -1,3 +1,4 @@ +# Not currently in use name: Regression Tests on: @@ -33,7 +34,7 @@ jobs: ) steps: - name: Create comment - uses: peter-evans/create-or-update-comment@v1 + uses: peter-evans/create-or-update-comment@v2 with: issue-number: ${{ github.event.pull_request.number }} body: | @@ -81,7 +82,7 @@ jobs: # branch: the branch name - name: Checkout Automlbenchmark - uses: actions/checkout@v2 + uses: actions/checkout@v3 with: repository: ${{ env.AUTOMLBENCHMARK_REPO }} ref: ${{ env.AUTOMLBENCHMARK_REF }} @@ -141,7 +142,7 @@ jobs: # value: The python version used by the installed system - name: Setup Python - uses: actions/setup-python@v2 + uses: actions/setup-python@v3 with: python-version: ${{ steps.python-version.outputs.value }} @@ -184,7 +185,7 @@ jobs: # filename: name of the results file - name: Upload ${{matrix.task_type}} results as artifact - uses: actions/upload-artifact@v2 + uses: actions/upload-artifact@v3 with: name: ${{ steps.benchmark.outputs.filename }} path: | diff --git a/.github/workflows/stale.yaml b/.github/workflows/stale.yaml index d95d344674..b4bb87fafd 100644 --- a/.github/workflows/stale.yaml +++ b/.github/workflows/stale.yaml @@ -9,7 +9,7 @@ jobs: stale: runs-on: ubuntu-latest steps: - - uses: actions/stale@v3 + - uses: actions/stale@v4 with: days-before-stale: 60 days-before-close: 7 From 8932524d471d3b0e3701d858e0f28572177aa0f8 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Thu, 2 Jun 2022 08:40:10 +0200 Subject: [PATCH 059/118] Update pytest in `Dockerfile` (#1491) --- Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Dockerfile b/Dockerfile index e2a74c04f6..d73f8c8fca 100644 --- a/Dockerfile +++ b/Dockerfile @@ -32,7 +32,7 @@ ADD . /auto-sklearn/ # Upgrade pip then install dependencies RUN pip3 install --upgrade pip -RUN pip3 install pytest==4.6.* pep8 codecov pytest-cov flake8 openml +RUN pip3 install pytest>=4.6.* pep8 codecov pytest-cov flake8 openml RUN cat /auto-sklearn/requirements.txt | xargs -n 1 -L 1 pip3 install RUN pip3 install jupyter From 4264c4189941d13e1a256e26d7cf9720c98cb07f Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Fri, 3 Jun 2022 17:19:45 +0200 Subject: [PATCH 060/118] Update submodule (#1493) --- autosklearn/automl_common | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/autosklearn/automl_common b/autosklearn/automl_common index 63877b2531..c760e29b5f 160000 --- a/autosklearn/automl_common +++ b/autosklearn/automl_common @@ -1 +1 @@ -Subproject commit 63877b253124ca7ac805aefaf02b69ebd7837010 +Subproject commit c760e29b5fbf90455d1a16aebbca6424468628c2 From 200720440fddb91f64c55a22a9375a018932f99a Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Wed, 8 Jun 2022 12:48:27 +0200 Subject: [PATCH 061/118] Update FAQ with text stuff --- doc/faq.rst | 34 +++++++++++++++++++--------------- doc/manual.rst | 4 +--- 2 files changed, 20 insertions(+), 18 deletions(-) diff --git a/doc/faq.rst b/doc/faq.rst index 23a0124ce1..255ce4c76d 100644 --- a/doc/faq.rst +++ b/doc/faq.rst @@ -31,26 +31,30 @@ General Optionally, you can measure the ability of this fitted model to generalize to unseen data by providing an optional testing pair (X_test/Y_test). For further details, please refer to the Example :ref:`sphx_glr_examples_40_advanced_example_pandas_train_test.py`. - Supported formats for these training and testing pairs are: np.ndarray, - pd.DataFrame, scipy.sparse.csr_matrix and python lists. - If your data contains categorical values (in the features or targets), autosklearn will automatically encode your - data using a `sklearn.preprocessing.LabelEncoder `_ - for unidimensional data and a `sklearn.preprocessing.OrdinalEncoder `_ - for multidimensional data. - - Regarding the features, there are two methods to guide *auto-sklearn* to properly encode categorical columns: + Regarding the features, there are multiple things to consider: * Providing a X_train/X_test numpy array with the optional flag feat_type. For further details, you can check the Example :ref:`sphx_glr_examples_40_advanced_example_feature_types.py`. - * You can provide a pandas DataFrame, with properly formatted columns. If a column has numerical - dtype, *auto-sklearn* will not encode it and it will be passed directly to scikit-learn. If the - column has a categorical/boolean class, it will be encoded. If the column is of any other type - (Object or Timeseries), an error will be raised. For further details on how to properly encode - your data, you can check the Pandas Example - `Working with categorical data `_). - If you are working with time series, it is recommended that you follow this approach + * You can provide a pandas DataFrame with properly formatted columns. If a column has numerical + dtype, *auto-sklearn* will not encode it and it will be passed directly to scikit-learn. *auto-sklearn* + supports both categorical or string as column type. Please ensure that you are using the correct + dtype for your task. By default *auto-sklearn* treats object and string columns as strings and + encodes the data using `sklearn.feature_extraction.text.CountVectorizer `_ + * If your data contains categorical values (in the features or targets), ensure that you explicitly label them as categorical. + data labeled as categorical is encoded by using a `sklearn.preprocessing.LabelEncoder `_ + for unidimensional data and a `sklearn.preprodcessing.OrdinalEncoder `_ for multidimensional data. + * For further details on how to properly encode your data, you can check the Pandas Example + `Working with categorical data `_). If you are working with time series, it is recommended that you follow this approach `Working with time data `_. + * If you prefer not using the string option at all you can disable this option. In this case + objects, strings and categorical columns are encoded as categorical. + + .. code:: python + + import autosklearn.classification + automl = autosklearn.classification.AutoSklearnClassifier(allow_string_features=False) + automl.fit(X_train, y_train) Regarding the targets (y_train/y_test), if the task involves a classification problem, such features will be automatically encoded. It is recommended to provide both y_train and y_test during fit, so that a common encoding diff --git a/doc/manual.rst b/doc/manual.rst index c3a37e19e3..e2b7e4d556 100644 --- a/doc/manual.rst +++ b/doc/manual.rst @@ -317,14 +317,12 @@ Other Optionally, you can measure the ability of this fitted model to generalize to unseen data by providing an optional testing pair (X_test/Y_test). For further details, please refer to the Example :ref:`sphx_glr_examples_40_advanced_example_pandas_train_test.py`. - Supported formats for these training and testing pairs are: np.ndarray, - pd.DataFrame, scipy.sparse.csr_matrix and python lists. Regarding the features, there are multiple things to consider: * Providing a X_train/X_test numpy array with the optional flag feat_type. For further details, you can check the Example :ref:`sphx_glr_examples_40_advanced_example_feature_types.py`. - * You can provide a pandas DataFrame, with properly formatted columns. If a column has numerical + * You can provide a pandas DataFrame with properly formatted columns. If a column has numerical dtype, *auto-sklearn* will not encode it and it will be passed directly to scikit-learn. *auto-sklearn* supports both categorical or string as column type. Please ensure that you are using the correct dtype for your task. By default *auto-sklearn* treats object and string columns as strings and From 56e6ac06eabc69e5e79cbbd52f92ee8feef0a30e Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Wed, 8 Jun 2022 12:52:38 +0200 Subject: [PATCH 062/118] Undo accidental commit --- doc/faq.rst | 34 +++++++++++++++------------------- doc/manual.rst | 4 +++- 2 files changed, 18 insertions(+), 20 deletions(-) diff --git a/doc/faq.rst b/doc/faq.rst index 255ce4c76d..23a0124ce1 100644 --- a/doc/faq.rst +++ b/doc/faq.rst @@ -31,30 +31,26 @@ General Optionally, you can measure the ability of this fitted model to generalize to unseen data by providing an optional testing pair (X_test/Y_test). For further details, please refer to the Example :ref:`sphx_glr_examples_40_advanced_example_pandas_train_test.py`. + Supported formats for these training and testing pairs are: np.ndarray, + pd.DataFrame, scipy.sparse.csr_matrix and python lists. - Regarding the features, there are multiple things to consider: + If your data contains categorical values (in the features or targets), autosklearn will automatically encode your + data using a `sklearn.preprocessing.LabelEncoder `_ + for unidimensional data and a `sklearn.preprocessing.OrdinalEncoder `_ + for multidimensional data. + + Regarding the features, there are two methods to guide *auto-sklearn* to properly encode categorical columns: * Providing a X_train/X_test numpy array with the optional flag feat_type. For further details, you can check the Example :ref:`sphx_glr_examples_40_advanced_example_feature_types.py`. - * You can provide a pandas DataFrame with properly formatted columns. If a column has numerical - dtype, *auto-sklearn* will not encode it and it will be passed directly to scikit-learn. *auto-sklearn* - supports both categorical or string as column type. Please ensure that you are using the correct - dtype for your task. By default *auto-sklearn* treats object and string columns as strings and - encodes the data using `sklearn.feature_extraction.text.CountVectorizer `_ - * If your data contains categorical values (in the features or targets), ensure that you explicitly label them as categorical. - data labeled as categorical is encoded by using a `sklearn.preprocessing.LabelEncoder `_ - for unidimensional data and a `sklearn.preprodcessing.OrdinalEncoder `_ for multidimensional data. - * For further details on how to properly encode your data, you can check the Pandas Example - `Working with categorical data `_). If you are working with time series, it is recommended that you follow this approach + * You can provide a pandas DataFrame, with properly formatted columns. If a column has numerical + dtype, *auto-sklearn* will not encode it and it will be passed directly to scikit-learn. If the + column has a categorical/boolean class, it will be encoded. If the column is of any other type + (Object or Timeseries), an error will be raised. For further details on how to properly encode + your data, you can check the Pandas Example + `Working with categorical data `_). + If you are working with time series, it is recommended that you follow this approach `Working with time data `_. - * If you prefer not using the string option at all you can disable this option. In this case - objects, strings and categorical columns are encoded as categorical. - - .. code:: python - - import autosklearn.classification - automl = autosklearn.classification.AutoSklearnClassifier(allow_string_features=False) - automl.fit(X_train, y_train) Regarding the targets (y_train/y_test), if the task involves a classification problem, such features will be automatically encoded. It is recommended to provide both y_train and y_test during fit, so that a common encoding diff --git a/doc/manual.rst b/doc/manual.rst index e2b7e4d556..c3a37e19e3 100644 --- a/doc/manual.rst +++ b/doc/manual.rst @@ -317,12 +317,14 @@ Other Optionally, you can measure the ability of this fitted model to generalize to unseen data by providing an optional testing pair (X_test/Y_test). For further details, please refer to the Example :ref:`sphx_glr_examples_40_advanced_example_pandas_train_test.py`. + Supported formats for these training and testing pairs are: np.ndarray, + pd.DataFrame, scipy.sparse.csr_matrix and python lists. Regarding the features, there are multiple things to consider: * Providing a X_train/X_test numpy array with the optional flag feat_type. For further details, you can check the Example :ref:`sphx_glr_examples_40_advanced_example_feature_types.py`. - * You can provide a pandas DataFrame with properly formatted columns. If a column has numerical + * You can provide a pandas DataFrame, with properly formatted columns. If a column has numerical dtype, *auto-sklearn* will not encode it and it will be passed directly to scikit-learn. *auto-sklearn* supports both categorical or string as column type. Please ensure that you are using the correct dtype for your task. By default *auto-sklearn* treats object and string columns as strings and From 09c4d2260673ab8c7cbd8cc2c59db661cc7daa50 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Thu, 9 Jun 2022 15:08:03 +0200 Subject: [PATCH 063/118] Update FAQ with text stuff (#1500) * Update FAQ with text stuff * Take suggestions into account --- doc/faq.rst | 34 +++++++++++++++++++--------------- doc/manual.rst | 6 ++---- 2 files changed, 21 insertions(+), 19 deletions(-) diff --git a/doc/faq.rst b/doc/faq.rst index 23a0124ce1..56fa360937 100644 --- a/doc/faq.rst +++ b/doc/faq.rst @@ -31,26 +31,30 @@ General Optionally, you can measure the ability of this fitted model to generalize to unseen data by providing an optional testing pair (X_test/Y_test). For further details, please refer to the Example :ref:`sphx_glr_examples_40_advanced_example_pandas_train_test.py`. - Supported formats for these training and testing pairs are: np.ndarray, - pd.DataFrame, scipy.sparse.csr_matrix and python lists. - If your data contains categorical values (in the features or targets), autosklearn will automatically encode your - data using a `sklearn.preprocessing.LabelEncoder `_ - for unidimensional data and a `sklearn.preprocessing.OrdinalEncoder `_ - for multidimensional data. - - Regarding the features, there are two methods to guide *auto-sklearn* to properly encode categorical columns: + Regarding the features, there are multiple things to consider: * Providing a X_train/X_test numpy array with the optional flag feat_type. For further details, you can check the Example :ref:`sphx_glr_examples_40_advanced_example_feature_types.py`. - * You can provide a pandas DataFrame, with properly formatted columns. If a column has numerical - dtype, *auto-sklearn* will not encode it and it will be passed directly to scikit-learn. If the - column has a categorical/boolean class, it will be encoded. If the column is of any other type - (Object or Timeseries), an error will be raised. For further details on how to properly encode - your data, you can check the Pandas Example - `Working with categorical data `_). - If you are working with time series, it is recommended that you follow this approach + * You can provide a pandas DataFrame with properly formatted columns. If a column has numerical + dtype, *auto-sklearn* will not encode it and it will be passed directly to scikit-learn. *auto-sklearn* + supports both categorical or string as column type. Please ensure that you are using the correct + dtype for your task. By default *auto-sklearn* treats object and string columns as strings and + encodes the data using `sklearn.feature_extraction.text.CountVectorizer `_ + * If your data contains categorical values (in the features or targets), ensure that you explicitly label them as categorical. + Data labeled as categorical is encoded by using a `sklearn.preprocessing.LabelEncoder `_ + for unidimensional data and a `sklearn.preprodcessing.OrdinalEncoder `_ for multidimensional data. + * For further details on how to properly encode your data, you can check the Pandas Example + `Working with categorical data `_). If you are working with time series, it is recommended that you follow this approach `Working with time data `_. + * If you prefer not using the string option at all you can disable this option. In this case + objects, strings and categorical columns are encoded as categorical. + + .. code:: python + + import autosklearn.classification + automl = autosklearn.classification.AutoSklearnClassifier(allow_string_features=False) + automl.fit(X_train, y_train) Regarding the targets (y_train/y_test), if the task involves a classification problem, such features will be automatically encoded. It is recommended to provide both y_train and y_test during fit, so that a common encoding diff --git a/doc/manual.rst b/doc/manual.rst index c3a37e19e3..7cdb162881 100644 --- a/doc/manual.rst +++ b/doc/manual.rst @@ -317,20 +317,18 @@ Other Optionally, you can measure the ability of this fitted model to generalize to unseen data by providing an optional testing pair (X_test/Y_test). For further details, please refer to the Example :ref:`sphx_glr_examples_40_advanced_example_pandas_train_test.py`. - Supported formats for these training and testing pairs are: np.ndarray, - pd.DataFrame, scipy.sparse.csr_matrix and python lists. Regarding the features, there are multiple things to consider: * Providing a X_train/X_test numpy array with the optional flag feat_type. For further details, you can check the Example :ref:`sphx_glr_examples_40_advanced_example_feature_types.py`. - * You can provide a pandas DataFrame, with properly formatted columns. If a column has numerical + * You can provide a pandas DataFrame with properly formatted columns. If a column has numerical dtype, *auto-sklearn* will not encode it and it will be passed directly to scikit-learn. *auto-sklearn* supports both categorical or string as column type. Please ensure that you are using the correct dtype for your task. By default *auto-sklearn* treats object and string columns as strings and encodes the data using `sklearn.feature_extraction.text.CountVectorizer `_ * If your data contains categorical values (in the features or targets), ensure that you explicitly label them as categorical. - data labeled as categorical is encoded by using a `sklearn.preprocessing.LabelEncoder `_ + Data labeled as categorical is encoded by using a `sklearn.preprocessing.LabelEncoder `_ for unidimensional data and a `sklearn.preprodcessing.OrdinalEncoder `_ for multidimensional data. * For further details on how to properly encode your data, you can check the Pandas Example `Working with categorical data `_). If you are working with time series, it is recommended that you follow this approach From fd71fea87f79e7f1a351f71d74cabce31fa46446 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Tue, 14 Jun 2022 14:34:47 +0200 Subject: [PATCH 064/118] Fix moo things (#1501) * Push * `fit_ensemble` now has priority for kwargs to take * Change ordering of prefernce for ensemble params * Add TODO note for metrics * Add `metrics` arg to `fit_ensemble` * Add test for pareto front sizes * Remove uneeded file * Re-added tests to `test_pareto_front` * Add descriptions to test files * Add test to ensure argument priority * Add test to make sure X_data only loaded when required * Remove part of test required for performance history * Default to `self._metrics` if `metrics` not available --- autosklearn/automl.py | 15 +- autosklearn/ensemble_building/builder.py | 133 +++++++++---- autosklearn/estimators.py | 7 +- test/fixtures/ensemble_building.py | 4 + test/fixtures/metrics.py | 26 +++ test/test_automl/__init__.py | 1 - test/test_automl/test_construction.py | 7 +- test/test_automl/test_dataset_compression.py | 1 + test/test_automl/test_dummy_predictions.py | 18 +- test/test_automl/test_fit.py | 1 + test/test_automl/test_fit_pipeline.py | 2 +- test/test_automl/test_model_predict.py | 4 + test/test_automl/test_outputs.py | 118 ----------- test/test_automl/test_pareto_front.py | 36 ++++ test/test_automl/test_performance.py | 47 +++++ .../test_automl/test_performance_over_time.py | 1 + test/test_automl/test_post_fit.py | 136 ++++++++++--- test/test_automl/test_predict.py | 2 +- test/test_automl/test_refit.py | 1 + test/test_automl/test_show_models.py | 1 + test/test_automl/test_sklearn_compliance.py | 6 +- .../test_ensemble_builder.py | 188 +++++++++++++++++- 22 files changed, 536 insertions(+), 219 deletions(-) create mode 100644 test/fixtures/metrics.py delete mode 100644 test/test_automl/test_outputs.py create mode 100644 test/test_automl/test_pareto_front.py diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 12e80b8e4e..3ce4f8edf9 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -1502,6 +1502,7 @@ def fit_ensemble( ensemble_nbest: Optional[int] = None, ensemble_class: Optional[AbstractEnsemble] = EnsembleSelection, ensemble_kwargs: Optional[Dict[str, Any]] = None, + metrics: Scorer | Sequence[Scorer] | None = None, ): check_is_fitted(self) @@ -1532,6 +1533,10 @@ def fit_ensemble( else: self._is_dask_client_internally_created = False + metrics = metrics if metrics is not None else self._metrics + if not isinstance(metrics, Sequence): + metrics = [metrics] + # Use the current thread to start the ensemble builder process # The function ensemble_builder_process will internally create a ensemble # builder in the provide dask client @@ -1541,7 +1546,7 @@ def fit_ensemble( backend=copy.deepcopy(self._backend), dataset_name=dataset_name if dataset_name else self._dataset_name, task=task if task else self._task, - metrics=self._metrics, + metrics=metrics if metrics is not None else self._metrics, ensemble_class=( ensemble_class if ensemble_class is not None else self._ensemble_class ), @@ -1652,9 +1657,6 @@ def _load_best_individual_model(self): return ensemble def _load_pareto_set(self) -> Sequence[VotingClassifier | VotingRegressor]: - if len(self._metrics) <= 1: - raise ValueError("Pareto set is only available for two or more metrics.") - if self._ensemble_class is not None: self.ensemble_ = self._backend.load_ensemble(self._seed) else: @@ -1662,10 +1664,7 @@ def _load_pareto_set(self) -> Sequence[VotingClassifier | VotingRegressor]: # If no ensemble is loaded we cannot do anything if not self.ensemble_: - - raise ValueError( - "Pareto set can only be accessed if an ensemble is available." - ) + raise ValueError("Pareto set only available if ensemble can be loaded.") if isinstance(self.ensemble_, AbstractMultiObjectiveEnsemble): pareto_set = self.ensemble_.get_pareto_set() diff --git a/autosklearn/ensemble_building/builder.py b/autosklearn/ensemble_building/builder.py index 487332cbe1..50f69eb35a 100644 --- a/autosklearn/ensemble_building/builder.py +++ b/autosklearn/ensemble_building/builder.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import Any, Dict, Iterable, Sequence, Type, cast +from typing import Any, Iterable, Mapping, Sequence, Type, cast import logging.handlers import multiprocessing @@ -46,7 +46,7 @@ def __init__( task_type: int, metrics: Sequence[Scorer], ensemble_class: Type[AbstractEnsemble] = EnsembleSelection, - ensemble_kwargs: Dict[str, Any] | None = None, + ensemble_kwargs: Mapping[str, Any] | None = None, ensemble_nbest: int | float = 50, max_models_on_disc: int | float | None = 100, seed: int = 1, @@ -71,9 +71,11 @@ def __init__( metrics: Sequence[Scorer] Metrics to optimize the ensemble for. These must be non-duplicated. - ensemble_class + ensemble_class: Type[AbstractEnsemble] + Implementation of the ensemble algorithm. - ensemble_kwargs + ensemble_kwargs: Mapping[str, Any] | None + Arguments passed to the constructor of the ensemble algorithm. ensemble_nbest: int | float = 50 @@ -169,6 +171,8 @@ def __init__( self.validation_performance_ = np.inf # Data we may need + # TODO: The test data is needlessly loaded but automl_common has no concept of + # these and is perhaps too rigid datamanager: XYDataManager = self.backend.load_datamanager() self._X_test: SUPPORTED_FEAT_TYPES | None = datamanager.data.get("X_test", None) self._y_test: np.ndarray | None = datamanager.data.get("Y_test", None) @@ -442,6 +446,17 @@ def main( self.logger.debug("Found no runs") raise RuntimeError("Found no runs") + # We load in `X_data` if we need it + if any(m._needs_X for m in self.metrics): + ensemble_X_data = self.X_data("ensemble") + + if ensemble_X_data is None: + msg = "No `X_data` for 'ensemble' which was required by metrics" + self.logger.debug(msg) + raise RuntimeError(msg) + else: + ensemble_X_data = None + # Calculate the loss for those that require it requires_update = self.requires_loss_update(runs) if self.read_at_most is not None: @@ -450,9 +465,7 @@ def main( for run in requires_update: run.record_modified_times() # So we don't count as modified next time run.losses = { - metric.name: self.loss( - run, metric=metric, X_data=self.X_data("ensemble") - ) + metric.name: self.loss(run, metric=metric, X_data=ensemble_X_data) for metric in self.metrics } @@ -549,15 +562,14 @@ def main( return self.ensemble_history, self.ensemble_nbest targets = cast(np.ndarray, self.targets("ensemble")) # Sure they exist - X_data = self.X_data("ensemble") ensemble = self.fit_ensemble( candidates=candidates, - X_data=X_data, targets=targets, runs=runs, ensemble_class=self.ensemble_class, ensemble_kwargs=self.ensemble_kwargs, + X_data=ensemble_X_data, task=self.task_type, metrics=self.metrics, precision=self.precision, @@ -587,7 +599,15 @@ def main( run_preds = [r.predictions(kind, precision=self.precision) for r in models] pred = ensemble.predict(run_preds) - X_data = self.X_data(kind) + + if any(m._needs_X for m in self.metrics): + X_data = self.X_data(kind) + if X_data is None: + msg = f"No `X` data for '{kind}' which was required by metrics" + self.logger.debug(msg) + raise RuntimeError(msg) + else: + X_data = None scores = calculate_scores( solution=pred_targets, @@ -597,10 +617,19 @@ def main( X_data=X_data, scoring_functions=None, ) + + # TODO only one metric in history + # + # We should probably return for all metrics but this makes + # automl::performance_history a lot more complicated, will + # tackle in a future PR + first_metric = self.metrics[0] performance_stamp[f"ensemble_{score_name}_score"] = scores[ - self.metrics[0].name + first_metric.name ] - self.ensemble_history.append(performance_stamp) + + # Add the performance stamp to the history + self.ensemble_history.append(performance_stamp) # Lastly, delete any runs that need to be deleted. We save this as the last step # so that we have an ensemble saved that is up to date. If we do not do so, @@ -805,13 +834,13 @@ def candidate_selection( def fit_ensemble( self, - candidates: list[Run], - X_data: SUPPORTED_FEAT_TYPES, - targets: np.ndarray, + candidates: Sequence[Run], + runs: Sequence[Run], *, - runs: list[Run], + targets: np.ndarray | None = None, ensemble_class: Type[AbstractEnsemble] = EnsembleSelection, - ensemble_kwargs: Dict[str, Any] | None = None, + ensemble_kwargs: Mapping[str, Any] | None = None, + X_data: SUPPORTED_FEAT_TYPES | None = None, task: int | None = None, metrics: Sequence[Scorer] | None = None, precision: int | None = None, @@ -825,24 +854,24 @@ def fit_ensemble( Parameters ---------- - candidates: list[Run] + candidates: Sequence[Run] List of runs to build an ensemble from - X_data: SUPPORTED_FEAT_TYPES - The base level data. + runs: Sequence[Run] + List of all runs (also pruned ones and dummy runs) - targets: np.ndarray + targets: np.ndarray | None = None The targets to build the ensemble with - runs: list[Run] - List of all runs (also pruned ones and dummy runs) - - ensemble_class: AbstractEnsemble + ensemble_class: Type[AbstractEnsemble] Implementation of the ensemble algorithm. - ensemble_kwargs: Dict[str, Any] + ensemble_kwargs: Mapping[str, Any] | None Arguments passed to the constructor of the ensemble algorithm. + X_data: SUPPORTED_FEAT_TYPES | None = None + The base level data. + task: int | None = None The kind of task performed @@ -859,24 +888,42 @@ def fit_ensemble( ------- AbstractEnsemble """ - task = task if task is not None else self.task_type + # Validate we have targets if None specified + if targets is None: + targets = self.targets("ensemble") + if targets is None: + path = self.backend._get_targets_ensemble_filename() + raise ValueError(f"`fit_ensemble` could not find any targets at {path}") + ensemble_class = ( ensemble_class if ensemble_class is not None else self.ensemble_class ) - ensemble_kwargs = ( - ensemble_kwargs if ensemble_kwargs is not None else self.ensemble_kwargs - ) - ensemble_kwargs = ensemble_kwargs if ensemble_kwargs is not None else {} - metrics = metrics if metrics is not None else self.metrics - rs = random_state if random_state is not None else self.random_state - ensemble = ensemble_class( - task_type=task, - metrics=metrics, - random_state=rs, - backend=self.backend, - **ensemble_kwargs, - ) # type: AbstractEnsemble + # Create the ensemble_kwargs, favouring in order: + # 1) function kwargs, 2) function params 3) init_kwargs 4) init_params + + # Collect func params in dict if they're not None + params = { + k: v + for k, v in [ + ("task_type", task), + ("metrics", metrics), + ("random_state", random_state), + ] + if v is not None + } + + kwargs = { + "backend": self.backend, + "task_type": self.task_type, + "metrics": self.metrics, + "random_state": self.random_state, + **(self.ensemble_kwargs or {}), + **params, + **(ensemble_kwargs or {}), + } + + ensemble = ensemble_class(**kwargs) # type: AbstractEnsemble self.logger.debug(f"Fitting ensemble on {len(candidates)} models") start_time = time.time() @@ -995,7 +1042,8 @@ def loss( self, run: Run, metric: Scorer, - X_data: SUPPORTED_FEAT_TYPES, + *, + X_data: SUPPORTED_FEAT_TYPES | None = None, kind: str = "ensemble", ) -> float: """Calculate the loss for a run @@ -1008,6 +1056,9 @@ def loss( metric: Scorer The metric to calculate the loss of + X_data: SUPPORTED_FEAT_TYPES | None = None + Any X_data required to be passed to the metric + kind: str = "ensemble" The kind of targets to use for the run diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 7144fcc39c..9d729133f3 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -601,6 +601,7 @@ def fit_ensemble( ensemble_kwargs: Optional[Dict[str, Any]] = None, ensemble_nbest: Optional[int] = None, ensemble_class: Optional[AbstractEnsemble] = EnsembleSelection, + metrics: Scorer | Sequence[Scorer] | None = None, ): """Fit an ensemble to models trained during an optimization process. @@ -650,12 +651,13 @@ def fit_ensemble( to obtain only use the single best model instead of an ensemble. + metrics: Scorer | Sequence[Scorer] | None = None + A metric or list of metrics to score the ensemble with + Returns ------- self - """ - # User specified `ensemble_size` explicitly, warn them about deprecation if ensemble_size is not None: # Keep consistent behaviour @@ -708,6 +710,7 @@ def fit_ensemble( ensemble_nbest=ensemble_nbest, ensemble_class=ensemble_class, ensemble_kwargs=ensemble_kwargs, + metrics=metrics, ) return self diff --git a/test/fixtures/ensemble_building.py b/test/fixtures/ensemble_building.py index cca68f76d0..548d1c5d72 100644 --- a/test/fixtures/ensemble_building.py +++ b/test/fixtures/ensemble_building.py @@ -164,6 +164,10 @@ def _make( backend.save_additional_data( datamanager.data["Y_train"], what="targets_ensemble" ) + if "X_train" in datamanager.data: + backend.save_additional_data( + datamanager.data["X_train"], what="input_ensemble" + ) builder = EnsembleBuilder( backend=backend, diff --git a/test/fixtures/metrics.py b/test/fixtures/metrics.py new file mode 100644 index 0000000000..8bf754aea5 --- /dev/null +++ b/test/fixtures/metrics.py @@ -0,0 +1,26 @@ +from typing import Any + +import numpy as np + +from autosklearn.metrics import accuracy, make_scorer + + +def _accuracy_requiring_X_data( + y_true: np.ndarray, + y_pred: np.ndarray, + X_data: Any, +) -> float: + """Dummy metric that needs X Data""" + if X_data is None: + raise ValueError() + return accuracy(y_true, y_pred) + + +acc_with_X_data = make_scorer( + name="acc_with_X_data", + score_func=_accuracy_requiring_X_data, + needs_X=True, + optimum=1, + worst_possible_result=0, + greater_is_better=True, +) diff --git a/test/test_automl/__init__.py b/test/test_automl/__init__.py index dae354a675..e69de29bb2 100644 --- a/test/test_automl/__init__.py +++ b/test/test_automl/__init__.py @@ -1 +0,0 @@ -# -*- encoding: utf-8 -*- diff --git a/test/test_automl/test_construction.py b/test/test_automl/test_construction.py index 5b15812acd..5b68d35118 100644 --- a/test/test_automl/test_construction.py +++ b/test/test_automl/test_construction.py @@ -1,9 +1,4 @@ -"""Property based Tests - -These test are for checking properties of already fitted models. Any test that does -tests using cases should not modify the state as these models are cached between tests -to reduce training time. -""" +"""Test things related to only constructing an AutoML instance""" from typing import Any, Dict, Optional, Union from autosklearn.automl import AutoML diff --git a/test/test_automl/test_dataset_compression.py b/test/test_automl/test_dataset_compression.py index d50869ebbf..0a7e5a18bf 100644 --- a/test/test_automl/test_dataset_compression.py +++ b/test/test_automl/test_dataset_compression.py @@ -1,3 +1,4 @@ +"""Test things related to how AutoML compresses the dataset size""" from typing import Any, Callable, Dict import numpy as np diff --git a/test/test_automl/test_dummy_predictions.py b/test/test_automl/test_dummy_predictions.py index c4aa560791..c593e7f4cf 100644 --- a/test/test_automl/test_dummy_predictions.py +++ b/test/test_automl/test_dummy_predictions.py @@ -1,3 +1,7 @@ +"""Test the dummy predictor of AutoML + +Dummy models can serve as an early warning of issues with parameters during fit +""" from __future__ import annotations from typing import Callable, Sequence, Tuple @@ -183,6 +187,11 @@ def test_crash_due_to_memory_exception( def test_raises_if_no_metric_set(make_automl: Callable[..., AutoML]) -> None: + """ + Expects + ------- + * raise if there was no metric set when calling `_do_dummy_prediction()` + """ automl = make_automl() with pytest.raises(ValueError, match="Metric/Metrics was/were not set"): automl._do_dummy_prediction() @@ -193,10 +202,17 @@ def test_raises_invalid_metric( make_automl: Callable[..., AutoML], make_sklearn_dataset: Callable[..., XYDataManager], ) -> None: + """ + Expects + ------- + * Should raise an error if the given metric is not applicable to a given task type + """ + # `precision` is not applicable to MULTICLASS_CLASSIFICATION dataset = "iris" task = MULTICLASS_CLASSIFICATION + metrics = [accuracy, precision] - automl = make_automl(metrics=[accuracy, precision]) + automl = make_automl(metrics=metrics) automl._logger = mock_logger datamanager = make_sklearn_dataset( diff --git a/test/test_automl/test_fit.py b/test/test_automl/test_fit.py index 2defa2518b..02992e5e13 100644 --- a/test/test_automl/test_fit.py +++ b/test/test_automl/test_fit.py @@ -1,3 +1,4 @@ +"""Test specific ways of calling `fit` of AutoML""" from typing import Any, Callable, Dict, Optional, Tuple, Union import numpy as np diff --git a/test/test_automl/test_fit_pipeline.py b/test/test_automl/test_fit_pipeline.py index 137b57a5c3..5d1d648fc4 100644 --- a/test/test_automl/test_fit_pipeline.py +++ b/test/test_automl/test_fit_pipeline.py @@ -1 +1 @@ -"""TODO""" +"""Test specific ways of calling `fit_pipeline`""" diff --git a/test/test_automl/test_model_predict.py b/test/test_automl/test_model_predict.py index a301d1a9a5..b43c488220 100644 --- a/test/test_automl/test_model_predict.py +++ b/test/test_automl/test_model_predict.py @@ -1,3 +1,4 @@ +"""Test the _model_predict helper function such that it shapes output correctly""" from typing import Callable, Dict, Tuple import warnings @@ -19,7 +20,10 @@ class WarningModel: + """Simple model that returns incorrect shape and issues warning""" + def predict(self, X: np.ndarray) -> np.ndarray: + """Shout a warning during prediction""" warnings.warn("shout") return X diff --git a/test/test_automl/test_outputs.py b/test/test_automl/test_outputs.py deleted file mode 100644 index 5b31e60331..0000000000 --- a/test/test_automl/test_outputs.py +++ /dev/null @@ -1,118 +0,0 @@ -from pathlib import Path - -from autosklearn.automl import AutoML -from autosklearn.ensemble_building.builder import CANDIDATES_FILENAME - -from pytest import mark -from pytest_cases import parametrize_with_cases -from pytest_cases.filters import has_tag - -import test.test_automl.cases as cases -from test.conftest import DEFAULT_SEED - -# Some filters -has_ensemble = has_tag("fitted") & ~has_tag("no_ensemble") -no_ensemble = has_tag("fitted") & has_tag("no_ensemble") - - -@mark.todo -def test_datamanager_stored_contents() -> None: - ... - - -@parametrize_with_cases("automl", cases=cases, filter=has_ensemble) -def test_paths_created(automl: AutoML) -> None: - """ - Parameters - ---------- - automl : AutoML - A previously fitted automl - - Expects - ------- - * The given paths should exist after the automl has been run and fitted - """ - assert automl._backend is not None - - partial = Path(automl._backend.internals_directory) - expected = [ - partial / fixture - for fixture in ( - "true_targets_ensemble.npy", - f"start_time_{DEFAULT_SEED}", - "datamanager.pkl", - "runs", - ) - ] - - for path in expected: - assert path.exists() - - -@parametrize_with_cases("automl", cases=cases, filter=has_ensemble) -def test_paths_created_with_ensemble(automl: AutoML) -> None: - """ - Parameters - ---------- - automl : AutoML - A previously fitted automl - - Expects - ------- - * The given paths for an automl with an ensemble should include paths - specific to ensemble building - """ - assert automl._backend is not None - - partial = Path(automl._backend.internals_directory) - expected = [ - partial / fixture - for fixture in ( - "ensembles", - "ensemble_history.json", - CANDIDATES_FILENAME, - ) - ] - - for path in expected: - assert path.exists() - - -@parametrize_with_cases("automl", cases=cases, filter=has_ensemble) -def test_at_least_one_model_and_predictions(automl: AutoML) -> None: - """ - Expects - ------- - * There should be at least one models saved - * Each model saved should have predictions for the ensemble - """ - assert automl._backend is not None - runs_dir = Path(automl._backend.get_runs_directory()) - - runs = list(runs_dir.iterdir()) - assert len(runs) > 0 - - at_least_one = False - for run in runs: - prediction_files = run.glob("predictions_ensemble*.npy") - model_files = run.glob("*.*.model") - - if any(prediction_files): - at_least_one = True - assert any(model_files), "Run produced prediction but no model" - - assert at_least_one, "No runs produced predictions" - - -@parametrize_with_cases("automl", cases=cases, filter=has_ensemble) -def test_at_least_one_ensemble(automl: AutoML) -> None: - """ - Expects - ------- - * There should be at least one ensemble generated - """ - assert automl._backend is not None - ens_dir = Path(automl._backend.get_ensemble_dir()) - - # TODO make more generic - assert len(list(ens_dir.glob("*.ensemble"))) > 0 diff --git a/test/test_automl/test_pareto_front.py b/test/test_automl/test_pareto_front.py new file mode 100644 index 0000000000..8ff38a04a6 --- /dev/null +++ b/test/test_automl/test_pareto_front.py @@ -0,0 +1,36 @@ +"""Test the output of loading the pareto set from an automl instance""" +from autosklearn.automl import AutoML + +from pytest_cases import parametrize_with_cases +from pytest_cases.filters import has_tag + +import test.test_automl.cases as cases + +has_ensemble = has_tag("fitted") & ~has_tag("no_ensemble") + +single_objective = has_ensemble & ~has_tag("multiobjective") +multi_objective = has_ensemble & has_tag("multiobjective") + + +@parametrize_with_cases("automl", cases=cases, filter=single_objective) +def test_can_output_pareto_front_singleobjective(automl: AutoML) -> None: + """ + Expects + ------- + * Non-multiobjective instances should have a pareto set of size 1 + """ + pareto_set = automl._load_pareto_set() + + assert len(pareto_set) == 1 + + +@parametrize_with_cases("automl", cases=cases, filter=multi_objective) +def test_can_output_pareto_front_multiobjective(automl: AutoML) -> None: + """ + Expects + ------- + * Multiobjective ensembles should return >= 1, #TODO should test it's pareto optimal + """ + pareto_set = automl._load_pareto_set() + + assert len(pareto_set) >= 1 diff --git a/test/test_automl/test_performance.py b/test/test_automl/test_performance.py index e69de29bb2..76c1a0d9d4 100644 --- a/test/test_automl/test_performance.py +++ b/test/test_automl/test_performance.py @@ -0,0 +1,47 @@ +"""Test the performance of automl instances after fitting""" + +import numpy as np +from sklearn.ensemble import VotingClassifier, VotingRegressor + +from autosklearn.automl import AutoML + +from pytest_cases import parametrize_with_cases + +import test.test_automl.cases as cases + + +@parametrize_with_cases("automl", cases.case_classifier_fitted_holdout_multiobjective) +def test_performance_with_multiobjective(automl: AutoML) -> None: + """ + Expects + ------- + * Auto-sklearn can predict/predict_proba and has a model + * Each ensemble in the pareto_set can predict/predict_proba + """ + # TODO: This test is hyperspecific to this one case + # + # Long term we probably want to return additional info about the case so we can + # test things for other than this case + + # Check that the predict function works + X = np.array([[1.0, 1.0, 1.0, 1.0]]) + + assert automl.predict_proba(X).shape == (1, 3) + assert automl.predict(X).shape == (1,) + + pareto_front = automl._load_pareto_set() + for ensemble in pareto_front: + + assert isinstance(ensemble, (VotingClassifier, VotingRegressor)) + + y_pred = ensemble.predict_proba(X) + assert y_pred.shape == (1, 3) + + y_pred = ensemble.predict(X) + assert y_pred in ["setosa", "versicolor", "virginica"] + + statistics = automl.sprint_statistics() + assert "Metrics" in statistics + assert ("Best validation score: 0.9" in statistics) or ( + "Best validation score: 1.0" in statistics + ), statistics diff --git a/test/test_automl/test_performance_over_time.py b/test/test_automl/test_performance_over_time.py index d5cc327a41..f38bdd7ee2 100644 --- a/test/test_automl/test_performance_over_time.py +++ b/test/test_automl/test_performance_over_time.py @@ -1,3 +1,4 @@ +"""Test the performance over time functionality of automl instances""" from autosklearn.automl import AutoML from pytest_cases import parametrize_with_cases diff --git a/test/test_automl/test_post_fit.py b/test/test_automl/test_post_fit.py index ccc5f25b9b..37fcd63eca 100644 --- a/test/test_automl/test_post_fit.py +++ b/test/test_automl/test_post_fit.py @@ -1,11 +1,20 @@ -import numpy as np -from sklearn.ensemble import VotingClassifier, VotingRegressor +"""Check the internal state of the automl instances after it has been fitted""" + +from pathlib import Path from autosklearn.automl import AutoML +from autosklearn.ensemble_building.builder import CANDIDATES_FILENAME +from pytest import mark from pytest_cases import parametrize_with_cases +from pytest_cases.filters import has_tag import test.test_automl.cases as cases +from test.conftest import DEFAULT_SEED + +# Some filters +has_ensemble = has_tag("fitted") & ~has_tag("no_ensemble") +no_ensemble = has_tag("fitted") & has_tag("no_ensemble") @parametrize_with_cases("automl", cases=cases, has_tag=["fitted", "holdout"]) @@ -52,7 +61,7 @@ def test_cv_loaded_models(automl: AutoML) -> None: assert set(automl.cv_models_.keys()) == set(ensemble_identifiers) -@parametrize_with_cases("automl", cases=cases, has_tag=["fitted", "no_ensemble"]) +@parametrize_with_cases("automl", cases=cases, has_tag=no_ensemble) def test_no_ensemble(automl: AutoML) -> None: """ Parameters @@ -71,36 +80,99 @@ def test_no_ensemble(automl: AutoML) -> None: assert len(automl.cv_models_) == 0 -@parametrize_with_cases("automl", cases, has_tag=["multiobjective"]) -def test__load_pareto_front(automl: AutoML) -> None: +@mark.todo +def test_datamanager_stored_contents() -> None: """ - Parameters - ---------- - automl : AutoML - An AutoML object fitted with multiple objective metrics + Expects + ------- + * TODO + """ + ... + + +@parametrize_with_cases("automl", cases=cases, filter=has_ensemble) +def test_paths_created(automl: AutoML) -> None: + """ + Expects + ------- + * The given paths should exist after the automl has been run and fitted + """ + assert automl._backend is not None + + partial = Path(automl._backend.internals_directory) + expected = [ + partial / fixture + for fixture in ( + "true_targets_ensemble.npy", + f"start_time_{DEFAULT_SEED}", + "datamanager.pkl", + "runs", + ) + ] + + for path in expected: + assert path.exists() + + +@parametrize_with_cases("automl", cases=cases, filter=has_ensemble) +def test_paths_created_with_ensemble(automl: AutoML) -> None: + """ + Expects + ------- + * The given paths for an automl with an ensemble should include paths + specific to ensemble building + """ + assert automl._backend is not None + + partial = Path(automl._backend.internals_directory) + expected = [ + partial / fixture + for fixture in ( + "ensembles", + "ensemble_history.json", + CANDIDATES_FILENAME, + ) + ] + + for path in expected: + assert path.exists() + +@parametrize_with_cases("automl", cases=cases, filter=has_ensemble) +def test_at_least_one_model_and_predictions(automl: AutoML) -> None: + """ Expects ------- - * Auto-sklearn can predict and has a model - * _load_pareto_front returns one scikit-learn ensemble - """ - # Check that the predict function works - X = np.array([[1.0, 1.0, 1.0, 1.0]]) - - assert automl.predict_proba(X).shape == (1, 3) - assert automl.predict(X).shape == (1,) - - pareto_front = automl._load_pareto_set() - assert len(pareto_front) == 1 - for ensemble in pareto_front: - assert isinstance(ensemble, (VotingClassifier, VotingRegressor)) - y_pred = ensemble.predict_proba(X) - assert y_pred.shape == (1, 3) - y_pred = ensemble.predict(X) - assert y_pred in ["setosa", "versicolor", "virginica"] - - statistics = automl.sprint_statistics() - assert "Metrics" in statistics - assert ("Best validation score: 0.9" in statistics) or ( - "Best validation score: 1.0" in statistics - ), statistics + * There should be at least one models saved + * Each model saved should have predictions for the ensemble + """ + assert automl._backend is not None + runs_dir = Path(automl._backend.get_runs_directory()) + + runs = list(runs_dir.iterdir()) + assert len(runs) > 0 + + at_least_one = False + for run in runs: + prediction_files = run.glob("predictions_ensemble*.npy") + model_files = run.glob("*.*.model") + + if any(prediction_files): + at_least_one = True + assert any(model_files), "Run produced prediction but no model" + + assert at_least_one, "No runs produced predictions" + + +@parametrize_with_cases("automl", cases=cases, filter=has_ensemble) +def test_at_least_one_ensemble(automl: AutoML) -> None: + """ + Expects + ------- + * There should be at least one ensemble generated + """ + assert automl._backend is not None + ens_dir = Path(automl._backend.get_ensemble_dir()) + + # TODO make more generic + assert len(list(ens_dir.glob("*.ensemble"))) > 0 diff --git a/test/test_automl/test_predict.py b/test/test_automl/test_predict.py index 137b57a5c3..4bad9859be 100644 --- a/test/test_automl/test_predict.py +++ b/test/test_automl/test_predict.py @@ -1 +1 @@ -"""TODO""" +"""Test predictions of an automl instance""" diff --git a/test/test_automl/test_refit.py b/test/test_automl/test_refit.py index 341486ab13..0f5b42fff0 100644 --- a/test/test_automl/test_refit.py +++ b/test/test_automl/test_refit.py @@ -1,3 +1,4 @@ +"""Test the refitting functionality of an automl instance""" from typing import Callable, Union from itertools import repeat diff --git a/test/test_automl/test_show_models.py b/test/test_automl/test_show_models.py index 72b2e4f8d6..93a4aac651 100644 --- a/test/test_automl/test_show_models.py +++ b/test/test_automl/test_show_models.py @@ -1,3 +1,4 @@ +"""Test the show models functinality of an automl instance""" from autosklearn.automl import AutoML from pytest_cases import parametrize_with_cases diff --git a/test/test_automl/test_sklearn_compliance.py b/test/test_automl/test_sklearn_compliance.py index ce747e1bb8..c96468e0e2 100644 --- a/test/test_automl/test_sklearn_compliance.py +++ b/test/test_automl/test_sklearn_compliance.py @@ -1,7 +1,9 @@ -""" +"""Test that autosklearn is sklearn compliant + Note ---- -This is far from complete at the moment +* This is far from complete at the moment +* This should probably be tested on AutoSklearnEstimators not AutoML """ from typing import List, Union diff --git a/test/test_ensemble_builder/test_ensemble_builder.py b/test/test_ensemble_builder/test_ensemble_builder.py index a46da42ef1..9a7927384d 100644 --- a/test/test_ensemble_builder/test_ensemble_builder.py +++ b/test/test_ensemble_builder/test_ensemble_builder.py @@ -10,14 +10,15 @@ from autosklearn.automl_common.common.utils.backend import Backend from autosklearn.ensemble_building import EnsembleBuilder, Run -from autosklearn.metrics import make_scorer +from autosklearn.metrics import Scorer, accuracy, make_scorer from autosklearn.util.functional import bound, pairs import pytest from pytest_cases import fixture, parametrize -from unittest.mock import patch +from unittest.mock import Mock, patch from test.conftest import DEFAULT_SEED +from test.fixtures.metrics import acc_with_X_data @fixture @@ -673,6 +674,27 @@ def test_delete_runs_does_not_delete_dummy( assert set(loaded.values()) == set(dummy_runs) +def test_fit_ensemble_with_no_targets_raises( + builder: EnsembleBuilder, + make_run: Callable[..., Run], +) -> None: + """ + Expects + ------- + * If no ensemble targets can be found then `fit_ensemble` should fail + """ + # Delete the targets and then try fit ensemble + targets_path = Path(builder.backend._get_targets_ensemble_filename()) + targets_path.unlink() + + candidates = [make_run(backend=builder.backend) for _ in range(5)] + with pytest.raises(ValueError, match="`fit_ensemble` could not find any .*"): + builder.fit_ensemble( + candidates=candidates, + runs=candidates, + ) + + def test_fit_ensemble_produces_ensemble( builder: EnsembleBuilder, make_run: Callable[..., Run], @@ -682,16 +704,13 @@ def test_fit_ensemble_produces_ensemble( ------- * Should produce an ensemble if all runs have predictions """ - X_data = builder.X_data("ensemble") targets = builder.targets("ensemble") assert targets is not None predictions = targets runs = [make_run(predictions={"ensemble": predictions}) for _ in range(10)] - ensemble = builder.fit_ensemble( - candidates=runs, X_data=X_data, targets=targets, runs=runs - ) + ensemble = builder.fit_ensemble(candidates=runs, runs=runs) assert ensemble is not None @@ -823,3 +842,160 @@ def test_deletion_will_not_break_current_ensemble( for run in new_runs: assert run in available_runs + + +@parametrize("metrics", [accuracy, acc_with_X_data, [accuracy, acc_with_X_data]]) +def test_will_build_ensemble_with_different_metrics( + make_ensemble_builder: Callable[..., EnsembleBuilder], + make_run: Callable[..., Run], + metrics: Scorer | list[Scorer], +) -> None: + """ + Expects + ------- + * Should be able to build a valid ensemble with different combinations of metrics + * Should produce a validation score for both "ensemble" and "test" scores + """ + if not isinstance(metrics, list): + metrics = [metrics] + + builder = make_ensemble_builder(metrics=metrics) + + # Make some runs and stick them in the same backend as the builder + # Dummy just has a terrible loss for all metrics + make_run( + dummy=True, + losses={m.name: 1000 for m in metrics}, + backend=builder.backend, + ) + + # "Proper" runs will have the correct targets and so be better than dummy + run_predictions = { + "ensemble": builder.targets("ensemble"), + "test": builder.targets("test"), + } + for _ in range(5): + make_run(predictions=run_predictions, backend=builder.backend) + + history, nbest = builder.main() + + # Should only produce one step + assert len(history) == 1 + hist = history[0] + + # Each of these two keys should be present + for key in ["ensemble_optimization_score", "ensemble_test_score"]: + assert key in hist + + # TODO should be updated in next PR + # Each of these scores should contain all the metrics + # for metric in metrics: + # assert metric.name in hist[key] + + +@parametrize("n_least_prioritized", [1, 2, 3, 4]) +@parametrize("metrics", [accuracy, acc_with_X_data, [accuracy, acc_with_X_data]]) +def test_fit_ensemble_kwargs_priorities( + make_ensemble_builder: Callable[..., EnsembleBuilder], + make_run: Callable[..., Run], + metrics: Scorer | list[Scorer], + n_least_prioritized: int, +) -> None: + """ + Expects + ------- + * Should favour 1) function kwargs, 2) function params 3) init_kwargs 4) init_params + """ + if not isinstance(metrics, list): + metrics = [metrics] + + class FakeEnsembleClass: + def __init__(self, *args, **kwargs): + self.args = args + self.kwargs = kwargs + + def fit(*args, **kwargs) -> None: + pass + + # We establish the priorty order and give each one of them a custom metric + priority = ["function_kwargs", "function_params", "init_kwargs", "init_params"] + + # We reverse the priority and use the `n_least_prioritized` ones + # with `n_least_prioritized = 3` + # reversed = ["init_params", "init_kwargs", "function_params", "function_kwargs"] + # used = ["init_params", "init_kwargs", "function_params"] + # highest = "function_params" + reversed_priority = list(reversed(priority)) + used = reversed_priority[:n_least_prioritized] + highest_priority = used[-1] + + def S(name: str) -> Scorer: + return make_scorer(name, lambda: None) + + # We now pass in all the places this arguments could be specified + # Naming them specifically to make it more clear in setup below + builder_metric = [S("init_params")] if "init_params" in used else None + fit_ensemble_metric = [S("function_params")] if "function_params" in used else None + + builder_ensemble_kwargs = ( + {"metrics": [S("init_kwargs")]} if "init_kwargs" in used else None + ) + fit_ensemble_kwargs = ( + {"metrics": [S("function_kwargs")]} if "function_kwargs" in used else None + ) + + builder = make_ensemble_builder( + metrics=builder_metric, + ensemble_kwargs=builder_ensemble_kwargs, + ) + + candidates = [make_run() for _ in range(5)] # Just so something can be run + + ensemble = builder.fit_ensemble( + metrics=fit_ensemble_metric, + ensemble_class=FakeEnsembleClass, + ensemble_kwargs=fit_ensemble_kwargs, + candidates=candidates, + runs=candidates, + ) + + # These are the final metrics passed to the ensemble builder when constructed + passed_metrics = ensemble.kwargs["metrics"] + metric = passed_metrics[0] + + assert metric.name == highest_priority + + +@parametrize("metric, should_be_loaded", [(accuracy, False), (acc_with_X_data, True)]) +def test_X_data_only_loaded_when_required( + make_ensemble_builder: Callable[..., EnsembleBuilder], + make_run: Callable[..., Run], + metric: Scorer, + should_be_loaded: bool, +) -> None: + """ + Expects + ------- + * Should only load X_train if it's required + * TODO should only load X_test if it's required + """ + metrics = [metric] + builder = make_ensemble_builder(metrics=metrics) + + # Make a dummy which is required for the whole pipeline to run + make_run(dummy=True, losses={metric.name: 1000}, backend=builder.backend) + + # Make a run that has no losses recorded, forcing us to use the metric + make_run( + dummy=False, + predictions={"ensemble": builder.targets("ensemble")}, + losses=None, + backend=builder.backend, + ) + + ret_value = builder.X_data() + builder.X_data = Mock(return_value=ret_value) + + builder.main() + + assert builder.X_data.called == should_be_loaded From 23aba24b454bc092f2c39142d0082914300e4393 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Tue, 14 Jun 2022 14:35:10 +0200 Subject: [PATCH 065/118] Create simple example and doc for naive early stopping (#1476) * Create simple example and doc for naive early stopping * Fix doc, pass through SMAC callbacks directly * Fix `isinstance` check * Add test for early stopping * Fix signature of early stopping example/test * Fix doc build --- autosklearn/automl.py | 12 ++- autosklearn/estimators.py | 20 +++-- autosklearn/util/smac_wrap.py | 43 ++++++++++ doc/manual.rst | 5 ++ .../example_early_stopping_and_callbacks.py | 81 +++++++++++++++++++ test/test_automl/test_early_stopping.py | 37 +++++++++ 6 files changed, 192 insertions(+), 6 deletions(-) create mode 100644 autosklearn/util/smac_wrap.py create mode 100644 examples/40_advanced/example_early_stopping_and_callbacks.py create mode 100644 test/test_automl/test_early_stopping.py diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 3ce4f8edf9..c3167ff093 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -120,6 +120,7 @@ ) from autosklearn.util.parallel import preload_modules from autosklearn.util.single_thread_client import SingleThreadedClient +from autosklearn.util.smac_wrap import SMACCallback, SmacRunCallback from autosklearn.util.stopwatch import StopWatch import unittest.mock @@ -235,7 +236,7 @@ def __init__( logging_config: Optional[Mapping] = None, metrics: Sequence[Scorer] | None = None, scoring_functions: Optional[list[Scorer]] = None, - get_trials_callback: Optional[IncorporateRunResultCallback] = None, + get_trials_callback: SMACCallback | None = None, dataset_compression: bool | Mapping[str, Any] = True, allow_string_features: bool = True, ): @@ -264,6 +265,15 @@ def __init__( memory_limit=memory_limit, ) + # If we got something callable for `get_trials_callback`, wrap it so SMAC + # will accept it. + if ( + get_trials_callback is not None + and callable(get_trials_callback) + and not isinstance(get_trials_callback, IncorporateRunResultCallback) + ): + get_trials_callback = SmacRunCallback(get_trials_callback) + self._delete_tmp_folder_after_terminate = delete_tmp_folder_after_terminate self._time_for_task = time_left_for_this_task self._per_run_time_limit = per_run_time_limit diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 9d729133f3..4f788ccfca 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -38,6 +38,7 @@ from autosklearn.ensembles.ensemble_selection import EnsembleSelection from autosklearn.metrics import Scorer from autosklearn.pipeline.base import BasePipeline +from autosklearn.util.smac_wrap import SMACCallback class AutoSklearnEstimator(BaseEstimator): @@ -69,7 +70,7 @@ def __init__( metric: Scorer | Sequence[Scorer] | None = None, scoring_functions: Optional[List[Scorer]] = None, load_models: bool = True, - get_trials_callback=None, + get_trials_callback: SMACCallback | None = None, dataset_compression: Union[bool, Mapping[str, Any]] = True, allow_string_features: bool = True, ): @@ -301,10 +302,19 @@ def __init__( Whether to load the models after fitting Auto-sklearn. get_trials_callback: callable - Callback function to create an object of subclass defined in module - `smac.callbacks `_. - This is an advanced feature. Use only if you are familiar with - `SMAC `_. + A callable with the following definition. + + * (smac.SMBO, smac.RunInfo, smac.RunValue, time_left: float) -> bool | None + + This will be called after SMAC, the underlying optimizer for autosklearn, + finishes training each run. + + You can use this to record your own information about the optimization + process. You can also use this to enable a early stopping based on some + critera. + + See the example: + :ref:`Early Stopping And Callbacks `. dataset_compression: Union[bool, Mapping[str, Any]] = True We compress datasets so that they fit into some predefined amount of memory. diff --git a/autosklearn/util/smac_wrap.py b/autosklearn/util/smac_wrap.py new file mode 100644 index 0000000000..bf3202bbb3 --- /dev/null +++ b/autosklearn/util/smac_wrap.py @@ -0,0 +1,43 @@ +from __future__ import annotations + +from typing import Callable, Union + +from smac.callbacks import IncorporateRunResultCallback +from smac.optimizer.smbo import SMBO +from smac.runhistory.runhistory import RunInfo, RunValue + +SMACCallback = Callable[[SMBO, RunInfo, RunValue, float], Union[bool, None]] + + +class SmacRunCallback(IncorporateRunResultCallback): + def __init__(self, f: SMACCallback): + self.f = f + + def __call__( + self, + smbo: SMBO, + run_info: RunInfo, + result: RunValue, + time_left: float, + ) -> bool | None: + """ + Parameters + ---------- + smbo: SMBO + The SMAC SMBO object + + run_info: RunInfo + Information about the run completed + + result: RunValue + The results of the run + + time_left: float + How much time is left for the remaining runs + + Returns + ------- + bool | None + If False is returned, the optimization loop will stop + """ + return self.f(smbo, run_info, result, time_left) diff --git a/doc/manual.rst b/doc/manual.rst index 7cdb162881..7ab31ce727 100644 --- a/doc/manual.rst +++ b/doc/manual.rst @@ -374,3 +374,8 @@ Other according to its performance on the validation set. Setting the initial configurations found by meta-learning to zero makes *auto-sklearn* use the regular SMAC algorithm for suggesting new hyperparameter configurations. + +.. collapse:: Early stopping and Callbacks + + By using the parameter ``get_trials_callback``, we can get access to the results + of runs as they occur. See this example :ref:`Early Stopping And Callbacks ` for more! diff --git a/examples/40_advanced/example_early_stopping_and_callbacks.py b/examples/40_advanced/example_early_stopping_and_callbacks.py new file mode 100644 index 0000000000..84dae5dced --- /dev/null +++ b/examples/40_advanced/example_early_stopping_and_callbacks.py @@ -0,0 +1,81 @@ +""" +============================ +Early stopping and Callbacks +============================ + +The example below shows how we can use the ``get_trials_callback`` parameter of +auto-sklearn to implement an early-stopping mechanism through a callback. + +These callbacks give access to the result of each model + hyperparameter configuration +optimized by SMAC, the underlying optimizer for autosklearn. By checking the cost of +a result, we can implement a simple yet effective early stopping mechanism! + +Do note however, this does not provide any access to the ensembles that autosklearn +produces, only the individual models. You may wish to perform a more sophisticated +early stopping mechanism such that there are enough good models for autosklearn to build +and ensemble with. This is here to provide a simple example. +""" +from __future__ import annotations + +from pprint import pprint + +import sklearn.datasets +import sklearn.metrics + +import autosklearn.classification + +from smac.optimizer.smbo import SMBO +from smac.runhistory.runhistory import RunInfo, RunValue + + +############################################################################ +# Build and fit a classifier +# ========================== +def callback( + smbo: SMBO, + run_info: RunInfo, + result: RunValue, + time_left: float, +) -> bool | None: + """Stop early if we get a very low cost value for a single run + + The return value indicates to SMAC whether to stop or not. False will + stop the search process while any other value will mean it continues. + """ + # You can find out the parameters in the SMAC documentation + # https://automl.github.io/SMAC3/main/ + if result.cost <= 0.02: + print("Stopping!") + print(run_info) + print(result) + return False + + +X, y = sklearn.datasets.load_breast_cancer(return_X_y=True) +X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + X, y, random_state=1 +) + +automl = autosklearn.classification.AutoSklearnClassifier( + time_left_for_this_task=120, per_run_time_limit=30, get_trials_callback=callback +) +automl.fit(X_train, y_train, dataset_name="breast_cancer") + +############################################################################ +# View the models found by auto-sklearn +# ===================================== + +print(automl.leaderboard()) + +############################################################################ +# Print the final ensemble constructed by auto-sklearn +# ==================================================== + +pprint(automl.show_models(), indent=4) + +########################################################################### +# Get the Score of the final ensemble +# =================================== + +predictions = automl.predict(X_test) +print("Accuracy score:", sklearn.metrics.accuracy_score(y_test, predictions)) diff --git a/test/test_automl/test_early_stopping.py b/test/test_automl/test_early_stopping.py new file mode 100644 index 0000000000..4aa7192180 --- /dev/null +++ b/test/test_automl/test_early_stopping.py @@ -0,0 +1,37 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Callable + +if TYPE_CHECKING: + import numpy as np + from smac.optimizer.smbo import SMBO + from smac.runhistory.runhistory import RunInfo, RunValue + + from autosklearn.automl import AutoMLClassifier + + +def test_early_stopping( + make_automl_classifier: Callable[..., AutoMLClassifier], + make_sklearn_dataset: Callable[..., tuple[np.ndarray, ...]], +) -> None: + """ + Expects + ------- + * Should early after fitting 2 models + """ + + def callback( + smbo: SMBO, + run_info: RunInfo, + result: RunValue, + time_left: float, + ) -> bool | None: + if int(result.additional_info["num_run"]) >= 2: + return False + + automl = make_automl_classifier(get_trials_callback=callback) + + X_train, Y_train, X_test, Y_test = make_sklearn_dataset("iris") + automl.fit(X_train, Y_train) + + assert len(automl.runhistory_.data) == 2 From 47bbe9ca4a9f7bbc0bb45b9c4cacfd1f685065c9 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Tue, 14 Jun 2022 14:35:51 +0200 Subject: [PATCH 066/118] Rename `rval` to `return_value` or `run_value` (#1504) --- autosklearn/automl.py | 8 +- autosklearn/estimators.py | 34 ++-- autosklearn/evaluation/__init__.py | 2 +- autosklearn/evaluation/abstract_evaluator.py | 6 +- autosklearn/evaluation/util.py | 8 +- autosklearn/experimental/selector.py | 16 +- .../metalearning/kNearestDatasets/kND.py | 6 +- autosklearn/pipeline/base.py | 6 +- scripts/2015_nips_paper/run/score_ensemble.py | 4 +- test/test_evaluation/test_test_evaluator.py | 38 ++--- test/test_evaluation/test_train_evaluator.py | 154 +++++++++--------- test/test_scripts/test_metadata_generation.py | 30 ++-- 12 files changed, 157 insertions(+), 155 deletions(-) diff --git a/autosklearn/automl.py b/autosklearn/automl.py index c3167ff093..3902aded6e 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -2147,10 +2147,10 @@ def has_key(rv, key): return rv.additional_info and key in rv.additional_info table_dict = {} - for rkey, rval in self.runhistory_.data.items(): - if has_key(rval, "num_run"): - model_id = rval.additional_info["num_run"] - table_dict[model_id] = {"model_id": model_id, "cost": rval.cost} + for run_key, run_val in self.runhistory_.data.items(): + if has_key(run_val, "num_run"): + model_id = run_val.additional_info["num_run"] + table_dict[model_id] = {"model_id": model_id, "cost": run_val.cost} # Checking if the dictionary is empty if not table_dict: diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 4f788ccfca..96e7f07d26 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -1054,31 +1054,31 @@ def additional_info_has_key(rv, key): return rv.additional_info and key in rv.additional_info model_runs = {} - for rkey, rval in self.automl_.runhistory_.data.items(): - if not additional_info_has_key(rval, "num_run"): + for run_key, run_val in self.automl_.runhistory_.data.items(): + if not additional_info_has_key(run_val, "num_run"): continue else: - model_key = rval.additional_info["num_run"] + model_key = run_val.additional_info["num_run"] model_run = { - "model_id": rval.additional_info["num_run"], - "seed": rkey.seed, - "budget": rkey.budget, - "duration": rval.time, - "config_id": rkey.config_id, - "start_time": rval.starttime, - "end_time": rval.endtime, - "status": str(rval.status), - "train_loss": rval.additional_info["train_loss"] - if additional_info_has_key(rval, "train_loss") + "model_id": run_val.additional_info["num_run"], + "seed": run_key.seed, + "budget": run_key.budget, + "duration": run_val.time, + "config_id": run_key.config_id, + "start_time": run_val.starttime, + "end_time": run_val.endtime, + "status": str(run_val.status), + "train_loss": run_val.additional_info["train_loss"] + if additional_info_has_key(run_val, "train_loss") else None, - "config_origin": rval.additional_info["configuration_origin"] - if additional_info_has_key(rval, "configuration_origin") + "config_origin": run_val.additional_info["configuration_origin"] + if additional_info_has_key(run_val, "configuration_origin") else None, } if num_metrics == 1: - model_run["cost"] = rval.cost + model_run["cost"] = run_val.cost else: - for cost_idx, cost in enumerate(rval.cost): + for cost_idx, cost in enumerate(run_val.cost): model_run[f"cost_{cost_idx}"] = cost model_runs[model_key] = model_run diff --git a/autosklearn/evaluation/__init__.py b/autosklearn/evaluation/__init__.py index aace158c00..9563f4ef8b 100644 --- a/autosklearn/evaluation/__init__.py +++ b/autosklearn/evaluation/__init__.py @@ -71,7 +71,7 @@ def fit_predict_try_except_decorator( # File "auto-sklearn/autosklearn/evaluation/train_evaluator.py", line 616, in fit_predict_and_loss, # noqa E501 # status=status # File "auto-sklearn/autosklearn/evaluation/abstract_evaluator.py", line 320, in finish_up # noqa E501 - # self.queue.put(rval_dict) + # self.queue.put(return_value_dict) # File "miniconda/3-4.5.4/envs/autosklearn/lib/python3.7/multiprocessing/queues.py", line 87, in put # noqa E501 # self._start_thread() # File "miniconda/3-4.5.4/envs/autosklearn/lib/python3.7/multiprocessing/queues.py", line 170, in _start_thread # noqa E501 diff --git a/autosklearn/evaluation/abstract_evaluator.py b/autosklearn/evaluation/abstract_evaluator.py index efd87c6cc3..6a189a86a0 100644 --- a/autosklearn/evaluation/abstract_evaluator.py +++ b/autosklearn/evaluation/abstract_evaluator.py @@ -429,15 +429,15 @@ def finish_up( if test_loss is not None: additional_run_info["test_loss"] = test_loss - rval_dict = { + return_value_dict = { "loss": loss, "additional_run_info": additional_run_info, "status": status, } if final_call: - rval_dict["final_queue_element"] = True + return_value_dict["final_queue_element"] = True - self.queue.put(rval_dict) + self.queue.put(return_value_dict) return self.duration, loss_, self.seed, additional_run_info_ def calculate_auxiliary_losses( diff --git a/autosklearn/evaluation/util.py b/autosklearn/evaluation/util.py index c249c8be1c..158825786b 100644 --- a/autosklearn/evaluation/util.py +++ b/autosklearn/evaluation/util.py @@ -12,19 +12,19 @@ def read_queue( stack = [] while True: try: - rval = queue_.get(timeout=1) + return_value = queue_.get(timeout=1) except queue.Empty: break # Check if there is a special placeholder value which tells us that # we don't have to wait until the queue times out in order to # retrieve the final value! - if "final_queue_element" in rval: - del rval["final_queue_element"] + if "final_queue_element" in return_value: + del return_value["final_queue_element"] do_break = True else: do_break = False - stack.append(rval) + stack.append(return_value) if do_break: break diff --git a/autosklearn/experimental/selector.py b/autosklearn/experimental/selector.py index 125cba6125..b854c7b440 100644 --- a/autosklearn/experimental/selector.py +++ b/autosklearn/experimental/selector.py @@ -297,17 +297,17 @@ def _predict( wins = wins / np.sum(wins) predictions[X.index[x_idx]] = wins - rval = { + return_value = { task_id: { strategy: predictions[task_id][strategy_idx] for strategy_idx, strategy in enumerate(self.strategies_) } for task_id in X.index } - rval = pd.DataFrame(rval).transpose().astype(float) - rval = rval[self.strategies_] - rval = rval.fillna(0.0) - return rval + return_value = pd.DataFrame(return_value).transpose().astype(float) + return_value = return_value[self.strategies_] + return_value = return_value.fillna(0.0) + return return_value def fit_pairwise_model(self, X, y, weights, rng, configuration): raise NotImplementedError() @@ -346,14 +346,14 @@ def fit( ) -> None: self.X_ = X self.strategies_ = y.columns - self.rval_ = np.array( + self.return_value_ = np.array( [ (len(self.strategies_) - self.default_strategies.index(strategy) - 1) / (len(self.strategies_) - 1) for strategy in self.strategies_ ] ) - self.rval_ = self.rval_ / np.sum(self.rval_) + self.return_value_ = self.return_value_ / np.sum(self.return_value_) self.selector.fit(X, y, minima, maxima) def _predict( @@ -377,7 +377,7 @@ def _predict( prediction.loc[task_id] = pd.Series( { strategy: value - for strategy, value in zip(self.strategies_, self.rval_) + for strategy, value in zip(self.strategies_, self.return_value_) } ) diff --git a/autosklearn/metalearning/metalearning/kNearestDatasets/kND.py b/autosklearn/metalearning/metalearning/kNearestDatasets/kND.py index f6c10c95d2..f49ed8ccab 100644 --- a/autosklearn/metalearning/metalearning/kNearestDatasets/kND.py +++ b/autosklearn/metalearning/metalearning/kNearestDatasets/kND.py @@ -122,7 +122,7 @@ def kNearestDatasets(self, x, k=1, return_distance=False): assert k == neighbor_indices.shape[1] - rval = [ + return_value = [ self.metafeatures.index[i] # Neighbor indices is 2d, each row is the indices for one # dataset in x. @@ -130,9 +130,9 @@ def kNearestDatasets(self, x, k=1, return_distance=False): ] if return_distance is False: - return rval + return return_value else: - return rval, distances[0] + return return_value, distances[0] def kBestSuggestions(self, x, k=1, exclude_double_configurations=True): assert type(x) == pd.Series diff --git a/autosklearn/pipeline/base.py b/autosklearn/pipeline/base.py index 93c73b4716..3a13364ea6 100644 --- a/autosklearn/pipeline/base.py +++ b/autosklearn/pipeline/base.py @@ -495,15 +495,15 @@ def __repr__(self): dataset_properties_string.append("}") dataset_properties_string = "".join(dataset_properties_string) - rval = "%s(%s,\n%s)" % ( + return_value = "%s(%s,\n%s)" % ( class_name, configuration, dataset_properties_string, ) else: - rval = "%s(%s)" % (class_name, configuration_string) + return_value = "%s(%s)" % (class_name, configuration_string) - return rval + return return_value def _get_pipeline_steps(self, dataset_properties): raise NotImplementedError() diff --git a/scripts/2015_nips_paper/run/score_ensemble.py b/scripts/2015_nips_paper/run/score_ensemble.py index 1e873f01fd..9842359225 100644 --- a/scripts/2015_nips_paper/run/score_ensemble.py +++ b/scripts/2015_nips_paper/run/score_ensemble.py @@ -227,14 +227,14 @@ def evaluate(input_directory, validation_files, test_files, ensemble_size=50): ensemble_time = time.time() - start - rval = { + return_value = { "ensemble_time": ensemble_time, "time_function_evaluation": time_function_evaluation, "ensemble_error": ensemble_error, "ensemble_test_error": ensemble_test_error, } - return rval + return return_value if __name__ == "__main__": diff --git a/test/test_evaluation/test_test_evaluator.py b/test/test_evaluation/test_test_evaluator.py index 457661df03..02eedcca91 100644 --- a/test/test_evaluation/test_test_evaluator.py +++ b/test/test_evaluation/test_test_evaluator.py @@ -80,10 +80,10 @@ def test_datasets(self): ) evaluator.fit_predict_and_loss() - rval = read_queue(evaluator.queue) - self.assertEqual(len(rval), 1) - self.assertEqual(len(rval[0]), 3) - self.assertTrue(np.isfinite(rval[0]["loss"])) + return_value = read_queue(evaluator.queue) + self.assertEqual(len(return_value), 1) + self.assertEqual(len(return_value[0]), 3) + self.assertTrue(np.isfinite(return_value[0]["loss"])) class FunctionsTest(unittest.TestCase): @@ -124,11 +124,11 @@ def test_eval_test(self): port=self.port, additional_components=dict(), ) - rval = read_queue(self.queue) - self.assertEqual(len(rval), 1) - self.assertAlmostEqual(rval[0]["loss"], 0.07999999999999996) - self.assertEqual(rval[0]["status"], StatusType.SUCCESS) - self.assertNotIn("bac_metric", rval[0]["additional_run_info"]) + return_value = read_queue(self.queue) + self.assertEqual(len(return_value), 1) + self.assertAlmostEqual(return_value[0]["loss"], 0.07999999999999996) + self.assertEqual(return_value[0]["status"], StatusType.SUCCESS) + self.assertNotIn("bac_metric", return_value[0]["additional_run_info"]) def test_eval_test_multi_objective(self): metrics = { @@ -151,12 +151,12 @@ def test_eval_test_multi_objective(self): port=self.port, additional_components=dict(), ) - rval = read_queue(self.queue) - self.assertEqual(len(rval), 1) + return_value = read_queue(self.queue) + self.assertEqual(len(return_value), 1) for metric, loss in metrics.items(): - self.assertAlmostEqual(rval[0]["loss"][metric.name], loss) - self.assertEqual(rval[0]["status"], StatusType.SUCCESS) - self.assertNotIn("bac_metric", rval[0]["additional_run_info"]) + self.assertAlmostEqual(return_value[0]["loss"][metric.name], loss) + self.assertEqual(return_value[0]["status"], StatusType.SUCCESS) + self.assertNotIn("bac_metric", return_value[0]["additional_run_info"]) def test_eval_test_all_loss_functions(self): eval_t( @@ -175,8 +175,8 @@ def test_eval_test_all_loss_functions(self): port=self.port, additional_components=dict(), ) - rval = read_queue(self.queue) - self.assertEqual(len(rval), 1) + return_value = read_queue(self.queue) + self.assertEqual(len(return_value), 1) # Note: All metric here should be minimized fixture = { @@ -195,7 +195,7 @@ def test_eval_test_all_loss_functions(self): "num_run": -1, } - additional_run_info = rval[0]["additional_run_info"] + additional_run_info = return_value[0]["additional_run_info"] for key, value in fixture.items(): self.assertAlmostEqual(additional_run_info[key], fixture[key], msg=key) self.assertEqual( @@ -204,5 +204,5 @@ def test_eval_test_all_loss_functions(self): msg=sorted(additional_run_info.items()), ) self.assertIn("duration", additional_run_info) - self.assertAlmostEqual(rval[0]["loss"], 0.040000000000000036) - self.assertEqual(rval[0]["status"], StatusType.SUCCESS) + self.assertAlmostEqual(return_value[0]["loss"], 0.040000000000000036) + self.assertEqual(return_value[0]["status"], StatusType.SUCCESS) diff --git a/test/test_evaluation/test_train_evaluator.py b/test/test_evaluation/test_train_evaluator.py index 9413af5509..14c36f2afc 100644 --- a/test/test_evaluation/test_train_evaluator.py +++ b/test/test_evaluation/test_train_evaluator.py @@ -150,10 +150,10 @@ def test_holdout(self, pipeline_mock): evaluator.fit_predict_and_loss() - rval = read_queue(evaluator.queue) - self.assertEqual(len(rval), 1) - result = rval[0]["loss"] - self.assertEqual(len(rval[0]), 3) + return_value = read_queue(evaluator.queue) + self.assertEqual(len(return_value), 1) + result = return_value[0]["loss"] + self.assertEqual(len(return_value[0]), 3) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(evaluator.file_output.call_count, 1) @@ -294,15 +294,15 @@ def side_effect(self, *args, **kwargs): self.assertEqual(evaluator.file_output.call_count, 9) for i in range(1, 10): - rval = evaluator.queue.get(timeout=1) - result = rval["loss"] + return_value = evaluator.queue.get(timeout=1) + result = return_value["loss"] self.assertAlmostEqual(result, 1.0 - (0.1 * (i - 1))) if i < 9: - self.assertEqual(rval["status"], StatusType.DONOTADVANCE) - self.assertEqual(len(rval), 3) + self.assertEqual(return_value["status"], StatusType.DONOTADVANCE) + self.assertEqual(len(return_value), 3) else: - self.assertEqual(rval["status"], StatusType.SUCCESS) - self.assertEqual(len(rval), 4) + self.assertEqual(return_value["status"], StatusType.SUCCESS) + self.assertEqual(len(return_value), 4) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(pipeline_mock.iterative_fit.call_count, 9) @@ -438,8 +438,8 @@ def side_effect(self, *args, **kwargs): self.assertEqual(evaluator.file_output.call_count, 2) for i in range(1, 3): - rval = evaluator.queue.get(timeout=1) - self.assertAlmostEqual(rval["loss"], 1.0 - (0.2 * i)) + return_value = evaluator.queue.get(timeout=1) + self.assertAlmostEqual(return_value["loss"], 1.0 - (0.2 * i)) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(pipeline_mock.iterative_fit.call_count, 2) @@ -499,8 +499,8 @@ def test_iterative_holdout_not_iterative(self, pipeline_mock): evaluator.fit_predict_and_loss(iterative=True) self.assertEqual(evaluator.file_output.call_count, 1) - rval = evaluator.queue.get(timeout=1) - self.assertAlmostEqual(rval["loss"], 0.47826086956521741) + return_value = evaluator.queue.get(timeout=1) + self.assertAlmostEqual(return_value["loss"], 0.47826086956521741) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(pipeline_mock.iterative_fit.call_count, 0) @@ -554,10 +554,10 @@ def test_cv(self, pipeline_mock): evaluator.fit_predict_and_loss() - rval = read_queue(evaluator.queue) - self.assertEqual(len(rval), 1) - result = rval[0]["loss"] - self.assertEqual(len(rval[0]), 3) + return_value = read_queue(evaluator.queue) + self.assertEqual(len(return_value), 1) + result = return_value[0]["loss"] + self.assertEqual(len(return_value[0]), 3) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(evaluator.file_output.call_count, 1) @@ -623,11 +623,11 @@ def test_partial_cv(self, pipeline_mock): evaluator.partial_fit_predict_and_loss(fold=1) - rval = evaluator.queue.get(timeout=1) + return_value = evaluator.queue.get(timeout=1) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(evaluator.file_output.call_count, 0) - self.assertEqual(rval["loss"], 0.5) + self.assertEqual(return_value["loss"], 0.5) self.assertEqual(pipeline_mock.fit.call_count, 1) self.assertEqual(pipeline_mock.predict_proba.call_count, 4) # The model prior to fitting is saved, this cannot be directly tested @@ -760,12 +760,12 @@ def side_effect(self, *args, **kwargs): self.assertEqual(evaluator.file_output.call_count, 0) for i in range(1, 10): - rval = evaluator.queue.get(timeout=1) - self.assertAlmostEqual(rval["loss"], 1.0 - (0.1 * (i - 1))) + return_value = evaluator.queue.get(timeout=1) + self.assertAlmostEqual(return_value["loss"], 1.0 - (0.1 * (i - 1))) if i < 9: - self.assertEqual(rval["status"], StatusType.DONOTADVANCE) + self.assertEqual(return_value["status"], StatusType.DONOTADVANCE) else: - self.assertEqual(rval["status"], StatusType.SUCCESS) + self.assertEqual(return_value["status"], StatusType.SUCCESS) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(pipeline_mock.iterative_fit.call_count, 9) @@ -809,13 +809,13 @@ def test_file_output(self, loss_mock, model_mock): self.backend_mock.get_model_dir.return_value = True evaluator.model = "model" evaluator.Y_optimization = D.data["Y_train"] - rval = evaluator.file_output( + return_value = evaluator.file_output( D.data["Y_train"], D.data["Y_valid"], D.data["Y_test"], ) - self.assertEqual(rval, (None, {})) + self.assertEqual(return_value, (None, {})) self.assertEqual(self.backend_mock.save_additional_data.call_count, 2) self.assertEqual(self.backend_mock.save_numrun_to_dir.call_count, 1) self.assertEqual( @@ -839,12 +839,12 @@ def test_file_output(self, loss_mock, model_mock): ) evaluator.models = ["model2", "model2"] - rval = evaluator.file_output( + return_value = evaluator.file_output( D.data["Y_train"], D.data["Y_valid"], D.data["Y_test"], ) - self.assertEqual(rval, (None, {})) + self.assertEqual(return_value, (None, {})) self.assertEqual(self.backend_mock.save_additional_data.call_count, 4) self.assertEqual(self.backend_mock.save_numrun_to_dir.call_count, 2) self.assertEqual( @@ -870,26 +870,26 @@ def test_file_output(self, loss_mock, model_mock): # Check for not containing NaNs - that the models don't predict nonsense # for unseen data D.data["Y_valid"][0] = np.NaN - rval = evaluator.file_output( + return_value = evaluator.file_output( D.data["Y_train"], D.data["Y_valid"], D.data["Y_test"], ) self.assertEqual( - rval, + return_value, ( 1.0, {"error": "Model predictions for validation set contains NaNs."}, ), ) D.data["Y_train"][0] = np.NaN - rval = evaluator.file_output( + return_value = evaluator.file_output( D.data["Y_train"], D.data["Y_valid"], D.data["Y_test"], ) self.assertEqual( - rval, + return_value, ( 1.0, {"error": "Model predictions for optimization set contains NaNs."}, @@ -1112,8 +1112,8 @@ def test_fit_predict_and_loss_standard_additional_run_info( evaluator.X_targets[0] = np.array([1, 0] * 23) evaluator.Y_targets[0] = np.array([1] * 23) evaluator.Y_train_targets = np.array([1] * 69) - rval = evaluator.fit_predict_and_loss(iterative=False) - self.assertIsNone(rval) + return_value = evaluator.fit_predict_and_loss(iterative=False) + self.assertIsNone(return_value) element = queue_.get() self.assertEqual(element["status"], StatusType.SUCCESS) self.assertEqual(element["additional_run_info"]["a"], 5) @@ -1219,8 +1219,8 @@ def __call__(self): evaluator.file_output.return_value = (None, {}) evaluator.Y_targets[0] = np.array([1] * 23).reshape((-1, 1)) evaluator.Y_train_targets = np.array([1] * 69).reshape((-1, 1)) - rval = evaluator.fit_predict_and_loss(iterative=True) - self.assertIsNone(rval) + return_value = evaluator.fit_predict_and_loss(iterative=True) + self.assertIsNone(return_value) self.assertEqual(finish_up_mock.call_count, 1) self.assertEqual(finish_up_mock.call_args[1]["additional_run_info"], 14678) @@ -1265,8 +1265,8 @@ def test_fit_predict_and_loss_iterative_noniterativemodel_additional_run_info( evaluator.Y_targets[0] = np.array([1] * 23).reshape((-1, 1)) evaluator.Y_train_targets = np.array([1] * 69).reshape((-1, 1)) - rval = evaluator.fit_predict_and_loss(iterative=True) - self.assertIsNone(rval) + return_value = evaluator.fit_predict_and_loss(iterative=True) + self.assertIsNone(return_value) self.assertEqual(finish_up_mock.call_count, 1) self.assertEqual(finish_up_mock.call_args[1]["additional_run_info"], 14678) @@ -1326,8 +1326,8 @@ def __call__(self): evaluator.Y_targets[0] = np.array([1] * 23).reshape((-1, 1)) evaluator.Y_train_targets = np.array([1] * 69).reshape((-1, 1)) - rval = evaluator.fit_predict_and_loss(iterative=False) - self.assertIsNone(rval) + return_value = evaluator.fit_predict_and_loss(iterative=False) + self.assertIsNone(return_value) self.assertEqual(finish_up_mock.call_count, 1) self.assertEqual( finish_up_mock.call_args[1]["additional_run_info"], {"val": 14678} @@ -1373,8 +1373,8 @@ def test_fit_predict_and_loss_budget_2_additional_run_info( evaluator.Y_targets[0] = np.array([1] * 23).reshape((-1, 1)) evaluator.Y_train_targets = np.array([1] * 69).reshape((-1, 1)) - rval = evaluator.fit_predict_and_loss(iterative=False) - self.assertIsNone(rval) + return_value = evaluator.fit_predict_and_loss(iterative=False) + self.assertIsNone(return_value) self.assertEqual(finish_up_mock.call_count, 1) self.assertEqual( finish_up_mock.call_args[1]["additional_run_info"], {"val": 14678} @@ -1422,8 +1422,8 @@ def test_datasets(self): ) evaluator.fit_predict_and_loss() - rval = evaluator.queue.get(timeout=1) - self.assertTrue(np.isfinite(rval["loss"])) + return_value = evaluator.queue.get(timeout=1) + self.assertTrue(np.isfinite(return_value["loss"])) ############################################################################ # Test obtaining a splitter object from scikit-learn @@ -3053,8 +3053,8 @@ def test_eval_holdout_all_loss_functions(self): metrics=[accuracy], additional_components=dict(), ) - rval = read_queue(self.queue) - self.assertEqual(len(rval), 1) + return_value = read_queue(self.queue) + self.assertEqual(len(return_value), 1) fixture = { "accuracy": 0.030303030303030276, @@ -3075,7 +3075,7 @@ def test_eval_holdout_all_loss_functions(self): "train_loss": 0.0, } - additional_run_info = rval[0]["additional_run_info"] + additional_run_info = return_value[0]["additional_run_info"] for key, value in fixture.items(): self.assertAlmostEqual(additional_run_info[key], fixture[key], msg=key) self.assertIn("duration", additional_run_info) @@ -3085,8 +3085,8 @@ def test_eval_holdout_all_loss_functions(self): msg=sorted(additional_run_info.items()), ) - self.assertAlmostEqual(rval[0]["loss"], 0.030303030303030276, places=3) - self.assertEqual(rval[0]["status"], StatusType.SUCCESS) + self.assertAlmostEqual(return_value[0]["loss"], 0.030303030303030276, places=3) + self.assertEqual(return_value[0]["status"], StatusType.SUCCESS) def test_eval_holdout_iterative_fit_no_timeout(self): eval_iterative_holdout( @@ -3107,11 +3107,11 @@ def test_eval_holdout_iterative_fit_no_timeout(self): metrics=[accuracy], additional_components=dict(), ) - rval = read_queue(self.queue) - self.assertEqual(len(rval), 9) - self.assertAlmostEqual(rval[-1]["loss"], 0.030303030303030276) - self.assertEqual(rval[0]["status"], StatusType.DONOTADVANCE) - self.assertEqual(rval[-1]["status"], StatusType.SUCCESS) + return_value = read_queue(self.queue) + self.assertEqual(len(return_value), 9) + self.assertAlmostEqual(return_value[-1]["loss"], 0.030303030303030276) + self.assertEqual(return_value[0]["status"], StatusType.DONOTADVANCE) + self.assertEqual(return_value[-1]["status"], StatusType.SUCCESS) def test_eval_holdout_iterative_fit_no_timeout_multi_objective(self): metrics = { @@ -3136,12 +3136,12 @@ def test_eval_holdout_iterative_fit_no_timeout_multi_objective(self): metrics=list(metrics.keys()), additional_components=dict(), ) - rval = read_queue(self.queue) - self.assertEqual(len(rval), 9) + return_value = read_queue(self.queue) + self.assertEqual(len(return_value), 9) for metric, loss in metrics.items(): - self.assertAlmostEqual(rval[-1]["loss"][metric.name], loss) - self.assertEqual(rval[0]["status"], StatusType.DONOTADVANCE) - self.assertEqual(rval[-1]["status"], StatusType.SUCCESS) + self.assertAlmostEqual(return_value[-1]["loss"][metric.name], loss) + self.assertEqual(return_value[0]["status"], StatusType.DONOTADVANCE) + self.assertEqual(return_value[-1]["status"], StatusType.SUCCESS) def test_eval_holdout_budget_iterations(self): eval_holdout( @@ -3405,11 +3405,11 @@ def test_eval_cv(self): metrics=[accuracy], additional_components=dict(), ) - rval = read_queue(self.queue) - self.assertEqual(len(rval), 1) - self.assertAlmostEqual(rval[0]["loss"], 0.04999999999999997) - self.assertEqual(rval[0]["status"], StatusType.SUCCESS) - self.assertNotIn("bac_metric", rval[0]["additional_run_info"]) + return_value = read_queue(self.queue) + self.assertEqual(len(return_value), 1) + self.assertAlmostEqual(return_value[0]["loss"], 0.04999999999999997) + self.assertEqual(return_value[0]["status"], StatusType.SUCCESS) + self.assertNotIn("bac_metric", return_value[0]["additional_run_info"]) def test_eval_cv_all_loss_functions(self): eval_cv( @@ -3430,8 +3430,8 @@ def test_eval_cv_all_loss_functions(self): metrics=[accuracy], additional_components=dict(), ) - rval = read_queue(self.queue) - self.assertEqual(len(rval), 1) + return_value = read_queue(self.queue) + self.assertEqual(len(return_value), 1) fixture = { "accuracy": 0.04999999999999997, @@ -3452,7 +3452,7 @@ def test_eval_cv_all_loss_functions(self): "train_loss": 0.0, } - additional_run_info = rval[0]["additional_run_info"] + additional_run_info = return_value[0]["additional_run_info"] for key, value in fixture.items(): self.assertAlmostEqual(additional_run_info[key], fixture[key], msg=key) self.assertIn("duration", additional_run_info) @@ -3462,8 +3462,8 @@ def test_eval_cv_all_loss_functions(self): msg=sorted(additional_run_info.items()), ) - self.assertAlmostEqual(rval[0]["loss"], 0.04999999999999997) - self.assertEqual(rval[0]["status"], StatusType.SUCCESS) + self.assertAlmostEqual(return_value[0]["loss"], 0.04999999999999997) + self.assertEqual(return_value[0]["status"], StatusType.SUCCESS) # def test_eval_cv_on_subset(self): # backend_api = backend.create(self.tmp_dir, self.tmp_dir) @@ -3504,10 +3504,10 @@ def test_eval_partial_cv(self): metrics=[accuracy], additional_components=dict(), ) - rval = read_queue(self.queue) - self.assertEqual(len(rval), 1) - self.assertAlmostEqual(rval[0]["loss"], results[fold]) - self.assertEqual(rval[0]["status"], StatusType.SUCCESS) + return_value = read_queue(self.queue) + self.assertEqual(len(return_value), 1) + self.assertAlmostEqual(return_value[0]["loss"], results[fold]) + self.assertEqual(return_value[0]["status"], StatusType.SUCCESS) def test_eval_partial_cv_multi_objective(self): metrics = { @@ -3547,8 +3547,8 @@ def test_eval_partial_cv_multi_objective(self): metrics=list(metrics.keys()), additional_components=dict(), ) - rval = read_queue(self.queue) - self.assertEqual(len(rval), 1) + return_value = read_queue(self.queue) + self.assertEqual(len(return_value), 1) for metric, loss in metrics.items(): - self.assertAlmostEqual(rval[0]["loss"][metric.name], loss[fold]) - self.assertEqual(rval[0]["status"], StatusType.SUCCESS) + self.assertAlmostEqual(return_value[0]["loss"][metric.name], loss[fold]) + self.assertEqual(return_value[0]["status"], StatusType.SUCCESS) diff --git a/test/test_scripts/test_metadata_generation.py b/test/test_scripts/test_metadata_generation.py index 929b90e029..89999d6be1 100644 --- a/test/test_scripts/test_metadata_generation.py +++ b/test/test_scripts/test_metadata_generation.py @@ -52,10 +52,10 @@ def test_metadata_generation(self): script_filename, self.working_directory, ) - rval = subprocess.run( + return_value = subprocess.run( cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE ) - self.assertEqual(rval.returncode, 0, msg=str(rval)) + self.assertEqual(return_value.returncode, 0, msg=str(return_value)) # 4. run one of the commands to get some data commands_output_file = os.path.join( @@ -99,11 +99,11 @@ def test_metadata_generation(self): # for training. In production, it would use twice as much! cmd = cmd.replace("-s 1", "-s 1 --unittest") print("COMMAND: %s" % cmd) - rval = subprocess.run( + return_value = subprocess.run( cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE ) - print("STDOUT: %s" % repr(rval.stdout), flush=True) - print("STDERR: %s" % repr(rval.stderr), flush=True) + print("STDOUT: %s" % repr(return_value.stdout), flush=True) + print("STDERR: %s" % repr(return_value.stderr), flush=True) self.print_files() @@ -123,7 +123,9 @@ def test_metadata_generation(self): ) with open(smac_log) as fh: smac_output = fh.read() - self.assertEqual(rval.returncode, 0, msg=str(rval) + "\n" + smac_output) + self.assertEqual( + return_value.returncode, 0, msg=str(return_value) + "\n" + smac_output + ) expected_validation_output = os.path.join( expected_output_directory, "..", "validation_trajectory_1.json" ) @@ -172,12 +174,12 @@ def test_metadata_generation(self): self.working_directory, ) print("COMMAND: %s" % cmd) - rval = subprocess.run( + return_value = subprocess.run( cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE ) - print("STDOUT: %s" % repr(rval.stdout), flush=True) - print("STDERR: %s" % repr(rval.stderr), flush=True) - self.assertEqual(rval.returncode, 0, msg=str(rval)) + print("STDOUT: %s" % repr(return_value.stdout), flush=True) + print("STDERR: %s" % repr(return_value.stderr), flush=True) + self.assertEqual(return_value.returncode, 0, msg=str(return_value)) for file in [ "algorithm_runs.arff", @@ -215,10 +217,10 @@ def test_metadata_generation(self): script_filename, self.working_directory, ) - rval = subprocess.run( + return_value = subprocess.run( cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE ) - self.assertEqual(rval.returncode, 0, msg=str(rval)) + self.assertEqual(return_value.returncode, 0, msg=str(return_value)) for task_type in ("classification", "regression"): for file in [ "calculation_times.csv", @@ -271,10 +273,10 @@ def test_metadata_generation(self): script_filename, self.working_directory, ) - rval = subprocess.run( + return_value = subprocess.run( cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE ) - self.assertEqual(rval.returncode, 0, msg=str(rval)) + self.assertEqual(return_value.returncode, 0, msg=str(return_value)) for metric_, combination in ( (metric, "%s_binary.classification_dense" % metric), From df47a0719801c4aadc7e5961b20659269872cdc9 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 14 Jun 2022 17:05:33 +0200 Subject: [PATCH 067/118] Bump actions/setup-python from 3 to 4 (#1511) Bumps [actions/setup-python](https://github.com/actions/setup-python) from 3 to 4. - [Release notes](https://github.com/actions/setup-python/releases) - [Commits](https://github.com/actions/setup-python/compare/v3...v4) --- updated-dependencies: - dependency-name: actions/setup-python dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/dist.yml | 2 +- .github/workflows/docs.yml | 2 +- .github/workflows/generate-baselines.yml | 2 +- .github/workflows/pre-commit.yaml | 2 +- .github/workflows/pytest.yml | 2 +- .github/workflows/regressions.yml | 4 ++-- 6 files changed, 7 insertions(+), 7 deletions(-) diff --git a/.github/workflows/dist.yml b/.github/workflows/dist.yml index 24fc6bbf1d..07ad9366a2 100644 --- a/.github/workflows/dist.yml +++ b/.github/workflows/dist.yml @@ -27,7 +27,7 @@ jobs: submodules: recursive - name: Setup Python - uses: actions/setup-python@v3 + uses: actions/setup-python@v4 with: python-version: 3.8 diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 2b5d32a4f4..83510c5483 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -28,7 +28,7 @@ jobs: submodules: recursive - name: Setup Python - uses: actions/setup-python@v3 + uses: actions/setup-python@v4 with: python-version: 3.8 diff --git a/.github/workflows/generate-baselines.yml b/.github/workflows/generate-baselines.yml index 337fdd269e..ef356d0c20 100644 --- a/.github/workflows/generate-baselines.yml +++ b/.github/workflows/generate-baselines.yml @@ -59,7 +59,7 @@ jobs: # value: The python version used by the installed system - name: Setup Python - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: ${{ steps.python-version.outputs.value }} diff --git a/.github/workflows/pre-commit.yaml b/.github/workflows/pre-commit.yaml index 2d28dd1eae..c7e5b94438 100644 --- a/.github/workflows/pre-commit.yaml +++ b/.github/workflows/pre-commit.yaml @@ -25,7 +25,7 @@ jobs: submodules: recursive - name: Setup Python 3.7 - uses: actions/setup-python@v3 + uses: actions/setup-python@v4 with: python-version: 3.7 diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index de29e860fc..92a66ed573 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -79,7 +79,7 @@ jobs: submodules: recursive - name: Setup Python ${{ matrix.python-version }} - uses: actions/setup-python@v3 + uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} diff --git a/.github/workflows/regressions.yml b/.github/workflows/regressions.yml index 70be44beff..f86d06c12c 100644 --- a/.github/workflows/regressions.yml +++ b/.github/workflows/regressions.yml @@ -142,7 +142,7 @@ jobs: # value: The python version used by the installed system - name: Setup Python - uses: actions/setup-python@v3 + uses: actions/setup-python@v4 with: python-version: ${{ steps.python-version.outputs.value }} @@ -206,7 +206,7 @@ jobs: # value: The python version used by the installed system - name: Setup Python - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: ${{ steps.python-version.outputs.value }} From ef9c08bde9de804b0003844c35723255a969fa51 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 14 Jun 2022 17:06:01 +0200 Subject: [PATCH 068/118] Bump actions/download-artifact from 2 to 3 (#1512) Bumps [actions/download-artifact](https://github.com/actions/download-artifact) from 2 to 3. - [Release notes](https://github.com/actions/download-artifact/releases) - [Commits](https://github.com/actions/download-artifact/compare/v2...v3) --- updated-dependencies: - dependency-name: actions/download-artifact dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/regressions.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/regressions.yml b/.github/workflows/regressions.yml index f86d06c12c..701c602c87 100644 --- a/.github/workflows/regressions.yml +++ b/.github/workflows/regressions.yml @@ -250,7 +250,7 @@ jobs: # - baseline_regression_x_x_x.csv - name: Download workflow artifacts - uses: actions/download-artifact@v2 + uses: actions/download-artifact@v3 with: path: artifacts From ff04a6fa636cfb5951a8708c20ee07ae2dc96a0f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 14 Jun 2022 17:06:25 +0200 Subject: [PATCH 069/118] Bump codecov/codecov-action from 2 to 3 (#1513) Bumps [codecov/codecov-action](https://github.com/codecov/codecov-action) from 2 to 3. - [Release notes](https://github.com/codecov/codecov-action/releases) - [Changelog](https://github.com/codecov/codecov-action/blob/master/CHANGELOG.md) - [Commits](https://github.com/codecov/codecov-action/compare/v2...v3) --- updated-dependencies: - dependency-name: codecov/codecov-action dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/pytest.