diff --git a/.gitignore b/.gitignore index 22f54346f..e8ad80c8c 100644 --- a/.gitignore +++ b/.gitignore @@ -414,3 +414,4 @@ TSWLatexianTemp* # exclude notebooks directory: this is generated during build /notebooks/ +/sphinx/base/ diff --git a/.idea/facet.iml b/.idea/facet.iml index 9ec4437f5..f440f9ceb 100644 --- a/.idea/facet.iml +++ b/.idea/facet.iml @@ -8,8 +8,12 @@ + + + + diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 737da2c