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index 92a66ed573..2e140d8832 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -150,7 +150,7 @@ jobs: - name: Upload coverage if: matrix.code-cov && always() - uses: codecov/codecov-action@v2 + uses: codecov/codecov-action@v3 with: fail_ci_if_error: true verbose: true From e866b062529b26dbbe514229fdd4e8afff62277a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 14 Jun 2022 17:06:40 +0200 Subject: [PATCH 070/118] Bump actions/upload-artifact from 2 to 3 (#1514) Bumps [actions/upload-artifact](https://github.com/actions/upload-artifact) from 2 to 3. - [Release notes](https://github.com/actions/upload-artifact/releases) - [Commits](https://github.com/actions/upload-artifact/compare/v2...v3) --- updated-dependencies: - dependency-name: actions/upload-artifact dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/generate-baselines.yml | 2 +- .github/workflows/regressions.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/generate-baselines.yml b/.github/workflows/generate-baselines.yml index ef356d0c20..5149dd57d8 100644 --- a/.github/workflows/generate-baselines.yml +++ b/.github/workflows/generate-baselines.yml @@ -109,7 +109,7 @@ jobs: # results_path: path to the benchmark results - name: Upload Results as Artifact - uses: actions/upload-artifact@v2 + uses: actions/upload-artifact@v3 with: name: baselines path: | diff --git a/.github/workflows/regressions.yml b/.github/workflows/regressions.yml index 701c602c87..46414dadc9 100644 --- a/.github/workflows/regressions.yml +++ b/.github/workflows/regressions.yml @@ -307,7 +307,7 @@ jobs: # compared_means: path to the results of regression test vs baseline - name: Upload all results together as an artifact - uses: actions/upload-artifact@v2 + uses: actions/upload-artifact@v3 with: name: ${{ github.repository_owner }}_${{ steps.extract.outputs.branch }}_${{ github.sha }} path: | From 49ea63937c159e6bd8ec0999e2bf5cb2bb6b2662 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Wed, 15 Jun 2022 08:17:27 +0200 Subject: [PATCH 071/118] Fix logging server cleanup (#1503) * Fix logging server cleanup * Add comment relating to the `try: finally:` * Remove nested try: except: from `fit` --- autosklearn/automl.py | 556 ++++++++++++++++++++++-------------------- 1 file changed, 286 insertions(+), 270 deletions(-) diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 3902aded6e..34fd185a6a 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -656,273 +656,282 @@ def fit( # By default try to use the TCP logging port or get a new port self._logger_port = logging.handlers.DEFAULT_TCP_LOGGING_PORT - self._logger = self._get_logger(dataset_name) - # The first thing we have to do is create the logger to update the backend - self._backend.setup_logger(self._logger_port) + # Once we start the logging server, it starts in a new process + # If an error occurs then we want to make sure that we exit cleanly + # and shut it down, else it might hang + # https://github.com/automl/auto-sklearn/issues/1480 + try: + self._logger = self._get_logger(dataset_name) - if not only_return_configuration_space: - # If only querying the configuration space, we do not save the start time - # The start time internally checks for the fit() method to execute only once - # But this does not apply when only querying the configuration space - self._backend.save_start_time(self._seed) + # The first thing we have to do is create the logger to update the backend + self._backend.setup_logger(self._logger_port) - self._stopwatch = StopWatch() + if not only_return_configuration_space: + # If only querying the configuration space, we do not save the start + # time The start time internally checks for the fit() method to execute + # only once but this does not apply when only querying the configuration + # space + self._backend.save_start_time(self._seed) - # Make sure that input is valid - # Performs Ordinal one hot encoding to the target - # both for train and test data - self.InputValidator = InputValidator( - is_classification=is_classification, - feat_type=feat_type, - logger_port=self._logger_port, - allow_string_features=self.allow_string_features, - ) - self.InputValidator.fit(X_train=X, y_train=y, X_test=X_test, y_test=y_test) - X, y = self.InputValidator.transform(X, y) + self._stopwatch = StopWatch() - if X_test is not None and y_test is not None: - X_test, y_test = self.InputValidator.transform(X_test, y_test) + # Make sure that input is valid + # Performs Ordinal one hot encoding to the target + # both for train and test data + self.InputValidator = InputValidator( + is_classification=is_classification, + feat_type=feat_type, + logger_port=self._logger_port, + allow_string_features=self.allow_string_features, + ) + self.InputValidator.fit(X_train=X, y_train=y, X_test=X_test, y_test=y_test) + X, y = self.InputValidator.transform(X, y) - # We don't support size reduction on pandas type object yet - if ( - self._dataset_compression is not None - and not isinstance(X, pd.DataFrame) - and not (isinstance(y, pd.Series) or isinstance(y, pd.DataFrame)) - ): - methods = self._dataset_compression["methods"] - memory_allocation = self._dataset_compression["memory_allocation"] - - # Remove precision reduction if we can't perform it - if "precision" in methods and X.dtype not in supported_precision_reductions: - methods = [method for method in methods if method != "precision"] - - with warnings_to(self._logger): - X, y = reduce_dataset_size_if_too_large( - X=X, - y=y, - memory_limit=self._memory_limit, - is_classification=is_classification, - random_state=self._seed, - operations=methods, - memory_allocation=memory_allocation, - ) + if X_test is not None and y_test is not None: + X_test, y_test = self.InputValidator.transform(X_test, y_test) - # Check the re-sampling strategy - try: + # We don't support size reduction on pandas type object yet + if ( + self._dataset_compression is not None + and not isinstance(X, pd.DataFrame) + and not (isinstance(y, pd.Series) or isinstance(y, pd.DataFrame)) + ): + methods = self._dataset_compression["methods"] + memory_allocation = self._dataset_compression["memory_allocation"] + + # Remove precision reduction if we can't perform it + if ( + "precision" in methods + and X.dtype not in supported_precision_reductions + ): + methods = [method for method in methods if method != "precision"] + + with warnings_to(self._logger): + X, y = reduce_dataset_size_if_too_large( + X=X, + y=y, + memory_limit=self._memory_limit, + is_classification=is_classification, + random_state=self._seed, + operations=methods, + memory_allocation=memory_allocation, + ) + + # Check the re-sampling strategy self._check_resampling_strategy( X=X, y=y, task=self._task, ) - except Exception as e: - self._fit_cleanup() - raise e - - # Reset learnt stuff - self.models_ = None - self.cv_models_ = None - self.ensemble_ = None - # The metric must exist as of this point - # It can be provided in the constructor, or automatically - # defined in the estimator fit call - if isinstance(self._metrics, Sequence): - for entry in self._metrics: - if not isinstance(entry, Scorer): - raise ValueError( - "Metric {entry} must be instance of autosklearn.metrics.Scorer." - ) - else: - raise ValueError( - "Metric must be a sequence of instances of " - "autosklearn.metrics.Scorer." - ) + # Reset learnt stuff + self.models_ = None + self.cv_models_ = None + self.ensemble_ = None - # If no dask client was provided, we create one, so that we can - # start a ensemble process in parallel to smbo optimize - if self._dask_client is None and ( - self._ensemble_class is not None - or self._n_jobs is not None - and self._n_jobs > 1 - ): - self._create_dask_client() - else: - self._is_dask_client_internally_created = False + # The metric must exist as of this point + # It can be provided in the constructor, or automatically + # defined in the estimator fit call + if isinstance(self._metrics, Sequence): + for entry in self._metrics: + if not isinstance(entry, Scorer): + raise ValueError( + f"Metric {entry} must be instance of" + " autosklearn.metrics.Scorer." + ) + else: + raise ValueError( + "Metric must be a sequence of instances of " + "autosklearn.metrics.Scorer." + ) - self._dataset_name = dataset_name - self._stopwatch.start(self._dataset_name) + # If no dask client was provided, we create one, so that we can + # start a ensemble process in parallel to smbo optimize + if self._dask_client is None and ( + self._ensemble_class is not None + or self._n_jobs is not None + and self._n_jobs > 1 + ): + self._create_dask_client() + else: + self._is_dask_client_internally_created = False - # Take the feature types from the validator - self._feat_type = self.InputValidator.feature_validator.feat_type + self._dataset_name = dataset_name + self._stopwatch.start(self._dataset_name) - self._log_fit_setup() + # Take the feature types from the validator + self._feat_type = self.InputValidator.feature_validator.feat_type - # == Pickle the data manager to speed up loading - with self._stopwatch.time("Save Datamanager"): - datamanager = XYDataManager( - X, - y, - X_test=X_test, - y_test=y_test, - task=self._task, - feat_type=self._feat_type, - dataset_name=dataset_name, - ) + self._log_fit_setup() - self._backend._make_internals_directory() - self._label_num = datamanager.info["label_num"] - - self._backend.save_datamanager(datamanager) - - # = Create a searchspace - # Do this before One Hot Encoding to make sure that it creates a - # search space for a dense classifier even if one hot encoding would - # make it sparse (tradeoff; if one hot encoding would make it sparse, - # densifier and truncatedSVD would probably lead to a MemoryError, - # like this we can't use some of the preprocessing methods in case - # the data became sparse) - with self._stopwatch.time("Create Search space"): - self.configuration_space, configspace_path = self._create_search_space( - self._backend.temporary_directory, - self._backend, - datamanager, - include=self._include, - exclude=self._exclude, - ) - - if only_return_configuration_space: - self._fit_cleanup() - return self.configuration_space - - # == Perform dummy predictions - with self._stopwatch.time("Dummy predictions"): - self.num_run += 1 - self._do_dummy_prediction() - - # == RUN ensemble builder - # Do this before calculating the meta-features to make sure that the - # dummy predictions are actually included in the ensemble even if - # calculating the meta-features takes very long - with self._stopwatch.time("Run Ensemble Builder"): - - elapsed_time = self._stopwatch.time_since(self._dataset_name, "start") - - time_left_for_ensembles = max(0, self._time_for_task - elapsed_time) - proc_ensemble = None - if time_left_for_ensembles <= 0: - # Fit only raises error when an ensemble class is given but - # time_left_for_ensembles is zero. - if self._ensemble_class is not None: - raise ValueError( - "Not starting ensemble builder because there " - "is no time left. Try increasing the value " - "of time_left_for_this_task." - ) - elif self._ensemble_class is None: - self._logger.info( - "Not starting ensemble builder because no ensemble class is given." - ) - else: - self._logger.info( - "Start Ensemble with %5.2fsec time left" % time_left_for_ensembles - ) - - proc_ensemble = EnsembleBuilderManager( - start_time=time.time(), - time_left_for_ensembles=time_left_for_ensembles, - backend=copy.deepcopy(self._backend), - dataset_name=dataset_name, + # == Pickle the data manager to speed up loading + with self._stopwatch.time("Save Datamanager"): + datamanager = XYDataManager( + X, + y, + X_test=X_test, + y_test=y_test, task=self._task, - metrics=self._metrics, - ensemble_class=self._ensemble_class, - ensemble_kwargs=self._ensemble_kwargs, - ensemble_nbest=self._ensemble_nbest, - max_models_on_disc=self._max_models_on_disc, - seed=self._seed, - precision=self.precision, - max_iterations=self._max_ensemble_build_iterations, - read_at_most=self._read_at_most, - memory_limit=self._memory_limit, - random_state=self._seed, - logger_port=self._logger_port, - pynisher_context=self._multiprocessing_context, + feat_type=self._feat_type, + dataset_name=dataset_name, ) - # kill the datamanager as it will be re-loaded anyways from sub processes - try: - del self._datamanager - except Exception: - pass + self._backend._make_internals_directory() + self._label_num = datamanager.info["label_num"] + + self._backend.save_datamanager(datamanager) + + # = Create a searchspace + # Do this before One Hot Encoding to make sure that it creates a + # search space for a dense classifier even if one hot encoding would + # make it sparse (tradeoff; if one hot encoding would make it sparse, + # densifier and truncatedSVD would probably lead to a MemoryError, + # like this we can't use some of the preprocessing methods in case + # the data became sparse) + with self._stopwatch.time("Create Search space"): + self.configuration_space, configspace_path = self._create_search_space( + self._backend.temporary_directory, + self._backend, + datamanager, + include=self._include, + exclude=self._exclude, + ) - # => RUN SMAC - with self._stopwatch.time("Run SMAC"): - elapsed_time = self._stopwatch.time_since(self._dataset_name, "start") - time_left = self._time_for_task - elapsed_time - - if self._logger: - self._logger.info("Start SMAC with %5.2fsec time left" % time_left) - if time_left <= 0: - self._logger.warning("Not starting SMAC because there is no time left.") - _proc_smac = None - self._budget_type = None - else: - if ( - self._per_run_time_limit is None - or self._per_run_time_limit > time_left - ): - self._logger.warning( - "Time limit for a single run is higher than total time " - "limit. Capping the limit for a single run to the total " - "time given to SMAC (%f)" % time_left + if only_return_configuration_space: + return self.configuration_space + + # == Perform dummy predictions + with self._stopwatch.time("Dummy predictions"): + self.num_run += 1 + self._do_dummy_prediction() + + # == RUN ensemble builder + # Do this before calculating the meta-features to make sure that the + # dummy predictions are actually included in the ensemble even if + # calculating the meta-features takes very long + with self._stopwatch.time("Run Ensemble Builder"): + + elapsed_time = self._stopwatch.time_since(self._dataset_name, "start") + + time_left_for_ensembles = max(0, self._time_for_task - elapsed_time) + proc_ensemble = None + if time_left_for_ensembles <= 0: + # Fit only raises error when an ensemble class is given but + # time_left_for_ensembles is zero. + if self._ensemble_class is not None: + raise ValueError( + "Not starting ensemble builder because there " + "is no time left. Try increasing the value " + "of time_left_for_this_task." + ) + elif self._ensemble_class is None: + self._logger.info( + "No ensemble buildin because no ensemble class was given." ) - per_run_time_limit = time_left else: - per_run_time_limit = self._per_run_time_limit + self._logger.info( + "Start Ensemble with %5.2fsec time left" + % time_left_for_ensembles + ) + + proc_ensemble = EnsembleBuilderManager( + start_time=time.time(), + time_left_for_ensembles=time_left_for_ensembles, + backend=copy.deepcopy(self._backend), + dataset_name=dataset_name, + task=self._task, + metrics=self._metrics, + ensemble_class=self._ensemble_class, + ensemble_kwargs=self._ensemble_kwargs, + ensemble_nbest=self._ensemble_nbest, + max_models_on_disc=self._max_models_on_disc, + seed=self._seed, + precision=self.precision, + max_iterations=self._max_ensemble_build_iterations, + read_at_most=self._read_at_most, + memory_limit=self._memory_limit, + random_state=self._seed, + logger_port=self._logger_port, + pynisher_context=self._multiprocessing_context, + ) - # Make sure that at least 2 models are created for the ensemble process - num_models = time_left // per_run_time_limit - if num_models < 2: - per_run_time_limit = time_left // 2 + # kill the datamanager as it will be re-loaded anyways from sub processes + try: + del self._datamanager + except Exception: + pass + + # => RUN SMAC + with self._stopwatch.time("Run SMAC"): + elapsed_time = self._stopwatch.time_since(self._dataset_name, "start") + time_left = self._time_for_task - elapsed_time + + if self._logger: + self._logger.info("Start SMAC with %5.2fsec time left" % time_left) + if time_left <= 0: self._logger.warning( - "Capping the per_run_time_limit to {} to have " - "time for a least 2 models in each process.".format( - per_run_time_limit - ) + "Not starting SMAC because there is no time left." ) + _proc_smac = None + self._budget_type = None + else: + if ( + self._per_run_time_limit is None + or self._per_run_time_limit > time_left + ): + self._logger.warning( + "Time limit for a single run is higher than total time " + "limit. Capping the limit for a single run to the total " + "time given to SMAC (%f)" % time_left + ) + per_run_time_limit = time_left + else: + per_run_time_limit = self._per_run_time_limit + + # At least 2 models are created for the ensemble process + num_models = time_left // per_run_time_limit + if num_models < 2: + per_run_time_limit = time_left // 2 + self._logger.warning( + "Capping the per_run_time_limit to {} to have " + "time for a least 2 models in each process.".format( + per_run_time_limit + ) + ) - _proc_smac = AutoMLSMBO( - config_space=self.configuration_space, - dataset_name=self._dataset_name, - backend=self._backend, - total_walltime_limit=time_left, - func_eval_time_limit=per_run_time_limit, - memory_limit=self._memory_limit, - data_memory_limit=self._data_memory_limit, - stopwatch=self._stopwatch, - n_jobs=self._n_jobs, - dask_client=self._dask_client, - start_num_run=self.num_run, - num_metalearning_cfgs=self._initial_configurations_via_metalearning, - config_file=configspace_path, - seed=self._seed, - metadata_directory=self._metadata_directory, - metrics=self._metrics, - resampling_strategy=self._resampling_strategy, - resampling_strategy_args=self._resampling_strategy_arguments, - include=self._include, - exclude=self._exclude, - disable_file_output=self._disable_evaluator_output, - get_smac_object_callback=self._get_smac_object_callback, - smac_scenario_args=self._smac_scenario_args, - scoring_functions=self._scoring_functions, - port=self._logger_port, - pynisher_context=self._multiprocessing_context, - ensemble_callback=proc_ensemble, - trials_callback=self._get_trials_callback, - ) + n_meta_configs = self._initial_configurations_via_metalearning + _proc_smac = AutoMLSMBO( + config_space=self.configuration_space, + dataset_name=self._dataset_name, + backend=self._backend, + total_walltime_limit=time_left, + func_eval_time_limit=per_run_time_limit, + memory_limit=self._memory_limit, + data_memory_limit=self._data_memory_limit, + stopwatch=self._stopwatch, + n_jobs=self._n_jobs, + dask_client=self._dask_client, + start_num_run=self.num_run, + num_metalearning_cfgs=n_meta_configs, + config_file=configspace_path, + seed=self._seed, + metadata_directory=self._metadata_directory, + metrics=self._metrics, + resampling_strategy=self._resampling_strategy, + resampling_strategy_args=self._resampling_strategy_arguments, + include=self._include, + exclude=self._exclude, + disable_file_output=self._disable_evaluator_output, + get_smac_object_callback=self._get_smac_object_callback, + smac_scenario_args=self._smac_scenario_args, + scoring_functions=self._scoring_functions, + port=self._logger_port, + pynisher_context=self._multiprocessing_context, + ensemble_callback=proc_ensemble, + trials_callback=self._get_trials_callback, + ) - try: ( self.runhistory_, self.trajectory_, @@ -938,42 +947,49 @@ def fit( ] with open(trajectory_filename, "w") as fh: json.dump(saveable_trajectory, fh) - except Exception as e: - self._logger.exception(e) - raise - - self._logger.info("Starting shutdown...") - # Wait until the ensemble process is finished to avoid shutting down - # while the ensemble builder tries to access the data - if proc_ensemble is not None: - self.ensemble_performance_history = list(proc_ensemble.history) - - if len(proc_ensemble.futures) > 0: - # Now we need to wait for the future to return as it cannot be cancelled - # while it is running: https://stackoverflow.com/a/49203129 - self._logger.info( - "Ensemble script still running, waiting for it to finish." - ) - result = proc_ensemble.futures.pop().result() - if result: - ensemble_history, _ = result - self.ensemble_performance_history.extend(ensemble_history) - self._logger.info("Ensemble script finished, continue shutdown.") - - # save the ensemble performance history file - if len(self.ensemble_performance_history) > 0: - pd.DataFrame(self.ensemble_performance_history).to_json( - os.path.join( - self._backend.internals_directory, "ensemble_history.json" + + self._logger.info("Starting shutdown...") + # Wait until the ensemble process is finished to avoid shutting down + # while the ensemble builder tries to access the data + if proc_ensemble is not None: + self.ensemble_performance_history = list(proc_ensemble.history) + + if len(proc_ensemble.futures) > 0: + # Now we wait for the future to return as it cannot be cancelled + # while it is running: https://stackoverflow.com/a/49203129 + self._logger.info( + "Ensemble script still running, waiting for it to finish." + ) + result = proc_ensemble.futures.pop().result() + if result: + ensemble_history, _ = result + self.ensemble_performance_history.extend(ensemble_history) + self._logger.info("Ensemble script finished, continue shutdown.") + + # save the ensemble performance history file + if len(self.ensemble_performance_history) > 0: + pd.DataFrame(self.ensemble_performance_history).to_json( + os.path.join( + self._backend.internals_directory, "ensemble_history.json" + ) ) - ) - if load_models: - self._logger.info("Loading models...") - self._load_models() - self._logger.info("Finished loading models...") + if load_models: + self._logger.info("Loading models...") + self._load_models() + self._logger.info("Finished loading models...") + + # The whole logic above from where we begin the logging server is capture + # in a try: finally: so that if something goes wrong, we at least close + # down the logging server, preventing it from hanging and not closing + # until ctrl+c is pressed + except Exception as e: + # This will be called before the _fit_cleanup + self._logger.exception(e) + raise e + finally: + self._fit_cleanup() - self._fit_cleanup() self.fitted = True return self From 374b8e00179df12be38a66ada6a350c8495d85cf Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 15 Jun 2022 16:55:05 +0200 Subject: [PATCH 072/118] Bump peter-evans/find-comment from 1 to 2 (#1520) Bumps [peter-evans/find-comment](https://github.com/peter-evans/find-comment) from 1 to 2. - [Release notes](https://github.com/peter-evans/find-comment/releases) - [Commits](https://github.com/peter-evans/find-comment/compare/v1...v2) --- updated-dependencies: - dependency-name: peter-evans/find-comment dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/regressions.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/regressions.yml b/.github/workflows/regressions.yml index 46414dadc9..8bb0addcf4 100644 --- a/.github/workflows/regressions.yml +++ b/.github/workflows/regressions.yml @@ -327,7 +327,7 @@ jobs: && github.event.action == 'labeled' && github.event.label.name == 'regression-tests' ) - uses: peter-evans/find-comment@v1 + uses: peter-evans/find-comment@v2 id: comment_finder with: issue-number: ${{ github.event.pull_request.number }} From 3863a169fa404393b26444f3608d6b4eef652045 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 15 Jun 2022 16:55:17 +0200 Subject: [PATCH 073/118] Bump actions/stale from 4 to 5 (#1521) Bumps [actions/stale](https://github.com/actions/stale) from 4 to 5. - [Release notes](https://github.com/actions/stale/releases) - [Changelog](https://github.com/actions/stale/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/stale/compare/v4...v5) --- updated-dependencies: - dependency-name: actions/stale dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/stale.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/stale.yaml b/.github/workflows/stale.yaml index b4bb87fafd..5d24ae0627 100644 --- a/.github/workflows/stale.yaml +++ b/.github/workflows/stale.yaml @@ -9,7 +9,7 @@ jobs: stale: runs-on: ubuntu-latest steps: - - uses: actions/stale@v4 + - uses: actions/stale@v5 with: days-before-stale: 60 days-before-close: 7 From 4b40d455895798bc3827248fc497d0704dd9325b Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Fri, 17 Jun 2022 14:26:12 +0200 Subject: [PATCH 074/118] Remove references to validation set in evaluator (#1517) * Init commit * Fix logging server cleanup (#1503) * Fix logging server cleanup * Add comment relating to the `try: finally:` * Remove nested try: except: from `fit` * Bump peter-evans/find-comment from 1 to 2 (#1520) Bumps [peter-evans/find-comment](https://github.com/peter-evans/find-comment) from 1 to 2. - [Release notes](https://github.com/peter-evans/find-comment/releases) - [Commits](https://github.com/peter-evans/find-comment/compare/v1...v2) --- updated-dependencies: - dependency-name: peter-evans/find-comment dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Bump actions/stale from 4 to 5 (#1521) Bumps [actions/stale](https://github.com/actions/stale) from 4 to 5. - [Release notes](https://github.com/actions/stale/releases) - [Changelog](https://github.com/actions/stale/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/stale/compare/v4...v5) --- updated-dependencies: - dependency-name: actions/stale dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Init commit * Update evaluation module * Clean up other occurences of the word validation * Re-add test for test predictions Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- autosklearn/automl.py | 2 +- autosklearn/estimators.py | 4 +- autosklearn/evaluation/__init__.py | 24 +- autosklearn/evaluation/abstract_evaluator.py | 65 ++---- autosklearn/evaluation/test_evaluator.py | 2 - autosklearn/evaluation/train_evaluator.py | 73 +----- test/test_evaluation/evaluation_util.py | 73 +----- .../test_abstract_evaluator.py | 47 +--- test/test_evaluation/test_train_evaluator.py | 216 +++++------------- 9 files changed, 88 insertions(+), 418 deletions(-) diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 34fd185a6a..8726ab8c6d 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -244,7 +244,7 @@ def __init__( if isinstance(disable_evaluator_output, Iterable): disable_evaluator_output = list(disable_evaluator_output) # Incase iterator - allowed = set(["model", "cv_model", "y_optimization", "y_test", "y_valid"]) + allowed = set(["model", "cv_model", "y_optimization", "y_test"]) unknown = allowed - set(disable_evaluator_output) if any(unknown): raise ValueError( diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 96e7f07d26..5afd8c597c 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -262,8 +262,8 @@ def __init__( list are: * ``'y_optimization'`` : do not save the predictions for the - optimization/validation set, which would later on be used to build - an ensemble. + optimization set, which would later on be used to build an ensemble. + * ``model`` : do not save any model files smac_scenario_args : dict, optional (None) diff --git a/autosklearn/evaluation/__init__.py b/autosklearn/evaluation/__init__.py index 9563f4ef8b..ba17513ae0 100644 --- a/autosklearn/evaluation/__init__.py +++ b/autosklearn/evaluation/__init__.py @@ -230,14 +230,7 @@ def __init__( self.memory_limit = memory_limit dm = self.backend.load_datamanager() - if "X_valid" in dm.data and "Y_valid" in dm.data: - self._get_validation_loss = True - else: - self._get_validation_loss = False - if "X_test" in dm.data and "Y_test" in dm.data: - self._get_test_loss = True - else: - self._get_test_loss = False + self._get_test_loss = "X_test" in dm.data and "Y_test" in dm.data self.port = port self.pynisher_context = pynisher_context @@ -533,21 +526,6 @@ def run( additional_run_info["train_learning_curve"] = train_learning_curve additional_run_info["learning_curve_runtime"] = learning_curve_runtime - if self._get_validation_loss: - validation_learning_curve = ( - autosklearn.evaluation.util.extract_learning_curve( - info, - "validation_loss", - ) - ) - if len(validation_learning_curve) > 1: - additional_run_info[ - "validation_learning_curve" - ] = validation_learning_curve - additional_run_info[ - "learning_curve_runtime" - ] = learning_curve_runtime - if self._get_test_loss: test_learning_curve = ( autosklearn.evaluation.util.extract_learning_curve( diff --git a/autosklearn/evaluation/abstract_evaluator.py b/autosklearn/evaluation/abstract_evaluator.py index 6a189a86a0..b97f588a45 100644 --- a/autosklearn/evaluation/abstract_evaluator.py +++ b/autosklearn/evaluation/abstract_evaluator.py @@ -220,8 +220,6 @@ def __init__( self.include = include self.exclude = exclude - self.X_valid = self.datamanager.data.get("X_valid") - self.y_valid = self.datamanager.data.get("Y_valid") self.X_test = self.datamanager.data.get("X_test") self.y_test = self.datamanager.data.get("Y_test") @@ -359,7 +357,6 @@ def finish_up( loss: Union[Dict[str, float], float], train_loss: Optional[Dict[str, float]], opt_pred: np.ndarray, - valid_pred: np.ndarray, test_pred: np.ndarray, additional_run_info: Optional[TYPE_ADDITIONAL_INFO], file_output: bool, @@ -382,19 +379,12 @@ def finish_up( self.duration = time.time() - self.starttime if file_output: - file_out_loss, additional_run_info_ = self.file_output( - opt_pred, - valid_pred, - test_pred, - ) + file_out_loss, additional_run_info_ = self.file_output(opt_pred, test_pred) else: file_out_loss = None additional_run_info_ = {} - validation_loss, test_loss = self.calculate_auxiliary_losses( - valid_pred, - test_pred, - ) + test_loss = self.calculate_auxiliary_losses(test_pred) if file_out_loss is not None: return self.duration, file_out_loss, self.seed, additional_run_info_ @@ -424,8 +414,6 @@ def finish_up( additional_run_info["train_loss"] = [ train_loss[metric.name] for metric in self.metrics ] - if validation_loss is not None: - additional_run_info["validation_loss"] = validation_loss if test_loss is not None: additional_run_info["test_loss"] = test_loss @@ -442,41 +430,22 @@ def finish_up( def calculate_auxiliary_losses( self, - Y_valid_pred: np.ndarray, - Y_test_pred: np.ndarray, - ) -> Tuple[Optional[float | Sequence[float]], Optional[float | Sequence[float]]]: - if Y_valid_pred is not None: - if self.y_valid is not None: - validation_loss: Optional[Union[float, Dict[str, float]]] = self._loss( - self.y_valid, Y_valid_pred - ) - if len(self.metrics) == 1: - validation_loss = validation_loss[self.metrics[0].name] - else: - validation_loss = None - else: - validation_loss = None + Y_test_pred: np.ndarray | None, + ) -> float | dict[str, float] | None: + if Y_test_pred is None or self.y_test is None: + return None - if Y_test_pred is not None: - if self.y_test is not None: - test_loss: Optional[Union[float, Dict[str, float]]] = self._loss( - self.y_test, Y_test_pred - ) - if len(self.metrics) == 1: - test_loss = test_loss[self.metrics[0].name] - else: - test_loss = None - else: - test_loss = None + test_loss = self._loss(self.y_test, Y_test_pred) + if len(self.metrics) == 1: + test_loss = test_loss[self.metrics[0].name] - return validation_loss, test_loss + return test_loss def file_output( self, Y_optimization_pred: np.ndarray, - Y_valid_pred: np.ndarray, Y_test_pred: np.ndarray, - ) -> Tuple[Optional[float], Dict[str, Union[str, int, float, List, Dict, Tuple]]]: + ) -> tuple[float | None, dict[str, Any]]: # Abort if self.Y_optimization is None # self.Y_optimization can be None if we use partial-cv, then, # obviously no output should be saved. @@ -496,12 +465,7 @@ def file_output( ) # Abort if predictions contain NaNs - for y, s in [ - # Y_train_pred deleted here. Fix unittest accordingly. - [Y_optimization_pred, "optimization"], - [Y_valid_pred, "validation"], - [Y_test_pred, "test"], - ]: + for y, s in [(Y_optimization_pred, "optimization"), (Y_test_pred, "test")]: if y is not None and not np.all(np.isfinite(y)): return ( 1.0, @@ -553,14 +517,13 @@ def file_output( budget=self.budget, model=self.model if "model" not in self.disable_file_output else None, cv_model=models if "cv_model" not in self.disable_file_output else None, + # TODO: below line needs to be deleted once backend is updated + valid_predictions=None, ensemble_predictions=( Y_optimization_pred if "y_optimization" not in self.disable_file_output else None ), - valid_predictions=( - Y_valid_pred if "y_valid" not in self.disable_file_output else None - ), test_predictions=( Y_test_pred if "y_test" not in self.disable_file_output else None ), diff --git a/autosklearn/evaluation/test_evaluator.py b/autosklearn/evaluation/test_evaluator.py index e76186aa06..d624c1a44d 100644 --- a/autosklearn/evaluation/test_evaluator.py +++ b/autosklearn/evaluation/test_evaluator.py @@ -67,7 +67,6 @@ def fit_predict_and_loss(self) -> None: loss=loss, train_loss=None, opt_pred=Y_pred, - valid_pred=None, test_pred=None, file_output=False, final_call=True, @@ -78,7 +77,6 @@ def fit_predict_and_loss(self) -> None: def predict_and_loss( self, train: bool = False ) -> Tuple[Union[Dict[str, float], float], np.array, Any, Any]: - if train: Y_pred = self.predict_function( self.X_train, self.model, self.task_type, self.Y_train diff --git a/autosklearn/evaluation/train_evaluator.py b/autosklearn/evaluation/train_evaluator.py index a8433c2136..f19db473bf 100644 --- a/autosklearn/evaluation/train_evaluator.py +++ b/autosklearn/evaluation/train_evaluator.py @@ -316,7 +316,6 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: Y_train_pred = [None] * self.num_cv_folds Y_optimization_pred = [None] * self.num_cv_folds - Y_valid_pred = [None] * self.num_cv_folds Y_test_pred = [None] * self.num_cv_folds train_splits = [None] * self.num_cv_folds @@ -417,7 +416,7 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: **fit_params_array[i], ) - (train_pred, opt_pred, valid_pred, test_pred) = self._predict( + (train_pred, opt_pred, test_pred) = self._predict( model, train_indices=train_indices, test_indices=test_indices, @@ -425,7 +424,6 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: Y_train_pred[i] = train_pred Y_optimization_pred[i] = opt_pred - Y_valid_pred[i] = valid_pred Y_test_pred[i] = test_pred train_splits[i] = train_indices @@ -499,20 +497,6 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: X_targets = concat_data(X_targets, num_cv_folds=self.num_cv_folds) Y_targets = concat_data(Y_targets, num_cv_folds=self.num_cv_folds) - if self.X_valid is not None: - Y_valid_preds = np.array( - [ - Y_valid_pred[i] - for i in range(self.num_cv_folds) - if Y_valid_pred[i] is not None - ] - ) - # Average the predictions of several models - if len(Y_valid_preds.shape) == 3: - Y_valid_preds = np.nanmean(Y_valid_preds, axis=0) - else: - Y_valid_preds = None - if self.X_test is not None: Y_test_preds = np.array( [ @@ -544,7 +528,6 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: loss=opt_loss, train_loss=train_loss, opt_pred=Y_optimization_pred_concat, - valid_pred=Y_valid_preds, test_pred=Y_test_preds, additional_run_info=additional_run_info, file_output=True, @@ -558,7 +541,6 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: Y_train_pred = [None] * self.num_cv_folds Y_optimization_pred = [None] * self.num_cv_folds - Y_valid_pred = [None] * self.num_cv_folds Y_test_pred = [None] * self.num_cv_folds train_splits = [None] * self.num_cv_folds @@ -586,7 +568,6 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: ( train_pred, opt_pred, - valid_pred, test_pred, additional_run_info, ) = self._partial_fit_and_predict_standard( @@ -599,7 +580,6 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: ( train_pred, opt_pred, - valid_pred, test_pred, additional_run_info, ) = self._partial_fit_and_predict_budget( @@ -622,7 +602,6 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: Y_train_pred[i] = train_pred Y_optimization_pred[i] = opt_pred - Y_valid_pred[i] = valid_pred Y_test_pred[i] = test_pred train_splits[i] = train_split @@ -683,18 +662,6 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: X_targets = concat_data(X_targets, num_cv_folds=self.num_cv_folds) Y_targets = concat_data(Y_targets, num_cv_folds=self.num_cv_folds) - if self.X_valid is not None: - Y_valid_pred = np.array( - [ - Y_valid_pred[i] - for i in range(self.num_cv_folds) - if Y_valid_pred[i] is not None - ] - ) - # Average the predictions of several models - if len(np.shape(Y_valid_pred)) == 3: - Y_valid_pred = np.nanmean(Y_valid_pred, axis=0) - if self.X_test is not None: Y_test_pred = np.array( [ @@ -746,7 +713,6 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: loss=opt_loss, train_loss=train_loss, opt_pred=Y_optimization_pred, - valid_pred=Y_valid_pred if self.X_valid is not None else None, test_pred=Y_test_pred if self.X_test is not None else None, additional_run_info=additional_run_info, file_output=True, @@ -793,7 +759,6 @@ def partial_fit_predict_and_loss(self, fold: int, iterative: bool = False) -> No ( train_pred, opt_pred, - valid_pred, test_pred, additional_run_info, ) = self._partial_fit_and_predict_standard( @@ -819,7 +784,6 @@ def partial_fit_predict_and_loss(self, fold: int, iterative: bool = False) -> No loss=loss, train_loss=train_loss, opt_pred=opt_pred, - valid_pred=valid_pred, test_pred=test_pred, file_output=False, final_call=True, @@ -883,12 +847,7 @@ def _partial_fit_and_predict_iterative( n_iter=n_iter, **fit_params, ) - ( - Y_train_pred, - Y_optimization_pred, - Y_valid_pred, - Y_test_pred, - ) = self._predict( + (Y_train_pred, Y_optimization_pred, Y_test_pred,) = self._predict( model, train_indices=train_indices, test_indices=test_indices, @@ -921,7 +880,6 @@ def _partial_fit_and_predict_iterative( loss=loss, train_loss=train_loss, opt_pred=Y_optimization_pred, - valid_pred=Y_valid_pred, test_pred=Y_test_pred, additional_run_info=additional_run_info, file_output=file_output, @@ -936,7 +894,6 @@ def _partial_fit_and_predict_iterative( ( Y_train_pred, Y_optimization_pred, - Y_valid_pred, Y_test_pred, additional_run_info, ) = self._partial_fit_and_predict_standard( @@ -962,7 +919,6 @@ def _partial_fit_and_predict_iterative( loss=loss, train_loss=train_loss, opt_pred=Y_optimization_pred, - valid_pred=Y_valid_pred, test_pred=Y_test_pred, additional_run_info=additional_run_info, file_output=file_output, @@ -980,7 +936,6 @@ def _partial_fit_and_predict_standard( ) -> Tuple[ PIPELINE_DATA_DTYPE, # train_pred PIPELINE_DATA_DTYPE, # opt_pred - PIPELINE_DATA_DTYPE, # valid_pred PIPELINE_DATA_DTYPE, # test_pred TYPE_ADDITIONAL_INFO, ]: @@ -1020,7 +975,7 @@ def _partial_fit_and_predict_standard( else self.Y_train[train_indices] ) - train_pred, opt_pred, valid_pred, test_pred = self._predict( + train_pred, opt_pred, test_pred = self._predict( model=model, train_indices=train_indices, test_indices=test_indices, @@ -1029,7 +984,6 @@ def _partial_fit_and_predict_standard( return ( train_pred, opt_pred, - valid_pred, test_pred, additional_run_info, ) @@ -1043,7 +997,6 @@ def _partial_fit_and_predict_budget( ) -> Tuple[ PIPELINE_DATA_DTYPE, # train_pred PIPELINE_DATA_DTYPE, # opt_pred - PIPELINE_DATA_DTYPE, # valid_pred PIPELINE_DATA_DTYPE, # test_pred TYPE_ADDITIONAL_INFO, ]: @@ -1073,7 +1026,7 @@ def _partial_fit_and_predict_budget( task_type=self.task_type, ) - train_pred, opt_pred, valid_pred, test_pred = self._predict( + train_pred, opt_pred, test_pred = self._predict( model, train_indices=train_indices, test_indices=test_indices, @@ -1088,19 +1041,13 @@ def _partial_fit_and_predict_budget( return ( train_pred, opt_pred, - valid_pred, test_pred, additional_run_info, ) def _predict( self, model: BaseEstimator, test_indices: List[int], train_indices: List[int] - ) -> Tuple[ - PIPELINE_DATA_DTYPE, - PIPELINE_DATA_DTYPE, - PIPELINE_DATA_DTYPE, - PIPELINE_DATA_DTYPE, - ]: + ) -> Tuple[PIPELINE_DATA_DTYPE, PIPELINE_DATA_DTYPE, PIPELINE_DATA_DTYPE]: train_pred = self.predict_function( self.X_train.iloc[train_indices] if hasattr(self.X_train, "iloc") @@ -1123,14 +1070,6 @@ def _predict( else self.Y_train[train_indices], ) - if self.X_valid is not None: - X_valid = self.X_valid.copy() - valid_pred = self.predict_function( - X_valid, model, self.task_type, self.Y_train[train_indices] - ) - else: - valid_pred = None - if self.X_test is not None: X_test = self.X_test.copy() test_pred = self.predict_function( @@ -1144,7 +1083,7 @@ def _predict( else: test_pred = None - return train_pred, opt_pred, valid_pred, test_pred + return train_pred, opt_pred, test_pred def get_splitter( self, D: AbstractDataManager diff --git a/test/test_evaluation/evaluation_util.py b/test/test_evaluation/evaluation_util.py index 62623a50ba..38040f2e4e 100644 --- a/test/test_evaluation/evaluation_util.py +++ b/test/test_evaluation/evaluation_util.py @@ -133,27 +133,14 @@ def get_multiclass_classification_datamanager(): np.random.shuffle(indices) X_train = X_train[indices] Y_train = Y_train[indices] - - X_valid = X_test[ - :25, - ] - Y_valid = Y_test[ - :25, - ] - X_test = X_test[ - 25:, - ] - Y_test = Y_test[ - 25:, - ] + X_test = X_test[25:] + Y_test = Y_test[25:] D = Dummy() D.info = {"task": MULTICLASS_CLASSIFICATION, "is_sparse": False, "label_num": 3} D.data = { "X_train": X_train, "Y_train": Y_train, - "X_valid": X_valid, - "Y_valid": Y_valid, "X_test": X_test, "Y_test": Y_test, } @@ -196,34 +183,16 @@ def get_multilabel_classification_datamanager(): Y_train = Y_train[indices] Y_train = np.array(convert_to_bin(Y_train, 3)) - # for i in range(Y_train_.shape[0]): - # Y_train_[:, Y_train[i]] = 1 - # Y_train = Y_train_ Y_test = np.array(convert_to_bin(Y_test, 3)) - # for i in range(Y_test_.shape[0]): - # Y_test_[:, Y_test[i]] = 1 - # Y_test = Y_test_ - X_valid = X_test[ - :25, - ] - Y_valid = Y_test[ - :25, - ] - X_test = X_test[ - 25:, - ] - Y_test = Y_test[ - 25:, - ] + X_test = X_test[25:] + Y_test = Y_test[25:] D = Dummy() D.info = {"task": MULTILABEL_CLASSIFICATION, "is_sparse": False, "label_num": 3} D.data = { "X_train": X_train, "Y_train": Y_train, - "X_valid": X_valid, - "Y_valid": Y_valid, "X_test": X_test, "Y_test": Y_test, } @@ -247,26 +216,14 @@ def get_binary_classification_datamanager(): X_test = X_test[eliminate_class_two] Y_test = Y_test[eliminate_class_two] - X_valid = X_test[ - :25, - ] - Y_valid = Y_test[ - :25, - ] - X_test = X_test[ - 25:, - ] - Y_test = Y_test[ - 25:, - ] + X_test = X_test[25:] + Y_test = Y_test[25:] D = Dummy() D.info = {"task": BINARY_CLASSIFICATION, "is_sparse": False, "label_num": 2} D.data = { "X_train": X_train, "Y_train": Y_train.reshape((-1, 1)), - "X_valid": X_valid, - "Y_valid": Y_valid.reshape((-1, 1)), "X_test": X_test, "Y_test": Y_test.reshape((-1, 1)), } @@ -282,26 +239,14 @@ def get_regression_datamanager(): X_train = X_train[indices] Y_train = Y_train[indices] - X_valid = X_test[ - :200, - ] - Y_valid = Y_test[ - :200, - ] - X_test = X_test[ - 200:, - ] - Y_test = Y_test[ - 200:, - ] + X_test = X_test[200:] + Y_test = Y_test[200:] D = Dummy() D.info = {"task": REGRESSION, "is_sparse": False, "label_num": 1} D.data = { "X_train": X_train, "Y_train": Y_train.reshape((-1, 1)), - "X_valid": X_valid, - "Y_valid": Y_valid.reshape((-1, 1)), "X_test": X_test, "Y_test": Y_test.reshape((-1, 1)), } @@ -334,8 +279,6 @@ def get_500_classes_datamanager(): D.data = { "X_train": X[:700], "Y_train": Y[:700], - "X_valid": X[700:710], - "Y_valid": Y[700:710], "X_test": X[710:], "Y_test": Y[710:], } diff --git a/test/test_evaluation/test_abstract_evaluator.py b/test/test_evaluation/test_abstract_evaluator.py index 7bd52c0f76..e2473d738b 100644 --- a/test/test_evaluation/test_abstract_evaluator.py +++ b/test/test_evaluation/test_abstract_evaluator.py @@ -71,7 +71,6 @@ def test_finish_up_model_predicts_NaN(self): ae.Y_optimization = rs.rand(33, 3) predictions_ensemble = rs.rand(33, 3) predictions_test = rs.rand(25, 3) - predictions_valid = rs.rand(25, 3) # NaNs in prediction ensemble predictions_ensemble[5, 2] = np.NaN @@ -79,7 +78,6 @@ def test_finish_up_model_predicts_NaN(self): loss=0.1, train_loss=0.1, opt_pred=predictions_ensemble, - valid_pred=predictions_valid, test_pred=predictions_test, additional_run_info=None, final_call=True, @@ -89,37 +87,15 @@ def test_finish_up_model_predicts_NaN(self): self.assertEqual(loss, 1.0) self.assertEqual( additional_run_info, - {"error": "Model predictions for optimization set " "contains NaNs."}, + {"error": "Model predictions for optimization set contains NaNs."}, ) - # NaNs in prediction validation - predictions_ensemble[5, 2] = 0.5 - predictions_valid[5, 2] = np.NaN - _, loss, _, additional_run_info = ae.finish_up( - loss=0.1, - train_loss=0.1, - opt_pred=predictions_ensemble, - valid_pred=predictions_valid, - test_pred=predictions_test, - additional_run_info=None, - final_call=True, - file_output=True, - status=StatusType.SUCCESS, - ) - self.assertEqual(loss, 1.0) - self.assertEqual( - additional_run_info, - {"error": "Model predictions for validation set " "contains NaNs."}, - ) - - # NaNs in prediction test - predictions_valid[5, 2] = 0.5 + predictions_ensemble = rs.rand(33, 3) predictions_test[5, 2] = np.NaN _, loss, _, additional_run_info = ae.finish_up( loss=0.1, train_loss=0.1, opt_pred=predictions_ensemble, - valid_pred=predictions_valid, test_pred=predictions_test, additional_run_info=None, final_call=True, @@ -129,9 +105,8 @@ def test_finish_up_model_predicts_NaN(self): self.assertEqual(loss, 1.0) self.assertEqual( additional_run_info, - {"error": "Model predictions for test set contains " "NaNs."}, + {"error": "Model predictions for test set contains NaNs."}, ) - self.assertEqual(self.backend_mock.save_predictions_as_npy.call_count, 0) def test_disable_file_output(self): @@ -150,11 +125,9 @@ def test_disable_file_output(self): predictions_ensemble = rs.rand(33, 3) predictions_test = rs.rand(25, 3) - predictions_valid = rs.rand(25, 3) loss_, additional_run_info_ = ae.file_output( predictions_ensemble, - predictions_valid, predictions_test, ) @@ -179,7 +152,6 @@ def test_disable_file_output(self): loss_, additional_run_info_ = ae.file_output( predictions_ensemble, - predictions_valid, predictions_test, ) @@ -211,11 +183,6 @@ def test_disable_file_output(self): "ensemble_predictions" ] ) - self.assertIsNotNone( - self.backend_mock.save_numrun_to_dir.call_args_list[-1][1][ - "valid_predictions" - ] - ) self.assertIsNotNone( self.backend_mock.save_numrun_to_dir.call_args_list[-1][1][ "test_predictions" @@ -237,7 +204,6 @@ def test_disable_file_output(self): loss_, additional_run_info_ = ae.file_output( predictions_ensemble, - predictions_valid, predictions_test, ) @@ -249,11 +215,6 @@ def test_disable_file_output(self): "ensemble_predictions" ] ) - self.assertIsNotNone( - self.backend_mock.save_numrun_to_dir.call_args_list[-1][1][ - "valid_predictions" - ] - ) self.assertIsNotNone( self.backend_mock.save_numrun_to_dir.call_args_list[-1][1][ "test_predictions" @@ -296,11 +257,9 @@ def test_file_output(self): ae.Y_optimization = rs.rand(33, 3) predictions_ensemble = rs.rand(33, 3) predictions_test = rs.rand(25, 3) - predictions_valid = rs.rand(25, 3) ae.file_output( Y_optimization_pred=predictions_ensemble, - Y_valid_pred=predictions_valid, Y_test_pred=predictions_test, ) diff --git a/test/test_evaluation/test_train_evaluator.py b/test/test_evaluation/test_train_evaluator.py index 14c36f2afc..c8fe1c5f87 100644 --- a/test/test_evaluation/test_train_evaluator.py +++ b/test/test_evaluation/test_train_evaluator.py @@ -7,6 +7,7 @@ import shutil import sys import tempfile +from itertools import chain import numpy as np import sklearn.model_selection @@ -68,6 +69,24 @@ ) +class LossSideEffect(object): + """Some kind of re-used fixture for losses calculated""" + + def __init__(self): + # The 3 below is related to train, test, opt sets + self.losses = [ + {"accuracy": value} + for value in chain.from_iterable( + [i] * 3 for i in [1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2] + ) + ] + self.iteration = 0 + + def side_effect(self, *args, **kwargs): + self.iteration += 1 + return self.losses[self.iteration - 1] + + class Dummy(object): def __init__(self): self.name = "dummy" @@ -159,15 +178,14 @@ def test_holdout(self, pipeline_mock): self.assertEqual(evaluator.file_output.call_count, 1) self.assertEqual(result, 0.45833333333333337) self.assertEqual(pipeline_mock.fit.call_count, 1) - # four calls because of train, holdout, validation and test set - self.assertEqual(pipeline_mock.predict_proba.call_count, 4) + # four calls because of train, holdout and test set + self.assertEqual(pipeline_mock.predict_proba.call_count, 3) self.assertEqual(evaluator.file_output.call_count, 1) + self.assertEqual(evaluator.file_output.call_args[0][0].shape[0], 24) + self.assertEqual( - evaluator.file_output.call_args[0][1].shape[0], D.data["Y_valid"].shape[0] - ) - self.assertEqual( - evaluator.file_output.call_args[0][2].shape[0], D.data["Y_test"].shape[0] + evaluator.file_output.call_args[0][1].shape[0], D.data["Y_test"].shape[0] ) self.assertEqual(evaluator.model.fit.call_count, 1) @@ -240,46 +258,12 @@ def configuration_fully_fitted(self): class LossSideEffect(object): def __init__(self): + # The 3 below is related to train, test, opt sets self.losses = [ {"accuracy": value} - for value in [ - 1.0, - 1.0, - 1.0, - 1.0, - 0.9, - 0.9, - 0.9, - 0.9, - 0.8, - 0.8, - 0.8, - 0.8, - 0.7, - 0.7, - 0.7, - 0.7, - 0.6, - 0.6, - 0.6, - 0.6, - 0.5, - 0.5, - 0.5, - 0.5, - 0.4, - 0.4, - 0.4, - 0.4, - 0.3, - 0.3, - 0.3, - 0.3, - 0.2, - 0.2, - 0.2, - 0.2, - ] + for value in chain.from_iterable( + [i] * 3 for i in [1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2] + ) ] self.iteration = 0 @@ -310,16 +294,12 @@ def side_effect(self, *args, **kwargs): [cal[1]["n_iter"] for cal in pipeline_mock.iterative_fit.call_args_list], [2, 2, 4, 8, 16, 32, 64, 128, 256], ) - # 20 calls because of train, holdout, validation and test set - # and a total of five calls because of five iterations of fitting - self.assertEqual(evaluator.model.predict_proba.call_count, 36) - # 1/3 of 69 + + # 9 per split type + self.assertEqual(evaluator.model.predict_proba.call_count, 27) self.assertEqual(evaluator.file_output.call_args[0][0].shape[0], 23) self.assertEqual( - evaluator.file_output.call_args[0][1].shape[0], D.data["Y_valid"].shape[0] - ) - self.assertEqual( - evaluator.file_output.call_args[0][2].shape[0], D.data["Y_test"].shape[0] + evaluator.file_output.call_args[0][1].shape[0], D.data["Y_test"].shape[0] ) self.assertEqual(evaluator.file_output.call_count, 9) self.assertEqual(evaluator.model.fit.call_count, 0) @@ -443,15 +423,14 @@ def side_effect(self, *args, **kwargs): self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(pipeline_mock.iterative_fit.call_count, 2) - # eight calls because of train, holdout, the validation and the test set + + # 6 calls because of train, holdout and test set # and a total of two calls each because of two iterations of fitting - self.assertEqual(evaluator.model.predict_proba.call_count, 8) + self.assertEqual(evaluator.model.predict_proba.call_count, 6) + self.assertEqual(evaluator.file_output.call_args[0][0].shape[0], 23) self.assertEqual( - evaluator.file_output.call_args[0][1].shape[0], D.data["Y_valid"].shape[0] - ) - self.assertEqual( - evaluator.file_output.call_args[0][2].shape[0], D.data["Y_test"].shape[0] + evaluator.file_output.call_args[0][1].shape[0], D.data["Y_test"].shape[0] ) self.assertEqual(evaluator.file_output.call_count, 2) self.assertEqual(evaluator.model.fit.call_count, 0) @@ -504,14 +483,13 @@ def test_iterative_holdout_not_iterative(self, pipeline_mock): self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(pipeline_mock.iterative_fit.call_count, 0) - # four calls for train, opt, valid and test - self.assertEqual(evaluator.model.predict_proba.call_count, 4) + + # 3 calls for train, opt and test + self.assertEqual(evaluator.model.predict_proba.call_count, 3) + self.assertEqual(evaluator.file_output.call_args[0][0].shape[0], 23) self.assertEqual( - evaluator.file_output.call_args[0][1].shape[0], D.data["Y_valid"].shape[0] - ) - self.assertEqual( - evaluator.file_output.call_args[0][2].shape[0], D.data["Y_test"].shape[0] + evaluator.file_output.call_args[0][1].shape[0], D.data["Y_test"].shape[0] ) self.assertEqual(evaluator.file_output.call_count, 1) self.assertEqual(evaluator.model.fit.call_count, 1) @@ -563,17 +541,14 @@ def test_cv(self, pipeline_mock): self.assertEqual(evaluator.file_output.call_count, 1) self.assertEqual(result, 0.463768115942029) self.assertEqual(pipeline_mock.fit.call_count, 5) - # Fifteen calls because of the training, holdout, validation and - # test set (4 sets x 5 folds = 20) - self.assertEqual(pipeline_mock.predict_proba.call_count, 20) + + # 15 calls because of the training (5), holdout (5) and test set (5) + self.assertEqual(pipeline_mock.predict_proba.call_count, 15) self.assertEqual( evaluator.file_output.call_args[0][0].shape[0], D.data["Y_train"].shape[0] ) self.assertEqual( - evaluator.file_output.call_args[0][1].shape[0], D.data["Y_valid"].shape[0] - ) - self.assertEqual( - evaluator.file_output.call_args[0][2].shape[0], D.data["Y_test"].shape[0] + evaluator.file_output.call_args[0][1].shape[0], D.data["Y_test"].shape[0] ) # The model prior to fitting is saved, this cannot be directly tested # because of the way the mock module is used. Instead, we test whether @@ -629,7 +604,7 @@ def test_partial_cv(self, pipeline_mock): self.assertEqual(evaluator.file_output.call_count, 0) self.assertEqual(return_value["loss"], 0.5) self.assertEqual(pipeline_mock.fit.call_count, 1) - self.assertEqual(pipeline_mock.predict_proba.call_count, 4) + self.assertEqual(pipeline_mock.predict_proba.call_count, 3) # The model prior to fitting is saved, this cannot be directly tested # because of the way the mock module is used. Instead, we test whether # the if block in which model assignment is done is accessed @@ -703,55 +678,6 @@ def configuration_fully_fitted(self): evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) evaluator.file_output.return_value = (None, {}) - class LossSideEffect(object): - def __init__(self): - self.losses = [ - {"accuracy": value} - for value in [ - 1.0, - 1.0, - 1.0, - 1.0, - 0.9, - 0.9, - 0.9, - 0.9, - 0.8, - 0.8, - 0.8, - 0.8, - 0.7, - 0.7, - 0.7, - 0.7, - 0.6, - 0.6, - 0.6, - 0.6, - 0.5, - 0.5, - 0.5, - 0.5, - 0.4, - 0.4, - 0.4, - 0.4, - 0.3, - 0.3, - 0.3, - 0.3, - 0.2, - 0.2, - 0.2, - 0.2, - ] - ] - self.iteration = 0 - - def side_effect(self, *args, **kwargs): - self.iteration += 1 - return self.losses[self.iteration - 1] - evaluator._loss = unittest.mock.Mock() evaluator._loss.side_effect = LossSideEffect().side_effect @@ -773,13 +699,9 @@ def side_effect(self, *args, **kwargs): [cal[1]["n_iter"] for cal in pipeline_mock.iterative_fit.call_args_list], [2, 2, 4, 8, 16, 32, 64, 128, 256], ) - # fifteen calls because of the holdout, the validation and the test set - # and a total of five calls because of five iterations of fitting self.assertTrue(hasattr(evaluator, "model")) self.assertEqual(pipeline_mock.iterative_fit.call_count, 9) - # 20 calls because of train, holdout, the validation and the test set - # and a total of five calls because of five iterations of fitting - self.assertEqual(pipeline_mock.predict_proba.call_count, 36) + self.assertEqual(pipeline_mock.predict_proba.call_count, 27) @unittest.mock.patch.object(TrainEvaluator, "_loss") @unittest.mock.patch.object(TrainEvaluator, "_get_model") @@ -809,11 +731,7 @@ def test_file_output(self, loss_mock, model_mock): self.backend_mock.get_model_dir.return_value = True evaluator.model = "model" evaluator.Y_optimization = D.data["Y_train"] - return_value = evaluator.file_output( - D.data["Y_train"], - D.data["Y_valid"], - D.data["Y_test"], - ) + return_value = evaluator.file_output(D.data["Y_train"], D.data["Y_test"]) self.assertEqual(return_value, (None, {})) self.assertEqual(self.backend_mock.save_additional_data.call_count, 2) @@ -826,8 +744,8 @@ def test_file_output(self, loss_mock, model_mock): "budget", "model", "cv_model", + "valid_predictions", # TODO remove once backend updated "ensemble_predictions", - "valid_predictions", "test_predictions", }, ) @@ -839,11 +757,7 @@ def test_file_output(self, loss_mock, model_mock): ) evaluator.models = ["model2", "model2"] - return_value = evaluator.file_output( - D.data["Y_train"], - D.data["Y_valid"], - D.data["Y_test"], - ) + return_value = evaluator.file_output(D.data["Y_train"], D.data["Y_test"]) self.assertEqual(return_value, (None, {})) self.assertEqual(self.backend_mock.save_additional_data.call_count, 4) self.assertEqual(self.backend_mock.save_numrun_to_dir.call_count, 2) @@ -855,8 +769,8 @@ def test_file_output(self, loss_mock, model_mock): "budget", "model", "cv_model", + "valid_predictions", # TODO remove once backend updated "ensemble_predictions", - "valid_predictions", "test_predictions", }, ) @@ -867,27 +781,8 @@ def test_file_output(self, loss_mock, model_mock): self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]["cv_model"] ) - # Check for not containing NaNs - that the models don't predict nonsense - # for unseen data - D.data["Y_valid"][0] = np.NaN - return_value = evaluator.file_output( - D.data["Y_train"], - D.data["Y_valid"], - D.data["Y_test"], - ) - self.assertEqual( - return_value, - ( - 1.0, - {"error": "Model predictions for validation set contains NaNs."}, - ), - ) D.data["Y_train"][0] = np.NaN - return_value = evaluator.file_output( - D.data["Y_train"], - D.data["Y_valid"], - D.data["Y_test"], - ) + return_value = evaluator.file_output(D.data["Y_train"], D.data["Y_test"]) self.assertEqual( return_value, ( @@ -1086,7 +981,6 @@ def test_fit_predict_and_loss_standard_additional_run_info( _partial_fit_and_predict_mock.return_value = ( np.array([[0.1, 0.9]] * 46), np.array([[0.1, 0.9]] * 23), - np.array([[0.1, 0.9]] * 25), np.array([[0.1, 0.9]] * 6), {"a": 5}, ) @@ -1129,7 +1023,6 @@ def __call__(self, *args, **kwargs): return ( np.array([[0.1, 0.9]] * 34), np.array([[0.1, 0.9]] * 35), - np.array([[0.1, 0.9]] * 25), np.array([[0.1, 0.9]] * 6), {"a": 5}, ) @@ -1137,7 +1030,6 @@ def __call__(self, *args, **kwargs): return ( np.array([[0.1, 0.9]] * 34), np.array([[0.1, 0.9]] * 34), - np.array([[0.1, 0.9]] * 25), np.array([[0.1, 0.9]] * 6), {"a": 5}, ) @@ -3070,7 +2962,6 @@ def test_eval_holdout_all_loss_functions(self): "recall_micro": 0.030303030303030276, "recall_weighted": 0.030303030303030276, "num_run": 1, - "validation_loss": 0.0, "test_loss": 0.04, "train_loss": 0.0, } @@ -3447,7 +3338,6 @@ def test_eval_cv_all_loss_functions(self): "recall_micro": 0.04999999999999997, "recall_weighted": 0.04999999999999997, "num_run": 1, - "validation_loss": 0.04, "test_loss": 0.04, "train_loss": 0.0, } From 9914168e71ab903964b6cb3ec2473e74d20f5d8e Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Fri, 17 Jun 2022 15:26:34 +0200 Subject: [PATCH 075/118] Fix timeouts related to metalearnings tests (#1508) * Add debug statements and 30s timeouts * Fix formatting * Update internal timeout param * +timeout, use allocated tmpdir * +timeout, use allocated tmpdir * Remove another occurence of explicit `tmp` * Increase timelimits once again * Remove incomplete comment --- .github/workflows/pytest.yml | 2 +- scripts/03_calculate_metafeatures.py | 15 ++--- test/test_scripts/test_metadata_generation.py | 58 ++++++++++++++----- 3 files changed, 49 insertions(+), 26 deletions(-) diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index 2e140d8832..794157f602 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -26,7 +26,7 @@ env: pytest-args: >- --forked --durations=20 - --timeout=300 + --timeout=600 --timeout-method=thread -s diff --git a/scripts/03_calculate_metafeatures.py b/scripts/03_calculate_metafeatures.py index 95d857145a..d7e08ffea8 100644 --- a/scripts/03_calculate_metafeatures.py +++ b/scripts/03_calculate_metafeatures.py @@ -5,6 +5,7 @@ import os import sys import unittest.mock +import tempfile import arff import joblib @@ -82,10 +83,7 @@ def calculate_metafeatures(task_id): for task_type in ("classification", "regression"): output_directory = os.path.join(working_directory, "metafeatures", task_type) - try: - os.makedirs(output_directory) - except: - pass + os.makedirs(output_directory, exist_ok=True) all_metafeatures = {} @@ -100,13 +98,10 @@ def calculate_metafeatures(task_id): tasks = copy.deepcopy(tasks) np.random.shuffle(tasks) - def producer(): - for task_id in tasks: - yield task_id - - memory = joblib.Memory(location="/tmp/joblib", verbose=10) + tmpdir = os.path.join(tempfile.gettempdir(), "joblib") + memory = joblib.Memory(location=tmpdir, verbose=10) cached_calculate_metafeatures = memory.cache(calculate_metafeatures) - mfs = [cached_calculate_metafeatures(task_id) for task_id in producer()] + mfs = [cached_calculate_metafeatures(task_id) for task_id in tasks] for mf in mfs: if mf is not None: diff --git a/test/test_scripts/test_metadata_generation.py b/test/test_scripts/test_metadata_generation.py index 89999d6be1..25c4855b08 100644 --- a/test/test_scripts/test_metadata_generation.py +++ b/test/test_scripts/test_metadata_generation.py @@ -4,6 +4,7 @@ import shutil import socket import subprocess +import tempfile import arff import numpy as np @@ -15,10 +16,12 @@ class TestMetadataGeneration(unittest.TestCase): def setUp(self): - self.working_directory = "/tmp/autosklearn-unittest-tmp-dir-%s-%d-%d" % ( - socket.gethostname(), - os.getpid(), - random.randint(0, 1000000), + host = socket.gethostname() + pid = os.getpid() + rint = random.randint(0, 1000000) + + self.working_directory = os.path.join( + tempfile.gettempdir(), f"autosklearn-unittest-tmp-dir-{host}-{pid}-{rint}" ) def print_files(self): @@ -27,7 +30,6 @@ def print_files(self): print(dirpath, dirnames, filenames) def test_metadata_generation(self): - regression_task_id = 360029 regression_dataset_name = "SWD".lower() classification_task_id = 245 @@ -52,10 +54,15 @@ def test_metadata_generation(self): script_filename, self.working_directory, ) + return_value = subprocess.run( - cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE + cmd, + shell=True, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + timeout=30, ) - self.assertEqual(return_value.returncode, 0, msg=str(return_value)) + self.assertEqual(return_value.returncode, 0, msg=f"{cmd}\n{str(return_value)}") # 4. run one of the commands to get some data commands_output_file = os.path.join( @@ -99,8 +106,13 @@ def test_metadata_generation(self): # for training. In production, it would use twice as much! cmd = cmd.replace("-s 1", "-s 1 --unittest") print("COMMAND: %s" % cmd) + return_value = subprocess.run( - cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE + cmd, + shell=True, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + timeout=180, ) print("STDOUT: %s" % repr(return_value.stdout), flush=True) print("STDERR: %s" % repr(return_value.stderr), flush=True) @@ -124,7 +136,9 @@ def test_metadata_generation(self): with open(smac_log) as fh: smac_output = fh.read() self.assertEqual( - return_value.returncode, 0, msg=str(return_value) + "\n" + smac_output + return_value.returncode, + 0, + msg=f"{cmd}\n{str(return_value)}" + "\n" + smac_output, ) expected_validation_output = os.path.join( expected_output_directory, "..", "validation_trajectory_1.json" @@ -174,12 +188,17 @@ def test_metadata_generation(self): self.working_directory, ) print("COMMAND: %s" % cmd) + return_value = subprocess.run( - cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE + cmd, + shell=True, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + timeout=60, ) print("STDOUT: %s" % repr(return_value.stdout), flush=True) print("STDERR: %s" % repr(return_value.stderr), flush=True) - self.assertEqual(return_value.returncode, 0, msg=str(return_value)) + self.assertEqual(return_value.returncode, 0, msg=f"{cmd}\n{str(return_value)}") for file in [ "algorithm_runs.arff", @@ -218,9 +237,13 @@ def test_metadata_generation(self): self.working_directory, ) return_value = subprocess.run( - cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE + cmd, + shell=True, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + timeout=90, ) - self.assertEqual(return_value.returncode, 0, msg=str(return_value)) + self.assertEqual(return_value.returncode, 0, msg=f"{cmd}\n{str(return_value)}") for task_type in ("classification", "regression"): for file in [ "calculation_times.csv", @@ -273,10 +296,15 @@ def test_metadata_generation(self): script_filename, self.working_directory, ) + return_value = subprocess.run( - cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE + cmd, + shell=True, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + timeout=45, ) - self.assertEqual(return_value.returncode, 0, msg=str(return_value)) + self.assertEqual(return_value.returncode, 0, msg=f"{cmd}\n{str(return_value)}") for metric_, combination in ( (metric, "%s_binary.classification_dense" % metric), From 9209452b111e9ed19e6f7dbb605d2d87b9189f64 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Thu, 23 Jun 2022 14:31:11 +0200 Subject: [PATCH 076/118] Fix prediction fails with MOO ensemble and dummy is best (#1518) * Init commit * Fix DummyClassifiers in _load_pareto_set * Add test for dummy only in classifiers * Update no ensemble docstring * Add automl case where automl only has dummy * Remove tmp file * Fix `include` statement to be regressor --- autosklearn/automl.py | 38 ++++++++++--- test/test_automl/cases.py | 109 ++++++++++++++++++++++++++++++++++---- 2 files changed, 131 insertions(+), 16 deletions(-) diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 8726ab8c6d..278cd5c146 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -48,6 +48,7 @@ BaseShuffleSplit, _RepeatedSplits, ) +from sklearn.pipeline import Pipeline from sklearn.utils import check_random_state from sklearn.utils.validation import check_is_fitted from smac.callbacks import IncorporateRunResultCallback @@ -1473,6 +1474,7 @@ def predict(self, X, batch_size=None, n_jobs=1): # Each process computes predictions in chunks of batch_size rows. try: for i, tmp_model in enumerate(self.models_.values()): + # TODO, modify this if isinstance(tmp_model, (DummyRegressor, DummyClassifier)): check_is_fitted(tmp_model) else: @@ -1683,10 +1685,8 @@ def _load_best_individual_model(self): return ensemble def _load_pareto_set(self) -> Sequence[VotingClassifier | VotingRegressor]: - if self._ensemble_class is not None: + if self.ensemble_ is None: self.ensemble_ = self._backend.load_ensemble(self._seed) - else: - self.ensemble_ = None # If no ensemble is loaded we cannot do anything if not self.ensemble_: @@ -1716,8 +1716,10 @@ def _load_pareto_set(self) -> Sequence[VotingClassifier | VotingRegressor]: estimators=None, voting="soft", ) + kind = "classifier" else: voter = VotingRegressor(estimators=None) + kind = "regeressor" if self._resampling_strategy in ("cv", "cv-iterative-fit"): models = self._backend.load_cv_models_by_identifiers(identifiers) @@ -1730,8 +1732,32 @@ def _load_pareto_set(self) -> Sequence[VotingClassifier | VotingRegressor]: weight_vector = [] estimators = [] for identifier in identifiers: - weight_vector.append(weights[identifier]) - estimators.append(models[identifier]) + estimator = models[identifier] + weight = weights[identifier] + + # Kind of hacky, really the dummy models should + # act like everything else does. Doing this is + # required so that the VotingClassifier/Regressor + # can use it as intended + if not isinstance(estimator, Pipeline): + if kind == "classifier": + steps = [ + ("data_preprocessor", None), + ("balancing", None), + ("feature_preprocessor", None), + (kind, estimator), + ] + else: + steps = [ + ("data_preprocessor", None), + ("feature_preprocessor", None), + (kind, estimator), + ] + + estimator = Pipeline(steps=steps) + + weight_vector.append(weight) + estimators.append(estimator) voter.estimators = estimators voter.estimators_ = estimators @@ -2148,7 +2174,7 @@ def show_models(self) -> dict[int, Any]: ensemble_dict = {} - if self._ensemble_class is not None: + if self._ensemble_class is None: warnings.warn( "No models in the ensemble. Kindly provide an ensemble class." ) diff --git a/test/test_automl/cases.py b/test/test_automl/cases.py index c80d3d3b5f..9583c7b31d 100644 --- a/test/test_automl/cases.py +++ b/test/test_automl/cases.py @@ -14,7 +14,6 @@ {fitted} - If the automl case has been fitted {cv, holdout} - Whether explicitly cv or holdout was used {no_ensemble} - Fit with no ensemble size - {cached} - If the resulting case is then cached {multiobjective} - If the automl instance is multiobjective """ from __future__ import annotations @@ -24,10 +23,15 @@ from pathlib import Path import numpy as np +import sklearn.model_selection import autosklearn.metrics from autosklearn.automl import AutoMLClassifier, AutoMLRegressor from autosklearn.automl_common.common.utils.backend import Backend +from autosklearn.evaluation.abstract_evaluator import ( + MyDummyClassifier, + MyDummyRegressor, +) from pytest_cases import case, parametrize @@ -35,6 +39,11 @@ from test.fixtures.caching import Cache +def stop_at_first(smbo, run_info, result, time_left) -> bool: + """Used in some cases to enforce the only valid model is the dummy model""" + return False + + @case(tags=["classifier"]) def case_classifier( tmp_dir: str, @@ -60,7 +69,7 @@ def case_regressor( # ################################### # The following are fitted and cached # ################################### -@case(tags=["classifier", "fitted", "holdout", "cached"]) +@case(tags=["classifier", "fitted", "holdout"]) @parametrize("dataset", ["iris"]) def case_classifier_fitted_holdout_iterative( dataset: str, @@ -97,7 +106,7 @@ def case_classifier_fitted_holdout_iterative( return model -@case(tags=["classifier", "fitted", "cv", "cached"]) +@case(tags=["classifier", "fitted", "cv"]) @parametrize("dataset", ["iris"]) def case_classifier_fitted_cv( make_cache: Callable[[str], Cache], @@ -134,7 +143,7 @@ def case_classifier_fitted_cv( return model -@case(tags=["classifier", "fitted", "holdout", "cached", "multiobjective"]) +@case(tags=["classifier", "fitted", "holdout", "multiobjective"]) @parametrize("dataset", ["iris"]) def case_classifier_fitted_holdout_multiobjective( dataset: str, @@ -177,7 +186,7 @@ def case_classifier_fitted_holdout_multiobjective( return model -@case(tags=["regressor", "fitted", "holdout", "cached"]) +@case(tags=["regressor", "fitted", "holdout"]) @parametrize("dataset", ["boston"]) def case_regressor_fitted_holdout( make_cache: Callable[[str], Cache], @@ -212,7 +221,7 @@ def case_regressor_fitted_holdout( return model -@case(tags=["regressor", "fitted", "cv", "cached"]) +@case(tags=["regressor", "fitted", "cv"]) @parametrize("dataset", ["boston"]) def case_regressor_fitted_cv( make_cache: Callable[[str], Cache], @@ -249,7 +258,7 @@ def case_regressor_fitted_cv( return model -@case(tags=["classifier", "fitted", "no_ensemble", "cached"]) +@case(tags=["classifier", "fitted", "no_ensemble"]) @parametrize("dataset", ["iris"]) def case_classifier_fitted_no_ensemble( make_cache: Callable[[str], Cache], @@ -258,8 +267,7 @@ def case_classifier_fitted_no_ensemble( make_automl_classifier: Callable[..., AutoMLClassifier], make_sklearn_dataset: Callable[..., Tuple[np.ndarray, ...]], ) -> AutoMLClassifier: - """Case of a fitted classifier but ensemble was disabled by - not writing models to disk""" + """Case of a fitted classifier but ensemble was disabled""" key = f"case_classifier_fitted_no_ensemble_{dataset}" # This locks the cache for this item while we check, required for pytest-xdist @@ -270,7 +278,6 @@ def case_classifier_fitted_no_ensemble( temporary_directory=cache.path("backend"), delete_tmp_folder_after_terminate=False, ensemble_class=None, - disable_evaluator_output=True, ) X, y, Xt, yt = make_sklearn_dataset(name=dataset) @@ -282,3 +289,85 @@ def case_classifier_fitted_no_ensemble( model._backend = copy_backend(old=model._backend, new=make_backend()) return model + + +@case(tags=["classifier", "fitted"]) +def case_classifier_fitted_only_dummy( + make_cache: Callable[[str], Cache], + make_backend: Callable[..., Backend], + make_automl_classifier: Callable[..., AutoMLClassifier], +) -> AutoMLClassifier: + """Case of a fitted classifier but only dummy was found""" + key = "case_classifier_fitted_only_dummy" + + # This locks the cache for this item while we check, required for pytest-xdist + + with make_cache(key) as cache: + if "model" not in cache: + model = make_automl_classifier( + temporary_directory=cache.path("backend"), + delete_tmp_folder_after_terminate=False, + include={"classifier": ["bernoulli_nb"]}, # Just a meh model + get_trials_callback=stop_at_first, + ) + rand = np.random.RandomState(2) + _X = rand.random((100, 50)) + _y = rand.randint(0, 2, (100,)) + X, Xt, y, yt = sklearn.model_selection.train_test_split( + _X, _y, random_state=1 # Required to ensure dummy is best + ) + model.fit(X, y, dataset_name="random") + + # We now validate that indeed, the only model is the Dummy + members = list(model.models_.values()) + if len(members) != 1 and not isinstance(members[0], MyDummyClassifier): + raise ValueError("Should only have one model, dummy\n", members) + + cache.save(model, "model") + + model = cache.load("model") + model._backend = copy_backend(old=model._backend, new=make_backend()) + + return model + + +@case(tags=["regressor", "fitted"]) +def case_regressor_fitted_only_dummy( + make_cache: Callable[[str], Cache], + make_backend: Callable[..., Backend], + make_automl_regressor: Callable[..., AutoMLRegressor], +) -> AutoMLRegressor: + """Case of a fitted classifier but only dummy was found""" + key = "case_regressor_fitted_only_dummy" + + # This locks the cache for this item while we check, required for pytest-xdist + + with make_cache(key) as cache: + if "model" not in cache: + model = make_automl_regressor( + temporary_directory=cache.path("backend"), + delete_tmp_folder_after_terminate=False, + include={"regressor": ["k_nearest_neighbors"]}, # Just a meh model + get_trials_callback=stop_at_first, + ) + + rand = np.random.RandomState(2) + _X = rand.random((100, 50)) + _y = rand.random((100,)) + + X, Xt, y, yt = sklearn.model_selection.train_test_split( + _X, _y, random_state=1 # Required to ensure dummy is best + ) + model.fit(X, y, dataset_name="random") + + # We now validate that indeed, the only model is the Dummy + members = list(model.models_.values()) + if len(members) != 1 and not isinstance(members[0], MyDummyRegressor): + raise ValueError("Should only have one model, dummy\n", members) + + cache.save(model, "model") + + model = cache.load("model") + model._backend = copy_backend(old=model._backend, new=make_backend()) + + return model From 6d44fa6dde9ebbff9b726bfc9fde9821e0f47e54 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Thu, 23 Jun 2022 17:40:00 +0200 Subject: [PATCH 077/118] fix-1527-Fix-mlp-regressor-test-fixture-values (#1528) * Create PR * Update MLP regressor values --- test/test_pipeline/components/regression/test_mlp.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/test_pipeline/components/regression/test_mlp.py b/test/test_pipeline/components/regression/test_mlp.py index 9e2a92acac..941e30bf32 100644 --- a/test/test_pipeline/components/regression/test_mlp.py +++ b/test/test_pipeline/components/regression/test_mlp.py @@ -29,7 +29,7 @@ class MLPComponentTest(BaseRegressionComponentTest): # # Seems there is a consistently different values for boston so: # * include two valuess for n_iter in 'boston_iterative_n_iter' - # known-values = [236, 331] + # known-values = [236, 331, 327] # # * decreased places from 6 -> 5 in 'default_boston_{sparse,_iterative_sparse}' # to check for for iterations and expanded the default places for checking @@ -47,7 +47,7 @@ class MLPComponentTest(BaseRegressionComponentTest): res["default_boston"] = 0.2750079862455884 res["default_boston_places"] = 1 res["boston_n_calls"] = [8, 9] - res["boston_iterative_n_iter"] = [236, 331] + res["boston_iterative_n_iter"] = [236, 331, 327] res["default_boston_iterative"] = res["default_boston"] res["default_boston_iterative_places"] = 1 res["default_boston_sparse"] = -0.10972947168054104 From db982d55f467cb7473e5fccd3629056b0f1cf81a Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Thu, 23 Jun 2022 17:40:24 +0200 Subject: [PATCH 078/118] fix docker workflow (#1526) * Make docker file install from `setup.py` * Add pytest cache to gitignore * Up timeouts on test_metadata_generation --- .gitignore | 3 +++ Dockerfile | 5 +---- test/test_scripts/test_metadata_generation.py | 11 +++++++++++ 3 files changed, 15 insertions(+), 4 deletions(-) diff --git a/.gitignore b/.gitignore index 07d38690ff..03cbf93339 100755 --- a/.gitignore +++ b/.gitignore @@ -38,6 +38,9 @@ htmlcov .coverage .tox +# Pytest cache +.pytest_cache + # pycharm .idea diff --git a/Dockerfile b/Dockerfile index d73f8c8fca..cb71f2f6ff 100644 --- a/Dockerfile +++ b/Dockerfile @@ -32,9 +32,6 @@ ADD . /auto-sklearn/ # Upgrade pip then install dependencies RUN pip3 install --upgrade pip -RUN pip3 install pytest>=4.6.* pep8 codecov pytest-cov flake8 openml -RUN cat /auto-sklearn/requirements.txt | xargs -n 1 -L 1 pip3 install -RUN pip3 install jupyter # Install -RUN pip3 install /auto-sklearn/ +RUN pip3 install "/auto-sklearn[test, examples]" diff --git a/test/test_scripts/test_metadata_generation.py b/test/test_scripts/test_metadata_generation.py index 25c4855b08..d888e8589c 100644 --- a/test/test_scripts/test_metadata_generation.py +++ b/test/test_scripts/test_metadata_generation.py @@ -13,6 +13,17 @@ import unittest +# Abstracted to here to make changing them easier +# seems to be quite fidely with github actions +# Recommended to set a minimum of 60 +timeouts = { + 1: 60, # create commands + 2: 180, # generate metadata + 3: 60, # get performance of configurations + 4: 90, # Calculate metafeatures + 5: 60, # Create aslib files +} + class TestMetadataGeneration(unittest.TestCase): def setUp(self): From 40f134589a8f27c5975b63b19edb8d494cbfdf04 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Fri, 8 Jul 2022 14:46:25 +0200 Subject: [PATCH 079/118] fix-1535-Exception-in-the-fit()-call-of-AutoSklearn (#1539) * Create PR * Fix test fixture --- test/fixtures/ensemble_building.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/test/fixtures/ensemble_building.py b/test/fixtures/ensemble_building.py index 548d1c5d72..20e771fd1a 100644 --- a/test/fixtures/ensemble_building.py +++ b/test/fixtures/ensemble_building.py @@ -231,7 +231,10 @@ def _make( # Hence, we take the y_train of the datamanager and use that as the # the targets if "Y_train" in datamanager.data: - backend.save_targets_ensemble(datamanager.data["Y_train"]) + backend.save_additional_data( + datamanager.data["Y_train"], + what="targets_ensemble", + ) return EnsembleBuilderManager( backend=backend, From a2e63c8fdae141f5821d092dfbd8a493c9e18894 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 11 Jul 2022 11:46:23 +0200 Subject: [PATCH 080/118] Bump docker/build-push-action from 1 to 3 (#1515) * Bump docker/build-push-action from 1 to 3 Bumps [docker/build-push-action](https://github.com/docker/build-push-action) from 1 to 3. - [Release notes](https://github.com/docker/build-push-action/releases) - [Commits](https://github.com/docker/build-push-action/compare/v1...v3) --- updated-dependencies: - dependency-name: docker/build-push-action dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] * Update docker-publish.yml Replace password by token Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Matthias Feurer --- .github/workflows/docker-publish.yml | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/.github/workflows/docker-publish.yml b/.github/workflows/docker-publish.yml index 4c4773700e..525004269e 100644 --- a/.github/workflows/docker-publish.yml +++ b/.github/workflows/docker-publish.yml @@ -26,9 +26,8 @@ jobs: run: echo "##[set-output name=branch;]$(echo ${GITHUB_REF#refs/heads/})" id: extract_branch - # Updating this to @v2 requires a github auth token - name: Push to GitHub Packages - uses: docker/build-push-action@v1 + uses: docker/build-push-action@v3 with: username: ${{ github.actor }} password: ${{ secrets.GITHUB_TOKEN }} @@ -37,12 +36,11 @@ jobs: tag_with_ref: true tags: ${{ steps.extract_branch.outputs.branch }} - # Updating this to @v2 requires a github auth token - name: Push to Docker Hub - uses: docker/build-push-action@v1 + uses: docker/build-push-action@v3 with: username: ${{ secrets.DOCKER_USERNAME }} - password: ${{ secrets.DOCKER_PASSWORD }} + password: ${{ secrets.DOCKER_TOKEN }} repository: mfeurer/auto-sklearn tags: ${{ steps.extract_branch.outputs.branch }} From a66361b01327b8539dfaf1d78dbc084ad060131c Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Sat, 16 Jul 2022 20:35:59 +0200 Subject: [PATCH 081/118] fix-1532-_ERROR_-asyncio.exceptions.CancelledError (#1540) * Create PR * Abstract out dask client types * Fix _ issue * Extend scope of dask_client in automl.py * Add docstring to dask module * Indent result addition * Add basic tests for Dask wrappers --- autosklearn/automl.py | 282 +++++++++++------------- autosklearn/util/dask.py | 142 ++++++++++++ test/test_automl/test_construction.py | 6 +- test/test_estimators/test_estimators.py | 2 - test/test_util/test_dask.py | 75 +++++++ 5 files changed, 347 insertions(+), 160 deletions(-) create mode 100644 autosklearn/util/dask.py create mode 100644 test/test_util/test_dask.py diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 278cd5c146..f76a03adec 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -21,7 +21,6 @@ import os import platform import sys -import tempfile import time import types import uuid @@ -37,7 +36,7 @@ import sklearn.utils from ConfigSpace.configuration_space import Configuration, ConfigurationSpace from ConfigSpace.read_and_write import json as cs_json -from dask.distributed import Client, LocalCluster +from dask.distributed import Client from scipy.sparse import spmatrix from sklearn.base import BaseEstimator from sklearn.dummy import DummyClassifier, DummyRegressor @@ -105,6 +104,7 @@ from autosklearn.pipeline.components.regression import RegressorChoice from autosklearn.smbo import AutoMLSMBO from autosklearn.util import RE_PATTERN, pipeline +from autosklearn.util.dask import Dask, LocalDask, UserDask from autosklearn.util.data import ( DatasetCompressionSpec, default_dataset_compression_arg, @@ -120,7 +120,6 @@ warnings_to, ) from autosklearn.util.parallel import preload_modules -from autosklearn.util.single_thread_client import SingleThreadedClient from autosklearn.util.smac_wrap import SMACCallback, SmacRunCallback from autosklearn.util.stopwatch import StopWatch @@ -299,21 +298,22 @@ def __init__( self._initial_configurations_via_metalearning = ( initial_configurations_via_metalearning ) + self._n_jobs = n_jobs self._scoring_functions = scoring_functions or [] self._resampling_strategy_arguments = resampling_strategy_arguments or {} + self._multiprocessing_context = "forkserver" # Single core, local runs should use fork to prevent the __main__ requirements # in examples. Nevertheless, multi-process runs have spawn as requirement to # reduce the possibility of a deadlock - if n_jobs == 1 and dask_client is None: - self._multiprocessing_context = "fork" - self._dask_client = SingleThreadedClient() - self._n_jobs = 1 + self._dask: Dask + if dask_client is not None: + self._dask = UserDask(client=dask_client) else: - self._multiprocessing_context = "forkserver" - self._dask_client = dask_client - self._n_jobs = n_jobs + self._dask = LocalDask(n_jobs=n_jobs) + if n_jobs == 1: + self._multiprocessing_context = "fork" # Create the backend self._backend: Backend = create( @@ -350,38 +350,6 @@ def __init__( self.num_run = 0 self.fitted = False - def _create_dask_client(self) -> None: - self._is_dask_client_internally_created = True - self._dask_client = Client( - LocalCluster( - n_workers=self._n_jobs, - processes=False, - threads_per_worker=1, - # We use the temporal directory to save the - # dask workers, because deleting workers takes - # more time than deleting backend directories - # This prevent an error saying that the worker - # file was deleted, so the client could not close - # the worker properly - local_directory=tempfile.gettempdir(), - # Memory is handled by the pynisher, not by the dask worker/nanny - memory_limit=0, - ), - # Heartbeat every 10s - heartbeat_interval=10000, - ) - - def _close_dask_client(self, force: bool = False) -> None: - if getattr(self, "_dask_client", None) is not None and ( - force or getattr(self, "_is_dask_client_internally_created", False) - ): - self._dask_client.shutdown() - self._dask_client.close() - del self._dask_client - self._dask_client = None - self._is_dask_client_internally_created = False - del self._is_dask_client_internally_created - def _get_logger(self, name: str) -> PicklableClientLogger: logger_name = "AutoML(%d):%s" % (self._seed, name) @@ -747,17 +715,6 @@ def fit( "autosklearn.metrics.Scorer." ) - # If no dask client was provided, we create one, so that we can - # start a ensemble process in parallel to smbo optimize - if self._dask_client is None and ( - self._ensemble_class is not None - or self._n_jobs is not None - and self._n_jobs > 1 - ): - self._create_dask_client() - else: - self._is_dask_client_internally_created = False - self._dataset_name = dataset_name self._stopwatch.start(self._dataset_name) @@ -902,70 +859,85 @@ def fit( ) n_meta_configs = self._initial_configurations_via_metalearning - _proc_smac = AutoMLSMBO( - config_space=self.configuration_space, - dataset_name=self._dataset_name, - backend=self._backend, - total_walltime_limit=time_left, - func_eval_time_limit=per_run_time_limit, - memory_limit=self._memory_limit, - data_memory_limit=self._data_memory_limit, - stopwatch=self._stopwatch, - n_jobs=self._n_jobs, - dask_client=self._dask_client, - start_num_run=self.num_run, - num_metalearning_cfgs=n_meta_configs, - config_file=configspace_path, - seed=self._seed, - metadata_directory=self._metadata_directory, - metrics=self._metrics, - resampling_strategy=self._resampling_strategy, - resampling_strategy_args=self._resampling_strategy_arguments, - include=self._include, - exclude=self._exclude, - disable_file_output=self._disable_evaluator_output, - get_smac_object_callback=self._get_smac_object_callback, - smac_scenario_args=self._smac_scenario_args, - scoring_functions=self._scoring_functions, - port=self._logger_port, - pynisher_context=self._multiprocessing_context, - ensemble_callback=proc_ensemble, - trials_callback=self._get_trials_callback, - ) + with self._dask as dask_client: + resamp_args = self._resampling_strategy_arguments + _proc_smac = AutoMLSMBO( + config_space=self.configuration_space, + dataset_name=self._dataset_name, + backend=self._backend, + total_walltime_limit=time_left, + func_eval_time_limit=per_run_time_limit, + memory_limit=self._memory_limit, + data_memory_limit=self._data_memory_limit, + stopwatch=self._stopwatch, + n_jobs=self._n_jobs, + dask_client=dask_client, + start_num_run=self.num_run, + num_metalearning_cfgs=n_meta_configs, + config_file=configspace_path, + seed=self._seed, + metadata_directory=self._metadata_directory, + metrics=self._metrics, + resampling_strategy=self._resampling_strategy, + resampling_strategy_args=resamp_args, + include=self._include, + exclude=self._exclude, + disable_file_output=self._disable_evaluator_output, + get_smac_object_callback=self._get_smac_object_callback, + smac_scenario_args=self._smac_scenario_args, + scoring_functions=self._scoring_functions, + port=self._logger_port, + pynisher_context=self._multiprocessing_context, + ensemble_callback=proc_ensemble, + trials_callback=self._get_trials_callback, + ) - ( - self.runhistory_, - self.trajectory_, - self._budget_type, - ) = _proc_smac.run_smbo() - trajectory_filename = os.path.join( - self._backend.get_smac_output_directory_for_run(self._seed), - "trajectory.json", - ) - saveable_trajectory = [ - list(entry[:2]) + [entry[2].get_dictionary()] + list(entry[3:]) - for entry in self.trajectory_ - ] - with open(trajectory_filename, "w") as fh: - json.dump(saveable_trajectory, fh) - - self._logger.info("Starting shutdown...") - # Wait until the ensemble process is finished to avoid shutting down - # while the ensemble builder tries to access the data - if proc_ensemble is not None: - self.ensemble_performance_history = list(proc_ensemble.history) - - if len(proc_ensemble.futures) > 0: - # Now we wait for the future to return as it cannot be cancelled - # while it is running: https://stackoverflow.com/a/49203129 - self._logger.info( - "Ensemble script still running, waiting for it to finish." - ) - result = proc_ensemble.futures.pop().result() - if result: - ensemble_history, _ = result - self.ensemble_performance_history.extend(ensemble_history) - self._logger.info("Ensemble script finished, continue shutdown.") + ( + self.runhistory_, + self.trajectory_, + self._budget_type, + ) = _proc_smac.run_smbo() + + trajectory_filename = os.path.join( + self._backend.get_smac_output_directory_for_run(self._seed), + "trajectory.json", + ) + saveable_trajectory = [ + list(entry[:2]) + + [entry[2].get_dictionary()] + + list(entry[3:]) + for entry in self.trajectory_ + ] + with open(trajectory_filename, "w") as fh: + json.dump(saveable_trajectory, fh) + + self._logger.info("Starting shutdown...") + # Wait until the ensemble process is finished to avoid shutting + # down while the ensemble builder tries to access the data + if proc_ensemble is not None: + self.ensemble_performance_history = list( + proc_ensemble.history + ) + + if len(proc_ensemble.futures) > 0: + # Now we wait for the future to return as it cannot be + # cancelled while it is running + # * https://stackoverflow.com/a/49203129 + self._logger.info( + "Ensemble script still running," + " waiting for it to finish." + ) + result = proc_ensemble.futures.pop().result() + + if result: + ensemble_history, _ = result + self.ensemble_performance_history.extend( + ensemble_history + ) + + self._logger.info( + "Ensemble script finished, continue shutdown." + ) # save the ensemble performance history file if len(self.ensemble_performance_history) > 0: @@ -1054,7 +1026,7 @@ def _log_fit_setup(self) -> None: self._logger.debug( " multiprocessing_context: %s", str(self._multiprocessing_context) ) - self._logger.debug(" dask_client: %s", str(self._dask_client)) + self._logger.debug(" dask_client: %s", str(self._dask)) self._logger.debug(" precision: %s", str(self.precision)) self._logger.debug( " disable_evaluator_output: %s", str(self._disable_evaluator_output) @@ -1090,7 +1062,6 @@ def __sklearn_is_fitted__(self) -> bool: def _fit_cleanup(self) -> None: self._logger.info("Closing the dask infrastructure") - self._close_dask_client() self._logger.info("Finished closing the dask infrastructure") # Clean up the logger @@ -1555,12 +1526,6 @@ def fit_ensemble( # Make sure that input is valid y = self.InputValidator.target_validator.transform(y) - # Create a client if needed - if self._dask_client is None: - self._create_dask_client() - else: - self._is_dask_client_internally_created = False - metrics = metrics if metrics is not None else self._metrics if not isinstance(metrics, Sequence): metrics = [metrics] @@ -1568,35 +1533,41 @@ def fit_ensemble( # Use the current thread to start the ensemble builder process # The function ensemble_builder_process will internally create a ensemble # builder in the provide dask client - manager = EnsembleBuilderManager( - start_time=time.time(), - time_left_for_ensembles=self._time_for_task, - backend=copy.deepcopy(self._backend), - dataset_name=dataset_name if dataset_name else self._dataset_name, - task=task if task else self._task, - metrics=metrics if metrics is not None else self._metrics, - ensemble_class=( - ensemble_class if ensemble_class is not None else self._ensemble_class - ), - ensemble_kwargs=( - ensemble_kwargs - if ensemble_kwargs is not None - else self._ensemble_kwargs - ), - ensemble_nbest=ensemble_nbest if ensemble_nbest else self._ensemble_nbest, - max_models_on_disc=self._max_models_on_disc, - seed=self._seed, - precision=precision if precision else self.precision, - max_iterations=1, - read_at_most=None, - memory_limit=self._memory_limit, - random_state=self._seed, - logger_port=self._logger_port, - pynisher_context=self._multiprocessing_context, - ) - manager.build_ensemble(self._dask_client) - future = manager.futures.pop() - result = future.result() + with self._dask as dask_client: + manager = EnsembleBuilderManager( + start_time=time.time(), + time_left_for_ensembles=self._time_for_task, + backend=copy.deepcopy(self._backend), + dataset_name=dataset_name if dataset_name else self._dataset_name, + task=task if task else self._task, + metrics=metrics if metrics is not None else self._metrics, + ensemble_class=( + ensemble_class + if ensemble_class is not None + else self._ensemble_class + ), + ensemble_kwargs=( + ensemble_kwargs + if ensemble_kwargs is not None + else self._ensemble_kwargs + ), + ensemble_nbest=ensemble_nbest + if ensemble_nbest + else self._ensemble_nbest, + max_models_on_disc=self._max_models_on_disc, + seed=self._seed, + precision=precision if precision else self.precision, + max_iterations=1, + read_at_most=None, + memory_limit=self._memory_limit, + random_state=self._seed, + logger_port=self._logger_port, + pynisher_context=self._multiprocessing_context, + ) + manager.build_ensemble(dask_client) + future = manager.futures.pop() + result = future.result() + if result is None: raise ValueError( "Error building the ensemble - please check the log file and command " @@ -1606,7 +1577,6 @@ def fit_ensemble( self._ensemble_class = ensemble_class self._load_models() - self._close_dask_client() return self def _load_models(self): @@ -2295,7 +2265,7 @@ def _create_search_space( def __getstate__(self) -> dict[str, Any]: # Cannot serialize a client! - self._dask_client = None + self._dask = None self.logging_server = None self.stop_logging_server = None return self.__dict__ @@ -2304,8 +2274,6 @@ def __del__(self) -> None: # Clean up the logger self._clean_logger() - self._close_dask_client() - class AutoMLClassifier(AutoML): diff --git a/autosklearn/util/dask.py b/autosklearn/util/dask.py new file mode 100644 index 0000000000..624fecfae9 --- /dev/null +++ b/autosklearn/util/dask.py @@ -0,0 +1,142 @@ +""" Provides simplified 2 use cases of dask that we consider + +1. A UserDask is when a user supplies a dask client, in which case +we don't close this down and leave it up to the user to control its lifetime. +2. A LocalDask is one we use when no user dask is supplied. In this case +we make sure to spin up and close down clients as needed. + +Both of these can be uniformly accessed as a context manager. + +.. code:: python + + # Locally controlled dask client + local_dask = LocalDask(n_jobs=2) + with local_dask as client: + # Do stuff with client + ... + + # `client` is shutdown properly + + # ---------------- + + # User controlled dask client + user_dask = UserDask(user_client) + + with user_dask as client: + # Do stuff with (client == user_client) + ... + + # `user_client` is still open and up to the user to close +""" +from __future__ import annotations + +from abc import ABC, abstractmethod +from typing import Any + +import tempfile + +from dask.distributed import Client, LocalCluster + +from autosklearn.util.single_thread_client import SingleThreadedClient + + +class Dask(ABC): + @abstractmethod + def client(self) -> Client: + """Should return a dask client""" + ... + + @abstractmethod + def close(self) -> None: + """Should close up any resources needed for the dask client""" + ... + + def __enter__(self) -> Client: + return self.client() + + def __exit__(self, *args: Any, **kwargs: Any) -> None: + self.close() + + @abstractmethod + def __repr__(self) -> str: + ... + + +class UserDask(Dask): + """A dask instance created by a user""" + + def __init__(self, client: Client): + """ + Parameters + ---------- + client : Client + The client they passed in + """ + self._client = client + + def client(self) -> Client: + """The dask client""" + return self._client + + def close(self) -> None: + """Close the dask client""" + # We do nothing, it's user provided + pass + + def __repr__(self) -> str: + return "UserDask(...)" + + +class LocalDask(Dask): + def __init__(self, n_jobs: int | None = None) -> None: + self.n_jobs = n_jobs + self._client: Client | None = None + self._cluster: LocalCluster | None = None + + def client(self) -> Client: + """Creates a usable dask client or returns an existing one + + If there is not current client, because it has been closed, create + a new one. + * If ``n_jobs == 1``, create a ``SingleThreadedClient`` + * Else create a ``Client`` with a ``LocalCluster`` + """ + if self._client is not None: + return self._client + + if self.n_jobs == 1: + cluster = None + client = SingleThreadedClient() + else: + cluster = LocalCluster( + n_workers=self.n_jobs, + processes=False, + threads_per_worker=1, + # We use tmpdir to save the workers as deleting workers takes + # more time than deleting backend directories. + # This prevent an error saying that the worker file was deleted, + # so the client could not close the worker properly + local_directory=tempfile.gettempdir(), + # Memory is handled by the pynisher, not by the dask worker/nanny + memory_limit=0, + ) + client = Client(cluster, heartbeat_interval=10000) # 10s + + self._client = client + self._cluster = cluster + return self._client + + def close(self) -> None: + """Closes any open dask client""" + if self._client is None: + return + + self._client.close() + if self._cluster is not None: + self._cluster.close() + + self._client = None + self._cluster = None + + def __repr__(self) -> str: + return f"LocalDask(n_jobs = {self.n_jobs})" diff --git a/test/test_automl/test_construction.py b/test/test_automl/test_construction.py index 5b68d35118..be6fe0e39b 100644 --- a/test/test_automl/test_construction.py +++ b/test/test_automl/test_construction.py @@ -2,6 +2,7 @@ from typing import Any, Dict, Optional, Union from autosklearn.automl import AutoML +from autosklearn.util.dask import LocalDask from autosklearn.util.data import default_dataset_compression_arg from autosklearn.util.single_thread_client import SingleThreadedClient @@ -87,4 +88,7 @@ def test_single_job_and_no_dask_client_sets_correct_multiprocessing_context() -> assert automl._multiprocessing_context == "fork" assert automl._n_jobs == 1 - assert isinstance(automl._dask_client, SingleThreadedClient) + assert isinstance(automl._dask, LocalDask) + + with automl._dask as client: + assert isinstance(client, SingleThreadedClient) diff --git a/test/test_estimators/test_estimators.py b/test/test_estimators/test_estimators.py index d0d3f28bdb..e1e33d684a 100644 --- a/test/test_estimators/test_estimators.py +++ b/test/test_estimators/test_estimators.py @@ -140,8 +140,6 @@ def __call__(self, *args, **kwargs): assert count_succeses(automl.cv_results_) > 0 assert includes_train_scores(automl.performance_over_time_.columns) is True assert performance_over_time_is_plausible(automl.performance_over_time_) is True - # For travis-ci it is important that the client no longer exists - assert automl.automl_._dask_client is None def test_feat_type_wrong_arguments(): diff --git a/test/test_util/test_dask.py b/test/test_util/test_dask.py new file mode 100644 index 0000000000..1dbc290500 --- /dev/null +++ b/test/test_util/test_dask.py @@ -0,0 +1,75 @@ +from pathlib import Path + +from dask.distributed import Client, LocalCluster + +from autosklearn.util.dask import LocalDask, UserDask + +import pytest + + +@pytest.mark.parametrize("n_jobs", [1, 2]) +def test_user_dask(tmp_path: Path, n_jobs: int) -> None: + """ + Expects + ------- + * A UserDask should not close the client after exiting context + """ + cluster = LocalCluster( + n_workers=n_jobs, + processes=False, + threads_per_worker=1, + local_directory=tmp_path, + ) + client = Client(cluster, heartbeat_interval=10000) + + # Active at creation + dask = UserDask(client) + + client_1 = None + with dask as user_client: + client_1 = user_client + assert user_client.status == "running" + + client_2 = None + with dask as user_client: + assert user_client.status == "running" + client_2 = user_client + + # Make sure they are the same client + assert id(client_1) == id(client_2) + + # Remains running after context + assert client_1.status == "running" + + cluster.close() + client.close() + + assert client.status == "closed" + + +def test_local_dask_creates_new_clients(tmp_path: Path) -> None: + """ + Expects + ------- + * A LocalDask should create new dask clusters at each context usage + """ + # We need 2 to use an actual dask client and not a SingleThreadedClient + local_dask = LocalDask(n_jobs=2) + + client_1 = None + with local_dask as client: + client_1 = client + assert client_1.status == "running" + + assert client_1.status == "closed" + + client_2 = None + with local_dask as client: + client_2 = client + assert client_2.status == "running" + + # Make sure they were different clients + assert id(client_1) != id(client_2) + + assert client_2.status == "closed" + assert client_1.status == "closed" From 542625073ba579537b86e2c349f59060bbfe5ff1 Mon Sep 17 00:00:00 2001 From: SkBlaz Date: Wed, 3 Aug 2022 14:03:13 +0200 Subject: [PATCH 082/118] More explicit subsample bounds check (#1545) The subsample float can be further validated via 0<=subsample<1 (not only the upper bound) --- autosklearn/evaluation/train_evaluator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/autosklearn/evaluation/train_evaluator.py b/autosklearn/evaluation/train_evaluator.py index f19db473bf..b7f9be73d3 100644 --- a/autosklearn/evaluation/train_evaluator.py +++ b/autosklearn/evaluation/train_evaluator.py @@ -81,7 +81,7 @@ def subsample_indices( elif subsample > 1: raise ValueError("Subsample must not be larger than 1, but is %f" % subsample) - if subsample is not None and subsample < 1: + if subsample is not None and 0 <= subsample < 1: # Only subsample if there are more indices given to this method than # required to subsample because otherwise scikit-learn will complain From ce96a2dbaaf19dc7dc5a8a6cedbd6031e26b46eb Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Wed, 3 Aug 2022 08:04:29 -0400 Subject: [PATCH 083/118] Add workflow update pre-commit versions (#1541) --- .github/workflows/pre-commit-update.yml | 26 +++++++++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 .github/workflows/pre-commit-update.yml diff --git a/.github/workflows/pre-commit-update.yml b/.github/workflows/pre-commit-update.yml new file mode 100644 index 0000000000..4d1e42f807 --- /dev/null +++ b/.github/workflows/pre-commit-update.yml @@ -0,0 +1,26 @@ +name: Pre-commit auto-update + +on: + # every day at midnight + schedule: + - cron: "0 0 * * *" + # on demand + workflow_dispatch: + +jobs: + auto-update: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + + - uses: actions/setup-python@v2 + + - uses: browniebroke/pre-commit-autoupdate-action@main + + - uses: peter-evans/create-pull-request@v3 + with: + token: ${{ secrets.GITHUB_TOKEN }} + branch: update/pre-commit-hooks + title: Update pre-commit hooks + commit-message: "chore: update pre-commit hooks" + body: Update versions of pre-commit hooks to latest version. From 0c711343668f2fcd5daa87b41f0463709b5ed1b1 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Thu, 4 Aug 2022 11:02:02 +0200 Subject: [PATCH 084/118] Update code for latest scipy and sphinx-gallery (#1551) * Make meta-feature computation compatible with scipy 1.9 * Also fix doc for latest sphinx-gallery --- .../metalearning/metafeatures/metafeatures.py | 36 +++++++++---- doc/faq.rst | 2 +- doc/manual.rst | 2 +- doc/releases.rst | 2 +- .../pyMetaLearn/test_meta_features.py | 18 ++----- .../pyMetaLearn/test_meta_features_sparse.py | 53 +++++++++---------- 6 files changed, 59 insertions(+), 54 deletions(-) diff --git a/autosklearn/metalearning/metafeatures/metafeatures.py b/autosklearn/metalearning/metafeatures/metafeatures.py index 2c4d9cf4bd..8a1c3c98d5 100644 --- a/autosklearn/metalearning/metafeatures/metafeatures.py +++ b/autosklearn/metalearning/metafeatures/metafeatures.py @@ -530,11 +530,16 @@ def _calculate(self, X, y, logger, feat_type): kurts = [] for i in range(X.shape[1]): if numerical[X.columns[i] if hasattr(X, "columns") else i]: - kurts.append( - scipy.stats.kurtosis( - X.iloc[:, i] if hasattr(X, "iloc") else X[:, i] + if np.isclose( + np.var(X.iloc[:, i] if hasattr(X, "iloc") else X[:, i]), 0 + ): + kurts.append(0) + else: + kurts.append( + scipy.stats.kurtosis( + X.iloc[:, i] if hasattr(X, "iloc") else X[:, i] + ) ) - ) return kurts def _calculate_sparse(self, X, y, logger, feat_type): @@ -548,7 +553,10 @@ def _calculate_sparse(self, X, y, logger, feat_type): if numerical[X.columns[i] if hasattr(X, "columns") else i]: start = X_new.indptr[i] stop = X_new.indptr[i + 1] - kurts.append(scipy.stats.kurtosis(X_new.data[start:stop])) + if np.isclose(np.var(X_new.data[start:stop]), 0): + kurts.append(0) + else: + kurts.append(scipy.stats.kurtosis(X_new.data[start:stop])) return kurts @@ -594,9 +602,16 @@ def _calculate(self, X, y, logger, feat_type): skews = [] for i in range(X.shape[1]): if numerical[X.columns[i] if hasattr(X, "columns") else i]: - skews.append( - scipy.stats.skew(X.iloc[:, i] if hasattr(X, "iloc") else X[:, i]) - ) + if np.isclose( + np.var(X.iloc[:, i] if hasattr(X, "iloc") else X[:, i]), 0 + ): + skews.append(0) + else: + skews.append( + scipy.stats.skew( + X.iloc[:, i] if hasattr(X, "iloc") else X[:, i] + ) + ) return skews def _calculate_sparse(self, X, y, logger, feat_type): @@ -610,7 +625,10 @@ def _calculate_sparse(self, X, y, logger, feat_type): if numerical[X.columns[i] if hasattr(X, "columns") else i]: start = X_new.indptr[i] stop = X_new.indptr[i + 1] - skews.append(scipy.stats.skew(X_new.data[start:stop])) + if np.isclose(np.var(X_new.data[start:stop]), 0): + skews.append(0) + else: + skews.append(scipy.stats.skew(X_new.data[start:stop])) return skews diff --git a/doc/faq.rst b/doc/faq.rst index 56fa360937..14cdb392b5 100644 --- a/doc/faq.rst +++ b/doc/faq.rst @@ -13,7 +13,7 @@ General We provide examples on using *auto-sklearn* for multiple use cases ranging from simple classification to advanced uses such as feature importance, parallel runs - and customization. They can be found in the :ref:`sphx_glr_examples`. + and customization. They can be found in the :ref:`examples`. .. collapse:: What type of tasks can auto-sklearn tackle? diff --git a/doc/manual.rst b/doc/manual.rst index 7ab31ce727..1322ecfdbc 100644 --- a/doc/manual.rst +++ b/doc/manual.rst @@ -13,7 +13,7 @@ give short explanations (click the title to expand text), e.g. We provide examples on using *auto-sklearn* for multiple use cases ranging from simple classification to advanced uses such as feature importance, parallel runs - and customization. They can be found in the :ref:`sphx_glr_examples`. + and customization. They can be found in the :ref:`examples`. .. collapse:: Material from talks and presentations diff --git a/doc/releases.rst b/doc/releases.rst index 456d7f60c7..5ba959e6f3 100644 --- a/doc/releases.rst +++ b/doc/releases.rst @@ -657,7 +657,7 @@ Version 0.4.0 for the ensemble. * Adds an operating system check at import and installation time to make sure to not accidentaly run on a Windows machine. -* New examples gallery using sphinx gallery: :ref:`sphx_glr_examples` +* New examples gallery using sphinx gallery: :ref:`examples` * Safeguard Auto-sklearn against deleting directories it did not create (Issue `#317 `_. diff --git a/test/test_metalearning/pyMetaLearn/test_meta_features.py b/test/test_metalearning/pyMetaLearn/test_meta_features.py index 3dea2c762b..9f6ae8ae6f 100644 --- a/test/test_metalearning/pyMetaLearn/test_meta_features.py +++ b/test/test_metalearning/pyMetaLearn/test_meta_features.py @@ -148,17 +148,7 @@ def meta_train_data_transformed(request): if request.param == "numpy": return X_transformed, y, feat_type_transformed elif request.param == "pandas": - dtypes = {} - for key, value in feat_type.items(): - if value == "categorical": - dtypes[key] = "category" - elif value == "numerical": - dtypes[key] = "float64" - elif value == "string": - dtypes[key] = "string" - else: - raise KeyError - X_transformed = pd.DataFrame(X_transformed).astype(dtypes) + X_transformed = pd.DataFrame(X_transformed) return X_transformed, y, feat_type_transformed else: raise ValueError(request.param) @@ -881,10 +871,10 @@ def test_calculate_all_metafeatures_same_results_across_datatypes(): "SkewnessMean": 1.47397188548894, "SkewnessMax": 29.916569235579203, "SkewnessMin": -29.916569235579203, - "KurtosisSTD": 153.0563504598898, - "KurtosisMean": 56.998860939761165, + "KurtosisSTD": 152.95700852863064, + "KurtosisMean": 57.258120199020425, "KurtosisMax": 893.0011148272025, - "KurtosisMin": -3.0, + "KurtosisMin": -1.998392219134577, "SymbolsSum": 49, "SymbolsSTD": 1.3679553264445183, "SymbolsMean": 1.8846153846153846, diff --git a/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py b/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py index 66eb2072e1..e4f2c7647b 100644 --- a/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py +++ b/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py @@ -338,38 +338,36 @@ def test_skewnesses(sparse_data_transformed): mf = meta_features.helper_functions["Skewnesses"]( X_transformed, y, logging.getLogger("Meta") ) - print(mf.value) - print(fixture) np.testing.assert_allclose(mf.value, fixture) def test_kurtosisses(sparse_data_transformed): fixture = [ - -3.0, - -3.0, - -3.0, - -3.0, - -3.0, - -3.0, - -3.0, - -3.0, - -3.0, - -3.0, - -3.0, - -3.0, - -3.0, - -3.0, - -3.0, - -3.0, - -3.0, - -3.0, - -3.0, - -3.0, - -3.0, - -3.0, - -3.0, - -3.0, - -3.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, -1.1005836114255763, -1.1786325509475744, -1.23879983823279, @@ -381,7 +379,6 @@ def test_kurtosisses(sparse_data_transformed): mf = meta_features.helper_functions["Kurtosisses"]( X_transformed, y, logging.getLogger("Meta") ) - print(mf.value) np.testing.assert_allclose(mf.value, fixture) From 7fc3a91b480b4a01c7e43f61c19ffef56204e7cb Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Thu, 4 Aug 2022 11:03:29 +0200 Subject: [PATCH 085/118] Dummy implementation of a multi-objective ensemble. (#1523) * Dummy implementation of a multi-objective ensemble. * Fix bug * One bugfix and suggestions from Eddie * Move single best common code into parent class * fix docstring * Add tests + improve docs + simplify code * Factor out pareto_front into stand alone function * Make the pareto set a property By encapsulating it this way, type checker is more friendly, the @property is always available and will throw an error if not fitted. IN contrast, a non-existent property is likely to give a much more inuintive error that "attribute pareto_set_ does not exist" * Add None defaults, fix indentation * Move resolve ensemble class check to init * Revert "Move resolve ensemble class check to init" This reverts commit 446b7d688369e23112fc9a00c89d34c6c1e228fe. * Fix `_resolve_ensemble_class` and make it private See the comment string for the fix * Fix variable name * Fix missing parameter names * Fix bug, update docs * Implement requested changes, fix bug Co-authored-by: eddiebergman --- autosklearn/automl.py | 9 +- autosklearn/ensembles/__init__.py | 10 +- autosklearn/ensembles/abstract_ensemble.py | 43 +- autosklearn/ensembles/ensemble_selection.py | 47 +- .../multiobjective_dummy_ensemble.py | 220 +++++++++ autosklearn/ensembles/singlebest_ensemble.py | 423 ++++++++++++++---- autosklearn/estimators.py | 62 ++- autosklearn/metrics/__init__.py | 24 +- autosklearn/util/multiobjective.py | 40 ++ doc/api.rst | 24 +- 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index f76a03adec..75a19acc57 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -79,7 +79,7 @@ AbstractMultiObjectiveEnsemble, ) from autosklearn.ensembles.ensemble_selection import EnsembleSelection -from autosklearn.ensembles.singlebest_ensemble import SingleBest +from autosklearn.ensembles.singlebest_ensemble import SingleBestFromRunhistory from autosklearn.evaluation import ExecuteTaFuncWithQueue, get_cost_of_crash from autosklearn.evaluation.abstract_evaluator import _fit_and_suppress_warnings from autosklearn.evaluation.train_evaluator import TrainEvaluator, _fit_with_budget @@ -1638,7 +1638,7 @@ def _load_best_individual_model(self): return None # SingleBest contains the best model found by AutoML - ensemble = SingleBest( + ensemble = SingleBestFromRunhistory( metrics=self._metrics, task_type=self._task, seed=self._seed, @@ -1663,13 +1663,12 @@ def _load_pareto_set(self) -> Sequence[VotingClassifier | VotingRegressor]: raise ValueError("Pareto set only available if ensemble can be loaded.") if isinstance(self.ensemble_, AbstractMultiObjectiveEnsemble): - pareto_set = self.ensemble_.get_pareto_set() + pareto_set = self.ensemble_.pareto_set else: self._logger.warning( "Pareto set not available for single objective ensemble " "method. The Pareto set will only include the single ensemble " - "constructed by %s", - type(self.ensemble_), + f"constructed by {type(self.ensemble_)}," ) pareto_set = [self.ensemble_] diff --git a/autosklearn/ensembles/__init__.py b/autosklearn/ensembles/__init__.py index 4e1af926cf..40bb380bdb 100644 --- a/autosklearn/ensembles/__init__.py +++ b/autosklearn/ensembles/__init__.py @@ -1,10 +1,18 @@ from .abstract_ensemble import AbstractEnsemble, AbstractMultiObjectiveEnsemble from .ensemble_selection import EnsembleSelection -from .singlebest_ensemble import SingleBest +from .multiobjective_dummy_ensemble import MultiObjectiveDummyEnsemble +from .singlebest_ensemble import ( + SingleBest, + SingleBestFromRunhistory, + SingleModelEnsemble, +) __all__ = [ "AbstractEnsemble", "AbstractMultiObjectiveEnsemble", "EnsembleSelection", + "SingleBestFromRunhistory", "SingleBest", + "SingleModelEnsemble", + "MultiObjectiveDummyEnsemble", ] diff --git a/autosklearn/ensembles/abstract_ensemble.py b/autosklearn/ensembles/abstract_ensemble.py index ea14a45e90..c4388b71fd 100644 --- a/autosklearn/ensembles/abstract_ensemble.py +++ b/autosklearn/ensembles/abstract_ensemble.py @@ -1,7 +1,7 @@ from __future__ import annotations from abc import ABC, abstractmethod -from typing import Dict, List, Sequence, Tuple, Union +from typing import Any, Dict, List, Sequence, Tuple, Union import numpy as np @@ -18,19 +18,29 @@ def __init__( self, task_type: int, metrics: Sequence[Scorer] | Scorer, - random_state: int | np.random.RandomState | None, backend: Backend, + random_state: int | np.random.RandomState | None = None, ): pass + def __getstate__(self) -> Dict[str, Any]: + # Cannot serialize a metric if + # it is user defined. + # That is, if doing pickle dump + # the metric won't be the same as the + # one in __main__. we don't use the metric + # in the EnsembleSelection so this should + # be fine + return {key: value for key, value in self.__dict__.items() if key != "metrics"} + @abstractmethod def fit( self, base_models_predictions: np.ndarray | List[np.ndarray], - X_data: SUPPORTED_FEAT_TYPES, true_targets: np.ndarray, model_identifiers: List[Tuple[int, int, float]], runs: Sequence[Run], + X_data: SUPPORTED_FEAT_TYPES | None = None, ) -> "AbstractEnsemble": """Fit an ensemble given predictions of base models and targets. @@ -79,7 +89,7 @@ def predict( Returns ------- - array : [n_data_points] + np.ndarray """ pass @@ -97,7 +107,7 @@ def get_models_with_weights( Returns ------- - array : [(weight_1, model_1), ..., (weight_n, model_n)] + List[Tuple[float, BasePipeline]] """ @abstractmethod @@ -115,7 +125,7 @@ def get_identifiers_with_weights( Returns ------- - array : [(identifier_1, weight_1), ..., (identifier_n, weight_n)] + List[Tuple[Tuple[int, int, float], float] """ @abstractmethod @@ -133,12 +143,25 @@ def get_selected_model_identifiers(self) -> List[Tuple[int, int, float]]: def get_validation_performance(self) -> float: """Return validation performance of ensemble. - Return - ------ + Returns + ------- float """ class AbstractMultiObjectiveEnsemble(AbstractEnsemble): - def get_pareto_set(self) -> Sequence[AbstractEnsemble]: - pass + @property + @abstractmethod + def pareto_set(self) -> Sequence[AbstractEnsemble]: + """Get a sequence on ensembles that are on the pareto front + + Raises + ------ + SklearnNotFittedError + If ``fit`` has not been called and the pareto set does not exist yet + + Returns + ------- + Sequence[AbstractEnsemble] + """ + ... diff --git a/autosklearn/ensembles/ensemble_selection.py b/autosklearn/ensembles/ensemble_selection.py index 310f2eb6a4..e5e3c835b5 100644 --- a/autosklearn/ensembles/ensemble_selection.py +++ b/autosklearn/ensembles/ensemble_selection.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import Any, Dict, List, Optional, Sequence, Tuple, Union +from typing import Dict, List, Sequence, Tuple, Union import random import warnings @@ -23,11 +23,11 @@ def __init__( self, task_type: int, metrics: Sequence[Scorer] | Scorer, - random_state: Optional[Union[int, np.random.RandomState]], backend: Backend, ensemble_size: int = 50, bagging: bool = False, mode: str = "fast", + random_state: int | np.random.RandomState | None = None, ) -> None: """An ensemble of selected algorithms @@ -43,14 +43,6 @@ def __init__( The metric used to evaluate the models. If multiple metrics are passed, ensemble selection only optimizes for the first - random_state: Optional[int | RandomState] = None - The random_state used for ensemble selection. - - * None - Uses numpy's default RandomState object - * int - Successive calls to fit will produce the same results - * RandomState - Truly random, each call to fit will produce - different results, even with the same object. - backend : Backend Gives access to the backend of Auto-sklearn. Not used by Ensemble Selection. @@ -62,6 +54,14 @@ def __init__( * 'slow' - The original method used in Rich Caruana's ensemble selection. * 'fast' - A faster version of Rich Caruanas' ensemble selection. + random_state: int | RandomState | None = None + The random_state used for ensemble selection. + + * None - Uses numpy's default RandomState object + * int - Successive calls to fit will produce the same results + * RandomState - Truly random, each call to fit will produce + different results, even with the same object. + References ---------- | Ensemble selection from libraries of models @@ -92,23 +92,13 @@ def __init__( # https://scikit-learn.org/stable/common_pitfalls.html#controlling-randomness self.random_state = random_state - def __getstate__(self) -> Dict[str, Any]: - # Cannot serialize a metric if - # it is user defined. - # That is, if doing pickle dump - # the metric won't be the same as the - # one in __main__. we don't use the metric - # in the EnsembleSelection so this should - # be fine - return {key: value for key, value in self.__dict__.items() if key != "metrics"} - def fit( self, base_models_predictions: List[np.ndarray], - X_data: SUPPORTED_FEAT_TYPES, true_targets: np.ndarray, model_identifiers: List[Tuple[int, int, float]], runs: Sequence[Run], + X_data: SUPPORTED_FEAT_TYPES | None = None, ) -> EnsembleSelection: self.ensemble_size = int(self.ensemble_size) if self.ensemble_size < 1: @@ -141,20 +131,22 @@ def fit( def _fit( self, predictions: List[np.ndarray], - X_data: SUPPORTED_FEAT_TYPES, labels: np.ndarray, + *, + X_data: SUPPORTED_FEAT_TYPES | None = None, ) -> EnsembleSelection: if self.mode == "fast": - self._fast(predictions, X_data, labels) + self._fast(predictions=predictions, X_data=X_data, labels=labels) else: - self._slow(predictions, X_data, labels) + self._slow(predictions=predictions, X_data=X_data, labels=labels) return self def _fast( self, predictions: List[np.ndarray], - X_data: SUPPORTED_FEAT_TYPES, labels: np.ndarray, + *, + X_data: SUPPORTED_FEAT_TYPES | None = None, ) -> None: """Fast version of Rich Caruana's ensemble selection method.""" self.num_input_models_ = len(predictions) @@ -231,8 +223,9 @@ def _fast( def _slow( self, predictions: List[np.ndarray], - X_data: SUPPORTED_FEAT_TYPES, labels: np.ndarray, + *, + X_data: SUPPORTED_FEAT_TYPES | None = None, ) -> None: """Rich Caruana's ensemble selection method.""" self.num_input_models_ = len(predictions) @@ -311,7 +304,7 @@ def _bagging( # Bagging a set of models indices = sorted(random.sample(range(0, n_models), bag_size)) bag = predictions[indices, :, :] - order, _ = self._fit(bag, labels) + order, _ = self._fit(predictions=bag, labels=labels) order_of_each_bag.append(order) return np.array( diff --git a/autosklearn/ensembles/multiobjective_dummy_ensemble.py b/autosklearn/ensembles/multiobjective_dummy_ensemble.py new file mode 100644 index 0000000000..8751507ecd --- /dev/null +++ b/autosklearn/ensembles/multiobjective_dummy_ensemble.py @@ -0,0 +1,220 @@ +from __future__ import annotations + +from typing import Sequence + +import warnings + +import numpy as np +from sklearn.exceptions import NotFittedError + +from autosklearn.automl_common.common.utils.backend import Backend +from autosklearn.constants import TASK_TYPES +from autosklearn.data.validation import SUPPORTED_FEAT_TYPES +from autosklearn.ensemble_building.run import Run +from autosklearn.ensembles.abstract_ensemble import ( + AbstractEnsemble, + AbstractMultiObjectiveEnsemble, +) +from autosklearn.ensembles.singlebest_ensemble import SingleModelEnsemble +from autosklearn.metrics import Scorer, calculate_losses +from autosklearn.pipeline.base import BasePipeline +from autosklearn.util.multiobjective import pareto_front + + +class MultiObjectiveDummyEnsemble(AbstractMultiObjectiveEnsemble): + def __init__( + self, + task_type: int, + metrics: Sequence[Scorer] | Scorer, + backend: Backend, + random_state: int | np.random.RandomState | None = None, + ) -> None: + """A dummy implementation of a multi-objective ensemble. + + Builds ensembles that are individual models on the Pareto front each. + + Parameters + ---------- + task_type: int + An identifier indicating which task is being performed. + + metrics: Sequence[Scorer] | Scorer + The metrics used to evaluate the models. + + backend : Backend + Gives access to the backend of Auto-sklearn. Not used. + + random_state: int | RandomState | None = None + Not used. + """ + self.task_type = task_type + if isinstance(metrics, Sequence): + if len(metrics) == 1: + warnings.warn( + "Passed only a single metric to a multi-objective ensemble. " + "Please use a single-objective ensemble in such cases." + ) + self.metrics = metrics + else: + self.metric = [metrics] + self.random_state = random_state + self.backend = backend + + @property + def pareto_set(self) -> Sequence[AbstractEnsemble]: + if not hasattr(self, "pareto_set_"): + raise NotFittedError("`pareto_set` not created, please call `fit()` first") + return self.pareto_set_ + + def fit( + self, + base_models_predictions: list[np.ndarray], + true_targets: np.ndarray, + model_identifiers: list[tuple[int, int, float]], + runs: Sequence[Run], + X_data: SUPPORTED_FEAT_TYPES | None = None, + ) -> MultiObjectiveDummyEnsemble: + """Select dummy ensembles given predictions of base models and targets. + + Parameters + ---------- + base_models_predictions: np.ndarray + shape = (n_base_models, n_data_points, n_targets) + n_targets is the number of classes in case of classification, + n_targets is 0 or 1 in case of regression + + Can be a list of 2d numpy arrays as well to prevent copying all + predictions into a single, large numpy array. + + true_targets : array of shape [n_targets] + + model_identifiers : identifier for each base model. + Can be used for practical text output of the ensemble. + + runs: Sequence[Run] + Additional information for each run executed by SMAC that was + considered by the ensemble builder. Not used. + + X_data : list-like | sparse matrix | None = None + X data to give to the metric if required + + Returns + ------- + self + """ + if self.task_type not in TASK_TYPES: + raise ValueError("Unknown task type %s." % self.task_type) + + all_costs = np.empty((len(base_models_predictions), len(self.metrics))) + for i, base_model_prediction in enumerate(base_models_predictions): + losses = calculate_losses( + solution=true_targets, + prediction=base_model_prediction, + task_type=self.task_type, + metrics=self.metrics, + X_data=X_data, + ) + all_costs[i] = [losses[metric.name] for metric in self.metrics] + + all_costs = np.array(all_costs) + sort_by_first_metric = np.argsort(all_costs[:, 0]) + efficient_points = pareto_front(all_costs, is_loss=True) + pareto_set = [] + + for argsort_idx in sort_by_first_metric: + if not efficient_points[argsort_idx]: + continue + ensemble = SingleModelEnsemble( + task_type=self.task_type, + metrics=self.metrics, + random_state=self.random_state, + backend=self.backend, + model_index=argsort_idx, + ) + ensemble.fit( + base_models_predictions=base_models_predictions, + true_targets=true_targets, + model_identifiers=model_identifiers, + runs=runs, + X_data=X_data, + ) + pareto_set.append(ensemble) + + self.pareto_set_ = pareto_set + return self + + def predict( + self, base_models_predictions: np.ndarray | list[np.ndarray] + ) -> np.ndarray: + """Predict using the ensemble which is best for the 1st metric. + + Parameters + ---------- + base_models_predictions : np.ndarray + shape = (n_base_models, n_data_points, n_targets) + Same as in the fit method. + + Returns + ------- + np.ndarray + """ + return self.pareto_set[0].predict(base_models_predictions) + + def __str__(self) -> str: + return "MultiObjectiveDummyEnsemble: %d models" % len(self.pareto_set) + + def get_models_with_weights( + self, models: dict[tuple[int, int, float], BasePipeline] + ) -> list[tuple[float, BasePipeline]]: + """Return a list of (weight, model) pairs for the ensemble that is + best for the 1st metric. + + Parameters + ---------- + models : dict {identifier : model object} + The identifiers are the same as the one presented to the fit() + method. Models can be used for nice printing. + + Returns + ------- + list[tuple[float, BasePipeline]] + """ + return self.pareto_set[0].get_models_with_weights(models) + + def get_identifiers_with_weights( + self, + ) -> list[tuple[tuple[int, int, float], float]]: + """Return a (identifier, weight)-pairs for all models that were passed to the + ensemble builder based on the ensemble that is best for the 1st metric. + + Parameters + ---------- + models : dict {identifier : model object} + The identifiers are the same as the one presented to the fit() + method. Models can be used for nice printing. + + Returns + ------- + list[tuple[tuple[int, int, float], float] + """ + return self.pareto_set[0].get_identifiers_with_weights() + + def get_selected_model_identifiers(self) -> list[tuple[int, int, float]]: + """Return identifiers of models in the ensemble that is best for the 1st metric. + + This includes models which have a weight of zero! + + Returns + ------- + list + """ + return self.pareto_set[0].get_selected_model_identifiers() + + def get_validation_performance(self) -> float: + """Return validation performance of the ensemble that is best for the 1st metric. + + Returns + ------- + float + """ + return self.pareto_set[0].get_validation_performance() diff --git a/autosklearn/ensembles/singlebest_ensemble.py b/autosklearn/ensembles/singlebest_ensemble.py index 497ee4d495..b072bcab11 100644 --- a/autosklearn/ensembles/singlebest_ensemble.py +++ b/autosklearn/ensembles/singlebest_ensemble.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import Dict, List, Sequence, Tuple, Union +from typing import Sequence import os @@ -11,29 +11,36 @@ from autosklearn.data.validation import SUPPORTED_FEAT_TYPES from autosklearn.ensemble_building.run import Run from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble -from autosklearn.metrics import Scorer +from autosklearn.metrics import Scorer, calculate_losses from autosklearn.pipeline.base import BasePipeline -class SingleBest(AbstractEnsemble): - """ - In the case of a crash, this class searches - for the best individual model. +class AbstractSingleModelEnsemble(AbstractEnsemble): + """Ensemble consisting of a single model. - Such model is returned as an ensemble of a single - object, to comply with the expected interface of an - AbstractEnsemble. + Parameters + ---------- + task_type: int + An identifier indicating which task is being performed. + + metrics: Sequence[Scorer] | Scorer + The metrics used to evaluate the models. + + backend : Backend + Gives access to the backend of Auto-sklearn. Not used. + + random_state: int | RandomState | None = None + Not used. """ def __init__( self, task_type: int, metrics: Sequence[Scorer] | Scorer, - random_state: int | np.random.RandomState | None, backend: Backend, - run_history: RunHistory, - seed: int, + random_state: int | np.random.RandomState | None = None, ): + self.weights_ = [1.0] self.task_type = task_type if isinstance(metrics, Sequence): self.metrics = metrics @@ -41,26 +48,344 @@ def __init__( self.metrics = [metrics] else: raise TypeError(type(metrics)) - self.seed = seed + self.random_state = random_state self.backend = backend - # Add some default values -- at least 1 model in ensemble is assumed - self.indices_ = [0] - self.weights_ = [1.0] - self.run_history = run_history - self.identifiers_ = self.get_identifiers_from_run_history() + def fit( + self, + base_models_predictions: np.ndarray | list[np.ndarray], + true_targets: np.ndarray, + model_identifiers: list[tuple[int, int, float]], + runs: Sequence[Run], + X_data: SUPPORTED_FEAT_TYPES | None = None, + ) -> AbstractSingleModelEnsemble: + """Fit the ensemble + + Parameters + ---------- + base_models_predictions: np.ndarray + shape = (n_base_models, n_data_points, n_targets) + n_targets is the number of classes in case of classification, + n_targets is 0 or 1 in case of regression + + Can be a list of 2d numpy arrays as well to prevent copying all + predictions into a single, large numpy array. + + true_targets : array of shape [n_targets] + + model_identifiers : identifier for each base model. + Can be used for practical text output of the ensemble. + + runs: Sequence[Run] + Additional information for each run executed by SMAC that was + considered by the ensemble builder. + + X_data : list-like | sparse matrix | None = None + + Returns + ------- + self + """ + return self + + def predict(self, predictions: np.ndarray | list[np.ndarray]) -> np.ndarray: + """Select the predictions of the selected model. + + Parameters + ---------- + base_models_predictions : np.ndarray + shape = (n_base_models, n_data_points, n_targets) + Same as in the fit method. + + Returns + ------- + np.ndarray + """ + return predictions[0] + + def __str__(self) -> str: + return "%s:\n\tMembers: %s" "\n\tWeights: %s\n\tIdentifiers: [%s]" % ( + self.__class__.__name__, + self.indices_, # type: ignore [attr-defined] + self.weights_, + self.identifiers_[0], # type: ignore [attr-defined] + ) + + def get_models_with_weights( + self, models: dict[tuple[int, int, float], BasePipeline] + ) -> list[tuple[float, BasePipeline]]: + """Return a list of (weight, model) pairs for the model selected by this ensemble. + + Parameters + ---------- + models : dict {identifier : model object} + The identifiers are the same as the one presented to the fit() + method. Models can be used for nice printing. + + Returns + ------- + list[tuple[float, BasePipeline]] + """ + return [(self.weights_[0], models[self.identifiers_[0]])] # type: ignore [attr-defined] # noqa: E501 + + def get_identifiers_with_weights( + self, + ) -> list[tuple[tuple[int, int, float], float]]: + """Return a (identifier, weight)-pairs for the model selected by this ensemble. + + Parameters + ---------- + models : dict {identifier : model object} + The identifiers are the same as the one presented to the fit() + method. Models can be used for nice printing. + + Returns + ------- + list[tuple[tuple[int, int, float], float] + """ + return list(zip(self.identifiers_, self.weights_)) # type: ignore [attr-defined] # noqa: E501 + + def get_selected_model_identifiers(self) -> list[tuple[int, int, float]]: + """Return identifier of models in the ensemble. + + This includes models which have a weight of zero! + + Returns + ------- + list + """ + return self.identifiers_ # type: ignore [attr-defined] + + def get_validation_performance(self) -> float: + """Return validation performance of ensemble. + + In case of multi-objective problem, only the first metric will be returned. + + Return + ------ + float + """ + return self.best_model_score_ # type: ignore [attr-defined] + + +class SingleModelEnsemble(AbstractSingleModelEnsemble): + """Ensemble consisting of a single model. + + This class is used by the :class:`MultiObjectiveDummyEnsemble` to represent + ensembles consisting of a single model, and this class should not be used + on its own. + + Do not use by yourself! + + Parameters + ---------- + task_type: int + An identifier indicating which task is being performed. + + metrics: Sequence[Scorer] | Scorer + The metrics used to evaluate the models. + + backend : Backend + Gives access to the backend of Auto-sklearn. Not used. + + model_index : int + Index of the model that constitutes the ensemble. This index will + be used to select the correct predictions that will be passed during + ``fit`` and ``predict``. + + random_state: int | RandomState | None = None + Not used. + """ + + def __init__( + self, + task_type: int, + metrics: Sequence[Scorer] | Scorer, + backend: Backend, + model_index: int, + random_state: int | np.random.RandomState | None = None, + ): + super().__init__( + task_type=task_type, + metrics=metrics, + random_state=random_state, + backend=backend, + ) + self.indices_ = [model_index] + + def fit( + self, + base_models_predictions: np.ndarray | list[np.ndarray], + true_targets: np.ndarray, + model_identifiers: list[tuple[int, int, float]], + runs: Sequence[Run], + X_data: SUPPORTED_FEAT_TYPES | None = None, + ) -> SingleModelEnsemble: + """Dummy implementation of the ``fit`` method. + + Actualy work of passing the model index is done in the constructor. This + method only stores the identifier of the selected model and computes it's + validation loss. + + Parameters + ---------- + base_models_predictions: np.ndarray + shape = (n_base_models, n_data_points, n_targets) + n_targets is the number of classes in case of classification, + n_targets is 0 or 1 in case of regression + + Can be a list of 2d numpy arrays as well to prevent copying all + predictions into a single, large numpy array. + + true_targets : array of shape [n_targets] + + model_identifiers : identifier for each base model. + Can be used for practical text output of the ensemble. + + runs: Sequence[Run] + Additional information for each run executed by SMAC that was + considered by the ensemble builder. Not used. + + X_data : list-like | spmatrix | None = None + X data to feed to a metric if it requires it + + Returns + ------- + self + """ + self.identifiers_ = [model_identifiers[self.indices_[0]]] + loss = calculate_losses( + solution=true_targets, + prediction=base_models_predictions[self.indices_[0]], + task_type=self.task_type, + metrics=self.metrics, + X_data=X_data, + ) + self.best_model_score_ = loss[self.metrics[0].name] + return self + + +class SingleBest(AbstractSingleModelEnsemble): + """Ensemble consisting of the single best model. + + Parameters + ---------- + task_type: int + An identifier indicating which task is being performed. + + metrics: Sequence[Scorer] | Scorer + The metrics used to evaluate the models. + + random_state: int | RandomState | None = None + Not used. + + backend : Backend + Gives access to the backend of Auto-sklearn. Not used. + """ + + def __init__( + self, + task_type: int, + metrics: Sequence[Scorer] | Scorer, + backend: Backend, + random_state: int | np.random.RandomState | None = None, + ): + super().__init__( + task_type=task_type, + metrics=metrics, + random_state=random_state, + backend=backend, + ) def fit( self, - base_models_predictions: np.ndarray | List[np.ndarray], - X_data: SUPPORTED_FEAT_TYPES, + base_models_predictions: np.ndarray | list[np.ndarray], true_targets: np.ndarray, - model_identifiers: List[Tuple[int, int, float]], + model_identifiers: list[tuple[int, int, float]], runs: Sequence[Run], - ) -> "AbstractEnsemble": + X_data: SUPPORTED_FEAT_TYPES | None = None, + ) -> SingleBest: + """Select the single best model. + + Parameters + ---------- + base_models_predictions: np.ndarray + shape = (n_base_models, n_data_points, n_targets) + n_targets is the number of classes in case of classification, + n_targets is 0 or 1 in case of regression + + Can be a list of 2d numpy arrays as well to prevent copying all + predictions into a single, large numpy array. + + true_targets : array of shape [n_targets] + + model_identifiers : identifier for each base model. + Can be used for practical text output of the ensemble. + + runs: Sequence[Run] + Additional information for each run executed by SMAC that was + considered by the ensemble builder. Not used. + + X_data : array-like | sparse matrix | None = None + + Returns + ------- + self + """ + losses = [ + calculate_losses( + solution=true_targets, + prediction=base_model_prediction, + task_type=self.task_type, + metrics=self.metrics, + X_data=X_data, + )[self.metrics[0].name] + for base_model_prediction in base_models_predictions + ] + argmin = np.argmin(losses) + self.indices_ = [argmin] + self.identifiers_ = [model_identifiers[argmin]] + self.best_model_score_ = losses[argmin] return self - def get_identifiers_from_run_history(self) -> List[Tuple[int, int, float]]: + +class SingleBestFromRunhistory(AbstractSingleModelEnsemble): + """ + In the case of a crash, this class searches + for the best individual model. + + Such model is returned as an ensemble of a single + object, to comply with the expected interface of an + AbstractEnsemble. + + Do not use by yourself! + """ + + def __init__( + self, + task_type: int, + metrics: Sequence[Scorer] | Scorer, + backend: Backend, + run_history: RunHistory, + seed: int, + random_state: int | np.random.RandomState | None = None, + ): + super().__init__( + task_type=task_type, + metrics=metrics, + random_state=random_state, + backend=backend, + ) + + # The seed here is seperate from RandomState and is used to indiicate a + # directory for the backend to search in + self.seed = seed + self.indices_ = [0] + self.weights_ = [1.0] + self.run_history = run_history + self.identifiers_ = self.get_identifiers_from_run_history() + + def get_identifiers_from_run_history(self) -> list[tuple[int, int, float]]: """Parses the run history, to identify the best performing model Populates the identifiers attribute, which is used by the backend to access @@ -115,55 +440,3 @@ def get_identifiers_from_run_history(self) -> List[Tuple[int, int, float]]: self.best_model_score_ = best_model_score return best_model_identifier - - def predict(self, predictions: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray: - return predictions[0] - - def __str__(self) -> str: - return ( - "Single Model Selection:\n\tMembers: %s" - "\n\tWeights: %s\n\tIdentifiers: %s" - % ( - self.indices_, - self.weights_, - " ".join( - [ - str(identifier) - for idx, identifier in enumerate(self.identifiers_) - if self.weights_[idx] > 0 - ] - ), - ) - ) - - def get_models_with_weights( - self, models: Dict[Tuple[int, int, float], BasePipeline] - ) -> List[Tuple[float, BasePipeline]]: - output = [] - for i, weight in enumerate(self.weights_): - if weight > 0.0: - identifier = self.identifiers_[i] - model = models[identifier] - output.append((weight, model)) - - output.sort(reverse=True, key=lambda t: t[0]) - - return output - - def get_identifiers_with_weights( - self, - ) -> List[Tuple[Tuple[int, int, float], float]]: - return list(zip(self.identifiers_, self.weights_)) - - def get_selected_model_identifiers(self) -> List[Tuple[int, int, float]]: - output = [] - - for i, weight in enumerate(self.weights_): - identifier = self.identifiers_[i] - if weight > 0.0: - output.append(identifier) - - return output - - def get_validation_performance(self) -> float: - return self.best_model_score_ diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 5afd8c597c..607fb3dc2f 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -36,6 +36,9 @@ ) from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble from autosklearn.ensembles.ensemble_selection import EnsembleSelection +from autosklearn.ensembles.multiobjective_dummy_ensemble import ( + MultiObjectiveDummyEnsemble, +) from autosklearn.metrics import Scorer from autosklearn.pipeline.base import BasePipeline from autosklearn.util.smac_wrap import SMACCallback @@ -48,7 +51,7 @@ def __init__( per_run_time_limit=None, initial_configurations_via_metalearning=25, ensemble_size: int | None = None, - ensemble_class: Type[AbstractEnsemble] | None = EnsembleSelection, + ensemble_class: Type[AbstractEnsemble] | Literal["default"] | None = "default", ensemble_kwargs: Dict[str, Any] | None = None, ensemble_nbest=50, max_models_on_disc=50, @@ -104,12 +107,16 @@ def __init__( this argument via ``ensemble_kwargs={"ensemble_size": int}`` if you want to change the ensemble size for ensemble selection. - ensemble_class : Type[AbstractEnsemble], optional (default=EnsembleSelection) + ensemble_class : Type[AbstractEnsemble] | "default", optional (default="default") Class implementing the post-hoc ensemble algorithm. Set to - ``None`` to disable ensemble building or use ``SingleBest`` + ``None`` to disable ensemble building or use :class:`SingleBest` to obtain only use the single best model instead of an ensemble. + If set to "default" it will use :class:`EnsembleSelection` for + single-objective problems and :class:`MultiObjectiveDummyEnsemble` + for multi-objective problems. + ensemble_kwargs : Dict, optional Keyword arguments that are passed to the ensemble class upon initialization. @@ -396,6 +403,11 @@ def __init__( self.initial_configurations_via_metalearning = ( initial_configurations_via_metalearning ) + + # Need to resolve the ensemble class here so we can act on it below. + if ensemble_class == "default": + ensemble_class = self._resolve_ensemble_class(metric) + self.ensemble_class = ensemble_class # User specified `ensemble_size` explicitly, warn them about deprecation @@ -610,8 +622,8 @@ def fit_ensemble( ensemble_size: int | None = None, ensemble_kwargs: Optional[Dict[str, Any]] = None, ensemble_nbest: Optional[int] = None, - ensemble_class: Optional[AbstractEnsemble] = EnsembleSelection, - metrics: Scorer | Sequence[Scorer] | None = None, + ensemble_class: Type[AbstractEnsemble] | Literal["default"] | None = "default", + metric: Scorer | Sequence[Scorer] | None = None, ): """Fit an ensemble to models trained during an optimization process. @@ -655,19 +667,39 @@ def fit_ensemble( is independent of the ``ensemble_class`` argument and this pruning step is done prior to constructing an ensemble. - ensemble_class : Type[AbstractEnsemble], optional (default=EnsembleSelection) + ensemble_class : Type[AbstractEnsemble] | "default", optional (default="default") Class implementing the post-hoc ensemble algorithm. Set to - ``None`` to disable ensemble building or use ``SingleBest`` + ``None`` to disable ensemble building or use class:`SingleBest` to obtain only use the single best model instead of an ensemble. - metrics: Scorer | Sequence[Scorer] | None = None + If set to "default" it will use :class:`EnsembleSelection` for + single-objective problems and :class:`MultiObjectiveDummyEnsemble` + for multi-objective problems. + + metric: Scorer | Sequence[Scorer] | None = None A metric or list of metrics to score the ensemble with Returns ------- self - """ + """ # noqa: E501 + + if ensemble_class == "default": + # Things are actually a little more nuanced here: + # * If they passed `metric=None` at init, we would infer this in automl + # during `fit` and store it in the automl instance. + # * If they passed a `metric` in init and left it `None` here, this would + # also be in the automl instance + # => We can use self.automl_ as ground truth for metric if no metrics passed + # and we have one created + if metric is None and self.automl_ is not None and self.automl_._metrics: + metric = self.automl_._metrics + + ensemble_class = self._resolve_ensemble_class(metric) + + self.ensemble_class = ensemble_class + # User specified `ensemble_size` explicitly, warn them about deprecation if ensemble_size is not None: # Keep consistent behaviour @@ -720,10 +752,20 @@ def fit_ensemble( ensemble_nbest=ensemble_nbest, ensemble_class=ensemble_class, ensemble_kwargs=ensemble_kwargs, - metrics=metrics, + metrics=metric, ) return self + def _resolve_ensemble_class( + self, + metric: Scorer | Sequence[Scorer] | None, + ) -> type[AbstractEnsemble]: + return ( + EnsembleSelection + if metric is None or isinstance(metric, Scorer) or len(metric) == 1 + else MultiObjectiveDummyEnsemble + ) + def refit(self, X, y): """Refit all models found with fit to new data. diff --git a/autosklearn/metrics/__init__.py b/autosklearn/metrics/__init__.py index 0485db892c..e6b2e87802 100644 --- a/autosklearn/metrics/__init__.py +++ b/autosklearn/metrics/__init__.py @@ -48,6 +48,7 @@ def __call__( self, y_true: np.ndarray, y_pred: np.ndarray, + *, X_data: Optional[SUPPORTED_XDATA_TYPES] = None, sample_weight: Optional[List[float]] = None, ) -> float: @@ -62,6 +63,7 @@ def __call__( self, y_true: np.ndarray, y_pred: np.ndarray, + *, X_data: Optional[SUPPORTED_XDATA_TYPES] = None, sample_weight: Optional[List[float]] = None, ) -> float: @@ -129,6 +131,7 @@ def __call__( self, y_true: np.ndarray, y_pred: np.ndarray, + *, X_data: Optional[SUPPORTED_XDATA_TYPES] = None, sample_weight: Optional[List[float]] = None, ) -> float: @@ -189,6 +192,7 @@ def __call__( self, y_true: np.ndarray, y_pred: np.ndarray, + *, X_data: Optional[SUPPORTED_XDATA_TYPES] = None, sample_weight: Optional[List[float]] = None, ) -> float: @@ -508,11 +512,11 @@ def calculate_scores( try: score_dict[metric_.name] = _compute_single_scorer( - metric_, - prediction, - solution, - task_type, - X_data, + metric=metric_, + prediction=prediction, + solution=solution, + task_type=task_type, + X_data=X_data, ) except ValueError as e: print(e, e.args[0]) @@ -532,11 +536,11 @@ def calculate_scores( try: score_dict[metric_.name] = _compute_single_scorer( - metric_, - prediction, - solution, - task_type, - X_data, + metric=metric_, + prediction=prediction, + solution=solution, + task_type=task_type, + X_data=X_data, ) except ValueError as e: if e.args[0] == "multiclass format is not supported": diff --git a/autosklearn/util/multiobjective.py b/autosklearn/util/multiobjective.py new file mode 100644 index 0000000000..f12b111153 --- /dev/null +++ b/autosklearn/util/multiobjective.py @@ -0,0 +1,40 @@ +import operator + +import numpy as np + + +def pareto_front(values: np.ndarray, *, is_loss: bool = True) -> np.ndarray: + """Calculate the pareto front + + source from: https://stackoverflow.com/a/40239615 + + Note + ---- + Works on the assumption that every value is either something to minimize or + something to maximize, based on ``is_loss``. + + Parameters + ---------- + values: np.ndarray [n_models, n_metrics] + The value for each of the metrics + + is_loss: bool = True + Whether the metrics are a loss or a score + + Returns + ------- + np.ndarray + A boolean mask where true indicates if the model on the pareto front + """ + op = operator.lt if is_loss else operator.gt + + is_efficient = np.ones(values.shape[0], dtype=bool) + for i, c in enumerate(values): + if is_efficient[i]: + # Keep any point with a lower cost + is_efficient[is_efficient] = np.any(op(values[is_efficient], c), axis=1) + + # And keep self + is_efficient[i] = True + + return is_efficient diff --git a/doc/api.rst b/doc/api.rst index 789d2d24e3..581c74d98d 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -117,14 +117,30 @@ Extension Interfaces Ensembles ========= -.. autoclass:: autosklearn.ensembles.AbstractEnsemble +~~~~~~~~~~~~~~~~ +Single objective +~~~~~~~~~~~~~~~~ + +.. autoclass:: autosklearn.ensembles.EnsembleSelection + :members: + +Single model classes +~~~~~~~~~~~~~~~~~~~~ + +These classes wrap a single model to provide a unified interface in Auto-sklearn. + +.. autoclass:: autosklearn.ensembles.SingleBest :members: -.. autoclass:: autosklearn.ensembles.AbstractMultiObjectiveEnsemble +.. autoclass:: autosklearn.ensembles.SingleModelEnsemble :members: -.. autoclass:: autosklearn.ensembles.EnsembleSelection +.. autoclass:: autosklearn.ensembles.SingleBestFromRunhistory :members: -.. autoclass:: autosklearn.ensembles.SingleBest +~~~~~~~~~~~~~~~ +Multi-objective +~~~~~~~~~~~~~~~ + +.. autoclass:: autosklearn.ensembles.MultiObjectiveDummyEnsemble :members: diff --git a/examples/60_search/example_parallel_manual_spawning_cli.py b/examples/60_search/example_parallel_manual_spawning_cli.py index 8a1a9d5a7c..7df0907527 100644 --- a/examples/60_search/example_parallel_manual_spawning_cli.py +++ b/examples/60_search/example_parallel_manual_spawning_cli.py @@ -193,7 +193,7 @@ def cli_start_worker(scheduler_file_name): delete_tmp_folder_after_terminate=False, time_left_for_this_task=30, per_run_time_limit=10, - memory_limit=1024, + memory_limit=2048, tmp_folder=tmp_folder, seed=777, # n_jobs is ignored internally as we pass a dask client. diff --git a/test/test_ensemble_builder/test.test_ensemble_builder.test_ensemble_testEntireEnsembleBuilder/.auto-sklearn/runs/0_1_0.0/0.1.0.0.model b/test/test_ensemble_builder/test.test_ensemble_builder.test_ensemble_testEntireEnsembleBuilder/.auto-sklearn/runs/0_1_0.0/0.1.0.0.model deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/test/test_ensemble_builder/test.test_ensemble_builder.test_ensemble_testEntireEnsembleBuilder/.auto-sklearn/runs/0_2_0.0/0.2.0.0.model b/test/test_ensemble_builder/test.test_ensemble_builder.test_ensemble_testEntireEnsembleBuilder/.auto-sklearn/runs/0_2_0.0/0.2.0.0.model deleted file mode 100644 index e69de29bb2..0000000000 diff --git 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a/test/test_ensemble_builder/test_multiobjective_dummy_ensemble.py b/test/test_ensemble_builder/test_multiobjective_dummy_ensemble.py new file mode 100644 index 0000000000..eb639236fd --- /dev/null +++ b/test/test_ensemble_builder/test_multiobjective_dummy_ensemble.py @@ -0,0 +1,73 @@ +import numpy as np +import sklearn.metrics + +from autosklearn.constants import REGRESSION +from autosklearn.ensembles.multiobjective_dummy_ensemble import ( + MultiObjectiveDummyEnsemble, +) +from autosklearn.ensembles.singlebest_ensemble import SingleModelEnsemble +from autosklearn.metrics import MAXINT, make_scorer, root_mean_squared_error + + +def test_MultiObjectiveDummyEnsemble(backend): + negative_root_mean_squared_error = make_scorer( + "negative_root_mean_squared_error", + sklearn.metrics.mean_squared_error, + optimum=0, + worst_possible_result=-MAXINT, + greater_is_better=True, + squared=False, + ) + + ensemble = MultiObjectiveDummyEnsemble( + task_type=REGRESSION, + metrics=[root_mean_squared_error, negative_root_mean_squared_error], + random_state=0, + backend=backend, + ) + + # fit the whole thing + X_data = np.random.random(size=(100, 2)) + y_true = np.full((100), 5.5) + predictions = [] + for i in range(20, 1, -1): + pred = np.ones((100), dtype=np.float32) * i + predictions.append(pred) + identifiers = [(i, i, 0.0) for i in range(20)] + + ensemble.fit( + base_models_predictions=predictions, + X_data=X_data, + true_targets=y_true, + model_identifiers=identifiers, + runs=[], + ) + + # Because the target value is 5.5 some of the predictions have the same error + # -> not every prediction is on the Pareto front + assert len(ensemble.pareto_set_) == 15 + for sub_ensemble in ensemble.pareto_set_: + assert isinstance(sub_ensemble, SingleModelEnsemble) + + # test the prediction + ensemble_prediction = np.random.random((1, 100, 2)) + prediction = ensemble.predict(ensemble_prediction) + assert prediction.shape == (100, 2) + + assert str(ensemble) == """MultiObjectiveDummyEnsemble: 15 models""" + + # From here on everything is supposed to return the output of the best + # ensemble according to the 1st metric + models_with_weights = ensemble.get_models_with_weights( + {identifier: i for i, identifier in enumerate(identifiers)} + ) + assert models_with_weights == [(1.0, 14)] + + identifiers_with_weights = ensemble.get_identifiers_with_weights() + assert identifiers_with_weights == [((14, 14, 0.0), 1.0)] + + selected_model_identifiers = ensemble.get_selected_model_identifiers() + assert selected_model_identifiers == [(14, 14, 0.0)] + + best_model_score = ensemble.get_validation_performance() + assert best_model_score == 0.5 diff --git a/test/test_ensemble_builder/test_singlebest_ensemble.py b/test/test_ensemble_builder/test_singlebest_ensemble.py new file mode 100644 index 0000000000..915f4bc6cf --- /dev/null +++ b/test/test_ensemble_builder/test_singlebest_ensemble.py @@ -0,0 +1,132 @@ +import numpy as np + +from autosklearn.constants import REGRESSION +from autosklearn.ensembles.singlebest_ensemble import SingleBest, SingleModelEnsemble +from autosklearn.metrics import root_mean_squared_error + + +def test_SingleBest(backend): + + ensemble = SingleBest( + task_type=REGRESSION, + metrics=[root_mean_squared_error], + random_state=0, + backend=backend, + ) + + # fit the whole thing + X_data = np.random.random(size=(100, 2)) + y_true = np.full((100), 5.5) + predictions = [] + for i in range(20, 1, -1): + pred = np.ones((100), dtype=np.float32) * i + predictions.append(pred) + identifiers = [(i, i, 0.0) for i in range(20)] + + # Check that the weight isn't touched + assert ensemble.weights_ == [1.0] + + ensemble.fit( + base_models_predictions=predictions, + X_data=X_data, + true_targets=y_true, + model_identifiers=identifiers, + runs=[], + ) + + assert ensemble.weights_ == [1.0] + assert ensemble.indices_ == [14] + assert ensemble.identifiers_ == [(14, 14, 0.0)] + assert ensemble.best_model_score_ == 0.5 + + # test the prediction + ensemble_prediction = np.random.random((1, 100, 2)) + prediction = ensemble.predict(ensemble_prediction) + assert prediction.shape == (100, 2) + + # test string representation; it selects the 14th prediction (6) + # and ties with the 15th (5), but then afterwards the RMSE goes up again + assert ( + str(ensemble) + == """SingleBest: +\tMembers: [14] +\tWeights: [1.0] +\tIdentifiers: [(14, 14, 0.0)]""" + ) + + models_with_weights = ensemble.get_models_with_weights( + {identifier: i for i, identifier in enumerate(identifiers)} + ) + assert models_with_weights == [(1.0, 14)] + + identifiers_with_weights = ensemble.get_identifiers_with_weights() + assert identifiers_with_weights == [((14, 14, 0.0), 1.0)] + + selected_model_identifiers = ensemble.get_selected_model_identifiers() + assert selected_model_identifiers == [(14, 14, 0.0)] + + best_model_score = ensemble.get_validation_performance() + assert best_model_score == 0.5 + + +def test_SingleModelEnsemble(backend): + + ensemble = SingleModelEnsemble( + task_type=REGRESSION, + metrics=[root_mean_squared_error], + random_state=0, + backend=backend, + model_index=5, + ) + + # fit the whole thing + X_data = np.random.random(size=(100, 2)) + y_true = np.full((100), 5.5) + predictions = [] + for i in range(20, 1, -1): + pred = np.ones((100), dtype=np.float32) * i + predictions.append(pred) + identifiers = [(i, i, 0.0) for i in range(20)] + + # Check that the weight isn't touched + assert ensemble.weights_ == [1.0] + + ensemble.fit( + base_models_predictions=predictions, + X_data=X_data, + true_targets=y_true, + model_identifiers=identifiers, + runs=[], + ) + + assert ensemble.weights_ == [1.0] + assert ensemble.indices_ == [5] + assert ensemble.identifiers_ == [(5, 5, 0.0)] + assert ensemble.best_model_score_ == 9.5 + + # test the prediction + ensemble_prediction = np.random.random((1, 100, 2)) + prediction = ensemble.predict(ensemble_prediction) + assert prediction.shape == (100, 2) + + assert ( + str(ensemble) + == """SingleModelEnsemble: +\tMembers: [5] +\tWeights: [1.0] +\tIdentifiers: [(5, 5, 0.0)]""" + ) + + models_with_weights = ensemble.get_models_with_weights( + {identifier: i for i, identifier in enumerate(identifiers)} + ) + assert models_with_weights == [(1.0, 5)] + + identifiers_with_weights = ensemble.get_identifiers_with_weights() + assert identifiers_with_weights == [((5, 5, 0.0), 1.0)] + + selected_model_identifiers = ensemble.get_selected_model_identifiers() + assert selected_model_identifiers == [(5, 5, 0.0)] + + best_model_score = ensemble.get_validation_performance() + assert best_model_score == 9.5 diff --git a/test/test_estimators/test_estimators.py b/test/test_estimators/test_estimators.py index e1e33d684a..c3a50a37fe 100644 --- a/test/test_estimators/test_estimators.py +++ b/test/test_estimators/test_estimators.py @@ -29,6 +29,7 @@ from autosklearn.automl import AutoMLClassifier from autosklearn.data.validation import InputValidator from autosklearn.ensemble_building.run import Run +from autosklearn.ensembles import EnsembleSelection, MultiObjectiveDummyEnsemble from autosklearn.estimators import ( AutoSklearnClassifier, AutoSklearnEstimator, @@ -1727,3 +1728,16 @@ def test_param_dataset_compression(dataset_compression: Union[bool, Dict[str, An model = AutoSklearnClassifier(dataset_compression=dataset_compression) assert model.dataset_compression == dataset_compression + + +def test_ensemble_default_resolves(): + model = AutoSklearnClassifier( + ensemble_class="default", + metric=accuracy, + ) + assert model.ensemble_class == EnsembleSelection + model = AutoSklearnClassifier( + ensemble_class="default", + metric=[accuracy, f1_macro], + ) + assert model.ensemble_class == MultiObjectiveDummyEnsemble From a9d49d46aad14b70c0a05e171b0affe93d16eb1d Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Thu, 4 Aug 2022 11:07:57 +0200 Subject: [PATCH 086/118] Update pre-commit hooks (#1552) * chore: update pre-commit hooks * Update .pre-commit-config.yaml Co-authored-by: eddiebergman Co-authored-by: Eddie Bergman --- .pre-commit-config.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index ab3472ddc6..4f0694daf3 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -15,7 +15,7 @@ repos: files: test/.* - repo: https://github.com/psf/black - rev: 22.3.0 + rev: 22.6.0 hooks: - id: black name: black formatter autosklearn @@ -39,14 +39,14 @@ repos: additional_dependencies: ["toml"] # Needed to parse pyproject.toml - repo: https://github.com/pre-commit/mirrors-mypy - rev: v0.942 + rev: v0.971 hooks: - id: mypy name: mypy auto-sklearn files: autosklearn/.* - repo: https://gitlab.com/pycqa/flake8 - rev: 4.0.1 + rev: 5.0.4 hooks: - id: flake8 name: flake8 autosklearn From 66b782a8dde3d182aa8a5532ee66b0172adb01f5 Mon Sep 17 00:00:00 2001 From: Lukas Strack <73999018+Louquinze@users.noreply.github.com> Date: Thu, 4 Aug 2022 13:53:45 +0200 Subject: [PATCH 087/118] Fixing hps remain active & meta hp configuration (#1536) * fixing the issue that metalearning tries to use every hp defined in the csv files. Also fixing the hps remain active bug. * fixing the issue that metalearning tries to use every hp defined in the csv files. Also fixing the hps remain active bug. * fixing the issue that metalearning tries to use every hp defined in the csv files. Also fixing the hps remain active bug. * fixing the issue that metalearning tries to use every hp defined in the csv files. Also fixing the hps remain active bug. * fixing the issue that metalearning tries to use every hp defined in the csv files. Also fixing the hps remain active bug. * fixing ensemble builder * fixing ensemble builder * fixing ensemble builder * fixing ensemble builder * fixing ensemble builder * fixing ensemble builder * fixing ensemble builder * fixing ensemble builder * fixing ensemble builder * fixing ensemble builder * fixing ensemble builder * fixing ensemble builder * fixing ensemble builder * fixing ensemble builder * fixing ensemble builder * fixing ensemble builder * fixing ensemble builder * fixing ensemble builder * fix search space bug * fix search space bug * fix search space bug * fix search space bug * fix search space bug * fix search space bug * fix search space bug * fix search space bug * fix search space bug * fix search space bug * fix search space bug * fix typing * fixing pre-commit * fixing pre-commit * fixing pre-commit * fixing pre-commit * fixing pre-commit * fixing pre-commit * fixing pre-commit * fixing pre-commit * fixing pre-commit * adding new test. Test if new configuration space is correct. * add new tests and fixing some issues from PR * add new tests and fixing some issues from PR * add new tests and fixing some issues from PR * add new tests and fixing some issues from PR * fix-1535-Exception-in-the-fit()-call-of-AutoSklearn (#1539) * Create PR * Fix test fixture * add new tests and fixing some issues from PR * add new tests and fixing some issues from PR * chancing metalearning test to check whether the configspace adapts to feat_type or not * in `test_data_preprocessing_feat_type.py` check for configuration space size * in `test_data_preprocessing_feat_type.py` check for configuration space size * in `test_data_preprocessing_feat_type.py` check for configuration space size * include feedback from review * include feedback from review * include feedback from review * adapted meta_data_generation search space * Bump docker/build-push-action from 1 to 3 (#1515) * Bump docker/build-push-action from 1 to 3 Bumps [docker/build-push-action](https://github.com/docker/build-push-action) from 1 to 3. - [Release notes](https://github.com/docker/build-push-action/releases) - [Commits](https://github.com/docker/build-push-action/compare/v1...v3) --- updated-dependencies: - dependency-name: docker/build-push-action dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] * Update docker-publish.yml Replace password by token Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Matthias Feurer * fix-1532-_ERROR_-asyncio.exceptions.CancelledError (#1540) * Create PR * Abstract out dask client types * Fix _ issue * Extend scope of dask_client in automl.py * Add docstring to dask module * Indent result addition * Add basic tests for Dask wrappers * Update config space in meta-data generation script Co-authored-by: Eddie Bergman Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Matthias Feurer --- autosklearn/askl_typing.py | 3 + autosklearn/automl.py | 2 +- autosklearn/evaluation/abstract_evaluator.py | 12 +- autosklearn/evaluation/test_evaluator.py | 2 +- autosklearn/evaluation/train_evaluator.py | 17 ++- autosklearn/experimental/askl2.py | 8 +- .../metalearning/input/aslib_simple.py | 10 +- .../metalearning/metalearning/meta_base.py | 4 +- autosklearn/pipeline/base.py | 67 +++++++-- autosklearn/pipeline/classification.py | 23 ++- autosklearn/pipeline/components/base.py | 37 ++++- .../components/classification/__init__.py | 13 +- .../components/classification/adaboost.py | 7 +- .../components/classification/bernoulli_nb.py | 7 +- .../classification/decision_tree.py | 7 +- .../components/classification/extra_trees.py | 7 +- .../components/classification/gaussian_nb.py | 7 +- .../classification/gradient_boosting.py | 7 +- .../classification/k_nearest_neighbors.py | 7 +- .../pipeline/components/classification/lda.py | 7 +- .../classification/liblinear_svc.py | 7 +- .../components/classification/libsvm_svc.py | 7 +- .../pipeline/components/classification/mlp.py | 7 +- .../classification/multinomial_nb.py | 7 +- .../classification/passive_aggressive.py | 7 +- .../pipeline/components/classification/qda.py | 7 +- .../classification/random_forest.py | 7 +- .../pipeline/components/classification/sgd.py | 7 +- .../components/data_preprocessing/__init__.py | 12 +- .../data_preprocessing/balancing/balancing.py | 2 + .../categorical_encoding/__init__.py | 9 +- .../categorical_encoding/encoding.py | 2 + .../categorical_encoding/no_encoding.py | 2 + .../categorical_encoding/one_hot_encoding.py | 2 + .../category_shift/category_shift.py | 2 + .../data_preprocessing/feature_type.py | 84 +++++++---- .../feature_type_categorical.py | 22 ++- .../feature_type_numerical.py | 21 ++- .../data_preprocessing/feature_type_text.py | 24 ++-- .../imputation/categorical_imputation.py | 2 + .../imputation/numerical_imputation.py | 2 + .../minority_coalescense/__init__.py | 10 +- .../minority_coalescer.py | 3 + .../minority_coalescense/no_coalescense.py | 6 +- .../data_preprocessing/rescaling/__init__.py | 6 +- .../rescaling/abstract_rescaling.py | 2 + .../rescaling/quantile_transformer.py | 2 + .../rescaling/robust_scaler.py | 2 + .../text_encoding/__init__.py | 11 +- .../text_encoding/bag_of_word_encoding.py | 2 + .../bag_of_word_encoding_distinct.py | 2 + .../text_encoding/tfidf_encoding.py | 2 + .../text_feature_reduction/truncated_svd.py | 2 + .../variance_threshold/variance_threshold.py | 2 + .../feature_preprocessing/__init__.py | 13 +- .../feature_preprocessing/densifier.py | 7 +- .../extra_trees_preproc_for_classification.py | 7 +- .../extra_trees_preproc_for_regression.py | 7 +- .../feature_preprocessing/fast_ica.py | 7 +- .../feature_agglomeration.py | 7 +- .../feature_preprocessing/kernel_pca.py | 7 +- .../feature_preprocessing/kitchen_sinks.py | 5 +- .../liblinear_svc_preprocessor.py | 7 +- .../feature_preprocessing/no_preprocessing.py | 7 +- .../feature_preprocessing/nystroem_sampler.py | 10 +- .../components/feature_preprocessing/pca.py | 7 +- .../feature_preprocessing/polynomial.py | 7 +- .../random_trees_embedding.py | 7 +- .../select_percentile_classification.py | 7 +- .../select_percentile_regression.py | 7 +- .../select_rates_classification.py | 7 +- .../select_rates_regression.py | 7 +- .../feature_preprocessing/truncatedSVD.py | 7 +- .../components/regression/__init__.py | 13 +- .../components/regression/adaboost.py | 7 +- .../components/regression/ard_regression.py | 7 +- .../components/regression/decision_tree.py | 7 +- .../components/regression/extra_trees.py | 7 +- .../components/regression/gaussian_process.py | 7 +- .../regression/gradient_boosting.py | 7 +- .../regression/k_nearest_neighbors.py | 7 +- .../components/regression/liblinear_svr.py | 7 +- .../components/regression/libsvm_svr.py | 7 +- .../pipeline/components/regression/mlp.py | 7 +- .../components/regression/random_forest.py | 7 +- .../pipeline/components/regression/sgd.py | 7 +- autosklearn/pipeline/regression.py | 20 ++- autosklearn/util/pipeline.py | 43 +++--- .../40_advanced/example_text_preprocessing.py | 1 - .../example_extending_classification.py | 7 +- .../example_extending_data_preprocessor.py | 7 +- .../example_extending_preprocessor.py | 7 +- .../example_extending_regression.py | 7 +- ...mple_restrict_number_of_hyperparameters.py | 6 +- scripts/02_retrieve_metadata.py | 24 +++- test/fixtures/ensembles.py | 27 +++- test/test_evaluation/test_dummy_pipelines.py | 4 +- test/test_evaluation/test_test_evaluator.py | 13 +- test/test_evaluation/test_train_evaluator.py | 19 ++- .../pyMetaLearn/test_metalearner.py | 1 + .../test_metalearning_configuration.py | 40 ++++++ .../data_preprocessing/test_balancing.py | 2 +- .../test_data_preprocessing_feat_type.py | 135 ++++++++++++++++++ .../test_data_preprocessing_numerical.py | 20 ++- .../data_preprocessing/test_scaling.py | 2 +- test/test_pipeline/test_base.py | 37 ++++- test/test_pipeline/test_classification.py | 98 ++++++++++--- 107 files changed, 1077 insertions(+), 228 deletions(-) create mode 100644 autosklearn/askl_typing.py create mode 100644 test/test_metalearning/pyMetaLearn/test_metalearning_configuration.py create mode 100644 test/test_pipeline/components/data_preprocessing/test_data_preprocessing_feat_type.py diff --git a/autosklearn/askl_typing.py b/autosklearn/askl_typing.py new file mode 100644 index 0000000000..61d01bef30 --- /dev/null +++ b/autosklearn/askl_typing.py @@ -0,0 +1,3 @@ +from typing import Dict, Union + +FEAT_TYPE_TYPE = Dict[Union[str, int], str] diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 75a19acc57..e242fbbc08 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -2250,7 +2250,7 @@ def _create_search_space( ) -> Tuple[ConfigurationSpace, str]: configspace_path = os.path.join(tmp_dir, "space.json") configuration_space = pipeline.get_configuration_space( - datamanager.info, + datamanager, include=include, exclude=exclude, ) diff --git a/autosklearn/evaluation/abstract_evaluator.py b/autosklearn/evaluation/abstract_evaluator.py index b97f588a45..1dd77479bc 100644 --- a/autosklearn/evaluation/abstract_evaluator.py +++ b/autosklearn/evaluation/abstract_evaluator.py @@ -17,6 +17,7 @@ import autosklearn.pipeline.classification import autosklearn.pipeline.regression +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.automl_common.common.utils.backend import Backend from autosklearn.constants import ( CLASSIFICATION_TASKS, @@ -45,6 +46,7 @@ def __init__( self, config: Configuration, random_state: Optional[Union[int, np.random.RandomState]], + feat_type: Optional[FEAT_TYPE_TYPE] = None, init_params: Optional[Dict[str, Any]] = None, dataset_properties: Dict[str, Any] = {}, include: Optional[List[str]] = None, @@ -61,6 +63,7 @@ def __init__( self.dataset_properties = dataset_properties self.include = include self.exclude = exclude + self.feat_type = feat_type def pre_transform( self, @@ -108,6 +111,7 @@ def __init__( self, config: Configuration, random_state: Optional[Union[int, np.random.RandomState]], + feat_type: Optional[FEAT_TYPE_TYPE] = None, init_params: Optional[Dict[str, Any]] = None, dataset_properties: Dict[str, Any] = {}, include: Optional[List[str]] = None, @@ -123,6 +127,7 @@ def __init__( self.dataset_properties = dataset_properties self.include = include self.exclude = exclude + self.feat_type = feat_type def pre_transform( self, @@ -217,6 +222,7 @@ def __init__( self.queue = queue self.datamanager = self.backend.load_datamanager() + self.feat_type = self.datamanager.feat_type self.include = include self.exclude = exclude @@ -294,11 +300,12 @@ def __init__( _addons[key].add_component(component) # Please mypy to prevent not defined attr - self.model = self._get_model() + self.model = self._get_model(feat_type=self.feat_type) - def _get_model(self) -> BaseEstimator: + def _get_model(self, feat_type: Optional[FEAT_TYPE_TYPE]) -> BaseEstimator: if not isinstance(self.configuration, Configuration): model = self.model_class( + feat_type=feat_type, config=self.configuration, random_state=self.seed, init_params=self._init_params, @@ -318,6 +325,7 @@ def _get_model(self) -> BaseEstimator: "multiclass": self.task_type == MULTICLASS_CLASSIFICATION, } model = self.model_class( + feat_type=feat_type, config=self.configuration, dataset_properties=dataset_properties, random_state=self.seed, diff --git a/autosklearn/evaluation/test_evaluator.py b/autosklearn/evaluation/test_evaluator.py index d624c1a44d..fc1134819d 100644 --- a/autosklearn/evaluation/test_evaluator.py +++ b/autosklearn/evaluation/test_evaluator.py @@ -58,7 +58,7 @@ def __init__( self.X_test = self.datamanager.data.get("X_test") self.Y_test = self.datamanager.data.get("Y_test") - self.model = self._get_model() + self.model = self._get_model(self.feat_type) def fit_predict_and_loss(self) -> None: _fit_and_suppress_warnings(self.logger, self.model, self.X_train, self.Y_train) diff --git a/autosklearn/evaluation/train_evaluator.py b/autosklearn/evaluation/train_evaluator.py index b7f9be73d3..4757091190 100644 --- a/autosklearn/evaluation/train_evaluator.py +++ b/autosklearn/evaluation/train_evaluator.py @@ -247,6 +247,7 @@ def __init__( budget_type=budget_type, ) + self.feat_type = self.backend.load_datamanager().feat_type self.resampling_strategy = resampling_strategy if resampling_strategy_args is None: self.resampling_strategy_args = {} @@ -305,7 +306,7 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: # Test if the model allows for an iterative fit, if not, # call this method again without the iterative argument - model = self._get_model() + model = self._get_model(self.feat_type) if not model.estimator_supports_iterative_fit(): self.fit_predict_and_loss(iterative=False) return @@ -319,7 +320,9 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: Y_test_pred = [None] * self.num_cv_folds train_splits = [None] * self.num_cv_folds - self.models = [self._get_model() for i in range(self.num_cv_folds)] + self.models = [ + self._get_model(self.feat_type) for i in range(self.num_cv_folds) + ] iterations = [1] * self.num_cv_folds total_n_iterations = [0] * self.num_cv_folds # model.estimator_supports_iterative_fit -> true @@ -515,7 +518,7 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: self.Y_optimization = Y_targets self.Y_actual_train = Y_train_targets - self.model = self._get_model() + self.model = self._get_model(self.feat_type) status = StatusType.DONOTADVANCE if any( [ @@ -679,7 +682,7 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: self.Y_actual_train = Y_train_targets if self.num_cv_folds > 1: - self.model = self._get_model() + self.model = self._get_model(self.feat_type) # Bad style, but necessary for unit testing that self.model is # actually a new model self._added_empty_model = True @@ -798,7 +801,7 @@ def _partial_fit_and_predict_iterative( test_indices: List[int], add_model_to_self: bool, ) -> None: - model = self._get_model() + model = self._get_model(self.feat_type) self.indices[fold] = (train_indices, test_indices) @@ -939,7 +942,7 @@ def _partial_fit_and_predict_standard( PIPELINE_DATA_DTYPE, # test_pred TYPE_ADDITIONAL_INFO, ]: - model = self._get_model() + model = self._get_model(self.feat_type) self.indices[fold] = (train_indices, test_indices) @@ -1005,7 +1008,7 @@ def _partial_fit_and_predict_budget( # Add this statement for mypy assert self.budget is not None - model = self._get_model() + model = self._get_model(self.feat_type) self.indices[fold] = (train_indices, test_indices) self.X_targets[fold] = self.X_train[test_indices] self.Y_targets[fold] = self.Y_train[test_indices] diff --git a/autosklearn/experimental/askl2.py b/autosklearn/experimental/askl2.py index 4303c953dc..078355dfbb 100644 --- a/autosklearn/experimental/askl2.py +++ b/autosklearn/experimental/askl2.py @@ -51,8 +51,10 @@ def __call__( initial_configurations = [] for member in self.portfolio.values(): try: + hp_names = scenario.cs.get_hyperparameter_names() + _member = {key: member[key] for key in member if key in hp_names} initial_configurations.append( - Configuration(configuration_space=scenario.cs, values=member) + Configuration(configuration_space=scenario.cs, values=_member) ) except ValueError: pass @@ -103,8 +105,10 @@ def __call__( initial_configurations = [] for member in self.portfolio.values(): try: + hp_names = scenario.cs.get_hyperparameter_names() + _member = {key: member[key] for key in member if key in hp_names} initial_configurations.append( - Configuration(configuration_space=scenario.cs, values=member) + Configuration(configuration_space=scenario.cs, values=_member) ) except ValueError: pass diff --git a/autosklearn/metalearning/input/aslib_simple.py b/autosklearn/metalearning/input/aslib_simple.py index 833242729d..871cccd2c1 100644 --- a/autosklearn/metalearning/input/aslib_simple.py +++ b/autosklearn/metalearning/input/aslib_simple.py @@ -5,13 +5,15 @@ import arff import pandas as pd +from ConfigSpace.configuration_space import ConfigurationSpace class AlgorithmSelectionProblem(object): - def __init__(self, directory): + def __init__(self, directory: str, cs: ConfigurationSpace): self.logger = logging.getLogger(__name__) # Create data structures + self.cs = cs self.dir_ = directory self.algorithm_runs = None self.configurations = None @@ -143,13 +145,17 @@ def _read_configurations(self, filename): csv_reader = csv.DictReader(fh) configurations = dict() + hp_names = self.cs.get_hyperparameter_names() for line in csv_reader: configuration = dict() algorithm_id = line["idx"] for hp_name, value in line.items(): if not value or hp_name == "idx": continue - + if hp_name not in hp_names: + # skip hyperparameter + # if it is not existing in the current search space + continue try: value = int(value) except Exception: diff --git a/autosklearn/metalearning/metalearning/meta_base.py b/autosklearn/metalearning/metalearning/meta_base.py index 826bfaeda3..4cb116ef21 100644 --- a/autosklearn/metalearning/metalearning/meta_base.py +++ b/autosklearn/metalearning/metalearning/meta_base.py @@ -42,7 +42,9 @@ def __init__(self, configuration_space, aslib_directory, logger): self.configuration_space = configuration_space self.aslib_directory = aslib_directory - aslib_reader = aslib_simple.AlgorithmSelectionProblem(self.aslib_directory) + aslib_reader = aslib_simple.AlgorithmSelectionProblem( + self.aslib_directory, self.configuration_space + ) self.metafeatures = aslib_reader.metafeatures self.algorithm_runs: OrderedDict[ str, pd.DataFrame diff --git a/autosklearn/pipeline/base.py b/autosklearn/pipeline/base.py index 3a13364ea6..b4647215c6 100644 --- a/autosklearn/pipeline/base.py +++ b/autosklearn/pipeline/base.py @@ -1,5 +1,5 @@ from abc import ABCMeta -from typing import Dict, Union +from typing import Dict, Optional, Union import numpy as np import scipy.sparse @@ -7,6 +7,7 @@ from sklearn.pipeline import Pipeline import autosklearn.pipeline.create_searchspace_util +from autosklearn.askl_typing import FEAT_TYPE_TYPE from .components.base import AutoSklearnChoice, AutoSklearnComponent @@ -35,6 +36,7 @@ class BasePipeline(Pipeline): def __init__( self, config=None, + feat_type: Optional[FEAT_TYPE_TYPE] = None, steps=None, dataset_properties=None, include=None, @@ -50,15 +52,18 @@ def __init__( dataset_properties if dataset_properties is not None else {} ) self.random_state = random_state + self.feat_type = feat_type if steps is None: - self.steps = self._get_pipeline_steps(dataset_properties=dataset_properties) + self.steps = self._get_pipeline_steps( + feat_type=feat_type, dataset_properties=dataset_properties + ) else: self.steps = steps self._validate_include_exclude_params() - self.config_space = self.get_hyperparameter_search_space() + self.config_space = self.get_hyperparameter_search_space(feat_type=feat_type) if config is None: self.config = self.config_space.get_default_configuration() @@ -82,7 +87,9 @@ def __init__( ) self.config = config - self.set_hyperparameters(self.config, init_params=init_params) + self.set_hyperparameters( + self.config, feat_type=feat_type, init_params=init_params + ) super().__init__(steps=self.steps) @@ -202,14 +209,19 @@ def predict(self, X, batch_size=None): return y - def set_hyperparameters(self, configuration, init_params=None): + def set_hyperparameters( + self, + configuration, + feat_type: Optional[FEAT_TYPE_TYPE] = None, + init_params=None, + ): self.config = configuration for node_idx, n_ in enumerate(self.steps): node_name, node = n_ sub_configuration_space = node.get_hyperparameter_search_space( - dataset_properties=self.dataset_properties + feat_type=feat_type, dataset_properties=self.dataset_properties ) sub_config_dict = {} for param in configuration: @@ -236,7 +248,9 @@ def set_hyperparameters(self, configuration, init_params=None): node, (AutoSklearnChoice, AutoSklearnComponent, BasePipeline) ): node.set_hyperparameters( - configuration=sub_configuration, init_params=sub_init_params_dict + feat_type=feat_type, + configuration=sub_configuration, + init_params=sub_init_params_dict, ) else: raise NotImplementedError("Not supported yet!") @@ -247,7 +261,9 @@ def set_hyperparameters(self, configuration, init_params=None): return self - def get_hyperparameter_search_space(self, dataset_properties=None): + def get_hyperparameter_search_space( + self, feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): """Return the configuration space for the CASH problem. Returns @@ -258,6 +274,7 @@ def get_hyperparameter_search_space(self, dataset_properties=None): """ if not hasattr(self, "config_space") or self.config_space is None: self.config_space = self._get_hyperparameter_search_space( + feat_type=feat_type, include=self.include, exclude=self.exclude, dataset_properties=self.dataset_properties, @@ -265,7 +282,11 @@ def get_hyperparameter_search_space(self, dataset_properties=None): return self.config_space def _get_hyperparameter_search_space( - self, include=None, exclude=None, dataset_properties=None + self, + feat_type: Optional[FEAT_TYPE_TYPE] = None, + include=None, + exclude=None, + dataset_properties=None, ): """Return the configuration space for the CASH problem. @@ -278,6 +299,9 @@ def _get_hyperparameter_search_space( Parameters ---------- + feat_type: dict + python dictionary which maps the columns of the dataset to the data types + estimator_name : str Name of the estimator hyperparameter which will be used in the configuration space. For a classification task, this would be @@ -307,7 +331,13 @@ def _get_hyperparameter_search_space( raise NotImplementedError() def _get_base_search_space( - self, cs, dataset_properties, exclude, include, pipeline + self, + cs, + dataset_properties, + include, + exclude, + pipeline, + feat_type: Optional[FEAT_TYPE_TYPE] = None, ): if include is None: if self.include is None: @@ -343,7 +373,10 @@ def _get_base_search_space( dataset_properties["signed"] = False matches = autosklearn.pipeline.create_searchspace_util.get_match_array( - pipeline, dataset_properties, include=include, exclude=exclude + pipeline=pipeline, + dataset_properties=dataset_properties, + include=include, + exclude=exclude, ) # Now we have only legal combinations at this step of the pipeline @@ -370,7 +403,9 @@ def _get_base_search_space( if not is_choice: cs.add_configuration_space( node_name, - node.get_hyperparameter_search_space(dataset_properties), + node.get_hyperparameter_search_space( + dataset_properties=dataset_properties, feat_type=feat_type + ), ) # If the node is a choice, we have to figure out which of its # choices are actually legal choices @@ -386,7 +421,9 @@ def _get_base_search_space( ) ) sub_config_space = node.get_hyperparameter_search_space( - dataset_properties, include=choices_list + feat_type=feat_type, + dataset_properties=dataset_properties, + include=choices_list, ) cs.add_configuration_space(node_name, sub_config_space) @@ -505,7 +542,9 @@ def __repr__(self): return return_value - def _get_pipeline_steps(self, dataset_properties): + def _get_pipeline_steps( + self, dataset_properties, feat_type: Optional[FEAT_TYPE_TYPE] = None + ): raise NotImplementedError() def _get_estimator_hyperparameter_name(self): diff --git a/autosklearn/pipeline/classification.py b/autosklearn/pipeline/classification.py index 1686e02809..332c076b9b 100644 --- a/autosklearn/pipeline/classification.py +++ b/autosklearn/pipeline/classification.py @@ -8,6 +8,7 @@ from ConfigSpace.forbidden import ForbiddenAndConjunction, ForbiddenEqualsClause from sklearn.base import ClassifierMixin +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import BasePipeline from autosklearn.pipeline.components.classification import ClassifierChoice from autosklearn.pipeline.components.data_preprocessing import DataPreprocessorChoice @@ -71,6 +72,7 @@ class SimpleClassificationPipeline(BasePipeline, ClassifierMixin): def __init__( self, config: Optional[Configuration] = None, + feat_type: Optional[FEAT_TYPE_TYPE] = None, steps=None, dataset_properties=None, include=None, @@ -84,6 +86,7 @@ def __init__( if "target_type" not in dataset_properties: dataset_properties["target_type"] = "classification" super().__init__( + feat_type=feat_type, config=config, steps=steps, dataset_properties=dataset_properties, @@ -109,7 +112,9 @@ def fit_transformer(self, X, y, fit_params=None): ) _init_params.update(self.init_params) self.set_hyperparameters( - configuration=self.config, init_params=_init_params + feat_type=self.feat_type, + configuration=self.config, + init_params=_init_params, ) if _fit_params is not None: @@ -166,12 +171,18 @@ def predict_proba(self, X, batch_size=None): return y def _get_hyperparameter_search_space( - self, include=None, exclude=None, dataset_properties=None + self, + feat_type: Optional[FEAT_TYPE_TYPE] = None, + include=None, + exclude=None, + dataset_properties=None, ): """Create the hyperparameter configuration space. Parameters ---------- + feat_type : dict, maps columns to there datatypes + include : dict (optional, default=None) Returns @@ -194,6 +205,7 @@ def _get_hyperparameter_search_space( cs = self._get_base_search_space( cs=cs, + feat_type=feat_type, dataset_properties=dataset_properties, exclude=exclude, include=include, @@ -344,7 +356,9 @@ def _get_hyperparameter_search_space( self.dataset_properties = dataset_properties return cs - def _get_pipeline_steps(self, dataset_properties): + def _get_pipeline_steps( + self, dataset_properties, feat_type: Optional[FEAT_TYPE_TYPE] = None + ): steps = [] default_dataset_properties = {"target_type": "classification"} @@ -356,6 +370,7 @@ def _get_pipeline_steps(self, dataset_properties): [ "data_preprocessor", DataPreprocessorChoice( + feat_type=feat_type, dataset_properties=default_dataset_properties, random_state=self.random_state, ), @@ -364,6 +379,7 @@ def _get_pipeline_steps(self, dataset_properties): [ "feature_preprocessor", FeaturePreprocessorChoice( + feat_type=feat_type, dataset_properties=default_dataset_properties, random_state=self.random_state, ), @@ -371,6 +387,7 @@ def _get_pipeline_steps(self, dataset_properties): [ "classifier", ClassifierChoice( + feat_type=feat_type, dataset_properties=default_dataset_properties, random_state=self.random_state, ), diff --git a/autosklearn/pipeline/components/base.py b/autosklearn/pipeline/components/base.py index c4a95df08c..7b496842b2 100644 --- a/autosklearn/pipeline/components/base.py +++ b/autosklearn/pipeline/components/base.py @@ -1,4 +1,4 @@ -from typing import Dict +from typing import Dict, Optional import importlib import inspect @@ -8,6 +8,7 @@ from sklearn.base import BaseEstimator, TransformerMixin +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.constants import SPARSE _addons = dict() # type: Dict[str, 'ThirdPartyComponents'] @@ -98,12 +99,14 @@ def get_properties(dataset_properties=None): raise NotImplementedError() @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): """Return the configuration space of this classification algorithm. Parameters ---------- - + feat_type : FEAT_TYPE_TYPE (default=None) dataset_properties : dict, optional (default=None) Returns @@ -136,7 +139,12 @@ def fit(self, X, y): for further information.""" raise NotImplementedError() - def set_hyperparameters(self, configuration, init_params=None): + def set_hyperparameters( + self, + configuration, + feat_type: Optional[FEAT_TYPE_TYPE] = None, + init_params=None, + ): params = configuration.get_dictionary() for param, value in params.items(): @@ -339,7 +347,12 @@ def get_estimator(self): class AutoSklearnChoice(object): - def __init__(self, dataset_properties, random_state=None): + def __init__( + self, + dataset_properties, + feat_type: Optional[FEAT_TYPE_TYPE] = None, + random_state=None, + ): """ Parameters ---------- @@ -414,7 +427,12 @@ def get_available_components( return components_dict - def set_hyperparameters(self, configuration, init_params=None): + def set_hyperparameters( + self, + configuration, + feat_type: Optional[FEAT_TYPE_TYPE] = None, + init_params=None, + ): new_params = {} params = configuration.get_dictionary() @@ -438,7 +456,12 @@ def set_hyperparameters(self, configuration, init_params=None): return self def get_hyperparameter_search_space( - self, dataset_properties=None, default=None, include=None, exclude=None + self, + feat_type: FEAT_TYPE_TYPE, + dataset_properties=None, + default=None, + include=None, + exclude=None, ): raise NotImplementedError() diff --git a/autosklearn/pipeline/components/classification/__init__.py b/autosklearn/pipeline/components/classification/__init__.py index c95334273a..31fa2ea9ca 100644 --- a/autosklearn/pipeline/components/classification/__init__.py +++ b/autosklearn/pipeline/components/classification/__init__.py @@ -8,6 +8,8 @@ from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter +from autosklearn.askl_typing import FEAT_TYPE_TYPE + from ..base import ( AutoSklearnChoice, AutoSklearnClassificationAlgorithm, @@ -86,7 +88,12 @@ def get_available_components( return components_dict def get_hyperparameter_search_space( - self, dataset_properties=None, default=None, include=None, exclude=None + self, + feat_type: FEAT_TYPE_TYPE, + dataset_properties=None, + default=None, + include=None, + exclude=None, ): if dataset_properties is None: dataset_properties = {} @@ -126,7 +133,9 @@ def get_hyperparameter_search_space( for estimator_name in available_estimators.keys(): estimator_configuration_space = available_estimators[ estimator_name - ].get_hyperparameter_search_space(dataset_properties) + ].get_hyperparameter_search_space( + feat_type=feat_type, dataset_properties=dataset_properties + ) parent_hyperparameter = {"parent": estimator, "value": estimator_name} cs.add_configuration_space( estimator_name, diff --git a/autosklearn/pipeline/components/classification/adaboost.py b/autosklearn/pipeline/components/classification/adaboost.py index 3634f53956..08a9bc06bd 100644 --- a/autosklearn/pipeline/components/classification/adaboost.py +++ b/autosklearn/pipeline/components/classification/adaboost.py @@ -1,3 +1,5 @@ +from typing import Optional + from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( CategoricalHyperparameter, @@ -5,6 +7,7 @@ UniformIntegerHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA @@ -68,7 +71,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() n_estimators = UniformIntegerHyperparameter( diff --git a/autosklearn/pipeline/components/classification/bernoulli_nb.py b/autosklearn/pipeline/components/classification/bernoulli_nb.py index 8271c5f602..de52bc939c 100644 --- a/autosklearn/pipeline/components/classification/bernoulli_nb.py +++ b/autosklearn/pipeline/components/classification/bernoulli_nb.py @@ -1,3 +1,5 @@ +from typing import Optional + import numpy as np from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( @@ -5,6 +7,7 @@ UniformFloatHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA from autosklearn.util.common import check_for_bool @@ -64,7 +67,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() # the smoothing parameter is a non-negative float diff --git a/autosklearn/pipeline/components/classification/decision_tree.py b/autosklearn/pipeline/components/classification/decision_tree.py index fbfc6b7c6a..1369ecf906 100644 --- a/autosklearn/pipeline/components/classification/decision_tree.py +++ b/autosklearn/pipeline/components/classification/decision_tree.py @@ -1,3 +1,5 @@ +from typing import Optional + import numpy as np from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( @@ -8,6 +10,7 @@ UnParametrizedHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA from autosklearn.pipeline.implementations.util import ( @@ -106,7 +109,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() criterion = CategoricalHyperparameter( diff --git a/autosklearn/pipeline/components/classification/extra_trees.py b/autosklearn/pipeline/components/classification/extra_trees.py index 5c7ce1879a..36edd82584 100644 --- a/autosklearn/pipeline/components/classification/extra_trees.py +++ b/autosklearn/pipeline/components/classification/extra_trees.py @@ -1,3 +1,5 @@ +from typing import Optional + from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( CategoricalHyperparameter, @@ -6,6 +8,7 @@ UnParametrizedHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import ( AutoSklearnClassificationAlgorithm, IterativeComponentWithSampleWeight, @@ -156,7 +159,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() criterion = CategoricalHyperparameter( diff --git a/autosklearn/pipeline/components/classification/gaussian_nb.py b/autosklearn/pipeline/components/classification/gaussian_nb.py index 8e978e9631..bf43f4e4a5 100644 --- a/autosklearn/pipeline/components/classification/gaussian_nb.py +++ b/autosklearn/pipeline/components/classification/gaussian_nb.py @@ -1,6 +1,9 @@ +from typing import Optional + import numpy as np from ConfigSpace.configuration_space import ConfigurationSpace +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm from autosklearn.pipeline.constants import DENSE, PREDICTIONS, UNSIGNED_DATA @@ -55,6 +58,8 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() return cs diff --git a/autosklearn/pipeline/components/classification/gradient_boosting.py b/autosklearn/pipeline/components/classification/gradient_boosting.py index 50b0b284bd..618028dff7 100644 --- a/autosklearn/pipeline/components/classification/gradient_boosting.py +++ b/autosklearn/pipeline/components/classification/gradient_boosting.py @@ -1,3 +1,5 @@ +from typing import Optional + import numpy as np from ConfigSpace.conditions import EqualsCondition, InCondition from ConfigSpace.configuration_space import ConfigurationSpace @@ -9,6 +11,7 @@ UnParametrizedHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import ( AutoSklearnClassificationAlgorithm, IterativeComponentWithSampleWeight, @@ -182,7 +185,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() loss = Constant("loss", "auto") learning_rate = UniformFloatHyperparameter( diff --git a/autosklearn/pipeline/components/classification/k_nearest_neighbors.py b/autosklearn/pipeline/components/classification/k_nearest_neighbors.py index fe55e0783d..d524bd42d9 100644 --- a/autosklearn/pipeline/components/classification/k_nearest_neighbors.py +++ b/autosklearn/pipeline/components/classification/k_nearest_neighbors.py @@ -1,9 +1,12 @@ +from typing import Optional + from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( CategoricalHyperparameter, UniformIntegerHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA @@ -57,7 +60,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() n_neighbors = UniformIntegerHyperparameter( diff --git a/autosklearn/pipeline/components/classification/lda.py b/autosklearn/pipeline/components/classification/lda.py index 29a08f80b5..e7ebec290b 100644 --- a/autosklearn/pipeline/components/classification/lda.py +++ b/autosklearn/pipeline/components/classification/lda.py @@ -1,3 +1,5 @@ +from typing import Optional + from ConfigSpace.conditions import EqualsCondition from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( @@ -5,6 +7,7 @@ UniformFloatHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm from autosklearn.pipeline.constants import DENSE, PREDICTIONS, UNSIGNED_DATA from autosklearn.pipeline.implementations.util import softmax @@ -76,7 +79,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() shrinkage = CategoricalHyperparameter( "shrinkage", ["None", "auto", "manual"], default_value="None" diff --git a/autosklearn/pipeline/components/classification/liblinear_svc.py b/autosklearn/pipeline/components/classification/liblinear_svc.py index 3f57ef8f94..d1beb08837 100644 --- a/autosklearn/pipeline/components/classification/liblinear_svc.py +++ b/autosklearn/pipeline/components/classification/liblinear_svc.py @@ -1,3 +1,5 @@ +from typing import Optional + from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.forbidden import ForbiddenAndConjunction, ForbiddenEqualsClause from ConfigSpace.hyperparameters import ( @@ -6,6 +8,7 @@ UniformFloatHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA from autosklearn.pipeline.implementations.util import softmax @@ -104,7 +107,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() penalty = CategoricalHyperparameter("penalty", ["l1", "l2"], default_value="l2") diff --git a/autosklearn/pipeline/components/classification/libsvm_svc.py b/autosklearn/pipeline/components/classification/libsvm_svc.py index ba423161c1..43bd017c5c 100644 --- a/autosklearn/pipeline/components/classification/libsvm_svc.py +++ b/autosklearn/pipeline/components/classification/libsvm_svc.py @@ -1,3 +1,5 @@ +from typing import Optional + import resource import sys @@ -10,6 +12,7 @@ UnParametrizedHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA from autosklearn.pipeline.implementations.util import softmax @@ -138,7 +141,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): C = UniformFloatHyperparameter("C", 0.03125, 32768, log=True, default_value=1.0) # No linear kernel here, because we have liblinear kernel = CategoricalHyperparameter( diff --git a/autosklearn/pipeline/components/classification/mlp.py b/autosklearn/pipeline/components/classification/mlp.py index f7001d7bc1..d8c95fa9d0 100644 --- a/autosklearn/pipeline/components/classification/mlp.py +++ b/autosklearn/pipeline/components/classification/mlp.py @@ -1,3 +1,5 @@ +from typing import Optional + import copy import numpy as np @@ -11,6 +13,7 @@ UnParametrizedHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import ( AutoSklearnClassificationAlgorithm, IterativeComponent, @@ -203,7 +206,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() hidden_layer_depth = UniformIntegerHyperparameter( name="hidden_layer_depth", lower=1, upper=3, default_value=1 diff --git a/autosklearn/pipeline/components/classification/multinomial_nb.py b/autosklearn/pipeline/components/classification/multinomial_nb.py index 7b65be8a5c..dee1507f01 100644 --- a/autosklearn/pipeline/components/classification/multinomial_nb.py +++ b/autosklearn/pipeline/components/classification/multinomial_nb.py @@ -1,3 +1,5 @@ +from typing import Optional + import numpy as np from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( @@ -5,6 +7,7 @@ UniformFloatHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SIGNED_DATA, SPARSE from autosklearn.util.common import check_for_bool @@ -76,7 +79,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() # the smoothing parameter is a non-negative float diff --git a/autosklearn/pipeline/components/classification/passive_aggressive.py b/autosklearn/pipeline/components/classification/passive_aggressive.py index 494ea7db06..97a11a0283 100644 --- a/autosklearn/pipeline/components/classification/passive_aggressive.py +++ b/autosklearn/pipeline/components/classification/passive_aggressive.py @@ -1,3 +1,5 @@ +from typing import Optional + import numpy as np from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( @@ -6,6 +8,7 @@ UnParametrizedHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import ( AutoSklearnClassificationAlgorithm, IterativeComponentWithSampleWeight, @@ -152,7 +155,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): C = UniformFloatHyperparameter("C", 1e-5, 10, 1.0, log=True) fit_intercept = UnParametrizedHyperparameter("fit_intercept", "True") loss = CategoricalHyperparameter( diff --git a/autosklearn/pipeline/components/classification/qda.py b/autosklearn/pipeline/components/classification/qda.py index 7b25858392..0b6f6f7653 100644 --- a/autosklearn/pipeline/components/classification/qda.py +++ b/autosklearn/pipeline/components/classification/qda.py @@ -1,7 +1,10 @@ +from typing import Optional + import numpy as np from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformFloatHyperparameter +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm from autosklearn.pipeline.constants import DENSE, PREDICTIONS, UNSIGNED_DATA from autosklearn.pipeline.implementations.util import softmax @@ -72,7 +75,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): reg_param = UniformFloatHyperparameter("reg_param", 0.0, 1.0, default_value=0.0) cs = ConfigurationSpace() cs.add_hyperparameter(reg_param) diff --git a/autosklearn/pipeline/components/classification/random_forest.py b/autosklearn/pipeline/components/classification/random_forest.py index 6ccd720b3a..892d8611d5 100644 --- a/autosklearn/pipeline/components/classification/random_forest.py +++ b/autosklearn/pipeline/components/classification/random_forest.py @@ -1,3 +1,5 @@ +from typing import Optional + from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( CategoricalHyperparameter, @@ -6,6 +8,7 @@ UnParametrizedHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import ( AutoSklearnClassificationAlgorithm, IterativeComponentWithSampleWeight, @@ -149,7 +152,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() criterion = CategoricalHyperparameter( "criterion", ["gini", "entropy"], default_value="gini" diff --git a/autosklearn/pipeline/components/classification/sgd.py b/autosklearn/pipeline/components/classification/sgd.py index 469c2605dd..5073f8ec20 100644 --- a/autosklearn/pipeline/components/classification/sgd.py +++ b/autosklearn/pipeline/components/classification/sgd.py @@ -1,3 +1,5 @@ +from typing import Optional + from ConfigSpace.conditions import EqualsCondition, InCondition from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( @@ -6,6 +8,7 @@ UnParametrizedHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import ( AutoSklearnClassificationAlgorithm, IterativeComponentWithSampleWeight, @@ -169,7 +172,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() loss = CategoricalHyperparameter( diff --git a/autosklearn/pipeline/components/data_preprocessing/__init__.py b/autosklearn/pipeline/components/data_preprocessing/__init__.py index 5693efd441..3cc968f7d1 100644 --- a/autosklearn/pipeline/components/data_preprocessing/__init__.py +++ b/autosklearn/pipeline/components/data_preprocessing/__init__.py @@ -6,6 +6,7 @@ from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import PIPELINE_DATA_DTYPE from ..base import ( @@ -105,6 +106,7 @@ def get_available_components( def get_hyperparameter_search_space( self, + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[Dict] = None, default: str = None, include: Optional[Dict] = None, @@ -136,8 +138,8 @@ def get_hyperparameter_search_space( cs.add_hyperparameter(preprocessor) for name in available_preprocessors: preprocessor_configuration_space = available_preprocessors[name]( - dataset_properties=dataset_properties - ).get_hyperparameter_search_space(dataset_properties) + feat_type=feat_type, dataset_properties=dataset_properties + ).get_hyperparameter_search_space(dataset_properties=dataset_properties) parent_hyperparameter = {"parent": preprocessor, "value": name} cs.add_configuration_space( name, @@ -150,7 +152,10 @@ def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: return self.choice.transform(X) def set_hyperparameters( - self, configuration: ConfigurationSpace, init_params: Optional[Dict] = None + self, + configuration: ConfigurationSpace, + feat_type: Optional[FEAT_TYPE_TYPE] = None, + init_params: Optional[Dict] = None, ) -> "DataPreprocessorChoice": config = {} params = configuration.get_dictionary() @@ -162,7 +167,6 @@ def set_hyperparameters( config[param] = value new_params = {} - feat_type = None if init_params is not None: for param, value in init_params.items(): param = param.replace(choice, "").split(":", 1)[-1] diff --git a/autosklearn/pipeline/components/data_preprocessing/balancing/balancing.py b/autosklearn/pipeline/components/data_preprocessing/balancing/balancing.py index 721fe63fc5..106eb377f7 100644 --- a/autosklearn/pipeline/components/data_preprocessing/balancing/balancing.py +++ b/autosklearn/pipeline/components/data_preprocessing/balancing/balancing.py @@ -5,6 +5,7 @@ from ConfigSpace.hyperparameters import CategoricalHyperparameter from sklearn.base import BaseEstimator +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import ( @@ -139,6 +140,7 @@ def get_properties( @staticmethod def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: # TODO add replace by zero! diff --git a/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/__init__.py b/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/__init__.py index 5d1647b24a..5b1cf075b3 100644 --- a/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/__init__.py +++ b/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/__init__.py @@ -8,6 +8,7 @@ from ConfigSpace.hyperparameters import CategoricalHyperparameter from sklearn.base import BaseEstimator +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from ...base import ( @@ -38,6 +39,7 @@ def get_components(cls: BaseEstimator) -> Dict[str, BaseEstimator]: def get_hyperparameter_search_space( self, + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, default: Optional[str] = None, include: Optional[Dict[str, str]] = None, @@ -73,7 +75,7 @@ def get_hyperparameter_search_space( for name in available_preprocessors: preprocessor_configuration_space = available_preprocessors[ name - ].get_hyperparameter_search_space(dataset_properties) + ].get_hyperparameter_search_space(dataset_properties=dataset_properties) parent_hyperparameter = {"parent": preprocessor, "value": name} cs.add_configuration_space( name, @@ -86,7 +88,10 @@ def get_hyperparameter_search_space( return cs def set_hyperparameters( - self, configuration: Configuration, init_params: Optional[Dict[str, Any]] = None + self, + feat_type: FEAT_TYPE_TYPE, + configuration: Configuration, + init_params: Optional[Dict[str, Any]] = None, ) -> "OHEChoice": new_params = {} diff --git a/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/encoding.py b/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/encoding.py index 43d578219f..7c904635f8 100644 --- a/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/encoding.py +++ b/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/encoding.py @@ -5,6 +5,7 @@ from ConfigSpace.configuration_space import ConfigurationSpace from sklearn.preprocessing import OrdinalEncoder +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA @@ -69,6 +70,7 @@ def get_properties( @staticmethod def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: return ConfigurationSpace() diff --git a/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/no_encoding.py b/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/no_encoding.py index 028a4fb9c1..cead9331d4 100644 --- a/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/no_encoding.py +++ b/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/no_encoding.py @@ -3,6 +3,7 @@ import numpy as np from ConfigSpace.configuration_space import ConfigurationSpace +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA @@ -44,6 +45,7 @@ def get_properties( @staticmethod def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: cs = ConfigurationSpace() diff --git a/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/one_hot_encoding.py b/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/one_hot_encoding.py index 9b9ee87c81..989cf86680 100644 --- a/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/one_hot_encoding.py +++ b/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/one_hot_encoding.py @@ -5,6 +5,7 @@ from ConfigSpace.configuration_space import ConfigurationSpace from sklearn.preprocessing import OneHotEncoder as DenseOneHotEncoder +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA @@ -55,6 +56,7 @@ def get_properties( @staticmethod def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: return ConfigurationSpace() diff --git a/autosklearn/pipeline/components/data_preprocessing/category_shift/category_shift.py b/autosklearn/pipeline/components/data_preprocessing/category_shift/category_shift.py index f2dc2bf304..65ec36f7e7 100644 --- a/autosklearn/pipeline/components/data_preprocessing/category_shift/category_shift.py +++ b/autosklearn/pipeline/components/data_preprocessing/category_shift/category_shift.py @@ -4,6 +4,7 @@ from ConfigSpace.configuration_space import ConfigurationSpace import autosklearn.pipeline.implementations.CategoryShift +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA @@ -63,6 +64,7 @@ def get_properties( @staticmethod def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: return ConfigurationSpace() diff --git a/autosklearn/pipeline/components/data_preprocessing/feature_type.py b/autosklearn/pipeline/components/data_preprocessing/feature_type.py index bd42d8a67a..11085a1f74 100644 --- a/autosklearn/pipeline/components/data_preprocessing/feature_type.py +++ b/autosklearn/pipeline/components/data_preprocessing/feature_type.py @@ -7,6 +7,7 @@ from scipy import sparse from sklearn.base import BaseEstimator +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.data.validation import SUPPORTED_FEAT_TYPES, SUPPORTED_TARGET_TYPES from autosklearn.pipeline.base import ( DATASET_PROPERTIES_TYPE, @@ -46,7 +47,7 @@ def __init__( exclude: Optional[Dict[str, str]] = None, random_state: Optional[Union[int, np.random.RandomState]] = None, init_params: Optional[Dict[str, Any]] = None, - feat_type: Optional[Dict[Union[str, int], str]] = None, + feat_type: Optional[FEAT_TYPE_TYPE] = None, force_sparse_output: bool = False, column_transformer: Optional[sklearn.compose.ColumnTransformer] = None, ): @@ -72,6 +73,7 @@ def __init__( # TODO: Extract the child configuration space from the FeatTypeSplit to the # pipeline if needed self.categ_ppl = CategoricalPreprocessingPipeline( + feat_type=self.feat_type, config=None, steps=pipeline, dataset_properties=dataset_properties, @@ -88,6 +90,7 @@ def __init__( # TODO: Extract the child configuration space from the FeatTypeSplit to the # pipeline if needed self.numer_ppl = NumericalPreprocessingPipeline( + feat_type=self.feat_type, config=None, steps=pipeline, dataset_properties=dataset_properties, @@ -105,6 +108,7 @@ def __init__( # TODO: Extract the child configuration space from the FeatTypeSplit to the # pipeline if needed self.txt_ppl = TextPreprocessingPipeline( + feat_type=self.feat_type, config=None, steps=pipeline, dataset_properties=dataset_properties, @@ -114,13 +118,28 @@ def __init__( init_params=init_params, ) - self._transformers: List[Tuple[str, AutoSklearnComponent]] = [ - ("categorical_transformer", self.categ_ppl), - ("numerical_transformer", self.numer_ppl), - ("text_transformer", self.txt_ppl), - ] + if self.feat_type is None: + self._transformers: List[Tuple[str, AutoSklearnComponent]] = [ + ("categorical_transformer", self.categ_ppl), + ("numerical_transformer", self.numer_ppl), + ("text_transformer", self.txt_ppl), + ] + else: + self._transformers: List[Tuple[str, AutoSklearnComponent]] = [] + if "categorical" in self.feat_type.values(): + self._transformers.append(("categorical_transformer", self.categ_ppl)) + if "numerical" in self.feat_type.values(): + self._transformers.append(("numerical_transformer", self.numer_ppl)) + if "string" in self.feat_type.values(): + self._transformers.append(("text_transformer", self.txt_ppl)) + if self.config: - self.set_hyperparameters(self.config, init_params=init_params) + self.set_hyperparameters( + feat_type=self.feat_type, + configuration=self.config, + init_params=init_params, + ) + self.column_transformer = column_transformer def fit( @@ -128,9 +147,6 @@ def fit( ) -> "FeatTypeSplit": n_feats = X.shape[1] - categorical_features = [] - numerical_features = [] - text_features = [] if self.feat_type is not None: # Make sure that we are not missing any column! expected = set(self.feat_type.keys()) @@ -143,31 +159,37 @@ def fit( f"Train data has columns={expected} yet the" f" feat_types are feat={columns}" ) + sklearn_transf_spec = [] + categorical_features = [ key for key, value in self.feat_type.items() if value.lower() == "categorical" ] + if len(categorical_features) > 0: + sklearn_transf_spec.append( + ("categorical_transformer", self.categ_ppl, categorical_features) + ) + numerical_features = [ key for key, value in self.feat_type.items() if value.lower() == "numerical" ] + if len(numerical_features) > 0: + sklearn_transf_spec.append( + ("numerical_transformer", self.numer_ppl, numerical_features) + ) + text_features = [ key for key, value in self.feat_type.items() if value.lower() == "string" ] - - sklearn_transf_spec = [ - (name, transformer, feature_columns) - for name, transformer, feature_columns in [ - ("categorical_transformer", self.categ_ppl, categorical_features), - ("numerical_transformer", self.numer_ppl, numerical_features), - ("text_transformer", self.txt_ppl, text_features), - ] - if len(feature_columns) > 0 - ] + if len(text_features) > 0: + sklearn_transf_spec.append( + ("text_transformer", self.txt_ppl, text_features) + ) else: # self.feature_type == None assumes numerical case sklearn_transf_spec = [ @@ -223,7 +245,10 @@ def get_properties( } def set_hyperparameters( - self, configuration: Configuration, init_params: Optional[Dict[str, Any]] = None + self, + feat_type: FEAT_TYPE_TYPE, + configuration: Configuration, + init_params: Optional[Dict[str, Any]] = None, ) -> "FeatTypeSplit": if init_params is not None and "feat_type" in init_params.keys(): self.feat_type = init_params["feat_type"] @@ -232,7 +257,7 @@ def set_hyperparameters( for transf_name, transf_op in self._transformers: sub_configuration_space = transf_op.get_hyperparameter_search_space( - dataset_properties=self.dataset_properties + dataset_properties=self.dataset_properties, feat_type=feat_type ) sub_config_dict = {} for param in configuration: @@ -258,7 +283,9 @@ def set_hyperparameters( transf_op, (AutoSklearnChoice, AutoSklearnComponent, BasePipeline) ): transf_op.set_hyperparameters( - configuration=sub_configuration, init_params=sub_init_params_dict + feat_type=feat_type, + configuration=sub_configuration, + init_params=sub_init_params_dict, ) else: raise NotImplementedError("Not supported yet!") @@ -267,12 +294,16 @@ def set_hyperparameters( def get_hyperparameter_search_space( self, + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: self.dataset_properties = dataset_properties cs = ConfigurationSpace() cs = FeatTypeSplit._get_hyperparameter_search_space_recursevely( - dataset_properties, cs, self._transformers + feat_type=feat_type, + dataset_properties=dataset_properties, + cs=cs, + transformer=self._transformers, ) return cs @@ -281,12 +312,15 @@ def _get_hyperparameter_search_space_recursevely( dataset_properties: DATASET_PROPERTIES_TYPE, cs: ConfigurationSpace, transformer: BaseEstimator, + feat_type: Optional[FEAT_TYPE_TYPE] = None, ) -> ConfigurationSpace: for st_name, st_operation in transformer: if hasattr(st_operation, "get_hyperparameter_search_space"): cs.add_configuration_space( st_name, - st_operation.get_hyperparameter_search_space(dataset_properties), + st_operation.get_hyperparameter_search_space( + dataset_properties=dataset_properties + ), ) else: return FeatTypeSplit._get_hyperparameter_search_space_recursevely( diff --git a/autosklearn/pipeline/components/data_preprocessing/feature_type_categorical.py b/autosklearn/pipeline/components/data_preprocessing/feature_type_categorical.py index dfdaf7af62..07cfeb7fa5 100644 --- a/autosklearn/pipeline/components/data_preprocessing/feature_type_categorical.py +++ b/autosklearn/pipeline/components/data_preprocessing/feature_type_categorical.py @@ -4,6 +4,7 @@ from ConfigSpace.configuration_space import Configuration, ConfigurationSpace from sklearn.base import BaseEstimator +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, BasePipeline from autosklearn.pipeline.components.data_preprocessing.categorical_encoding import ( # noqa: E501 OHEChoice, @@ -46,6 +47,7 @@ class CategoricalPreprocessingPipeline(BasePipeline): def __init__( self, + feat_type: Optional[FEAT_TYPE_TYPE] = None, config: Optional[Configuration] = None, steps: Optional[List[Tuple[str, BaseEstimator]]] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, @@ -56,13 +58,14 @@ def __init__( ) -> None: self._output_dtype = np.int32 super().__init__( - config, - steps, - dataset_properties, - include, - exclude, - random_state, - init_params, + config=config, + steps=steps, + dataset_properties=dataset_properties, + include=include, + exclude=exclude, + random_state=random_state, + init_params=init_params, + feat_type=feat_type, ) @staticmethod @@ -92,6 +95,7 @@ def get_properties( def _get_hyperparameter_search_space( self, + feat_type: Optional[FEAT_TYPE_TYPE] = None, include: Optional[Dict[str, str]] = None, exclude: Optional[Dict[str, str]] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, @@ -110,6 +114,7 @@ def _get_hyperparameter_search_space( cs = self._get_base_search_space( cs=cs, + feat_type=feat_type, dataset_properties=dataset_properties, exclude=exclude, include=include, @@ -120,6 +125,7 @@ def _get_hyperparameter_search_space( def _get_pipeline_steps( self, + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[Dict[str, str]] = None, ) -> List[Tuple[str, BaseEstimator]]: steps = [] @@ -135,6 +141,7 @@ def _get_pipeline_steps( ( "category_coalescence", CoalescenseChoice( + feat_type=feat_type, dataset_properties=default_dataset_properties, random_state=self.random_state, ), @@ -142,6 +149,7 @@ def _get_pipeline_steps( ( "categorical_encoding", OHEChoice( + feat_type=feat_type, dataset_properties=default_dataset_properties, random_state=self.random_state, ), diff --git a/autosklearn/pipeline/components/data_preprocessing/feature_type_numerical.py b/autosklearn/pipeline/components/data_preprocessing/feature_type_numerical.py index b50bf0d357..5cc3f19561 100644 --- a/autosklearn/pipeline/components/data_preprocessing/feature_type_numerical.py +++ b/autosklearn/pipeline/components/data_preprocessing/feature_type_numerical.py @@ -4,6 +4,7 @@ from ConfigSpace.configuration_space import Configuration, ConfigurationSpace from sklearn.base import BaseEstimator +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, BasePipeline from autosklearn.pipeline.components.data_preprocessing import ( rescaling as rescaling_components, @@ -39,6 +40,7 @@ class NumericalPreprocessingPipeline(BasePipeline): def __init__( self, + feat_type: Optional[FEAT_TYPE_TYPE] = None, config: Optional[Configuration] = None, steps: Optional[List[Tuple[str, BaseEstimator]]] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, @@ -49,13 +51,14 @@ def __init__( ) -> None: self._output_dtype = np.int32 super().__init__( - config, - steps, - dataset_properties, - include, - exclude, - random_state, - init_params, + config=config, + steps=steps, + dataset_properties=dataset_properties, + include=include, + exclude=exclude, + random_state=random_state, + init_params=init_params, + feat_type=feat_type, ) @staticmethod @@ -85,6 +88,7 @@ def get_properties( def _get_hyperparameter_search_space( self, + feat_type: Optional[FEAT_TYPE_TYPE] = None, include: Optional[Dict[str, str]] = None, exclude: Optional[Dict[str, str]] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, @@ -110,12 +114,14 @@ def _get_hyperparameter_search_space( exclude=exclude, include=include, pipeline=self.steps, + feat_type=feat_type, ) return cs def _get_pipeline_steps( self, + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[Dict[str, str]] = None, ) -> List[Tuple[str, BaseEstimator]]: steps = [] @@ -134,6 +140,7 @@ def _get_pipeline_steps( ( "rescaling", rescaling_components.RescalingChoice( + feat_type=feat_type, dataset_properties=default_dataset_properties, random_state=self.random_state, ), diff --git a/autosklearn/pipeline/components/data_preprocessing/feature_type_text.py b/autosklearn/pipeline/components/data_preprocessing/feature_type_text.py index 8924d568a6..e92ef09c03 100644 --- a/autosklearn/pipeline/components/data_preprocessing/feature_type_text.py +++ b/autosklearn/pipeline/components/data_preprocessing/feature_type_text.py @@ -4,6 +4,7 @@ from ConfigSpace.configuration_space import Configuration, ConfigurationSpace from sklearn.base import BaseEstimator +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, BasePipeline from autosklearn.pipeline.components.data_preprocessing.text_encoding import ( BagOfWordChoice, @@ -34,6 +35,7 @@ class TextPreprocessingPipeline(BasePipeline): def __init__( self, + feat_type: Optional[FEAT_TYPE_TYPE] = None, config: Optional[Configuration] = None, steps: Optional[List[Tuple[str, BaseEstimator]]] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, @@ -44,13 +46,14 @@ def __init__( ) -> None: self._output_dtype = np.int32 super().__init__( - config, - steps, - dataset_properties, - include, - exclude, - random_state, - init_params, + config=config, + steps=steps, + dataset_properties=dataset_properties, + include=include, + exclude=exclude, + random_state=random_state, + init_params=init_params, + feat_type=feat_type, ) @staticmethod @@ -79,6 +82,7 @@ def get_properties( def _get_hyperparameter_search_space( self, + feat_type: Optional[FEAT_TYPE_TYPE] = None, include: Optional[Dict[str, str]] = None, exclude: Optional[Dict[str, str]] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, @@ -104,12 +108,14 @@ def _get_hyperparameter_search_space( exclude=exclude, include=include, pipeline=self.steps, + feat_type=feat_type, ) return cs def _get_pipeline_steps( self, + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[Dict[str, str]] = None, ) -> List[Tuple[str, BaseEstimator]]: steps = [] @@ -123,7 +129,9 @@ def _get_pipeline_steps( ( "text_encoding", BagOfWordChoice( - default_dataset_properties, random_state=self.random_state + feat_type=feat_type, + dataset_properties=default_dataset_properties, + random_state=self.random_state, ), ), ( diff --git a/autosklearn/pipeline/components/data_preprocessing/imputation/categorical_imputation.py b/autosklearn/pipeline/components/data_preprocessing/imputation/categorical_imputation.py index 00b627daed..31b762eb60 100644 --- a/autosklearn/pipeline/components/data_preprocessing/imputation/categorical_imputation.py +++ b/autosklearn/pipeline/components/data_preprocessing/imputation/categorical_imputation.py @@ -4,6 +4,7 @@ from ConfigSpace.configuration_space import ConfigurationSpace from scipy.sparse import spmatrix +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA @@ -91,6 +92,7 @@ def get_properties( @staticmethod def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: return ConfigurationSpace() diff --git a/autosklearn/pipeline/components/data_preprocessing/imputation/numerical_imputation.py b/autosklearn/pipeline/components/data_preprocessing/imputation/numerical_imputation.py index d7d6a645ab..0d09b7bf11 100644 --- a/autosklearn/pipeline/components/data_preprocessing/imputation/numerical_imputation.py +++ b/autosklearn/pipeline/components/data_preprocessing/imputation/numerical_imputation.py @@ -4,6 +4,7 @@ from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA @@ -62,6 +63,7 @@ def get_properties( @staticmethod def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: # TODO add replace by zero! diff --git a/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/__init__.py b/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/__init__.py index fbf999761c..85002ec349 100644 --- a/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/__init__.py +++ b/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/__init__.py @@ -8,6 +8,7 @@ from ConfigSpace.hyperparameters import CategoricalHyperparameter from sklearn.base import BaseEstimator +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from ...base import ( @@ -38,6 +39,7 @@ def get_components(cls: BaseEstimator) -> Dict[str, BaseEstimator]: def get_hyperparameter_search_space( self, + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, default: Optional[str] = None, include: Optional[Dict[str, str]] = None, @@ -73,7 +75,7 @@ def get_hyperparameter_search_space( for name in available_preprocessors: preprocessor_configuration_space = available_preprocessors[ name - ].get_hyperparameter_search_space(dataset_properties) + ].get_hyperparameter_search_space(dataset_properties=dataset_properties) parent_hyperparameter = {"parent": preprocessor, "value": name} cs.add_configuration_space( name, @@ -86,7 +88,10 @@ def get_hyperparameter_search_space( return cs def set_hyperparameters( - self, configuration: Configuration, init_params: Optional[Dict[str, Any]] = None + self, + configuration: Configuration, + init_params: Optional[Dict[str, Any]] = None, + feat_type: Optional[FEAT_TYPE_TYPE] = None, ) -> "CoalescenseChoice": new_params = {} @@ -111,6 +116,7 @@ def set_hyperparameters( new_params["random_state"] = self.random_state self.new_params = new_params + new_params["feat_type"] = feat_type self.choice = self.get_components()[choice](**new_params) return self diff --git a/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/minority_coalescer.py b/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/minority_coalescer.py index 278cf0bfb9..2533e92e8d 100644 --- a/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/minority_coalescer.py +++ b/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/minority_coalescer.py @@ -5,6 +5,7 @@ from ConfigSpace.hyperparameters import UniformFloatHyperparameter import autosklearn.pipeline.implementations.MinorityCoalescer +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA @@ -15,6 +16,7 @@ class MinorityCoalescer(AutoSklearnPreprocessingAlgorithm): def __init__( self, + feat_type: Optional[FEAT_TYPE_TYPE] = None, minimum_fraction: float = 0.01, random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> None: @@ -59,6 +61,7 @@ def get_properties( @staticmethod def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: cs = ConfigurationSpace() diff --git a/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/no_coalescense.py b/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/no_coalescense.py index d05c146d98..2732795649 100644 --- a/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/no_coalescense.py +++ b/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/no_coalescense.py @@ -3,6 +3,7 @@ import numpy as np from ConfigSpace.configuration_space import ConfigurationSpace +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA @@ -10,7 +11,9 @@ class NoCoalescence(AutoSklearnPreprocessingAlgorithm): def __init__( - self, random_state: Optional[Union[int, np.random.RandomState]] = None + self, + feat_type: Optional[FEAT_TYPE_TYPE] = None, + random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> None: pass @@ -43,6 +46,7 @@ def get_properties( @staticmethod def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: cs = ConfigurationSpace() diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/__init__.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/__init__.py index 2a9fbdb842..9f83881472 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/__init__.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/__init__.py @@ -7,6 +7,7 @@ from ConfigSpace.hyperparameters import CategoricalHyperparameter from sklearn.base import BaseEstimator +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling import ( # noqa: E501 Rescaling, @@ -42,6 +43,7 @@ def get_components(cls: BaseEstimator) -> Dict[str, BaseEstimator]: def get_hyperparameter_search_space( self, + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, default: Optional[str] = None, include: Optional[Dict[str, str]] = None, @@ -74,7 +76,9 @@ def get_hyperparameter_search_space( for name in available_preprocessors: preprocessor_configuration_space = available_preprocessors[ name - ].get_hyperparameter_search_space(dataset_properties) + ].get_hyperparameter_search_space( + feat_type=feat_type, dataset_properties=dataset_properties + ) parent_hyperparameter = {"parent": preprocessor, "value": name} cs.add_configuration_space( name, diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py index 05e1a4e898..0b3244cc62 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py @@ -5,6 +5,7 @@ from sklearn.base import BaseEstimator from sklearn.exceptions import NotFittedError +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm @@ -38,6 +39,7 @@ def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: @staticmethod def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: cs = ConfigurationSpace() diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py index 2611c0650d..51beabcc7a 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py @@ -7,6 +7,7 @@ UniformIntegerHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling import ( # noqa: E501 @@ -62,6 +63,7 @@ def get_properties( @staticmethod def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: cs = ConfigurationSpace() diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/robust_scaler.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/robust_scaler.py index af3b4c0558..8762c1be96 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/robust_scaler.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/robust_scaler.py @@ -6,6 +6,7 @@ from scipy import sparse from sklearn.exceptions import NotFittedError +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling import ( # noqa: E501 @@ -59,6 +60,7 @@ def get_properties( @staticmethod def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: cs = ConfigurationSpace() diff --git a/autosklearn/pipeline/components/data_preprocessing/text_encoding/__init__.py b/autosklearn/pipeline/components/data_preprocessing/text_encoding/__init__.py index 990ad579ca..75c173e181 100644 --- a/autosklearn/pipeline/components/data_preprocessing/text_encoding/__init__.py +++ b/autosklearn/pipeline/components/data_preprocessing/text_encoding/__init__.py @@ -8,6 +8,7 @@ from ConfigSpace.hyperparameters import CategoricalHyperparameter from sklearn.base import BaseEstimator +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from ...base import ( @@ -39,6 +40,7 @@ def get_components(cls: BaseEstimator) -> Dict[str, BaseEstimator]: def get_hyperparameter_search_space( self, + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, default: Optional[str] = None, include: Optional[Dict[str, str]] = None, @@ -75,7 +77,9 @@ def get_hyperparameter_search_space( for name in available_preprocessors: preprocessor_configuration_space = available_preprocessors[ name - ].get_hyperparameter_search_space(dataset_properties) + ].get_hyperparameter_search_space( + feat_type=feat_type, dataset_properties=dataset_properties + ) parent_hyperparameter = {"parent": preprocessor, "value": name} cs.add_configuration_space( name, @@ -88,7 +92,10 @@ def get_hyperparameter_search_space( return cs def set_hyperparameters( - self, configuration: Configuration, init_params: Optional[Dict[str, Any]] = None + self, + configuration: Configuration, + feat_type: Optional[FEAT_TYPE_TYPE] = None, + init_params: Optional[Dict[str, Any]] = None, ) -> "BagOfWordChoice": new_params = {} diff --git a/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding.py b/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding.py index b8a62ccd89..a90b1c1fa4 100644 --- a/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding.py +++ b/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding.py @@ -9,6 +9,7 @@ from ConfigSpace.configuration_space import ConfigurationSpace from sklearn.feature_extraction.text import CountVectorizer +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA @@ -95,6 +96,7 @@ def get_properties( @staticmethod def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: cs = ConfigurationSpace() diff --git a/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding_distinct.py b/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding_distinct.py index 90a43b0f48..de852b5d6b 100644 --- a/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding_distinct.py +++ b/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding_distinct.py @@ -8,6 +8,7 @@ from scipy.sparse import hstack from sklearn.feature_extraction.text import CountVectorizer +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA @@ -101,6 +102,7 @@ def get_properties( @staticmethod def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: cs = ConfigurationSpace() diff --git a/autosklearn/pipeline/components/data_preprocessing/text_encoding/tfidf_encoding.py b/autosklearn/pipeline/components/data_preprocessing/text_encoding/tfidf_encoding.py index f20d24f769..3956ec9eff 100644 --- a/autosklearn/pipeline/components/data_preprocessing/text_encoding/tfidf_encoding.py +++ b/autosklearn/pipeline/components/data_preprocessing/text_encoding/tfidf_encoding.py @@ -9,6 +9,7 @@ from ConfigSpace.configuration_space import ConfigurationSpace from sklearn.feature_extraction.text import TfidfVectorizer +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA @@ -100,6 +101,7 @@ def get_properties( @staticmethod def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: cs = ConfigurationSpace() diff --git a/autosklearn/pipeline/components/data_preprocessing/text_feature_reduction/truncated_svd.py b/autosklearn/pipeline/components/data_preprocessing/text_feature_reduction/truncated_svd.py index beecefb028..d6380e03dd 100644 --- a/autosklearn/pipeline/components/data_preprocessing/text_feature_reduction/truncated_svd.py +++ b/autosklearn/pipeline/components/data_preprocessing/text_feature_reduction/truncated_svd.py @@ -5,6 +5,7 @@ from ConfigSpace.configuration_space import ConfigurationSpace from sklearn.decomposition import TruncatedSVD +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA @@ -74,6 +75,7 @@ def get_properties( @staticmethod def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: cs = ConfigurationSpace() diff --git a/autosklearn/pipeline/components/data_preprocessing/variance_threshold/variance_threshold.py b/autosklearn/pipeline/components/data_preprocessing/variance_threshold/variance_threshold.py index 365ae405a0..eb917d6915 100644 --- a/autosklearn/pipeline/components/data_preprocessing/variance_threshold/variance_threshold.py +++ b/autosklearn/pipeline/components/data_preprocessing/variance_threshold/variance_threshold.py @@ -4,6 +4,7 @@ import sklearn.feature_selection from ConfigSpace.configuration_space import ConfigurationSpace +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA @@ -49,6 +50,7 @@ def get_properties( @staticmethod def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: cs = ConfigurationSpace() diff --git a/autosklearn/pipeline/components/feature_preprocessing/__init__.py b/autosklearn/pipeline/components/feature_preprocessing/__init__.py index cd52d6ad34..9a0bf69a30 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/__init__.py +++ b/autosklearn/pipeline/components/feature_preprocessing/__init__.py @@ -1,4 +1,4 @@ -from typing import Type +from typing import Optional, Type import os from collections import OrderedDict @@ -6,6 +6,8 @@ from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter +from autosklearn.askl_typing import FEAT_TYPE_TYPE + from ..base import ( AutoSklearnChoice, AutoSklearnPreprocessingAlgorithm, @@ -101,7 +103,12 @@ def get_available_components( return components_dict def get_hyperparameter_search_space( - self, dataset_properties=None, default=None, include=None, exclude=None + self, + feat_type: Optional[FEAT_TYPE_TYPE] = None, + dataset_properties=None, + default=None, + include=None, + exclude=None, ): cs = ConfigurationSpace() @@ -130,7 +137,7 @@ def get_hyperparameter_search_space( for name in available_preprocessors: preprocessor_configuration_space = available_preprocessors[ name - ].get_hyperparameter_search_space(dataset_properties) + ].get_hyperparameter_search_space(dataset_properties=dataset_properties) parent_hyperparameter = {"parent": preprocessor, "value": name} cs.add_configuration_space( name, diff --git a/autosklearn/pipeline/components/feature_preprocessing/densifier.py b/autosklearn/pipeline/components/feature_preprocessing/densifier.py index f5c88ecadf..f571d6abee 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/densifier.py +++ b/autosklearn/pipeline/components/feature_preprocessing/densifier.py @@ -1,5 +1,8 @@ +from typing import Optional + from ConfigSpace.configuration_space import ConfigurationSpace +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA @@ -36,6 +39,8 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() return cs diff --git a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py index dad45795b8..904004b201 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py +++ b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py @@ -1,3 +1,5 @@ +from typing import Optional + from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( CategoricalHyperparameter, @@ -7,6 +9,7 @@ UnParametrizedHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA from autosklearn.util.common import check_for_bool, check_none @@ -123,7 +126,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() n_estimators = Constant("n_estimators", 100) diff --git a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py index 3287b837c5..10e741a44e 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py +++ b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py @@ -1,3 +1,5 @@ +from typing import Optional + import numpy as np from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( @@ -8,6 +10,7 @@ UnParametrizedHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA from autosklearn.util.common import check_for_bool, check_none @@ -125,7 +128,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() n_estimators = Constant("n_estimators", 100) diff --git a/autosklearn/pipeline/components/feature_preprocessing/fast_ica.py b/autosklearn/pipeline/components/feature_preprocessing/fast_ica.py index 695ff3c2cc..fe23177fc9 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/fast_ica.py +++ b/autosklearn/pipeline/components/feature_preprocessing/fast_ica.py @@ -1,3 +1,5 @@ +from typing import Optional + import warnings from ConfigSpace.conditions import EqualsCondition @@ -7,6 +9,7 @@ UniformIntegerHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, UNSIGNED_DATA from autosklearn.util.common import check_for_bool, check_none @@ -74,7 +77,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() n_components = UniformIntegerHyperparameter( diff --git a/autosklearn/pipeline/components/feature_preprocessing/feature_agglomeration.py b/autosklearn/pipeline/components/feature_preprocessing/feature_agglomeration.py index d51242de21..2a8db4eaad 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/feature_agglomeration.py +++ b/autosklearn/pipeline/components/feature_preprocessing/feature_agglomeration.py @@ -1,3 +1,5 @@ +from typing import Optional + import numpy as np from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.forbidden import ( @@ -10,6 +12,7 @@ UniformIntegerHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, UNSIGNED_DATA @@ -63,7 +66,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() n_clusters = UniformIntegerHyperparameter("n_clusters", 2, 400, 25) affinity = CategoricalHyperparameter( diff --git a/autosklearn/pipeline/components/feature_preprocessing/kernel_pca.py b/autosklearn/pipeline/components/feature_preprocessing/kernel_pca.py index 4e96bfb1c2..08c72efb6f 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/kernel_pca.py +++ b/autosklearn/pipeline/components/feature_preprocessing/kernel_pca.py @@ -1,3 +1,5 @@ +from typing import Optional + import warnings import numpy as np @@ -9,6 +11,7 @@ UniformIntegerHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA @@ -82,7 +85,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): n_components = UniformIntegerHyperparameter( "n_components", 10, 2000, default_value=100 ) diff --git a/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py b/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py index a81e9ddd78..4e6a348f17 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py +++ b/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py @@ -7,6 +7,7 @@ ) from numpy.random import RandomState +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA @@ -69,7 +70,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): gamma = UniformFloatHyperparameter( "gamma", 3.0517578125e-05, 8, default_value=1.0, log=True ) diff --git a/autosklearn/pipeline/components/feature_preprocessing/liblinear_svc_preprocessor.py b/autosklearn/pipeline/components/feature_preprocessing/liblinear_svc_preprocessor.py index 546c8742ad..7031089e91 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/liblinear_svc_preprocessor.py +++ b/autosklearn/pipeline/components/feature_preprocessing/liblinear_svc_preprocessor.py @@ -1,3 +1,5 @@ +from typing import Optional + from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.forbidden import ForbiddenAndConjunction, ForbiddenEqualsClause from ConfigSpace.hyperparameters import ( @@ -6,6 +8,7 @@ UniformFloatHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA from autosklearn.util.common import check_for_bool, check_none @@ -91,7 +94,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() penalty = Constant("penalty", "l1") diff --git a/autosklearn/pipeline/components/feature_preprocessing/no_preprocessing.py b/autosklearn/pipeline/components/feature_preprocessing/no_preprocessing.py index 550872d551..38c11bdb58 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/no_preprocessing.py +++ b/autosklearn/pipeline/components/feature_preprocessing/no_preprocessing.py @@ -1,5 +1,8 @@ +from typing import Optional + from ConfigSpace.configuration_space import ConfigurationSpace +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA @@ -34,6 +37,8 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() return cs diff --git a/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py b/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py index 097f59e0f1..a7dc227056 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py +++ b/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py @@ -1,4 +1,5 @@ -import numpy as np +from typing import Optional + from ConfigSpace.conditions import EqualsCondition, InCondition from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( @@ -7,6 +8,7 @@ UniformIntegerHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import ( DENSE, @@ -54,7 +56,7 @@ def fit(self, X, Y=None): else: X[X < 0] = 0.0 - self.preprocessor.fit(X.astype(np.float64)) + self.preprocessor.fit(X) return self def transform(self, X): @@ -94,7 +96,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): if dataset_properties is not None and ( dataset_properties.get("sparse") is True or dataset_properties.get("signed") is False diff --git a/autosklearn/pipeline/components/feature_preprocessing/pca.py b/autosklearn/pipeline/components/feature_preprocessing/pca.py index a1ad9f3981..7c69f8eb80 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/pca.py +++ b/autosklearn/pipeline/components/feature_preprocessing/pca.py @@ -1,3 +1,5 @@ +from typing import Optional + import numpy as np from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( @@ -5,6 +7,7 @@ UniformFloatHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA from autosklearn.util.common import check_for_bool @@ -55,7 +58,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): keep_variance = UniformFloatHyperparameter( "keep_variance", 0.5, 0.9999, default_value=0.9999 ) diff --git a/autosklearn/pipeline/components/feature_preprocessing/polynomial.py b/autosklearn/pipeline/components/feature_preprocessing/polynomial.py index bd5312bba0..78e3ff2676 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/polynomial.py +++ b/autosklearn/pipeline/components/feature_preprocessing/polynomial.py @@ -1,9 +1,12 @@ +from typing import Optional + from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( CategoricalHyperparameter, UniformIntegerHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA from autosklearn.util.common import check_for_bool @@ -54,7 +57,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): # More than degree 3 is too expensive! degree = UniformIntegerHyperparameter("degree", 2, 3, 2) interaction_only = CategoricalHyperparameter( diff --git a/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py b/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py index 9daed1ae97..2b5aa340a9 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py +++ b/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py @@ -1,3 +1,5 @@ +from typing import Optional + from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( CategoricalHyperparameter, @@ -6,6 +8,7 @@ UnParametrizedHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, SIGNED_DATA, SPARSE, UNSIGNED_DATA from autosklearn.util.common import check_for_bool, check_none @@ -94,7 +97,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): n_estimators = UniformIntegerHyperparameter( name="n_estimators", lower=10, upper=100, default_value=10 ) diff --git a/autosklearn/pipeline/components/feature_preprocessing/select_percentile_classification.py b/autosklearn/pipeline/components/feature_preprocessing/select_percentile_classification.py index 3caa50b46d..98495eaedb 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/select_percentile_classification.py +++ b/autosklearn/pipeline/components/feature_preprocessing/select_percentile_classification.py @@ -1,3 +1,5 @@ +from typing import Optional + from functools import partial from ConfigSpace.configuration_space import ConfigurationSpace @@ -7,6 +9,7 @@ UniformFloatHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.components.feature_preprocessing.select_percentile import ( SelectPercentileBase, @@ -110,7 +113,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): percentile = UniformFloatHyperparameter( name="percentile", lower=1, upper=99, default_value=50 ) diff --git a/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py b/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py index e9343fead4..a653dc4a7e 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py +++ b/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py @@ -1,3 +1,5 @@ +from typing import Optional + from functools import partial from ConfigSpace.configuration_space import ConfigurationSpace @@ -6,6 +8,7 @@ UniformFloatHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.components.feature_preprocessing.select_percentile import ( SelectPercentileBase, @@ -53,7 +56,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): percentile = UniformFloatHyperparameter( "percentile", lower=1, upper=99, default_value=50 ) diff --git a/autosklearn/pipeline/components/feature_preprocessing/select_rates_classification.py b/autosklearn/pipeline/components/feature_preprocessing/select_rates_classification.py index 0c4768d000..3a728d753e 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/select_rates_classification.py +++ b/autosklearn/pipeline/components/feature_preprocessing/select_rates_classification.py @@ -1,3 +1,5 @@ +from typing import Optional + from functools import partial from ConfigSpace import NotEqualsCondition @@ -7,6 +9,7 @@ UniformFloatHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import ( DENSE, @@ -116,7 +119,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): alpha = UniformFloatHyperparameter( name="alpha", lower=0.01, upper=0.5, default_value=0.1 ) diff --git a/autosklearn/pipeline/components/feature_preprocessing/select_rates_regression.py b/autosklearn/pipeline/components/feature_preprocessing/select_rates_regression.py index ffec19e6ec..89c84905b2 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/select_rates_regression.py +++ b/autosklearn/pipeline/components/feature_preprocessing/select_rates_regression.py @@ -1,3 +1,5 @@ +from typing import Optional + from functools import partial from ConfigSpace import NotEqualsCondition @@ -7,6 +9,7 @@ UniformFloatHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA @@ -84,7 +87,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): alpha = UniformFloatHyperparameter( name="alpha", lower=0.01, upper=0.5, default_value=0.1 ) diff --git a/autosklearn/pipeline/components/feature_preprocessing/truncatedSVD.py b/autosklearn/pipeline/components/feature_preprocessing/truncatedSVD.py index 4d6f6b7ca9..0c61e72c1c 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/truncatedSVD.py +++ b/autosklearn/pipeline/components/feature_preprocessing/truncatedSVD.py @@ -1,6 +1,9 @@ +from typing import Optional + from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformIntegerHyperparameter +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA @@ -48,7 +51,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): target_dim = UniformIntegerHyperparameter( "target_dim", 10, 256, default_value=128 ) diff --git a/autosklearn/pipeline/components/regression/__init__.py b/autosklearn/pipeline/components/regression/__init__.py index 73033467a7..9d1ef58650 100644 --- a/autosklearn/pipeline/components/regression/__init__.py +++ b/autosklearn/pipeline/components/regression/__init__.py @@ -6,6 +6,8 @@ from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter +from autosklearn.askl_typing import FEAT_TYPE_TYPE + from ..base import ( AutoSklearnChoice, AutoSklearnRegressionAlgorithm, @@ -79,7 +81,12 @@ def get_available_components( return components_dict def get_hyperparameter_search_space( - self, dataset_properties=None, default=None, include=None, exclude=None + self, + feat_type: FEAT_TYPE_TYPE, + dataset_properties=None, + default=None, + include=None, + exclude=None, ): if include is not None and exclude is not None: raise ValueError( @@ -116,7 +123,9 @@ def get_hyperparameter_search_space( for estimator_name in available_estimators.keys(): estimator_configuration_space = available_estimators[ estimator_name - ].get_hyperparameter_search_space(dataset_properties) + ].get_hyperparameter_search_space( + feat_type=feat_type, dataset_properties=dataset_properties + ) parent_hyperparameter = {"parent": estimator, "value": estimator_name} cs.add_configuration_space( estimator_name, diff --git a/autosklearn/pipeline/components/regression/adaboost.py b/autosklearn/pipeline/components/regression/adaboost.py index e78a57e6a2..8faae821c3 100644 --- a/autosklearn/pipeline/components/regression/adaboost.py +++ b/autosklearn/pipeline/components/regression/adaboost.py @@ -1,3 +1,5 @@ +from typing import Optional + from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( CategoricalHyperparameter, @@ -5,6 +7,7 @@ UniformIntegerHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA @@ -62,7 +65,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() # base_estimator = Constant(name="base_estimator", value="None") diff --git a/autosklearn/pipeline/components/regression/ard_regression.py b/autosklearn/pipeline/components/regression/ard_regression.py index 219cb775af..758c4b04d7 100644 --- a/autosklearn/pipeline/components/regression/ard_regression.py +++ b/autosklearn/pipeline/components/regression/ard_regression.py @@ -1,9 +1,12 @@ +from typing import Optional + from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( UniformFloatHyperparameter, UnParametrizedHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm from autosklearn.pipeline.constants import DENSE, PREDICTIONS, UNSIGNED_DATA from autosklearn.util.common import check_for_bool @@ -89,7 +92,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() n_iter = UnParametrizedHyperparameter("n_iter", value=300) tol = UniformFloatHyperparameter( diff --git a/autosklearn/pipeline/components/regression/decision_tree.py b/autosklearn/pipeline/components/regression/decision_tree.py index db59767587..80890889f9 100644 --- a/autosklearn/pipeline/components/regression/decision_tree.py +++ b/autosklearn/pipeline/components/regression/decision_tree.py @@ -1,3 +1,5 @@ +from typing import Optional + import numpy as np from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( @@ -8,6 +10,7 @@ UnParametrizedHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA from autosklearn.util.common import check_none @@ -96,7 +99,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() criterion = CategoricalHyperparameter( diff --git a/autosklearn/pipeline/components/regression/extra_trees.py b/autosklearn/pipeline/components/regression/extra_trees.py index c4646a2709..b1d8eeb00a 100644 --- a/autosklearn/pipeline/components/regression/extra_trees.py +++ b/autosklearn/pipeline/components/regression/extra_trees.py @@ -1,3 +1,5 @@ +from typing import Optional + from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( CategoricalHyperparameter, @@ -6,6 +8,7 @@ UnParametrizedHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import ( AutoSklearnRegressionAlgorithm, IterativeComponent, @@ -148,7 +151,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() criterion = CategoricalHyperparameter( diff --git a/autosklearn/pipeline/components/regression/gaussian_process.py b/autosklearn/pipeline/components/regression/gaussian_process.py index 1acf238cd1..d08a3b0239 100644 --- a/autosklearn/pipeline/components/regression/gaussian_process.py +++ b/autosklearn/pipeline/components/regression/gaussian_process.py @@ -1,6 +1,9 @@ +from typing import Optional + from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformFloatHyperparameter +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm from autosklearn.pipeline.constants import DENSE, PREDICTIONS, UNSIGNED_DATA @@ -65,7 +68,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): alpha = UniformFloatHyperparameter( name="alpha", lower=1e-14, upper=1.0, default_value=1e-8, log=True ) diff --git a/autosklearn/pipeline/components/regression/gradient_boosting.py b/autosklearn/pipeline/components/regression/gradient_boosting.py index b7503f5fd0..16b7df965d 100644 --- a/autosklearn/pipeline/components/regression/gradient_boosting.py +++ b/autosklearn/pipeline/components/regression/gradient_boosting.py @@ -1,3 +1,5 @@ +from typing import Optional + import numpy as np from ConfigSpace.conditions import EqualsCondition, InCondition from ConfigSpace.configuration_space import ConfigurationSpace @@ -9,6 +11,7 @@ UnParametrizedHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import ( AutoSklearnRegressionAlgorithm, IterativeComponent, @@ -166,7 +169,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() loss = CategoricalHyperparameter( "loss", ["least_squares"], default_value="least_squares" diff --git a/autosklearn/pipeline/components/regression/k_nearest_neighbors.py b/autosklearn/pipeline/components/regression/k_nearest_neighbors.py index 83c13cd191..c16e8a6404 100644 --- a/autosklearn/pipeline/components/regression/k_nearest_neighbors.py +++ b/autosklearn/pipeline/components/regression/k_nearest_neighbors.py @@ -1,9 +1,12 @@ +from typing import Optional + from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( CategoricalHyperparameter, UniformIntegerHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA @@ -52,7 +55,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() n_neighbors = UniformIntegerHyperparameter( diff --git a/autosklearn/pipeline/components/regression/liblinear_svr.py b/autosklearn/pipeline/components/regression/liblinear_svr.py index e129331298..62e38c1551 100644 --- a/autosklearn/pipeline/components/regression/liblinear_svr.py +++ b/autosklearn/pipeline/components/regression/liblinear_svr.py @@ -1,3 +1,5 @@ +from typing import Optional + from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.forbidden import ForbiddenAndConjunction, ForbiddenEqualsClause from ConfigSpace.hyperparameters import ( @@ -6,6 +8,7 @@ UniformFloatHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA from autosklearn.util.common import check_for_bool @@ -83,7 +86,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() C = UniformFloatHyperparameter("C", 0.03125, 32768, log=True, default_value=1.0) loss = CategoricalHyperparameter( diff --git a/autosklearn/pipeline/components/regression/libsvm_svr.py b/autosklearn/pipeline/components/regression/libsvm_svr.py index d4173d7f01..c3ac98b1f9 100644 --- a/autosklearn/pipeline/components/regression/libsvm_svr.py +++ b/autosklearn/pipeline/components/regression/libsvm_svr.py @@ -1,3 +1,5 @@ +from typing import Optional + import resource import sys @@ -10,6 +12,7 @@ UnParametrizedHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA from autosklearn.util.common import check_for_bool, check_none @@ -149,7 +152,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): C = UniformFloatHyperparameter( name="C", lower=0.03125, upper=32768, log=True, default_value=1.0 ) diff --git a/autosklearn/pipeline/components/regression/mlp.py b/autosklearn/pipeline/components/regression/mlp.py index 645c29403a..42ceff4556 100644 --- a/autosklearn/pipeline/components/regression/mlp.py +++ b/autosklearn/pipeline/components/regression/mlp.py @@ -1,3 +1,5 @@ +from typing import Optional + import numpy as np from ConfigSpace.conditions import InCondition from ConfigSpace.configuration_space import ConfigurationSpace @@ -9,6 +11,7 @@ UnParametrizedHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import ( AutoSklearnRegressionAlgorithm, IterativeComponent, @@ -225,7 +228,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() hidden_layer_depth = UniformIntegerHyperparameter( name="hidden_layer_depth", lower=1, upper=3, default_value=1 diff --git a/autosklearn/pipeline/components/regression/random_forest.py b/autosklearn/pipeline/components/regression/random_forest.py index 128113fc43..043d62e16b 100644 --- a/autosklearn/pipeline/components/regression/random_forest.py +++ b/autosklearn/pipeline/components/regression/random_forest.py @@ -1,3 +1,5 @@ +from typing import Optional + from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( CategoricalHyperparameter, @@ -6,6 +8,7 @@ UnParametrizedHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import ( AutoSklearnRegressionAlgorithm, IterativeComponent, @@ -135,7 +138,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() criterion = CategoricalHyperparameter( "criterion", ["mse", "friedman_mse", "mae"] diff --git a/autosklearn/pipeline/components/regression/sgd.py b/autosklearn/pipeline/components/regression/sgd.py index 3b3f939fa8..915e45169f 100644 --- a/autosklearn/pipeline/components/regression/sgd.py +++ b/autosklearn/pipeline/components/regression/sgd.py @@ -1,3 +1,5 @@ +from typing import Optional + from ConfigSpace.conditions import EqualsCondition, InCondition from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( @@ -6,6 +8,7 @@ UnParametrizedHyperparameter, ) +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import ( AutoSklearnRegressionAlgorithm, IterativeComponent, @@ -185,7 +188,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() loss = CategoricalHyperparameter( diff --git a/autosklearn/pipeline/regression.py b/autosklearn/pipeline/regression.py index 638f8ae3cb..dcc2fa3fcf 100644 --- a/autosklearn/pipeline/regression.py +++ b/autosklearn/pipeline/regression.py @@ -8,6 +8,7 @@ from ConfigSpace.forbidden import ForbiddenAndConjunction, ForbiddenEqualsClause from sklearn.base import RegressorMixin +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import BasePipeline from autosklearn.pipeline.components import ( feature_preprocessing as feature_preprocessing_components, @@ -68,6 +69,7 @@ class SimpleRegressionPipeline(RegressorMixin, BasePipeline): def __init__( self, config: Optional[Configuration] = None, + feat_type: Optional[FEAT_TYPE_TYPE] = None, steps=None, dataset_properties=None, include=None, @@ -81,6 +83,7 @@ def __init__( if "target_type" not in dataset_properties: dataset_properties["target_type"] = "regression" super().__init__( + feat_type=feat_type, config=config, steps=steps, dataset_properties=dataset_properties, @@ -112,7 +115,11 @@ def predict(self, X, batch_size=None): return y def _get_hyperparameter_search_space( - self, include=None, exclude=None, dataset_properties=None + self, + feat_type: Optional[FEAT_TYPE_TYPE] = None, + include=None, + exclude=None, + dataset_properties=None, ): """Return the configuration space for the CASH problem. @@ -149,6 +156,7 @@ def _get_hyperparameter_search_space( cs = self._get_base_search_space( cs=cs, + feat_type=feat_type, dataset_properties=dataset_properties, exclude=exclude, include=include, @@ -259,7 +267,9 @@ def _get_hyperparameter_search_space( def _get_estimator_components(self): return regression_components._regressors - def _get_pipeline_steps(self, dataset_properties, init_params=None): + def _get_pipeline_steps( + self, dataset_properties, feat_type: Optional[FEAT_TYPE_TYPE] = None + ): steps = [] default_dataset_properties = {"target_type": "regression"} @@ -271,6 +281,7 @@ def _get_pipeline_steps(self, dataset_properties, init_params=None): [ "data_preprocessor", DataPreprocessorChoice( + feat_type=feat_type, dataset_properties=default_dataset_properties, random_state=self.random_state, ), @@ -278,6 +289,7 @@ def _get_pipeline_steps(self, dataset_properties, init_params=None): [ "feature_preprocessor", feature_preprocessing_components.FeaturePreprocessorChoice( + feat_type=feat_type, dataset_properties=default_dataset_properties, random_state=self.random_state, ), @@ -285,7 +297,9 @@ def _get_pipeline_steps(self, dataset_properties, init_params=None): [ "regressor", regression_components.RegressorChoice( - default_dataset_properties, random_state=self.random_state + feat_type=feat_type, + dataset_properties=default_dataset_properties, + random_state=self.random_state, ), ], ] diff --git a/autosklearn/util/pipeline.py b/autosklearn/util/pipeline.py index d3291069f5..f0a66a2a86 100755 --- a/autosklearn/util/pipeline.py +++ b/autosklearn/util/pipeline.py @@ -1,5 +1,5 @@ # -*- encoding: utf-8 -*- -from typing import Any, Dict, List, Optional, Union +from typing import Dict, List, Optional, Union import numpy as np from ConfigSpace.configuration_space import ConfigurationSpace @@ -11,6 +11,7 @@ MULTIOUTPUT_REGRESSION, REGRESSION_TASKS, ) +from autosklearn.data.abstract_data_manager import AbstractDataManager from autosklearn.pipeline.classification import SimpleClassificationPipeline from autosklearn.pipeline.regression import SimpleRegressionPipeline @@ -18,7 +19,7 @@ def get_configuration_space( - info: Dict[str, Any], + datamanager: AbstractDataManager, include: Optional[Dict[str, List[str]]] = None, exclude: Optional[Dict[str, List[str]]] = None, random_state: Optional[Union[int, np.random.RandomState]] = None, @@ -27,8 +28,8 @@ def get_configuration_space( Parameters ---------- - info: Dict[str, Any] - Information about the dataset + datamanager: AbstractDataManager + AbstractDataManager object storing all important information about the dataset include: Optional[Dict[str, List[str]]] = None A dictionary of what components to include for each pipeline step @@ -44,16 +45,18 @@ def get_configuration_space( ConfigurationSpace The configuration space for the pipeline """ - if info["task"] in REGRESSION_TASKS: - return _get_regression_configuration_space(info, include, exclude, random_state) + if datamanager.info["task"] in REGRESSION_TASKS: + return _get_regression_configuration_space( + datamanager, include, exclude, random_state + ) else: return _get_classification_configuration_space( - info, include, exclude, random_state + datamanager, include, exclude, random_state ) def _get_regression_configuration_space( - info: Dict[str, Any], + datamanager: AbstractDataManager, include: Optional[Dict[str, List[str]]], exclude: Optional[Dict[str, List[str]]], random_state: Optional[Union[int, np.random.RandomState]] = None, @@ -62,8 +65,8 @@ def _get_regression_configuration_space( Parameters ---------- - info: Dict[str, Any] - Information about the dataset + datamanager: AbstractDataManager + AbstractDataManager object storing all important information about the dataset include: Optional[Dict[str, List[str]]] = None A dictionary of what components to include for each pipeline step @@ -79,28 +82,29 @@ def _get_regression_configuration_space( ConfigurationSpace The configuration space for the regression pipeline """ - task_type = info["task"] + task_type = datamanager.info["task"] sparse = False multioutput = False if task_type == MULTIOUTPUT_REGRESSION: multioutput = True - if info["is_sparse"] == 1: + if datamanager.info["is_sparse"] == 1: sparse = True dataset_properties = {"multioutput": multioutput, "sparse": sparse} configuration_space = SimpleRegressionPipeline( + feat_type=datamanager.feat_type, dataset_properties=dataset_properties, include=include, exclude=exclude, random_state=random_state, - ).get_hyperparameter_search_space() + ).get_hyperparameter_search_space(feat_type=datamanager.feat_type) return configuration_space def _get_classification_configuration_space( - info: Dict[str, Any], + datamanager: AbstractDataManager, include: Optional[Dict[str, List[str]]], exclude: Optional[Dict[str, List[str]]], random_state: Optional[Union[int, np.random.RandomState]] = None, @@ -109,8 +113,8 @@ def _get_classification_configuration_space( Parameters ---------- - info: Dict[str, Any] - Information about the dataset + datamanager: AbstractDataManager + AbstractDataManager object storing all important information about the dataset include: Optional[Dict[str, List[str]]] = None A dictionary of what components to include for each pipeline step @@ -126,7 +130,7 @@ def _get_classification_configuration_space( ConfigurationSpace The configuration space for the classification pipeline """ - task_type = info["task"] + task_type = datamanager.info["task"] multilabel = False multiclass = False @@ -139,7 +143,7 @@ def _get_classification_configuration_space( if task_type == BINARY_CLASSIFICATION: pass - if info["is_sparse"] == 1: + if datamanager.info["is_sparse"] == 1: sparse = True dataset_properties = { @@ -149,8 +153,9 @@ def _get_classification_configuration_space( } return SimpleClassificationPipeline( + feat_type=datamanager.feat_type, dataset_properties=dataset_properties, include=include, exclude=exclude, random_state=random_state, - ).get_hyperparameter_search_space() + ).get_hyperparameter_search_space(feat_type=datamanager.feat_type) diff --git a/examples/40_advanced/example_text_preprocessing.py b/examples/40_advanced/example_text_preprocessing.py index 7c65825b7b..ba7deffe03 100644 --- a/examples/40_advanced/example_text_preprocessing.py +++ b/examples/40_advanced/example_text_preprocessing.py @@ -59,7 +59,6 @@ automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=60, per_run_time_limit=30, - tmp_folder="/tmp/autosklearn_text_example_tmp", ) automl.fit(X_train, y_train, dataset_name="20_Newsgroups") # fit the automl model diff --git a/examples/80_extending/example_extending_classification.py b/examples/80_extending/example_extending_classification.py index b5112c022b..9e46b9e8cd 100644 --- a/examples/80_extending/example_extending_classification.py +++ b/examples/80_extending/example_extending_classification.py @@ -6,6 +6,7 @@ The following example demonstrates how to create a new classification component for using in auto-sklearn. """ +from typing import Optional from pprint import pprint from ConfigSpace.configuration_space import ConfigurationSpace @@ -16,6 +17,8 @@ ) import sklearn.metrics + +from autosklearn.askl_typing import FEAT_TYPE_TYPE import autosklearn.classification import autosklearn.pipeline.components.classification from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm @@ -100,7 +103,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() hidden_layer_depth = UniformIntegerHyperparameter( name="hidden_layer_depth", lower=1, upper=3, default_value=1 diff --git a/examples/80_extending/example_extending_data_preprocessor.py b/examples/80_extending/example_extending_data_preprocessor.py index aa5c443255..eb0325d9df 100644 --- a/examples/80_extending/example_extending_data_preprocessor.py +++ b/examples/80_extending/example_extending_data_preprocessor.py @@ -5,12 +5,15 @@ The following example demonstrates how to turn off data preprocessing step in auto-skearn. """ +from typing import Optional from pprint import pprint import autosklearn.classification import autosklearn.pipeline.components.data_preprocessing import sklearn.metrics from ConfigSpace.configuration_space import ConfigurationSpace + +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import SPARSE, DENSE, UNSIGNED_DATA, INPUT from sklearn.datasets import load_breast_cancer @@ -49,7 +52,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): return ConfigurationSpace() # Return an empty configuration as there is None diff --git a/examples/80_extending/example_extending_preprocessor.py b/examples/80_extending/example_extending_preprocessor.py index 1eb3fc1daf..8516931780 100644 --- a/examples/80_extending/example_extending_preprocessor.py +++ b/examples/80_extending/example_extending_preprocessor.py @@ -7,6 +7,7 @@ discriminant analysis (LDA) algorithm from sklearn and use it as a preprocessor in auto-sklearn. """ +from typing import Optional from pprint import pprint from ConfigSpace.configuration_space import ConfigurationSpace @@ -17,6 +18,8 @@ from ConfigSpace.conditions import InCondition import sklearn.metrics + +from autosklearn.askl_typing import FEAT_TYPE_TYPE import autosklearn.classification import autosklearn.pipeline.components.feature_preprocessing from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm @@ -76,7 +79,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() solver = CategoricalHyperparameter( name="solver", choices=["svd", "lsqr", "eigen"], default_value="svd" diff --git a/examples/80_extending/example_extending_regression.py b/examples/80_extending/example_extending_regression.py index 4d6987a9db..ad2fb8850f 100644 --- a/examples/80_extending/example_extending_regression.py +++ b/examples/80_extending/example_extending_regression.py @@ -6,6 +6,7 @@ The following example demonstrates how to create a new regression component for using in auto-sklearn. """ +from typing import Optional from pprint import pprint from ConfigSpace.configuration_space import ConfigurationSpace @@ -17,6 +18,8 @@ from ConfigSpace.conditions import EqualsCondition import sklearn.metrics + +from autosklearn.askl_typing import FEAT_TYPE_TYPE import autosklearn.regression import autosklearn.pipeline.components.regression from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm @@ -86,7 +89,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() alpha = UniformFloatHyperparameter( name="alpha", lower=10**-5, upper=1, log=True, default_value=1.0 diff --git a/examples/80_extending/example_restrict_number_of_hyperparameters.py b/examples/80_extending/example_restrict_number_of_hyperparameters.py index d8bd2f4a98..a17aa128aa 100644 --- a/examples/80_extending/example_restrict_number_of_hyperparameters.py +++ b/examples/80_extending/example_restrict_number_of_hyperparameters.py @@ -7,6 +7,7 @@ component with a new component, implementing the same classifier, but with different hyperparameters . """ +from typing import Optional from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import ( @@ -17,6 +18,7 @@ from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split +from autosklearn.askl_typing import FEAT_TYPE_TYPE import autosklearn.classification import autosklearn.pipeline.components.classification from autosklearn.pipeline.components.classification import ( @@ -84,7 +86,9 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space( + feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None + ): cs = ConfigurationSpace() # The maximum number of features used in the forest is calculated as m^max_features, where diff --git a/scripts/02_retrieve_metadata.py b/scripts/02_retrieve_metadata.py index f87f65ecc4..56a0395b9e 100644 --- a/scripts/02_retrieve_metadata.py +++ b/scripts/02_retrieve_metadata.py @@ -1,5 +1,4 @@ from argparse import ArgumentParser -from collections import defaultdict import csv import glob import itertools @@ -10,6 +9,7 @@ import numpy as np from ConfigSpace.configuration_space import Configuration +from ConfigSpace.util import deactivate_inactive_hyperparameters from autosklearn.constants import * from autosklearn.metrics import CLASSIFICATION_METRICS, REGRESSION_METRICS @@ -66,8 +66,18 @@ def retrieve_matadata( n_better += 1 try: + for hp in configuration_space.get_hyperparameters(): + if hp.name not in config: + config[hp.name] = hp.default_value + best_configuration = Configuration( - configuration_space=configuration_space, values=config + configuration_space=configuration_space, + values=config, + allow_inactive_with_values=True, + ) + best_configuration = deactivate_inactive_hyperparameters( + configuration=best_configuration, + configuration_space=configuration_space, ) best_value = score best_configuration_dir = validation_trajectory_file @@ -178,6 +188,11 @@ def write_output(outputs, configurations, output_dir, configuration_space, metri fh.write("%s: %s\n" % (key, description[key])) +class DummyDatamanager(): + def __init__(self, info, feat_type=None): + self.info = info + self.feat_type = feat_type + def main(): parser = ArgumentParser() @@ -220,7 +235,10 @@ def main(): ) configuration_space = pipeline.get_configuration_space( - {"is_sparse": sparse, "task": task} + DummyDatamanager( + info={"is_sparse": sparse, "task": task}, + feat_type={"A": "numerical", "B": "categorical"} + ) ) outputs, configurations = retrieve_matadata( diff --git a/test/fixtures/ensembles.py b/test/fixtures/ensembles.py index 467c53822f..250841511a 100644 --- a/test/fixtures/ensembles.py +++ b/test/fixtures/ensembles.py @@ -5,7 +5,11 @@ import numpy as np from sklearn.ensemble import VotingClassifier, VotingRegressor -from autosklearn.data.validation import SUPPORTED_FEAT_TYPES, SUPPORTED_TARGET_TYPES +from autosklearn.data.validation import ( + SUPPORTED_FEAT_TYPES, + SUPPORTED_TARGET_TYPES, + InputValidator, +) from autosklearn.evaluation.abstract_evaluator import ( MyDummyClassifier, MyDummyRegressor, @@ -42,9 +46,16 @@ def _make( seed: Union[int, None, np.random.RandomState] = DEFAULT_SEED, ) -> VotingClassifier: assert not (X is None) ^ (y is None) - if not models: - models = [MyDummyClassifier(config=1, random_state=seed) for _ in range(5)] + validator = InputValidator(is_classification=True).fit(X, y) + models = [ + MyDummyClassifier( + feat_type=validator.feature_validator.feat_type, + config=1, + random_state=seed, + ) + for _ in range(5) + ] if X is not None: for model in models: @@ -81,7 +92,15 @@ def _make( assert not (X is None) ^ (y is None) if not models: - models = [MyDummyRegressor(config=1, random_state=seed) for _ in range(5)] + validator = InputValidator(is_classification=False).fit(X, y) + models = [ + MyDummyRegressor( + feat_type=validator.feature_validator.feat_type, + config=1, + random_state=seed, + ) + for _ in range(5) + ] if X is not None: for model in models: diff --git a/test/test_evaluation/test_dummy_pipelines.py b/test/test_evaluation/test_dummy_pipelines.py index 8d1005e178..c69578420f 100644 --- a/test/test_evaluation/test_dummy_pipelines.py +++ b/test/test_evaluation/test_dummy_pipelines.py @@ -23,8 +23,10 @@ def test_dummy_pipeline(task_type: str) -> None: pytest.fail(task_type) return - estimator = estimator_class(config=1, random_state=0) X, y = data_maker(random_state=0) + estimator = estimator_class( + feat_type={i: "numerical" for i in range(X.shape[1])}, config=1, random_state=0 + ) estimator.fit(X, y) check_is_fitted(estimator) diff --git a/test/test_evaluation/test_test_evaluator.py b/test/test_evaluation/test_test_evaluator.py index 02eedcca91..52eccf489b 100644 --- a/test/test_evaluation/test_test_evaluator.py +++ b/test/test_evaluation/test_test_evaluator.py @@ -86,11 +86,22 @@ def test_datasets(self): self.assertTrue(np.isfinite(return_value[0]["loss"])) +class DummyDatamanager: + def __init__(self): + self.info = {"task": MULTICLASS_CLASSIFICATION, "is_sparse": False} + self.feat_type = { + 0: "numerical", + 1: "Numerical", + 2: "numerical", + 3: "numerical", + } + + class FunctionsTest(unittest.TestCase): def setUp(self): self.queue = multiprocessing.Queue() self.configuration = get_configuration_space( - {"task": MULTICLASS_CLASSIFICATION, "is_sparse": False} + DummyDatamanager() ).get_default_configuration() self.data = get_multiclass_classification_datamanager() self.tmp_dir = os.path.join(os.path.dirname(__file__), ".test_cv_functions") diff --git a/test/test_evaluation/test_train_evaluator.py b/test/test_evaluation/test_train_evaluator.py index c8fe1c5f87..e23c706847 100644 --- a/test/test_evaluation/test_train_evaluator.py +++ b/test/test_evaluation/test_train_evaluator.py @@ -2833,11 +2833,22 @@ def test_holdout_split_size(self, te_mock): self.assertEqual(len(test_samples), 3) +class DummyDatamanager: + def __init__(self): + self.info = {"task": MULTICLASS_CLASSIFICATION, "is_sparse": False} + self.feat_type = { + 0: "numerical", + 1: "Numerical", + 2: "numerical", + 3: "numerical", + } + + class FunctionsTest(unittest.TestCase): def setUp(self): self.queue = multiprocessing.Queue() self.configuration = get_configuration_space( - {"task": MULTICLASS_CLASSIFICATION, "is_sparse": False} + DummyDatamanager() ).get_default_configuration() self.data = get_multiclass_classification_datamanager() self.tmp_dir = os.path.join( @@ -3096,7 +3107,7 @@ def test_eval_holdout_budget_iterations_multi_objective(self): def test_eval_holdout_budget_iterations_converged_multi_objective(self): configuration = get_configuration_space( exclude={"classifier": ["random_forest", "liblinear_svc"]}, - info={"task": MULTICLASS_CLASSIFICATION, "is_sparse": False}, + datamanager=DummyDatamanager(), ).get_default_configuration() eval_holdout( queue=self.queue, @@ -3131,7 +3142,7 @@ def test_eval_holdout_budget_iterations_converged(self): } configuration = get_configuration_space( exclude={"classifier": ["random_forest", "liblinear_svc"]}, - info={"task": MULTICLASS_CLASSIFICATION, "is_sparse": False}, + datamanager=DummyDatamanager(), ).get_default_configuration() eval_holdout( queue=self.queue, @@ -3248,7 +3259,7 @@ def test_eval_holdout_budget_mixed_iterations(self): def test_eval_holdout_budget_mixed_subsample(self): configuration = get_configuration_space( exclude={"classifier": ["random_forest"]}, - info={"task": MULTICLASS_CLASSIFICATION, "is_sparse": False}, + datamanager=DummyDatamanager(), ).get_default_configuration() self.assertEqual(configuration["classifier:__choice__"], "liblinear_svc") eval_holdout( diff --git a/test/test_metalearning/pyMetaLearn/test_metalearner.py b/test/test_metalearning/pyMetaLearn/test_metalearner.py index 42d27d49da..33ce3f9f88 100644 --- a/test/test_metalearning/pyMetaLearn/test_metalearner.py +++ b/test/test_metalearning/pyMetaLearn/test_metalearner.py @@ -25,6 +25,7 @@ def setUp(self): pipeline = autosklearn.pipeline.classification.SimpleClassificationPipeline() self.cs = pipeline.get_hyperparameter_search_space() + # print(self.cs.get_default_configuration()) self.logger = logging.getLogger() meta_base = MetaBase(self.cs, data_dir, logger=self.logger) diff --git a/test/test_metalearning/pyMetaLearn/test_metalearning_configuration.py b/test/test_metalearning/pyMetaLearn/test_metalearning_configuration.py new file mode 100644 index 0000000000..1e08805d87 --- /dev/null +++ b/test/test_metalearning/pyMetaLearn/test_metalearning_configuration.py @@ -0,0 +1,40 @@ +import logging +import os + +import autosklearn.metalearning.optimizers.metalearn_optimizer.metalearner as metalearner # noqa: E501 +import autosklearn.pipeline.classification +from autosklearn.metalearning.metalearning.meta_base import MetaBase + +import unittest + +logging.basicConfig() + + +class MetalearningConfiguration(unittest.TestCase): + def test_metalearning_cs_size(self): + self.cwd = os.getcwd() + data_dir = os.path.dirname(__file__) + data_dir = os.path.join(data_dir, "test_meta_base_data") + os.chdir(data_dir) + + for feat_type, cs_size in [ + ({"A": "numerical"}, 165), + ({"A": "categorical"}, 162), + ({"A": "string"}, 174), + ({"A": "numerical", "B": "categorical"}, 168), + ({"A": "numerical", "B": "string"}, 180), + ({"A": "categorical", "B": "string"}, 177), + ({"A": "categorical", "B": "string", "C": "numerical"}, 183), + ]: + pipeline = autosklearn.pipeline.classification.SimpleClassificationPipeline( + feat_type=feat_type + ) + self.cs = pipeline.get_hyperparameter_search_space(feat_type=feat_type) + # print(self.cs.get_default_configuration()) + + self.logger = logging.getLogger() + meta_base = MetaBase(self.cs, data_dir, logger=self.logger) + self.meta_optimizer = metalearner.MetaLearningOptimizer( + "233", self.cs, meta_base, logger=self.logger + ) + self.assertEqual(len(self.meta_optimizer.configuration_space), cs_size) diff --git a/test/test_pipeline/components/data_preprocessing/test_balancing.py b/test/test_pipeline/components/data_preprocessing/test_balancing.py index 6a76ce419c..a128559833 100644 --- a/test/test_pipeline/components/data_preprocessing/test_balancing.py +++ b/test/test_pipeline/components/data_preprocessing/test_balancing.py @@ -215,7 +215,7 @@ def test_weighting_effect(self): default._values["balancing:strategy"] = strategy classifier = SimpleClassificationPipeline( - default, random_state=1, include=include + config=default, random_state=1, include=include ) Xt, fit_params = classifier.fit_transformer(X_train, Y_train) classifier.fit_estimator(Xt, Y_train, **fit_params) diff --git a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_feat_type.py b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_feat_type.py new file mode 100644 index 0000000000..0cf5ee6bd2 --- /dev/null +++ b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_feat_type.py @@ -0,0 +1,135 @@ +from autosklearn.pipeline.components.data_preprocessing.feature_type import ( + FeatTypeSplit, +) + +import unittest + + +class PreprocessingPipelineFeatTypeTest(unittest.TestCase): + def test_single_type(self): + DPP = FeatTypeSplit(feat_type={"A": "numerical"}) + cs = DPP.get_hyperparameter_search_space( + feat_type={"A": "numerical"}, + dataset_properties={ + "task": 1, + "sparse": False, + "multilabel": False, + "multiclass": False, + "target_type": "classification", + "signed": False, + }, + ) + for key in cs.get_hyperparameters_dict().keys(): + self.assertNotIn("text", key.split(":")[0]) + self.assertNotIn("categorical", key.split(":")[0]) + self.assertEqual(len(cs), 6) + + DPP = FeatTypeSplit(feat_type={"A": "categorical"}) + cs = DPP.get_hyperparameter_search_space( + feat_type={"A": "categorical"}, + dataset_properties={ + "task": 1, + "sparse": False, + "multilabel": False, + "multiclass": False, + "target_type": "classification", + "signed": False, + }, + ) + for key in cs.get_hyperparameters_dict().keys(): + self.assertNotIn("text", key.split(":")[0]) + self.assertNotIn("numerical", key.split(":")[0]) + self.assertEqual(len(cs), 3) + + DPP = FeatTypeSplit(feat_type={"A": "string"}) + cs = DPP.get_hyperparameter_search_space( + feat_type={"A": "string"}, + dataset_properties={ + "task": 1, + "sparse": False, + "multilabel": False, + "multiclass": False, + "target_type": "classification", + "signed": False, + }, + ) + for key in cs.get_hyperparameters_dict().keys(): + self.assertNotIn("numerical", key.split(":")[0]) + self.assertNotIn("categorical", key.split(":")[0]) + self.assertEqual(len(cs), 15) + + def test_dual_type(self): + DPP = FeatTypeSplit(feat_type={"A": "numerical", "B": "categorical"}) + cs = DPP.get_hyperparameter_search_space( + feat_type={"A": "numerical", "B": "categorical"}, + dataset_properties={ + "task": 1, + "sparse": False, + "multilabel": False, + "multiclass": False, + "target_type": "classification", + "signed": False, + }, + ) + for key in cs.get_hyperparameters_dict().keys(): + self.assertNotIn("text", key.split(":")[0]) + self.assertEqual(len(cs), 9) + + DPP = FeatTypeSplit(feat_type={"A": "categorical", "B": "string"}) + cs = DPP.get_hyperparameter_search_space( + feat_type={"A": "categorical", "B": "string"}, + dataset_properties={ + "task": 1, + "sparse": False, + "multilabel": False, + "multiclass": False, + "target_type": "classification", + "signed": False, + }, + ) + for key in cs.get_hyperparameters_dict().keys(): + self.assertNotIn("numerical", key.split(":")[0]) + self.assertEqual(len(cs), 18) + + DPP = FeatTypeSplit(feat_type={"A": "string", "B": "numerical"}) + cs = DPP.get_hyperparameter_search_space( + feat_type={"A": "string", "B": "numerical"}, + dataset_properties={ + "task": 1, + "sparse": False, + "multilabel": False, + "multiclass": False, + "target_type": "classification", + "signed": False, + }, + ) + for key in cs.get_hyperparameters_dict().keys(): + self.assertNotIn("categorical", key.split(":")[0]) + self.assertEqual(len(cs), 21) + + def test_triple_type(self): + DPP = FeatTypeSplit( + feat_type={"A": "numerical", "B": "categorical", "C": "string"} + ) + cs = DPP.get_hyperparameter_search_space( + feat_type={"A": "numerical", "B": "categorical", "C": "string"}, + dataset_properties={ + "task": 1, + "sparse": False, + "multilabel": False, + "multiclass": False, + "target_type": "classification", + "signed": False, + }, + ) + truth_table = [False] * 3 + for key in cs.get_hyperparameters_dict().keys(): + if "text" in key.split(":")[0]: + truth_table[0] = True + elif "categorical" in key.split(":")[0]: + truth_table[1] = True + elif "numerical" in key.split(":")[0]: + truth_table[2] = True + + self.assertEqual(sum(truth_table), 3) + self.assertEqual(len(cs), 24) diff --git a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_numerical.py b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_numerical.py index d25cef2a2b..6110793c8c 100644 --- a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_numerical.py +++ b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_numerical.py @@ -11,13 +11,17 @@ class NumericalPreprocessingPipelineTest(unittest.TestCase): def test_data_type_consistency(self): X = np.random.rand(3, 4) - Y = NumericalPreprocessingPipeline().fit_transform(X) + Y = NumericalPreprocessingPipeline( + feat_type={0: "numerical", 1: "numerical", 2: "numerical"} + ).fit_transform(X) self.assertFalse(sparse.issparse(Y)) X = sparse.csc_matrix( ([3.0, 6.0, 4.0, 5.0], ([0, 1, 2, 1], [3, 2, 1, 0])), shape=(3, 4) ) - Y = NumericalPreprocessingPipeline().fit_transform(X) + Y = NumericalPreprocessingPipeline( + feat_type={0: "numerical", 1: "numerical", 2: "numerical"} + ).fit_transform(X) self.assertTrue(sparse.issparse(Y)) def test_fit_transform(self): @@ -37,12 +41,16 @@ def test_fit_transform(self): ] ) # noqa : matrix legibility # dense input - Yt = NumericalPreprocessingPipeline().fit_transform(X) + Yt = NumericalPreprocessingPipeline( + feat_type={0: "numerical", 1: "numerical", 2: "numerical"} + ).fit_transform(X) np.testing.assert_array_almost_equal(Yt, Y1) # sparse input (uses with_mean=False) Y2 = np.array([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0]]) / sdev X_sparse = sparse.csc_matrix(X) - Yt = NumericalPreprocessingPipeline().fit_transform(X_sparse) + Yt = NumericalPreprocessingPipeline( + feat_type={0: "numerical", 1: "numerical", 2: "numerical"} + ).fit_transform(X_sparse) np.testing.assert_array_almost_equal(Yt.todense(), Y2) def test_transform(self): @@ -51,7 +59,9 @@ def test_transform(self): ) # noqa : matrix legibility sdev = np.sqrt(2 / 3) # fit - NPP = NumericalPreprocessingPipeline() + NPP = NumericalPreprocessingPipeline( + feat_type={0: "numerical", 1: "numerical", 2: "numerical"} + ) NPP.fit_transform(X1) # transform X2 = np.array([[1.0, 5.0, 8.0], [2.0, 6.0, 9.0], [3.0, 7.0, np.nan]]) diff --git a/test/test_pipeline/components/data_preprocessing/test_scaling.py b/test/test_pipeline/components/data_preprocessing/test_scaling.py index b87223d14d..faa5b3f1e1 100644 --- a/test/test_pipeline/components/data_preprocessing/test_scaling.py +++ b/test/test_pipeline/components/data_preprocessing/test_scaling.py @@ -19,7 +19,7 @@ def _test_helper(self, Preprocessor, dataset=None, make_sparse=False): original_X_train = X_train.copy() configuration_space = Preprocessor( dataset_properties - ).get_hyperparameter_search_space(dataset_properties) + ).get_hyperparameter_search_space(dataset_properties=dataset_properties) default = configuration_space.get_default_configuration() preprocessor = Preprocessor(dataset_properties, random_state=1) diff --git a/test/test_pipeline/test_base.py b/test/test_pipeline/test_base.py index f8cfe26912..1b604caf58 100644 --- a/test/test_pipeline/test_base.py +++ b/test/test_pipeline/test_base.py @@ -1,17 +1,20 @@ +from typing import Optional + import ConfigSpace.configuration_space import autosklearn.pipeline.base import autosklearn.pipeline.components.base import autosklearn.pipeline.components.classification as classification import autosklearn.pipeline.components.feature_preprocessing as feature_preprocessing +from autosklearn.askl_typing import FEAT_TYPE_TYPE import unittest import unittest.mock class BasePipelineMock(autosklearn.pipeline.base.BasePipeline): - def __init__(self): - pass + def __init__(self, feat_type: Optional[FEAT_TYPE_TYPE] = None): + self.feat_type = feat_type class BaseTest(unittest.TestCase): @@ -37,7 +40,11 @@ def test_get_hyperparameter_configuration_space_3choices(self): base = BasePipelineMock() cs = base._get_base_search_space( - cs, dataset_properties, exclude, include, pipeline + cs=cs, + dataset_properties=dataset_properties, + exclude=exclude, + include=include, + pipeline=pipeline, ) self.assertEqual(len(cs.get_hyperparameter("p0:__choice__").choices), 13) @@ -51,7 +58,11 @@ def test_get_hyperparameter_configuration_space_3choices(self): dataset_properties = {"target_type": "classification", "signed": True} include = {"c": ["multinomial_nb"]} cs = base._get_base_search_space( - cs, dataset_properties, exclude, include, pipeline + cs=cs, + dataset_properties=dataset_properties, + exclude=exclude, + include=include, + pipeline=pipeline, ) self.assertEqual(len(cs.get_hyperparameter("p0:__choice__").choices), 13) self.assertEqual(len(cs.get_hyperparameter("p1:__choice__").choices), 10) @@ -66,7 +77,11 @@ def test_get_hyperparameter_configuration_space_3choices(self): dataset_properties = {"target_type": "classification", "signed": True} include = {} cs = base._get_base_search_space( - cs, dataset_properties, exclude, include, pipeline + cs=cs, + dataset_properties=dataset_properties, + exclude=exclude, + include=include, + pipeline=pipeline, ) self.assertEqual(len(cs.get_hyperparameter("p0:__choice__").choices), 13) self.assertEqual(len(cs.get_hyperparameter("p1:__choice__").choices), 15) @@ -78,7 +93,11 @@ def test_get_hyperparameter_configuration_space_3choices(self): cs = ConfigSpace.configuration_space.ConfigurationSpace() dataset_properties = {"target_type": "classification", "sparse": True} cs = base._get_base_search_space( - cs, dataset_properties, exclude, include, pipeline + cs=cs, + dataset_properties=dataset_properties, + exclude=exclude, + include=include, + pipeline=pipeline, ) self.assertEqual(len(cs.get_hyperparameter("p0:__choice__").choices), 12) self.assertEqual(len(cs.get_hyperparameter("p1:__choice__").choices), 15) @@ -93,7 +112,11 @@ def test_get_hyperparameter_configuration_space_3choices(self): "signed": True, } cs = base._get_base_search_space( - cs, dataset_properties, exclude, include, pipeline + cs=cs, + dataset_properties=dataset_properties, + exclude=exclude, + include=include, + pipeline=pipeline, ) self.assertEqual(len(cs.get_hyperparameter("p0:__choice__").choices), 12) diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index 7be8038119..94fd8c7a65 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, Union +from typing import Any, Dict, Optional, Union import copy import itertools @@ -20,6 +20,7 @@ import autosklearn.pipeline.components.classification as classification_components import autosklearn.pipeline.components.feature_preprocessing as preprocessing_components +from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.classification import SimpleClassificationPipeline from autosklearn.pipeline.components.base import ( AutoSklearnChoice, @@ -61,7 +62,7 @@ def get_properties(dataset_properties=None): } @staticmethod - def get_hyperparameter_search_space(dataset_properties=None): + def get_hyperparameter_search_space(feat_type=None, dataset_properties=None): cs = ConfigurationSpace() return cs @@ -352,16 +353,6 @@ def test_configurations_categorical_data(self): ------- * All configurations should fit, predict and predict_proba successfully """ - pipeline = SimpleClassificationPipeline( - dataset_properties={"sparse": False}, - include={ - "feature_preprocessor": ["no_preprocessing"], - "classifier": ["sgd", "adaboost"], - }, - ) - - cs = pipeline.get_hyperparameter_search_space() - categorical_columns = [ True, True, @@ -407,6 +398,17 @@ def test_configurations_categorical_data(self): for i, is_categorical in enumerate(categorical_columns) } + pipeline = SimpleClassificationPipeline( + feat_type=categorical, + dataset_properties={"sparse": False}, + include={ + "feature_preprocessor": ["no_preprocessing"], + "classifier": ["sgd", "adaboost"], + }, + ) + + cs = pipeline.get_hyperparameter_search_space(feat_type=categorical) + here = os.path.dirname(__file__) dataset_path = os.path.join( here, "components", "data_preprocessing", "dataset.pkl" @@ -429,7 +431,10 @@ def test_configurations_categorical_data(self): init_params = {"data_preprocessor:feat_type": categorical} self._test_configurations( - configurations_space=cs, dataset=data, init_params=init_params + configurations_space=cs, + dataset=data, + init_params=init_params, + feat_type=categorical, ) @unittest.mock.patch( @@ -459,7 +464,8 @@ def test_categorical_passed_to_one_hot_encoder(self, ohe_mock): feat_types = {0: "categorical", 1: "numerical"} cls = SimpleClassificationPipeline( - init_params={"data_preprocessor:feat_type": feat_types} + feat_type=feat_types, + init_params={"data_preprocessor:feat_type": feat_types}, ) init_args = ohe_mock.call_args[1]["init_params"] @@ -468,8 +474,11 @@ def test_categorical_passed_to_one_hot_encoder(self, ohe_mock): # Check through `set_hyperparameters` feat_types = {0: "categorical", 1: "categorical", 2: "numerical"} - default = cls.get_hyperparameter_search_space().get_default_configuration() + default = cls.get_hyperparameter_search_space( + feat_type=feat_types + ).get_default_configuration() cls.set_hyperparameters( + feat_type=feat_types, configuration=default, init_params={"data_preprocessor:feat_type": feat_types}, ) @@ -485,6 +494,7 @@ def _test_configurations( init_params: Dict[str, Any] = None, dataset_properties: Dict[str, Any] = None, n_samples: int = 10, + feat_type: Optional[FEAT_TYPE_TYPE] = None, ): """Tests a configuration space by taking multiple samples and fiting each before calling predict and predict_proba. @@ -560,9 +570,13 @@ def _test_configurations( init_params_ = copy.deepcopy(init_params) cls = SimpleClassificationPipeline( - dataset_properties=dataset_properties, init_params=init_params_ + feat_type=feat_type, + dataset_properties=dataset_properties, + init_params=init_params_, + ) + cls.set_hyperparameters( + config, init_params=init_params_, feat_type=feat_type ) - cls.set_hyperparameters(config, init_params=init_params_) # First make sure that for this configuration, setting the parameters # does not mistakenly set the estimator as fitted @@ -659,7 +673,9 @@ def test_get_hyperparameter_search_space(self): * (n_hyperparameters - 4) different conditionals for the pipeline * 53 forbidden combinations """ - pipeline = SimpleClassificationPipeline() + pipeline = SimpleClassificationPipeline( + feat_type={"A": "numerical", "B": "categorical", "C": "string"} + ) cs = pipeline.get_hyperparameter_search_space() self.assertIsInstance(cs, ConfigurationSpace) @@ -897,7 +913,10 @@ def test_predict_proba_batched(self): perform near identically """ # Multiclass - cls = SimpleClassificationPipeline(include={"classifier": ["sgd"]}) + cls = SimpleClassificationPipeline( + feat_type={i: "numerical" for i in range(0, 64)}, + include={"classifier": ["sgd"]}, + ) X_train, Y_train, X_test, Y_test = get_dataset(dataset="digits") with ignore_warnings(classifier_warnings): @@ -951,6 +970,7 @@ def test_predict_proba_batched_sparse(self): perform near identically """ cls = SimpleClassificationPipeline( + feat_type={i: "numerical" for i in range(0, 64)}, dataset_properties={"sparse": True, "multiclass": True}, include={"classifier": ["sgd"]}, ) @@ -1313,3 +1333,43 @@ def test_fit_instantiates_component(self): del preprocessing_components.additional_components.components[ "CrashPreprocessor" ] + + def test_get_hyperparameter_search_space_feat_type(self): + cs_mc = SimpleClassificationPipeline( + feat_type={"1": "numerical"} + ).get_hyperparameter_search_space(dataset_properties={"multiclass": True}) + self.assertNotIn("data_preprocessor:feature_type:categorical", str(cs_mc)) + self.assertNotIn("data_preprocessor:feature_type:text", str(cs_mc)) + + cs_mc = SimpleClassificationPipeline( + feat_type={"1": "categorical"} + ).get_hyperparameter_search_space(dataset_properties={"multilabel": True}) + self.assertNotIn("data_preprocessor:feature_type:numerical", str(cs_mc)) + self.assertNotIn("data_preprocessor:feature_type:text", str(cs_mc)) + + cs_mc = SimpleClassificationPipeline( + feat_type={"1": "string"} + ).get_hyperparameter_search_space(dataset_properties={"sparse": True}) + self.assertNotIn("data_preprocessor:feature_type:numerical", str(cs_mc)) + self.assertNotIn("data_preprocessor:feature_type:categorical", str(cs_mc)) + + cs_mc = SimpleClassificationPipeline( + feat_type={"1": "numerical", "2": "categorical"} + ).get_hyperparameter_search_space( + dataset_properties={"multilabel": True, "multiclass": True} + ) + self.assertNotIn("data_preprocessor:feature_type:text", str(cs_mc)) + + cs_mc = SimpleClassificationPipeline( + feat_type={"1": "numerical", "2": "string"} + ).get_hyperparameter_search_space( + dataset_properties={"multilabel": True, "multiclass": True} + ) + self.assertNotIn("data_preprocessor:feature_type:categorical", str(cs_mc)) + + cs_mc = SimpleClassificationPipeline( + feat_type={"1": "categorical", "2": "string"} + ).get_hyperparameter_search_space( + dataset_properties={"multilabel": True, "multiclass": True} + ) + self.assertNotIn("data_preprocessor:feature_type:numerical", str(cs_mc)) From c38671b7444b1f80c402dbe6843840b5d9ccf7b7 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Fri, 5 Aug 2022 09:39:43 +0200 Subject: [PATCH 088/118] Update pre-commit hooks (#1555) * chore: update pre-commit hooks * Update .pre-commit-config.yaml * Update .pre-commit-config.yaml Co-authored-by: eddiebergman Co-authored-by: Eddie Bergman --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 4f0694daf3..b296a920cb 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -45,7 +45,7 @@ repos: name: mypy auto-sklearn files: autosklearn/.* - - repo: https://gitlab.com/pycqa/flake8 + - repo: https://github.com/PyCQA/flake8 rev: 5.0.4 hooks: - id: flake8 From 2649b3a942ae155ff68e3e42091bb8a1a79ae639 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Fri, 5 Aug 2022 09:47:48 +0200 Subject: [PATCH 089/118] Bump peter-evans/create-pull-request from 3 to 4 (#1554) Bumps [peter-evans/create-pull-request](https://github.com/peter-evans/create-pull-request) from 3 to 4. - [Release notes](https://github.com/peter-evans/create-pull-request/releases) - [Commits](https://github.com/peter-evans/create-pull-request/compare/v3...v4) --- updated-dependencies: - dependency-name: peter-evans/create-pull-request dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/pre-commit-update.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pre-commit-update.yml b/.github/workflows/pre-commit-update.yml index 4d1e42f807..3bfede916f 100644 --- a/.github/workflows/pre-commit-update.yml +++ b/.github/workflows/pre-commit-update.yml @@ -17,7 +17,7 @@ jobs: - uses: browniebroke/pre-commit-autoupdate-action@main - - uses: peter-evans/create-pull-request@v3 + - uses: peter-evans/create-pull-request@v4 with: token: ${{ secrets.GITHUB_TOKEN }} branch: update/pre-commit-hooks From cb5ba133ae3d3ae4f4ab33f4dc481d3a3215c382 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Fri, 5 Aug 2022 03:48:52 -0400 Subject: [PATCH 090/118] Fix line lengths for flake8 (#1556) --- autosklearn/ensembles/abstract_ensemble.py | 2 +- autosklearn/ensembles/multiobjective_dummy_ensemble.py | 2 +- autosklearn/ensembles/singlebest_ensemble.py | 2 +- test/conftest.py | 4 ++-- test/test_estimators/test_estimators.py | 9 ++------- test/test_pipeline/test_classification.py | 2 +- 6 files changed, 8 insertions(+), 13 deletions(-) diff --git a/autosklearn/ensembles/abstract_ensemble.py b/autosklearn/ensembles/abstract_ensemble.py index c4388b71fd..81959ae014 100644 --- a/autosklearn/ensembles/abstract_ensemble.py +++ b/autosklearn/ensembles/abstract_ensemble.py @@ -97,7 +97,7 @@ def predict( def get_models_with_weights( self, models: Dict[Tuple[int, int, float], BasePipeline] ) -> List[Tuple[float, BasePipeline]]: - """Return a list of (weight, model) pairs for all models included in the ensemble. + """List of (weight, model) pairs for all models included in the ensemble. Parameters ---------- diff --git a/autosklearn/ensembles/multiobjective_dummy_ensemble.py b/autosklearn/ensembles/multiobjective_dummy_ensemble.py index 8751507ecd..132783527d 100644 --- a/autosklearn/ensembles/multiobjective_dummy_ensemble.py +++ b/autosklearn/ensembles/multiobjective_dummy_ensemble.py @@ -211,7 +211,7 @@ def get_selected_model_identifiers(self) -> list[tuple[int, int, float]]: return self.pareto_set[0].get_selected_model_identifiers() def get_validation_performance(self) -> float: - """Return validation performance of the ensemble that is best for the 1st metric. + """Validation performance of the ensemble that is best for the 1st metric. Returns ------- diff --git a/autosklearn/ensembles/singlebest_ensemble.py b/autosklearn/ensembles/singlebest_ensemble.py index b072bcab11..cfcc01a242 100644 --- a/autosklearn/ensembles/singlebest_ensemble.py +++ b/autosklearn/ensembles/singlebest_ensemble.py @@ -114,7 +114,7 @@ def __str__(self) -> str: def get_models_with_weights( self, models: dict[tuple[int, int, float], BasePipeline] ) -> list[tuple[float, BasePipeline]]: - """Return a list of (weight, model) pairs for the model selected by this ensemble. + """List of (weight, model) pairs for the model selected by this ensemble. Parameters ---------- diff --git a/test/conftest.py b/test/conftest.py index f2d9e3a6f3..0d27b26b91 100644 --- a/test/conftest.py +++ b/test/conftest.py @@ -142,8 +142,8 @@ def pytest_runtest_setup(item: Item) -> None: def pytest_sessionstart(session: Session) -> None: - """Called after the ``Session`` object has been created and before performing collection - and entering the run test loop. + """Called after the ``Session`` object has been created and before performing + collection and entering the run test loop. Parameters ---------- diff --git a/test/test_estimators/test_estimators.py b/test/test_estimators/test_estimators.py index c3a50a37fe..9231757b4a 100644 --- a/test/test_estimators/test_estimators.py +++ b/test/test_estimators/test_estimators.py @@ -1713,13 +1713,8 @@ def test_autosklearn_anneal(as_frame): "dataset_compression", [False, True, {"memory_allocation": 0.2}] ) def test_param_dataset_compression(dataset_compression: Union[bool, Dict[str, Any]]): - """We expect this does not get parsed and modified until it gets to the AutoML class, - In the meantime, it's value remains whatever was passed in. - - Parameters - ---------- - dataset_compression: Union[bool, Dict[str, Any] - The arg to pass to the estimator + """We expect this does not get parsed and modified until it gets to the AutoML + class, In the meantime, it's value remains whatever was passed in. Expects ------- diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index 94fd8c7a65..9721f88e31 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -306,7 +306,7 @@ def test_multilabel(self): self._test_configurations(configurations_space=cs, dataset=data) def test_configurations(self): - """Tests a non-seeded random set of configurations with default dataset properties + """Test non-seeded random sets of configurations with default dataset properties Expects ------- From 0c2832fd7e16d4cf61a17fdfb4c11757619b91b6 Mon Sep 17 00:00:00 2001 From: Lukas Strack <73999018+Louquinze@users.noreply.github.com> Date: Fri, 5 Aug 2022 15:28:25 +0200 Subject: [PATCH 091/118] Optimize metalearning configuration data --- .../RF_None_10CV_iterative_es_if.json | 224 ++-------- .../RF_None_3CV_iterative_es_if.json | 224 ++-------- .../RF_None_5CV_iterative_es_if.json | 224 ++-------- .../RF_None_holdout_iterative_es_if.json | 224 ++-------- .../RF_SH-eta4-i_10CV_iterative_es_if.json | 224 ++-------- .../RF_SH-eta4-i_3CV_iterative_es_if.json | 224 ++-------- .../RF_SH-eta4-i_5CV_iterative_es_if.json | 224 ++-------- .../RF_SH-eta4-i_holdout_iterative_es_if.json | 224 ++-------- .../RF_None_10CV_iterative_es_if.json | 224 ++-------- .../RF_None_3CV_iterative_es_if.json | 224 ++-------- .../RF_None_5CV_iterative_es_if.json | 224 ++-------- .../RF_None_holdout_iterative_es_if.json | 224 ++-------- .../RF_SH-eta4-i_10CV_iterative_es_if.json | 224 ++-------- .../RF_SH-eta4-i_3CV_iterative_es_if.json | 224 ++-------- .../RF_SH-eta4-i_5CV_iterative_es_if.json | 224 ++-------- .../RF_SH-eta4-i_holdout_iterative_es_if.json | 224 ++-------- .../RF_None_10CV_iterative_es_if.json | 224 ++-------- .../RF_None_3CV_iterative_es_if.json | 224 ++-------- .../RF_None_5CV_iterative_es_if.json | 224 ++-------- .../RF_None_holdout_iterative_es_if.json | 224 ++-------- .../RF_SH-eta4-i_10CV_iterative_es_if.json | 224 ++-------- .../RF_SH-eta4-i_3CV_iterative_es_if.json | 224 ++-------- .../RF_SH-eta4-i_5CV_iterative_es_if.json | 224 ++-------- .../RF_SH-eta4-i_holdout_iterative_es_if.json | 224 ++-------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- 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.../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../r2_regression_dense/configurations.csv | 196 ++++----- .../r2_regression_sparse/configurations.csv | 196 ++++----- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 412 +++++++++--------- .../configurations.csv | 396 ++++++++--------- .../configurations.csv | 196 ++++----- .../configurations.csv | 196 ++++----- .../metalearning/metalearning/meta_base.py | 10 +- 117 files changed, 18113 insertions(+), 21945 deletions(-) diff --git a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_10CV_iterative_es_if.json b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_10CV_iterative_es_if.json index 217c28e771..752ffd8213 100644 --- a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_10CV_iterative_es_if.json +++ b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_10CV_iterative_es_if.json @@ -20,12 +20,7 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.11840768063684459, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7668727846319956, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.28633349214759163, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "f1500cd": { "balancing:strategy": "weighting", @@ -47,12 +42,7 @@ "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0013138596672908974, "classifier:gradient_boosting:n_iter_no_change": 13, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "3e1a332": { "balancing:strategy": "weighting", @@ -72,12 +62,7 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.006544845548292656, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1062, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "22a29ef": { "balancing:strategy": "weighting", @@ -101,12 +86,7 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "classifier:gradient_boosting:n_iter_no_change": 6, "classifier:gradient_boosting:validation_fraction": 0.11549557161401015, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "5886349": { "balancing:strategy": "weighting", @@ -128,12 +108,7 @@ "classifier:gradient_boosting:tol": 1e-07, "classifier:gradient_boosting:n_iter_no_change": 1, "classifier:gradient_boosting:validation_fraction": 0.16202792486532844, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "8f54538": { "balancing:strategy": "none", @@ -158,12 +133,7 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.004667222369408079, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - 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b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_5CV_iterative_es_if.json @@ -22,12 +22,7 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.026511611819517358, "classifier:gradient_boosting:n_iter_no_change": 11, "classifier:gradient_boosting:validation_fraction": 0.1, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "153cd48": { "balancing:strategy": 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100644 --- a/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json +++ b/autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json @@ -20,12 +20,7 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.11840768063684459, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7668727846319956, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.28633349214759163, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - 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"data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.05231190906849011, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "d38f297": { "balancing:strategy": "weighting", @@ -101,12 +86,7 @@ "classifier:sgd:tol": 0.002985016693669331, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 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"data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.0004981969019895562, "classifier:gradient_boosting:n_iter_no_change": 4, "classifier:gradient_boosting:validation_fraction": 0.23374473517320618, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "a4cc318": { "balancing:strategy": "weighting", @@ -50,12 +45,7 @@ "classifier:mlp:shuffle": "True", 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{ "balancing:strategy": "none", @@ -716,12 +591,7 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "classifier:sgd:epsilon": 0.0012221439818245812, "classifier:sgd:eta0": 0.008943497619680189, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "5fc1b32": { "balancing:strategy": "none", @@ -744,12 +614,7 @@ 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"classifier:gradient_boosting:tol": 1e-07, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "e5c9543": { "balancing:strategy": "weighting", @@ -844,12 +694,7 @@ "classifier:sgd:loss": "squared_hinge", "classifier:sgd:penalty": "l1", "classifier:sgd:tol": 0.0004953514665899356, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": 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"data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.043137864752964175, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "d4b1080": { "balancing:strategy": "weighting", @@ -829,12 +684,7 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "normal", "classifier:sgd:eta0": 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"data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.40377267259792243, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" } }, "cutoffs": [ diff --git a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_10CV_iterative_es_if.json b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_10CV_iterative_es_if.json index 71eaa43a07..6be8f31b8c 100644 --- a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_10CV_iterative_es_if.json +++ b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_10CV_iterative_es_if.json @@ -20,12 +20,7 @@ "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.8406370831514932, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.20389889461388738, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - 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"data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "b03f092": { "balancing:strategy": "none", @@ -734,12 +604,7 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "classifier:sgd:eta0": 4.881378632007281e-07, "classifier:sgd:l1_ratio": 0.20434166925375527, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "27509eb": { "balancing:strategy": "none", @@ -761,12 +626,7 @@ "classifier:gradient_boosting:tol": 1e-07, "classifier:gradient_boosting:n_iter_no_change": 2, "classifier:gradient_boosting:validation_fraction": 0.24884856514753817, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "f208775": { "balancing:strategy": "none", @@ -787,12 +647,7 @@ "classifier:gradient_boosting:scoring": "loss", "classifier:gradient_boosting:tol": 1e-07, "classifier:gradient_boosting:n_iter_no_change": 18, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "e3b07fe": { "balancing:strategy": "none", @@ -818,12 +673,7 @@ "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.04337401371806109, "classifier:mlp:validation_fraction": 0.1, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "32ab8ad": { "balancing:strategy": "none", @@ -845,12 +695,7 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.4884138170289074, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles": 1000, "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "952e617": { "balancing:strategy": "none", @@ -869,12 +714,7 @@ "classifier:random_forest:min_samples_leaf": 15, "classifier:random_forest:min_samples_split": 19, "classifier:random_forest:min_weight_fraction_leaf": 0.0, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" } }, "cutoffs": [ diff --git a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_3CV_iterative_es_if.json b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_3CV_iterative_es_if.json index d8b44f3c4c..1928878da9 100644 --- a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_3CV_iterative_es_if.json +++ b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_3CV_iterative_es_if.json @@ -19,12 +19,7 @@ "classifier:gradient_boosting:scoring": "loss", "classifier:gradient_boosting:tol": 1e-07, "classifier:gradient_boosting:n_iter_no_change": 1, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "057a961": { "balancing:strategy": "none", @@ -49,12 +44,7 @@ "classifier:mlp:solver": "adam", "classifier:mlp:tol": 0.0001, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.019274771652010168, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "18b9a26": { "balancing:strategy": "none", @@ -76,12 +66,7 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.01722698747911545, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.7057003246095424, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.19450556868583982, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "32b95ed": { "balancing:strategy": "weighting", @@ -103,12 +88,7 @@ "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.3101738987949935, "classifier:gradient_boosting:n_iter_no_change": 9, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "3b9d012": { "balancing:strategy": "weighting", @@ -129,12 +109,7 @@ "classifier:gradient_boosting:scoring": "loss", "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.02647964603290406, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "e94ffc2": { "balancing:strategy": "none", @@ -157,12 +132,7 @@ "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.012839593604617927, "classifier:gradient_boosting:n_iter_no_change": 3, "classifier:gradient_boosting:validation_fraction": 0.3750029060371645, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "611fcd2": { "balancing:strategy": "none", @@ -183,12 +153,7 @@ "classifier:gradient_boosting:scoring": "loss", "classifier:gradient_boosting:tol": 1e-07, "data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.04973487764532246, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "2cc7fa6": { "balancing:strategy": "none", @@ -212,12 +177,7 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "classifier:gradient_boosting:n_iter_no_change": 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{ "balancing:strategy": "weighting", @@ -859,12 +709,7 @@ "classifier:gradient_boosting:min_samples_leaf": 43, "classifier:gradient_boosting:scoring": "loss", "classifier:gradient_boosting:tol": 1e-07, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "b7a81a4": { "balancing:strategy": "none", @@ -887,12 +732,7 @@ 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diff --git a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_5CV_iterative_es_if.json b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_5CV_iterative_es_if.json index 2f41ca5ffb..4752cc9ecc 100644 --- a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_5CV_iterative_es_if.json +++ b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_5CV_iterative_es_if.json @@ -23,12 +23,7 @@ "data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution": "uniform", "classifier:gradient_boosting:n_iter_no_change": 14, "classifier:gradient_boosting:validation_fraction": 0.12446922535601551, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - 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"data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max": 0.8406370831514932, "data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min": 0.20389889461388738, - "data_preprocessor:__choice__": "feature_type", - "data_preprocessor:feature_type:text_transformer:text_encoding:__choice__": "bag_of_word_encoding", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" }, "078caa9": { "balancing:strategy": "none", @@ -49,12 +44,7 @@ "classifier:mlp:shuffle": "True", "classifier:mlp:solver": "adam", "classifier:mlp:tol": 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"data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_absolute": 1, - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:min_df_choice": "min_df_absolute", - "data_preprocessor:feature_type:text_transformer:text_encoding:bag_of_word_encoding:ngram_upper_bound": 1, - "data_preprocessor:feature_type:text_transformer:text_feature_reduction:n_components": 100 + "data_preprocessor:__choice__": "feature_type" } }, "cutoffs": [ diff --git a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json index c88e7485a4..a74fd998d7 100644 --- a/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json +++ b/autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json @@ -19,12 +19,7 @@ "classifier:gradient_boosting:scoring": "loss", 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+556,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2243702699735874,median,robust_scaler,,,0.9398079108777535,0.06751508236447328,extra_trees_preproc_for_classification,False,gini,None,0.5400657457230515,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type diff --git a/autosklearn/metalearning/files/average_precision_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/average_precision_binary.classification_dense/configurations.csv index 5c38cb6a1a..c2a4c9fde1 100644 --- a/autosklearn/metalearning/files/average_precision_binary.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/average_precision_binary.classification_dense/configurations.csv @@ -1,206 +1,206 @@ 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+556,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2243702699735874,median,robust_scaler,,,0.9398079108777535,0.06751508236447328,extra_trees_preproc_for_classification,False,gini,None,0.5400657457230515,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type diff --git a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/configurations.csv index f203d80d68..e6da571793 100644 --- a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/configurations.csv @@ -1,206 +1,206 @@ 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a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/configurations.csv @@ -1,206 +1,206 @@ 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a/autosklearn/metalearning/files/f1_binary.classification_sparse/configurations.csv +++ b/autosklearn/metalearning/files/f1_binary.classification_sparse/configurations.csv @@ -1,198 +1,198 @@ 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+554,weighting,adaboost,SAMME,0.0898116612384131,5,305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,,feature_type +556,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2243702699735874,median,robust_scaler,,,0.9398079108777535,0.06751508236447328,extra_trees_preproc_for_classification,False,gini,None,0.5400657457230515,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/configurations.csv index 8a5d9c876f..55f176a35a 100644 --- a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/configurations.csv @@ -1,206 +1,206 @@ 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+++ b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/configurations.csv @@ -1,206 +1,206 @@ 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a/autosklearn/metalearning/files/f1_multiclass.classification_sparse/configurations.csv +++ b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/configurations.csv @@ -1,198 +1,198 @@ 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+556,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2243702699735874,median,robust_scaler,,,0.9398079108777535,0.06751508236447328,extra_trees_preproc_for_classification,False,gini,None,0.5400657457230515,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type diff --git a/autosklearn/metalearning/files/log_loss_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/log_loss_binary.classification_dense/configurations.csv index dffc9876db..4c4979b7ef 100644 --- a/autosklearn/metalearning/files/log_loss_binary.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/log_loss_binary.classification_dense/configurations.csv @@ -1,206 +1,206 @@ 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a/autosklearn/metalearning/files/log_loss_binary.classification_sparse/configurations.csv +++ b/autosklearn/metalearning/files/log_loss_binary.classification_sparse/configurations.csv @@ -1,198 +1,198 @@ 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+556,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2243702699735874,median,robust_scaler,,,0.9398079108777535,0.06751508236447328,extra_trees_preproc_for_classification,False,gini,None,0.5400657457230515,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type diff --git a/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/configurations.csv index dffc9876db..4c4979b7ef 100644 --- a/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/configurations.csv @@ -1,206 +1,206 @@ 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a/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/configurations.csv +++ b/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/configurations.csv @@ -1,98 +1,98 @@ 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a/autosklearn/metalearning/files/median_absolute_error_regression_dense/configurations.csv +++ b/autosklearn/metalearning/files/median_absolute_error_regression_dense/configurations.csv @@ -1,98 +1,98 @@ 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a/autosklearn/metalearning/files/median_absolute_error_regression_sparse/configurations.csv +++ b/autosklearn/metalearning/files/median_absolute_error_regression_sparse/configurations.csv @@ -1,98 +1,98 @@ 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a/autosklearn/metalearning/files/precision_binary.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/precision_binary.classification_dense/configurations.csv @@ -1,206 +1,206 @@ 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a/autosklearn/metalearning/files/precision_binary.classification_sparse/configurations.csv +++ b/autosklearn/metalearning/files/precision_binary.classification_sparse/configurations.csv @@ -1,198 +1,198 @@ 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+556,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2243702699735874,median,robust_scaler,,,0.9398079108777535,0.06751508236447328,extra_trees_preproc_for_classification,False,gini,None,0.5400657457230515,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type diff --git a/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/configurations.csv index d65b1b6024..66acbc15b2 100644 --- a/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/configurations.csv @@ -1,206 +1,206 @@ 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6d087cedf5..5130389441 100644 --- a/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/configurations.csv +++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/configurations.csv @@ -1,198 +1,198 @@ 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--- a/autosklearn/metalearning/files/precision_multiclass.classification_sparse/configurations.csv +++ b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/configurations.csv @@ -1,198 +1,198 @@ 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+556,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2243702699735874,median,robust_scaler,,,0.9398079108777535,0.06751508236447328,extra_trees_preproc_for_classification,False,gini,None,0.5400657457230515,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type diff --git a/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/configurations.csv index 0f6c3b44bb..4ce962a76d 100644 --- a/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/configurations.csv @@ -1,206 +1,206 @@ 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+556,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2243702699735874,median,robust_scaler,,,0.9398079108777535,0.06751508236447328,extra_trees_preproc_for_classification,False,gini,None,0.5400657457230515,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type diff --git a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/configurations.csv index 32cf9506ae..b16451c8b6 100644 --- a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/configurations.csv @@ -1,206 +1,206 @@ 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+556,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2243702699735874,median,robust_scaler,,,0.9398079108777535,0.06751508236447328,extra_trees_preproc_for_classification,False,gini,None,0.5400657457230515,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type diff --git a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/configurations.csv index c09338ef0b..42f5608918 100644 --- a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/configurations.csv @@ -1,206 +1,206 @@ 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a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/configurations.csv @@ -1,206 +1,206 @@ 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+554,weighting,adaboost,SAMME,0.0898116612384131,5,305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,,feature_type +556,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2243702699735874,median,robust_scaler,,,0.9398079108777535,0.06751508236447328,extra_trees_preproc_for_classification,False,gini,None,0.5400657457230515,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type diff --git a/autosklearn/metalearning/files/recall_micro_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/configurations.csv index 56ccc24865..3fcf2da7c1 100644 --- a/autosklearn/metalearning/files/recall_micro_binary.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/configurations.csv @@ -1,206 +1,206 @@ 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+556,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2243702699735874,median,robust_scaler,,,0.9398079108777535,0.06751508236447328,extra_trees_preproc_for_classification,False,gini,None,0.5400657457230515,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type diff --git a/autosklearn/metalearning/files/recall_samples_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/recall_samples_binary.classification_dense/configurations.csv index 0f6c3b44bb..4ce962a76d 100644 --- a/autosklearn/metalearning/files/recall_samples_binary.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/recall_samples_binary.classification_dense/configurations.csv @@ -1,206 +1,206 @@ 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+556,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2243702699735874,median,robust_scaler,,,0.9398079108777535,0.06751508236447328,extra_trees_preproc_for_classification,False,gini,None,0.5400657457230515,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type diff --git a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/configurations.csv index 56ccc24865..3fcf2da7c1 100644 --- a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/configurations.csv @@ -1,206 +1,206 @@ 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+556,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2243702699735874,median,robust_scaler,,,0.9398079108777535,0.06751508236447328,extra_trees_preproc_for_classification,False,gini,None,0.5400657457230515,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type diff --git a/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/configurations.csv index 35dc07e42c..9140a334b1 100644 --- a/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/configurations.csv @@ -1,206 +1,206 @@ 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+285,no_encoding,no_coalescense,,mean,minmax,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2186105871515939,fdr,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.10377482408306521,0.016255400771699312,least_squares,255,None,65,70,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type diff --git a/autosklearn/metalearning/metalearning/meta_base.py b/autosklearn/metalearning/metalearning/meta_base.py index 4cb116ef21..95cca964d1 100644 --- a/autosklearn/metalearning/metalearning/meta_base.py +++ b/autosklearn/metalearning/metalearning/meta_base.py @@ -40,6 +40,9 @@ def __init__(self, configuration_space, aslib_directory, logger): self.logger = logger self.configuration_space = configuration_space + self.default_configuration_space_dict = dict( + configuration_space.get_default_configuration() + ) self.aslib_directory = aslib_directory aslib_reader = aslib_simple.AlgorithmSelectionProblem( @@ -55,8 +58,13 @@ def __init__(self, configuration_space, aslib_directory, logger): for algorithm_id in self.configurations: configuration = self.configurations[algorithm_id] try: + for key in self.default_configuration_space_dict.keys(): + if key not in configuration: + configuration[key] = self.default_configuration_space_dict[key] configurations[str(algorithm_id)] = Configuration( - configuration_space, values=configuration + configuration_space, + values=configuration, + allow_inactive_with_values=True, ) except (ValueError, KeyError) as e: self.logger.debug("Error reading configurations: %s", e) From 306263c7ab65d2df24dbd03b35ffe366767c7dd3 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Mon, 8 Aug 2022 12:40:14 -0400 Subject: [PATCH 092/118] fix-1547-Bug-in-_partial_fit_and_predict_iterative-in-train_evaluator.py_ (#1550) * Create PR * Fix issue with incorrect splits and data * Remove extraneous indexing * Fill in missing points of X_data to self._loss * Fix typevar * Fix X data passed in to loss * Fix indexing of X_fold for train evalutor * Fix cv-iterative-fit successive halving example --- autosklearn/evaluation/train_evaluator.py | 282 ++++++++-------------- 1 file changed, 95 insertions(+), 187 deletions(-) diff --git a/autosklearn/evaluation/train_evaluator.py b/autosklearn/evaluation/train_evaluator.py index 4757091190..eb98fb8a2a 100644 --- a/autosklearn/evaluation/train_evaluator.py +++ b/autosklearn/evaluation/train_evaluator.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast +from typing import Any, Dict, List, Optional, Sequence, Tuple, TypeVar, Union, cast import copy import json @@ -67,6 +67,14 @@ def _get_y_array(y: SUPPORTED_TARGET_TYPES, task_type: int) -> SUPPORTED_TARGET_ return y +T = TypeVar("T", SUPPORTED_FEAT_TYPES, SUPPORTED_TARGET_TYPES) + + +def select(data: T, indices: np.ndarray) -> T: + """Select into some data by indices""" + return data.iloc[indices] if hasattr(data, "iloc") else data[indices] + + def subsample_indices( train_indices: List[int], subsample: Optional[float], @@ -86,11 +94,7 @@ def subsample_indices( # required to subsample because otherwise scikit-learn will complain if task_type in CLASSIFICATION_TASKS and task_type != MULTILABEL_CLASSIFICATION: - stratify: Optional[SUPPORTED_TARGET_TYPES] = ( - Y_train.iloc[train_indices] - if hasattr(Y_train, "iloc") - else Y_train[train_indices] - ) + stratify: Optional[SUPPORTED_TARGET_TYPES] = select(Y_train, train_indices) else: stratify = None @@ -118,50 +122,24 @@ def _fit_with_budget( train_indices: List[int], task_type: int, ) -> None: - if ( - budget_type == "iterations" - or budget_type == "mixed" - and model.estimator_supports_iterative_fit() + if budget_type == "iterations" or ( + budget_type == "mixed" and model.estimator_supports_iterative_fit() ): + X = select(X_train, train_indices) + y = select(Y_train, train_indices) + if model.estimator_supports_iterative_fit(): budget_factor = model.get_max_iter() - Xt, fit_params = model.fit_transformer( - X_train.iloc[train_indices] - if hasattr(X_train, "iloc") - else X_train[train_indices], - Y_train.iloc[train_indices] - if hasattr(Y_train, "iloc") - else Y_train[train_indices], - ) + Xt, fit_params = model.fit_transformer(X, y) n_iter = int(np.ceil(budget / 100 * budget_factor)) - model.iterative_fit( - Xt, - Y_train.iloc[train_indices] - if hasattr(Y_train, "iloc") - else Y_train[train_indices], - n_iter=n_iter, - refit=True, - **fit_params, - ) + model.iterative_fit(Xt, y, n_iter=n_iter, refit=True, **fit_params) else: - _fit_and_suppress_warnings( - logger, - model, - X_train.iloc[train_indices] - if hasattr(X_train, "iloc") - else X_train[train_indices], - Y_train.iloc[train_indices] - if hasattr(Y_train, "iloc") - else Y_train[train_indices], - ) + _fit_and_suppress_warnings(logger, model, X, y) - elif ( - budget_type == "subsample" - or budget_type == "mixed" - and not model.estimator_supports_iterative_fit() + elif budget_type == "subsample" or ( + budget_type == "mixed" and not model.estimator_supports_iterative_fit() ): - subsample = budget / 100 train_indices_subset = subsample_indices( train_indices, @@ -169,12 +147,9 @@ def _fit_with_budget( task_type, Y_train, ) - _fit_and_suppress_warnings( - logger, - model, - X_train[train_indices_subset], - Y_train[train_indices_subset], - ) + X = select(X_train, train_indices_subset) + y = select(Y_train, train_indices_subset) + _fit_and_suppress_warnings(logger, model, X, y) else: raise ValueError(budget_type) @@ -289,13 +264,9 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: self.Y_train, groups=self.resampling_strategy_args.get("groups"), ): - self.X_optimization = ( - self.X_train.iloc[test_split] - if hasattr(self.X_train, "iloc") - else self.X_train[test_split] - ) - self.Y_optimization = self.Y_train[test_split] - self.Y_actual_train = self.Y_train[train_split] + self.X_optimization = select(self.X_train, test_split) + self.Y_optimization = select(self.Y_train, test_split) + self.Y_actual_train = select(self.Y_train, train_split) self._partial_fit_and_predict_iterative( 0, train_indices=train_split, @@ -384,37 +355,27 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: model = self.models[i] if iterations[i] == 1: - self.Y_train_targets[train_indices] = ( - self.Y_train.iloc[train_indices] - if hasattr(self.Y_train, "iloc") - else self.Y_train[train_indices] - ) - self.X_targets[i] = ( - self.X_train.iloc[test_indices] - if hasattr(self.X_train, "iloc") - else self.X_train[train_indices] + self.Y_train_targets[train_indices] = select( + self.Y_train, train_indices ) + self.X_targets[i] = select(self.X_train, test_indices) + self.Y_targets[i] = select(self.Y_train, test_indices) - self.Y_targets[i] = self.Y_train[test_indices] - + # Note: Be careful moving these into variables, caused a + # headache when trying to debug why things were breaking Xt, fit_params = model.fit_transformer( - self.X_train.iloc[train_indices] - if hasattr(self.X_train, "iloc") - else self.X_train[train_indices], - self.Y_train.iloc[train_indices] - if hasattr(self.Y_train, "iloc") - else self.Y_train[train_indices], + select(self.X_train, train_indices), + select(self.Y_train, train_indices), ) Xt_array[i] = Xt fit_params_array[i] = fit_params + n_iter = int(2 ** iterations[i] / 2) if iterations[i] > 1 else 2 total_n_iterations[i] = total_n_iterations[i] + n_iter model.iterative_fit( Xt_array[i], - self.Y_train.iloc[train_indices] - if hasattr(self.Y_train, "iloc") - else self.Y_train[train_indices], + select(self.Y_train, train_indices), n_iter=n_iter, **fit_params_array[i], ) @@ -431,9 +392,7 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: train_splits[i] = train_indices train_loss = self._loss( - self.Y_train.iloc[train_indices] - if hasattr(self.Y_train, "iloc") - else self.Y_train[train_indices], + select(self.Y_train, train_indices), train_pred, X_data=Xt_array[i], ) @@ -608,13 +567,10 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: Y_test_pred[i] = test_pred train_splits[i] = train_split - train_loss = self._loss( - self.Y_train_targets[train_split], - train_pred, - X_data=self.X_train.iloc[train_split] - if hasattr(self.X_train, "iloc") - else self.X_train[train_split], - ) + X = select(self.X_train, train_split) + y = select(self.Y_train_targets, train_split) + + train_loss = self._loss(y, train_pred, X_data=X) train_losses.append(train_loss) # number of training data points for this fold. Used for weighting # the average. @@ -622,7 +578,9 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None: # Compute validation loss of this fold and store it. optimization_loss = self._loss( - self.Y_targets[i], opt_pred, X_data=self.X_targets[i] + self.Y_targets[i], + opt_pred, + X_data=self.X_targets[i], ) opt_losses.append(optimization_loss) # number of optimization data points for this fold. Used for weighting @@ -745,9 +703,9 @@ def partial_fit_predict_and_loss(self, fold: int, iterative: bool = False) -> No break if self.num_cv_folds > 1: - self.X_optimization = self.X_train[test_split] - self.Y_optimization = self.Y_train[test_split] - self.Y_actual_train = self.Y_train[train_split] + self.X_optimization = select(self.X_train, test_split) + self.Y_optimization = select(self.Y_train, test_split) + self.Y_actual_train = select(self.Y_train, train_split) if iterative: self._partial_fit_and_predict_iterative( @@ -770,8 +728,12 @@ def partial_fit_predict_and_loss(self, fold: int, iterative: bool = False) -> No test_indices=test_split, add_model_to_self=True, ) - train_loss = self._loss(self.Y_actual_train, train_pred) - loss = self._loss(self.Y_targets[fold], opt_pred) + + # This is my best guess at what the X_data for these should be + X_train = select(self.X_train, train_split) # From above (only cv?) + X_fold = self.X_targets[fold] + train_loss = self._loss(self.Y_actual_train, train_pred, X_data=X_train) + loss = self._loss(self.Y_targets[fold], opt_pred, X_data=X_fold) if self.model.estimator_supports_iterative_fit(): model_max_iter = self.model.get_max_iter() @@ -811,20 +773,15 @@ def _partial_fit_and_predict_iterative( file_output = True if self.num_cv_folds == 1 else False if model.estimator_supports_iterative_fit(): - Xt, fit_params = model.fit_transformer( - self.X_train.iloc[train_indices] - if hasattr(self.Y_train, "iloc") - else self.X_train[train_indices], - self.Y_train.iloc[train_indices] - if hasattr(self.Y_train, "iloc") - else self.Y_train[train_indices], - ) + X = select(self.X_train, train_indices) + y = select(self.Y_train, train_indices) - self.Y_train_targets[train_indices] = ( - self.Y_train.iloc[train_indices] - if hasattr(self.Y_train, "iloc") - else self.Y_train[train_indices] - ) + X_test = select(self.X_train, test_indices) + y_test = select(self.Y_train, test_indices) + + Xt, fit_params = model.fit_transformer(X, y) + + self.Y_train_targets[train_indices] = y iteration = 1 total_n_iteration = 0 @@ -842,14 +799,8 @@ def _partial_fit_and_predict_iterative( ): n_iter = int(2**iteration / 2) if iteration > 1 else 2 total_n_iteration += n_iter - model.iterative_fit( - Xt, - self.Y_train.iloc[train_indices] - if hasattr(self.Y_train, "iloc") - else self.Y_train[train_indices], - n_iter=n_iter, - **fit_params, - ) + model.iterative_fit(Xt, y, n_iter=n_iter, **fit_params) + (Y_train_pred, Y_optimization_pred, Y_test_pred,) = self._predict( model, train_indices=train_indices, @@ -859,13 +810,9 @@ def _partial_fit_and_predict_iterative( if add_model_to_self: self.model = model - train_loss = self._loss( - self.Y_train.iloc[train_indices] - if hasattr(self.Y_train, "iloc") - else self.Y_train[train_indices], - Y_train_pred, - ) - loss = self._loss(self.Y_train[test_indices], Y_optimization_pred) + train_loss = self._loss(y, Y_train_pred, X_data=X) + loss = self._loss(y_test, Y_optimization_pred, X_data=X_test) + additional_run_info = model.get_additional_run_info() model_current_iter = model.get_current_iter() @@ -902,13 +849,15 @@ def _partial_fit_and_predict_iterative( ) = self._partial_fit_and_predict_standard( fold, train_indices, test_indices, add_model_to_self ) - train_loss = self._loss( - self.Y_train.iloc[train_indices] - if hasattr(self.Y_train, "iloc") - else self.Y_train[train_indices], - Y_train_pred, - ) - loss = self._loss(self.Y_train[test_indices], Y_optimization_pred) + + X = select(self.X_train, train_indices) + y = select(self.Y_train, train_indices) + train_loss = self._loss(y, Y_train_pred, X_data=X) + + X_test = select(self.X_train, test_indices) + y_test = select(self.Y_train, test_indices) + loss = self._loss(y_test, Y_optimization_pred, X_data=X_test) + if self.model.estimator_supports_iterative_fit(): model_max_iter = self.model.get_max_iter() model_current_iter = self.model.get_current_iter() @@ -946,37 +895,18 @@ def _partial_fit_and_predict_standard( self.indices[fold] = (train_indices, test_indices) - _fit_and_suppress_warnings( - self.logger, - model, - self.X_train.iloc[train_indices] - if hasattr(self.X_train, "iloc") - else self.X_train[train_indices], - self.Y_train.iloc[train_indices] - if hasattr(self.Y_train, "iloc") - else self.Y_train[train_indices], - ) + X = select(self.X_train, train_indices) + y = select(self.Y_train, train_indices) + _fit_and_suppress_warnings(self.logger, model, X, y) if add_model_to_self: self.model = model else: self.models[fold] = model - self.X_targets[fold] = ( - self.X_train.iloc[test_indices] - if hasattr(self.X_train, "iloc") - else self.X_train[test_indices] - ) - self.Y_targets[fold] = ( - self.Y_train.iloc[test_indices] - if hasattr(self.Y_train, "iloc") - else self.Y_train[test_indices] - ) - self.Y_train_targets[train_indices] = ( - self.Y_train.iloc[train_indices] - if hasattr(self.Y_train, "iloc") - else self.Y_train[train_indices] - ) + self.X_targets[fold] = select(self.X_train, test_indices) + self.Y_targets[fold] = select(self.Y_train, test_indices) + self.Y_train_targets[train_indices] = select(self.Y_train, train_indices) train_pred, opt_pred, test_pred = self._predict( model=model, @@ -1010,13 +940,9 @@ def _partial_fit_and_predict_budget( model = self._get_model(self.feat_type) self.indices[fold] = (train_indices, test_indices) - self.X_targets[fold] = self.X_train[test_indices] - self.Y_targets[fold] = self.Y_train[test_indices] - self.Y_train_targets[train_indices] = ( - self.Y_train.iloc[train_indices] - if hasattr(self.Y_train, "iloc") - else self.Y_train[train_indices], - ) + self.X_targets[fold] = select(self.X_train, test_indices) + self.Y_targets[fold] = select(self.Y_train, test_indices) + self.Y_train_targets[train_indices] = select(self.Y_train, train_indices) _fit_with_budget( X_train=self.X_train, @@ -1051,38 +977,20 @@ def _partial_fit_and_predict_budget( def _predict( self, model: BaseEstimator, test_indices: List[int], train_indices: List[int] ) -> Tuple[PIPELINE_DATA_DTYPE, PIPELINE_DATA_DTYPE, PIPELINE_DATA_DTYPE]: - train_pred = self.predict_function( - self.X_train.iloc[train_indices] - if hasattr(self.X_train, "iloc") - else self.X_train[train_indices], - model, - self.task_type, - self.Y_train.iloc[train_indices] - if hasattr(self.Y_train, "iloc") - else self.Y_train[train_indices], - ) + y_train = select(self.Y_train, train_indices) + X_train = select(self.X_train, train_indices) + X_test = select(self.X_train, test_indices) - opt_pred = self.predict_function( - self.X_train.iloc[test_indices] - if hasattr(self.X_train, "iloc") - else self.X_train[test_indices], - model, - self.task_type, - self.Y_train.iloc[train_indices] - if hasattr(self.Y_train, "iloc") - else self.Y_train[train_indices], - ) + # The y_train here does not correspond to the X, there to ensure output shape + # will be correct in case labels were missing from some split + train_pred = self.predict_function(X_train, model, self.task_type, y_train) + opt_pred = self.predict_function(X_test, model, self.task_type, y_train) + # This is the test data the user can pass in if self.X_test is not None: - X_test = self.X_test.copy() - test_pred = self.predict_function( - X_test, - model, - self.task_type, - self.Y_train.iloc[train_indices] - if hasattr(self.Y_train, "iloc") - else self.Y_train[train_indices], - ) + # See comment above about y_train + X_user = self.X_test.copy() + test_pred = self.predict_function(X_user, model, self.task_type, y_train) else: test_pred = None From 391680c78b7557e7006dcd5bee0b4d8be3f25bdc Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Wed, 10 Aug 2022 10:34:39 +0200 Subject: [PATCH 093/118] Minor updates to meta-data loading (#1559) --- autosklearn/metalearning/metalearning/meta_base.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/autosklearn/metalearning/metalearning/meta_base.py b/autosklearn/metalearning/metalearning/meta_base.py index 95cca964d1..b5ef9db083 100644 --- a/autosklearn/metalearning/metalearning/meta_base.py +++ b/autosklearn/metalearning/metalearning/meta_base.py @@ -3,6 +3,7 @@ import numpy as np import pandas as pd from ConfigSpace.configuration_space import Configuration +from ConfigSpace.util import deactivate_inactive_hyperparameters from ..input import aslib_simple from ..metafeatures.metafeature import DatasetMetafeatures @@ -40,8 +41,8 @@ def __init__(self, configuration_space, aslib_directory, logger): self.logger = logger self.configuration_space = configuration_space - self.default_configuration_space_dict = dict( - configuration_space.get_default_configuration() + self.default_configuration_space_dict = ( + configuration_space.get_default_configuration().get_dictionary() ) self.aslib_directory = aslib_directory @@ -61,11 +62,15 @@ def __init__(self, configuration_space, aslib_directory, logger): for key in self.default_configuration_space_dict.keys(): if key not in configuration: configuration[key] = self.default_configuration_space_dict[key] - configurations[str(algorithm_id)] = Configuration( + configuration = Configuration( configuration_space, values=configuration, allow_inactive_with_values=True, ) + configuration = deactivate_inactive_hyperparameters( + configuration, configuration_space + ) + configurations[str(algorithm_id)] = configuration except (ValueError, KeyError) as e: self.logger.debug("Error reading configurations: %s", e) From 990631a5846158a60153360caa66eac3bab421d2 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Wed, 10 Aug 2022 19:03:35 +0200 Subject: [PATCH 094/118] Prepare release 0.15.0 (#1558) * Prepare release 0.15.0 * Extend release notes with commit history * Fix release notes Co-authored-by: eddiebergman --- autosklearn/__version__.py | 2 +- doc/releases.rst | 58 +++++++++++++++++++++++++++++++++++++- 2 files changed, 58 insertions(+), 2 deletions(-) diff --git a/autosklearn/__version__.py b/autosklearn/__version__.py index 6928aee41e..a6b8488c18 100644 --- a/autosklearn/__version__.py +++ b/autosklearn/__version__.py @@ -1,4 +1,4 @@ """Version information.""" # The following line *must* be the last in the module, exactly as formatted: -__version__ = "0.14.6" +__version__ = "0.15.0" diff --git a/doc/releases.rst b/doc/releases.rst index 5ba959e6f3..56115f2357 100644 --- a/doc/releases.rst +++ b/doc/releases.rst @@ -3,12 +3,68 @@ .. The following command allows to retrieve all commiters since a specified commit. From https://stackoverflow.com/questions/6482436/list-of-authors-in-git-since-a-given-commit - git log 3d53cd9b5011258c4fdcce9d02e252d0248e5f1d.. --format="%aN <%aE>" --reverse | perl -e 'my %dedupe; while () { print unless $dedupe{$_}++}' + git log 6cc8bb179fcb023d1c341cf33d2958a16a6935be.. --format="%aN <%aE>" --reverse | perl -e 'my %dedupe; while () { print unless $dedupe{$_}++}' ======== Releases ======== +Version 0.15.0 +============== + +* ADD #1317, #1455, #1485, #1501, #1518, #1523: Initial support for multi-objective Auto-sklearn. +* ADD #1300, #1410, #1414, #1415, #1420, #1468, #1500: Intial support for text features Auto-sklearn. You can now pass in columns identified as `"string"` columns which will be tokenized using pure sklearn methods. +* ADD #1475: Support for passing `X` data to metrics, as required by [`fairlearn`](https://github.com/fairlearn/fairlearn) metrics +* ADD #1341, #1250: Expose interface to interact with how auto-sklearn performs dataset compression when required + +* DOC #1304: This adds documentation for SMAC callbacks that can be used by Auto-sklearn. +* DOC #1476: Example on how to interupt autosklearn with a callback, implementing a very naive early stopping + +* MAINT #1364, #1473: Improve import time of Auto-sklearn 2 by moving the construction of the selector + model from import time to construction time. +* MAINT #1425: Update `StopWatch` to be context manager. +* MAINT #1454: Rename interal bool parameters `categorical` to `feat_type` to reflect the use of different feature types +* MAINT #1474: remove left-overs of a "public test set" from the code. This has no influence on + any user-facing code. +* MAINT #1487: Replace deprecated of `DataFrame.append` +* MAINT #1504: Rename `rval` to `return_value` or `run_value` to remove ambiguity +* MAINT #1506: Increase the time given to meta-learning-related unit tests to decrease the amount + of timeouts on github. +* MAINT #1527: Relax MLPRegressor unit tests precision. +* MAINT #1545: Add explicit lower bound subsample check in the train evaluator +* MAINT #1551: Fix issue with updated scipy skew see [here](https://github.com/scipy/scipy/issues/16765). +* MAINT #1434: Refactor the ensemble building process +* MAINT #1464: Improve testing, with caching (#1464), modularity (#1417) +* MAINT #1358: Add tooling Mypy, Flake8, isort, black + +* FIX #741: Disable hyperparameters for a special data modality if it is not present, for example + disable one hot encoding if no categorical features are present. +* FIX #1365, #1369: Fix an issue with `ensemble_size == 0`. +* FIX #1374: Pass random state to all components of a pipeline. +* FIX #1432: Fixes an issue in which the `AutoSklearnClassifier.leaderboard()` or + `AutoSklearnRegressor.leaderboard()` could fail to display results. +* FIX #1480: Properly terminate Auto-sklearn on an exception or a keyboard interrupt. +* FIX #1532: Removes exception printing at shutdown for latest dask versions. The printed + exceptions did not impact performance at all and were only confusing as they suggested failures + of Auto-sklearn. +* FIX #1547: Fixes a bug in Auto-sklearn 2 that could silently break it when passing in pandas + DataFrames. +* FIX #1550: Fix recent bug when performing evaluations with pandas Y. + +Contributors v0.15.0 +******************** + +* Matthias Feurer +* Eddie Bergman +* Katharina Eggensperger +* Sagar Kaushik +* partev +* Lukas Strack +* Basavasagar K Patil +* Eric Pedley +* Aseem Kannal +* SkBlaz + Version 0.14.6 ============== From 5396856b38084c777744836ae39b49ae4fd76240 Mon Sep 17 00:00:00 2001 From: eddiebergman Date: Mon, 15 Aug 2022 17:28:15 +0200 Subject: [PATCH 095/118] Cleanup files from merge --- autosklearn/util/single_thread_client.py | 1 - test/test_ensemble_builder/ensemble_utils.py | 116 +++++++++++++++++++ test/test_pipeline/test_regression.py | 5 + 3 files changed, 121 insertions(+), 1 deletion(-) create mode 100644 test/test_ensemble_builder/ensemble_utils.py diff --git a/autosklearn/util/single_thread_client.py b/autosklearn/util/single_thread_client.py index fc0c238f8d..5c158192a0 100644 --- a/autosklearn/util/single_thread_client.py +++ b/autosklearn/util/single_thread_client.py @@ -2,7 +2,6 @@ from typing import Any from pathlib import Path -from typing import Any import dask.distributed diff --git a/test/test_ensemble_builder/ensemble_utils.py b/test/test_ensemble_builder/ensemble_utils.py new file mode 100644 index 0000000000..7a3cd7f252 --- /dev/null +++ b/test/test_ensemble_builder/ensemble_utils.py @@ -0,0 +1,116 @@ +import os +import shutil + +import numpy as np + +from autosklearn.automl_common.common.ensemble_building.abstract_ensemble import ( + AbstractEnsemble, +) +from autosklearn.ensemble_builder import EnsembleBuilder +from autosklearn.metrics import make_scorer + +import unittest +import unittest.mock + + +def scorer_function(a, b): + return 0.9 + + +MockMetric = make_scorer("mock", scorer_function) + + +class BackendMock(object): + def __init__(self, target_directory): + this_directory = os.path.abspath(os.path.dirname(__file__)) + shutil.copytree( + os.path.join(this_directory, "data"), os.path.join(target_directory) + ) + self.temporary_directory = target_directory + self.internals_directory = os.path.join( + self.temporary_directory, ".auto-sklearn" + ) + + def load_datamanager(self): + manager = unittest.mock.Mock() + manager.__reduce__ = lambda self: (unittest.mock.MagicMock, ()) + array = np.load( + os.path.join( + self.temporary_directory, + ".auto-sklearn", + "runs", + "0_3_100.0", + "predictions_test_0_3_100.0.npy", + ) + ) + manager.data.get.return_value = array + return manager + + def load_targets_ensemble(self): + with open( + os.path.join( + self.temporary_directory, + ".auto-sklearn", + "predictions_ensemble_true.npy", + ), + "rb", + ) as fp: + y = np.load(fp, allow_pickle=True) + return y + + def save_ensemble(self, ensemble, index_run, seed): + return + + def save_predictions_as_txt(self, predictions, subset, idx, prefix, precision): + return + + def get_runs_directory(self) -> str: + return os.path.join(self.temporary_directory, ".auto-sklearn", "runs") + + def get_numrun_directory(self, seed: int, num_run: int, budget: float) -> str: + return os.path.join( + self.get_runs_directory(), "%d_%d_%s" % (seed, num_run, budget) + ) + + def get_model_filename(self, seed: int, idx: int, budget: float) -> str: + return "%s.%s.%s.model" % (seed, idx, budget) + + +def compare_read_preds(read_preds1, read_preds2): + """ + compares read_preds attribute. An alternative to + assert Dict Equal as it contains np arrays, so we have + to use np testing utilities accordingly + """ + + # Both arrays should have the same splits + assert set(read_preds1.keys()) == set(read_preds2.keys()) + + for k, v in read_preds1.items(): + + # Each split should have the same elements + assert set(read_preds1[k].keys()) == set(read_preds2[k].keys()) + + # This level contains the scores/ensmebles/etc + for actual_k, actual_v in read_preds1[k].items(): + + # If it is a numpy array, make sure it is the same + if type(actual_v) is np.ndarray: + np.testing.assert_array_equal(actual_v, read_preds2[k][actual_k]) + else: + assert actual_v == read_preds2[k][actual_k] + + +class EnsembleBuilderMemMock(EnsembleBuilder): + def fit_ensemble(self, selected_keys): + return True + + def predict( + self, + set_: str, + ensemble: AbstractEnsemble, + selected_keys: list, + n_preds: int, + index_run: int, + ): + np.ones([10000000, 1000000]) diff --git a/test/test_pipeline/test_regression.py b/test/test_pipeline/test_regression.py index 3a50decb8c..66738a1f3a 100644 --- a/test/test_pipeline/test_regression.py +++ b/test/test_pipeline/test_regression.py @@ -247,6 +247,11 @@ def _test_configurations( "identity" in e.args[0] ): continue + elif ( + "zero-size array to reduction operation maximum which has no " + "identity" in e.args[0] + ): + continue else: e.args += (f"config={config}",) raise e From fd4e25f71891e8e1abfde9ae0c8ee3525505a30f Mon Sep 17 00:00:00 2001 From: eddiebergman Date: Tue, 16 Aug 2022 10:15:51 +0200 Subject: [PATCH 096/118] Remove odd duplications --- CONTRIBUTING.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 73ce781618..376f717c63 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -46,7 +46,7 @@ Following that we'll tell you about how you can test your changes locally and th # If you missed the --recurse-submodules arg during clone or need to install the # submodule manually, then execute the following line: # - # git submodule udate --init --recursive + # git submodule update --init --recursive # ... Alternatively, if you would prefer a more manual method # Show all the available branches with a * beside your current one From 8f3f7128b7f47d15f8ed0d90a3f6e3ae481671c3 Mon Sep 17 00:00:00 2001 From: eddiebergman Date: Tue, 16 Aug 2022 10:17:24 +0200 Subject: [PATCH 097/118] Fix broken links --- autosklearn/estimators.py | 6 +++--- autosklearn/experimental/askl2.py | 2 +- examples/40_advanced/example_get_pipeline_components.py | 2 +- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 607fb3dc2f..0549abf2d8 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -275,16 +275,16 @@ def __init__( smac_scenario_args : dict, optional (None) Additional arguments inserted into the scenario of SMAC. See the - `SMAC documentation `_ + `SMAC documentation `_ for a list of available arguments. get_smac_object_callback : callable Callback function to create an object of class - `smac.optimizer.smbo.SMBO `_. + `smac.optimizer.smbo.SMBO `_. The function must accept the arguments ``scenario_dict``, ``instances``, ``num_params``, ``runhistory``, ``seed`` and ``ta``. This is an advanced feature. Use only if you are familiar with - `SMAC `_. + `SMAC `_. logging_config : dict, optional (None) dictionary object specifying the logger configuration. If None, diff --git a/autosklearn/experimental/askl2.py b/autosklearn/experimental/askl2.py index 078355dfbb..47a3d4c5ab 100644 --- a/autosklearn/experimental/askl2.py +++ b/autosklearn/experimental/askl2.py @@ -263,7 +263,7 @@ def __init__( smac_scenario_args : dict, optional (None) Additional arguments inserted into the scenario of SMAC. See the - `SMAC documentation `_ + `SMAC documentation `_ for a list of available arguments. logging_config : dict, optional (None) diff --git a/examples/40_advanced/example_get_pipeline_components.py b/examples/40_advanced/example_get_pipeline_components.py index 80686889ac..4cf113aa4b 100644 --- a/examples/40_advanced/example_get_pipeline_components.py +++ b/examples/40_advanced/example_get_pipeline_components.py @@ -87,7 +87,7 @@ # # Auto-sklearn also keeps detailed statistics of the hyperparameter # optimization procedurce, which are stored in a so-called -# `run history `_. print(automl.automl_.runhistory_) From 2f355452e6bb1dc5365f00d1b57a7f286aeec2a2 Mon Sep 17 00:00:00 2001 From: eddiebergman Date: Tue, 16 Aug 2022 10:32:06 +0200 Subject: [PATCH 098/118] Drop 3.10 testing until scikit learn is updated --- .github/workflows/pytest.yml | 2 +- setup.py | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index 794157f602..64e1f26b5b 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -46,7 +46,7 @@ jobs: fail-fast: false matrix: os: [windows-latest, macos-latest, ubuntu-latest] - python-version: ['3.7', '3.8', '3.9', '3.10'] + python-version: ['3.7', '3.8', '3.9'] # 3.10 once updated kind: ['conda', 'source', 'dist'] exclude: diff --git a/setup.py b/setup.py index 84af24be33..aa6e42669e 100644 --- a/setup.py +++ b/setup.py @@ -1,4 +1,3 @@ -# -*- encoding: utf-8 -*- import os import sys From cd2f0debdd36b1a677a9afb84efd6f79e0267bfc Mon Sep 17 00:00:00 2001 From: eddiebergman Date: Tue, 16 Aug 2022 13:40:07 +0200 Subject: [PATCH 099/118] Fix doc links --- autosklearn/estimators.py | 4 ++-- autosklearn/experimental/askl2.py | 2 +- examples/40_advanced/example_get_pipeline_components.py | 3 +-- 3 files changed, 4 insertions(+), 5 deletions(-) diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 0549abf2d8..1a094d2582 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -275,12 +275,12 @@ def __init__( smac_scenario_args : dict, optional (None) Additional arguments inserted into the scenario of SMAC. See the - `SMAC documentation `_ + `SMAC documentation `_ for a list of available arguments. get_smac_object_callback : callable Callback function to create an object of class - `smac.optimizer.smbo.SMBO `_. + `smac.optimizer.smbo.SMBO `_. The function must accept the arguments ``scenario_dict``, ``instances``, ``num_params``, ``runhistory``, ``seed`` and ``ta``. This is an advanced feature. Use only if you are familiar with diff --git a/autosklearn/experimental/askl2.py b/autosklearn/experimental/askl2.py index 47a3d4c5ab..317f0be5b1 100644 --- a/autosklearn/experimental/askl2.py +++ b/autosklearn/experimental/askl2.py @@ -263,7 +263,7 @@ def __init__( smac_scenario_args : dict, optional (None) Additional arguments inserted into the scenario of SMAC. See the - `SMAC documentation `_ + `SMAC documentation `_ for a list of available arguments. logging_config : dict, optional (None) diff --git a/examples/40_advanced/example_get_pipeline_components.py b/examples/40_advanced/example_get_pipeline_components.py index 4cf113aa4b..81b23be3b5 100644 --- a/examples/40_advanced/example_get_pipeline_components.py +++ b/examples/40_advanced/example_get_pipeline_components.py @@ -87,8 +87,7 @@ # # Auto-sklearn also keeps detailed statistics of the hyperparameter # optimization procedurce, which are stored in a so-called -# `run history `_. +# `run history `_. print(automl.automl_.runhistory_) From 59b449e91e6a4e7208d0b24226e4468d38472a46 Mon Sep 17 00:00:00 2001 From: eddiebergman Date: Wed, 17 Aug 2022 12:38:46 +0200 Subject: [PATCH 100/118] Double timeout for 03_calculate_metafeatures --- test/test_scripts/test_metadata_generation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_scripts/test_metadata_generation.py b/test/test_scripts/test_metadata_generation.py index d888e8589c..088d5c93c9 100644 --- a/test/test_scripts/test_metadata_generation.py +++ b/test/test_scripts/test_metadata_generation.py @@ -252,7 +252,7 @@ def test_metadata_generation(self): shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, - timeout=90, + timeout=180, ) self.assertEqual(return_value.returncode, 0, msg=f"{cmd}\n{str(return_value)}") for task_type in ("classification", "regression"): From 6a54ab1599fd9f3c79af9ea440ee79b1c1e04bf4 Mon Sep 17 00:00:00 2001 From: eddiebergman Date: Thu, 18 Aug 2022 19:29:53 +0200 Subject: [PATCH 101/118] Revert "Double timeout for 03_calculate_metafeatures" This reverts commit b762b7c993d6ce4eef812df5e826a898a2953663. --- test/test_scripts/test_metadata_generation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_scripts/test_metadata_generation.py b/test/test_scripts/test_metadata_generation.py index 088d5c93c9..d888e8589c 100644 --- a/test/test_scripts/test_metadata_generation.py +++ b/test/test_scripts/test_metadata_generation.py @@ -252,7 +252,7 @@ def test_metadata_generation(self): shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, - timeout=180, + timeout=90, ) self.assertEqual(return_value.returncode, 0, msg=f"{cmd}\n{str(return_value)}") for task_type in ("classification", "regression"): From 38d96b02addb722418521eca88c0fe93b8029212 Mon Sep 17 00:00:00 2001 From: eddiebergman Date: Thu, 18 Aug 2022 19:30:45 +0200 Subject: [PATCH 102/118] Revert "Fix doc links" This reverts commit d7e75ef95d36fe44fff0c83f7b1de15f26367266. --- autosklearn/estimators.py | 4 ++-- autosklearn/experimental/askl2.py | 2 +- examples/40_advanced/example_get_pipeline_components.py | 3 ++- 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 1a094d2582..0549abf2d8 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -275,12 +275,12 @@ def __init__( smac_scenario_args : dict, optional (None) Additional arguments inserted into the scenario of SMAC. See the - `SMAC documentation `_ + `SMAC documentation `_ for a list of available arguments. get_smac_object_callback : callable Callback function to create an object of class - `smac.optimizer.smbo.SMBO `_. + `smac.optimizer.smbo.SMBO `_. The function must accept the arguments ``scenario_dict``, ``instances``, ``num_params``, ``runhistory``, ``seed`` and ``ta``. This is an advanced feature. Use only if you are familiar with diff --git a/autosklearn/experimental/askl2.py b/autosklearn/experimental/askl2.py index 317f0be5b1..47a3d4c5ab 100644 --- a/autosklearn/experimental/askl2.py +++ b/autosklearn/experimental/askl2.py @@ -263,7 +263,7 @@ def __init__( smac_scenario_args : dict, optional (None) Additional arguments inserted into the scenario of SMAC. See the - `SMAC documentation `_ + `SMAC documentation `_ for a list of available arguments. logging_config : dict, optional (None) diff --git a/examples/40_advanced/example_get_pipeline_components.py b/examples/40_advanced/example_get_pipeline_components.py index 81b23be3b5..4cf113aa4b 100644 --- a/examples/40_advanced/example_get_pipeline_components.py +++ b/examples/40_advanced/example_get_pipeline_components.py @@ -87,7 +87,8 @@ # # Auto-sklearn also keeps detailed statistics of the hyperparameter # optimization procedurce, which are stored in a so-called -# `run history `_. +# `run history `_. print(automl.automl_.runhistory_) From 33fcb330565bc527806657c8503732d56ff78cf4 Mon Sep 17 00:00:00 2001 From: eddiebergman Date: Thu, 18 Aug 2022 19:30:47 +0200 Subject: [PATCH 103/118] Revert "Drop 3.10 testing until scikit learn is updated" This reverts commit 686a56788ebaefbb96cd3dd81b665cd0a8ec48d9. --- .github/workflows/pytest.yml | 2 +- setup.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index 64e1f26b5b..794157f602 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -46,7 +46,7 @@ jobs: fail-fast: false matrix: os: [windows-latest, macos-latest, ubuntu-latest] - python-version: ['3.7', '3.8', '3.9'] # 3.10 once updated + python-version: ['3.7', '3.8', '3.9', '3.10'] kind: ['conda', 'source', 'dist'] exclude: diff --git a/setup.py b/setup.py index aa6e42669e..84af24be33 100644 --- a/setup.py +++ b/setup.py @@ -1,3 +1,4 @@ +# -*- encoding: utf-8 -*- import os import sys From 75d492edcb9c83e7a7f09e116115edd1e3ab5890 Mon Sep 17 00:00:00 2001 From: eddiebergman Date: Thu, 18 Aug 2022 19:30:48 +0200 Subject: [PATCH 104/118] Revert "Fix broken links" This reverts commit 0ed38e04ca544c68e0483503afd28e909bb75dbb. --- autosklearn/estimators.py | 6 +++--- autosklearn/experimental/askl2.py | 2 +- examples/40_advanced/example_get_pipeline_components.py | 2 +- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 0549abf2d8..607fb3dc2f 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -275,16 +275,16 @@ def __init__( smac_scenario_args : dict, optional (None) Additional arguments inserted into the scenario of SMAC. See the - `SMAC documentation `_ + `SMAC documentation `_ for a list of available arguments. get_smac_object_callback : callable Callback function to create an object of class - `smac.optimizer.smbo.SMBO `_. + `smac.optimizer.smbo.SMBO `_. The function must accept the arguments ``scenario_dict``, ``instances``, ``num_params``, ``runhistory``, ``seed`` and ``ta``. This is an advanced feature. Use only if you are familiar with - `SMAC `_. + `SMAC `_. logging_config : dict, optional (None) dictionary object specifying the logger configuration. If None, diff --git a/autosklearn/experimental/askl2.py b/autosklearn/experimental/askl2.py index 47a3d4c5ab..078355dfbb 100644 --- a/autosklearn/experimental/askl2.py +++ b/autosklearn/experimental/askl2.py @@ -263,7 +263,7 @@ def __init__( smac_scenario_args : dict, optional (None) Additional arguments inserted into the scenario of SMAC. See the - `SMAC documentation `_ + `SMAC documentation `_ for a list of available arguments. logging_config : dict, optional (None) diff --git a/examples/40_advanced/example_get_pipeline_components.py b/examples/40_advanced/example_get_pipeline_components.py index 4cf113aa4b..80686889ac 100644 --- a/examples/40_advanced/example_get_pipeline_components.py +++ b/examples/40_advanced/example_get_pipeline_components.py @@ -87,7 +87,7 @@ # # Auto-sklearn also keeps detailed statistics of the hyperparameter # optimization procedurce, which are stored in a so-called -# `run history `_. print(automl.automl_.runhistory_) From 59ef9a31d9f9ef0f2cbfde85793c88bf8c606506 Mon Sep 17 00:00:00 2001 From: eddiebergman Date: Thu, 18 Aug 2022 19:30:49 +0200 Subject: [PATCH 105/118] Revert "Remove odd duplications" This reverts commit 7065060544b33c04bbae1efd94691b999578d300. --- CONTRIBUTING.md | 2 +- .../pipeline/components/regression/gradient_boosting.py | 3 +++ autosklearn/pipeline/components/regression/random_forest.py | 3 +++ 3 files changed, 7 insertions(+), 1 deletion(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 376f717c63..73ce781618 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -46,7 +46,7 @@ Following that we'll tell you about how you can test your changes locally and th # If you missed the --recurse-submodules arg during clone or need to install the # submodule manually, then execute the following line: # - # git submodule update --init --recursive + # git submodule udate --init --recursive # ... Alternatively, if you would prefer a more manual method # Show all the available branches with a * beside your current one diff --git a/autosklearn/pipeline/components/regression/gradient_boosting.py b/autosklearn/pipeline/components/regression/gradient_boosting.py index 16b7df965d..04bc969b7f 100644 --- a/autosklearn/pipeline/components/regression/gradient_boosting.py +++ b/autosklearn/pipeline/components/regression/gradient_boosting.py @@ -130,6 +130,9 @@ def iterative_fit(self, X, y, n_iter=2, refit=False): if y.ndim == 2 and y.shape[1] == 1: y = y.flatten() + if y.ndim == 2 and y.shape[1] == 1: + y = y.flatten() + self.estimator.fit(X, y) if ( diff --git a/autosklearn/pipeline/components/regression/random_forest.py b/autosklearn/pipeline/components/regression/random_forest.py index 043d62e16b..068429e842 100644 --- a/autosklearn/pipeline/components/regression/random_forest.py +++ b/autosklearn/pipeline/components/regression/random_forest.py @@ -107,6 +107,9 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): if y.ndim == 2 and y.shape[1] == 1: y = y.flatten() + if y.ndim == 2 and y.shape[1] == 1: + y = y.flatten() + self.estimator.fit(X, y) return self From 3c52ac379317c00f4998045af88d2eed390d052a Mon Sep 17 00:00:00 2001 From: eddiebergman Date: Thu, 18 Aug 2022 19:30:50 +0200 Subject: [PATCH 106/118] Revert "Cleanup files from merge" This reverts commit a7f73f1563a25a74200692f615fa44b34a8a942c. --- autosklearn/automl.py | 1 + autosklearn/ensemble_builder.py | 1502 +++++++++++++++++ autosklearn/evaluation/abstract_evaluator.py | 2 + autosklearn/pipeline/components/base.py | 2 + autosklearn/util/single_thread_client.py | 1 + test/test_automl/test_automl.py | 1048 ++++++++++++ test/test_automl/test_estimators.py | 1278 ++++++++++++++ test/test_ensemble_builder/ensemble_utils.py | 75 +- test/test_evaluation/test_train_evaluator.py | 2 + .../components/classification/test_base.py | 2 + .../feature_preprocessing/test_liblinear.py | 3 + .../components/regression/test_base.py | 6 + test/test_pipeline/test_classification.py | 4 +- test/test_pipeline/test_regression.py | 8 +- 14 files changed, 3885 insertions(+), 49 deletions(-) create mode 100644 autosklearn/ensemble_builder.py create mode 100644 test/test_automl/test_automl.py create mode 100644 test/test_automl/test_estimators.py diff --git a/autosklearn/automl.py b/autosklearn/automl.py index e242fbbc08..25325d50ac 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -13,6 +13,7 @@ ) import copy +import distro import io import itertools import json diff --git a/autosklearn/ensemble_builder.py b/autosklearn/ensemble_builder.py new file mode 100644 index 0000000000..e337726b0e --- /dev/null +++ b/autosklearn/ensemble_builder.py @@ -0,0 +1,1502 @@ +# -*- encoding: utf-8 -*- +import glob +import gzip +import math +import numbers +import logging.handlers +import multiprocessing +import os +import pickle +import re +import shutil +import time +import traceback +from typing import List, Optional, Tuple, Union +import zlib + +import dask.distributed + +import numpy as np +import pandas as pd +import pynisher +from smac.callbacks import IncorporateRunResultCallback +from smac.optimizer.smbo import SMBO +from smac.runhistory.runhistory import RunInfo, RunValue +from smac.tae.base import StatusType + +from autosklearn.automl_common.common.utils.backend import Backend +from autosklearn.automl_common.common.ensemble_building.abstract_ensemble import AbstractEnsemble + +from autosklearn.constants import BINARY_CLASSIFICATION +from autosklearn.metrics import calculate_score, calculate_loss, Scorer +from autosklearn.ensembles.ensemble_selection import EnsembleSelection +from autosklearn.util.logging_ import get_named_client_logger +from autosklearn.util.parallel import preload_modules + +Y_ENSEMBLE = 0 +Y_VALID = 1 +Y_TEST = 2 + +MODEL_FN_RE = r'_([0-9]*)_([0-9]*)_([0-9]{1,3}\.[0-9]*)\.npy' + + +class EnsembleBuilderManager(IncorporateRunResultCallback): + def __init__( + self, + start_time: float, + time_left_for_ensembles: float, + backend: Backend, + dataset_name: str, + task: int, + metric: Scorer, + ensemble_size: int, + ensemble_nbest: int, + max_models_on_disc: Union[float, int], + seed: int, + precision: int, + max_iterations: Optional[int], + read_at_most: int, + ensemble_memory_limit: Optional[int], + random_state: Union[int, np.random.RandomState], + logger_port: int = logging.handlers.DEFAULT_TCP_LOGGING_PORT, + pynisher_context: str = 'fork', + ): + """ SMAC callback to handle ensemble building + + Parameters + ---------- + start_time: int + the time when this job was started, to account for any latency in job allocation + time_left_for_ensemble: int + How much time is left for the task. Job should finish within this allocated time + backend: util.backend.Backend + backend to write and read files + dataset_name: str + name of dataset + task_type: int + type of ML task + metric: str + name of metric to compute the loss of the given predictions + ensemble_size: int + maximal size of ensemble (passed to autosklearn.ensemble.ensemble_selection) + ensemble_nbest: int/float + if int: consider only the n best prediction + if float: consider only this fraction of the best models + Both wrt to validation predictions + If performance_range_threshold > 0, might return less models + max_models_on_disc: int + Defines the maximum number of models that are kept in the disc. + If int, it must be greater or equal than 1, and dictates the max number of + models to keep. + If float, it will be interpreted as the max megabytes allowed of disc space. That + is, if the number of ensemble candidates require more disc space than this float + value, the worst models will be deleted to keep within this budget. + Models and predictions of the worst-performing models will be deleted then. + If None, the feature is disabled. + It defines an upper bound on the models that can be used in the ensemble. + seed: int + random seed + max_iterations: int + maximal number of iterations to run this script + (default None --> deactivated) + precision: [16,32,64,128] + precision of floats to read the predictions + memory_limit: Optional[int] + memory limit in mb. If ``None``, no memory limit is enforced. + read_at_most: int + read at most n new prediction files in each iteration + logger_port: int + port that receives logging records + pynisher_context: str + The multiprocessing context for pynisher. One of spawn/fork/forkserver. + + Returns + ------- + List[Tuple[int, float, float, float]]: + A list with the performance history of this ensemble, of the form + [[pandas_timestamp, train_performance, val_performance, test_performance], ...] + """ + self.start_time = start_time + self.time_left_for_ensembles = time_left_for_ensembles + self.backend = backend + self.dataset_name = dataset_name + self.task = task + self.metric = metric + self.ensemble_size = ensemble_size + self.ensemble_nbest = ensemble_nbest + self.max_models_on_disc = max_models_on_disc + self.seed = seed + self.precision = precision + self.max_iterations = max_iterations + self.read_at_most = read_at_most + self.ensemble_memory_limit = ensemble_memory_limit + self.random_state = random_state + self.logger_port = logger_port + self.pynisher_context = pynisher_context + + # Store something similar to SMAC's runhistory + self.history = [] + + # We only submit new ensembles when there is not an active ensemble job + self.futures = [] + + # The last criteria is the number of iterations + self.iteration = 0 + + # Keep track of when we started to know when we need to finish! + self.start_time = time.time() + + def __call__( + self, + smbo: 'SMBO', + run_info: RunInfo, + result: RunValue, + time_left: float, + ): + if result.status in (StatusType.STOP, StatusType.ABORT) or smbo._stop: + return + self.build_ensemble(smbo.tae_runner.client) + + def build_ensemble( + self, + dask_client: dask.distributed.Client, + unit_test: bool = False + ) -> None: + + # The second criteria is elapsed time + elapsed_time = time.time() - self.start_time + + logger = get_named_client_logger( + name='EnsembleBuilder', + port=self.logger_port, + ) + + # First test for termination conditions + if self.time_left_for_ensembles < elapsed_time: + logger.info( + "Terminate ensemble building as not time is left (run for {}s)".format( + elapsed_time + ), + ) + return + if self.max_iterations is not None and self.max_iterations <= self.iteration: + logger.info( + "Terminate ensemble building because of max iterations: {} of {}".format( + self.max_iterations, + self.iteration + ) + ) + return + + if len(self.futures) != 0: + if self.futures[0].done(): + result = self.futures.pop().result() + if result: + ensemble_history, self.ensemble_nbest, _, _, _ = result + logger.debug("iteration={} @ elapsed_time={} has history={}".format( + self.iteration, + elapsed_time, + ensemble_history, + )) + self.history.extend(ensemble_history) + + # Only submit new jobs if the previous ensemble job finished + if len(self.futures) == 0: + + # Add the result of the run + # On the next while iteration, no references to + # ensemble builder object, so it should be garbage collected to + # save memory while waiting for resources + # Also, notice how ensemble nbest is returned, so we don't waste + # iterations testing if the deterministic predictions size can + # be fitted in memory + try: + # Submit a Dask job from this job, to properly + # see it in the dask diagnostic dashboard + # Notice that the forked ensemble_builder_process will + # wait for the below function to be done + self.futures.append(dask_client.submit( + fit_and_return_ensemble, + backend=self.backend, + dataset_name=self.dataset_name, + task_type=self.task, + metric=self.metric, + ensemble_size=self.ensemble_size, + ensemble_nbest=self.ensemble_nbest, + max_models_on_disc=self.max_models_on_disc, + seed=self.seed, + precision=self.precision, + memory_limit=self.ensemble_memory_limit, + read_at_most=self.read_at_most, + random_state=self.random_state, + end_at=self.start_time + self.time_left_for_ensembles, + iteration=self.iteration, + return_predictions=False, + priority=100, + pynisher_context=self.pynisher_context, + logger_port=self.logger_port, + unit_test=unit_test, + )) + + logger.info( + "{}/{} Started Ensemble builder job at {} for iteration {}.".format( + # Log the client to make sure we + # remain connected to the scheduler + self.futures[0], + dask_client, + time.strftime("%Y.%m.%d-%H.%M.%S"), + self.iteration, + ), + ) + self.iteration += 1 + except Exception as e: + exception_traceback = traceback.format_exc() + error_message = repr(e) + logger.critical(exception_traceback) + logger.critical(error_message) + + +def fit_and_return_ensemble( + backend: Backend, + dataset_name: str, + task_type: str, + metric: Scorer, + ensemble_size: int, + ensemble_nbest: int, + max_models_on_disc: Union[float, int], + seed: int, + precision: int, + read_at_most: int, + end_at: float, + iteration: int, + return_predictions: bool, + pynisher_context: str, + logger_port: int = logging.handlers.DEFAULT_TCP_LOGGING_PORT, + unit_test: bool = False, + memory_limit: Optional[int] = None, + random_state: Optional[Union[int, np.random.RandomState]] = None, +) -> Tuple[ + List[Tuple[int, float, float, float]], + int, + Optional[np.ndarray], + Optional[np.ndarray], + Optional[np.ndarray], +]: + """ + + A short function to fit and create an ensemble. It is just a wrapper to easily send + a request to dask to create an ensemble and clean the memory when finished + + Parameters + ---------- + backend: util.backend.Backend + backend to write and read files + dataset_name: str + name of dataset + metric: str + name of metric to compute the loss of the given predictions + task_type: int + type of ML task + ensemble_size: int + maximal size of ensemble (passed to autosklearn.ensemble.ensemble_selection) + ensemble_nbest: int/float + if int: consider only the n best prediction + if float: consider only this fraction of the best models + Both wrt to validation predictions + If performance_range_threshold > 0, might return less models + max_models_on_disc: int + Defines the maximum number of models that are kept in the disc. + If int, it must be greater or equal than 1, and dictates the max number of + models to keep. + If float, it will be interpreted as the max megabytes allowed of disc space. That + is, if the number of ensemble candidates require more disc space than this float + value, the worst models will be deleted to keep within this budget. + Models and predictions of the worst-performing models will be deleted then. + If None, the feature is disabled. + It defines an upper bound on the models that can be used in the ensemble. + seed: int + random seed + precision: [16,32,64,128] + precision of floats to read the predictions + read_at_most: int + read at most n new prediction files in each iteration + end_at: float + At what time the job must finish. Needs to be the endtime and not the time left + because we do not know when dask schedules the job. + iteration: int + The current iteration + pynisher_context: str + Context to use for multiprocessing, can be either fork, spawn or forkserver. + logger_port: int = DEFAULT_TCP_LOGGING_PORT + The port where the logging server is listening to. + unit_test: bool = False + Turn on unit testing mode. This currently makes fit_ensemble raise a MemoryError. + Having this is very bad coding style, but I did not find a way to make + unittest.mock work through the pynisher with all spawn contexts. If you know a + better solution, please let us know by opening an issue. + memory_limit: Optional[int] = None + memory limit in mb. If ``None``, no memory limit is enforced. + random_state: Optional[int | RandomState] = None + A random state used for the ensemble selection process. + + Returns + ------- + List[Tuple[int, float, float, float]] + A list with the performance history of this ensemble, of the form + [[pandas_timestamp, train_performance, val_performance, test_performance], ...] + + """ + result = EnsembleBuilder( + backend=backend, + dataset_name=dataset_name, + task_type=task_type, + metric=metric, + ensemble_size=ensemble_size, + ensemble_nbest=ensemble_nbest, + max_models_on_disc=max_models_on_disc, + seed=seed, + precision=precision, + memory_limit=memory_limit, + read_at_most=read_at_most, + random_state=random_state, + logger_port=logger_port, + unit_test=unit_test, + ).run( + end_at=end_at, + iteration=iteration, + return_predictions=return_predictions, + pynisher_context=pynisher_context, + ) + return result + + +class EnsembleBuilder(object): + def __init__( + self, + backend: Backend, + dataset_name: str, + task_type: int, + metric: Scorer, + ensemble_size: int = 10, + ensemble_nbest: Union[int, float] = 100, + max_models_on_disc: int = 100, + performance_range_threshold: float = 0, + seed: int = 1, + precision: int = 32, + memory_limit: Optional[int] = 1024, + read_at_most: int = 5, + logger_port: int = logging.handlers.DEFAULT_TCP_LOGGING_PORT, + random_state: Optional[Union[int, np.random.RandomState]] = None, + unit_test: bool = False, + ): + """ + Constructor + + Parameters + ---------- + backend: util.backend.Backend + backend to write and read files + dataset_name: str + name of dataset + task_type: int + type of ML task + metric: str + name of metric to compute the loss of the given predictions + ensemble_size: int = 10 + maximal size of ensemble (passed to autosklearn.ensemble.ensemble_selection) + ensemble_nbest: int | float = 100 + if int: consider only the n best prediction + if float: consider only this fraction of the best models + Both with respect to the validation predictions + If performance_range_threshold > 0, might return less models + max_models_on_disc: int = 100 + Defines the maximum number of models that are kept in the disc. + If int, it must be greater or equal than 1, and dictates the max number of + models to keep. + If float, it will be interpreted as the max megabytes allowed of disc space. That + is, if the number of ensemble candidates require more disc space than this float + value, the worst models will be deleted to keep within this budget. + Models and predictions of the worst-performing models will be deleted then. + If None, the feature is disabled. + It defines an upper bound on the models that can be used in the ensemble. + performance_range_threshold: float = 0 + Keep only models that are better than: + dummy + (best - dummy)*performance_range_threshold + E.g dummy=2, best=4, thresh=0.5 --> only consider models with loss > 3 + Will at most return the minimum between ensemble_nbest models, + and max_models_on_disc. Might return less + seed: int = 1 + random seed that is used as part of the filename + precision: int in [16,32,64,128] = 32 + precision of floats to read the predictions + memory_limit: Optional[int] = 1024 + memory limit in mb. If ``None``, no memory limit is enforced. + read_at_most: int = 5 + read at most n new prediction files in each iteration + logger_port: int = DEFAULT_TCP_LOGGING_PORT + port that receives logging records + random_state: Optional[int | RandomState] = None + An int or RandomState object used for generating the ensemble. + unit_test: bool = False + Turn on unit testing mode. This currently makes fit_ensemble raise a MemoryError. + Having this is very bad coding style, but I did not find a way to make + unittest.mock work through the pynisher with all spawn contexts. If you know a + better solution, please let us know by opening an issue. + """ + + super(EnsembleBuilder, self).__init__() + + self.backend = backend # communication with filesystem + self.dataset_name = dataset_name + self.task_type = task_type + self.metric = metric + self.ensemble_size = ensemble_size + self.performance_range_threshold = performance_range_threshold + + if isinstance(ensemble_nbest, numbers.Integral) and ensemble_nbest < 1: + raise ValueError("Integer ensemble_nbest has to be larger 1: %s" % + ensemble_nbest) + elif not isinstance(ensemble_nbest, numbers.Integral): + if ensemble_nbest < 0 or ensemble_nbest > 1: + raise ValueError( + "Float ensemble_nbest best has to be >= 0 and <= 1: %s" % + ensemble_nbest) + + self.ensemble_nbest = ensemble_nbest + + # max_models_on_disc can be a float, in such case we need to + # remember the user specified Megabytes and translate this to + # max number of ensemble models. max_resident_models keeps the + # maximum number of models in disc + if max_models_on_disc is not None and max_models_on_disc < 0: + raise ValueError( + "max_models_on_disc has to be a positive number or None" + ) + self.max_models_on_disc = max_models_on_disc + self.max_resident_models = None + + self.seed = seed + self.precision = precision + self.memory_limit = memory_limit + self.read_at_most = read_at_most + self.random_state = random_state + self.unit_test = unit_test + + # Setup the logger + self.logger_port = logger_port + self.logger = get_named_client_logger( + name='EnsembleBuilder', + port=self.logger_port, + ) + + if ensemble_nbest == 1: + self.logger.debug("Behaviour depends on int/float: %s, %s (ensemble_nbest, type)" % + (ensemble_nbest, type(ensemble_nbest))) + + self.start_time = 0 + self.model_fn_re = re.compile(MODEL_FN_RE) + + self.last_hash = None # hash of ensemble training data + self.y_true_ensemble = None + self.SAVE2DISC = True + + # already read prediction files + # {"file name": { + # "ens_loss": float + # "mtime_ens": str, + # "mtime_valid": str, + # "mtime_test": str, + # "seed": int, + # "num_run": int, + # }} + self.read_losses = {} + # {"file_name": { + # Y_ENSEMBLE: np.ndarray + # Y_VALID: np.ndarray + # Y_TEST: np.ndarray + # } + # } + self.read_preds = {} + + # Depending on the dataset dimensions, + # regenerating every iteration, the predictions + # losses for self.read_preds + # is too computationally expensive + # As the ensemble builder is stateless + # (every time the ensemble builder gets resources + # from dask, it builds this object from scratch) + # we save the state of this dictionary to memory + # and read it if available + self.ensemble_memory_file = os.path.join( + self.backend.internals_directory, + 'ensemble_read_preds.pkl' + ) + if os.path.exists(self.ensemble_memory_file): + try: + with (open(self.ensemble_memory_file, "rb")) as memory: + self.read_preds, self.last_hash = pickle.load(memory) + except Exception as e: + self.logger.warning( + "Could not load the previous iterations of ensemble_builder predictions." + "This might impact the quality of the run. Exception={} {}".format( + e, + traceback.format_exc(), + ) + ) + self.ensemble_loss_file = os.path.join( + self.backend.internals_directory, + 'ensemble_read_losses.pkl' + ) + if os.path.exists(self.ensemble_loss_file): + try: + with (open(self.ensemble_loss_file, "rb")) as memory: + self.read_losses = pickle.load(memory) + except Exception as e: + self.logger.warning( + "Could not load the previous iterations of ensemble_builder losses." + "This might impact the quality of the run. Exception={} {}".format( + e, + traceback.format_exc(), + ) + ) + + # hidden feature which can be activated via an environment variable. This keeps all + # models and predictions which have ever been a candidate. This is necessary to post-hoc + # compute the whole ensemble building trajectory. + self._has_been_candidate = set() + + self.validation_performance_ = np.inf + + # Track the ensemble performance + datamanager = self.backend.load_datamanager() + self.y_valid = datamanager.data.get('Y_valid') + self.y_test = datamanager.data.get('Y_test') + del datamanager + self.ensemble_history = [] + + def run( + self, + iteration: int, + pynisher_context: str, + time_left: Optional[float] = None, + end_at: Optional[float] = None, + time_buffer=5, + return_predictions: bool = False, + ): + + if time_left is None and end_at is None: + raise ValueError('Must provide either time_left or end_at.') + elif time_left is not None and end_at is not None: + raise ValueError('Cannot provide both time_left and end_at.') + + self.logger = get_named_client_logger( + name='EnsembleBuilder', + port=self.logger_port, + ) + + process_start_time = time.time() + while True: + + if time_left is not None: + time_elapsed = time.time() - process_start_time + time_left -= time_elapsed + else: + current_time = time.time() + if current_time > end_at: + break + else: + time_left = end_at - current_time + + wall_time_in_s = int(time_left - time_buffer) + if wall_time_in_s < 1: + break + context = multiprocessing.get_context(pynisher_context) + preload_modules(context) + + safe_ensemble_script = pynisher.enforce_limits( + wall_time_in_s=wall_time_in_s, + mem_in_mb=self.memory_limit, + logger=self.logger, + context=context, + )(self.main) + safe_ensemble_script(time_left, iteration, return_predictions) + if safe_ensemble_script.exit_status is pynisher.MemorylimitException: + # if ensemble script died because of memory error, + # reduce nbest to reduce memory consumption and try it again + + # ATTENTION: main will start from scratch; # all data structures are empty again + try: + os.remove(self.ensemble_memory_file) + except: # noqa E722 + pass + + if isinstance(self.ensemble_nbest, numbers.Integral) and self.ensemble_nbest <= 1: + if self.read_at_most == 1: + self.logger.error( + "Memory Exception -- Unable to further reduce the number of ensemble " + "members and can no further limit the number of ensemble members " + "loaded per iteration -- please restart Auto-sklearn with a higher " + "value for the argument `memory_limit` (current limit is %s MB). " + "The ensemble builder will keep running to delete files from disk in " + "case this was enabled.", self.memory_limit + ) + self.ensemble_nbest = 0 + else: + self.read_at_most = 1 + self.logger.warning( + "Memory Exception -- Unable to further reduce the number of ensemble " + "members -- Now reducing the number of predictions per call to read " + "at most to 1." + ) + else: + if isinstance(self.ensemble_nbest, numbers.Integral): + self.ensemble_nbest = max(1, int(self.ensemble_nbest / 2)) + else: + self.ensemble_nbest = self.ensemble_nbest / 2 + self.logger.warning("Memory Exception -- restart with " + "less ensemble_nbest: %d" % self.ensemble_nbest) + return [], self.ensemble_nbest, None, None, None + else: + return safe_ensemble_script.result + + return [], self.ensemble_nbest, None, None, None + + def main(self, time_left, iteration, return_predictions): + + # Pynisher jobs inside dask 'forget' + # the logger configuration. So we have to set it up + # accordingly + self.logger = get_named_client_logger( + name='EnsembleBuilder', + port=self.logger_port, + ) + + self.start_time = time.time() + train_pred, valid_pred, test_pred = None, None, None + + used_time = time.time() - self.start_time + self.logger.debug( + 'Starting iteration %d, time left: %f', + iteration, + time_left - used_time, + ) + + # populates self.read_preds and self.read_losses + if not self.compute_loss_per_model(): + if return_predictions: + return self.ensemble_history, self.ensemble_nbest, train_pred, valid_pred, test_pred + else: + return self.ensemble_history, self.ensemble_nbest, None, None, None + + # Only the models with the n_best predictions are candidates + # to be in the ensemble + candidate_models = self.get_n_best_preds() + if not candidate_models: # no candidates yet + if return_predictions: + return self.ensemble_history, self.ensemble_nbest, train_pred, valid_pred, test_pred + else: + return self.ensemble_history, self.ensemble_nbest, None, None, None + + # populates predictions in self.read_preds + # reduces selected models if file reading failed + n_sel_valid, n_sel_test = self. \ + get_valid_test_preds(selected_keys=candidate_models) + + # If valid/test predictions loaded, then reduce candidate models to this set + if len(n_sel_test) != 0 and len(n_sel_valid) != 0 \ + and len(set(n_sel_valid).intersection(set(n_sel_test))) == 0: + # Both n_sel_* have entries, but there is no overlap, this is critical + self.logger.error("n_sel_valid and n_sel_test are not empty, but do not overlap") + if return_predictions: + return self.ensemble_history, self.ensemble_nbest, train_pred, valid_pred, test_pred + else: + return self.ensemble_history, self.ensemble_nbest, None, None, None + + # If any of n_sel_* is not empty and overlaps with candidate_models, + # then ensure candidate_models AND n_sel_test are sorted the same + candidate_models_set = set(candidate_models) + if candidate_models_set.intersection(n_sel_valid).intersection(n_sel_test): + candidate_models = sorted(list(candidate_models_set.intersection( + n_sel_valid).intersection(n_sel_test))) + n_sel_test = candidate_models + n_sel_valid = candidate_models + elif candidate_models_set.intersection(n_sel_valid): + candidate_models = sorted(list(candidate_models_set.intersection( + n_sel_valid))) + n_sel_valid = candidate_models + elif candidate_models_set.intersection(n_sel_test): + candidate_models = sorted(list(candidate_models_set.intersection( + n_sel_test))) + n_sel_test = candidate_models + else: + # This has to be the case + n_sel_test = [] + n_sel_valid = [] + + if os.environ.get('ENSEMBLE_KEEP_ALL_CANDIDATES'): + for candidate in candidate_models: + self._has_been_candidate.add(candidate) + + # train ensemble + ensemble = self.fit_ensemble(selected_keys=candidate_models) + + # Save the ensemble for later use in the main auto-sklearn module! + if ensemble is not None and self.SAVE2DISC: + self.backend.save_ensemble(ensemble, iteration, self.seed) + + # Delete files of non-candidate models - can only be done after fitting the ensemble and + # saving it to disc so we do not accidentally delete models in the previous ensemble + if self.max_resident_models is not None: + self._delete_excess_models(selected_keys=candidate_models) + + # Save the read losses status for the next iteration + with open(self.ensemble_loss_file, "wb") as memory: + pickle.dump(self.read_losses, memory) + + if ensemble is not None: + train_pred = self.predict(set_="train", + ensemble=ensemble, + selected_keys=candidate_models, + n_preds=len(candidate_models), + index_run=iteration) + # We can't use candidate_models here, as n_sel_* might be empty + valid_pred = self.predict(set_="valid", + ensemble=ensemble, + selected_keys=n_sel_valid, + n_preds=len(candidate_models), + index_run=iteration) + # TODO if predictions fails, build the model again during the + # next iteration! + test_pred = self.predict(set_="test", + ensemble=ensemble, + selected_keys=n_sel_test, + n_preds=len(candidate_models), + index_run=iteration) + + # Add a score to run history to see ensemble progress + self._add_ensemble_trajectory( + train_pred, + valid_pred, + test_pred + ) + + # The loaded predictions and the hash can only be saved after the ensemble has been + # built, because the hash is computed during the construction of the ensemble + with open(self.ensemble_memory_file, "wb") as memory: + pickle.dump((self.read_preds, self.last_hash), memory) + + if return_predictions: + return self.ensemble_history, self.ensemble_nbest, train_pred, valid_pred, test_pred + else: + return self.ensemble_history, self.ensemble_nbest, None, None, None + + def get_disk_consumption(self, pred_path): + """ + gets the cost of a model being on disc + """ + + match = self.model_fn_re.search(pred_path) + if not match: + raise ValueError("Invalid path format %s" % pred_path) + _seed = int(match.group(1)) + _num_run = int(match.group(2)) + _budget = float(match.group(3)) + + stored_files_for_run = os.listdir( + self.backend.get_numrun_directory(_seed, _num_run, _budget)) + stored_files_for_run = [ + os.path.join(self.backend.get_numrun_directory(_seed, _num_run, _budget), file_name) + for file_name in stored_files_for_run] + this_model_cost = sum([os.path.getsize(path) for path in stored_files_for_run]) + + # get the megabytes + return round(this_model_cost / math.pow(1024, 2), 2) + + def compute_loss_per_model(self): + """ + Compute the loss of the predictions on ensemble building data set; + populates self.read_preds and self.read_losses + """ + + self.logger.debug("Read ensemble data set predictions") + + if self.y_true_ensemble is None: + try: + self.y_true_ensemble = self.backend.load_targets_ensemble() + except FileNotFoundError: + self.logger.debug( + "Could not find true targets on ensemble data set: %s", + traceback.format_exc(), + ) + return False + + pred_path = os.path.join( + glob.escape(self.backend.get_runs_directory()), + '%d_*_*' % self.seed, + 'predictions_ensemble_%s_*_*.npy*' % self.seed, + ) + y_ens_files = glob.glob(pred_path) + y_ens_files = [y_ens_file for y_ens_file in y_ens_files + if y_ens_file.endswith('.npy') or y_ens_file.endswith('.npy.gz')] + self.y_ens_files = y_ens_files + # no validation predictions so far -- no files + if len(self.y_ens_files) == 0: + self.logger.debug("Found no prediction files on ensemble data set:" + " %s" % pred_path) + return False + + # First sort files chronologically + to_read = [] + for y_ens_fn in self.y_ens_files: + match = self.model_fn_re.search(y_ens_fn) + _seed = int(match.group(1)) + _num_run = int(match.group(2)) + _budget = float(match.group(3)) + mtime = os.path.getmtime(y_ens_fn) + + to_read.append([y_ens_fn, match, _seed, _num_run, _budget, mtime]) + + n_read_files = 0 + # Now read file wrt to num_run + for y_ens_fn, match, _seed, _num_run, _budget, mtime in \ + sorted(to_read, key=lambda x: x[5]): + if self.read_at_most and n_read_files >= self.read_at_most: + # limit the number of files that will be read + # to limit memory consumption + break + + if not y_ens_fn.endswith(".npy") and not y_ens_fn.endswith(".npy.gz"): + self.logger.info('Error loading file (not .npy or .npy.gz): %s', y_ens_fn) + continue + + if not self.read_losses.get(y_ens_fn): + self.read_losses[y_ens_fn] = { + "ens_loss": np.inf, + "mtime_ens": 0, + "mtime_valid": 0, + "mtime_test": 0, + "seed": _seed, + "num_run": _num_run, + "budget": _budget, + "disc_space_cost_mb": None, + # Lazy keys so far: + # 0 - not loaded + # 1 - loaded and in memory + # 2 - loaded but dropped again + # 3 - deleted from disk due to space constraints + "loaded": 0 + } + if not self.read_preds.get(y_ens_fn): + self.read_preds[y_ens_fn] = { + Y_ENSEMBLE: None, + Y_VALID: None, + Y_TEST: None, + } + + if self.read_losses[y_ens_fn]["mtime_ens"] == mtime: + # same time stamp; nothing changed; + continue + + # actually read the predictions and compute their respective loss + try: + y_ensemble = self._read_np_fn(y_ens_fn) + loss = calculate_loss(solution=self.y_true_ensemble, + prediction=y_ensemble, + task_type=self.task_type, + metric=self.metric, + scoring_functions=None) + + if np.isfinite(self.read_losses[y_ens_fn]["ens_loss"]): + self.logger.debug( + 'Changing ensemble loss for file %s from %f to %f ' + 'because file modification time changed? %f - %f', + y_ens_fn, + self.read_losses[y_ens_fn]["ens_loss"], + loss, + self.read_losses[y_ens_fn]["mtime_ens"], + os.path.getmtime(y_ens_fn), + ) + + self.read_losses[y_ens_fn]["ens_loss"] = loss + + # It is not needed to create the object here + # To save memory, we just compute the loss. + self.read_losses[y_ens_fn]["mtime_ens"] = os.path.getmtime(y_ens_fn) + self.read_losses[y_ens_fn]["loaded"] = 2 + self.read_losses[y_ens_fn]["disc_space_cost_mb"] = self.get_disk_consumption( + y_ens_fn + ) + + n_read_files += 1 + + except Exception: + self.logger.warning( + 'Error loading %s: %s', + y_ens_fn, + traceback.format_exc(), + ) + self.read_losses[y_ens_fn]["ens_loss"] = np.inf + + self.logger.debug( + 'Done reading %d new prediction files. Loaded %d predictions in ' + 'total.', + n_read_files, + np.sum([pred["loaded"] > 0 for pred in self.read_losses.values()]) + ) + return True + + def get_n_best_preds(self): + """ + get best n predictions (i.e., keys of self.read_losses) + according to the loss on the "ensemble set" + n: self.ensemble_nbest + + Side effects: + ->Define the n-best models to use in ensemble + ->Only the best models are loaded + ->Any model that is not best is candidate to deletion + if max models in disc is exceeded. + """ + + sorted_keys = self._get_list_of_sorted_preds() + + # number of models available + num_keys = len(sorted_keys) + # remove all that are at most as good as random + # note: dummy model must have run_id=1 (there is no run_id=0) + dummy_losses = list(filter(lambda x: x[2] == 1, sorted_keys)) + # number of dummy models + num_dummy = len(dummy_losses) + dummy_loss = dummy_losses[0] + self.logger.debug("Use %f as dummy loss" % dummy_loss[1]) + + # sorted_keys looks like: (k, v["ens_loss"], v["num_run"]) + # On position 1 we have the loss of a minimization problem. + # keep only the predictions with a loss smaller than the dummy + # prediction + sorted_keys = filter(lambda x: x[1] < dummy_loss[1], sorted_keys) + + # remove Dummy Classifier + sorted_keys = list(filter(lambda x: x[2] > 1, sorted_keys)) + if not sorted_keys: + # no model left; try to use dummy loss (num_run==0) + # log warning when there are other models but not better than dummy model + if num_keys > num_dummy: + self.logger.warning("No models better than random - using Dummy loss!" + "Number of models besides current dummy model: %d. " + "Number of dummy models: %d", + num_keys - 1, + num_dummy) + sorted_keys = [ + (k, v["ens_loss"], v["num_run"]) for k, v in self.read_losses.items() + if v["seed"] == self.seed and v["num_run"] == 1 + ] + # reload predictions if losses changed over time and a model is + # considered to be in the top models again! + if not isinstance(self.ensemble_nbest, numbers.Integral): + # Transform to number of models to keep. Keep at least one + keep_nbest = max(1, min(len(sorted_keys), + int(len(sorted_keys) * self.ensemble_nbest))) + self.logger.debug( + "Library pruning: using only top %f percent of the models for ensemble " + "(%d out of %d)", + self.ensemble_nbest * 100, keep_nbest, len(sorted_keys) + ) + else: + # Keep only at most ensemble_nbest + keep_nbest = min(self.ensemble_nbest, len(sorted_keys)) + self.logger.debug("Library Pruning: using for ensemble only " + " %d (out of %d) models" % (keep_nbest, len(sorted_keys))) + + # If max_models_on_disc is None, do nothing + # One can only read at most max_models_on_disc models + if self.max_models_on_disc is not None: + if not isinstance(self.max_models_on_disc, numbers.Integral): + consumption = [ + [ + v["ens_loss"], + v["disc_space_cost_mb"], + ] for v in self.read_losses.values() if v["disc_space_cost_mb"] is not None + ] + max_consumption = max(c[1] for c in consumption) + + # We are pessimistic with the consumption limit indicated by + # max_models_on_disc by 1 model. Such model is assumed to spend + # max_consumption megabytes + if (sum(c[1] for c in consumption) + max_consumption) > self.max_models_on_disc: + + # just leave the best -- smaller is better! + # This list is in descending order, to preserve the best models + sorted_cum_consumption = np.cumsum([ + c[1] for c in list(sorted(consumption)) + ]) + max_consumption + max_models = np.argmax(sorted_cum_consumption > self.max_models_on_disc) + + # Make sure that at least 1 model survives + self.max_resident_models = max(1, max_models) + self.logger.warning( + "Limiting num of models via float max_models_on_disc={}" + " as accumulated={} worst={} num_models={}".format( + self.max_models_on_disc, + (sum(c[1] for c in consumption) + max_consumption), + max_consumption, + self.max_resident_models + ) + ) + else: + self.max_resident_models = None + else: + self.max_resident_models = self.max_models_on_disc + + if self.max_resident_models is not None and keep_nbest > self.max_resident_models: + self.logger.debug( + "Restricting the number of models to %d instead of %d due to argument " + "max_models_on_disc", + self.max_resident_models, keep_nbest, + ) + keep_nbest = self.max_resident_models + + # consider performance_range_threshold + if self.performance_range_threshold > 0: + best_loss = sorted_keys[0][1] + worst_loss = dummy_loss[1] + worst_loss -= (worst_loss - best_loss) * self.performance_range_threshold + if sorted_keys[keep_nbest - 1][1] > worst_loss: + # We can further reduce number of models + # since worst model is worse than thresh + for i in range(0, keep_nbest): + # Look at most at keep_nbest models, + # but always keep at least one model + current_loss = sorted_keys[i][1] + if current_loss >= worst_loss: + self.logger.debug("Dynamic Performance range: " + "Further reduce from %d to %d models", + keep_nbest, max(1, i)) + keep_nbest = max(1, i) + break + ensemble_n_best = keep_nbest + + # reduce to keys + sorted_keys = list(map(lambda x: x[0], sorted_keys)) + + # remove loaded predictions for non-winning models + for k in sorted_keys[ensemble_n_best:]: + if k in self.read_preds: + self.read_preds[k][Y_ENSEMBLE] = None + self.read_preds[k][Y_VALID] = None + self.read_preds[k][Y_TEST] = None + if self.read_losses[k]['loaded'] == 1: + self.logger.debug( + 'Dropping model %s (%d,%d) with loss %f.', + k, + self.read_losses[k]['seed'], + self.read_losses[k]['num_run'], + self.read_losses[k]['ens_loss'], + ) + self.read_losses[k]['loaded'] = 2 + + # Load the predictions for the winning + for k in sorted_keys[:ensemble_n_best]: + if ( + ( + k not in self.read_preds or + self.read_preds[k][Y_ENSEMBLE] is None + ) + and self.read_losses[k]['loaded'] != 3 + ): + self.read_preds[k][Y_ENSEMBLE] = self._read_np_fn(k) + # No need to load valid and test here because they are loaded + # only if the model ends up in the ensemble + self.read_losses[k]['loaded'] = 1 + + # return keys of self.read_losses with lowest losses + return sorted_keys[:ensemble_n_best] + + def get_valid_test_preds(self, selected_keys: List[str]) -> Tuple[List[str], List[str]]: + """ + get valid and test predictions from disc + and store them in self.read_preds + + Parameters + --------- + selected_keys: list + list of selected keys of self.read_preds + + Return + ------ + success_keys: + all keys in selected keys for which we could read the valid and + test predictions + """ + success_keys_valid = [] + success_keys_test = [] + + for k in selected_keys: + valid_fn = glob.glob( + os.path.join( + glob.escape(self.backend.get_runs_directory()), + '%d_%d_%s' % ( + self.read_losses[k]["seed"], + self.read_losses[k]["num_run"], + self.read_losses[k]["budget"], + ), + 'predictions_valid_%d_%d_%s.npy*' % ( + self.read_losses[k]["seed"], + self.read_losses[k]["num_run"], + self.read_losses[k]["budget"], + ) + ) + ) + valid_fn = [vfn for vfn in valid_fn if vfn.endswith('.npy') or vfn.endswith('.npy.gz')] + test_fn = glob.glob( + os.path.join( + glob.escape(self.backend.get_runs_directory()), + '%d_%d_%s' % ( + self.read_losses[k]["seed"], + self.read_losses[k]["num_run"], + self.read_losses[k]["budget"], + ), + 'predictions_test_%d_%d_%s.npy*' % ( + self.read_losses[k]["seed"], + self.read_losses[k]["num_run"], + self.read_losses[k]["budget"] + ) + ) + ) + test_fn = [tfn for tfn in test_fn if tfn.endswith('.npy') or tfn.endswith('.npy.gz')] + + if len(valid_fn) == 0: + # self.logger.debug("Not found validation prediction file " + # "(although ensemble predictions available): " + # "%s" % valid_fn) + pass + else: + valid_fn = valid_fn[0] + if ( + self.read_losses[k]["mtime_valid"] == os.path.getmtime(valid_fn) + and k in self.read_preds + and self.read_preds[k][Y_VALID] is not None + ): + success_keys_valid.append(k) + continue + try: + y_valid = self._read_np_fn(valid_fn) + self.read_preds[k][Y_VALID] = y_valid + success_keys_valid.append(k) + self.read_losses[k]["mtime_valid"] = os.path.getmtime(valid_fn) + except Exception: + self.logger.warning('Error loading %s: %s', + valid_fn, traceback.format_exc()) + + if len(test_fn) == 0: + # self.logger.debug("Not found test prediction file (although " + # "ensemble predictions available):%s" % + # test_fn) + pass + else: + test_fn = test_fn[0] + if ( + self.read_losses[k]["mtime_test"] == os.path.getmtime(test_fn) + and k in self.read_preds + and self.read_preds[k][Y_TEST] is not None + ): + success_keys_test.append(k) + continue + try: + y_test = self._read_np_fn(test_fn) + self.read_preds[k][Y_TEST] = y_test + success_keys_test.append(k) + self.read_losses[k]["mtime_test"] = os.path.getmtime(test_fn) + except Exception: + self.logger.warning('Error loading %s: %s', + test_fn, traceback.format_exc()) + + return success_keys_valid, success_keys_test + + def fit_ensemble(self, selected_keys: list): + """ + fit ensemble + + Parameters + --------- + selected_keys: list + list of selected keys of self.read_losses + + Returns + ------- + ensemble: EnsembleSelection + trained Ensemble + """ + + if self.unit_test: + raise MemoryError() + + predictions_train = [self.read_preds[k][Y_ENSEMBLE] for k in selected_keys] + include_num_runs = [ + ( + self.read_losses[k]["seed"], + self.read_losses[k]["num_run"], + self.read_losses[k]["budget"], + ) + for k in selected_keys] + + # check hash if ensemble training data changed + current_hash = "".join([ + str(zlib.adler32(predictions_train[i].data.tobytes())) + for i in range(len(predictions_train)) + ]) + if self.last_hash == current_hash: + self.logger.debug( + "No new model predictions selected -- skip ensemble building " + "-- current performance: %f", + self.validation_performance_, + ) + + return None + self.last_hash = current_hash + + ensemble = EnsembleSelection( + ensemble_size=self.ensemble_size, + task_type=self.task_type, + metric=self.metric, + random_state=self.random_state, + ) + + try: + self.logger.debug( + "Fitting the ensemble on %d models.", + len(predictions_train), + ) + start_time = time.time() + ensemble.fit(predictions_train, self.y_true_ensemble, + include_num_runs) + end_time = time.time() + self.logger.debug( + "Fitting the ensemble took %.2f seconds.", + end_time - start_time, + ) + self.logger.info(ensemble) + self.validation_performance_ = min( + self.validation_performance_, + ensemble.get_validation_performance(), + ) + + except ValueError: + self.logger.error('Caught ValueError: %s', traceback.format_exc()) + return None + except IndexError: + self.logger.error('Caught IndexError: %s' + traceback.format_exc()) + return None + finally: + # Explicitly free memory + del predictions_train + + return ensemble + + def predict(self, set_: str, + ensemble: AbstractEnsemble, + selected_keys: list, + n_preds: int, + index_run: int): + """ + save preditions on ensemble, validation and test data on disc + + Parameters + ---------- + set_: ["valid","test"] + data split name + ensemble: EnsembleSelection + trained Ensemble + selected_keys: list + list of selected keys of self.read_losses + n_preds: int + number of prediction models used for ensemble building + same number of predictions on valid and test are necessary + index_run: int + n-th time that ensemble predictions are written to disc + + Return + ------ + y: np.ndarray + """ + self.logger.debug("Predicting the %s set with the ensemble!", set_) + + if set_ == 'valid': + pred_set = Y_VALID + elif set_ == 'test': + pred_set = Y_TEST + else: + pred_set = Y_ENSEMBLE + predictions = [self.read_preds[k][pred_set] for k in selected_keys] + + if n_preds == len(predictions): + y = ensemble.predict(predictions) + if self.task_type == BINARY_CLASSIFICATION: + y = y[:, 1] + return y + else: + self.logger.info( + "Found inconsistent number of predictions and models (%d vs " + "%d) for subset %s", + len(predictions), + n_preds, + set_, + ) + return None + + def _add_ensemble_trajectory(self, train_pred, valid_pred, test_pred): + """ + Records a snapshot of how the performance look at a given training + time. + + Parameters + ---------- + ensemble: EnsembleSelection + The ensemble selection object to record + valid_pred: np.ndarray + The predictions on the validation set using ensemble + test_pred: np.ndarray + The predictions on the test set using ensemble + + """ + if self.task_type == BINARY_CLASSIFICATION: + if len(train_pred.shape) == 1 or train_pred.shape[1] == 1: + train_pred = np.vstack( + ((1 - train_pred).reshape((1, -1)), train_pred.reshape((1, -1))) + ).transpose() + if valid_pred is not None and (len(valid_pred.shape) == 1 or valid_pred.shape[1] == 1): + valid_pred = np.vstack( + ((1 - valid_pred).reshape((1, -1)), valid_pred.reshape((1, -1))) + ).transpose() + if test_pred is not None and (len(test_pred.shape) == 1 or test_pred.shape[1] == 1): + test_pred = np.vstack( + ((1 - test_pred).reshape((1, -1)), test_pred.reshape((1, -1))) + ).transpose() + + performance_stamp = { + 'Timestamp': pd.Timestamp.now(), + 'ensemble_optimization_score': calculate_score( + solution=self.y_true_ensemble, + prediction=train_pred, + task_type=self.task_type, + metric=self.metric, + scoring_functions=None + ) + } + if valid_pred is not None: + # TODO: valid_pred are a legacy from competition manager + # and this if never happens. Re-evaluate Y_valid support + performance_stamp['ensemble_val_score'] = calculate_score( + solution=self.y_valid, + prediction=valid_pred, + task_type=self.task_type, + metric=self.metric, + scoring_functions=None + ) + + # In case test_pred was provided + if test_pred is not None: + performance_stamp['ensemble_test_score'] = calculate_score( + solution=self.y_test, + prediction=test_pred, + task_type=self.task_type, + metric=self.metric, + scoring_functions=None + ) + + self.ensemble_history.append(performance_stamp) + + def _get_list_of_sorted_preds(self): + """ + Returns a list of sorted predictions in descending order + Losses are taken from self.read_losses. + + Parameters + ---------- + None + + Return + ------ + sorted_keys: list + """ + # Sort by loss - smaller is better! + sorted_keys = list(sorted( + [ + (k, v["ens_loss"], v["num_run"]) + for k, v in self.read_losses.items() + ], + # Sort by loss as priority 1 and then by num_run on a ascending order + # We want small num_run first + key=lambda x: (x[1], x[2]), + )) + return sorted_keys + + def _delete_excess_models(self, selected_keys: List[str]): + """ + Deletes models excess models on disc. self.max_models_on_disc + defines the upper limit on how many models to keep. + Any additional model with a worst loss than the top + self.max_models_on_disc is deleted. + + """ + + # Loop through the files currently in the directory + for pred_path in self.y_ens_files: + + # Do not delete candidates + if pred_path in selected_keys: + continue + + if pred_path in self._has_been_candidate: + continue + + match = self.model_fn_re.search(pred_path) + _seed = int(match.group(1)) + _num_run = int(match.group(2)) + _budget = float(match.group(3)) + + # Do not delete the dummy prediction + if _num_run == 1: + continue + + numrun_dir = self.backend.get_numrun_directory(_seed, _num_run, _budget) + try: + os.rename(numrun_dir, numrun_dir + '.old') + shutil.rmtree(numrun_dir + '.old') + self.logger.info("Deleted files of non-candidate model %s", pred_path) + self.read_losses[pred_path]["disc_space_cost_mb"] = None + self.read_losses[pred_path]["loaded"] = 3 + self.read_losses[pred_path]["ens_loss"] = np.inf + except Exception as e: + self.logger.error( + "Failed to delete files of non-candidate model %s due" + " to error %s", pred_path, e + ) + + def _read_np_fn(self, path): + + # Support for string precision + if isinstance(self.precision, str): + precision = int(self.precision) + self.logger.warning("Interpreted str-precision as {}".format( + precision + )) + else: + precision = self.precision + + if path.endswith("gz"): + open_method = gzip.open + elif path.endswith("npy"): + open_method = open + else: + raise ValueError("Unknown filetype %s" % path) + with open_method(path, 'rb') as fp: + if precision == 16: + predictions = np.load(fp, allow_pickle=True).astype(dtype=np.float16) + elif precision == 32: + predictions = np.load(fp, allow_pickle=True).astype(dtype=np.float32) + elif precision == 64: + predictions = np.load(fp, allow_pickle=True).astype(dtype=np.float64) + else: + predictions = np.load(fp, allow_pickle=True) + return predictions diff --git a/autosklearn/evaluation/abstract_evaluator.py b/autosklearn/evaluation/abstract_evaluator.py index 1dd77479bc..039a2bdb78 100644 --- a/autosklearn/evaluation/abstract_evaluator.py +++ b/autosklearn/evaluation/abstract_evaluator.py @@ -15,6 +15,8 @@ from smac.tae import StatusType from threadpoolctl import threadpool_limits +from autosklearn.automl_common.common.utils.backend import Backend + import autosklearn.pipeline.classification import autosklearn.pipeline.regression from autosklearn.askl_typing import FEAT_TYPE_TYPE diff --git a/autosklearn/pipeline/components/base.py b/autosklearn/pipeline/components/base.py index 7b496842b2..f2790c27ab 100644 --- a/autosklearn/pipeline/components/base.py +++ b/autosklearn/pipeline/components/base.py @@ -172,6 +172,7 @@ def __str__(self): class IterativeComponent(AutoSklearnComponent): + def fit(self, X, y, sample_weight=None): self.iterative_fit(X, y, n_iter=2, refit=True) @@ -192,6 +193,7 @@ def get_current_iter(self): class IterativeComponentWithSampleWeight(AutoSklearnComponent): + def fit(self, X, y, sample_weight=None): self.iterative_fit(X, y, n_iter=2, refit=True, sample_weight=sample_weight) diff --git a/autosklearn/util/single_thread_client.py b/autosklearn/util/single_thread_client.py index 5c158192a0..fc0c238f8d 100644 --- a/autosklearn/util/single_thread_client.py +++ b/autosklearn/util/single_thread_client.py @@ -2,6 +2,7 @@ from typing import Any from pathlib import Path +from typing import Any import dask.distributed diff --git a/test/test_automl/test_automl.py b/test/test_automl/test_automl.py new file mode 100644 index 0000000000..f021279dce --- /dev/null +++ b/test/test_automl/test_automl.py @@ -0,0 +1,1048 @@ +# -*- encoding: utf-8 -*- +import itertools +import os +import pickle +import sys +import time +import glob +import unittest +import unittest.mock +import warnings + +import numpy as np +import pandas as pd +import pytest +from scipy.sparse import csr_matrix +import sklearn.datasets +from sklearn.ensemble import VotingRegressor, VotingClassifier +from smac.scenario.scenario import Scenario +from smac.facade.roar_facade import ROAR + +from autosklearn.automl import AutoML, AutoMLClassifier, AutoMLRegressor, _model_predict +from autosklearn.data.validation import InputValidator +import autosklearn.automl +from autosklearn.data.xy_data_manager import XYDataManager +from autosklearn.metrics import ( + accuracy, log_loss, balanced_accuracy, default_metric_for_task +) +from autosklearn.evaluation.abstract_evaluator import MyDummyClassifier, MyDummyRegressor +from autosklearn.util.logging_ import PickableLoggerAdapter +import autosklearn.pipeline.util as putil +from autosklearn.constants import ( + MULTICLASS_CLASSIFICATION, + BINARY_CLASSIFICATION, + MULTILABEL_CLASSIFICATION, + REGRESSION, + MULTIOUTPUT_REGRESSION, + CLASSIFICATION_TASKS, + REGRESSION_TASKS, +) +from smac.tae import StatusType + +sys.path.append(os.path.dirname(__file__)) +from automl_utils import print_debug_information, count_succeses, AutoMLLogParser, includes_all_scores, includes_train_scores, performance_over_time_is_plausible # noqa (E402: module level import not at top of file) + + +class AutoMLStub(AutoML): + def __init__(self): + self.__class__ = AutoML + self._task = None + self._dask_client = None + self._is_dask_client_internally_created = False + + def __del__(self): + pass + + +def test_fit(dask_client): + + X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') + automl = autosklearn.automl.AutoML( + seed=0, + time_left_for_this_task=30, + per_run_time_limit=5, + metric=accuracy, + dask_client=dask_client, + ) + + automl.fit(X_train, Y_train, task=MULTICLASS_CLASSIFICATION) + + score = automl.score(X_test, Y_test) + assert score > 0.8 + assert count_succeses(automl.cv_results_) > 0 + assert includes_train_scores(automl.performance_over_time_.columns) is True + assert performance_over_time_is_plausible(automl.performance_over_time_) is True + assert automl._task == MULTICLASS_CLASSIFICATION + + del automl + + +def test_fit_roar(dask_client_single_worker): + def get_roar_object_callback( + scenario_dict, + seed, + ta, + ta_kwargs, + dask_client, + n_jobs, + **kwargs + ): + """Random online adaptive racing. + + http://ml.informatik.uni-freiburg.de/papers/11-LION5-SMAC.pdf""" + scenario = Scenario(scenario_dict) + return ROAR( + scenario=scenario, + rng=seed, + tae_runner=ta, + tae_runner_kwargs=ta_kwargs, + dask_client=dask_client, + n_jobs=n_jobs, + ) + + X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') + automl = autosklearn.automl.AutoML( + time_left_for_this_task=30, + per_run_time_limit=5, + initial_configurations_via_metalearning=0, + get_smac_object_callback=get_roar_object_callback, + metric=accuracy, + dask_client=dask_client_single_worker, + ) + + automl.fit(X_train, Y_train, task=MULTICLASS_CLASSIFICATION) + + score = automl.score(X_test, Y_test) + assert score > 0.8 + assert count_succeses(automl.cv_results_) > 0 + assert includes_train_scores(automl.performance_over_time_.columns) is True + assert automl._task == MULTICLASS_CLASSIFICATION + + del automl + + +def test_refit_shuffle_on_fail(dask_client): + + failing_model = unittest.mock.Mock() + failing_model.fit.side_effect = [ValueError(), ValueError(), None] + failing_model.fit_transformer.side_effect = [ + ValueError(), ValueError(), (None, {})] + failing_model.get_max_iter.return_value = 100 + + auto = AutoML(30, 5, dask_client=dask_client) + ensemble_mock = unittest.mock.Mock() + ensemble_mock.get_selected_model_identifiers.return_value = [(1, 1, 50.0)] + auto.ensemble_ = ensemble_mock + auto.InputValidator = InputValidator() + for budget_type in [None, 'iterations']: + auto._budget_type = budget_type + + auto.models_ = {(1, 1, 50.0): failing_model} + + # Make sure a valid 2D array is given to automl + X = np.array([1, 2, 3]).reshape(-1, 1) + y = np.array([1, 2, 3]) + auto.InputValidator.fit(X, y) + auto.refit(X, y) + + assert failing_model.fit.call_count == 3 + assert failing_model.fit_transformer.call_count == 3 + + del auto + + +def test_only_loads_ensemble_models(automl_stub): + + def side_effect(ids, *args, **kwargs): + return models if ids is identifiers else {} + + # Add a resampling strategy as this is required by load_models + automl_stub._resampling_strategy = 'holdout' + identifiers = [(1, 2), (3, 4)] + + models = [42] + load_ensemble_mock = unittest.mock.Mock() + load_ensemble_mock.get_selected_model_identifiers.return_value = identifiers + automl_stub._backend.load_ensemble.return_value = load_ensemble_mock + automl_stub._backend.load_models_by_identifiers.side_effect = side_effect + + automl_stub._load_models() + assert models == automl_stub.models_ + assert automl_stub.cv_models_ is None + + automl_stub._resampling_strategy = 'cv' + + models = [42] + automl_stub._backend.load_cv_models_by_identifiers.side_effect = side_effect + + automl_stub._load_models() + assert models == automl_stub.cv_models_ + + +def test_check_for_models_if_no_ensemble(automl_stub): + models = [42] + automl_stub._backend.load_ensemble.return_value = None + automl_stub._backend.list_all_models.return_value = models + automl_stub._disable_evaluator_output = False + + automl_stub._load_models() + + +def test_raises_if_no_models(automl_stub): + automl_stub._backend.load_ensemble.return_value = None + automl_stub._backend.list_all_models.return_value = [] + automl_stub._resampling_strategy = 'holdout' + + automl_stub._disable_evaluator_output = False + with pytest.raises(ValueError): + automl_stub._load_models() + + automl_stub._disable_evaluator_output = True + automl_stub._load_models() + + +def test_delete_non_candidate_models(dask_client): + + seed = 555 + X, Y, _, _ = putil.get_dataset('iris') + automl = autosklearn.automl.AutoML( + delete_tmp_folder_after_terminate=False, + time_left_for_this_task=60, + per_run_time_limit=5, + ensemble_nbest=3, + seed=seed, + initial_configurations_via_metalearning=0, + resampling_strategy='holdout', + include={ + 'classifier': ['sgd'], + 'feature_preprocessor': ['no_preprocessing'] + }, + metric=accuracy, + dask_client=dask_client, + # Force model to be deleted. That is, from 50 which is the + # default to 3 to make sure we delete models. + max_models_on_disc=3, + ) + + automl.fit(X, Y, task=MULTICLASS_CLASSIFICATION, X_test=X, y_test=Y) + + # Assert at least one model file has been deleted and that there were no + # deletion errors + log_file_path = glob.glob(os.path.join( + automl._backend.temporary_directory, 'AutoML(' + str(seed) + '):*.log')) + with open(log_file_path[0]) as log_file: + log_content = log_file.read() + assert 'Deleted files of non-candidate model' in log_content, log_content + assert 'Failed to delete files of non-candidate model' not in log_content, log_content + assert 'Failed to lock model' not in log_content, log_content + + # Assert that the files of the models used by the ensemble weren't deleted + model_files = automl._backend.list_all_models(seed=seed) + model_files_idx = set() + for m_file in model_files: + # Extract the model identifiers from the filename + m_file = os.path.split(m_file)[1].replace('.model', '').split('.', 2) + model_files_idx.add((int(m_file[0]), int(m_file[1]), float(m_file[2]))) + ensemble_members_idx = set(automl.ensemble_.identifiers_) + assert ensemble_members_idx.issubset(model_files_idx), (ensemble_members_idx, model_files_idx) + + del automl + + +def test_binary_score_and_include(dask_client): + """ + Test fix for binary classification prediction + taking the index 1 of second dimension in prediction matrix + """ + + data = sklearn.datasets.make_classification( + n_samples=400, n_features=10, n_redundant=1, n_informative=3, + n_repeated=1, n_clusters_per_class=2, random_state=1) + X_train = data[0][:200] + Y_train = data[1][:200] + X_test = data[0][200:] + Y_test = data[1][200:] + + automl = autosklearn.automl.AutoML( + 20, 5, + include={'classifier': ['sgd'], + 'feature_preprocessor': ['no_preprocessing']}, + metric=accuracy, + dask_client=dask_client, + ) + + automl.fit(X_train, Y_train, task=BINARY_CLASSIFICATION) + + assert automl._task == BINARY_CLASSIFICATION + + # TODO, the assumption from above is not really tested here + # Also, the score method should be removed, it only makes little sense + score = automl.score(X_test, Y_test) + assert score >= 0.4 + + del automl + + +def test_automl_outputs(dask_client): + + X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') + name = 'iris' + auto = autosklearn.automl.AutoML( + 30, 5, + initial_configurations_via_metalearning=0, + seed=100, + metric=accuracy, + dask_client=dask_client, + delete_tmp_folder_after_terminate=False, + ) + + auto.fit( + X=X_train, + y=Y_train, + X_test=X_test, + y_test=Y_test, + dataset_name=name, + task=MULTICLASS_CLASSIFICATION, + ) + + data_manager_file = os.path.join( + auto._backend.temporary_directory, + '.auto-sklearn', + 'datamanager.pkl' + ) + + # pickled data manager (without one hot encoding!) + with open(data_manager_file, 'rb') as fh: + D = pickle.load(fh) + assert np.allclose(D.data['X_train'], X_train) + + # Check that all directories are there + fixture = [ + 'true_targets_ensemble.npy', + 'start_time_100', + 'datamanager.pkl', + 'ensemble_read_preds.pkl', + 'ensemble_read_losses.pkl', + 'runs', + 'ensembles', + 'ensemble_history.json', + ] + assert ( + sorted(os.listdir(os.path.join(auto._backend.temporary_directory, + '.auto-sklearn'))) + == sorted(fixture) + ) + + # At least one ensemble, one validation, one test prediction and one + # model and one ensemble + fixture = glob.glob(os.path.join( + auto._backend.temporary_directory, + '.auto-sklearn', 'runs', '*', 'predictions_ensemble*npy', + )) + assert len(fixture) > 0 + + fixture = glob.glob(os.path.join(auto._backend.temporary_directory, '.auto-sklearn', + 'runs', '*', '100.*.model')) + assert len(fixture) > 0 + + fixture = os.listdir(os.path.join(auto._backend.temporary_directory, + '.auto-sklearn', 'ensembles')) + assert '100.0000000000.ensemble' in fixture + + # Start time + start_time_file_path = os.path.join(auto._backend.temporary_directory, + '.auto-sklearn', "start_time_100") + with open(start_time_file_path, 'r') as fh: + start_time = float(fh.read()) + assert time.time() - start_time >= 10, print_debug_information(auto) + + # Then check that the logger matches the run expectation + logfile = glob.glob(os.path.join( + auto._backend.temporary_directory, 'AutoML*.log'))[0] + parser = AutoMLLogParser(logfile) + + # The number of ensemble trajectories properly in log file + success_ensemble_iters_auto = len(auto.ensemble_performance_history) + success_ensemble_iters_log = parser.count_ensembler_success_pynisher_calls() + assert success_ensemble_iters_auto == success_ensemble_iters_log, "{} != {}".format( + auto.ensemble_performance_history, + print_debug_information(auto), + ) + + # We also care that no iteration got lost + # This is important because it counts for pynisher calls + # and whether a pynisher call actually called the ensemble + total_ensemble_iterations = parser.count_ensembler_iterations() + assert len(total_ensemble_iterations) > 1 # At least 1 iteration + assert range(1, max(total_ensemble_iterations) + 1), total_ensemble_iterations + + # a point where pynisher is called before budget exhaustion + # Dummy not in run history + total_calls_to_pynisher_log = parser.count_tae_pynisher_calls() - 1 + total_returns_from_pynisher_log = parser.count_tae_pynisher_returns() - 1 + total_elements_rh = len([run_value for run_value in auto.runhistory_.data.values( + ) if run_value.status == StatusType.RUNNING]) + + # Make sure we register all calls to pynisher + # The less than or equal here is added as a WA as + # https://github.com/automl/SMAC3/pull/712 is not yet integrated + assert total_elements_rh <= total_calls_to_pynisher_log, print_debug_information(auto) + + # Make sure we register all returns from pynisher + assert total_elements_rh <= total_returns_from_pynisher_log, print_debug_information(auto) + + # Lastly check that settings are print to logfile + ensemble_size = parser.get_automl_setting_from_log(auto._dataset_name, 'ensemble_size') + assert auto._ensemble_size == int(ensemble_size) + + del auto + + +@pytest.mark.parametrize("datasets", [('breast_cancer', BINARY_CLASSIFICATION), + ('wine', MULTICLASS_CLASSIFICATION), + ('diabetes', REGRESSION)]) +def test_do_dummy_prediction(dask_client, datasets): + + name, task = datasets + + X_train, Y_train, X_test, Y_test = putil.get_dataset(name) + datamanager = XYDataManager( + X_train, Y_train, + X_test, Y_test, + task=task, + dataset_name=name, + feat_type={i: 'numerical' for i in range(X_train.shape[1])}, + ) + + auto = autosklearn.automl.AutoML( + 20, 5, + initial_configurations_via_metalearning=25, + metric=accuracy, + dask_client=dask_client, + delete_tmp_folder_after_terminate=False, + ) + auto._backend = auto._create_backend() + + # Make a dummy logger + auto._logger_port = 9020 + auto._logger = unittest.mock.Mock() + auto._logger.info.return_value = None + + auto._backend.save_datamanager(datamanager) + D = auto._backend.load_datamanager() + + # Check if data manager is correcly loaded + assert D.info['task'] == datamanager.info['task'] + auto._do_dummy_prediction(D, 1) + + # Ensure that the dummy predictions are not in the current working + # directory, but in the temporary directory. + unexpected_directory = os.path.join(os.getcwd(), '.auto-sklearn') + expected_directory = os.path.join( + auto._backend.temporary_directory, + '.auto-sklearn', + 'runs', + '1_1_0.0', + 'predictions_ensemble_1_1_0.0.npy' + ) + assert not os.path.exists(unexpected_directory) + assert os.path.exists(expected_directory) + + auto._clean_logger() + + del auto + + +@unittest.mock.patch('autosklearn.evaluation.ExecuteTaFuncWithQueue.run') +def test_fail_if_dummy_prediction_fails(ta_run_mock, dask_client): + + X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') + datamanager = XYDataManager( + X_train, Y_train, + X_test, Y_test, + task=2, + feat_type={i: 'Numerical' for i in range(X_train.shape[1])}, + dataset_name='iris', + ) + + time_for_this_task = 30 + per_run_time = 10 + auto = autosklearn.automl.AutoML(time_for_this_task, + per_run_time, + initial_configurations_via_metalearning=25, + metric=accuracy, + dask_client=dask_client, + delete_tmp_folder_after_terminate=False, + ) + auto._backend = auto._create_backend() + auto._backend._make_internals_directory() + auto._backend.save_datamanager(datamanager) + + # Make a dummy logger + auto._logger_port = 9020 + auto._logger = unittest.mock.Mock() + auto._logger.info.return_value = None + + # First of all, check that ta.run() is actually called. + ta_run_mock.return_value = StatusType.SUCCESS, None, None, {} + auto._do_dummy_prediction(datamanager, 1) + ta_run_mock.assert_called_once_with(1, cutoff=time_for_this_task) + + # Case 1. Check that function raises no error when statustype == success. + # ta.run() returns status, cost, runtime, and additional info. + ta_run_mock.return_value = StatusType.SUCCESS, None, None, {} + raised = False + try: + auto._do_dummy_prediction(datamanager, 1) + except ValueError: + raised = True + assert not raised, 'Exception raised' + + # Case 2. Check that if statustype returned by ta.run() != success, + # the function raises error. + ta_run_mock.return_value = StatusType.CRASHED, None, None, {} + with pytest.raises( + ValueError, + match='Dummy prediction failed with run state StatusType.CRASHED and additional output: {}.' # noqa + ): + auto._do_dummy_prediction(datamanager, 1) + + ta_run_mock.return_value = StatusType.ABORT, None, None, {} + with pytest.raises( + ValueError, + match='Dummy prediction failed with run state StatusType.ABORT ' + 'and additional output: {}.', + ): + auto._do_dummy_prediction(datamanager, 1) + ta_run_mock.return_value = StatusType.TIMEOUT, None, None, {} + with pytest.raises( + ValueError, + match='Dummy prediction failed with run state StatusType.TIMEOUT ' + 'and additional output: {}.' + ): + auto._do_dummy_prediction(datamanager, 1) + ta_run_mock.return_value = StatusType.MEMOUT, None, None, {} + with pytest.raises( + ValueError, + match='Dummy prediction failed with run state StatusType.MEMOUT ' + 'and additional output: {}.', + ): + auto._do_dummy_prediction(datamanager, 1) + ta_run_mock.return_value = StatusType.CAPPED, None, None, {} + with pytest.raises( + ValueError, + match='Dummy prediction failed with run state StatusType.CAPPED ' + 'and additional output: {}.' + ): + auto._do_dummy_prediction(datamanager, 1) + + ta_run_mock.return_value = StatusType.CRASHED, None, None, {'exitcode': -6} + with pytest.raises( + ValueError, + match='The error suggests that the provided memory limits were too tight.', + ): + auto._do_dummy_prediction(datamanager, 1) + + +@unittest.mock.patch('autosklearn.smbo.AutoMLSMBO.run_smbo') +def test_exceptions_inside_log_in_smbo(smbo_run_mock, dask_client): + + # Below importing and shutdown is a workaround, to make sure + # we reset the port to collect messages. Randomly, when running + # this test with multiple other test at the same time causes this + # test to fail. This resets the singletons of the logging class + import logging + logging.shutdown() + + automl = autosklearn.automl.AutoML( + 20, + 5, + metric=accuracy, + dask_client=dask_client, + delete_tmp_folder_after_terminate=False, + ) + + dataset_name = 'test_exceptions_inside_log' + + # Create a custom exception to prevent other errors to slip in + class MyException(Exception): + pass + + X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') + # The first call is on dummy predictor failure + message = str(np.random.randint(100)) + '_run_smbo' + smbo_run_mock.side_effect = MyException(message) + + with pytest.raises(MyException): + automl.fit( + X_train, + Y_train, + task=MULTICLASS_CLASSIFICATION, + dataset_name=dataset_name, + ) + + # make sure that the logfile was created + logger_name = 'AutoML(%d):%s' % (1, dataset_name) + logger = logging.getLogger(logger_name) + logfile = os.path.join(automl._backend.temporary_directory, logger_name + '.log') + assert os.path.exists(logfile), print_debug_information(automl) + str(automl._clean_logger()) + + # Give some time for the error message to be printed in the + # log file + found_message = False + for incr_tolerance in range(5): + with open(logfile) as f: + lines = f.readlines() + if any(message in line for line in lines): + found_message = True + break + else: + time.sleep(incr_tolerance) + + # Speed up the closing after forced crash + automl._clean_logger() + + if not found_message: + pytest.fail("Did not find {} in the log file {} for logger {}/{}/{}".format( + message, + print_debug_information(automl), + vars(automl._logger.logger), + vars(logger), + vars(logging.getLogger()) + )) + + +@pytest.mark.parametrize("metric", [log_loss, balanced_accuracy]) +def test_load_best_individual_model(metric, dask_client): + + X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') + automl = autosklearn.automl.AutoML( + time_left_for_this_task=30, + per_run_time_limit=5, + metric=metric, + dask_client=dask_client, + delete_tmp_folder_after_terminate=False, + ) + + # We cannot easily mock a function sent to dask + # so for this test we create the whole set of models/ensembles + # but prevent it to be loaded + automl.fit(X_train, Y_train, task=MULTICLASS_CLASSIFICATION) + + automl._backend.load_ensemble = unittest.mock.MagicMock(return_value=None) + + # A memory error occurs in the ensemble construction + assert automl._backend.load_ensemble(automl._seed) is None + + # The load model is robust to this and loads the best model + automl._load_models() + assert automl.ensemble_ is not None + + # Just 1 model is there for ensemble and all weight must be on it + get_models_with_weights = automl.get_models_with_weights() + assert len(get_models_with_weights) == 1 + assert get_models_with_weights[0][0] == 1.0 + + # Match a toy dataset + if metric.name == 'balanced_accuracy': + assert automl.score(X_test, Y_test) > 0.9 + elif metric.name == 'log_loss': + # Seen values in github actions of 0.6978304740364537 + assert automl.score(X_test, Y_test) < 0.7 + else: + raise ValueError(metric.name) + + del automl + + +def test_fail_if_feat_type_on_pandas_input(dask_client): + """We do not support feat type when pandas + is provided as an input + """ + automl = autosklearn.automl.AutoML( + time_left_for_this_task=30, + per_run_time_limit=5, + metric=accuracy, + dask_client=dask_client, + ) + + X_train = pd.DataFrame({'a': [1, 1], 'c': [1, 2]}) + y_train = [1, 0] + with pytest.raises( + ValueError, + match="" + "providing the option feat_type to the fit method is not supported when using a Dataframe" + ): + automl.fit( + X_train, y_train, + task=BINARY_CLASSIFICATION, + feat_type={1: 'Categorical', 2: 'Numerical'}, + ) + + +@pytest.mark.parametrize( + 'memory_limit,precision,task', + [ + (memory_limit, precision, task) + for task in itertools.chain(CLASSIFICATION_TASKS, REGRESSION_TASKS) + for precision in (float, np.float32, np.float64, np.float128) + for memory_limit in (1, 100, None) + ] +) +def test_subsample_if_too_large(memory_limit, precision, task): + fixture = { + BINARY_CLASSIFICATION: { + 1: {float: 1310, np.float32: 2621, np.float64: 1310, np.float128: 655}, + 100: {float: 12000, np.float32: 12000, np.float64: 12000, np.float128: 12000}, + None: {float: 12000, np.float32: 12000, np.float64: 12000, np.float128: 12000}, + }, + MULTICLASS_CLASSIFICATION: { + 1: {float: 204, np.float32: 409, np.float64: 204, np.float128: 102}, + 100: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797}, + None: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797}, + }, + MULTILABEL_CLASSIFICATION: { + 1: {float: 204, np.float32: 409, np.float64: 204, np.float128: 102}, + 100: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797}, + None: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797}, + }, + REGRESSION: { + 1: {float: 655, np.float32: 1310, np.float64: 655, np.float128: 327}, + 100: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000}, + None: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000}, + }, + MULTIOUTPUT_REGRESSION: { + 1: {float: 655, np.float32: 1310, np.float64: 655, np.float128: 327}, + 100: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000}, + None: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000}, + } + } + mock = unittest.mock.Mock() + if task == BINARY_CLASSIFICATION: + X, y = sklearn.datasets.make_hastie_10_2() + elif task == MULTICLASS_CLASSIFICATION: + X, y = sklearn.datasets.load_digits(return_X_y=True) + elif task == MULTILABEL_CLASSIFICATION: + X, y_ = sklearn.datasets.load_digits(return_X_y=True) + y = np.zeros((X.shape[0], 10)) + for i, j in enumerate(y_): + y[i, j] = 1 + elif task == REGRESSION: + X, y = sklearn.datasets.make_friedman1(n_samples=5000, n_features=20) + elif task == MULTIOUTPUT_REGRESSION: + X, y = sklearn.datasets.make_friedman1(n_samples=5000, n_features=20) + y = np.vstack((y, y)).transpose() + else: + raise ValueError(task) + X = X.astype(precision) + + assert X.shape[0] == y.shape[0] + + X_new, y_new = AutoML.subsample_if_too_large(X, y, mock, 1, memory_limit, task) + assert X_new.shape[0] == fixture[task][memory_limit][precision] + if memory_limit == 1: + if precision in (np.float128, np.float64, float): + assert mock.warning.call_count == 2 + else: + assert mock.warning.call_count == 1 + else: + assert mock.warning.call_count == 0 + + +def data_input_and_target_types(): + n_rows = 100 + + # Create valid inputs + X_ndarray = np.random.random(size=(n_rows, 5)) + X_ndarray[X_ndarray < .9] = 0 + + # Binary Classificaiton + y_binary_ndarray = np.random.random(size=n_rows) + y_binary_ndarray[y_binary_ndarray >= 0.5] = 1 + y_binary_ndarray[y_binary_ndarray < 0.5] = 0 + + # Multiclass classification + y_multiclass_ndarray = np.random.random(size=n_rows) + y_multiclass_ndarray[y_multiclass_ndarray > 0.66] = 2 + y_multiclass_ndarray[(y_multiclass_ndarray <= 0.66) & (y_multiclass_ndarray >= 0.33)] = 1 + y_multiclass_ndarray[y_multiclass_ndarray < 0.33] = 0 + + # Multilabel classificaiton + y_multilabel_ndarray = np.random.random(size=(n_rows, 3)) + y_multilabel_ndarray[y_multilabel_ndarray > 0.5] = 1 + y_multilabel_ndarray[y_multilabel_ndarray <= 0.5] = 0 + + # Regression + y_regression_ndarray = np.random.random(size=n_rows) + + # Multioutput Regression + y_multioutput_regression_ndarray = np.random.random(size=(n_rows, 3)) + + xs = [ + X_ndarray, + list(X_ndarray), + csr_matrix(X_ndarray), + pd.DataFrame(data=X_ndarray), + ] + + ys_binary = [ + y_binary_ndarray, + list(y_binary_ndarray), + csr_matrix(y_binary_ndarray), + pd.Series(y_binary_ndarray), + pd.DataFrame(data=y_binary_ndarray), + ] + + ys_multiclass = [ + y_multiclass_ndarray, + list(y_multiclass_ndarray), + csr_matrix(y_multiclass_ndarray), + pd.Series(y_multiclass_ndarray), + pd.DataFrame(data=y_multiclass_ndarray), + ] + + ys_multilabel = [ + y_multilabel_ndarray, + list(y_multilabel_ndarray), + csr_matrix(y_multilabel_ndarray), + # pd.Series(y_multilabel_ndarray) + pd.DataFrame(data=y_multilabel_ndarray), + ] + + ys_regression = [ + y_regression_ndarray, + list(y_regression_ndarray), + csr_matrix(y_regression_ndarray), + pd.Series(y_regression_ndarray), + pd.DataFrame(data=y_regression_ndarray), + ] + + ys_multioutput_regression = [ + y_multioutput_regression_ndarray, + list(y_multioutput_regression_ndarray), + csr_matrix(y_multioutput_regression_ndarray), + # pd.Series(y_multioutput_regression_ndarray), + pd.DataFrame(data=y_multioutput_regression_ndarray), + ] + + # [ (X, y, X_test, y_test, task), ... ] + return ( + (X, y, X, y, task) + for X in xs + for y, task in itertools.chain( + itertools.product(ys_binary, [BINARY_CLASSIFICATION]), + itertools.product(ys_multiclass, [MULTICLASS_CLASSIFICATION]), + itertools.product(ys_multilabel, [MULTILABEL_CLASSIFICATION]), + itertools.product(ys_regression, [REGRESSION]), + itertools.product(ys_multioutput_regression, [MULTIOUTPUT_REGRESSION]), + ) + ) + + +@pytest.mark.parametrize("X, y, X_test, y_test, task", data_input_and_target_types()) +def test_input_and_target_types(dask_client, X, y, X_test, y_test, task): + + if task in CLASSIFICATION_TASKS: + automl = AutoMLClassifier( + time_left_for_this_task=15, + per_run_time_limit=5, + dask_client=dask_client, + ) + else: + automl = AutoMLRegressor( + time_left_for_this_task=15, + per_run_time_limit=5, + dask_client=dask_client, + ) + # To save time fitting and only validate the inputs we only return + # the configuration space + automl.fit( + X=X, + y=y, + X_test=X_test, + y_test=y_test, + only_return_configuration_space=True + ) + assert automl._task == task + assert automl._metric.name == default_metric_for_task[task].name + + +def data_test_model_predict_outsputs_correct_shapes(): + datasets = sklearn.datasets + binary = datasets.make_classification( + n_samples=5, n_classes=2, random_state=0 + ) + multiclass = datasets.make_classification( + n_samples=5, n_informative=3, n_classes=3, random_state=0 + ) + multilabel = datasets.make_multilabel_classification( + n_samples=5, n_classes=3, random_state=0 + ) + regression = datasets.make_regression( + n_samples=5, random_state=0 + ) + multioutput = datasets.make_regression( + n_samples=5, n_targets=3, random_state=0 + ) + + # TODO issue 1169 + # While testing output shapes, realised all models are wrapped to provide + # a special predict_proba that outputs a different shape than usual. + # This includes DummyClassifier and DummyRegressor which are wrapped as + # `MyDummyClassifier/Regressor` and require a config object. + # config == 1 : Classifier uses 'uniform', Regressor uses 'mean' + # else : Classifier uses 'most_frequent', Regressor uses 'median' + # + # This wrapping of probabilities with + # `convert_multioutput_multiclass_to_multilabel` + # can probably be just put into a base class which queries subclasses + # as to whether it's needed. + # + # tldr; thats why we use MyDummyX here instead of the default models + # from sklearn + def classifier(X, y): + return MyDummyClassifier(config=1, random_state=0).fit(X, y) + + def regressor(X, y): + return MyDummyRegressor(config=1, random_state=0).fit(X, y) + + # How cross validation models are currently grouped together + def voting_classifier(X, y): + classifiers = [ + MyDummyClassifier(config=1, random_state=0).fit(X, y) + for _ in range(5) + ] + vc = VotingClassifier(estimators=None, voting='soft') + vc.estimators_ = classifiers + return vc + + def voting_regressor(X, y): + regressors = [ + MyDummyRegressor(config=1, random_state=0).fit(X, y) + for _ in range(5) + ] + vr = VotingRegressor(estimators=None) + vr.estimators_ = regressors + return vr + + test_data = { + BINARY_CLASSIFICATION: { + 'models': [classifier(*binary), voting_classifier(*binary)], + 'data': binary, + # prob of false/true for the one class + 'expected_output_shape': (len(binary[0]), 2) + }, + MULTICLASS_CLASSIFICATION: { + 'models': [classifier(*multiclass), voting_classifier(*multiclass)], + 'data': multiclass, + # prob of true for each possible class + 'expected_output_shape': (len(multiclass[0]), 3) + }, + MULTILABEL_CLASSIFICATION: { + 'models': [classifier(*multilabel), voting_classifier(*multilabel)], + 'data': multilabel, + # probability of true for each binary label + 'expected_output_shape': (len(multilabel[0]), 3) # type: ignore + }, + REGRESSION: { + 'models': [regressor(*regression), voting_regressor(*regression)], + 'data': regression, + # array of single outputs + 'expected_output_shape': (len(regression[0]), ) + }, + MULTIOUTPUT_REGRESSION: { + 'models': [regressor(*multioutput), voting_regressor(*multioutput)], + 'data': multioutput, + # array of vector otuputs + 'expected_output_shape': (len(multioutput[0]), 3) + } + } + + return itertools.chain.from_iterable( + [ + (model, cfg['data'], task, cfg['expected_output_shape']) + for model in cfg['models'] + ] + for task, cfg in test_data.items() + ) + + +@pytest.mark.parametrize( + "model, data, task, expected_output_shape", + data_test_model_predict_outsputs_correct_shapes() +) +def test_model_predict_outputs_correct_shapes(model, data, task, expected_output_shape): + X, y = data + prediction = _model_predict(model=model, X=X, task=task) + assert prediction.shape == expected_output_shape + + +def test_model_predict_outputs_warnings_to_logs(): + X = list(range(20)) + task = REGRESSION + logger = PickableLoggerAdapter('test_model_predict_correctly_outputs_warnings') + logger.warning = unittest.mock.Mock() + + class DummyModel: + def predict(self, x): + warnings.warn('test warning', Warning) + return x + + model = DummyModel() + + _model_predict(model, X, task, logger=logger) + + assert logger.warning.call_count == 1, "Logger should have had warning called" + + +def test_model_predict_outputs_to_stdout_if_no_logger(): + X = list(range(20)) + task = REGRESSION + + class DummyModel: + def predict(self, x): + warnings.warn('test warning', Warning) + return x + + model = DummyModel() + + with warnings.catch_warnings(record=True) as w: + _model_predict(model, X, task, logger=None) + + assert len(w) == 1, "One warning sould have been emmited" + + +@pytest.mark.parametrize("task, y", [ + (BINARY_CLASSIFICATION, np.asarray( + 9999 * [0] + 1 * [1] + )), + (MULTICLASS_CLASSIFICATION, np.asarray( + 4999 * [1] + 4999 * [2] + 1 * [3] + 1 * [4] + )), + (MULTILABEL_CLASSIFICATION, np.asarray( + 4999 * [[0, 1, 1]] + 4999 * [[1, 1, 0]] + 1 * [[1, 0, 1]] + 1 * [[0, 0, 0]] + )) +]) +def test_subsample_classification_unique_labels_stay_in_training_set(task, y): + n_samples = 10000 + X = np.random.random(size=(n_samples, 3)) + memory_limit = 1 # Force subsampling + mock = unittest.mock.Mock() + + # Make sure our test assumptions are correct + assert len(y) == n_samples, "Ensure tests are correctly setup" + + values, counts = np.unique(y, axis=0, return_counts=True) + unique_labels = [value for value, count in zip(values, counts) if count == 1] + assert len(unique_labels), "Ensure we have unique labels in the test" + + _, y_sampled = AutoML.subsample_if_too_large(X, y, + logger=mock, + seed=1, + memory_limit=memory_limit, + task=task) + + assert len(y_sampled) <= len(y), \ + "Ensure sampling took place" + assert all(label in y_sampled for label in unique_labels), \ + "All unique labels present in the return sampled set" diff --git a/test/test_automl/test_estimators.py b/test/test_automl/test_estimators.py new file mode 100644 index 0000000000..4de0f767aa --- /dev/null +++ b/test/test_automl/test_estimators.py @@ -0,0 +1,1278 @@ +from typing import Type, cast + +import copy +import glob +import importlib +import os +import inspect +import itertools +import pickle +import re +import sys +import tempfile +import unittest +import unittest.mock +import pytest + +from ConfigSpace.configuration_space import Configuration +import joblib +from joblib import cpu_count +import numpy as np +import numpy.ma as npma +import pandas as pd +import sklearn +import sklearn.model_selection as model_selection +import sklearn.dummy +import sklearn.datasets +from sklearn.base import clone +from sklearn.base import ClassifierMixin, RegressorMixin +from sklearn.base import is_classifier +from smac.tae import StatusType +from dask.distributed import Client + +from autosklearn.data.validation import InputValidator +import autosklearn.pipeline.util as putil +from autosklearn.ensemble_builder import MODEL_FN_RE +import autosklearn.estimators # noqa F401 +from autosklearn.estimators import ( + AutoSklearnEstimator, AutoSklearnRegressor, AutoSklearnClassifier +) +from autosklearn.metrics import accuracy, f1_macro, mean_squared_error, r2 +from autosklearn.automl import AutoMLClassifier +from autosklearn.experimental.askl2 import AutoSklearn2Classifier +from autosklearn.smbo import get_smac_object + +sys.path.append(os.path.dirname(__file__)) +from automl_utils import print_debug_information, count_succeses, includes_train_scores, includes_all_scores, include_single_scores, performance_over_time_is_plausible # noqa (E402: module level import not at top of file) + + +def test_fit_n_jobs(tmp_dir): + + X_train, Y_train, X_test, Y_test = putil.get_dataset('breast_cancer') + + # test parallel Classifier to predict classes, not only indices + Y_train += 1 + Y_test += 1 + + class get_smac_object_wrapper: + + def __call__(self, *args, **kwargs): + self.n_jobs = kwargs['n_jobs'] + smac = get_smac_object(*args, **kwargs) + self.dask_n_jobs = smac.solver.tae_runner.n_workers + self.dask_client_n_jobs = len( + smac.solver.tae_runner.client.scheduler_info()['workers'] + ) + return smac + get_smac_object_wrapper_instance = get_smac_object_wrapper() + + automl = AutoSklearnClassifier( + delete_tmp_folder_after_terminate=False, + time_left_for_this_task=30, + per_run_time_limit=5, + tmp_folder=tmp_dir, + seed=1, + initial_configurations_via_metalearning=0, + ensemble_size=5, + n_jobs=2, + include={'classifier': ['sgd'], + 'feature_preprocessor': ['no_preprocessing']}, + get_smac_object_callback=get_smac_object_wrapper_instance, + max_models_on_disc=None, + ) + + automl.fit(X_train, Y_train) + + # Test that the argument is correctly passed to SMAC + assert getattr(get_smac_object_wrapper_instance, 'n_jobs') == 2 + assert getattr(get_smac_object_wrapper_instance, 'dask_n_jobs') == 2 + assert getattr(get_smac_object_wrapper_instance, 'dask_client_n_jobs') == 2 + + available_num_runs = set() + for run_key, run_value in automl.automl_.runhistory_.data.items(): + if run_value.additional_info is not None and 'num_run' in run_value.additional_info: + available_num_runs.add(run_value.additional_info['num_run']) + available_predictions = set() + predictions = glob.glob( + os.path.join(automl.automl_._backend.get_runs_directory(), '*', 'predictions_ensemble*.npy') + ) + seeds = set() + for prediction in predictions: + prediction = os.path.split(prediction)[1] + match = re.match(MODEL_FN_RE, prediction.replace("predictions_ensemble", "")) + if match: + num_run = int(match.group(2)) + available_predictions.add(num_run) + seed = int(match.group(1)) + seeds.add(seed) + + # Remove the dummy prediction, it is not part of the runhistory + available_predictions.remove(1) + assert available_num_runs.issubset(available_predictions) + + assert len(seeds) == 1 + + ensemble_dir = automl.automl_._backend.get_ensemble_dir() + ensembles = os.listdir(ensemble_dir) + + seeds = set() + for ensemble_file in ensembles: + seeds.add(int(ensemble_file.split('.')[0].split('_')[0])) + assert len(seeds) == 1 + + assert count_succeses(automl.cv_results_) > 0 + assert includes_train_scores(automl.performance_over_time_.columns) is True + assert performance_over_time_is_plausible(automl.performance_over_time_) is True + # For travis-ci it is important that the client no longer exists + assert automl.automl_._dask_client is None + + +def test_feat_type_wrong_arguments(): + + # Every Auto-Sklearn estimator has a backend, that allows a single + # call to fit + X = np.zeros((100, 100)) + y = np.zeros((100, )) + + cls = AutoSklearnClassifier(ensemble_size=0) + expected_msg = r".*feat_type does not have same number of " + "variables as X has features. 1 vs 100.*" + with pytest.raises(ValueError, match=expected_msg): + cls.fit(X=X, y=y, feat_type=[True]) + + cls = AutoSklearnClassifier(ensemble_size=0) + expected_msg = r".*feat_type must only contain strings.*" + with pytest.raises(ValueError, match=expected_msg): + cls.fit(X=X, y=y, feat_type=[True]*100) + + cls = AutoSklearnClassifier(ensemble_size=0) + expected_msg = r".*Only `Categorical` and `Numerical` are" + "valid feature types, you passed `Car`.*" + with pytest.raises(ValueError, match=expected_msg): + cls.fit(X=X, y=y, feat_type=['Car']*100) + + +# Mock AutoSklearnEstimator.fit so the test doesn't actually run fit(). +@unittest.mock.patch('autosklearn.estimators.AutoSklearnEstimator.fit') +def test_type_of_target(mock_estimator): + # Test that classifier raises error for illegal target types. + X = np.array([[1, 2], + [2, 3], + [3, 4], + [4, 5], + ]) + # Possible target types + y_binary = np.array([0, 0, 1, 1]) + y_continuous = np.array([0.1, 1.3, 2.1, 4.0]) + y_multiclass = np.array([0, 1, 2, 0]) + y_multilabel = np.array([[0, 1], + [1, 1], + [1, 0], + [0, 0], + ]) + y_multiclass_multioutput = np.array([[0, 1], + [1, 3], + [2, 2], + [5, 3], + ]) + y_continuous_multioutput = np.array([[0.1, 1.5], + [1.2, 3.5], + [2.7, 2.7], + [5.5, 3.9], + ]) + + cls = AutoSklearnClassifier(ensemble_size=0) + cls.automl_ = unittest.mock.Mock() + cls.automl_.InputValidator = unittest.mock.Mock() + cls.automl_.InputValidator.target_validator = unittest.mock.Mock() + + # Illegal target types for classification: continuous, + # multiclass-multioutput, continuous-multioutput. + expected_msg = r".*Classification with data of type" + " multiclass-multioutput is not supported.*" + with pytest.raises(ValueError, match=expected_msg): + cls.fit(X=X, y=y_multiclass_multioutput) + + expected_msg = r".*Classification with data of type" + " continuous is not supported.*" + with pytest.raises(ValueError, match=expected_msg): + cls.fit(X=X, y=y_continuous) + + expected_msg = r".*Classification with data of type" + " continuous-multioutput is not supported.*" + with pytest.raises(ValueError, match=expected_msg): + cls.fit(X=X, y=y_continuous_multioutput) + + # Legal target types for classification: binary, multiclass, + # multilabel-indicator. + try: + cls.fit(X, y_binary) + except ValueError: + pytest.fail("cls.fit() raised ValueError while fitting " + "binary targets") + + try: + cls.fit(X, y_multiclass) + except ValueError: + pytest.fail("cls.fit() raised ValueError while fitting " + "multiclass targets") + + try: + cls.fit(X, y_multilabel) + except ValueError: + pytest.fail("cls.fit() raised ValueError while fitting " + "multilabel-indicator targets") + + # Test that regressor raises error for illegal target types. + reg = AutoSklearnRegressor(ensemble_size=0) + # Illegal target types for regression: multilabel-indicator + # multiclass-multioutput + expected_msg = r".*Regression with data of type" + " multilabel-indicator is not supported.*" + with pytest.raises(ValueError, match=expected_msg): + reg.fit(X=X, y=y_multilabel,) + + expected_msg = r".*Regression with data of type" + " multiclass-multioutput is not supported.*" + with pytest.raises(ValueError, match=expected_msg): + reg.fit(X=X, y=y_multiclass_multioutput,) + + # Legal target types: continuous, multiclass, + # continuous-multioutput, + # binary + try: + reg.fit(X, y_continuous) + except ValueError: + pytest.fail("reg.fit() raised ValueError while fitting " + "continuous targets") + + try: + reg.fit(X, y_multiclass) + except ValueError: + pytest.fail("reg.fit() raised ValueError while fitting " + "multiclass targets") + + try: + reg.fit(X, y_continuous_multioutput) + except ValueError: + pytest.fail("reg.fit() raised ValueError while fitting " + "continuous_multioutput targets") + + try: + reg.fit(X, y_binary) + except ValueError: + pytest.fail("reg.fit() raised ValueError while fitting " + "binary targets") + + +def test_performance_over_time_no_ensemble(tmp_dir): + X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') + + cls = AutoSklearnClassifier(time_left_for_this_task=30, + per_run_time_limit=5, + tmp_folder=tmp_dir, + seed=1, + initial_configurations_via_metalearning=0, + ensemble_size=0,) + + cls.fit(X_train, Y_train, X_test, Y_test) + + performance_over_time = cls.performance_over_time_ + assert include_single_scores(performance_over_time.columns) is True + assert performance_over_time_is_plausible(performance_over_time) is True + + +def test_cv_results(tmp_dir): + # TODO restructure and actually use real SMAC output from a long run + # to do this unittest! + X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') + + cls = AutoSklearnClassifier(time_left_for_this_task=30, + per_run_time_limit=5, + tmp_folder=tmp_dir, + seed=1, + initial_configurations_via_metalearning=0, + ensemble_size=0, + scoring_functions=[autosklearn.metrics.precision, + autosklearn.metrics.roc_auc]) + + params = cls.get_params() + original_params = copy.deepcopy(params) + + cls.fit(X_train, Y_train) + + cv_results = cls.cv_results_ + assert isinstance(cv_results, dict), type(cv_results) + assert isinstance(cv_results['mean_test_score'], np.ndarray), type( + cv_results['mean_test_score']) + assert isinstance(cv_results['mean_fit_time'], np.ndarray), type( + cv_results['mean_fit_time'] + ) + assert isinstance(cv_results['params'], list), type(cv_results['params']) + assert isinstance(cv_results['rank_test_scores'], np.ndarray), type( + cv_results['rank_test_scores'] + ) + assert isinstance(cv_results['metric_precision'], npma.MaskedArray), type( + cv_results['metric_precision'] + ) + assert isinstance(cv_results['metric_roc_auc'], npma.MaskedArray), type( + cv_results['metric_roc_auc'] + ) + cv_result_items = [isinstance(val, npma.MaskedArray) for key, val in + cv_results.items() if key.startswith('param_')] + assert all(cv_result_items), cv_results.items() + + # Compare the state of the model parameters with the original parameters + new_params = clone(cls).get_params() + for param_name, original_value in original_params.items(): + new_value = new_params[param_name] + + # Taken from Sklearn code: + # We should never change or mutate the internal state of input + # parameters by default. To check this we use the joblib.hash function + # that introspects recursively any subobjects to compute a checksum. + # The only exception to this rule of immutable constructor parameters + # is possible RandomState instance but in this check we explicitly + # fixed the random_state params recursively to be integer seeds. + assert joblib.hash(new_value) == joblib.hash(original_value), ( + "Estimator %s should not change or mutate " + " the parameter %s from %s to %s during fit." + % (cls, param_name, original_value, new_value)) + + # Comply with https://scikit-learn.org/dev/glossary.html#term-classes + is_classifier(cls) + assert hasattr(cls, 'classes_') + + +@pytest.mark.parametrize('estimator_type,dataset_name', [ + (AutoSklearnClassifier, 'iris'), + (AutoSklearnRegressor, 'boston') +]) +def test_leaderboard( + tmp_dir: str, + estimator_type: Type[AutoSklearnEstimator], + dataset_name: str +): + # Comprehensive test tasks a substantial amount of time, manually set if + # required. + MAX_COMBO_SIZE_FOR_INCLUDE_PARAM = 3 # [0, len(valid_columns) + 1] + column_types = AutoSklearnEstimator._leaderboard_columns() + + # Create a dict of all possible param values for each param + # with some invalid one's of the incorrect type + include_combinations = itertools.chain( + itertools.combinations(column_types['all'], item_count) + for item_count in range(1, MAX_COMBO_SIZE_FOR_INCLUDE_PARAM) + ) + valid_params = { + 'detailed': [True, False], + 'ensemble_only': [True, False], + 'top_k': [-10, 0, 1, 10, 'all'], + 'sort_by': [*column_types['all'], 'invalid'], + 'sort_order': ['ascending', 'descending', 'auto', 'invalid', None], + 'include': itertools.chain([None, 'invalid', 'type'], include_combinations), + } + + # Create a generator of all possible combinations of valid_params + params_generator = iter( + dict(zip(valid_params.keys(), param_values)) + for param_values in itertools.product(*valid_params.values()) + ) + + X_train, Y_train, _, _ = putil.get_dataset(dataset_name) + model = estimator_type( + time_left_for_this_task=30, + per_run_time_limit=5, + tmp_folder=tmp_dir, + seed=1 + ) + + model.fit(X_train, Y_train) + + for params in params_generator: + # Convert from iterator to solid list + if params['include'] is not None and not isinstance(params['include'], str): + params['include'] = list(params['include']) + + # Invalid top_k should raise an error, is a positive int or 'all' + if not (params['top_k'] == 'all' or params['top_k'] > 0): + with pytest.raises(ValueError): + model.leaderboard(**params) + + # Invalid sort_by column + elif params['sort_by'] not in column_types['all']: + with pytest.raises(ValueError): + model.leaderboard(**params) + + # Shouldn't accept an invalid sort order + elif params['sort_order'] not in ['ascending', 'descending', 'auto']: + with pytest.raises(ValueError): + model.leaderboard(**params) + + # include is single str but not valid + elif ( + isinstance(params['include'], str) + and params['include'] not in column_types['all'] + ): + with pytest.raises(ValueError): + model.leaderboard(**params) + + # Crash if include is list but contains invalid column + elif ( + isinstance(params['include'], list) + and len(set(params['include']) - set(column_types['all'])) != 0 + ): + with pytest.raises(ValueError): + model.leaderboard(**params) + + # Can't have just model_id, in both single str and list case + elif ( + params['include'] == 'model_id' + or params['include'] == ['model_id'] + ): + with pytest.raises(ValueError): + model.leaderboard(**params) + + # Else all valid combinations should be validated + else: + leaderboard = model.leaderboard(**params) + + # top_k should never be less than the rows given back + # It can however be larger + if isinstance(params['top_k'], int): + assert params['top_k'] >= len(leaderboard) + + # Check the right columns are present and in the right order + # The model_id is set as the index, not included in pandas columns + columns = list(leaderboard.columns) + + def exclude(lst, s): + return [x for x in lst if x != s] + + if params['include'] is not None: + # Include with only single str should be the only column + if isinstance(params['include'], str): + assert params['include'] in columns and len(columns) == 1 + # Include as a list should have all the columns without model_id + else: + assert columns == exclude(params['include'], 'model_id') + elif params['detailed']: + assert columns == exclude(column_types['detailed'], 'model_id') + else: + assert columns == exclude(column_types['simple'], 'model_id') + + # Ensure that if it's ensemble only + # Can only check if 'ensemble_weight' is present + if ( + params['ensemble_only'] + and 'ensemble_weight' in columns + ): + assert all(leaderboard['ensemble_weight'] > 0) + + +@pytest.mark.parametrize('estimator', [AutoSklearnRegressor]) +@pytest.mark.parametrize('resampling_strategy', ['holdout']) +@pytest.mark.parametrize('X', [ + np.asarray([[1.0, 1.0, 1.0]] * 25 + [[2.0, 2.0, 2.0]] * 25 + + [[3.0, 3.0, 3.0]] * 25 + [[4.0, 4.0, 4.0]] * 25) +]) +@pytest.mark.parametrize('y', [ + np.asarray([1.0] * 25 + [2.0] * 25 + [3.0] * 25 + [4.0] * 25) +]) +def test_show_models_with_holdout( + tmp_dir: str, + dask_client: Client, + estimator: AutoSklearnEstimator, + resampling_strategy: str, + X: np.ndarray, + y: np.ndarray +) -> None: + """ + Parameters + ---------- + tmp_dir: str + The temporary directory to use for this test + + dask_client: dask.distributed.Client + The dask client to use for this test + + estimator: AutoSklearnEstimator + The estimator to train + + resampling_strategy: str + The resampling strategy to use + + X: np.ndarray + The X data to use for this estimator + + y: np.ndarray + The targets to use for this estimator + + Expects + ------- + * Expects all the model dictionaries to have ``model_keys`` + * Expects all models to have an auto-sklearn wrapped model ``regressor`` + * Expects all models to have a sklearn wrapped model ``sklearn_regressor`` + * Expects no model to have any ``None`` value + """ + + automl = estimator( + time_left_for_this_task=60, + per_run_time_limit=5, + tmp_folder=tmp_dir, + resampling_strategy=resampling_strategy, + dask_client=dask_client + ) + automl.fit(X, y) + + models = automl.show_models().values() + + model_keys = set([ + 'model_id', 'rank', 'cost', 'ensemble_weight', + 'data_preprocessor', 'feature_preprocessor', + 'regressor', 'sklearn_regressor' + ]) + + assert all([model_keys == set(model.keys()) for model in models]) + assert all([model['regressor'] for model in models]) + assert all([model['sklearn_regressor'] for model in models]) + assert not any([None in model.values() for model in models]) + + +@pytest.mark.parametrize('estimator', [AutoSklearnClassifier]) +@pytest.mark.parametrize('resampling_strategy', ['cv']) +@pytest.mark.parametrize('X', [ + np.asarray([[1.0, 1.0, 1.0]] * 50 + [[2.0, 2.0, 2.0]] * 50) +]) +@pytest.mark.parametrize('y', [ + np.asarray([1] * 50 + [2] * 50) +]) +def test_show_models_with_cv( + tmp_dir: str, + dask_client: Client, + estimator: AutoSklearnEstimator, + resampling_strategy: str, + X: np.ndarray, + y: np.ndarray +) -> None: + """ + Parameters + ---------- + tmp_dir: str + The temporary directory to use for this test + + dask_client: dask.distributed.Client + The dask client to use for this test + + estimator: AutoSklearnEstimator + The estimator to train + + resampling_strategy: str + The resampling strategy to use + + X: np.ndarray + The X data to use for this estimator + + y: np.ndarray + The targets to use for this estimator + + Expects + ------- + * Expects all the model dictionaries to have ``model_keys`` + * Expects no model to have any ``None`` value + * Expects all the estimators in a model to have ``estimator_keys`` + * Expects all model estimators to have an auto-sklearn wrapped model ``classifier`` + * Expects all model estimators to have a sklearn wrapped model ``sklearn_classifier`` + * Expects no estimator to have ``None`` value + """ + + automl = estimator( + time_left_for_this_task=120, + per_run_time_limit=5, + tmp_folder=tmp_dir, + resampling_strategy=resampling_strategy, + dask_client=dask_client + ) + automl.fit(X, y) + + models = automl.show_models().values() + + model_keys = set([ + 'model_id', 'rank', + 'cost', 'ensemble_weight', + 'voting_model', 'estimators' + ]) + + estimator_keys = set([ + 'data_preprocessor', 'balancing', + 'feature_preprocessor', 'classifier', + 'sklearn_classifier' + ]) + + assert all([model_keys == set(model.keys()) for model in models]) + assert not any([None in model.values() for model in models]) + assert all([estimator_keys == set(estimator.keys()) + for model in models for estimator in model['estimators']]) + assert all([estimator['classifier'] + for model in models for estimator in model['estimators']]) + assert all([estimator['sklearn_classifier'] + for model in models for estimator in model['estimators']]) + assert not any([None in estimator.values() + for model in models for estimator in model['estimators']]) + + +@unittest.mock.patch('autosklearn.estimators.AutoSklearnEstimator.build_automl') +def test_fit_n_jobs_negative(build_automl_patch): + n_cores = cpu_count() + cls = AutoSklearnEstimator(n_jobs=-1, ensemble_size=0) + cls.fit() + assert cls._n_jobs == n_cores + + +def test_get_number_of_available_cores(): + n_cores = cpu_count() + assert n_cores >= 1, n_cores + + +@unittest.mock.patch('autosklearn.automl.AutoML.predict') +def test_multiclass_prediction(predict_mock, dask_client): + predicted_probabilities = [[0, 0, 0.99], [0, 0.99, 0], [0.99, 0, 0], + [0, 0.99, 0], [0, 0, 0.99]] + predicted_indexes = [2, 1, 0, 1, 2] + expected_result = ['c', 'b', 'a', 'b', 'c'] + + predict_mock.return_value = np.array(predicted_probabilities) + + classifier = AutoMLClassifier( + time_left_for_this_task=1, + per_run_time_limit=1, + dask_client=dask_client, + ) + classifier.InputValidator = InputValidator(is_classification=True) + classifier.InputValidator.target_validator.fit( + pd.DataFrame(expected_result, dtype='category'), + ) + classifier.InputValidator._is_fitted = True + + actual_result = classifier.predict([None] * len(predicted_indexes)) + + np.testing.assert_array_equal(expected_result, actual_result) + + +@unittest.mock.patch('autosklearn.automl.AutoML.predict') +def test_multilabel_prediction(predict_mock, dask_client): + predicted_probabilities = [[0.99, 0], + [0.99, 0], + [0, 0.99], + [0.99, 0.99], + [0.99, 0.99]] + predicted_indexes = np.array([[1, 0], [1, 0], [0, 1], [1, 1], [1, 1]]) + + predict_mock.return_value = np.array(predicted_probabilities) + + classifier = AutoMLClassifier( + time_left_for_this_task=1, + per_run_time_limit=1, + dask_client=dask_client, + ) + classifier.InputValidator = InputValidator(is_classification=True) + classifier.InputValidator.target_validator.fit( + pd.DataFrame(predicted_indexes, dtype='int64'), + ) + classifier.InputValidator._is_fitted = True + + assert classifier.InputValidator.target_validator.type_of_target == 'multilabel-indicator' + + actual_result = classifier.predict([None] * len(predicted_indexes)) + + np.testing.assert_array_equal(predicted_indexes, actual_result) + + +def test_can_pickle_classifier(tmp_dir, dask_client): + X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') + automl = AutoSklearnClassifier(time_left_for_this_task=30, + delete_tmp_folder_after_terminate=False, + per_run_time_limit=5, + tmp_folder=tmp_dir, + dask_client=dask_client, + ) + + automl.fit(X_train, Y_train) + + initial_predictions = automl.predict(X_test) + initial_accuracy = sklearn.metrics.accuracy_score(Y_test, + initial_predictions) + assert initial_accuracy >= 0.75 + assert count_succeses(automl.cv_results_) > 0 + assert includes_train_scores(automl.performance_over_time_.columns) is True + assert performance_over_time_is_plausible(automl.performance_over_time_) is True + + # Test pickle + dump_file = os.path.join(tmp_dir, 'automl.dump.pkl') + + with open(dump_file, 'wb') as f: + pickle.dump(automl, f) + + with open(dump_file, 'rb') as f: + restored_automl = pickle.load(f) + + restored_predictions = restored_automl.predict(X_test) + restored_accuracy = sklearn.metrics.accuracy_score(Y_test, + restored_predictions) + assert restored_accuracy >= 0.75 + assert initial_accuracy == restored_accuracy + + # Test joblib + dump_file = os.path.join(tmp_dir, 'automl.dump.joblib') + + joblib.dump(automl, dump_file) + + restored_automl = joblib.load(dump_file) + + restored_predictions = restored_automl.predict(X_test) + restored_accuracy = sklearn.metrics.accuracy_score(Y_test, + restored_predictions) + assert restored_accuracy >= 0.75 + assert initial_accuracy == restored_accuracy + + +def test_multilabel(tmp_dir, dask_client): + + X_train, Y_train, X_test, Y_test = putil.get_dataset( + 'iris', make_multilabel=True) + automl = AutoSklearnClassifier(time_left_for_this_task=30, + per_run_time_limit=5, + tmp_folder=tmp_dir, + dask_client=dask_client, + ) + + automl.fit(X_train, Y_train) + + predictions = automl.predict(X_test) + assert predictions.shape == (50, 3), print_debug_information(automl) + assert count_succeses(automl.cv_results_) > 0, print_debug_information(automl) + assert includes_train_scores(automl.performance_over_time_.columns) is True + assert performance_over_time_is_plausible(automl.performance_over_time_) is True + + score = f1_macro(Y_test, predictions) + assert score >= 0.9, print_debug_information(automl) + + probs = automl.predict_proba(X_train) + assert np.mean(probs) == pytest.approx(0.33, rel=1e-1) + + +def test_binary(tmp_dir, dask_client): + + X_train, Y_train, X_test, Y_test = putil.get_dataset( + 'iris', make_binary=True) + automl = AutoSklearnClassifier(time_left_for_this_task=40, + delete_tmp_folder_after_terminate=False, + per_run_time_limit=10, + tmp_folder=tmp_dir, + dask_client=dask_client, + ) + + automl.fit(X_train, Y_train, X_test=X_test, y_test=Y_test, + dataset_name='binary_test_dataset') + + predictions = automl.predict(X_test) + assert predictions.shape == (50, ), print_debug_information(automl) + + score = accuracy(Y_test, predictions) + assert score > 0.9, print_debug_information(automl) + assert count_succeses(automl.cv_results_) > 0, print_debug_information(automl) + assert includes_all_scores(automl.performance_over_time_.columns) is True + assert performance_over_time_is_plausible(automl.performance_over_time_) is True + + +def test_classification_pandas_support(tmp_dir, dask_client): + + X, y = sklearn.datasets.fetch_openml( + data_id=2, # cat/num dataset + return_X_y=True, + as_frame=True, + ) + + # Drop NAN!! + X = X.dropna(axis='columns') + + # This test only make sense if input is dataframe + assert isinstance(X, pd.DataFrame) + assert isinstance(y, pd.Series) + automl = AutoSklearnClassifier( + time_left_for_this_task=30, + per_run_time_limit=5, + exclude={'classifier': ['libsvm_svc']}, + dask_client=dask_client, + seed=5, + tmp_folder=tmp_dir, + ) + + automl.fit(X, y) + + # Make sure that at least better than random. + # We use same X_train==X_test to test code quality + assert automl.score(X, y) > 0.555, print_debug_information(automl) + + automl.refit(X, y) + + # Make sure that at least better than random. + # accuracy in sklearn needs valid data + # It should be 0.555 as the dataset is unbalanced. + prediction = automl.predict(X) + assert accuracy(y, prediction) > 0.555 + assert count_succeses(automl.cv_results_) > 0 + assert includes_train_scores(automl.performance_over_time_.columns) is True + assert performance_over_time_is_plausible(automl.performance_over_time_) is True + + +def test_regression(tmp_dir, dask_client): + + X_train, Y_train, X_test, Y_test = putil.get_dataset('boston') + automl = AutoSklearnRegressor(time_left_for_this_task=30, + per_run_time_limit=5, + tmp_folder=tmp_dir, + dask_client=dask_client, + ) + + automl.fit(X_train, Y_train) + + predictions = automl.predict(X_test) + assert predictions.shape == (356,) + score = mean_squared_error(Y_test, predictions) + + # On average np.sqrt(30) away from the target -> ~5.5 on average + # Results with select rates drops avg score to a range of -32.40 to -37, on 30 seconds + # constraint. With more time_left_for_this_task this is no longer an issue + assert score >= -37, print_debug_information(automl) + assert count_succeses(automl.cv_results_) > 0 + assert includes_train_scores(automl.performance_over_time_.columns) is True + assert performance_over_time_is_plausible(automl.performance_over_time_) is True + + +def test_cv_regression(tmp_dir, dask_client): + """ + Makes sure that when using a cv strategy, we are able to fit + a regressor + """ + + X_train, Y_train, X_test, Y_test = putil.get_dataset('boston', train_size_maximum=300) + automl = AutoSklearnRegressor(time_left_for_this_task=60, + per_run_time_limit=10, + resampling_strategy='cv', + tmp_folder=tmp_dir, + dask_client=dask_client, + ) + + automl.fit(X_train, Y_train) + + predictions = automl.predict(X_test) + assert predictions.shape == (206,) + score = r2(Y_test, predictions) + assert score >= 0.1, print_debug_information(automl) + assert count_succeses(automl.cv_results_) > 0, print_debug_information(automl) + assert includes_train_scores(automl.performance_over_time_.columns) is True + assert performance_over_time_is_plausible(automl.performance_over_time_) is True + + +def test_regression_pandas_support(tmp_dir, dask_client): + + X, y = sklearn.datasets.fetch_openml( + data_id=41514, # diabetes + return_X_y=True, + as_frame=True, + ) + # This test only make sense if input is dataframe + assert isinstance(X, pd.DataFrame) + assert isinstance(y, pd.Series) + automl = AutoSklearnRegressor( + time_left_for_this_task=40, + per_run_time_limit=5, + dask_client=dask_client, + tmp_folder=tmp_dir, + ) + + # Make sure we error out because y is not encoded + automl.fit(X, y) + + # Make sure that at least better than random. + # We use same X_train==X_test to test code quality + assert automl.score(X, y) >= 0.5, print_debug_information(automl) + + automl.refit(X, y) + + # Make sure that at least better than random. + assert r2(y, automl.predict(X)) > 0.5, print_debug_information(automl) + assert count_succeses(automl.cv_results_) > 0, print_debug_information(automl) + assert includes_train_scores(automl.performance_over_time_.columns) is True + assert performance_over_time_is_plausible(automl.performance_over_time_) is True + + +def test_autosklearn_classification_methods_returns_self(dask_client): + """ + Currently this method only tests that the methods of AutoSklearnClassifier + is able to fit using fit(), fit_ensemble() and refit() + """ + X_train, y_train, X_test, y_test = putil.get_dataset('iris') + automl = AutoSklearnClassifier(time_left_for_this_task=60, + delete_tmp_folder_after_terminate=False, + per_run_time_limit=10, + ensemble_size=0, + dask_client=dask_client, + exclude={'feature_preprocessor': ['fast_ica']}) + + automl_fitted = automl.fit(X_train, y_train) + + assert automl is automl_fitted + + automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5) + assert automl is automl_ensemble_fitted + + automl_refitted = automl.refit(X_train.copy(), y_train.copy()) + + assert automl is automl_refitted + + +# Currently this class only tests that the methods of AutoSklearnRegressor +# that should return self actually return self. +def test_autosklearn_regression_methods_returns_self(dask_client): + X_train, y_train, X_test, y_test = putil.get_dataset('boston') + automl = AutoSklearnRegressor(time_left_for_this_task=30, + delete_tmp_folder_after_terminate=False, + per_run_time_limit=5, + dask_client=dask_client, + ensemble_size=0) + + automl_fitted = automl.fit(X_train, y_train) + assert automl is automl_fitted + + automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5) + assert automl is automl_ensemble_fitted + + automl_refitted = automl.refit(X_train.copy(), y_train.copy()) + assert automl is automl_refitted + + +def test_autosklearn2_classification_methods_returns_self(dask_client): + X_train, y_train, X_test, y_test = putil.get_dataset('iris') + automl = AutoSklearn2Classifier(time_left_for_this_task=60, ensemble_size=0, + delete_tmp_folder_after_terminate=False, + dask_client=dask_client) + + automl_fitted = automl.fit(X_train, y_train) + + assert automl is automl_fitted + + automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5) + assert automl is automl_ensemble_fitted + + automl_refitted = automl.refit(X_train.copy(), y_train.copy()) + + assert automl is automl_refitted + + predictions = automl_fitted.predict(X_test) + assert sklearn.metrics.accuracy_score( + y_test, predictions + ) >= 2 / 3, print_debug_information(automl) + + pickle.dumps(automl_fitted) + + +def test_autosklearn2_classification_methods_returns_self_sparse(dask_client): + X_train, y_train, X_test, y_test = putil.get_dataset('breast_cancer', make_sparse=True) + automl = AutoSklearn2Classifier(time_left_for_this_task=60, ensemble_size=0, + delete_tmp_folder_after_terminate=False, + dask_client=dask_client) + + automl_fitted = automl.fit(X_train, y_train) + + assert automl is automl_fitted + + automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5) + assert automl is automl_ensemble_fitted + + automl_refitted = automl.refit(X_train.copy(), y_train.copy()) + + assert automl is automl_refitted + + predictions = automl_fitted.predict(X_test) + assert sklearn.metrics.accuracy_score( + y_test, predictions + ) >= 2 / 3, print_debug_information(automl) + + assert "boosting" not in str(automl.get_configuration_space(X=X_train, y=y_train)) + + pickle.dumps(automl_fitted) + + +@pytest.mark.parametrize("class_", [AutoSklearnClassifier, AutoSklearnRegressor, + AutoSklearn2Classifier]) +def test_check_estimator_signature(class_): + # Make sure signature is store in self + expected_subclass = ClassifierMixin if 'Classifier' in str(class_) else RegressorMixin + assert issubclass(class_, expected_subclass) + estimator = class_() + for expected in list(inspect.signature(class_).parameters): + assert hasattr(estimator, expected) + + +@pytest.mark.parametrize("selector_path", [None, # No XDG_CACHE_HOME provided + '/', # XDG_CACHE_HOME has no permission + tempfile.gettempdir(), # in the user cache + ]) +def test_selector_file_askl2_can_be_created(selector_path): + with unittest.mock.patch('os.environ.get') as mock_foo: + mock_foo.return_value = selector_path + if selector_path is not None and not os.access(selector_path, os.W_OK): + with pytest.raises(PermissionError): + importlib.reload(autosklearn.experimental.askl2) + else: + importlib.reload(autosklearn.experimental.askl2) + for metric in autosklearn.experimental.askl2.metrics: + assert os.path.exists(autosklearn.experimental.askl2.selector_files[metric.name]) + if selector_path is None or not os.access(selector_path, os.W_OK): + # We default to home in worst case + assert os.path.expanduser("~") in str( + autosklearn.experimental.askl2.selector_files[metric.name] + ) + else: + # a dir provided via XDG_CACHE_HOME + assert selector_path in str( + autosklearn.experimental.askl2.selector_files[metric.name] + ) + # Re import it at the end so we do not affect other test + importlib.reload(autosklearn.experimental.askl2) + + +def test_check_askl2_same_arguments_as_askl(): + # In case a new attribute is created, make sure it is there also in + # ASKL2 + extra_arguments = list(set( + inspect.getfullargspec(AutoSklearnEstimator.__init__).args) - set( + inspect.getfullargspec(AutoSklearn2Classifier.__init__).args)) + expected_extra_args = ['exclude', + 'include', + 'resampling_strategy_arguments', + 'get_smac_object_callback', + 'initial_configurations_via_metalearning', + 'resampling_strategy', + 'metadata_directory', + 'get_trials_callback'] + unexpected_args = set(extra_arguments) - set(expected_extra_args) + assert len(unexpected_args) == 0, unexpected_args + + +@pytest.mark.parametrize("task_type", ['classification', 'regression']) +@pytest.mark.parametrize("resampling_strategy", ['test', 'cv', 'holdout']) +@pytest.mark.parametrize("disable_file_output", [True, False]) +def test_fit_pipeline(dask_client, task_type, resampling_strategy, disable_file_output): + """ + Tests that we can query the configuration space, and from the default configuration + space, fit a classification pipeline with an acceptable score + """ + X_train, y_train, X_test, y_test = putil.get_dataset( + 'iris' if task_type == 'classification' else 'boston' + ) + estimator = AutoSklearnClassifier if task_type == 'classification' else AutoSklearnRegressor + seed = 3 + if task_type == "classification": + include = {'classifier': ['random_forest']} + else: + include = {'regressor': ['random_forest']} + automl = estimator( + delete_tmp_folder_after_terminate=False, + time_left_for_this_task=120, + # Time left for task plays no role + # only per run time limit + per_run_time_limit=30, + ensemble_size=0, + dask_client=dask_client, + include=include, + seed=seed, + # We cannot get the configuration space with 'test' not fit with it + resampling_strategy=resampling_strategy if resampling_strategy != 'test' else 'holdout', + ) + config = automl.get_configuration_space(X_train, y_train, + X_test=X_test, y_test=y_test, + ).get_default_configuration() + + pipeline, run_info, run_value = automl.fit_pipeline( + X=X_train, + y=y_train, + config=config, + X_test=X_test, + y_test=y_test, + disable_file_output=disable_file_output, + resampling_strategy=resampling_strategy + ) + + assert isinstance(run_info.config, Configuration) + assert run_info.cutoff == 30 + assert run_value.status == StatusType.SUCCESS, f"{run_info}->{run_value}" + # We should produce a decent result + assert run_value.cost < 0.2 + + # Make sure that the pipeline can be pickled + dump_file = os.path.join(tempfile.gettempdir(), 'automl.dump.pkl') + with open(dump_file, 'wb') as f: + pickle.dump(pipeline, f) + + if resampling_strategy == 'test' or disable_file_output: + # We do not produce a pipeline in 'test' + assert pipeline is None + elif resampling_strategy == 'cv': + # We should have fitted a Voting estimator + assert hasattr(pipeline, 'estimators_') + else: + # We should have fitted a pipeline with named_steps + assert hasattr(pipeline, 'named_steps') + assert 'RandomForest' in pipeline.steps[-1][-1].choice.__class__.__name__ + + # Num run should be 2, as 1 is for dummy classifier and we have not launch + # another pipeline + num_run = 2 + + # Check the re-sampling strategy + num_run_dir = automl.automl_._backend.get_numrun_directory( + seed, num_run, budget=0.0) + cv_model_path = os.path.join(num_run_dir, automl.automl_._backend.get_cv_model_filename( + seed, num_run, budget=0.0)) + model_path = os.path.join(num_run_dir, automl.automl_._backend.get_model_filename( + seed, num_run, budget=0.0)) + if resampling_strategy == 'test' or disable_file_output: + # No file output is expected + assert not os.path.exists(num_run_dir) + else: + # We expect the model path always + # And the cv model only on 'cv' + assert os.path.exists(model_path) + if resampling_strategy == 'cv': + assert os.path.exists(cv_model_path) + elif resampling_strategy == 'holdout': + assert not os.path.exists(cv_model_path) + + +@pytest.mark.parametrize("data_type", ['pandas', 'numpy']) +@pytest.mark.parametrize("include_categorical", [True, False]) +def test_pass_categorical_and_numeric_columns_to_pipeline( + dask_client, data_type, include_categorical +): + + # Prepare the training data + X, y = sklearn.datasets.make_classification(random_state=0) + X = cast(np.ndarray, X) + + n_features = X.shape[1] + + # If categorical, insert a row of 'categorical' '0's at last col + if include_categorical: + X = np.insert(X, n_features, values=0, axis=1) + + if data_type == 'pandas': + X = pd.DataFrame(X) + y = pd.DataFrame(y, dtype="category") + + # Set the last column to categorical + if include_categorical: + X.loc[:, n_features] = X.loc[:, n_features].astype('category') # type: ignore + + # Specify the feature_types + if data_type == 'numpy' and include_categorical: + feat_type = ['numerical'] * n_features + ['categorical'] + else: + feat_type = None + + # Create the splits + X_train, X_test, y_train, y_test = model_selection.train_test_split( + X, y, test_size=0.5, random_state=3 + ) + + # Create Estimator + # Time left for task plays no role for fit_pipeline + automl = AutoSklearnClassifier( + delete_tmp_folder_after_terminate=False, + time_left_for_this_task=120, + per_run_time_limit=30, + ensemble_size=0, + seed=0, + dask_client=dask_client, + include={'classifier': ['random_forest']}, + ) + + config_space = automl.get_configuration_space( + X_train, y_train, X_test=X_test, y_test=y_test, feat_type=feat_type, + ) + config = config_space.get_default_configuration() + + pipeline, _, run_value = automl.fit_pipeline( + X=X_train, y=y_train, X_test=X_test, y_test=y_test, + config=config, feat_type=feat_type, + ) + + assert pipeline is not None, "Expected a pipeline from automl.fit_pipeline" + + feature_validator = automl.automl_.InputValidator.feature_validator # type: ignore + transformed_X_test = feature_validator.transform(X_test) + predictions = pipeline.predict(transformed_X_test) + + # We should produce a half decent result + assert run_value.cost < 0.40, f"Run value:\n {run_value}" + + # Outputs should be the correct length + assert np.shape(predictions)[0] == np.shape(y_test)[0] + + n_columns = np.shape(X)[1] + + if include_categorical: + expected_feat_types = { + i: feature_type + for i, feature_type + in enumerate(['numerical'] * (n_columns-1) + ['categorical']) + } + + else: + expected_feat_types = { + i: feature_type + for i, feature_type + in enumerate(['numerical'] * n_columns) + } + + pipeline_feat_types = pipeline.named_steps['data_preprocessor'].choice.feat_type + assert expected_feat_types == pipeline_feat_types + + +@pytest.mark.parametrize("as_frame", [True, False]) +def test_autosklearn_anneal(as_frame): + """ + This test makes sure that anneal dataset can be fitted and scored. + This dataset is quite complex, with NaN, categorical and numerical columns + so is a good testcase for unit-testing + """ + X, y = sklearn.datasets.fetch_openml(data_id=2, return_X_y=True, as_frame=as_frame) + automl = AutoSklearnClassifier(time_left_for_this_task=60, ensemble_size=0, + delete_tmp_folder_after_terminate=False, + initial_configurations_via_metalearning=0, + smac_scenario_args={'runcount_limit': 6}, + resampling_strategy='holdout-iterative-fit') + + if as_frame: + # Let autosklearn calculate the feat types + automl_fitted = automl.fit(X, y) + + else: + X_, y_ = sklearn.datasets.fetch_openml(data_id=2, return_X_y=True, as_frame=True) + feat_type = ['categorical' if X_[col].dtype.name == 'category' else 'numerical' + for col in X_.columns] + + automl_fitted = automl.fit(X, y, feat_type=feat_type) + + assert automl is automl_fitted + + automl_ensemble_fitted = automl.fit_ensemble(y, ensemble_size=5) + assert automl is automl_ensemble_fitted + + # We want to make sure we can learn from this data. + # This is a test to make sure the data format (numpy/pandas) + # can be used in a meaningful way -- not meant for generalization, + # hence we use the train dataset + assert automl_fitted.score(X, y) > 0.75 diff --git a/test/test_ensemble_builder/ensemble_utils.py b/test/test_ensemble_builder/ensemble_utils.py index 7a3cd7f252..b98021c7bd 100644 --- a/test/test_ensemble_builder/ensemble_utils.py +++ b/test/test_ensemble_builder/ensemble_utils.py @@ -1,60 +1,51 @@ import os import shutil +import unittest +import unittest.mock import numpy as np -from autosklearn.automl_common.common.ensemble_building.abstract_ensemble import ( - AbstractEnsemble, -) -from autosklearn.ensemble_builder import EnsembleBuilder -from autosklearn.metrics import make_scorer +from autosklearn.automl_common.common.ensemble_building.abstract_ensemble import AbstractEnsemble -import unittest -import unittest.mock +from autosklearn.metrics import make_scorer +from autosklearn.ensemble_builder import EnsembleBuilder def scorer_function(a, b): return 0.9 -MockMetric = make_scorer("mock", scorer_function) +MockMetric = make_scorer('mock', scorer_function) class BackendMock(object): + def __init__(self, target_directory): - this_directory = os.path.abspath(os.path.dirname(__file__)) - shutil.copytree( - os.path.join(this_directory, "data"), os.path.join(target_directory) + this_directory = os.path.abspath( + os.path.dirname(__file__) ) + shutil.copytree(os.path.join(this_directory, 'data'), os.path.join(target_directory)) self.temporary_directory = target_directory - self.internals_directory = os.path.join( - self.temporary_directory, ".auto-sklearn" - ) + self.internals_directory = os.path.join(self.temporary_directory, '.auto-sklearn') def load_datamanager(self): manager = unittest.mock.Mock() manager.__reduce__ = lambda self: (unittest.mock.MagicMock, ()) - array = np.load( - os.path.join( - self.temporary_directory, - ".auto-sklearn", - "runs", - "0_3_100.0", - "predictions_test_0_3_100.0.npy", - ) - ) + array = np.load(os.path.join( + self.temporary_directory, + '.auto-sklearn', + 'runs', '0_3_100.0', + 'predictions_test_0_3_100.0.npy' + )) manager.data.get.return_value = array return manager def load_targets_ensemble(self): - with open( - os.path.join( - self.temporary_directory, - ".auto-sklearn", - "predictions_ensemble_true.npy", - ), - "rb", - ) as fp: + with open(os.path.join( + self.temporary_directory, + ".auto-sklearn", + "predictions_ensemble_true.npy" + ), "rb") as fp: y = np.load(fp, allow_pickle=True) return y @@ -65,15 +56,13 @@ def save_predictions_as_txt(self, predictions, subset, idx, prefix, precision): return def get_runs_directory(self) -> str: - return os.path.join(self.temporary_directory, ".auto-sklearn", "runs") + return os.path.join(self.temporary_directory, '.auto-sklearn', 'runs') def get_numrun_directory(self, seed: int, num_run: int, budget: float) -> str: - return os.path.join( - self.get_runs_directory(), "%d_%d_%s" % (seed, num_run, budget) - ) + return os.path.join(self.get_runs_directory(), '%d_%d_%s' % (seed, num_run, budget)) def get_model_filename(self, seed: int, idx: int, budget: float) -> str: - return "%s.%s.%s.model" % (seed, idx, budget) + return '%s.%s.%s.model' % (seed, idx, budget) def compare_read_preds(read_preds1, read_preds2): @@ -102,15 +91,13 @@ def compare_read_preds(read_preds1, read_preds2): class EnsembleBuilderMemMock(EnsembleBuilder): + def fit_ensemble(self, selected_keys): return True - def predict( - self, - set_: str, - ensemble: AbstractEnsemble, - selected_keys: list, - n_preds: int, - index_run: int, - ): + def predict(self, set_: str, + ensemble: AbstractEnsemble, + selected_keys: list, + n_preds: int, + index_run: int): np.ones([10000000, 1000000]) diff --git a/test/test_evaluation/test_train_evaluator.py b/test/test_evaluation/test_train_evaluator.py index e23c706847..d969a2cc54 100644 --- a/test/test_evaluation/test_train_evaluator.py +++ b/test/test_evaluation/test_train_evaluator.py @@ -30,6 +30,8 @@ ) from smac.tae import StatusType, TAEAbortException +from autosklearn.automl_common.common.utils import backend + import autosklearn.evaluation.splitter from autosklearn.automl_common.common.utils import backend from autosklearn.constants import ( diff --git a/test/test_pipeline/components/classification/test_base.py b/test/test_pipeline/components/classification/test_base.py index 9fc54f4dba..fb2243b92f 100644 --- a/test/test_pipeline/components/classification/test_base.py +++ b/test/test_pipeline/components/classification/test_base.py @@ -14,6 +14,8 @@ from test.test_pipeline.ignored_warnings import classifier_warnings, ignore_warnings +from test.test_pipeline.ignored_warnings import ignore_warnings, classifier_warnings + class BaseClassificationComponentTest(unittest.TestCase): # Magic command to not run tests on base class diff --git a/test/test_pipeline/components/feature_preprocessing/test_liblinear.py b/test/test_pipeline/components/feature_preprocessing/test_liblinear.py index 0195dfb701..bd9aa60e34 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_liblinear.py +++ b/test/test_pipeline/components/feature_preprocessing/test_liblinear.py @@ -15,8 +15,11 @@ ignore_warnings, ) +from test.test_pipeline.ignored_warnings import ignore_warnings, feature_preprocessing_warnings + class LiblinearComponentTest(PreprocessingTestCase): + def test_default_configuration(self): with ignore_warnings(feature_preprocessing_warnings): transformation, original = _test_preprocessing(LibLinear_Preprocessor) diff --git a/test/test_pipeline/components/regression/test_base.py b/test/test_pipeline/components/regression/test_base.py index 7ed4afe79a..54cb7799e5 100644 --- a/test/test_pipeline/components/regression/test_base.py +++ b/test/test_pipeline/components/regression/test_base.py @@ -1,5 +1,7 @@ from typing import Container, Type +import pytest + import numpy as np import sklearn.metrics @@ -13,6 +15,10 @@ from test.test_pipeline.ignored_warnings import ignore_warnings, regressor_warnings +from autosklearn.pipeline.components.regression import _regressors, RegressorChoice + +from test.test_pipeline.ignored_warnings import regressor_warnings, ignore_warnings + class BaseRegressionComponentTest(unittest.TestCase): diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index 9721f88e31..bf1d8fc6f8 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -44,6 +44,8 @@ from test.test_pipeline.ignored_warnings import classifier_warnings, ignore_warnings +from test.test_pipeline.ignored_warnings import classifier_warnings, ignore_warnings + class DummyClassifier(AutoSklearnClassificationAlgorithm): @staticmethod @@ -572,7 +574,7 @@ def _test_configurations( cls = SimpleClassificationPipeline( feat_type=feat_type, dataset_properties=dataset_properties, - init_params=init_params_, + init_params=init_params_ ) cls.set_hyperparameters( config, init_params=init_params_, feat_type=feat_type diff --git a/test/test_pipeline/test_regression.py b/test/test_pipeline/test_regression.py index 66738a1f3a..f84fedacea 100644 --- a/test/test_pipeline/test_regression.py +++ b/test/test_pipeline/test_regression.py @@ -37,6 +37,8 @@ from test.test_pipeline.ignored_warnings import ignore_warnings, regressor_warnings +from test.test_pipeline.ignored_warnings import regressor_warnings, ignore_warnings + class SimpleRegressionPipelineTest(unittest.TestCase): _multiprocess_can_split_ = True @@ -247,10 +249,8 @@ def _test_configurations( "identity" in e.args[0] ): continue - elif ( - "zero-size array to reduction operation maximum which has no " - "identity" in e.args[0] - ): + elif "zero-size array to reduction operation maximum which has no " \ + "identity" in e.args[0]: continue else: e.args += (f"config={config}",) From fa4d040c682fbae8e238628bfc6b054a1df99ec2 Mon Sep 17 00:00:00 2001 From: eddiebergman Date: Thu, 18 Aug 2022 19:31:29 +0200 Subject: [PATCH 107/118] Revert "Merged" This reverts commit 801d8834bb2585826f363f6a4b3c8dcb88b2127f, reversing changes made to 48eb993652cb097b5451d04622737eb246c2c8e1. --- autosklearn/__init__.py | 2 +- autosklearn/automl.py | 1 - autosklearn/ensemble_builder.py | 1502 ----------------- autosklearn/evaluation/abstract_evaluator.py | 2 - autosklearn/pipeline/components/base.py | 2 - .../regression/gradient_boosting.py | 3 - .../components/regression/random_forest.py | 3 - autosklearn/util/dependencies.py | 4 - autosklearn/util/single_thread_client.py | 1 - requirements.txt | 6 +- test/test_automl/test_automl.py | 1048 ------------ test/test_automl/test_estimators.py | 1278 -------------- test/test_ensemble_builder/ensemble_utils.py | 103 -- test/test_evaluation/test_train_evaluator.py | 2 - .../components/classification/test_base.py | 2 - .../feature_preprocessing/test_liblinear.py | 3 - .../components/regression/test_base.py | 6 - test/test_pipeline/test_classification.py | 4 +- test/test_pipeline/test_regression.py | 5 - 19 files changed, 5 insertions(+), 3972 deletions(-) delete mode 100644 autosklearn/ensemble_builder.py delete mode 100644 test/test_automl/test_automl.py delete mode 100644 test/test_automl/test_estimators.py delete mode 100644 test/test_ensemble_builder/ensemble_utils.py diff --git a/autosklearn/__init__.py b/autosklearn/__init__.py index 05b89a50af..2bff637af8 100644 --- a/autosklearn/__init__.py +++ b/autosklearn/__init__.py @@ -19,7 +19,7 @@ "/auto-sklearn/stable/installation.html#windows-osx-compability" % sys.platform ) -if sys.version_info < (3, 7): +if sys.version_info < (3, 6): raise ValueError( "Unsupported python version %s found. Auto-sklearn requires Python " "3.6 or higher." % sys.version_info diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 25325d50ac..e242fbbc08 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -13,7 +13,6 @@ ) import copy -import distro import io import itertools import json diff --git a/autosklearn/ensemble_builder.py b/autosklearn/ensemble_builder.py deleted file mode 100644 index e337726b0e..0000000000 --- a/autosklearn/ensemble_builder.py +++ /dev/null @@ -1,1502 +0,0 @@ -# -*- encoding: utf-8 -*- -import glob -import gzip -import math -import numbers -import logging.handlers -import multiprocessing -import os -import pickle -import re -import shutil -import time -import traceback -from typing import List, Optional, Tuple, Union -import zlib - -import dask.distributed - -import numpy as np -import pandas as pd -import pynisher -from smac.callbacks import IncorporateRunResultCallback -from smac.optimizer.smbo import SMBO -from smac.runhistory.runhistory import RunInfo, RunValue -from smac.tae.base import StatusType - -from autosklearn.automl_common.common.utils.backend import Backend -from autosklearn.automl_common.common.ensemble_building.abstract_ensemble import AbstractEnsemble - -from autosklearn.constants import BINARY_CLASSIFICATION -from autosklearn.metrics import calculate_score, calculate_loss, Scorer -from autosklearn.ensembles.ensemble_selection import EnsembleSelection -from autosklearn.util.logging_ import get_named_client_logger -from autosklearn.util.parallel import preload_modules - -Y_ENSEMBLE = 0 -Y_VALID = 1 -Y_TEST = 2 - -MODEL_FN_RE = r'_([0-9]*)_([0-9]*)_([0-9]{1,3}\.[0-9]*)\.npy' - - -class EnsembleBuilderManager(IncorporateRunResultCallback): - def __init__( - self, - start_time: float, - time_left_for_ensembles: float, - backend: Backend, - dataset_name: str, - task: int, - metric: Scorer, - ensemble_size: int, - ensemble_nbest: int, - max_models_on_disc: Union[float, int], - seed: int, - precision: int, - max_iterations: Optional[int], - read_at_most: int, - ensemble_memory_limit: Optional[int], - random_state: Union[int, np.random.RandomState], - logger_port: int = logging.handlers.DEFAULT_TCP_LOGGING_PORT, - pynisher_context: str = 'fork', - ): - """ SMAC callback to handle ensemble building - - Parameters - ---------- - start_time: int - the time when this job was started, to account for any latency in job allocation - time_left_for_ensemble: int - How much time is left for the task. Job should finish within this allocated time - backend: util.backend.Backend - backend to write and read files - dataset_name: str - name of dataset - task_type: int - type of ML task - metric: str - name of metric to compute the loss of the given predictions - ensemble_size: int - maximal size of ensemble (passed to autosklearn.ensemble.ensemble_selection) - ensemble_nbest: int/float - if int: consider only the n best prediction - if float: consider only this fraction of the best models - Both wrt to validation predictions - If performance_range_threshold > 0, might return less models - max_models_on_disc: int - Defines the maximum number of models that are kept in the disc. - If int, it must be greater or equal than 1, and dictates the max number of - models to keep. - If float, it will be interpreted as the max megabytes allowed of disc space. That - is, if the number of ensemble candidates require more disc space than this float - value, the worst models will be deleted to keep within this budget. - Models and predictions of the worst-performing models will be deleted then. - If None, the feature is disabled. - It defines an upper bound on the models that can be used in the ensemble. - seed: int - random seed - max_iterations: int - maximal number of iterations to run this script - (default None --> deactivated) - precision: [16,32,64,128] - precision of floats to read the predictions - memory_limit: Optional[int] - memory limit in mb. If ``None``, no memory limit is enforced. - read_at_most: int - read at most n new prediction files in each iteration - logger_port: int - port that receives logging records - pynisher_context: str - The multiprocessing context for pynisher. One of spawn/fork/forkserver. - - Returns - ------- - List[Tuple[int, float, float, float]]: - A list with the performance history of this ensemble, of the form - [[pandas_timestamp, train_performance, val_performance, test_performance], ...] - """ - self.start_time = start_time - self.time_left_for_ensembles = time_left_for_ensembles - self.backend = backend - self.dataset_name = dataset_name - self.task = task - self.metric = metric - self.ensemble_size = ensemble_size - self.ensemble_nbest = ensemble_nbest - self.max_models_on_disc = max_models_on_disc - self.seed = seed - self.precision = precision - self.max_iterations = max_iterations - self.read_at_most = read_at_most - self.ensemble_memory_limit = ensemble_memory_limit - self.random_state = random_state - self.logger_port = logger_port - self.pynisher_context = pynisher_context - - # Store something similar to SMAC's runhistory - self.history = [] - - # We only submit new ensembles when there is not an active ensemble job - self.futures = [] - - # The last criteria is the number of iterations - self.iteration = 0 - - # Keep track of when we started to know when we need to finish! - self.start_time = time.time() - - def __call__( - self, - smbo: 'SMBO', - run_info: RunInfo, - result: RunValue, - time_left: float, - ): - if result.status in (StatusType.STOP, StatusType.ABORT) or smbo._stop: - return - self.build_ensemble(smbo.tae_runner.client) - - def build_ensemble( - self, - dask_client: dask.distributed.Client, - unit_test: bool = False - ) -> None: - - # The second criteria is elapsed time - elapsed_time = time.time() - self.start_time - - logger = get_named_client_logger( - name='EnsembleBuilder', - port=self.logger_port, - ) - - # First test for termination conditions - if self.time_left_for_ensembles < elapsed_time: - logger.info( - "Terminate ensemble building as not time is left (run for {}s)".format( - elapsed_time - ), - ) - return - if self.max_iterations is not None and self.max_iterations <= self.iteration: - logger.info( - "Terminate ensemble building because of max iterations: {} of {}".format( - self.max_iterations, - self.iteration - ) - ) - return - - if len(self.futures) != 0: - if self.futures[0].done(): - result = self.futures.pop().result() - if result: - ensemble_history, self.ensemble_nbest, _, _, _ = result - logger.debug("iteration={} @ elapsed_time={} has history={}".format( - self.iteration, - elapsed_time, - ensemble_history, - )) - self.history.extend(ensemble_history) - - # Only submit new jobs if the previous ensemble job finished - if len(self.futures) == 0: - - # Add the result of the run - # On the next while iteration, no references to - # ensemble builder object, so it should be garbage collected to - # save memory while waiting for resources - # Also, notice how ensemble nbest is returned, so we don't waste - # iterations testing if the deterministic predictions size can - # be fitted in memory - try: - # Submit a Dask job from this job, to properly - # see it in the dask diagnostic dashboard - # Notice that the forked ensemble_builder_process will - # wait for the below function to be done - self.futures.append(dask_client.submit( - fit_and_return_ensemble, - backend=self.backend, - dataset_name=self.dataset_name, - task_type=self.task, - metric=self.metric, - ensemble_size=self.ensemble_size, - ensemble_nbest=self.ensemble_nbest, - max_models_on_disc=self.max_models_on_disc, - seed=self.seed, - precision=self.precision, - memory_limit=self.ensemble_memory_limit, - read_at_most=self.read_at_most, - random_state=self.random_state, - end_at=self.start_time + self.time_left_for_ensembles, - iteration=self.iteration, - return_predictions=False, - priority=100, - pynisher_context=self.pynisher_context, - logger_port=self.logger_port, - unit_test=unit_test, - )) - - logger.info( - "{}/{} Started Ensemble builder job at {} for iteration {}.".format( - # Log the client to make sure we - # remain connected to the scheduler - self.futures[0], - dask_client, - time.strftime("%Y.%m.%d-%H.%M.%S"), - self.iteration, - ), - ) - self.iteration += 1 - except Exception as e: - exception_traceback = traceback.format_exc() - error_message = repr(e) - logger.critical(exception_traceback) - logger.critical(error_message) - - -def fit_and_return_ensemble( - backend: Backend, - dataset_name: str, - task_type: str, - metric: Scorer, - ensemble_size: int, - ensemble_nbest: int, - max_models_on_disc: Union[float, int], - seed: int, - precision: int, - read_at_most: int, - end_at: float, - iteration: int, - return_predictions: bool, - pynisher_context: str, - logger_port: int = logging.handlers.DEFAULT_TCP_LOGGING_PORT, - unit_test: bool = False, - memory_limit: Optional[int] = None, - random_state: Optional[Union[int, np.random.RandomState]] = None, -) -> Tuple[ - List[Tuple[int, float, float, float]], - int, - Optional[np.ndarray], - Optional[np.ndarray], - Optional[np.ndarray], -]: - """ - - A short function to fit and create an ensemble. It is just a wrapper to easily send - a request to dask to create an ensemble and clean the memory when finished - - Parameters - ---------- - backend: util.backend.Backend - backend to write and read files - dataset_name: str - name of dataset - metric: str - name of metric to compute the loss of the given predictions - task_type: int - type of ML task - ensemble_size: int - maximal size of ensemble (passed to autosklearn.ensemble.ensemble_selection) - ensemble_nbest: int/float - if int: consider only the n best prediction - if float: consider only this fraction of the best models - Both wrt to validation predictions - If performance_range_threshold > 0, might return less models - max_models_on_disc: int - Defines the maximum number of models that are kept in the disc. - If int, it must be greater or equal than 1, and dictates the max number of - models to keep. - If float, it will be interpreted as the max megabytes allowed of disc space. That - is, if the number of ensemble candidates require more disc space than this float - value, the worst models will be deleted to keep within this budget. - Models and predictions of the worst-performing models will be deleted then. - If None, the feature is disabled. - It defines an upper bound on the models that can be used in the ensemble. - seed: int - random seed - precision: [16,32,64,128] - precision of floats to read the predictions - read_at_most: int - read at most n new prediction files in each iteration - end_at: float - At what time the job must finish. Needs to be the endtime and not the time left - because we do not know when dask schedules the job. - iteration: int - The current iteration - pynisher_context: str - Context to use for multiprocessing, can be either fork, spawn or forkserver. - logger_port: int = DEFAULT_TCP_LOGGING_PORT - The port where the logging server is listening to. - unit_test: bool = False - Turn on unit testing mode. This currently makes fit_ensemble raise a MemoryError. - Having this is very bad coding style, but I did not find a way to make - unittest.mock work through the pynisher with all spawn contexts. If you know a - better solution, please let us know by opening an issue. - memory_limit: Optional[int] = None - memory limit in mb. If ``None``, no memory limit is enforced. - random_state: Optional[int | RandomState] = None - A random state used for the ensemble selection process. - - Returns - ------- - List[Tuple[int, float, float, float]] - A list with the performance history of this ensemble, of the form - [[pandas_timestamp, train_performance, val_performance, test_performance], ...] - - """ - result = EnsembleBuilder( - backend=backend, - dataset_name=dataset_name, - task_type=task_type, - metric=metric, - ensemble_size=ensemble_size, - ensemble_nbest=ensemble_nbest, - max_models_on_disc=max_models_on_disc, - seed=seed, - precision=precision, - memory_limit=memory_limit, - read_at_most=read_at_most, - random_state=random_state, - logger_port=logger_port, - unit_test=unit_test, - ).run( - end_at=end_at, - iteration=iteration, - return_predictions=return_predictions, - pynisher_context=pynisher_context, - ) - return result - - -class EnsembleBuilder(object): - def __init__( - self, - backend: Backend, - dataset_name: str, - task_type: int, - metric: Scorer, - ensemble_size: int = 10, - ensemble_nbest: Union[int, float] = 100, - max_models_on_disc: int = 100, - performance_range_threshold: float = 0, - seed: int = 1, - precision: int = 32, - memory_limit: Optional[int] = 1024, - read_at_most: int = 5, - logger_port: int = logging.handlers.DEFAULT_TCP_LOGGING_PORT, - random_state: Optional[Union[int, np.random.RandomState]] = None, - unit_test: bool = False, - ): - """ - Constructor - - Parameters - ---------- - backend: util.backend.Backend - backend to write and read files - dataset_name: str - name of dataset - task_type: int - type of ML task - metric: str - name of metric to compute the loss of the given predictions - ensemble_size: int = 10 - maximal size of ensemble (passed to autosklearn.ensemble.ensemble_selection) - ensemble_nbest: int | float = 100 - if int: consider only the n best prediction - if float: consider only this fraction of the best models - Both with respect to the validation predictions - If performance_range_threshold > 0, might return less models - max_models_on_disc: int = 100 - Defines the maximum number of models that are kept in the disc. - If int, it must be greater or equal than 1, and dictates the max number of - models to keep. - If float, it will be interpreted as the max megabytes allowed of disc space. That - is, if the number of ensemble candidates require more disc space than this float - value, the worst models will be deleted to keep within this budget. - Models and predictions of the worst-performing models will be deleted then. - If None, the feature is disabled. - It defines an upper bound on the models that can be used in the ensemble. - performance_range_threshold: float = 0 - Keep only models that are better than: - dummy + (best - dummy)*performance_range_threshold - E.g dummy=2, best=4, thresh=0.5 --> only consider models with loss > 3 - Will at most return the minimum between ensemble_nbest models, - and max_models_on_disc. Might return less - seed: int = 1 - random seed that is used as part of the filename - precision: int in [16,32,64,128] = 32 - precision of floats to read the predictions - memory_limit: Optional[int] = 1024 - memory limit in mb. If ``None``, no memory limit is enforced. - read_at_most: int = 5 - read at most n new prediction files in each iteration - logger_port: int = DEFAULT_TCP_LOGGING_PORT - port that receives logging records - random_state: Optional[int | RandomState] = None - An int or RandomState object used for generating the ensemble. - unit_test: bool = False - Turn on unit testing mode. This currently makes fit_ensemble raise a MemoryError. - Having this is very bad coding style, but I did not find a way to make - unittest.mock work through the pynisher with all spawn contexts. If you know a - better solution, please let us know by opening an issue. - """ - - super(EnsembleBuilder, self).__init__() - - self.backend = backend # communication with filesystem - self.dataset_name = dataset_name - self.task_type = task_type - self.metric = metric - self.ensemble_size = ensemble_size - self.performance_range_threshold = performance_range_threshold - - if isinstance(ensemble_nbest, numbers.Integral) and ensemble_nbest < 1: - raise ValueError("Integer ensemble_nbest has to be larger 1: %s" % - ensemble_nbest) - elif not isinstance(ensemble_nbest, numbers.Integral): - if ensemble_nbest < 0 or ensemble_nbest > 1: - raise ValueError( - "Float ensemble_nbest best has to be >= 0 and <= 1: %s" % - ensemble_nbest) - - self.ensemble_nbest = ensemble_nbest - - # max_models_on_disc can be a float, in such case we need to - # remember the user specified Megabytes and translate this to - # max number of ensemble models. max_resident_models keeps the - # maximum number of models in disc - if max_models_on_disc is not None and max_models_on_disc < 0: - raise ValueError( - "max_models_on_disc has to be a positive number or None" - ) - self.max_models_on_disc = max_models_on_disc - self.max_resident_models = None - - self.seed = seed - self.precision = precision - self.memory_limit = memory_limit - self.read_at_most = read_at_most - self.random_state = random_state - self.unit_test = unit_test - - # Setup the logger - self.logger_port = logger_port - self.logger = get_named_client_logger( - name='EnsembleBuilder', - port=self.logger_port, - ) - - if ensemble_nbest == 1: - self.logger.debug("Behaviour depends on int/float: %s, %s (ensemble_nbest, type)" % - (ensemble_nbest, type(ensemble_nbest))) - - self.start_time = 0 - self.model_fn_re = re.compile(MODEL_FN_RE) - - self.last_hash = None # hash of ensemble training data - self.y_true_ensemble = None - self.SAVE2DISC = True - - # already read prediction files - # {"file name": { - # "ens_loss": float - # "mtime_ens": str, - # "mtime_valid": str, - # "mtime_test": str, - # "seed": int, - # "num_run": int, - # }} - self.read_losses = {} - # {"file_name": { - # Y_ENSEMBLE: np.ndarray - # Y_VALID: np.ndarray - # Y_TEST: np.ndarray - # } - # } - self.read_preds = {} - - # Depending on the dataset dimensions, - # regenerating every iteration, the predictions - # losses for self.read_preds - # is too computationally expensive - # As the ensemble builder is stateless - # (every time the ensemble builder gets resources - # from dask, it builds this object from scratch) - # we save the state of this dictionary to memory - # and read it if available - self.ensemble_memory_file = os.path.join( - self.backend.internals_directory, - 'ensemble_read_preds.pkl' - ) - if os.path.exists(self.ensemble_memory_file): - try: - with (open(self.ensemble_memory_file, "rb")) as memory: - self.read_preds, self.last_hash = pickle.load(memory) - except Exception as e: - self.logger.warning( - "Could not load the previous iterations of ensemble_builder predictions." - "This might impact the quality of the run. Exception={} {}".format( - e, - traceback.format_exc(), - ) - ) - self.ensemble_loss_file = os.path.join( - self.backend.internals_directory, - 'ensemble_read_losses.pkl' - ) - if os.path.exists(self.ensemble_loss_file): - try: - with (open(self.ensemble_loss_file, "rb")) as memory: - self.read_losses = pickle.load(memory) - except Exception as e: - self.logger.warning( - "Could not load the previous iterations of ensemble_builder losses." - "This might impact the quality of the run. Exception={} {}".format( - e, - traceback.format_exc(), - ) - ) - - # hidden feature which can be activated via an environment variable. This keeps all - # models and predictions which have ever been a candidate. This is necessary to post-hoc - # compute the whole ensemble building trajectory. - self._has_been_candidate = set() - - self.validation_performance_ = np.inf - - # Track the ensemble performance - datamanager = self.backend.load_datamanager() - self.y_valid = datamanager.data.get('Y_valid') - self.y_test = datamanager.data.get('Y_test') - del datamanager - self.ensemble_history = [] - - def run( - self, - iteration: int, - pynisher_context: str, - time_left: Optional[float] = None, - end_at: Optional[float] = None, - time_buffer=5, - return_predictions: bool = False, - ): - - if time_left is None and end_at is None: - raise ValueError('Must provide either time_left or end_at.') - elif time_left is not None and end_at is not None: - raise ValueError('Cannot provide both time_left and end_at.') - - self.logger = get_named_client_logger( - name='EnsembleBuilder', - port=self.logger_port, - ) - - process_start_time = time.time() - while True: - - if time_left is not None: - time_elapsed = time.time() - process_start_time - time_left -= time_elapsed - else: - current_time = time.time() - if current_time > end_at: - break - else: - time_left = end_at - current_time - - wall_time_in_s = int(time_left - time_buffer) - if wall_time_in_s < 1: - break - context = multiprocessing.get_context(pynisher_context) - preload_modules(context) - - safe_ensemble_script = pynisher.enforce_limits( - wall_time_in_s=wall_time_in_s, - mem_in_mb=self.memory_limit, - logger=self.logger, - context=context, - )(self.main) - safe_ensemble_script(time_left, iteration, return_predictions) - if safe_ensemble_script.exit_status is pynisher.MemorylimitException: - # if ensemble script died because of memory error, - # reduce nbest to reduce memory consumption and try it again - - # ATTENTION: main will start from scratch; # all data structures are empty again - try: - os.remove(self.ensemble_memory_file) - except: # noqa E722 - pass - - if isinstance(self.ensemble_nbest, numbers.Integral) and self.ensemble_nbest <= 1: - if self.read_at_most == 1: - self.logger.error( - "Memory Exception -- Unable to further reduce the number of ensemble " - "members and can no further limit the number of ensemble members " - "loaded per iteration -- please restart Auto-sklearn with a higher " - "value for the argument `memory_limit` (current limit is %s MB). " - "The ensemble builder will keep running to delete files from disk in " - "case this was enabled.", self.memory_limit - ) - self.ensemble_nbest = 0 - else: - self.read_at_most = 1 - self.logger.warning( - "Memory Exception -- Unable to further reduce the number of ensemble " - "members -- Now reducing the number of predictions per call to read " - "at most to 1." - ) - else: - if isinstance(self.ensemble_nbest, numbers.Integral): - self.ensemble_nbest = max(1, int(self.ensemble_nbest / 2)) - else: - self.ensemble_nbest = self.ensemble_nbest / 2 - self.logger.warning("Memory Exception -- restart with " - "less ensemble_nbest: %d" % self.ensemble_nbest) - return [], self.ensemble_nbest, None, None, None - else: - return safe_ensemble_script.result - - return [], self.ensemble_nbest, None, None, None - - def main(self, time_left, iteration, return_predictions): - - # Pynisher jobs inside dask 'forget' - # the logger configuration. So we have to set it up - # accordingly - self.logger = get_named_client_logger( - name='EnsembleBuilder', - port=self.logger_port, - ) - - self.start_time = time.time() - train_pred, valid_pred, test_pred = None, None, None - - used_time = time.time() - self.start_time - self.logger.debug( - 'Starting iteration %d, time left: %f', - iteration, - time_left - used_time, - ) - - # populates self.read_preds and self.read_losses - if not self.compute_loss_per_model(): - if return_predictions: - return self.ensemble_history, self.ensemble_nbest, train_pred, valid_pred, test_pred - else: - return self.ensemble_history, self.ensemble_nbest, None, None, None - - # Only the models with the n_best predictions are candidates - # to be in the ensemble - candidate_models = self.get_n_best_preds() - if not candidate_models: # no candidates yet - if return_predictions: - return self.ensemble_history, self.ensemble_nbest, train_pred, valid_pred, test_pred - else: - return self.ensemble_history, self.ensemble_nbest, None, None, None - - # populates predictions in self.read_preds - # reduces selected models if file reading failed - n_sel_valid, n_sel_test = self. \ - get_valid_test_preds(selected_keys=candidate_models) - - # If valid/test predictions loaded, then reduce candidate models to this set - if len(n_sel_test) != 0 and len(n_sel_valid) != 0 \ - and len(set(n_sel_valid).intersection(set(n_sel_test))) == 0: - # Both n_sel_* have entries, but there is no overlap, this is critical - self.logger.error("n_sel_valid and n_sel_test are not empty, but do not overlap") - if return_predictions: - return self.ensemble_history, self.ensemble_nbest, train_pred, valid_pred, test_pred - else: - return self.ensemble_history, self.ensemble_nbest, None, None, None - - # If any of n_sel_* is not empty and overlaps with candidate_models, - # then ensure candidate_models AND n_sel_test are sorted the same - candidate_models_set = set(candidate_models) - if candidate_models_set.intersection(n_sel_valid).intersection(n_sel_test): - candidate_models = sorted(list(candidate_models_set.intersection( - n_sel_valid).intersection(n_sel_test))) - n_sel_test = candidate_models - n_sel_valid = candidate_models - elif candidate_models_set.intersection(n_sel_valid): - candidate_models = sorted(list(candidate_models_set.intersection( - n_sel_valid))) - n_sel_valid = candidate_models - elif candidate_models_set.intersection(n_sel_test): - candidate_models = sorted(list(candidate_models_set.intersection( - n_sel_test))) - n_sel_test = candidate_models - else: - # This has to be the case - n_sel_test = [] - n_sel_valid = [] - - if os.environ.get('ENSEMBLE_KEEP_ALL_CANDIDATES'): - for candidate in candidate_models: - self._has_been_candidate.add(candidate) - - # train ensemble - ensemble = self.fit_ensemble(selected_keys=candidate_models) - - # Save the ensemble for later use in the main auto-sklearn module! - if ensemble is not None and self.SAVE2DISC: - self.backend.save_ensemble(ensemble, iteration, self.seed) - - # Delete files of non-candidate models - can only be done after fitting the ensemble and - # saving it to disc so we do not accidentally delete models in the previous ensemble - if self.max_resident_models is not None: - self._delete_excess_models(selected_keys=candidate_models) - - # Save the read losses status for the next iteration - with open(self.ensemble_loss_file, "wb") as memory: - pickle.dump(self.read_losses, memory) - - if ensemble is not None: - train_pred = self.predict(set_="train", - ensemble=ensemble, - selected_keys=candidate_models, - n_preds=len(candidate_models), - index_run=iteration) - # We can't use candidate_models here, as n_sel_* might be empty - valid_pred = self.predict(set_="valid", - ensemble=ensemble, - selected_keys=n_sel_valid, - n_preds=len(candidate_models), - index_run=iteration) - # TODO if predictions fails, build the model again during the - # next iteration! - test_pred = self.predict(set_="test", - ensemble=ensemble, - selected_keys=n_sel_test, - n_preds=len(candidate_models), - index_run=iteration) - - # Add a score to run history to see ensemble progress - self._add_ensemble_trajectory( - train_pred, - valid_pred, - test_pred - ) - - # The loaded predictions and the hash can only be saved after the ensemble has been - # built, because the hash is computed during the construction of the ensemble - with open(self.ensemble_memory_file, "wb") as memory: - pickle.dump((self.read_preds, self.last_hash), memory) - - if return_predictions: - return self.ensemble_history, self.ensemble_nbest, train_pred, valid_pred, test_pred - else: - return self.ensemble_history, self.ensemble_nbest, None, None, None - - def get_disk_consumption(self, pred_path): - """ - gets the cost of a model being on disc - """ - - match = self.model_fn_re.search(pred_path) - if not match: - raise ValueError("Invalid path format %s" % pred_path) - _seed = int(match.group(1)) - _num_run = int(match.group(2)) - _budget = float(match.group(3)) - - stored_files_for_run = os.listdir( - self.backend.get_numrun_directory(_seed, _num_run, _budget)) - stored_files_for_run = [ - os.path.join(self.backend.get_numrun_directory(_seed, _num_run, _budget), file_name) - for file_name in stored_files_for_run] - this_model_cost = sum([os.path.getsize(path) for path in stored_files_for_run]) - - # get the megabytes - return round(this_model_cost / math.pow(1024, 2), 2) - - def compute_loss_per_model(self): - """ - Compute the loss of the predictions on ensemble building data set; - populates self.read_preds and self.read_losses - """ - - self.logger.debug("Read ensemble data set predictions") - - if self.y_true_ensemble is None: - try: - self.y_true_ensemble = self.backend.load_targets_ensemble() - except FileNotFoundError: - self.logger.debug( - "Could not find true targets on ensemble data set: %s", - traceback.format_exc(), - ) - return False - - pred_path = os.path.join( - glob.escape(self.backend.get_runs_directory()), - '%d_*_*' % self.seed, - 'predictions_ensemble_%s_*_*.npy*' % self.seed, - ) - y_ens_files = glob.glob(pred_path) - y_ens_files = [y_ens_file for y_ens_file in y_ens_files - if y_ens_file.endswith('.npy') or y_ens_file.endswith('.npy.gz')] - self.y_ens_files = y_ens_files - # no validation predictions so far -- no files - if len(self.y_ens_files) == 0: - self.logger.debug("Found no prediction files on ensemble data set:" - " %s" % pred_path) - return False - - # First sort files chronologically - to_read = [] - for y_ens_fn in self.y_ens_files: - match = self.model_fn_re.search(y_ens_fn) - _seed = int(match.group(1)) - _num_run = int(match.group(2)) - _budget = float(match.group(3)) - mtime = os.path.getmtime(y_ens_fn) - - to_read.append([y_ens_fn, match, _seed, _num_run, _budget, mtime]) - - n_read_files = 0 - # Now read file wrt to num_run - for y_ens_fn, match, _seed, _num_run, _budget, mtime in \ - sorted(to_read, key=lambda x: x[5]): - if self.read_at_most and n_read_files >= self.read_at_most: - # limit the number of files that will be read - # to limit memory consumption - break - - if not y_ens_fn.endswith(".npy") and not y_ens_fn.endswith(".npy.gz"): - self.logger.info('Error loading file (not .npy or .npy.gz): %s', y_ens_fn) - continue - - if not self.read_losses.get(y_ens_fn): - self.read_losses[y_ens_fn] = { - "ens_loss": np.inf, - "mtime_ens": 0, - "mtime_valid": 0, - "mtime_test": 0, - "seed": _seed, - "num_run": _num_run, - "budget": _budget, - "disc_space_cost_mb": None, - # Lazy keys so far: - # 0 - not loaded - # 1 - loaded and in memory - # 2 - loaded but dropped again - # 3 - deleted from disk due to space constraints - "loaded": 0 - } - if not self.read_preds.get(y_ens_fn): - self.read_preds[y_ens_fn] = { - Y_ENSEMBLE: None, - Y_VALID: None, - Y_TEST: None, - } - - if self.read_losses[y_ens_fn]["mtime_ens"] == mtime: - # same time stamp; nothing changed; - continue - - # actually read the predictions and compute their respective loss - try: - y_ensemble = self._read_np_fn(y_ens_fn) - loss = calculate_loss(solution=self.y_true_ensemble, - prediction=y_ensemble, - task_type=self.task_type, - metric=self.metric, - scoring_functions=None) - - if np.isfinite(self.read_losses[y_ens_fn]["ens_loss"]): - self.logger.debug( - 'Changing ensemble loss for file %s from %f to %f ' - 'because file modification time changed? %f - %f', - y_ens_fn, - self.read_losses[y_ens_fn]["ens_loss"], - loss, - self.read_losses[y_ens_fn]["mtime_ens"], - os.path.getmtime(y_ens_fn), - ) - - self.read_losses[y_ens_fn]["ens_loss"] = loss - - # It is not needed to create the object here - # To save memory, we just compute the loss. - self.read_losses[y_ens_fn]["mtime_ens"] = os.path.getmtime(y_ens_fn) - self.read_losses[y_ens_fn]["loaded"] = 2 - self.read_losses[y_ens_fn]["disc_space_cost_mb"] = self.get_disk_consumption( - y_ens_fn - ) - - n_read_files += 1 - - except Exception: - self.logger.warning( - 'Error loading %s: %s', - y_ens_fn, - traceback.format_exc(), - ) - self.read_losses[y_ens_fn]["ens_loss"] = np.inf - - self.logger.debug( - 'Done reading %d new prediction files. Loaded %d predictions in ' - 'total.', - n_read_files, - np.sum([pred["loaded"] > 0 for pred in self.read_losses.values()]) - ) - return True - - def get_n_best_preds(self): - """ - get best n predictions (i.e., keys of self.read_losses) - according to the loss on the "ensemble set" - n: self.ensemble_nbest - - Side effects: - ->Define the n-best models to use in ensemble - ->Only the best models are loaded - ->Any model that is not best is candidate to deletion - if max models in disc is exceeded. - """ - - sorted_keys = self._get_list_of_sorted_preds() - - # number of models available - num_keys = len(sorted_keys) - # remove all that are at most as good as random - # note: dummy model must have run_id=1 (there is no run_id=0) - dummy_losses = list(filter(lambda x: x[2] == 1, sorted_keys)) - # number of dummy models - num_dummy = len(dummy_losses) - dummy_loss = dummy_losses[0] - self.logger.debug("Use %f as dummy loss" % dummy_loss[1]) - - # sorted_keys looks like: (k, v["ens_loss"], v["num_run"]) - # On position 1 we have the loss of a minimization problem. - # keep only the predictions with a loss smaller than the dummy - # prediction - sorted_keys = filter(lambda x: x[1] < dummy_loss[1], sorted_keys) - - # remove Dummy Classifier - sorted_keys = list(filter(lambda x: x[2] > 1, sorted_keys)) - if not sorted_keys: - # no model left; try to use dummy loss (num_run==0) - # log warning when there are other models but not better than dummy model - if num_keys > num_dummy: - self.logger.warning("No models better than random - using Dummy loss!" - "Number of models besides current dummy model: %d. " - "Number of dummy models: %d", - num_keys - 1, - num_dummy) - sorted_keys = [ - (k, v["ens_loss"], v["num_run"]) for k, v in self.read_losses.items() - if v["seed"] == self.seed and v["num_run"] == 1 - ] - # reload predictions if losses changed over time and a model is - # considered to be in the top models again! - if not isinstance(self.ensemble_nbest, numbers.Integral): - # Transform to number of models to keep. Keep at least one - keep_nbest = max(1, min(len(sorted_keys), - int(len(sorted_keys) * self.ensemble_nbest))) - self.logger.debug( - "Library pruning: using only top %f percent of the models for ensemble " - "(%d out of %d)", - self.ensemble_nbest * 100, keep_nbest, len(sorted_keys) - ) - else: - # Keep only at most ensemble_nbest - keep_nbest = min(self.ensemble_nbest, len(sorted_keys)) - self.logger.debug("Library Pruning: using for ensemble only " - " %d (out of %d) models" % (keep_nbest, len(sorted_keys))) - - # If max_models_on_disc is None, do nothing - # One can only read at most max_models_on_disc models - if self.max_models_on_disc is not None: - if not isinstance(self.max_models_on_disc, numbers.Integral): - consumption = [ - [ - v["ens_loss"], - v["disc_space_cost_mb"], - ] for v in self.read_losses.values() if v["disc_space_cost_mb"] is not None - ] - max_consumption = max(c[1] for c in consumption) - - # We are pessimistic with the consumption limit indicated by - # max_models_on_disc by 1 model. Such model is assumed to spend - # max_consumption megabytes - if (sum(c[1] for c in consumption) + max_consumption) > self.max_models_on_disc: - - # just leave the best -- smaller is better! - # This list is in descending order, to preserve the best models - sorted_cum_consumption = np.cumsum([ - c[1] for c in list(sorted(consumption)) - ]) + max_consumption - max_models = np.argmax(sorted_cum_consumption > self.max_models_on_disc) - - # Make sure that at least 1 model survives - self.max_resident_models = max(1, max_models) - self.logger.warning( - "Limiting num of models via float max_models_on_disc={}" - " as accumulated={} worst={} num_models={}".format( - self.max_models_on_disc, - (sum(c[1] for c in consumption) + max_consumption), - max_consumption, - self.max_resident_models - ) - ) - else: - self.max_resident_models = None - else: - self.max_resident_models = self.max_models_on_disc - - if self.max_resident_models is not None and keep_nbest > self.max_resident_models: - self.logger.debug( - "Restricting the number of models to %d instead of %d due to argument " - "max_models_on_disc", - self.max_resident_models, keep_nbest, - ) - keep_nbest = self.max_resident_models - - # consider performance_range_threshold - if self.performance_range_threshold > 0: - best_loss = sorted_keys[0][1] - worst_loss = dummy_loss[1] - worst_loss -= (worst_loss - best_loss) * self.performance_range_threshold - if sorted_keys[keep_nbest - 1][1] > worst_loss: - # We can further reduce number of models - # since worst model is worse than thresh - for i in range(0, keep_nbest): - # Look at most at keep_nbest models, - # but always keep at least one model - current_loss = sorted_keys[i][1] - if current_loss >= worst_loss: - self.logger.debug("Dynamic Performance range: " - "Further reduce from %d to %d models", - keep_nbest, max(1, i)) - keep_nbest = max(1, i) - break - ensemble_n_best = keep_nbest - - # reduce to keys - sorted_keys = list(map(lambda x: x[0], sorted_keys)) - - # remove loaded predictions for non-winning models - for k in sorted_keys[ensemble_n_best:]: - if k in self.read_preds: - self.read_preds[k][Y_ENSEMBLE] = None - self.read_preds[k][Y_VALID] = None - self.read_preds[k][Y_TEST] = None - if self.read_losses[k]['loaded'] == 1: - self.logger.debug( - 'Dropping model %s (%d,%d) with loss %f.', - k, - self.read_losses[k]['seed'], - self.read_losses[k]['num_run'], - self.read_losses[k]['ens_loss'], - ) - self.read_losses[k]['loaded'] = 2 - - # Load the predictions for the winning - for k in sorted_keys[:ensemble_n_best]: - if ( - ( - k not in self.read_preds or - self.read_preds[k][Y_ENSEMBLE] is None - ) - and self.read_losses[k]['loaded'] != 3 - ): - self.read_preds[k][Y_ENSEMBLE] = self._read_np_fn(k) - # No need to load valid and test here because they are loaded - # only if the model ends up in the ensemble - self.read_losses[k]['loaded'] = 1 - - # return keys of self.read_losses with lowest losses - return sorted_keys[:ensemble_n_best] - - def get_valid_test_preds(self, selected_keys: List[str]) -> Tuple[List[str], List[str]]: - """ - get valid and test predictions from disc - and store them in self.read_preds - - Parameters - --------- - selected_keys: list - list of selected keys of self.read_preds - - Return - ------ - success_keys: - all keys in selected keys for which we could read the valid and - test predictions - """ - success_keys_valid = [] - success_keys_test = [] - - for k in selected_keys: - valid_fn = glob.glob( - os.path.join( - glob.escape(self.backend.get_runs_directory()), - '%d_%d_%s' % ( - self.read_losses[k]["seed"], - self.read_losses[k]["num_run"], - self.read_losses[k]["budget"], - ), - 'predictions_valid_%d_%d_%s.npy*' % ( - self.read_losses[k]["seed"], - self.read_losses[k]["num_run"], - self.read_losses[k]["budget"], - ) - ) - ) - valid_fn = [vfn for vfn in valid_fn if vfn.endswith('.npy') or vfn.endswith('.npy.gz')] - test_fn = glob.glob( - os.path.join( - glob.escape(self.backend.get_runs_directory()), - '%d_%d_%s' % ( - self.read_losses[k]["seed"], - self.read_losses[k]["num_run"], - self.read_losses[k]["budget"], - ), - 'predictions_test_%d_%d_%s.npy*' % ( - self.read_losses[k]["seed"], - self.read_losses[k]["num_run"], - self.read_losses[k]["budget"] - ) - ) - ) - test_fn = [tfn for tfn in test_fn if tfn.endswith('.npy') or tfn.endswith('.npy.gz')] - - if len(valid_fn) == 0: - # self.logger.debug("Not found validation prediction file " - # "(although ensemble predictions available): " - # "%s" % valid_fn) - pass - else: - valid_fn = valid_fn[0] - if ( - self.read_losses[k]["mtime_valid"] == os.path.getmtime(valid_fn) - and k in self.read_preds - and self.read_preds[k][Y_VALID] is not None - ): - success_keys_valid.append(k) - continue - try: - y_valid = self._read_np_fn(valid_fn) - self.read_preds[k][Y_VALID] = y_valid - success_keys_valid.append(k) - self.read_losses[k]["mtime_valid"] = os.path.getmtime(valid_fn) - except Exception: - self.logger.warning('Error loading %s: %s', - valid_fn, traceback.format_exc()) - - if len(test_fn) == 0: - # self.logger.debug("Not found test prediction file (although " - # "ensemble predictions available):%s" % - # test_fn) - pass - else: - test_fn = test_fn[0] - if ( - self.read_losses[k]["mtime_test"] == os.path.getmtime(test_fn) - and k in self.read_preds - and self.read_preds[k][Y_TEST] is not None - ): - success_keys_test.append(k) - continue - try: - y_test = self._read_np_fn(test_fn) - self.read_preds[k][Y_TEST] = y_test - success_keys_test.append(k) - self.read_losses[k]["mtime_test"] = os.path.getmtime(test_fn) - except Exception: - self.logger.warning('Error loading %s: %s', - test_fn, traceback.format_exc()) - - return success_keys_valid, success_keys_test - - def fit_ensemble(self, selected_keys: list): - """ - fit ensemble - - Parameters - --------- - selected_keys: list - list of selected keys of self.read_losses - - Returns - ------- - ensemble: EnsembleSelection - trained Ensemble - """ - - if self.unit_test: - raise MemoryError() - - predictions_train = [self.read_preds[k][Y_ENSEMBLE] for k in selected_keys] - include_num_runs = [ - ( - self.read_losses[k]["seed"], - self.read_losses[k]["num_run"], - self.read_losses[k]["budget"], - ) - for k in selected_keys] - - # check hash if ensemble training data changed - current_hash = "".join([ - str(zlib.adler32(predictions_train[i].data.tobytes())) - for i in range(len(predictions_train)) - ]) - if self.last_hash == current_hash: - self.logger.debug( - "No new model predictions selected -- skip ensemble building " - "-- current performance: %f", - self.validation_performance_, - ) - - return None - self.last_hash = current_hash - - ensemble = EnsembleSelection( - ensemble_size=self.ensemble_size, - task_type=self.task_type, - metric=self.metric, - random_state=self.random_state, - ) - - try: - self.logger.debug( - "Fitting the ensemble on %d models.", - len(predictions_train), - ) - start_time = time.time() - ensemble.fit(predictions_train, self.y_true_ensemble, - include_num_runs) - end_time = time.time() - self.logger.debug( - "Fitting the ensemble took %.2f seconds.", - end_time - start_time, - ) - self.logger.info(ensemble) - self.validation_performance_ = min( - self.validation_performance_, - ensemble.get_validation_performance(), - ) - - except ValueError: - self.logger.error('Caught ValueError: %s', traceback.format_exc()) - return None - except IndexError: - self.logger.error('Caught IndexError: %s' + traceback.format_exc()) - return None - finally: - # Explicitly free memory - del predictions_train - - return ensemble - - def predict(self, set_: str, - ensemble: AbstractEnsemble, - selected_keys: list, - n_preds: int, - index_run: int): - """ - save preditions on ensemble, validation and test data on disc - - Parameters - ---------- - set_: ["valid","test"] - data split name - ensemble: EnsembleSelection - trained Ensemble - selected_keys: list - list of selected keys of self.read_losses - n_preds: int - number of prediction models used for ensemble building - same number of predictions on valid and test are necessary - index_run: int - n-th time that ensemble predictions are written to disc - - Return - ------ - y: np.ndarray - """ - self.logger.debug("Predicting the %s set with the ensemble!", set_) - - if set_ == 'valid': - pred_set = Y_VALID - elif set_ == 'test': - pred_set = Y_TEST - else: - pred_set = Y_ENSEMBLE - predictions = [self.read_preds[k][pred_set] for k in selected_keys] - - if n_preds == len(predictions): - y = ensemble.predict(predictions) - if self.task_type == BINARY_CLASSIFICATION: - y = y[:, 1] - return y - else: - self.logger.info( - "Found inconsistent number of predictions and models (%d vs " - "%d) for subset %s", - len(predictions), - n_preds, - set_, - ) - return None - - def _add_ensemble_trajectory(self, train_pred, valid_pred, test_pred): - """ - Records a snapshot of how the performance look at a given training - time. - - Parameters - ---------- - ensemble: EnsembleSelection - The ensemble selection object to record - valid_pred: np.ndarray - The predictions on the validation set using ensemble - test_pred: np.ndarray - The predictions on the test set using ensemble - - """ - if self.task_type == BINARY_CLASSIFICATION: - if len(train_pred.shape) == 1 or train_pred.shape[1] == 1: - train_pred = np.vstack( - ((1 - train_pred).reshape((1, -1)), train_pred.reshape((1, -1))) - ).transpose() - if valid_pred is not None and (len(valid_pred.shape) == 1 or valid_pred.shape[1] == 1): - valid_pred = np.vstack( - ((1 - valid_pred).reshape((1, -1)), valid_pred.reshape((1, -1))) - ).transpose() - if test_pred is not None and (len(test_pred.shape) == 1 or test_pred.shape[1] == 1): - test_pred = np.vstack( - ((1 - test_pred).reshape((1, -1)), test_pred.reshape((1, -1))) - ).transpose() - - performance_stamp = { - 'Timestamp': pd.Timestamp.now(), - 'ensemble_optimization_score': calculate_score( - solution=self.y_true_ensemble, - prediction=train_pred, - task_type=self.task_type, - metric=self.metric, - scoring_functions=None - ) - } - if valid_pred is not None: - # TODO: valid_pred are a legacy from competition manager - # and this if never happens. Re-evaluate Y_valid support - performance_stamp['ensemble_val_score'] = calculate_score( - solution=self.y_valid, - prediction=valid_pred, - task_type=self.task_type, - metric=self.metric, - scoring_functions=None - ) - - # In case test_pred was provided - if test_pred is not None: - performance_stamp['ensemble_test_score'] = calculate_score( - solution=self.y_test, - prediction=test_pred, - task_type=self.task_type, - metric=self.metric, - scoring_functions=None - ) - - self.ensemble_history.append(performance_stamp) - - def _get_list_of_sorted_preds(self): - """ - Returns a list of sorted predictions in descending order - Losses are taken from self.read_losses. - - Parameters - ---------- - None - - Return - ------ - sorted_keys: list - """ - # Sort by loss - smaller is better! - sorted_keys = list(sorted( - [ - (k, v["ens_loss"], v["num_run"]) - for k, v in self.read_losses.items() - ], - # Sort by loss as priority 1 and then by num_run on a ascending order - # We want small num_run first - key=lambda x: (x[1], x[2]), - )) - return sorted_keys - - def _delete_excess_models(self, selected_keys: List[str]): - """ - Deletes models excess models on disc. self.max_models_on_disc - defines the upper limit on how many models to keep. - Any additional model with a worst loss than the top - self.max_models_on_disc is deleted. - - """ - - # Loop through the files currently in the directory - for pred_path in self.y_ens_files: - - # Do not delete candidates - if pred_path in selected_keys: - continue - - if pred_path in self._has_been_candidate: - continue - - match = self.model_fn_re.search(pred_path) - _seed = int(match.group(1)) - _num_run = int(match.group(2)) - _budget = float(match.group(3)) - - # Do not delete the dummy prediction - if _num_run == 1: - continue - - numrun_dir = self.backend.get_numrun_directory(_seed, _num_run, _budget) - try: - os.rename(numrun_dir, numrun_dir + '.old') - shutil.rmtree(numrun_dir + '.old') - self.logger.info("Deleted files of non-candidate model %s", pred_path) - self.read_losses[pred_path]["disc_space_cost_mb"] = None - self.read_losses[pred_path]["loaded"] = 3 - self.read_losses[pred_path]["ens_loss"] = np.inf - except Exception as e: - self.logger.error( - "Failed to delete files of non-candidate model %s due" - " to error %s", pred_path, e - ) - - def _read_np_fn(self, path): - - # Support for string precision - if isinstance(self.precision, str): - precision = int(self.precision) - self.logger.warning("Interpreted str-precision as {}".format( - precision - )) - else: - precision = self.precision - - if path.endswith("gz"): - open_method = gzip.open - elif path.endswith("npy"): - open_method = open - else: - raise ValueError("Unknown filetype %s" % path) - with open_method(path, 'rb') as fp: - if precision == 16: - predictions = np.load(fp, allow_pickle=True).astype(dtype=np.float16) - elif precision == 32: - predictions = np.load(fp, allow_pickle=True).astype(dtype=np.float32) - elif precision == 64: - predictions = np.load(fp, allow_pickle=True).astype(dtype=np.float64) - else: - predictions = np.load(fp, allow_pickle=True) - return predictions diff --git a/autosklearn/evaluation/abstract_evaluator.py b/autosklearn/evaluation/abstract_evaluator.py index 039a2bdb78..1dd77479bc 100644 --- a/autosklearn/evaluation/abstract_evaluator.py +++ b/autosklearn/evaluation/abstract_evaluator.py @@ -15,8 +15,6 @@ from smac.tae import StatusType from threadpoolctl import threadpool_limits -from autosklearn.automl_common.common.utils.backend import Backend - import autosklearn.pipeline.classification import autosklearn.pipeline.regression from autosklearn.askl_typing import FEAT_TYPE_TYPE diff --git a/autosklearn/pipeline/components/base.py b/autosklearn/pipeline/components/base.py index f2790c27ab..7b496842b2 100644 --- a/autosklearn/pipeline/components/base.py +++ b/autosklearn/pipeline/components/base.py @@ -172,7 +172,6 @@ def __str__(self): class IterativeComponent(AutoSklearnComponent): - def fit(self, X, y, sample_weight=None): self.iterative_fit(X, y, n_iter=2, refit=True) @@ -193,7 +192,6 @@ def get_current_iter(self): class IterativeComponentWithSampleWeight(AutoSklearnComponent): - def fit(self, X, y, sample_weight=None): self.iterative_fit(X, y, n_iter=2, refit=True, sample_weight=sample_weight) diff --git a/autosklearn/pipeline/components/regression/gradient_boosting.py b/autosklearn/pipeline/components/regression/gradient_boosting.py index 04bc969b7f..16b7df965d 100644 --- a/autosklearn/pipeline/components/regression/gradient_boosting.py +++ b/autosklearn/pipeline/components/regression/gradient_boosting.py @@ -130,9 +130,6 @@ def iterative_fit(self, X, y, n_iter=2, refit=False): if y.ndim == 2 and y.shape[1] == 1: y = y.flatten() - if y.ndim == 2 and y.shape[1] == 1: - y = y.flatten() - self.estimator.fit(X, y) if ( diff --git a/autosklearn/pipeline/components/regression/random_forest.py b/autosklearn/pipeline/components/regression/random_forest.py index 068429e842..043d62e16b 100644 --- a/autosklearn/pipeline/components/regression/random_forest.py +++ b/autosklearn/pipeline/components/regression/random_forest.py @@ -107,9 +107,6 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): if y.ndim == 2 and y.shape[1] == 1: y = y.flatten() - if y.ndim == 2 and y.shape[1] == 1: - y = y.flatten() - self.estimator.fit(X, y) return self diff --git a/autosklearn/util/dependencies.py b/autosklearn/util/dependencies.py index 390ac351bb..fb9037450b 100644 --- a/autosklearn/util/dependencies.py +++ b/autosklearn/util/dependencies.py @@ -18,10 +18,6 @@ def verify_packages(packages: Optional[Union[str, List[str]]]) -> None: if not package: continue - # Ignore comments - if package.startswith("#"): - continue - match = RE_PATTERN.match(package) if match: name = match.group("name") diff --git a/autosklearn/util/single_thread_client.py b/autosklearn/util/single_thread_client.py index fc0c238f8d..5c158192a0 100644 --- a/autosklearn/util/single_thread_client.py +++ b/autosklearn/util/single_thread_client.py @@ -2,7 +2,6 @@ from typing import Any from pathlib import Path -from typing import Any import dask.distributed diff --git a/requirements.txt b/requirements.txt index 76af7f4a06..b8b77798f9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -15,7 +15,7 @@ pandas>=1.0 liac-arff threadpoolctl -ConfigSpace>=0.4.21,<0.5 -pynisher>=0.6.3,<0.7 +ConfigSpace>=0.4.14,<0.5 +pynisher>=0.6.3 pyrfr>=0.8.1,<0.9 -smac>=1.2,<1.3 +smac>=0.14 diff --git a/test/test_automl/test_automl.py b/test/test_automl/test_automl.py deleted file mode 100644 index f021279dce..0000000000 --- a/test/test_automl/test_automl.py +++ /dev/null @@ -1,1048 +0,0 @@ -# -*- encoding: utf-8 -*- -import itertools -import os -import pickle -import sys -import time -import glob -import unittest -import unittest.mock -import warnings - -import numpy as np -import pandas as pd -import pytest -from scipy.sparse import csr_matrix -import sklearn.datasets -from sklearn.ensemble import VotingRegressor, VotingClassifier -from smac.scenario.scenario import Scenario -from smac.facade.roar_facade import ROAR - -from autosklearn.automl import AutoML, AutoMLClassifier, AutoMLRegressor, _model_predict -from autosklearn.data.validation import InputValidator -import autosklearn.automl -from autosklearn.data.xy_data_manager import XYDataManager -from autosklearn.metrics import ( - accuracy, log_loss, balanced_accuracy, default_metric_for_task -) -from autosklearn.evaluation.abstract_evaluator import MyDummyClassifier, MyDummyRegressor -from autosklearn.util.logging_ import PickableLoggerAdapter -import autosklearn.pipeline.util as putil -from autosklearn.constants import ( - MULTICLASS_CLASSIFICATION, - BINARY_CLASSIFICATION, - MULTILABEL_CLASSIFICATION, - REGRESSION, - MULTIOUTPUT_REGRESSION, - CLASSIFICATION_TASKS, - REGRESSION_TASKS, -) -from smac.tae import StatusType - -sys.path.append(os.path.dirname(__file__)) -from automl_utils import print_debug_information, count_succeses, AutoMLLogParser, includes_all_scores, includes_train_scores, performance_over_time_is_plausible # noqa (E402: module level import not at top of file) - - -class AutoMLStub(AutoML): - def __init__(self): - self.__class__ = AutoML - self._task = None - self._dask_client = None - self._is_dask_client_internally_created = False - - def __del__(self): - pass - - -def test_fit(dask_client): - - X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') - automl = autosklearn.automl.AutoML( - seed=0, - time_left_for_this_task=30, - per_run_time_limit=5, - metric=accuracy, - dask_client=dask_client, - ) - - automl.fit(X_train, Y_train, task=MULTICLASS_CLASSIFICATION) - - score = automl.score(X_test, Y_test) - assert score > 0.8 - assert count_succeses(automl.cv_results_) > 0 - assert includes_train_scores(automl.performance_over_time_.columns) is True - assert performance_over_time_is_plausible(automl.performance_over_time_) is True - assert automl._task == MULTICLASS_CLASSIFICATION - - del automl - - -def test_fit_roar(dask_client_single_worker): - def get_roar_object_callback( - scenario_dict, - seed, - ta, - ta_kwargs, - dask_client, - n_jobs, - **kwargs - ): - """Random online adaptive racing. - - http://ml.informatik.uni-freiburg.de/papers/11-LION5-SMAC.pdf""" - scenario = Scenario(scenario_dict) - return ROAR( - scenario=scenario, - rng=seed, - tae_runner=ta, - tae_runner_kwargs=ta_kwargs, - dask_client=dask_client, - n_jobs=n_jobs, - ) - - X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') - automl = autosklearn.automl.AutoML( - time_left_for_this_task=30, - per_run_time_limit=5, - initial_configurations_via_metalearning=0, - get_smac_object_callback=get_roar_object_callback, - metric=accuracy, - dask_client=dask_client_single_worker, - ) - - automl.fit(X_train, Y_train, task=MULTICLASS_CLASSIFICATION) - - score = automl.score(X_test, Y_test) - assert score > 0.8 - assert count_succeses(automl.cv_results_) > 0 - assert includes_train_scores(automl.performance_over_time_.columns) is True - assert automl._task == MULTICLASS_CLASSIFICATION - - del automl - - -def test_refit_shuffle_on_fail(dask_client): - - failing_model = unittest.mock.Mock() - failing_model.fit.side_effect = [ValueError(), ValueError(), None] - failing_model.fit_transformer.side_effect = [ - ValueError(), ValueError(), (None, {})] - failing_model.get_max_iter.return_value = 100 - - auto = AutoML(30, 5, dask_client=dask_client) - ensemble_mock = unittest.mock.Mock() - ensemble_mock.get_selected_model_identifiers.return_value = [(1, 1, 50.0)] - auto.ensemble_ = ensemble_mock - auto.InputValidator = InputValidator() - for budget_type in [None, 'iterations']: - auto._budget_type = budget_type - - auto.models_ = {(1, 1, 50.0): failing_model} - - # Make sure a valid 2D array is given to automl - X = np.array([1, 2, 3]).reshape(-1, 1) - y = np.array([1, 2, 3]) - auto.InputValidator.fit(X, y) - auto.refit(X, y) - - assert failing_model.fit.call_count == 3 - assert failing_model.fit_transformer.call_count == 3 - - del auto - - -def test_only_loads_ensemble_models(automl_stub): - - def side_effect(ids, *args, **kwargs): - return models if ids is identifiers else {} - - # Add a resampling strategy as this is required by load_models - automl_stub._resampling_strategy = 'holdout' - identifiers = [(1, 2), (3, 4)] - - models = [42] - load_ensemble_mock = unittest.mock.Mock() - load_ensemble_mock.get_selected_model_identifiers.return_value = identifiers - automl_stub._backend.load_ensemble.return_value = load_ensemble_mock - automl_stub._backend.load_models_by_identifiers.side_effect = side_effect - - automl_stub._load_models() - assert models == automl_stub.models_ - assert automl_stub.cv_models_ is None - - automl_stub._resampling_strategy = 'cv' - - models = [42] - automl_stub._backend.load_cv_models_by_identifiers.side_effect = side_effect - - automl_stub._load_models() - assert models == automl_stub.cv_models_ - - -def test_check_for_models_if_no_ensemble(automl_stub): - models = [42] - automl_stub._backend.load_ensemble.return_value = None - automl_stub._backend.list_all_models.return_value = models - automl_stub._disable_evaluator_output = False - - automl_stub._load_models() - - -def test_raises_if_no_models(automl_stub): - automl_stub._backend.load_ensemble.return_value = None - automl_stub._backend.list_all_models.return_value = [] - automl_stub._resampling_strategy = 'holdout' - - automl_stub._disable_evaluator_output = False - with pytest.raises(ValueError): - automl_stub._load_models() - - automl_stub._disable_evaluator_output = True - automl_stub._load_models() - - -def test_delete_non_candidate_models(dask_client): - - seed = 555 - X, Y, _, _ = putil.get_dataset('iris') - automl = autosklearn.automl.AutoML( - delete_tmp_folder_after_terminate=False, - time_left_for_this_task=60, - per_run_time_limit=5, - ensemble_nbest=3, - seed=seed, - initial_configurations_via_metalearning=0, - resampling_strategy='holdout', - include={ - 'classifier': ['sgd'], - 'feature_preprocessor': ['no_preprocessing'] - }, - metric=accuracy, - dask_client=dask_client, - # Force model to be deleted. That is, from 50 which is the - # default to 3 to make sure we delete models. - max_models_on_disc=3, - ) - - automl.fit(X, Y, task=MULTICLASS_CLASSIFICATION, X_test=X, y_test=Y) - - # Assert at least one model file has been deleted and that there were no - # deletion errors - log_file_path = glob.glob(os.path.join( - automl._backend.temporary_directory, 'AutoML(' + str(seed) + '):*.log')) - with open(log_file_path[0]) as log_file: - log_content = log_file.read() - assert 'Deleted files of non-candidate model' in log_content, log_content - assert 'Failed to delete files of non-candidate model' not in log_content, log_content - assert 'Failed to lock model' not in log_content, log_content - - # Assert that the files of the models used by the ensemble weren't deleted - model_files = automl._backend.list_all_models(seed=seed) - model_files_idx = set() - for m_file in model_files: - # Extract the model identifiers from the filename - m_file = os.path.split(m_file)[1].replace('.model', '').split('.', 2) - model_files_idx.add((int(m_file[0]), int(m_file[1]), float(m_file[2]))) - ensemble_members_idx = set(automl.ensemble_.identifiers_) - assert ensemble_members_idx.issubset(model_files_idx), (ensemble_members_idx, model_files_idx) - - del automl - - -def test_binary_score_and_include(dask_client): - """ - Test fix for binary classification prediction - taking the index 1 of second dimension in prediction matrix - """ - - data = sklearn.datasets.make_classification( - n_samples=400, n_features=10, n_redundant=1, n_informative=3, - n_repeated=1, n_clusters_per_class=2, random_state=1) - X_train = data[0][:200] - Y_train = data[1][:200] - X_test = data[0][200:] - Y_test = data[1][200:] - - automl = autosklearn.automl.AutoML( - 20, 5, - include={'classifier': ['sgd'], - 'feature_preprocessor': ['no_preprocessing']}, - metric=accuracy, - dask_client=dask_client, - ) - - automl.fit(X_train, Y_train, task=BINARY_CLASSIFICATION) - - assert automl._task == BINARY_CLASSIFICATION - - # TODO, the assumption from above is not really tested here - # Also, the score method should be removed, it only makes little sense - score = automl.score(X_test, Y_test) - assert score >= 0.4 - - del automl - - -def test_automl_outputs(dask_client): - - X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') - name = 'iris' - auto = autosklearn.automl.AutoML( - 30, 5, - initial_configurations_via_metalearning=0, - seed=100, - metric=accuracy, - dask_client=dask_client, - delete_tmp_folder_after_terminate=False, - ) - - auto.fit( - X=X_train, - y=Y_train, - X_test=X_test, - y_test=Y_test, - dataset_name=name, - task=MULTICLASS_CLASSIFICATION, - ) - - data_manager_file = os.path.join( - auto._backend.temporary_directory, - '.auto-sklearn', - 'datamanager.pkl' - ) - - # pickled data manager (without one hot encoding!) - with open(data_manager_file, 'rb') as fh: - D = pickle.load(fh) - assert np.allclose(D.data['X_train'], X_train) - - # Check that all directories are there - fixture = [ - 'true_targets_ensemble.npy', - 'start_time_100', - 'datamanager.pkl', - 'ensemble_read_preds.pkl', - 'ensemble_read_losses.pkl', - 'runs', - 'ensembles', - 'ensemble_history.json', - ] - assert ( - sorted(os.listdir(os.path.join(auto._backend.temporary_directory, - '.auto-sklearn'))) - == sorted(fixture) - ) - - # At least one ensemble, one validation, one test prediction and one - # model and one ensemble - fixture = glob.glob(os.path.join( - auto._backend.temporary_directory, - '.auto-sklearn', 'runs', '*', 'predictions_ensemble*npy', - )) - assert len(fixture) > 0 - - fixture = glob.glob(os.path.join(auto._backend.temporary_directory, '.auto-sklearn', - 'runs', '*', '100.*.model')) - assert len(fixture) > 0 - - fixture = os.listdir(os.path.join(auto._backend.temporary_directory, - '.auto-sklearn', 'ensembles')) - assert '100.0000000000.ensemble' in fixture - - # Start time - start_time_file_path = os.path.join(auto._backend.temporary_directory, - '.auto-sklearn', "start_time_100") - with open(start_time_file_path, 'r') as fh: - start_time = float(fh.read()) - assert time.time() - start_time >= 10, print_debug_information(auto) - - # Then check that the logger matches the run expectation - logfile = glob.glob(os.path.join( - auto._backend.temporary_directory, 'AutoML*.log'))[0] - parser = AutoMLLogParser(logfile) - - # The number of ensemble trajectories properly in log file - success_ensemble_iters_auto = len(auto.ensemble_performance_history) - success_ensemble_iters_log = parser.count_ensembler_success_pynisher_calls() - assert success_ensemble_iters_auto == success_ensemble_iters_log, "{} != {}".format( - auto.ensemble_performance_history, - print_debug_information(auto), - ) - - # We also care that no iteration got lost - # This is important because it counts for pynisher calls - # and whether a pynisher call actually called the ensemble - total_ensemble_iterations = parser.count_ensembler_iterations() - assert len(total_ensemble_iterations) > 1 # At least 1 iteration - assert range(1, max(total_ensemble_iterations) + 1), total_ensemble_iterations - - # a point where pynisher is called before budget exhaustion - # Dummy not in run history - total_calls_to_pynisher_log = parser.count_tae_pynisher_calls() - 1 - total_returns_from_pynisher_log = parser.count_tae_pynisher_returns() - 1 - total_elements_rh = len([run_value for run_value in auto.runhistory_.data.values( - ) if run_value.status == StatusType.RUNNING]) - - # Make sure we register all calls to pynisher - # The less than or equal here is added as a WA as - # https://github.com/automl/SMAC3/pull/712 is not yet integrated - assert total_elements_rh <= total_calls_to_pynisher_log, print_debug_information(auto) - - # Make sure we register all returns from pynisher - assert total_elements_rh <= total_returns_from_pynisher_log, print_debug_information(auto) - - # Lastly check that settings are print to logfile - ensemble_size = parser.get_automl_setting_from_log(auto._dataset_name, 'ensemble_size') - assert auto._ensemble_size == int(ensemble_size) - - del auto - - -@pytest.mark.parametrize("datasets", [('breast_cancer', BINARY_CLASSIFICATION), - ('wine', MULTICLASS_CLASSIFICATION), - ('diabetes', REGRESSION)]) -def test_do_dummy_prediction(dask_client, datasets): - - name, task = datasets - - X_train, Y_train, X_test, Y_test = putil.get_dataset(name) - datamanager = XYDataManager( - X_train, Y_train, - X_test, Y_test, - task=task, - dataset_name=name, - feat_type={i: 'numerical' for i in range(X_train.shape[1])}, - ) - - auto = autosklearn.automl.AutoML( - 20, 5, - initial_configurations_via_metalearning=25, - metric=accuracy, - dask_client=dask_client, - delete_tmp_folder_after_terminate=False, - ) - auto._backend = auto._create_backend() - - # Make a dummy logger - auto._logger_port = 9020 - auto._logger = unittest.mock.Mock() - auto._logger.info.return_value = None - - auto._backend.save_datamanager(datamanager) - D = auto._backend.load_datamanager() - - # Check if data manager is correcly loaded - assert D.info['task'] == datamanager.info['task'] - auto._do_dummy_prediction(D, 1) - - # Ensure that the dummy predictions are not in the current working - # directory, but in the temporary directory. - unexpected_directory = os.path.join(os.getcwd(), '.auto-sklearn') - expected_directory = os.path.join( - auto._backend.temporary_directory, - '.auto-sklearn', - 'runs', - '1_1_0.0', - 'predictions_ensemble_1_1_0.0.npy' - ) - assert not os.path.exists(unexpected_directory) - assert os.path.exists(expected_directory) - - auto._clean_logger() - - del auto - - -@unittest.mock.patch('autosklearn.evaluation.ExecuteTaFuncWithQueue.run') -def test_fail_if_dummy_prediction_fails(ta_run_mock, dask_client): - - X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') - datamanager = XYDataManager( - X_train, Y_train, - X_test, Y_test, - task=2, - feat_type={i: 'Numerical' for i in range(X_train.shape[1])}, - dataset_name='iris', - ) - - time_for_this_task = 30 - per_run_time = 10 - auto = autosklearn.automl.AutoML(time_for_this_task, - per_run_time, - initial_configurations_via_metalearning=25, - metric=accuracy, - dask_client=dask_client, - delete_tmp_folder_after_terminate=False, - ) - auto._backend = auto._create_backend() - auto._backend._make_internals_directory() - auto._backend.save_datamanager(datamanager) - - # Make a dummy logger - auto._logger_port = 9020 - auto._logger = unittest.mock.Mock() - auto._logger.info.return_value = None - - # First of all, check that ta.run() is actually called. - ta_run_mock.return_value = StatusType.SUCCESS, None, None, {} - auto._do_dummy_prediction(datamanager, 1) - ta_run_mock.assert_called_once_with(1, cutoff=time_for_this_task) - - # Case 1. Check that function raises no error when statustype == success. - # ta.run() returns status, cost, runtime, and additional info. - ta_run_mock.return_value = StatusType.SUCCESS, None, None, {} - raised = False - try: - auto._do_dummy_prediction(datamanager, 1) - except ValueError: - raised = True - assert not raised, 'Exception raised' - - # Case 2. Check that if statustype returned by ta.run() != success, - # the function raises error. - ta_run_mock.return_value = StatusType.CRASHED, None, None, {} - with pytest.raises( - ValueError, - match='Dummy prediction failed with run state StatusType.CRASHED and additional output: {}.' # noqa - ): - auto._do_dummy_prediction(datamanager, 1) - - ta_run_mock.return_value = StatusType.ABORT, None, None, {} - with pytest.raises( - ValueError, - match='Dummy prediction failed with run state StatusType.ABORT ' - 'and additional output: {}.', - ): - auto._do_dummy_prediction(datamanager, 1) - ta_run_mock.return_value = StatusType.TIMEOUT, None, None, {} - with pytest.raises( - ValueError, - match='Dummy prediction failed with run state StatusType.TIMEOUT ' - 'and additional output: {}.' - ): - auto._do_dummy_prediction(datamanager, 1) - ta_run_mock.return_value = StatusType.MEMOUT, None, None, {} - with pytest.raises( - ValueError, - match='Dummy prediction failed with run state StatusType.MEMOUT ' - 'and additional output: {}.', - ): - auto._do_dummy_prediction(datamanager, 1) - ta_run_mock.return_value = StatusType.CAPPED, None, None, {} - with pytest.raises( - ValueError, - match='Dummy prediction failed with run state StatusType.CAPPED ' - 'and additional output: {}.' - ): - auto._do_dummy_prediction(datamanager, 1) - - ta_run_mock.return_value = StatusType.CRASHED, None, None, {'exitcode': -6} - with pytest.raises( - ValueError, - match='The error suggests that the provided memory limits were too tight.', - ): - auto._do_dummy_prediction(datamanager, 1) - - -@unittest.mock.patch('autosklearn.smbo.AutoMLSMBO.run_smbo') -def test_exceptions_inside_log_in_smbo(smbo_run_mock, dask_client): - - # Below importing and shutdown is a workaround, to make sure - # we reset the port to collect messages. Randomly, when running - # this test with multiple other test at the same time causes this - # test to fail. This resets the singletons of the logging class - import logging - logging.shutdown() - - automl = autosklearn.automl.AutoML( - 20, - 5, - metric=accuracy, - dask_client=dask_client, - delete_tmp_folder_after_terminate=False, - ) - - dataset_name = 'test_exceptions_inside_log' - - # Create a custom exception to prevent other errors to slip in - class MyException(Exception): - pass - - X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') - # The first call is on dummy predictor failure - message = str(np.random.randint(100)) + '_run_smbo' - smbo_run_mock.side_effect = MyException(message) - - with pytest.raises(MyException): - automl.fit( - X_train, - Y_train, - task=MULTICLASS_CLASSIFICATION, - dataset_name=dataset_name, - ) - - # make sure that the logfile was created - logger_name = 'AutoML(%d):%s' % (1, dataset_name) - logger = logging.getLogger(logger_name) - logfile = os.path.join(automl._backend.temporary_directory, logger_name + '.log') - assert os.path.exists(logfile), print_debug_information(automl) + str(automl._clean_logger()) - - # Give some time for the error message to be printed in the - # log file - found_message = False - for incr_tolerance in range(5): - with open(logfile) as f: - lines = f.readlines() - if any(message in line for line in lines): - found_message = True - break - else: - time.sleep(incr_tolerance) - - # Speed up the closing after forced crash - automl._clean_logger() - - if not found_message: - pytest.fail("Did not find {} in the log file {} for logger {}/{}/{}".format( - message, - print_debug_information(automl), - vars(automl._logger.logger), - vars(logger), - vars(logging.getLogger()) - )) - - -@pytest.mark.parametrize("metric", [log_loss, balanced_accuracy]) -def test_load_best_individual_model(metric, dask_client): - - X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') - automl = autosklearn.automl.AutoML( - time_left_for_this_task=30, - per_run_time_limit=5, - metric=metric, - dask_client=dask_client, - delete_tmp_folder_after_terminate=False, - ) - - # We cannot easily mock a function sent to dask - # so for this test we create the whole set of models/ensembles - # but prevent it to be loaded - automl.fit(X_train, Y_train, task=MULTICLASS_CLASSIFICATION) - - automl._backend.load_ensemble = unittest.mock.MagicMock(return_value=None) - - # A memory error occurs in the ensemble construction - assert automl._backend.load_ensemble(automl._seed) is None - - # The load model is robust to this and loads the best model - automl._load_models() - assert automl.ensemble_ is not None - - # Just 1 model is there for ensemble and all weight must be on it - get_models_with_weights = automl.get_models_with_weights() - assert len(get_models_with_weights) == 1 - assert get_models_with_weights[0][0] == 1.0 - - # Match a toy dataset - if metric.name == 'balanced_accuracy': - assert automl.score(X_test, Y_test) > 0.9 - elif metric.name == 'log_loss': - # Seen values in github actions of 0.6978304740364537 - assert automl.score(X_test, Y_test) < 0.7 - else: - raise ValueError(metric.name) - - del automl - - -def test_fail_if_feat_type_on_pandas_input(dask_client): - """We do not support feat type when pandas - is provided as an input - """ - automl = autosklearn.automl.AutoML( - time_left_for_this_task=30, - per_run_time_limit=5, - metric=accuracy, - dask_client=dask_client, - ) - - X_train = pd.DataFrame({'a': [1, 1], 'c': [1, 2]}) - y_train = [1, 0] - with pytest.raises( - ValueError, - match="" - "providing the option feat_type to the fit method is not supported when using a Dataframe" - ): - automl.fit( - X_train, y_train, - task=BINARY_CLASSIFICATION, - feat_type={1: 'Categorical', 2: 'Numerical'}, - ) - - -@pytest.mark.parametrize( - 'memory_limit,precision,task', - [ - (memory_limit, precision, task) - for task in itertools.chain(CLASSIFICATION_TASKS, REGRESSION_TASKS) - for precision in (float, np.float32, np.float64, np.float128) - for memory_limit in (1, 100, None) - ] -) -def test_subsample_if_too_large(memory_limit, precision, task): - fixture = { - BINARY_CLASSIFICATION: { - 1: {float: 1310, np.float32: 2621, np.float64: 1310, np.float128: 655}, - 100: {float: 12000, np.float32: 12000, np.float64: 12000, np.float128: 12000}, - None: {float: 12000, np.float32: 12000, np.float64: 12000, np.float128: 12000}, - }, - MULTICLASS_CLASSIFICATION: { - 1: {float: 204, np.float32: 409, np.float64: 204, np.float128: 102}, - 100: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797}, - None: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797}, - }, - MULTILABEL_CLASSIFICATION: { - 1: {float: 204, np.float32: 409, np.float64: 204, np.float128: 102}, - 100: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797}, - None: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797}, - }, - REGRESSION: { - 1: {float: 655, np.float32: 1310, np.float64: 655, np.float128: 327}, - 100: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000}, - None: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000}, - }, - MULTIOUTPUT_REGRESSION: { - 1: {float: 655, np.float32: 1310, np.float64: 655, np.float128: 327}, - 100: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000}, - None: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000}, - } - } - mock = unittest.mock.Mock() - if task == BINARY_CLASSIFICATION: - X, y = sklearn.datasets.make_hastie_10_2() - elif task == MULTICLASS_CLASSIFICATION: - X, y = sklearn.datasets.load_digits(return_X_y=True) - elif task == MULTILABEL_CLASSIFICATION: - X, y_ = sklearn.datasets.load_digits(return_X_y=True) - y = np.zeros((X.shape[0], 10)) - for i, j in enumerate(y_): - y[i, j] = 1 - elif task == REGRESSION: - X, y = sklearn.datasets.make_friedman1(n_samples=5000, n_features=20) - elif task == MULTIOUTPUT_REGRESSION: - X, y = sklearn.datasets.make_friedman1(n_samples=5000, n_features=20) - y = np.vstack((y, y)).transpose() - else: - raise ValueError(task) - X = X.astype(precision) - - assert X.shape[0] == y.shape[0] - - X_new, y_new = AutoML.subsample_if_too_large(X, y, mock, 1, memory_limit, task) - assert X_new.shape[0] == fixture[task][memory_limit][precision] - if memory_limit == 1: - if precision in (np.float128, np.float64, float): - assert mock.warning.call_count == 2 - else: - assert mock.warning.call_count == 1 - else: - assert mock.warning.call_count == 0 - - -def data_input_and_target_types(): - n_rows = 100 - - # Create valid inputs - X_ndarray = np.random.random(size=(n_rows, 5)) - X_ndarray[X_ndarray < .9] = 0 - - # Binary Classificaiton - y_binary_ndarray = np.random.random(size=n_rows) - y_binary_ndarray[y_binary_ndarray >= 0.5] = 1 - y_binary_ndarray[y_binary_ndarray < 0.5] = 0 - - # Multiclass classification - y_multiclass_ndarray = np.random.random(size=n_rows) - y_multiclass_ndarray[y_multiclass_ndarray > 0.66] = 2 - y_multiclass_ndarray[(y_multiclass_ndarray <= 0.66) & (y_multiclass_ndarray >= 0.33)] = 1 - y_multiclass_ndarray[y_multiclass_ndarray < 0.33] = 0 - - # Multilabel classificaiton - y_multilabel_ndarray = np.random.random(size=(n_rows, 3)) - y_multilabel_ndarray[y_multilabel_ndarray > 0.5] = 1 - y_multilabel_ndarray[y_multilabel_ndarray <= 0.5] = 0 - - # Regression - y_regression_ndarray = np.random.random(size=n_rows) - - # Multioutput Regression - y_multioutput_regression_ndarray = np.random.random(size=(n_rows, 3)) - - xs = [ - X_ndarray, - list(X_ndarray), - csr_matrix(X_ndarray), - pd.DataFrame(data=X_ndarray), - ] - - ys_binary = [ - y_binary_ndarray, - list(y_binary_ndarray), - csr_matrix(y_binary_ndarray), - pd.Series(y_binary_ndarray), - pd.DataFrame(data=y_binary_ndarray), - ] - - ys_multiclass = [ - y_multiclass_ndarray, - list(y_multiclass_ndarray), - csr_matrix(y_multiclass_ndarray), - pd.Series(y_multiclass_ndarray), - pd.DataFrame(data=y_multiclass_ndarray), - ] - - ys_multilabel = [ - y_multilabel_ndarray, - list(y_multilabel_ndarray), - csr_matrix(y_multilabel_ndarray), - # pd.Series(y_multilabel_ndarray) - pd.DataFrame(data=y_multilabel_ndarray), - ] - - ys_regression = [ - y_regression_ndarray, - list(y_regression_ndarray), - csr_matrix(y_regression_ndarray), - pd.Series(y_regression_ndarray), - pd.DataFrame(data=y_regression_ndarray), - ] - - ys_multioutput_regression = [ - y_multioutput_regression_ndarray, - list(y_multioutput_regression_ndarray), - csr_matrix(y_multioutput_regression_ndarray), - # pd.Series(y_multioutput_regression_ndarray), - pd.DataFrame(data=y_multioutput_regression_ndarray), - ] - - # [ (X, y, X_test, y_test, task), ... ] - return ( - (X, y, X, y, task) - for X in xs - for y, task in itertools.chain( - itertools.product(ys_binary, [BINARY_CLASSIFICATION]), - itertools.product(ys_multiclass, [MULTICLASS_CLASSIFICATION]), - itertools.product(ys_multilabel, [MULTILABEL_CLASSIFICATION]), - itertools.product(ys_regression, [REGRESSION]), - itertools.product(ys_multioutput_regression, [MULTIOUTPUT_REGRESSION]), - ) - ) - - -@pytest.mark.parametrize("X, y, X_test, y_test, task", data_input_and_target_types()) -def test_input_and_target_types(dask_client, X, y, X_test, y_test, task): - - if task in CLASSIFICATION_TASKS: - automl = AutoMLClassifier( - time_left_for_this_task=15, - per_run_time_limit=5, - dask_client=dask_client, - ) - else: - automl = AutoMLRegressor( - time_left_for_this_task=15, - per_run_time_limit=5, - dask_client=dask_client, - ) - # To save time fitting and only validate the inputs we only return - # the configuration space - automl.fit( - X=X, - y=y, - X_test=X_test, - y_test=y_test, - only_return_configuration_space=True - ) - assert automl._task == task - assert automl._metric.name == default_metric_for_task[task].name - - -def data_test_model_predict_outsputs_correct_shapes(): - datasets = sklearn.datasets - binary = datasets.make_classification( - n_samples=5, n_classes=2, random_state=0 - ) - multiclass = datasets.make_classification( - n_samples=5, n_informative=3, n_classes=3, random_state=0 - ) - multilabel = datasets.make_multilabel_classification( - n_samples=5, n_classes=3, random_state=0 - ) - regression = datasets.make_regression( - n_samples=5, random_state=0 - ) - multioutput = datasets.make_regression( - n_samples=5, n_targets=3, random_state=0 - ) - - # TODO issue 1169 - # While testing output shapes, realised all models are wrapped to provide - # a special predict_proba that outputs a different shape than usual. - # This includes DummyClassifier and DummyRegressor which are wrapped as - # `MyDummyClassifier/Regressor` and require a config object. - # config == 1 : Classifier uses 'uniform', Regressor uses 'mean' - # else : Classifier uses 'most_frequent', Regressor uses 'median' - # - # This wrapping of probabilities with - # `convert_multioutput_multiclass_to_multilabel` - # can probably be just put into a base class which queries subclasses - # as to whether it's needed. - # - # tldr; thats why we use MyDummyX here instead of the default models - # from sklearn - def classifier(X, y): - return MyDummyClassifier(config=1, random_state=0).fit(X, y) - - def regressor(X, y): - return MyDummyRegressor(config=1, random_state=0).fit(X, y) - - # How cross validation models are currently grouped together - def voting_classifier(X, y): - classifiers = [ - MyDummyClassifier(config=1, random_state=0).fit(X, y) - for _ in range(5) - ] - vc = VotingClassifier(estimators=None, voting='soft') - vc.estimators_ = classifiers - return vc - - def voting_regressor(X, y): - regressors = [ - MyDummyRegressor(config=1, random_state=0).fit(X, y) - for _ in range(5) - ] - vr = VotingRegressor(estimators=None) - vr.estimators_ = regressors - return vr - - test_data = { - BINARY_CLASSIFICATION: { - 'models': [classifier(*binary), voting_classifier(*binary)], - 'data': binary, - # prob of false/true for the one class - 'expected_output_shape': (len(binary[0]), 2) - }, - MULTICLASS_CLASSIFICATION: { - 'models': [classifier(*multiclass), voting_classifier(*multiclass)], - 'data': multiclass, - # prob of true for each possible class - 'expected_output_shape': (len(multiclass[0]), 3) - }, - MULTILABEL_CLASSIFICATION: { - 'models': [classifier(*multilabel), voting_classifier(*multilabel)], - 'data': multilabel, - # probability of true for each binary label - 'expected_output_shape': (len(multilabel[0]), 3) # type: ignore - }, - REGRESSION: { - 'models': [regressor(*regression), voting_regressor(*regression)], - 'data': regression, - # array of single outputs - 'expected_output_shape': (len(regression[0]), ) - }, - MULTIOUTPUT_REGRESSION: { - 'models': [regressor(*multioutput), voting_regressor(*multioutput)], - 'data': multioutput, - # array of vector otuputs - 'expected_output_shape': (len(multioutput[0]), 3) - } - } - - return itertools.chain.from_iterable( - [ - (model, cfg['data'], task, cfg['expected_output_shape']) - for model in cfg['models'] - ] - for task, cfg in test_data.items() - ) - - -@pytest.mark.parametrize( - "model, data, task, expected_output_shape", - data_test_model_predict_outsputs_correct_shapes() -) -def test_model_predict_outputs_correct_shapes(model, data, task, expected_output_shape): - X, y = data - prediction = _model_predict(model=model, X=X, task=task) - assert prediction.shape == expected_output_shape - - -def test_model_predict_outputs_warnings_to_logs(): - X = list(range(20)) - task = REGRESSION - logger = PickableLoggerAdapter('test_model_predict_correctly_outputs_warnings') - logger.warning = unittest.mock.Mock() - - class DummyModel: - def predict(self, x): - warnings.warn('test warning', Warning) - return x - - model = DummyModel() - - _model_predict(model, X, task, logger=logger) - - assert logger.warning.call_count == 1, "Logger should have had warning called" - - -def test_model_predict_outputs_to_stdout_if_no_logger(): - X = list(range(20)) - task = REGRESSION - - class DummyModel: - def predict(self, x): - warnings.warn('test warning', Warning) - return x - - model = DummyModel() - - with warnings.catch_warnings(record=True) as w: - _model_predict(model, X, task, logger=None) - - assert len(w) == 1, "One warning sould have been emmited" - - -@pytest.mark.parametrize("task, y", [ - (BINARY_CLASSIFICATION, np.asarray( - 9999 * [0] + 1 * [1] - )), - (MULTICLASS_CLASSIFICATION, np.asarray( - 4999 * [1] + 4999 * [2] + 1 * [3] + 1 * [4] - )), - (MULTILABEL_CLASSIFICATION, np.asarray( - 4999 * [[0, 1, 1]] + 4999 * [[1, 1, 0]] + 1 * [[1, 0, 1]] + 1 * [[0, 0, 0]] - )) -]) -def test_subsample_classification_unique_labels_stay_in_training_set(task, y): - n_samples = 10000 - X = np.random.random(size=(n_samples, 3)) - memory_limit = 1 # Force subsampling - mock = unittest.mock.Mock() - - # Make sure our test assumptions are correct - assert len(y) == n_samples, "Ensure tests are correctly setup" - - values, counts = np.unique(y, axis=0, return_counts=True) - unique_labels = [value for value, count in zip(values, counts) if count == 1] - assert len(unique_labels), "Ensure we have unique labels in the test" - - _, y_sampled = AutoML.subsample_if_too_large(X, y, - logger=mock, - seed=1, - memory_limit=memory_limit, - task=task) - - assert len(y_sampled) <= len(y), \ - "Ensure sampling took place" - assert all(label in y_sampled for label in unique_labels), \ - "All unique labels present in the return sampled set" diff --git a/test/test_automl/test_estimators.py b/test/test_automl/test_estimators.py deleted file mode 100644 index 4de0f767aa..0000000000 --- a/test/test_automl/test_estimators.py +++ /dev/null @@ -1,1278 +0,0 @@ -from typing import Type, cast - -import copy -import glob -import importlib -import os -import inspect -import itertools -import pickle -import re -import sys -import tempfile -import unittest -import unittest.mock -import pytest - -from ConfigSpace.configuration_space import Configuration -import joblib -from joblib import cpu_count -import numpy as np -import numpy.ma as npma -import pandas as pd -import sklearn -import sklearn.model_selection as model_selection -import sklearn.dummy -import sklearn.datasets -from sklearn.base import clone -from sklearn.base import ClassifierMixin, RegressorMixin -from sklearn.base import is_classifier -from smac.tae import StatusType -from dask.distributed import Client - -from autosklearn.data.validation import InputValidator -import autosklearn.pipeline.util as putil -from autosklearn.ensemble_builder import MODEL_FN_RE -import autosklearn.estimators # noqa F401 -from autosklearn.estimators import ( - AutoSklearnEstimator, AutoSklearnRegressor, AutoSklearnClassifier -) -from autosklearn.metrics import accuracy, f1_macro, mean_squared_error, r2 -from autosklearn.automl import AutoMLClassifier -from autosklearn.experimental.askl2 import AutoSklearn2Classifier -from autosklearn.smbo import get_smac_object - -sys.path.append(os.path.dirname(__file__)) -from automl_utils import print_debug_information, count_succeses, includes_train_scores, includes_all_scores, include_single_scores, performance_over_time_is_plausible # noqa (E402: module level import not at top of file) - - -def test_fit_n_jobs(tmp_dir): - - X_train, Y_train, X_test, Y_test = putil.get_dataset('breast_cancer') - - # test parallel Classifier to predict classes, not only indices - Y_train += 1 - Y_test += 1 - - class get_smac_object_wrapper: - - def __call__(self, *args, **kwargs): - self.n_jobs = kwargs['n_jobs'] - smac = get_smac_object(*args, **kwargs) - self.dask_n_jobs = smac.solver.tae_runner.n_workers - self.dask_client_n_jobs = len( - smac.solver.tae_runner.client.scheduler_info()['workers'] - ) - return smac - get_smac_object_wrapper_instance = get_smac_object_wrapper() - - automl = AutoSklearnClassifier( - delete_tmp_folder_after_terminate=False, - time_left_for_this_task=30, - per_run_time_limit=5, - tmp_folder=tmp_dir, - seed=1, - initial_configurations_via_metalearning=0, - ensemble_size=5, - n_jobs=2, - include={'classifier': ['sgd'], - 'feature_preprocessor': ['no_preprocessing']}, - get_smac_object_callback=get_smac_object_wrapper_instance, - max_models_on_disc=None, - ) - - automl.fit(X_train, Y_train) - - # Test that the argument is correctly passed to SMAC - assert getattr(get_smac_object_wrapper_instance, 'n_jobs') == 2 - assert getattr(get_smac_object_wrapper_instance, 'dask_n_jobs') == 2 - assert getattr(get_smac_object_wrapper_instance, 'dask_client_n_jobs') == 2 - - available_num_runs = set() - for run_key, run_value in automl.automl_.runhistory_.data.items(): - if run_value.additional_info is not None and 'num_run' in run_value.additional_info: - available_num_runs.add(run_value.additional_info['num_run']) - available_predictions = set() - predictions = glob.glob( - os.path.join(automl.automl_._backend.get_runs_directory(), '*', 'predictions_ensemble*.npy') - ) - seeds = set() - for prediction in predictions: - prediction = os.path.split(prediction)[1] - match = re.match(MODEL_FN_RE, prediction.replace("predictions_ensemble", "")) - if match: - num_run = int(match.group(2)) - available_predictions.add(num_run) - seed = int(match.group(1)) - seeds.add(seed) - - # Remove the dummy prediction, it is not part of the runhistory - available_predictions.remove(1) - assert available_num_runs.issubset(available_predictions) - - assert len(seeds) == 1 - - ensemble_dir = automl.automl_._backend.get_ensemble_dir() - ensembles = os.listdir(ensemble_dir) - - seeds = set() - for ensemble_file in ensembles: - seeds.add(int(ensemble_file.split('.')[0].split('_')[0])) - assert len(seeds) == 1 - - assert count_succeses(automl.cv_results_) > 0 - assert includes_train_scores(automl.performance_over_time_.columns) is True - assert performance_over_time_is_plausible(automl.performance_over_time_) is True - # For travis-ci it is important that the client no longer exists - assert automl.automl_._dask_client is None - - -def test_feat_type_wrong_arguments(): - - # Every Auto-Sklearn estimator has a backend, that allows a single - # call to fit - X = np.zeros((100, 100)) - y = np.zeros((100, )) - - cls = AutoSklearnClassifier(ensemble_size=0) - expected_msg = r".*feat_type does not have same number of " - "variables as X has features. 1 vs 100.*" - with pytest.raises(ValueError, match=expected_msg): - cls.fit(X=X, y=y, feat_type=[True]) - - cls = AutoSklearnClassifier(ensemble_size=0) - expected_msg = r".*feat_type must only contain strings.*" - with pytest.raises(ValueError, match=expected_msg): - cls.fit(X=X, y=y, feat_type=[True]*100) - - cls = AutoSklearnClassifier(ensemble_size=0) - expected_msg = r".*Only `Categorical` and `Numerical` are" - "valid feature types, you passed `Car`.*" - with pytest.raises(ValueError, match=expected_msg): - cls.fit(X=X, y=y, feat_type=['Car']*100) - - -# Mock AutoSklearnEstimator.fit so the test doesn't actually run fit(). -@unittest.mock.patch('autosklearn.estimators.AutoSklearnEstimator.fit') -def test_type_of_target(mock_estimator): - # Test that classifier raises error for illegal target types. - X = np.array([[1, 2], - [2, 3], - [3, 4], - [4, 5], - ]) - # Possible target types - y_binary = np.array([0, 0, 1, 1]) - y_continuous = np.array([0.1, 1.3, 2.1, 4.0]) - y_multiclass = np.array([0, 1, 2, 0]) - y_multilabel = np.array([[0, 1], - [1, 1], - [1, 0], - [0, 0], - ]) - y_multiclass_multioutput = np.array([[0, 1], - [1, 3], - [2, 2], - [5, 3], - ]) - y_continuous_multioutput = np.array([[0.1, 1.5], - [1.2, 3.5], - [2.7, 2.7], - [5.5, 3.9], - ]) - - cls = AutoSklearnClassifier(ensemble_size=0) - cls.automl_ = unittest.mock.Mock() - cls.automl_.InputValidator = unittest.mock.Mock() - cls.automl_.InputValidator.target_validator = unittest.mock.Mock() - - # Illegal target types for classification: continuous, - # multiclass-multioutput, continuous-multioutput. - expected_msg = r".*Classification with data of type" - " multiclass-multioutput is not supported.*" - with pytest.raises(ValueError, match=expected_msg): - cls.fit(X=X, y=y_multiclass_multioutput) - - expected_msg = r".*Classification with data of type" - " continuous is not supported.*" - with pytest.raises(ValueError, match=expected_msg): - cls.fit(X=X, y=y_continuous) - - expected_msg = r".*Classification with data of type" - " continuous-multioutput is not supported.*" - with pytest.raises(ValueError, match=expected_msg): - cls.fit(X=X, y=y_continuous_multioutput) - - # Legal target types for classification: binary, multiclass, - # multilabel-indicator. - try: - cls.fit(X, y_binary) - except ValueError: - pytest.fail("cls.fit() raised ValueError while fitting " - "binary targets") - - try: - cls.fit(X, y_multiclass) - except ValueError: - pytest.fail("cls.fit() raised ValueError while fitting " - "multiclass targets") - - try: - cls.fit(X, y_multilabel) - except ValueError: - pytest.fail("cls.fit() raised ValueError while fitting " - "multilabel-indicator targets") - - # Test that regressor raises error for illegal target types. - reg = AutoSklearnRegressor(ensemble_size=0) - # Illegal target types for regression: multilabel-indicator - # multiclass-multioutput - expected_msg = r".*Regression with data of type" - " multilabel-indicator is not supported.*" - with pytest.raises(ValueError, match=expected_msg): - reg.fit(X=X, y=y_multilabel,) - - expected_msg = r".*Regression with data of type" - " multiclass-multioutput is not supported.*" - with pytest.raises(ValueError, match=expected_msg): - reg.fit(X=X, y=y_multiclass_multioutput,) - - # Legal target types: continuous, multiclass, - # continuous-multioutput, - # binary - try: - reg.fit(X, y_continuous) - except ValueError: - pytest.fail("reg.fit() raised ValueError while fitting " - "continuous targets") - - try: - reg.fit(X, y_multiclass) - except ValueError: - pytest.fail("reg.fit() raised ValueError while fitting " - "multiclass targets") - - try: - reg.fit(X, y_continuous_multioutput) - except ValueError: - pytest.fail("reg.fit() raised ValueError while fitting " - "continuous_multioutput targets") - - try: - reg.fit(X, y_binary) - except ValueError: - pytest.fail("reg.fit() raised ValueError while fitting " - "binary targets") - - -def test_performance_over_time_no_ensemble(tmp_dir): - X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') - - cls = AutoSklearnClassifier(time_left_for_this_task=30, - per_run_time_limit=5, - tmp_folder=tmp_dir, - seed=1, - initial_configurations_via_metalearning=0, - ensemble_size=0,) - - cls.fit(X_train, Y_train, X_test, Y_test) - - performance_over_time = cls.performance_over_time_ - assert include_single_scores(performance_over_time.columns) is True - assert performance_over_time_is_plausible(performance_over_time) is True - - -def test_cv_results(tmp_dir): - # TODO restructure and actually use real SMAC output from a long run - # to do this unittest! - X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') - - cls = AutoSklearnClassifier(time_left_for_this_task=30, - per_run_time_limit=5, - tmp_folder=tmp_dir, - seed=1, - initial_configurations_via_metalearning=0, - ensemble_size=0, - scoring_functions=[autosklearn.metrics.precision, - autosklearn.metrics.roc_auc]) - - params = cls.get_params() - original_params = copy.deepcopy(params) - - cls.fit(X_train, Y_train) - - cv_results = cls.cv_results_ - assert isinstance(cv_results, dict), type(cv_results) - assert isinstance(cv_results['mean_test_score'], np.ndarray), type( - cv_results['mean_test_score']) - assert isinstance(cv_results['mean_fit_time'], np.ndarray), type( - cv_results['mean_fit_time'] - ) - assert isinstance(cv_results['params'], list), type(cv_results['params']) - assert isinstance(cv_results['rank_test_scores'], np.ndarray), type( - cv_results['rank_test_scores'] - ) - assert isinstance(cv_results['metric_precision'], npma.MaskedArray), type( - cv_results['metric_precision'] - ) - assert isinstance(cv_results['metric_roc_auc'], npma.MaskedArray), type( - cv_results['metric_roc_auc'] - ) - cv_result_items = [isinstance(val, npma.MaskedArray) for key, val in - cv_results.items() if key.startswith('param_')] - assert all(cv_result_items), cv_results.items() - - # Compare the state of the model parameters with the original parameters - new_params = clone(cls).get_params() - for param_name, original_value in original_params.items(): - new_value = new_params[param_name] - - # Taken from Sklearn code: - # We should never change or mutate the internal state of input - # parameters by default. To check this we use the joblib.hash function - # that introspects recursively any subobjects to compute a checksum. - # The only exception to this rule of immutable constructor parameters - # is possible RandomState instance but in this check we explicitly - # fixed the random_state params recursively to be integer seeds. - assert joblib.hash(new_value) == joblib.hash(original_value), ( - "Estimator %s should not change or mutate " - " the parameter %s from %s to %s during fit." - % (cls, param_name, original_value, new_value)) - - # Comply with https://scikit-learn.org/dev/glossary.html#term-classes - is_classifier(cls) - assert hasattr(cls, 'classes_') - - -@pytest.mark.parametrize('estimator_type,dataset_name', [ - (AutoSklearnClassifier, 'iris'), - (AutoSklearnRegressor, 'boston') -]) -def test_leaderboard( - tmp_dir: str, - estimator_type: Type[AutoSklearnEstimator], - dataset_name: str -): - # Comprehensive test tasks a substantial amount of time, manually set if - # required. - MAX_COMBO_SIZE_FOR_INCLUDE_PARAM = 3 # [0, len(valid_columns) + 1] - column_types = AutoSklearnEstimator._leaderboard_columns() - - # Create a dict of all possible param values for each param - # with some invalid one's of the incorrect type - include_combinations = itertools.chain( - itertools.combinations(column_types['all'], item_count) - for item_count in range(1, MAX_COMBO_SIZE_FOR_INCLUDE_PARAM) - ) - valid_params = { - 'detailed': [True, False], - 'ensemble_only': [True, False], - 'top_k': [-10, 0, 1, 10, 'all'], - 'sort_by': [*column_types['all'], 'invalid'], - 'sort_order': ['ascending', 'descending', 'auto', 'invalid', None], - 'include': itertools.chain([None, 'invalid', 'type'], include_combinations), - } - - # Create a generator of all possible combinations of valid_params - params_generator = iter( - dict(zip(valid_params.keys(), param_values)) - for param_values in itertools.product(*valid_params.values()) - ) - - X_train, Y_train, _, _ = putil.get_dataset(dataset_name) - model = estimator_type( - time_left_for_this_task=30, - per_run_time_limit=5, - tmp_folder=tmp_dir, - seed=1 - ) - - model.fit(X_train, Y_train) - - for params in params_generator: - # Convert from iterator to solid list - if params['include'] is not None and not isinstance(params['include'], str): - params['include'] = list(params['include']) - - # Invalid top_k should raise an error, is a positive int or 'all' - if not (params['top_k'] == 'all' or params['top_k'] > 0): - with pytest.raises(ValueError): - model.leaderboard(**params) - - # Invalid sort_by column - elif params['sort_by'] not in column_types['all']: - with pytest.raises(ValueError): - model.leaderboard(**params) - - # Shouldn't accept an invalid sort order - elif params['sort_order'] not in ['ascending', 'descending', 'auto']: - with pytest.raises(ValueError): - model.leaderboard(**params) - - # include is single str but not valid - elif ( - isinstance(params['include'], str) - and params['include'] not in column_types['all'] - ): - with pytest.raises(ValueError): - model.leaderboard(**params) - - # Crash if include is list but contains invalid column - elif ( - isinstance(params['include'], list) - and len(set(params['include']) - set(column_types['all'])) != 0 - ): - with pytest.raises(ValueError): - model.leaderboard(**params) - - # Can't have just model_id, in both single str and list case - elif ( - params['include'] == 'model_id' - or params['include'] == ['model_id'] - ): - with pytest.raises(ValueError): - model.leaderboard(**params) - - # Else all valid combinations should be validated - else: - leaderboard = model.leaderboard(**params) - - # top_k should never be less than the rows given back - # It can however be larger - if isinstance(params['top_k'], int): - assert params['top_k'] >= len(leaderboard) - - # Check the right columns are present and in the right order - # The model_id is set as the index, not included in pandas columns - columns = list(leaderboard.columns) - - def exclude(lst, s): - return [x for x in lst if x != s] - - if params['include'] is not None: - # Include with only single str should be the only column - if isinstance(params['include'], str): - assert params['include'] in columns and len(columns) == 1 - # Include as a list should have all the columns without model_id - else: - assert columns == exclude(params['include'], 'model_id') - elif params['detailed']: - assert columns == exclude(column_types['detailed'], 'model_id') - else: - assert columns == exclude(column_types['simple'], 'model_id') - - # Ensure that if it's ensemble only - # Can only check if 'ensemble_weight' is present - if ( - params['ensemble_only'] - and 'ensemble_weight' in columns - ): - assert all(leaderboard['ensemble_weight'] > 0) - - -@pytest.mark.parametrize('estimator', [AutoSklearnRegressor]) -@pytest.mark.parametrize('resampling_strategy', ['holdout']) -@pytest.mark.parametrize('X', [ - np.asarray([[1.0, 1.0, 1.0]] * 25 + [[2.0, 2.0, 2.0]] * 25 + - [[3.0, 3.0, 3.0]] * 25 + [[4.0, 4.0, 4.0]] * 25) -]) -@pytest.mark.parametrize('y', [ - np.asarray([1.0] * 25 + [2.0] * 25 + [3.0] * 25 + [4.0] * 25) -]) -def test_show_models_with_holdout( - tmp_dir: str, - dask_client: Client, - estimator: AutoSklearnEstimator, - resampling_strategy: str, - X: np.ndarray, - y: np.ndarray -) -> None: - """ - Parameters - ---------- - tmp_dir: str - The temporary directory to use for this test - - dask_client: dask.distributed.Client - The dask client to use for this test - - estimator: AutoSklearnEstimator - The estimator to train - - resampling_strategy: str - The resampling strategy to use - - X: np.ndarray - The X data to use for this estimator - - y: np.ndarray - The targets to use for this estimator - - Expects - ------- - * Expects all the model dictionaries to have ``model_keys`` - * Expects all models to have an auto-sklearn wrapped model ``regressor`` - * Expects all models to have a sklearn wrapped model ``sklearn_regressor`` - * Expects no model to have any ``None`` value - """ - - automl = estimator( - time_left_for_this_task=60, - per_run_time_limit=5, - tmp_folder=tmp_dir, - resampling_strategy=resampling_strategy, - dask_client=dask_client - ) - automl.fit(X, y) - - models = automl.show_models().values() - - model_keys = set([ - 'model_id', 'rank', 'cost', 'ensemble_weight', - 'data_preprocessor', 'feature_preprocessor', - 'regressor', 'sklearn_regressor' - ]) - - assert all([model_keys == set(model.keys()) for model in models]) - assert all([model['regressor'] for model in models]) - assert all([model['sklearn_regressor'] for model in models]) - assert not any([None in model.values() for model in models]) - - -@pytest.mark.parametrize('estimator', [AutoSklearnClassifier]) -@pytest.mark.parametrize('resampling_strategy', ['cv']) -@pytest.mark.parametrize('X', [ - np.asarray([[1.0, 1.0, 1.0]] * 50 + [[2.0, 2.0, 2.0]] * 50) -]) -@pytest.mark.parametrize('y', [ - np.asarray([1] * 50 + [2] * 50) -]) -def test_show_models_with_cv( - tmp_dir: str, - dask_client: Client, - estimator: AutoSklearnEstimator, - resampling_strategy: str, - X: np.ndarray, - y: np.ndarray -) -> None: - """ - Parameters - ---------- - tmp_dir: str - The temporary directory to use for this test - - dask_client: dask.distributed.Client - The dask client to use for this test - - estimator: AutoSklearnEstimator - The estimator to train - - resampling_strategy: str - The resampling strategy to use - - X: np.ndarray - The X data to use for this estimator - - y: np.ndarray - The targets to use for this estimator - - Expects - ------- - * Expects all the model dictionaries to have ``model_keys`` - * Expects no model to have any ``None`` value - * Expects all the estimators in a model to have ``estimator_keys`` - * Expects all model estimators to have an auto-sklearn wrapped model ``classifier`` - * Expects all model estimators to have a sklearn wrapped model ``sklearn_classifier`` - * Expects no estimator to have ``None`` value - """ - - automl = estimator( - time_left_for_this_task=120, - per_run_time_limit=5, - tmp_folder=tmp_dir, - resampling_strategy=resampling_strategy, - dask_client=dask_client - ) - automl.fit(X, y) - - models = automl.show_models().values() - - model_keys = set([ - 'model_id', 'rank', - 'cost', 'ensemble_weight', - 'voting_model', 'estimators' - ]) - - estimator_keys = set([ - 'data_preprocessor', 'balancing', - 'feature_preprocessor', 'classifier', - 'sklearn_classifier' - ]) - - assert all([model_keys == set(model.keys()) for model in models]) - assert not any([None in model.values() for model in models]) - assert all([estimator_keys == set(estimator.keys()) - for model in models for estimator in model['estimators']]) - assert all([estimator['classifier'] - for model in models for estimator in model['estimators']]) - assert all([estimator['sklearn_classifier'] - for model in models for estimator in model['estimators']]) - assert not any([None in estimator.values() - for model in models for estimator in model['estimators']]) - - -@unittest.mock.patch('autosklearn.estimators.AutoSklearnEstimator.build_automl') -def test_fit_n_jobs_negative(build_automl_patch): - n_cores = cpu_count() - cls = AutoSklearnEstimator(n_jobs=-1, ensemble_size=0) - cls.fit() - assert cls._n_jobs == n_cores - - -def test_get_number_of_available_cores(): - n_cores = cpu_count() - assert n_cores >= 1, n_cores - - -@unittest.mock.patch('autosklearn.automl.AutoML.predict') -def test_multiclass_prediction(predict_mock, dask_client): - predicted_probabilities = [[0, 0, 0.99], [0, 0.99, 0], [0.99, 0, 0], - [0, 0.99, 0], [0, 0, 0.99]] - predicted_indexes = [2, 1, 0, 1, 2] - expected_result = ['c', 'b', 'a', 'b', 'c'] - - predict_mock.return_value = np.array(predicted_probabilities) - - classifier = AutoMLClassifier( - time_left_for_this_task=1, - per_run_time_limit=1, - dask_client=dask_client, - ) - classifier.InputValidator = InputValidator(is_classification=True) - classifier.InputValidator.target_validator.fit( - pd.DataFrame(expected_result, dtype='category'), - ) - classifier.InputValidator._is_fitted = True - - actual_result = classifier.predict([None] * len(predicted_indexes)) - - np.testing.assert_array_equal(expected_result, actual_result) - - -@unittest.mock.patch('autosklearn.automl.AutoML.predict') -def test_multilabel_prediction(predict_mock, dask_client): - predicted_probabilities = [[0.99, 0], - [0.99, 0], - [0, 0.99], - [0.99, 0.99], - [0.99, 0.99]] - predicted_indexes = np.array([[1, 0], [1, 0], [0, 1], [1, 1], [1, 1]]) - - predict_mock.return_value = np.array(predicted_probabilities) - - classifier = AutoMLClassifier( - time_left_for_this_task=1, - per_run_time_limit=1, - dask_client=dask_client, - ) - classifier.InputValidator = InputValidator(is_classification=True) - classifier.InputValidator.target_validator.fit( - pd.DataFrame(predicted_indexes, dtype='int64'), - ) - classifier.InputValidator._is_fitted = True - - assert classifier.InputValidator.target_validator.type_of_target == 'multilabel-indicator' - - actual_result = classifier.predict([None] * len(predicted_indexes)) - - np.testing.assert_array_equal(predicted_indexes, actual_result) - - -def test_can_pickle_classifier(tmp_dir, dask_client): - X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') - automl = AutoSklearnClassifier(time_left_for_this_task=30, - delete_tmp_folder_after_terminate=False, - per_run_time_limit=5, - tmp_folder=tmp_dir, - dask_client=dask_client, - ) - - automl.fit(X_train, Y_train) - - initial_predictions = automl.predict(X_test) - initial_accuracy = sklearn.metrics.accuracy_score(Y_test, - initial_predictions) - assert initial_accuracy >= 0.75 - assert count_succeses(automl.cv_results_) > 0 - assert includes_train_scores(automl.performance_over_time_.columns) is True - assert performance_over_time_is_plausible(automl.performance_over_time_) is True - - # Test pickle - dump_file = os.path.join(tmp_dir, 'automl.dump.pkl') - - with open(dump_file, 'wb') as f: - pickle.dump(automl, f) - - with open(dump_file, 'rb') as f: - restored_automl = pickle.load(f) - - restored_predictions = restored_automl.predict(X_test) - restored_accuracy = sklearn.metrics.accuracy_score(Y_test, - restored_predictions) - assert restored_accuracy >= 0.75 - assert initial_accuracy == restored_accuracy - - # Test joblib - dump_file = os.path.join(tmp_dir, 'automl.dump.joblib') - - joblib.dump(automl, dump_file) - - restored_automl = joblib.load(dump_file) - - restored_predictions = restored_automl.predict(X_test) - restored_accuracy = sklearn.metrics.accuracy_score(Y_test, - restored_predictions) - assert restored_accuracy >= 0.75 - assert initial_accuracy == restored_accuracy - - -def test_multilabel(tmp_dir, dask_client): - - X_train, Y_train, X_test, Y_test = putil.get_dataset( - 'iris', make_multilabel=True) - automl = AutoSklearnClassifier(time_left_for_this_task=30, - per_run_time_limit=5, - tmp_folder=tmp_dir, - dask_client=dask_client, - ) - - automl.fit(X_train, Y_train) - - predictions = automl.predict(X_test) - assert predictions.shape == (50, 3), print_debug_information(automl) - assert count_succeses(automl.cv_results_) > 0, print_debug_information(automl) - assert includes_train_scores(automl.performance_over_time_.columns) is True - assert performance_over_time_is_plausible(automl.performance_over_time_) is True - - score = f1_macro(Y_test, predictions) - assert score >= 0.9, print_debug_information(automl) - - probs = automl.predict_proba(X_train) - assert np.mean(probs) == pytest.approx(0.33, rel=1e-1) - - -def test_binary(tmp_dir, dask_client): - - X_train, Y_train, X_test, Y_test = putil.get_dataset( - 'iris', make_binary=True) - automl = AutoSklearnClassifier(time_left_for_this_task=40, - delete_tmp_folder_after_terminate=False, - per_run_time_limit=10, - tmp_folder=tmp_dir, - dask_client=dask_client, - ) - - automl.fit(X_train, Y_train, X_test=X_test, y_test=Y_test, - dataset_name='binary_test_dataset') - - predictions = automl.predict(X_test) - assert predictions.shape == (50, ), print_debug_information(automl) - - score = accuracy(Y_test, predictions) - assert score > 0.9, print_debug_information(automl) - assert count_succeses(automl.cv_results_) > 0, print_debug_information(automl) - assert includes_all_scores(automl.performance_over_time_.columns) is True - assert performance_over_time_is_plausible(automl.performance_over_time_) is True - - -def test_classification_pandas_support(tmp_dir, dask_client): - - X, y = sklearn.datasets.fetch_openml( - data_id=2, # cat/num dataset - return_X_y=True, - as_frame=True, - ) - - # Drop NAN!! - X = X.dropna(axis='columns') - - # This test only make sense if input is dataframe - assert isinstance(X, pd.DataFrame) - assert isinstance(y, pd.Series) - automl = AutoSklearnClassifier( - time_left_for_this_task=30, - per_run_time_limit=5, - exclude={'classifier': ['libsvm_svc']}, - dask_client=dask_client, - seed=5, - tmp_folder=tmp_dir, - ) - - automl.fit(X, y) - - # Make sure that at least better than random. - # We use same X_train==X_test to test code quality - assert automl.score(X, y) > 0.555, print_debug_information(automl) - - automl.refit(X, y) - - # Make sure that at least better than random. - # accuracy in sklearn needs valid data - # It should be 0.555 as the dataset is unbalanced. - prediction = automl.predict(X) - assert accuracy(y, prediction) > 0.555 - assert count_succeses(automl.cv_results_) > 0 - assert includes_train_scores(automl.performance_over_time_.columns) is True - assert performance_over_time_is_plausible(automl.performance_over_time_) is True - - -def test_regression(tmp_dir, dask_client): - - X_train, Y_train, X_test, Y_test = putil.get_dataset('boston') - automl = AutoSklearnRegressor(time_left_for_this_task=30, - per_run_time_limit=5, - tmp_folder=tmp_dir, - dask_client=dask_client, - ) - - automl.fit(X_train, Y_train) - - predictions = automl.predict(X_test) - assert predictions.shape == (356,) - score = mean_squared_error(Y_test, predictions) - - # On average np.sqrt(30) away from the target -> ~5.5 on average - # Results with select rates drops avg score to a range of -32.40 to -37, on 30 seconds - # constraint. With more time_left_for_this_task this is no longer an issue - assert score >= -37, print_debug_information(automl) - assert count_succeses(automl.cv_results_) > 0 - assert includes_train_scores(automl.performance_over_time_.columns) is True - assert performance_over_time_is_plausible(automl.performance_over_time_) is True - - -def test_cv_regression(tmp_dir, dask_client): - """ - Makes sure that when using a cv strategy, we are able to fit - a regressor - """ - - X_train, Y_train, X_test, Y_test = putil.get_dataset('boston', train_size_maximum=300) - automl = AutoSklearnRegressor(time_left_for_this_task=60, - per_run_time_limit=10, - resampling_strategy='cv', - tmp_folder=tmp_dir, - dask_client=dask_client, - ) - - automl.fit(X_train, Y_train) - - predictions = automl.predict(X_test) - assert predictions.shape == (206,) - score = r2(Y_test, predictions) - assert score >= 0.1, print_debug_information(automl) - assert count_succeses(automl.cv_results_) > 0, print_debug_information(automl) - assert includes_train_scores(automl.performance_over_time_.columns) is True - assert performance_over_time_is_plausible(automl.performance_over_time_) is True - - -def test_regression_pandas_support(tmp_dir, dask_client): - - X, y = sklearn.datasets.fetch_openml( - data_id=41514, # diabetes - return_X_y=True, - as_frame=True, - ) - # This test only make sense if input is dataframe - assert isinstance(X, pd.DataFrame) - assert isinstance(y, pd.Series) - automl = AutoSklearnRegressor( - time_left_for_this_task=40, - per_run_time_limit=5, - dask_client=dask_client, - tmp_folder=tmp_dir, - ) - - # Make sure we error out because y is not encoded - automl.fit(X, y) - - # Make sure that at least better than random. - # We use same X_train==X_test to test code quality - assert automl.score(X, y) >= 0.5, print_debug_information(automl) - - automl.refit(X, y) - - # Make sure that at least better than random. - assert r2(y, automl.predict(X)) > 0.5, print_debug_information(automl) - assert count_succeses(automl.cv_results_) > 0, print_debug_information(automl) - assert includes_train_scores(automl.performance_over_time_.columns) is True - assert performance_over_time_is_plausible(automl.performance_over_time_) is True - - -def test_autosklearn_classification_methods_returns_self(dask_client): - """ - Currently this method only tests that the methods of AutoSklearnClassifier - is able to fit using fit(), fit_ensemble() and refit() - """ - X_train, y_train, X_test, y_test = putil.get_dataset('iris') - automl = AutoSklearnClassifier(time_left_for_this_task=60, - delete_tmp_folder_after_terminate=False, - per_run_time_limit=10, - ensemble_size=0, - dask_client=dask_client, - exclude={'feature_preprocessor': ['fast_ica']}) - - automl_fitted = automl.fit(X_train, y_train) - - assert automl is automl_fitted - - automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5) - assert automl is automl_ensemble_fitted - - automl_refitted = automl.refit(X_train.copy(), y_train.copy()) - - assert automl is automl_refitted - - -# Currently this class only tests that the methods of AutoSklearnRegressor -# that should return self actually return self. -def test_autosklearn_regression_methods_returns_self(dask_client): - X_train, y_train, X_test, y_test = putil.get_dataset('boston') - automl = AutoSklearnRegressor(time_left_for_this_task=30, - delete_tmp_folder_after_terminate=False, - per_run_time_limit=5, - dask_client=dask_client, - ensemble_size=0) - - automl_fitted = automl.fit(X_train, y_train) - assert automl is automl_fitted - - automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5) - assert automl is automl_ensemble_fitted - - automl_refitted = automl.refit(X_train.copy(), y_train.copy()) - assert automl is automl_refitted - - -def test_autosklearn2_classification_methods_returns_self(dask_client): - X_train, y_train, X_test, y_test = putil.get_dataset('iris') - automl = AutoSklearn2Classifier(time_left_for_this_task=60, ensemble_size=0, - delete_tmp_folder_after_terminate=False, - dask_client=dask_client) - - automl_fitted = automl.fit(X_train, y_train) - - assert automl is automl_fitted - - automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5) - assert automl is automl_ensemble_fitted - - automl_refitted = automl.refit(X_train.copy(), y_train.copy()) - - assert automl is automl_refitted - - predictions = automl_fitted.predict(X_test) - assert sklearn.metrics.accuracy_score( - y_test, predictions - ) >= 2 / 3, print_debug_information(automl) - - pickle.dumps(automl_fitted) - - -def test_autosklearn2_classification_methods_returns_self_sparse(dask_client): - X_train, y_train, X_test, y_test = putil.get_dataset('breast_cancer', make_sparse=True) - automl = AutoSklearn2Classifier(time_left_for_this_task=60, ensemble_size=0, - delete_tmp_folder_after_terminate=False, - dask_client=dask_client) - - automl_fitted = automl.fit(X_train, y_train) - - assert automl is automl_fitted - - automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5) - assert automl is automl_ensemble_fitted - - automl_refitted = automl.refit(X_train.copy(), y_train.copy()) - - assert automl is automl_refitted - - predictions = automl_fitted.predict(X_test) - assert sklearn.metrics.accuracy_score( - y_test, predictions - ) >= 2 / 3, print_debug_information(automl) - - assert "boosting" not in str(automl.get_configuration_space(X=X_train, y=y_train)) - - pickle.dumps(automl_fitted) - - -@pytest.mark.parametrize("class_", [AutoSklearnClassifier, AutoSklearnRegressor, - AutoSklearn2Classifier]) -def test_check_estimator_signature(class_): - # Make sure signature is store in self - expected_subclass = ClassifierMixin if 'Classifier' in str(class_) else RegressorMixin - assert issubclass(class_, expected_subclass) - estimator = class_() - for expected in list(inspect.signature(class_).parameters): - assert hasattr(estimator, expected) - - -@pytest.mark.parametrize("selector_path", [None, # No XDG_CACHE_HOME provided - '/', # XDG_CACHE_HOME has no permission - tempfile.gettempdir(), # in the user cache - ]) -def test_selector_file_askl2_can_be_created(selector_path): - with unittest.mock.patch('os.environ.get') as mock_foo: - mock_foo.return_value = selector_path - if selector_path is not None and not os.access(selector_path, os.W_OK): - with pytest.raises(PermissionError): - importlib.reload(autosklearn.experimental.askl2) - else: - importlib.reload(autosklearn.experimental.askl2) - for metric in autosklearn.experimental.askl2.metrics: - assert os.path.exists(autosklearn.experimental.askl2.selector_files[metric.name]) - if selector_path is None or not os.access(selector_path, os.W_OK): - # We default to home in worst case - assert os.path.expanduser("~") in str( - autosklearn.experimental.askl2.selector_files[metric.name] - ) - else: - # a dir provided via XDG_CACHE_HOME - assert selector_path in str( - autosklearn.experimental.askl2.selector_files[metric.name] - ) - # Re import it at the end so we do not affect other test - importlib.reload(autosklearn.experimental.askl2) - - -def test_check_askl2_same_arguments_as_askl(): - # In case a new attribute is created, make sure it is there also in - # ASKL2 - extra_arguments = list(set( - inspect.getfullargspec(AutoSklearnEstimator.__init__).args) - set( - inspect.getfullargspec(AutoSklearn2Classifier.__init__).args)) - expected_extra_args = ['exclude', - 'include', - 'resampling_strategy_arguments', - 'get_smac_object_callback', - 'initial_configurations_via_metalearning', - 'resampling_strategy', - 'metadata_directory', - 'get_trials_callback'] - unexpected_args = set(extra_arguments) - set(expected_extra_args) - assert len(unexpected_args) == 0, unexpected_args - - -@pytest.mark.parametrize("task_type", ['classification', 'regression']) -@pytest.mark.parametrize("resampling_strategy", ['test', 'cv', 'holdout']) -@pytest.mark.parametrize("disable_file_output", [True, False]) -def test_fit_pipeline(dask_client, task_type, resampling_strategy, disable_file_output): - """ - Tests that we can query the configuration space, and from the default configuration - space, fit a classification pipeline with an acceptable score - """ - X_train, y_train, X_test, y_test = putil.get_dataset( - 'iris' if task_type == 'classification' else 'boston' - ) - estimator = AutoSklearnClassifier if task_type == 'classification' else AutoSklearnRegressor - seed = 3 - if task_type == "classification": - include = {'classifier': ['random_forest']} - else: - include = {'regressor': ['random_forest']} - automl = estimator( - delete_tmp_folder_after_terminate=False, - time_left_for_this_task=120, - # Time left for task plays no role - # only per run time limit - per_run_time_limit=30, - ensemble_size=0, - dask_client=dask_client, - include=include, - seed=seed, - # We cannot get the configuration space with 'test' not fit with it - resampling_strategy=resampling_strategy if resampling_strategy != 'test' else 'holdout', - ) - config = automl.get_configuration_space(X_train, y_train, - X_test=X_test, y_test=y_test, - ).get_default_configuration() - - pipeline, run_info, run_value = automl.fit_pipeline( - X=X_train, - y=y_train, - config=config, - X_test=X_test, - y_test=y_test, - disable_file_output=disable_file_output, - resampling_strategy=resampling_strategy - ) - - assert isinstance(run_info.config, Configuration) - assert run_info.cutoff == 30 - assert run_value.status == StatusType.SUCCESS, f"{run_info}->{run_value}" - # We should produce a decent result - assert run_value.cost < 0.2 - - # Make sure that the pipeline can be pickled - dump_file = os.path.join(tempfile.gettempdir(), 'automl.dump.pkl') - with open(dump_file, 'wb') as f: - pickle.dump(pipeline, f) - - if resampling_strategy == 'test' or disable_file_output: - # We do not produce a pipeline in 'test' - assert pipeline is None - elif resampling_strategy == 'cv': - # We should have fitted a Voting estimator - assert hasattr(pipeline, 'estimators_') - else: - # We should have fitted a pipeline with named_steps - assert hasattr(pipeline, 'named_steps') - assert 'RandomForest' in pipeline.steps[-1][-1].choice.__class__.__name__ - - # Num run should be 2, as 1 is for dummy classifier and we have not launch - # another pipeline - num_run = 2 - - # Check the re-sampling strategy - num_run_dir = automl.automl_._backend.get_numrun_directory( - seed, num_run, budget=0.0) - cv_model_path = os.path.join(num_run_dir, automl.automl_._backend.get_cv_model_filename( - seed, num_run, budget=0.0)) - model_path = os.path.join(num_run_dir, automl.automl_._backend.get_model_filename( - seed, num_run, budget=0.0)) - if resampling_strategy == 'test' or disable_file_output: - # No file output is expected - assert not os.path.exists(num_run_dir) - else: - # We expect the model path always - # And the cv model only on 'cv' - assert os.path.exists(model_path) - if resampling_strategy == 'cv': - assert os.path.exists(cv_model_path) - elif resampling_strategy == 'holdout': - assert not os.path.exists(cv_model_path) - - -@pytest.mark.parametrize("data_type", ['pandas', 'numpy']) -@pytest.mark.parametrize("include_categorical", [True, False]) -def test_pass_categorical_and_numeric_columns_to_pipeline( - dask_client, data_type, include_categorical -): - - # Prepare the training data - X, y = sklearn.datasets.make_classification(random_state=0) - X = cast(np.ndarray, X) - - n_features = X.shape[1] - - # If categorical, insert a row of 'categorical' '0's at last col - if include_categorical: - X = np.insert(X, n_features, values=0, axis=1) - - if data_type == 'pandas': - X = pd.DataFrame(X) - y = pd.DataFrame(y, dtype="category") - - # Set the last column to categorical - if include_categorical: - X.loc[:, n_features] = X.loc[:, n_features].astype('category') # type: ignore - - # Specify the feature_types - if data_type == 'numpy' and include_categorical: - feat_type = ['numerical'] * n_features + ['categorical'] - else: - feat_type = None - - # Create the splits - X_train, X_test, y_train, y_test = model_selection.train_test_split( - X, y, test_size=0.5, random_state=3 - ) - - # Create Estimator - # Time left for task plays no role for fit_pipeline - automl = AutoSklearnClassifier( - delete_tmp_folder_after_terminate=False, - time_left_for_this_task=120, - per_run_time_limit=30, - ensemble_size=0, - seed=0, - dask_client=dask_client, - include={'classifier': ['random_forest']}, - ) - - config_space = automl.get_configuration_space( - X_train, y_train, X_test=X_test, y_test=y_test, feat_type=feat_type, - ) - config = config_space.get_default_configuration() - - pipeline, _, run_value = automl.fit_pipeline( - X=X_train, y=y_train, X_test=X_test, y_test=y_test, - config=config, feat_type=feat_type, - ) - - assert pipeline is not None, "Expected a pipeline from automl.fit_pipeline" - - feature_validator = automl.automl_.InputValidator.feature_validator # type: ignore - transformed_X_test = feature_validator.transform(X_test) - predictions = pipeline.predict(transformed_X_test) - - # We should produce a half decent result - assert run_value.cost < 0.40, f"Run value:\n {run_value}" - - # Outputs should be the correct length - assert np.shape(predictions)[0] == np.shape(y_test)[0] - - n_columns = np.shape(X)[1] - - if include_categorical: - expected_feat_types = { - i: feature_type - for i, feature_type - in enumerate(['numerical'] * (n_columns-1) + ['categorical']) - } - - else: - expected_feat_types = { - i: feature_type - for i, feature_type - in enumerate(['numerical'] * n_columns) - } - - pipeline_feat_types = pipeline.named_steps['data_preprocessor'].choice.feat_type - assert expected_feat_types == pipeline_feat_types - - -@pytest.mark.parametrize("as_frame", [True, False]) -def test_autosklearn_anneal(as_frame): - """ - This test makes sure that anneal dataset can be fitted and scored. - This dataset is quite complex, with NaN, categorical and numerical columns - so is a good testcase for unit-testing - """ - X, y = sklearn.datasets.fetch_openml(data_id=2, return_X_y=True, as_frame=as_frame) - automl = AutoSklearnClassifier(time_left_for_this_task=60, ensemble_size=0, - delete_tmp_folder_after_terminate=False, - initial_configurations_via_metalearning=0, - smac_scenario_args={'runcount_limit': 6}, - resampling_strategy='holdout-iterative-fit') - - if as_frame: - # Let autosklearn calculate the feat types - automl_fitted = automl.fit(X, y) - - else: - X_, y_ = sklearn.datasets.fetch_openml(data_id=2, return_X_y=True, as_frame=True) - feat_type = ['categorical' if X_[col].dtype.name == 'category' else 'numerical' - for col in X_.columns] - - automl_fitted = automl.fit(X, y, feat_type=feat_type) - - assert automl is automl_fitted - - automl_ensemble_fitted = automl.fit_ensemble(y, ensemble_size=5) - assert automl is automl_ensemble_fitted - - # We want to make sure we can learn from this data. - # This is a test to make sure the data format (numpy/pandas) - # can be used in a meaningful way -- not meant for generalization, - # hence we use the train dataset - assert automl_fitted.score(X, y) > 0.75 diff --git a/test/test_ensemble_builder/ensemble_utils.py b/test/test_ensemble_builder/ensemble_utils.py deleted file mode 100644 index b98021c7bd..0000000000 --- a/test/test_ensemble_builder/ensemble_utils.py +++ /dev/null @@ -1,103 +0,0 @@ -import os -import shutil -import unittest -import unittest.mock - -import numpy as np - -from autosklearn.automl_common.common.ensemble_building.abstract_ensemble import AbstractEnsemble - -from autosklearn.metrics import make_scorer -from autosklearn.ensemble_builder import EnsembleBuilder - - -def scorer_function(a, b): - return 0.9 - - -MockMetric = make_scorer('mock', scorer_function) - - -class BackendMock(object): - - def __init__(self, target_directory): - this_directory = os.path.abspath( - os.path.dirname(__file__) - ) - shutil.copytree(os.path.join(this_directory, 'data'), os.path.join(target_directory)) - self.temporary_directory = target_directory - self.internals_directory = os.path.join(self.temporary_directory, '.auto-sklearn') - - def load_datamanager(self): - manager = unittest.mock.Mock() - manager.__reduce__ = lambda self: (unittest.mock.MagicMock, ()) - array = np.load(os.path.join( - self.temporary_directory, - '.auto-sklearn', - 'runs', '0_3_100.0', - 'predictions_test_0_3_100.0.npy' - )) - manager.data.get.return_value = array - return manager - - def load_targets_ensemble(self): - with open(os.path.join( - self.temporary_directory, - ".auto-sklearn", - "predictions_ensemble_true.npy" - ), "rb") as fp: - y = np.load(fp, allow_pickle=True) - return y - - def save_ensemble(self, ensemble, index_run, seed): - return - - def save_predictions_as_txt(self, predictions, subset, idx, prefix, precision): - return - - def get_runs_directory(self) -> str: - return os.path.join(self.temporary_directory, '.auto-sklearn', 'runs') - - def get_numrun_directory(self, seed: int, num_run: int, budget: float) -> str: - return os.path.join(self.get_runs_directory(), '%d_%d_%s' % (seed, num_run, budget)) - - def get_model_filename(self, seed: int, idx: int, budget: float) -> str: - return '%s.%s.%s.model' % (seed, idx, budget) - - -def compare_read_preds(read_preds1, read_preds2): - """ - compares read_preds attribute. An alternative to - assert Dict Equal as it contains np arrays, so we have - to use np testing utilities accordingly - """ - - # Both arrays should have the same splits - assert set(read_preds1.keys()) == set(read_preds2.keys()) - - for k, v in read_preds1.items(): - - # Each split should have the same elements - assert set(read_preds1[k].keys()) == set(read_preds2[k].keys()) - - # This level contains the scores/ensmebles/etc - for actual_k, actual_v in read_preds1[k].items(): - - # If it is a numpy array, make sure it is the same - if type(actual_v) is np.ndarray: - np.testing.assert_array_equal(actual_v, read_preds2[k][actual_k]) - else: - assert actual_v == read_preds2[k][actual_k] - - -class EnsembleBuilderMemMock(EnsembleBuilder): - - def fit_ensemble(self, selected_keys): - return True - - def predict(self, set_: str, - ensemble: AbstractEnsemble, - selected_keys: list, - n_preds: int, - index_run: int): - np.ones([10000000, 1000000]) diff --git a/test/test_evaluation/test_train_evaluator.py b/test/test_evaluation/test_train_evaluator.py index d969a2cc54..e23c706847 100644 --- a/test/test_evaluation/test_train_evaluator.py +++ b/test/test_evaluation/test_train_evaluator.py @@ -30,8 +30,6 @@ ) from smac.tae import StatusType, TAEAbortException -from autosklearn.automl_common.common.utils import backend - import autosklearn.evaluation.splitter from autosklearn.automl_common.common.utils import backend from autosklearn.constants import ( diff --git a/test/test_pipeline/components/classification/test_base.py b/test/test_pipeline/components/classification/test_base.py index fb2243b92f..9fc54f4dba 100644 --- a/test/test_pipeline/components/classification/test_base.py +++ b/test/test_pipeline/components/classification/test_base.py @@ -14,8 +14,6 @@ from test.test_pipeline.ignored_warnings import classifier_warnings, ignore_warnings -from test.test_pipeline.ignored_warnings import ignore_warnings, classifier_warnings - class BaseClassificationComponentTest(unittest.TestCase): # Magic command to not run tests on base class diff --git a/test/test_pipeline/components/feature_preprocessing/test_liblinear.py b/test/test_pipeline/components/feature_preprocessing/test_liblinear.py index bd9aa60e34..0195dfb701 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_liblinear.py +++ b/test/test_pipeline/components/feature_preprocessing/test_liblinear.py @@ -15,11 +15,8 @@ ignore_warnings, ) -from test.test_pipeline.ignored_warnings import ignore_warnings, feature_preprocessing_warnings - class LiblinearComponentTest(PreprocessingTestCase): - def test_default_configuration(self): with ignore_warnings(feature_preprocessing_warnings): transformation, original = _test_preprocessing(LibLinear_Preprocessor) diff --git a/test/test_pipeline/components/regression/test_base.py b/test/test_pipeline/components/regression/test_base.py index 54cb7799e5..7ed4afe79a 100644 --- a/test/test_pipeline/components/regression/test_base.py +++ b/test/test_pipeline/components/regression/test_base.py @@ -1,7 +1,5 @@ from typing import Container, Type -import pytest - import numpy as np import sklearn.metrics @@ -15,10 +13,6 @@ from test.test_pipeline.ignored_warnings import ignore_warnings, regressor_warnings -from autosklearn.pipeline.components.regression import _regressors, RegressorChoice - -from test.test_pipeline.ignored_warnings import regressor_warnings, ignore_warnings - class BaseRegressionComponentTest(unittest.TestCase): diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index bf1d8fc6f8..9721f88e31 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -44,8 +44,6 @@ from test.test_pipeline.ignored_warnings import classifier_warnings, ignore_warnings -from test.test_pipeline.ignored_warnings import classifier_warnings, ignore_warnings - class DummyClassifier(AutoSklearnClassificationAlgorithm): @staticmethod @@ -574,7 +572,7 @@ def _test_configurations( cls = SimpleClassificationPipeline( feat_type=feat_type, dataset_properties=dataset_properties, - init_params=init_params_ + init_params=init_params_, ) cls.set_hyperparameters( config, init_params=init_params_, feat_type=feat_type diff --git a/test/test_pipeline/test_regression.py b/test/test_pipeline/test_regression.py index f84fedacea..3a50decb8c 100644 --- a/test/test_pipeline/test_regression.py +++ b/test/test_pipeline/test_regression.py @@ -37,8 +37,6 @@ from test.test_pipeline.ignored_warnings import ignore_warnings, regressor_warnings -from test.test_pipeline.ignored_warnings import regressor_warnings, ignore_warnings - class SimpleRegressionPipelineTest(unittest.TestCase): _multiprocess_can_split_ = True @@ -249,9 +247,6 @@ def _test_configurations( "identity" in e.args[0] ): continue - elif "zero-size array to reduction operation maximum which has no " \ - "identity" in e.args[0]: - continue else: e.args += (f"config={config}",) raise e From 7d139e8c7a7f21d101428a6c20a91e3209c13ba5 Mon Sep 17 00:00:00 2001 From: eddiebergman Date: Thu, 18 Aug 2022 20:10:44 +0200 Subject: [PATCH 108/118] Revert "Revert "Double timeout for 03_calculate_metafeatures"" This reverts commit 6a54ab1599fd9f3c79af9ea440ee79b1c1e04bf4. --- test/test_scripts/test_metadata_generation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_scripts/test_metadata_generation.py b/test/test_scripts/test_metadata_generation.py index d888e8589c..088d5c93c9 100644 --- a/test/test_scripts/test_metadata_generation.py +++ b/test/test_scripts/test_metadata_generation.py @@ -252,7 +252,7 @@ def test_metadata_generation(self): shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, - timeout=90, + timeout=180, ) self.assertEqual(return_value.returncode, 0, msg=f"{cmd}\n{str(return_value)}") for task_type in ("classification", "regression"): From 11b78ad8c31123b64b4a0d7c3638454873d4dd73 Mon Sep 17 00:00:00 2001 From: eddiebergman Date: Thu, 18 Aug 2022 20:10:48 +0200 Subject: [PATCH 109/118] Revert "Revert "Drop 3.10 testing until scikit learn is updated"" This reverts commit 33fcb330565bc527806657c8503732d56ff78cf4. --- .github/workflows/pytest.yml | 2 +- setup.py | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index 794157f602..64e1f26b5b 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -46,7 +46,7 @@ jobs: fail-fast: false matrix: os: [windows-latest, macos-latest, ubuntu-latest] - python-version: ['3.7', '3.8', '3.9', '3.10'] + python-version: ['3.7', '3.8', '3.9'] # 3.10 once updated kind: ['conda', 'source', 'dist'] exclude: diff --git a/setup.py b/setup.py index 84af24be33..aa6e42669e 100644 --- a/setup.py +++ b/setup.py @@ -1,4 +1,3 @@ -# -*- encoding: utf-8 -*- import os import sys From 259ed3dbd08a203544b2fc7c77436e24c684de36 Mon Sep 17 00:00:00 2001 From: eddiebergman Date: Thu, 18 Aug 2022 20:11:56 +0200 Subject: [PATCH 110/118] Revert "Revert "Fix doc links"" This reverts commit 38d96b02addb722418521eca88c0fe93b8029212. --- autosklearn/estimators.py | 4 ++-- autosklearn/experimental/askl2.py | 2 +- examples/40_advanced/example_get_pipeline_components.py | 3 +-- 3 files changed, 4 insertions(+), 5 deletions(-) diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 607fb3dc2f..dde12a88d9 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -275,12 +275,12 @@ def __init__( smac_scenario_args : dict, optional (None) Additional arguments inserted into the scenario of SMAC. See the - `SMAC documentation `_ + `SMAC documentation `_ for a list of available arguments. get_smac_object_callback : callable Callback function to create an object of class - `smac.optimizer.smbo.SMBO `_. + `smac.optimizer.smbo.SMBO `_. The function must accept the arguments ``scenario_dict``, ``instances``, ``num_params``, ``runhistory``, ``seed`` and ``ta``. This is an advanced feature. Use only if you are familiar with diff --git a/autosklearn/experimental/askl2.py b/autosklearn/experimental/askl2.py index 078355dfbb..317f0be5b1 100644 --- a/autosklearn/experimental/askl2.py +++ b/autosklearn/experimental/askl2.py @@ -263,7 +263,7 @@ def __init__( smac_scenario_args : dict, optional (None) Additional arguments inserted into the scenario of SMAC. See the - `SMAC documentation `_ + `SMAC documentation `_ for a list of available arguments. logging_config : dict, optional (None) diff --git a/examples/40_advanced/example_get_pipeline_components.py b/examples/40_advanced/example_get_pipeline_components.py index 80686889ac..81b23be3b5 100644 --- a/examples/40_advanced/example_get_pipeline_components.py +++ b/examples/40_advanced/example_get_pipeline_components.py @@ -87,8 +87,7 @@ # # Auto-sklearn also keeps detailed statistics of the hyperparameter # optimization procedurce, which are stored in a so-called -# `run history `_. +# `run history `_. print(automl.automl_.runhistory_) From f7f1c1c63bfa89b349ed14b64e0d6d77e089626d Mon Sep 17 00:00:00 2001 From: eddiebergman Date: Thu, 18 Aug 2022 21:06:08 +0200 Subject: [PATCH 111/118] Merge pull request #1379 from automl/v0.14.4 V0.14.4 --- doc/releases.rst | 33 +++++++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) diff --git a/doc/releases.rst b/doc/releases.rst index 56115f2357..74d32d6c6a 100644 --- a/doc/releases.rst +++ b/doc/releases.rst @@ -84,6 +84,39 @@ Contributors v0.14.5 ******************** * Eddie Bergman +Version 0.14.4 +============== + +* Fix #1356: SVR degree hyperparameter now only active with "poly" kernel. +* Add #1311: Black format checking (non-strict). +* Maint #1306: Run history is now saved every iteration +* Doc #1309: Updated the doc faqs to include many use cases and the manual for early introductions +* Doc #1322: Fix typo in contribution guide +* Maint #1326: Add isort checker (non-strict) +* Maint #1238, #1346, #1368, #1370: Update warnings in tests +* Maint #1325: Test workflow can now be manually triggered +* Maint #1332: Update docstring and typing of ``include`` and ``exclude`` params +* Add #1260: Support for Python 3.10 +* Add #1318: First update to use the shared backend in a new submodule `automl_common `_ +* Fix #1339: Resolve dependancy issues with ``sphinx_toolbox`` +* Fix #1335: Fix issue where some regression algorithm gave incorrect output dimensions as raised in #1297 +* Doc #1340: Update example for predefined splits +* Fix #1329: Fix random state not being passed to the ConfigurationSpace +* Maint #1348: Stop double triggering of github workflows +* Doc #1349: Rename OSX to macOS in docs +* Add #1321: Change ``show_models()`` to produce actual pipeline objects and not a ``str`` +* Maint #1361: Remove ``flaky`` dependency +* Maint #1366: Make ``SimpleClassificationPipeline`` tests more deterministic +* Maint #1367: Update test values for ``MLPRegressor`` with newer numpy + +Contributors v0.14.4 +******************** + +* Eddie Bergman +* Matthias Feurer +* Katharina Eggensperger +* UserFindingSelf +* partev Version 0.14.3 ============== From b489e04b2504382cadb63fd19621438625329cd9 Mon Sep 17 00:00:00 2001 From: eddiebergman Date: Thu, 18 Aug 2022 21:17:27 +0200 Subject: [PATCH 112/118] Upper bound requirements (cherry picked from commit 5078e34537d9f354f89df84f6557922cc1283a09) --- autosklearn/util/dependencies.py | 4 ++++ doc/releases.rst | 9 +++++++++ requirements.txt | 7 ++++--- 3 files changed, 17 insertions(+), 3 deletions(-) diff --git a/autosklearn/util/dependencies.py b/autosklearn/util/dependencies.py index fb9037450b..390ac351bb 100644 --- a/autosklearn/util/dependencies.py +++ b/autosklearn/util/dependencies.py @@ -18,6 +18,10 @@ def verify_packages(packages: Optional[Union[str, List[str]]]) -> None: if not package: continue + # Ignore comments + if package.startswith("#"): + continue + match = RE_PATTERN.match(package) if match: name = match.group("name") diff --git a/doc/releases.rst b/doc/releases.rst index 74d32d6c6a..9dd86e91c5 100644 --- a/doc/releases.rst +++ b/doc/releases.rst @@ -65,6 +65,15 @@ Contributors v0.15.0 * Aseem Kannal * SkBlaz +Version 0.14.7 +============== + +* HOTFIX #1445: Locks `ConfigSpace` to `<0.5.0` and `smac` to `<1.3`. Adds upper bounds on `automl` packages to help prevent further issues. + +Contributors v0.14.7 +******************** +* Eddie Bergman + Version 0.14.6 ============== diff --git a/requirements.txt b/requirements.txt index b8b77798f9..029b9ba747 100644 --- a/requirements.txt +++ b/requirements.txt @@ -15,7 +15,8 @@ pandas>=1.0 liac-arff threadpoolctl -ConfigSpace>=0.4.14,<0.5 -pynisher>=0.6.3 +# Add upper bounds to our own packages to keep things stable +ConfigSpace>=0.4.21,<0.5 +pynisher>=0.6.3,<0.7 pyrfr>=0.8.1,<0.9 -smac>=0.14 +smac>=1.3.1,<1.4 From 7f1fb51c5cf9ba497556bbc43ea6c8dc6e163a18 Mon Sep 17 00:00:00 2001 From: eddiebergman Date: Thu, 18 Aug 2022 21:17:48 +0200 Subject: [PATCH 113/118] Lower requirement of smac (cherry picked from commit 4a184db8f38fe5e0c60980f1fab986a094719749) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 029b9ba747..cc9a52ed34 100644 --- a/requirements.txt +++ b/requirements.txt @@ -19,4 +19,4 @@ threadpoolctl ConfigSpace>=0.4.21,<0.5 pynisher>=0.6.3,<0.7 pyrfr>=0.8.1,<0.9 -smac>=1.3.1,<1.4 +smac>=1.2,<1.3 From 7eb667967c9f81de7cde5975024c858dd26982e2 Mon Sep 17 00:00:00 2001 From: eddiebergman Date: Thu, 18 Aug 2022 21:25:56 +0200 Subject: [PATCH 114/118] Remove comment from requirements file (cherry picked from commit aa0d24b97f3e5f509dfd8584d591d3c7bd9dc349) --- requirements.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index cc9a52ed34..76af7f4a06 100644 --- a/requirements.txt +++ b/requirements.txt @@ -15,7 +15,6 @@ pandas>=1.0 liac-arff threadpoolctl -# Add upper bounds to our own packages to keep things stable ConfigSpace>=0.4.21,<0.5 pynisher>=0.6.3,<0.7 pyrfr>=0.8.1,<0.9 From 268a14db76c2065a3cf438d2c7b5680d5feae057 Mon Sep 17 00:00:00 2001 From: eddiebergman Date: Fri, 19 Aug 2022 15:14:42 +0200 Subject: [PATCH 115/118] Fix twine check (cherry picked from commit b2ac331c500ebef7becf372802493a7b235f7cec) --- autosklearn/estimators.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index dde12a88d9..1a094d2582 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -284,7 +284,7 @@ def __init__( The function must accept the arguments ``scenario_dict``, ``instances``, ``num_params``, ``runhistory``, ``seed`` and ``ta``. This is an advanced feature. Use only if you are familiar with - `SMAC `_. + `SMAC `_. logging_config : dict, optional (None) dictionary object specifying the logger configuration. If None, From ee0916ece79fd8f27059c37acf08b695e2c81ac8 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Mon, 22 Aug 2022 13:54:57 +0200 Subject: [PATCH 116/118] Fix typing in unit test --- scripts/02_retrieve_metadata.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/scripts/02_retrieve_metadata.py b/scripts/02_retrieve_metadata.py index 56a0395b9e..1aa5e48405 100644 --- a/scripts/02_retrieve_metadata.py +++ b/scripts/02_retrieve_metadata.py @@ -11,6 +11,7 @@ from ConfigSpace.configuration_space import Configuration from ConfigSpace.util import deactivate_inactive_hyperparameters +from autosklearn.data.abstract_data_manager import AbstractDataManager from autosklearn.constants import * from autosklearn.metrics import CLASSIFICATION_METRICS, REGRESSION_METRICS from autosklearn.util import pipeline @@ -188,9 +189,10 @@ def write_output(outputs, configurations, output_dir, configuration_space, metri fh.write("%s: %s\n" % (key, description[key])) -class DummyDatamanager(): +class DummyDatamanager(AbstractDataManager): def __init__(self, info, feat_type=None): - self.info = info + super().__init__("Test") + self._info = info self.feat_type = feat_type def main(): From f121ee4e6c2541854c1d6eceec0a0c3fd11869f1 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Tue, 30 Aug 2022 16:35:25 +0200 Subject: [PATCH 117/118] Fix docker workflow (#1576) * Debug docker workflow failure * Use new login action * Remove broken arguments * Add new push tag * Use docker/meta action * First push to docker, then to github, update repository names * Add docker/meta workflow for docker push * Disambiguate names * Fix registry for github packages login * extract hard-coded names into variables --- .github/workflows/docker-publish.yml | 63 +++++++++++++++++++--------- 1 file changed, 44 insertions(+), 19 deletions(-) diff --git a/.github/workflows/docker-publish.yml b/.github/workflows/docker-publish.yml index 525004269e..a884e5f613 100644 --- a/.github/workflows/docker-publish.yml +++ b/.github/workflows/docker-publish.yml @@ -9,6 +9,11 @@ on: - development - docker_workflow +env: + REGISTRY: ghcr.io + IMAGE_NAME: ${{ github.repository }} + DOCKERHUB_IMAGE_NAME: mfeurer/auto-sklearn + jobs: push_to_registry: @@ -23,39 +28,59 @@ jobs: - name: Extract branch name shell: bash - run: echo "##[set-output name=branch;]$(echo ${GITHUB_REF#refs/heads/})" + run: | + echo "Working on GITHUB_REF ${GITHUB_REF}" + echo "##[set-output name=branch;]$(echo ${GITHUB_REF#refs/heads/})" id: extract_branch - - name: Push to GitHub Packages + - name: Login to Docker Hub + uses: docker/login-action@v2 + with: + username: ${{ secrets.DOCKER_USERNAME }} + password: ${{ secrets.DOCKER_TOKEN }} + + - name: Extract metadata (tags, labels) for Docker Hub + id: meta_dockerhub + uses: docker/metadata-action@v4 + with: + images: ${{ env.DOCKERHUB_IMAGE_NAME }} + + - name: Push to Docker Hub uses: docker/build-push-action@v3 with: + context: . + push: true + tags: ${{ steps.meta_dockerhub.outputs.tags }} + labels: ${{ steps.meta_dockerhub.outputs.labels }} + + - name: Login to Github + uses: docker/login-action@v2 + with: + registry: ${{ env.REGISTRY }} username: ${{ github.actor }} password: ${{ secrets.GITHUB_TOKEN }} - registry: docker.pkg.github.com - repository: automl/auto-sklearn/auto-sklearn - tag_with_ref: true - tags: ${{ steps.extract_branch.outputs.branch }} - - name: Push to Docker Hub - uses: docker/build-push-action@v3 + - name: Extract metadata (tags, labels) for Docker on Github Packages + id: meta_githubpackages + uses: docker/metadata-action@v4 with: - username: ${{ secrets.DOCKER_USERNAME }} - password: ${{ secrets.DOCKER_TOKEN }} - repository: mfeurer/auto-sklearn - tags: ${{ steps.extract_branch.outputs.branch }} + images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }} - - name: Docker Login - run: docker login docker.pkg.github.com -u $GITHUB_ACTOR -p $GITHUB_TOKEN - env: - GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}} + - name: Push to GitHub Packages + uses: docker/build-push-action@v3 + with: + context: . + push: true + tags: ${{ steps.meta_githubpackages.outputs.tags }} + labels: ${{ steps.meta_githubpackages.outputs.labels }} - - name: Pull Docker image - run: docker pull docker.pkg.github.com/$GITHUB_REPOSITORY/auto-sklearn:$BRANCH + - name: Pull Docker image from Github Packages + run: docker pull ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:$BRANCH env: BRANCH: ${{ steps.extract_branch.outputs.branch }} - name: Run image - run: docker run -i -d --name unittester -v $GITHUB_WORKSPACE:/workspace -w /workspace docker.pkg.github.com/$GITHUB_REPOSITORY/auto-sklearn:$BRANCH + run: docker run -i -d --name unittester -v $GITHUB_WORKSPACE:/workspace -w /workspace ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:$BRANCH env: BRANCH: ${{ steps.extract_branch.outputs.branch }} From 013d7eee3c46f0f0c0f66ab7eac9dd1945faf101 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Wed, 31 Aug 2022 18:32:01 +0200 Subject: [PATCH 118/118] Louquinze development (#1578) * create new text preprocessing cs * create new text preprocessing cs * set new defaults for text encoding * set new defaults for text encoding * set new defaults for text encoding * Fix bug, rework tests Co-authored-by: lukas --- .../data_preprocessing/feature_type.py | 4 +- .../feature_type_categorical.py | 3 - .../feature_type_numerical.py | 4 - .../text_encoding/__init__.py | 2 +- .../text_encoding/bag_of_word_encoding.py | 134 ------ .../bag_of_word_encoding_distinct.py | 140 ------ .../text_encoding/tfidf_encoding.py | 212 ++++++--- .../text_feature_reduction/truncated_svd.py | 10 +- .../test_meta_base_data/configurations.csv | 258 +++++------ .../test_metalearning_configuration.py | 24 +- .../test_data_preprocessing_feat_type.py | 21 +- .../test_data_preprocessing_text.py | 411 ++++++++++++------ test/test_pipeline/test_classification.py | 4 +- test/test_pipeline/test_regression.py | 2 +- 14 files changed, 584 insertions(+), 645 deletions(-) delete mode 100644 autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding.py delete mode 100644 autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding_distinct.py diff --git a/autosklearn/pipeline/components/data_preprocessing/feature_type.py b/autosklearn/pipeline/components/data_preprocessing/feature_type.py index 11085a1f74..7f52b07dbc 100644 --- a/autosklearn/pipeline/components/data_preprocessing/feature_type.py +++ b/autosklearn/pipeline/components/data_preprocessing/feature_type.py @@ -267,7 +267,9 @@ def set_hyperparameters( sub_config_dict[new_name] = value sub_configuration = Configuration( - sub_configuration_space, values=sub_config_dict + sub_configuration_space, + values=sub_config_dict, + allow_inactive_with_values=True, ) sub_init_params_dict: Optional[Dict[str, Any]] = None diff --git a/autosklearn/pipeline/components/data_preprocessing/feature_type_categorical.py b/autosklearn/pipeline/components/data_preprocessing/feature_type_categorical.py index 07cfeb7fa5..dbe8adca4a 100644 --- a/autosklearn/pipeline/components/data_preprocessing/feature_type_categorical.py +++ b/autosklearn/pipeline/components/data_preprocessing/feature_type_categorical.py @@ -33,12 +33,10 @@ class CategoricalPreprocessingPipeline(BasePipeline): 3 - Minority coalescence: Assign category 1 to all categories whose occurrence don't sum-up to a certain minimum fraction 4 - One hot encoding: usual sklearn one hot encoding - Parameters ---------- config : ConfigSpace.configuration_space.Configuration The configuration to evaluate. - random_state : Optional[int | RandomState] If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; @@ -101,7 +99,6 @@ def _get_hyperparameter_search_space( dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: """Create the hyperparameter configuration space. - Returns ------- cs : ConfigSpace.configuration_space.Configuration diff --git a/autosklearn/pipeline/components/data_preprocessing/feature_type_numerical.py b/autosklearn/pipeline/components/data_preprocessing/feature_type_numerical.py index 5cc3f19561..1344ac8a96 100644 --- a/autosklearn/pipeline/components/data_preprocessing/feature_type_numerical.py +++ b/autosklearn/pipeline/components/data_preprocessing/feature_type_numerical.py @@ -26,12 +26,10 @@ class NumericalPreprocessingPipeline(BasePipeline): 2 - VarianceThreshold: Removes low-variance features 3 - Rescaling: rescale features according to a certain rule (e.g. normalization, standartization or min-max) - Parameters ---------- config : ConfigSpace.configuration_space.Configuration The configuration to evaluate. - random_state : Optional[int | RandomState] If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; @@ -94,10 +92,8 @@ def _get_hyperparameter_search_space( dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: """Create the hyperparameter configuration space. - Parameters ---------- - Returns ------- cs : ConfigSpace.configuration_space.Configuration diff --git a/autosklearn/pipeline/components/data_preprocessing/text_encoding/__init__.py b/autosklearn/pipeline/components/data_preprocessing/text_encoding/__init__.py index 75c173e181..f858781997 100644 --- a/autosklearn/pipeline/components/data_preprocessing/text_encoding/__init__.py +++ b/autosklearn/pipeline/components/data_preprocessing/text_encoding/__init__.py @@ -63,7 +63,7 @@ def get_hyperparameter_search_space( ) if default is None: - defaults = ["bag_of_words_encoding"] + defaults = ["tfidf_encoding"] for default_ in defaults: if default_ in available_preprocessors: default = default_ diff --git a/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding.py b/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding.py deleted file mode 100644 index a90b1c1fa4..0000000000 --- a/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding.py +++ /dev/null @@ -1,134 +0,0 @@ -from typing import Dict, Optional, Tuple, Union - -import itertools - -import ConfigSpace.hyperparameters as CSH -import numpy as np -import pandas as pd -from ConfigSpace import EqualsCondition -from ConfigSpace.configuration_space import ConfigurationSpace -from sklearn.feature_extraction.text import CountVectorizer - -from autosklearn.askl_typing import FEAT_TYPE_TYPE -from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE -from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA - - -class BagOfWordEncoder(AutoSklearnPreprocessingAlgorithm): - def __init__( - self, - ngram_upper_bound: int = 1, - min_df_choice: str = "min_df_absolute", - min_df_absolute: int = 0, - min_df_relative: float = 0.01, - random_state: Optional[Union[int, np.random.RandomState]] = None, - ) -> None: - self.ngram_upper_bound = ngram_upper_bound - self.random_state = random_state - self.min_df_choice = min_df_choice - self.min_df_absolute = min_df_absolute - self.min_df_relative = min_df_relative - - def fit( - self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None - ) -> "BagOfWordEncoder": - - if not isinstance(X, pd.DataFrame): - raise ValueError( - "Your text data is not encoded in a pandas.DataFrame\n" - "Please make sure to use a pandas.DataFrame and ensure" - "that the text features are encoded as strings." - ) - - X.fillna("", inplace=True) - - # define a CountVectorizer for used on every feature - if self.min_df_choice == "min_df_absolute": - self.preprocessor = CountVectorizer( - min_df=self.min_df_absolute, - ngram_range=(1, self.ngram_upper_bound), - ) - - elif self.min_df_choice == "min_df_relative": - self.preprocessor = CountVectorizer( - min_df=self.min_df_relative, - ngram_range=(1, self.ngram_upper_bound), - ) - - else: - raise KeyError() - - all_text = itertools.chain.from_iterable(X[col] for col in X.columns) - self.preprocessor = self.preprocessor.fit(all_text) - - return self - - def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: - X.fillna("", inplace=True) - X_transformed = None - if self.preprocessor is None: - raise NotImplementedError() - for feature in X.columns: - if X_transformed is None: - X_transformed = self.preprocessor.transform(X[feature]) - else: - X_transformed += self.preprocessor.transform(X[feature]) - return X_transformed - - @staticmethod - def get_properties( - dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return { - "shortname": "BOW", - "name": "Bag Of Word Encoder", - "handles_regression": True, - "handles_classification": True, - "handles_multiclass": True, - "handles_multilabel": True, - "handles_multioutput": True, - "handles_sparse": True, - "handles_dense": True, - "input": (DENSE, SPARSE, UNSIGNED_DATA), - "output": (INPUT,), - } - - @staticmethod - def get_hyperparameter_search_space( - feat_type: Optional[FEAT_TYPE_TYPE] = None, - dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, - ) -> ConfigurationSpace: - cs = ConfigurationSpace() - hp_ngram_upper_bound = CSH.UniformIntegerHyperparameter( - name="ngram_upper_bound", lower=1, upper=3, default_value=1 - ) - hp_min_df_choice_bow = CSH.CategoricalHyperparameter( - "min_df_choice", choices=["min_df_absolute", "min_df_relative"] - ) - hp_min_df_absolute_bow = CSH.UniformIntegerHyperparameter( - name="min_df_absolute", lower=0, upper=10, default_value=0 - ) - hp_min_df_relative_bow = CSH.UniformFloatHyperparameter( - name="min_df_relative", lower=0.01, upper=1.0, default_value=0.01, log=True - ) - cs.add_hyperparameters( - [ - hp_ngram_upper_bound, - hp_min_df_choice_bow, - hp_min_df_absolute_bow, - hp_min_df_relative_bow, - ] - ) - - cond_min_df_absolute_bow = EqualsCondition( - hp_min_df_absolute_bow, hp_min_df_choice_bow, "min_df_absolute" - ) - cond_min_df_relative_bow = EqualsCondition( - hp_min_df_relative_bow, hp_min_df_choice_bow, "min_df_relative" - ) - cs.add_conditions([cond_min_df_absolute_bow, cond_min_df_relative_bow]) - - # maybe add bigrams ... - - return cs diff --git a/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding_distinct.py b/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding_distinct.py deleted file mode 100644 index de852b5d6b..0000000000 --- a/autosklearn/pipeline/components/data_preprocessing/text_encoding/bag_of_word_encoding_distinct.py +++ /dev/null @@ -1,140 +0,0 @@ -from typing import Dict, Optional, Tuple, Union - -import ConfigSpace.hyperparameters as CSH -import numpy as np -import pandas as pd -from ConfigSpace import EqualsCondition -from ConfigSpace.configuration_space import ConfigurationSpace -from scipy.sparse import hstack -from sklearn.feature_extraction.text import CountVectorizer - -from autosklearn.askl_typing import FEAT_TYPE_TYPE -from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE -from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm -from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA - - -class BagOfWordEncoder(AutoSklearnPreprocessingAlgorithm): - def __init__( - self, - ngram_upper_bound: int = 1, - min_df_choice: str = "min_df_absolute", - min_df_absolute: int = 0, - min_df_relative: float = 0.01, - random_state: Optional[Union[int, np.random.RandomState]] = None, - ) -> None: - self.ngram_upper_bound = ngram_upper_bound - self.random_state = random_state - self.min_df_choice = min_df_choice - self.min_df_absolute = min_df_absolute - self.min_df_relative = min_df_relative - - def fit( - self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None - ) -> "BagOfWordEncoder": - - if isinstance(X, pd.DataFrame): - X.fillna("", inplace=True) - if self.min_df_choice == "min_df_absolute": - - self.preprocessor = {} - - for feature in X.columns: - vectorizer = CountVectorizer( - min_df=self.min_df_absolute, - ngram_range=(1, self.ngram_upper_bound), - ).fit(X[feature]) - self.preprocessor[feature] = vectorizer - - elif self.min_df_choice == "min_df_relative": - - self.preprocessor = {} - - for feature in X.columns: - vectorizer = CountVectorizer( - min_df=self.min_df_relative, - ngram_range=(1, self.ngram_upper_bound), - ).fit(X[feature]) - self.preprocessor[feature] = vectorizer - else: - raise KeyError() - else: - raise ValueError( - "Your text data is not encoded in a pandas.DataFrame\n" - "Please make sure to use a pandas.DataFrame and ensure" - "that the text features are encoded as strings." - ) - return self - - def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: - X.fillna("", inplace=True) - X_new = None - if self.preprocessor is None: - raise NotImplementedError() - - for feature in self.preprocessor: - # the names in the dataframe must not change - if X_new is None: - X_new = self.preprocessor[feature].transform(X[feature]) - else: - X_transformed = self.preprocessor[feature].transform(X[feature]) - X_new = hstack([X_new, X_transformed]) - - return X_new - - @staticmethod - def get_properties( - dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, - ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: - return { - "shortname": "BOW", - "name": "Bag Of Word Encoder", - "handles_regression": True, - "handles_classification": True, - "handles_multiclass": True, - "handles_multilabel": True, - "handles_multioutput": True, - "handles_sparse": True, - "handles_dense": True, - "input": (DENSE, SPARSE, UNSIGNED_DATA), - "output": (INPUT,), - } - - @staticmethod - def get_hyperparameter_search_space( - feat_type: Optional[FEAT_TYPE_TYPE] = None, - dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, - ) -> ConfigurationSpace: - cs = ConfigurationSpace() - hp_ngram_upper_bound = CSH.UniformIntegerHyperparameter( - name="ngram_upper_bound", lower=1, upper=3, default_value=1 - ) - hp_min_df_choice_bow = CSH.CategoricalHyperparameter( - "min_df_choice", choices=["min_df_absolute", "min_df_relative"] - ) - hp_min_df_absolute_bow = CSH.UniformIntegerHyperparameter( - name="min_df_absolute", lower=0, upper=10, default_value=0 - ) - hp_min_df_relative_bow = CSH.UniformFloatHyperparameter( - name="min_df_relative", lower=0.01, upper=1.0, default_value=0.01, log=True - ) - cs.add_hyperparameters( - [ - hp_ngram_upper_bound, - hp_min_df_choice_bow, - hp_min_df_absolute_bow, - hp_min_df_relative_bow, - ] - ) - - cond_min_df_absolute_bow = EqualsCondition( - hp_min_df_absolute_bow, hp_min_df_choice_bow, "min_df_absolute" - ) - cond_min_df_relative_bow = EqualsCondition( - hp_min_df_relative_bow, hp_min_df_choice_bow, "min_df_relative" - ) - cs.add_conditions([cond_min_df_absolute_bow, cond_min_df_relative_bow]) - - # maybe add bigrams ... - - return cs diff --git a/autosklearn/pipeline/components/data_preprocessing/text_encoding/tfidf_encoding.py b/autosklearn/pipeline/components/data_preprocessing/text_encoding/tfidf_encoding.py index 3956ec9eff..39fa39265d 100644 --- a/autosklearn/pipeline/components/data_preprocessing/text_encoding/tfidf_encoding.py +++ b/autosklearn/pipeline/components/data_preprocessing/text_encoding/tfidf_encoding.py @@ -7,87 +7,125 @@ import pandas as pd from ConfigSpace import EqualsCondition from ConfigSpace.configuration_space import ConfigurationSpace +from scipy.sparse import hstack from sklearn.feature_extraction.text import TfidfVectorizer from autosklearn.askl_typing import FEAT_TYPE_TYPE from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA +from autosklearn.util.common import check_for_bool class TfidfEncoder(AutoSklearnPreprocessingAlgorithm): def __init__( self, - ngram_upper_bound: int = 1, - use_idf: bool = True, - min_df_choice: str = "min_df_absolute", - min_df_absolute: int = 0, - min_df_relative: float = 0.01, + ngram_range_word: int = 1, + ngram_range_char: int = 4, + min_df: float = 0.0, + max_df: float = 1.0, + binary: bool = False, + norm: str = "l2", + sublinear_tf: bool = False, + per_column: bool = False, + analyzer: str = "char", random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> None: - self.ngram_upper_bound = ngram_upper_bound self.random_state = random_state - self.use_idf = use_idf - self.min_df_choice = min_df_choice - self.min_df_absolute = min_df_absolute - self.min_df_relative = min_df_relative + self.min_df = min_df + self.max_df = max_df + self.binary = binary + self.norm = norm + self.sublinear_tf = sublinear_tf + self.per_column = per_column + self.analyzer = analyzer + self.ngram_range_word = ngram_range_word + self.ngram_range_char = ngram_range_char def fit( - self, - X: PIPELINE_DATA_DTYPE, - y: Optional[PIPELINE_DATA_DTYPE] = None, + self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None ) -> "TfidfEncoder": + if self.analyzer == "word": + ngram_range = self.ngram_range_word + elif self.analyzer == "char": + ngram_range = self.ngram_range_char + else: + raise KeyError(f"Analyzer is not defined for {self.analyzer}") + + self.sublinear_tf = check_for_bool(self.sublinear_tf) + self.binary = check_for_bool(self.binary) + + if isinstance(X, pd.DataFrame): + X.fillna("", inplace=True) + if self.per_column: + self.preprocessor = {} + + for feature in X.columns: + vectorizer = TfidfVectorizer( + ngram_range=(ngram_range, ngram_range), + min_df=self.min_df, + max_df=self.max_df, + binary=self.binary, + norm=self.norm, + sublinear_tf=self.sublinear_tf, + analyzer=self.analyzer, + ).fit(X[feature]) + self.preprocessor[feature] = vectorizer + else: + self.preprocessor = TfidfVectorizer( + ngram_range=(ngram_range, ngram_range), + min_df=self.min_df, + max_df=self.max_df, + binary=self.binary, + norm=self.norm, + sublinear_tf=self.sublinear_tf, + analyzer=self.analyzer, + ) + all_text = itertools.chain.from_iterable(X[col] for col in X.columns) + self.preprocessor = self.preprocessor.fit(all_text) - if not isinstance(X, pd.DataFrame): + else: raise ValueError( "Your text data is not encoded in a pandas.DataFrame\n" "Please make sure to use a pandas.DataFrame and ensure" - " that the text features are encoded as strings." - ) - - X.fillna("", inplace=True) - - if self.min_df_choice == "min_df_absolute": - self.preprocessor = TfidfVectorizer( - min_df=self.min_df_absolute, - use_idf=self.use_idf, - ngram_range=(1, self.ngram_upper_bound), + "that the text features are encoded as strings." ) - - elif self.min_df_choice == "min_df_relative": - self.preprocessor = TfidfVectorizer( - min_df=self.min_df_relative, - use_idf=self.use_idf, - ngram_range=(1, self.ngram_upper_bound), - ) - - else: - raise KeyError() - - all_text = itertools.chain.from_iterable(X[col] for col in X.columns) - self.preprocessor = self.preprocessor.fit(all_text) - return self def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: X.fillna("", inplace=True) - X_transformed = None - if self.preprocessor is None: - raise NotImplementedError() - for feature in X.columns: - if X_transformed is None: - X_transformed = self.preprocessor.transform(X[feature]) - else: - X_transformed += self.preprocessor.transform(X[feature]) - return X_transformed + if self.per_column: + X_new = None + if self.preprocessor is None: + raise NotImplementedError() + + for feature in self.preprocessor: + # the names in the dataframe must not change + if X_new is None: + X_new = self.preprocessor[feature].transform(X[feature]) + else: + X_transformed = self.preprocessor[feature].transform(X[feature]) + X_new = hstack([X_new, X_transformed]) + + return X_new + else: + X_transformed = None + if self.preprocessor is None: + raise NotImplementedError() + for feature in X.columns: + if X_transformed is None: + X_transformed = self.preprocessor.transform(X[feature]) + else: + X_transformed += self.preprocessor.transform(X[feature]) + return X_transformed @staticmethod def get_properties( dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: return { - "shortname": "RBOW", - "name": "Relative Bag Of Word Encoder", + "shortname": "TF/IDF", + "name": "TF/IDF Encoder", "handles_regression": True, "handles_classification": True, "handles_multiclass": True, @@ -105,37 +143,67 @@ def get_hyperparameter_search_space( dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> ConfigurationSpace: cs = ConfigurationSpace() - hp_ngram_upper_bound = CSH.UniformIntegerHyperparameter( - name="ngram_upper_bound", lower=1, upper=3, default_value=1 + + hp_analyzer = CSH.CategoricalHyperparameter( + name="analyzer", + choices=["word", "char"], + default_value="char", ) - hp_use_idf = CSH.CategoricalHyperparameter("use_idf", choices=[False, True]) - hp_min_df_choice = CSH.CategoricalHyperparameter( - "min_df_choice", choices=["min_df_absolute", "min_df_relative"] + + hp_ngram_range_char = CSH.UniformIntegerHyperparameter( + name="ngram_range_char", + lower=2, + upper=4, + default_value=4, ) - hp_min_df_absolute = CSH.UniformIntegerHyperparameter( - name="min_df_absolute", lower=0, upper=10, default_value=0 + + hp_ngram_range_word = CSH.UniformIntegerHyperparameter( + name="ngram_range_word", + lower=1, + upper=3, + default_value=1, ) - hp_min_df_relative = CSH.UniformFloatHyperparameter( - name="min_df_relative", lower=0.01, upper=1.0, default_value=0.01, log=True + + hp_min_df = CSH.UniformFloatHyperparameter( + name="min_df", + lower=0.0, + upper=0.3, + default_value=0.0, ) - cs.add_hyperparameters( - [ - hp_ngram_upper_bound, - hp_use_idf, - hp_min_df_choice, - hp_min_df_absolute, - hp_min_df_relative, - ] + + hp_max_df = CSH.UniformFloatHyperparameter( + name="max_df", lower=0.7, upper=1.0, default_value=1.0 ) - cond_min_df_absolute = EqualsCondition( - hp_min_df_absolute, hp_min_df_choice, "min_df_absolute" + hp_binary = CSH.UnParametrizedHyperparameter(name="binary", value="False") + + hp_norm = CSH.CategoricalHyperparameter( + name="norm", choices=["l2", "l1"], default_value="l2" + ) + + hp_sublinear_tf = CSH.UnParametrizedHyperparameter( + name="sublinear_tf", value="False" ) - cond_min_df_relative = EqualsCondition( - hp_min_df_relative, hp_min_df_choice, "min_df_relative" + + hp_per_column = CSH.CategoricalHyperparameter( + name="per_column", choices=[True, False], default_value=False ) - cs.add_conditions([cond_min_df_absolute, cond_min_df_relative]) - # maybe add bigrams ... + cs.add_hyperparameters( + [ + hp_analyzer, + hp_ngram_range_char, + hp_ngram_range_word, + hp_max_df, + hp_min_df, + hp_binary, + hp_norm, + hp_sublinear_tf, + hp_per_column, + ] + ) + cond_char = EqualsCondition(hp_ngram_range_char, hp_analyzer, "char") + cond_word = EqualsCondition(hp_ngram_range_word, hp_analyzer, "word") + cs.add_conditions([cond_char, cond_word]) return cs diff --git a/autosklearn/pipeline/components/data_preprocessing/text_feature_reduction/truncated_svd.py b/autosklearn/pipeline/components/data_preprocessing/text_feature_reduction/truncated_svd.py index d6380e03dd..1d8ce91c49 100644 --- a/autosklearn/pipeline/components/data_preprocessing/text_feature_reduction/truncated_svd.py +++ b/autosklearn/pipeline/components/data_preprocessing/text_feature_reduction/truncated_svd.py @@ -31,22 +31,28 @@ def fit( self.preprocessor = TruncatedSVD( n_components=self.n_components, random_state=self.random_state ) + self.preprocessor.fit(X) elif X.shape[1] <= self.n_components and X.shape[1] != 1: self.preprocessor = TruncatedSVD( n_components=X.shape[1] - 1, random_state=self.random_state ) + self.preprocessor.fit(X) + elif X.shape[1] == 1: + self.preprocessor = "passthrough" else: raise ValueError( "The text embedding consists only of a single dimension.\n" "Are you sure that your text data is necessary?" ) - self.preprocessor.fit(X) return self def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: if self.preprocessor is None: raise NotImplementedError() - return self.preprocessor.transform(X) + elif self.preprocessor == "passthrough": + return X + else: + return self.preprocessor.transform(X) @staticmethod def get_properties( diff --git a/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv b/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv index 36263d3333..506d45112b 100755 --- a/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv +++ b/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv @@ -1,129 +1,129 @@ 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+102,weighting,adaboost,SAMME.R,0.0190998863782481,7,99,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1177,uniform,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,7,None,1,3,1.0,97,,,,,,feature_type +103,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.8850157429082246,None,0.0,12,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.27673478870889345,median,none,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type +104,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type +105,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type +106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.527652780264615,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010739139664277112,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,feature_type +107,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6568118863259279,None,0.0,4,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,23,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type +108,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86.72140178707858,f_classif,,,,feature_type +109,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type +110,weighting,adaboost,SAMME.R,0.22665749778830807,7,78,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,373,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type +111,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1689.0860195745497,0.7404917548199534,2,0.011884114654356123,poly,-1,True,4.262566522678876e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004395163582476699,median,minmax,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,3,None,13,13,1.0,74,,,,,,feature_type +112,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.859589328406253e-07,0.25392293346701533,auto,255,None,4,80,,loss,1E-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.7135135608214,mutual_info,,,,feature_type +113,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,9.097196057095871e-06,0.32800101253288033,auto,255,None,53,28,20,loss,1E-07,0.101919468281566,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008121631984215255,median,robust_scaler,,,0.7654960296398138,0.25,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07288328235161678,fwe,chi2,feature_type +114,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5463209559127865,None,0.0,12,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.025636105021492692,mean,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.4472785394247571,False,True,1,squared_hinge,ovr,l1,0.00018809455411335498,,,,,,,,,,,,,,,,,,,,,,,feature_type +115,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.07970183198340376,None,0.0,9,9,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.008729901092151533,most_frequent,quantile_transformer,1028,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,feature_type +116,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.050858257794119e-10,0.0509713008465305,auto,255,None,17,16,,loss,1E-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0006772048110168395,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.42719682445182733,fdr,f_classif,feature_type +117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4886932005592788,None,0.0,1,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,2.2673178962517726,False,True,1,squared_hinge,ovr,l1,0.07576775715726437,,,,,,,,,,,,,,,,,,,,,,,feature_type +118,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1954.4001293172055,0.5941977727413141,3,0.01222672837922025,poly,-1,False,0.000868704184075337,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.023611476558497053,most_frequent,quantile_transformer,1312,normal,,,extra_trees_preproc_for_classification,True,entropy,None,0.26038719206370126,None,0.0,6,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type +119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.48772464140872207,None,0.0,1,16,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,feature_type +120,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,571.8976418358935,-0.6542106402522795,,5.0850539598583375e-05,sigmoid,-1,False,0.003954814208041632,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1378799965815952,fwe,f_classif,feature_type +121,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6204291847226782,None,0.0,2,7,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012802264108301202,most_frequent,normalize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type +122,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7159488281157247,None,0.0,15,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7546151696972261,0.25941712940346606,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,33,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type +123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4285190453868457,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.49851517731857553,most_frequent,quantile_transformer,958,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,feature_type +124,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.690301155272815,None,0.0,19,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00018373331741554494,most_frequent,normalize,,,,,extra_trees_preproc_for_classification,False,gini,None,0.5638614505566816,None,0.0,1,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type +125,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type +126,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.77554422882638,True,True,hinge,0.02071347009354619,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01999967026715386,most_frequent,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.011708181260754043,8770,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type +127,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.5396632902741157e-05,0.18584665494894462,auto,255,None,6,48,13,loss,1E-07,0.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,379,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type +128,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,921.894056649221,,,0.4428468266254544,rbf,-1,False,1.0295003364004332e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7588611364765459,0.25,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type diff --git a/test/test_metalearning/pyMetaLearn/test_metalearning_configuration.py b/test/test_metalearning/pyMetaLearn/test_metalearning_configuration.py index 1e08805d87..6bb822a9ee 100644 --- a/test/test_metalearning/pyMetaLearn/test_metalearning_configuration.py +++ b/test/test_metalearning/pyMetaLearn/test_metalearning_configuration.py @@ -17,24 +17,30 @@ def test_metalearning_cs_size(self): data_dir = os.path.join(data_dir, "test_meta_base_data") os.chdir(data_dir) + # Total: 176, categorical: 3, numerical: 7, string: 7 + total = 179 + num_numerical = 6 + num_string = 11 + num_categorical = 3 for feat_type, cs_size in [ - ({"A": "numerical"}, 165), - ({"A": "categorical"}, 162), - ({"A": "string"}, 174), - ({"A": "numerical", "B": "categorical"}, 168), - ({"A": "numerical", "B": "string"}, 180), - ({"A": "categorical", "B": "string"}, 177), - ({"A": "categorical", "B": "string", "C": "numerical"}, 183), + ({"A": "numerical"}, total - num_string - num_categorical), + ({"A": "categorical"}, total - num_string - num_numerical), + ({"A": "string"}, total - num_categorical - num_numerical), + ({"A": "numerical", "B": "categorical"}, total - num_string), + ({"A": "numerical", "B": "string"}, total - num_categorical), + ({"A": "categorical", "B": "string"}, total - num_numerical), + ({"A": "categorical", "B": "string", "C": "numerical"}, total), ]: pipeline = autosklearn.pipeline.classification.SimpleClassificationPipeline( feat_type=feat_type ) self.cs = pipeline.get_hyperparameter_search_space(feat_type=feat_type) - # print(self.cs.get_default_configuration()) self.logger = logging.getLogger() meta_base = MetaBase(self.cs, data_dir, logger=self.logger) self.meta_optimizer = metalearner.MetaLearningOptimizer( "233", self.cs, meta_base, logger=self.logger ) - self.assertEqual(len(self.meta_optimizer.configuration_space), cs_size) + self.assertEqual( + len(self.meta_optimizer.configuration_space), cs_size, feat_type + ) diff --git a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_feat_type.py b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_feat_type.py index 0cf5ee6bd2..8efdad5e6c 100644 --- a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_feat_type.py +++ b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_feat_type.py @@ -6,6 +6,11 @@ class PreprocessingPipelineFeatTypeTest(unittest.TestCase): + + num_numerical = 6 + num_categorical = 3 + num_text = 11 + def test_single_type(self): DPP = FeatTypeSplit(feat_type={"A": "numerical"}) cs = DPP.get_hyperparameter_search_space( @@ -22,7 +27,7 @@ def test_single_type(self): for key in cs.get_hyperparameters_dict().keys(): self.assertNotIn("text", key.split(":")[0]) self.assertNotIn("categorical", key.split(":")[0]) - self.assertEqual(len(cs), 6) + self.assertEqual(len(cs), self.num_numerical) DPP = FeatTypeSplit(feat_type={"A": "categorical"}) cs = DPP.get_hyperparameter_search_space( @@ -39,7 +44,7 @@ def test_single_type(self): for key in cs.get_hyperparameters_dict().keys(): self.assertNotIn("text", key.split(":")[0]) self.assertNotIn("numerical", key.split(":")[0]) - self.assertEqual(len(cs), 3) + self.assertEqual(len(cs), self.num_categorical) DPP = FeatTypeSplit(feat_type={"A": "string"}) cs = DPP.get_hyperparameter_search_space( @@ -56,7 +61,7 @@ def test_single_type(self): for key in cs.get_hyperparameters_dict().keys(): self.assertNotIn("numerical", key.split(":")[0]) self.assertNotIn("categorical", key.split(":")[0]) - self.assertEqual(len(cs), 15) + self.assertEqual(len(cs), self.num_text) def test_dual_type(self): DPP = FeatTypeSplit(feat_type={"A": "numerical", "B": "categorical"}) @@ -73,7 +78,7 @@ def test_dual_type(self): ) for key in cs.get_hyperparameters_dict().keys(): self.assertNotIn("text", key.split(":")[0]) - self.assertEqual(len(cs), 9) + self.assertEqual(len(cs), self.num_numerical + self.num_categorical) DPP = FeatTypeSplit(feat_type={"A": "categorical", "B": "string"}) cs = DPP.get_hyperparameter_search_space( @@ -89,7 +94,7 @@ def test_dual_type(self): ) for key in cs.get_hyperparameters_dict().keys(): self.assertNotIn("numerical", key.split(":")[0]) - self.assertEqual(len(cs), 18) + self.assertEqual(len(cs), self.num_categorical + self.num_text) DPP = FeatTypeSplit(feat_type={"A": "string", "B": "numerical"}) cs = DPP.get_hyperparameter_search_space( @@ -105,7 +110,7 @@ def test_dual_type(self): ) for key in cs.get_hyperparameters_dict().keys(): self.assertNotIn("categorical", key.split(":")[0]) - self.assertEqual(len(cs), 21) + self.assertEqual(len(cs), self.num_text + self.num_numerical) def test_triple_type(self): DPP = FeatTypeSplit( @@ -132,4 +137,6 @@ def test_triple_type(self): truth_table[2] = True self.assertEqual(sum(truth_table), 3) - self.assertEqual(len(cs), 24) + self.assertEqual( + len(cs), self.num_numerical + self.num_categorical + self.num_text + ) diff --git a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_text.py b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_text.py index eed5b01bea..bc81efca48 100644 --- a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_text.py +++ b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_text.py @@ -1,150 +1,281 @@ import numpy as np import pandas as pd -from autosklearn.pipeline.components.data_preprocessing.text_encoding.bag_of_word_encoding import ( # noqa: E501 - BagOfWordEncoder as BOW, +from autosklearn.pipeline.components.data_preprocessing.text_encoding.tfidf_encoding import ( # noqa: E501 + TfidfEncoder as Vectorizer, ) -from autosklearn.pipeline.components.data_preprocessing.text_encoding.bag_of_word_encoding_distinct import ( # noqa: E501 - BagOfWordEncoder as BOW_distinct, + +import pytest + + +@pytest.mark.parametrize( + "analyzer,per_column", + [("word", True), ("word", False), ("char", True), ("char", False)], ) +def test_fit_transform(analyzer, per_column): + X = pd.DataFrame( + { + "col1": ["hello world", "hello mars"], + "col2": ["Test Test", "This is a test column"], + } + ).astype({"col1": "string", "col2": "string"}) + Vectorizer_fitted = Vectorizer( + analyzer=analyzer, + per_column=per_column, + random_state=1, + ).fit(X.copy()) + + if per_column: + for column in X.columns: + Yt = Vectorizer_fitted.preprocessor[column].vocabulary_ + if column == "col1": + if analyzer == "word": + words = sorted( + [ + "hello", + "world", + "mars", + ] + ) # is ignored by TFIDFVectorizer + Y = {key: idx for idx, key in enumerate(words)} + assert Yt == Y + else: + words = sorted( + [ + "hell", + "ello", + "llo ", + "lo w", + "o wo", + " wor", + "worl", + "orld", + "lo m", + "o ma", + " mar", + "mars", + ] + ) + Y = {key: idx for idx, key in enumerate(words)} + assert Yt == Y + elif column == "col2": + if analyzer == "word": + words = sorted( + [ + "test", + "this", + "is", # "a" is not added, len(...)=1, + "column", + ] + ) # is ignored by TFIDFVectorizer + Y = {key: idx for idx, key in enumerate(words)} + assert Yt == Y + else: + words = sorted( + [ + "test", + "est ", + "st t", + "t te", + " tes", + "this", + "his ", + "is i", + "s is", + " is ", + "is a", + "s a ", + " a t", + "a te", + "st c", + "t co", + " col", + "colu", + "olum", + "lumn", + ] + ) + Y = {key: idx for idx, key in enumerate(words)} + assert Yt == Y + else: + raise ValueError(column) + else: + Yt = Vectorizer_fitted.preprocessor.vocabulary_ + if analyzer == "word": + words = sorted( + [ + "hello", + "world", + "mars", + "test", + "this", + "is", # "a" is not added, len(...)=1, + "column", + ] + ) # is ignored by TFIDFVectorizer + Y = {key: idx for idx, key in enumerate(words)} + assert Yt == Y + else: + words = sorted( + [ + "hell", + "ello", + "llo ", + "lo w", + "o wo", + " wor", + "worl", + "orld", + "lo m", + "o ma", + " mar", + "mars", + "test", + "est ", + "st t", + "t te", + " tes", + "this", + "his ", + "is i", + "s is", + " is ", + "is a", + "s a ", + " a t", + "a te", + "st c", + "t co", + " col", + "colu", + "olum", + "lumn", + ] + ) + Y = {key: idx for idx, key in enumerate(words)} + assert Yt == Y + + +@pytest.mark.parametrize("per_column", [True, False]) +def test_transform(per_column): + X = pd.DataFrame( + { + "col1": ["hello world", "hello mars"], + "col2": ["Test Test", "This is a test column"], + } + ).astype({"col1": "string", "col2": "string"}) + vectorizer = Vectorizer( + per_column=per_column, + analyzer="word", + random_state=1, + ) + X_t = vectorizer.fit_transform(X.copy()) -import unittest - - -class TextPreprocessingPipelineTest(unittest.TestCase): - def test_fit_transform(self): - X = pd.DataFrame( - { - "col1": ["hello world", "This is a test"], - "col2": ["hello mars", "This is the second column"], - } - ).astype({"col1": "string", "col2": "string"}) - BOW_fitted = BOW( - ngram_upper_bound=1, - min_df_choice="min_df_absolute", - min_df_absolute=0, - min_df_relative=0, - random_state=1, - ).fit(X.copy()) - - Yt = BOW_fitted.preprocessor.vocabulary_ - words = sorted( + if per_column: + # ['hello', 'mars', 'world', 'column', 'is', 'test', 'this'] + y = np.array( [ - "hello", - "world", - "this", - "is", - "test", # "a" is not added, len(...)=1 - "mars", - "the", - "second", - "column", + [ + 0.57974, + 0.0, + 0.8148, + 0.0, + 0.0, + 1.0, + 0.0, + ], + [0.57974, 0.8148, 0.0, 0.53405, 0.53405, 0.37998, 0.53405], ] - ) # is ignored by CountVectorizer - Y = {key: idx for idx, key in enumerate(words)} - - np.testing.assert_array_equal(Yt, Y) - - BOW_fitted = BOW_distinct( - ngram_upper_bound=1, - min_df_choice="min_df_absolute", - min_df_absolute=0, - min_df_relative=0, - random_state=1, - ).fit(X.copy()) - - for key in BOW_fitted.preprocessor: - y = [] - for col in X[key]: - y += [word for word in col.lower().split(" ") if len(word) > 1] - y = sorted(y) - yt = sorted(BOW_fitted.preprocessor[key].vocabulary_.keys()) - np.testing.assert_array_equal(yt, y) - - def test_transform(self): - X = pd.DataFrame( - { - "col1": ["hello world", "this is a test"], - "col2": ["hello mars", "this is the second column"], - } - ).astype({"col1": "string", "col2": "string"}) - X_t = BOW( - ngram_upper_bound=1, - min_df_choice="min_df_absolute", - min_df_absolute=0, - min_df_relative=0, - random_state=1, - ).fit_transform(X.copy()) - - # ['column', 'hello', 'is', 'mars', 'second', 'test', 'the', 'this', 'world'] - y = np.array([[0, 2, 0, 1, 0, 0, 0, 0, 1], [1, 0, 2, 0, 1, 1, 1, 2, 0]]) - np.testing.assert_array_equal(X_t.toarray(), y) - - X_t = BOW_distinct( - ngram_upper_bound=1, - min_df_choice="min_df_absolute", - min_df_absolute=0, - min_df_relative=0, - random_state=1, - ).fit_transform(X.copy()) - - # 'hello', 'is', 'test', 'this', 'world', - # 'column', 'hello', 'is', 'mars', 'second', 'the', 'this' + ) + np.testing.assert_almost_equal(X_t.toarray(), y, decimal=5) + else: + print(vectorizer.preprocessor.vocabulary_) + # 'column', 'hello', 'is', 'mars', 'test', 'this', 'world y = np.array( - [[1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0], [0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1]] + [ + [0.0, 0.61913, 0.0, 0.0, 1.0, 0.0, 0.78529], + [0.52547, 0.61913, 0.52547, 0.78529, 0.41429, 0.52547, 0.0], + ] ) - np.testing.assert_array_equal(X_t.toarray(), y) - - def test_check_shape(self): - X = pd.DataFrame( - { - "col1": ["hello world", "this is test"], - "col2": ["test test", "test test"], - } - ).astype({"col1": "string", "col2": "string"}) - X_t = BOW( - ngram_upper_bound=1, - min_df_choice="min_df_absolute", - min_df_absolute=0, - min_df_relative=0, - random_state=1, - ).fit_transform(X.copy()) - - self.assertEqual(X_t.shape, (2, 5)) - - X_t = BOW_distinct( - ngram_upper_bound=1, - min_df_choice="min_df_absolute", - min_df_absolute=0, - min_df_relative=0, - random_state=1, - ).fit_transform(X.copy()) - - self.assertEqual(X_t.shape, (2, 6)) - - def test_check_nan(self): - X = pd.DataFrame( - { - "col1": ["hello world", "this is test", None], - "col2": ["test test", "test test", "test"], - } - ).astype({"col1": "string", "col2": "string"}) - X_t = BOW( - ngram_upper_bound=1, - min_df_choice="min_df_absolute", - min_df_absolute=0, - min_df_relative=0, - random_state=1, - ).fit_transform(X.copy()) - - self.assertEqual(X_t.shape, (3, 5)) - - X_t = BOW_distinct( - ngram_upper_bound=1, - min_df_choice="min_df_absolute", - min_df_absolute=0, - min_df_relative=0, - random_state=1, - ).fit_transform(X.copy()) - - self.assertEqual(X_t.shape, (3, 6)) + np.testing.assert_almost_equal(X_t.toarray(), y, decimal=5) + + +def test_check_shape(): + X = pd.DataFrame( + { + "col1": ["hello world", "this is test"], + "col2": ["test test", "test test"], + } + ).astype({"col1": "string", "col2": "string"}) + X_t = Vectorizer( + per_column=True, + analyzer="word", + random_state=1, + ).fit_transform(X.copy()) + + assert X_t.shape == (2, 6) + + X_t = Vectorizer( + analyzer="word", + per_column=False, + random_state=1, + ).fit_transform(X.copy()) + + assert X_t.shape == (2, 5) + + +def test_check_nan(): + X = pd.DataFrame( + { + "col1": ["hello world", "this is test", None], + "col2": ["test test", "test test", "test"], + } + ).astype({"col1": "string", "col2": "string"}) + X_t = Vectorizer( + per_column=True, + analyzer="word", + random_state=1, + ).fit_transform(X.copy()) + assert X_t.shape == (3, 6) + + X_t = Vectorizer( + analyzer="word", + per_column=False, + random_state=1, + ).fit_transform(X.copy()) + assert X_t.shape == (3, 5) + + +def test_check_vocabulary(): + X = pd.DataFrame( + { + "col1": ["hello world", "this is test", None], + "col2": ["test test", "test test", "test"], + } + ).astype({"col1": "string", "col2": "string"}) + vectorizer = Vectorizer( + per_column=True, + analyzer="word", + random_state=1, + ).fit(X.copy()) + assert vectorizer.preprocessor["col1"].vocabulary_ == { + "hello": 0, + "world": 4, + "this": 3, + "is": 1, + "test": 2, + } + assert vectorizer.preprocessor["col2"].vocabulary_ == {"test": 0} + + vectorizer = Vectorizer( + analyzer="word", + per_column=False, + random_state=1, + ).fit(X.copy()) + assert vectorizer.preprocessor.vocabulary_ == { + "hello": 0, + "world": 4, + "this": 3, + "is": 1, + "test": 2, + } diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index 9721f88e31..5b23cb37c9 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -669,7 +669,7 @@ def test_get_hyperparameter_search_space(self): * 7 rescaling choices * 16 classifier choices * 13 features preprocessor choices - * 183 total hyperparameters + * 176 total hyperparameters * (n_hyperparameters - 4) different conditionals for the pipeline * 53 forbidden combinations """ @@ -694,7 +694,7 @@ def test_get_hyperparameter_search_space(self): self.assertEqual(n_preprocessors, 13) hyperparameters = cs.get_hyperparameters() - self.assertEqual(len(hyperparameters), 183) + self.assertEqual(len(hyperparameters), 179) # for hp in sorted([str(h) for h in hyperparameters]): # print hp diff --git a/test/test_pipeline/test_regression.py b/test/test_pipeline/test_regression.py index 3a50decb8c..9f6d524173 100644 --- a/test/test_pipeline/test_regression.py +++ b/test/test_pipeline/test_regression.py @@ -320,7 +320,7 @@ def test_get_hyperparameter_search_space(self): conditions = cs.get_conditions() hyperparameters = cs.get_hyperparameters() forbiddens = cs.get_forbiddens() - self.assertEqual(171, len(hyperparameters)) + self.assertEqual(167, len(hyperparameters)) self.assertEqual(len(hyperparameters) - 3, len(conditions)) self.assertEqual(len(forbiddens), 35)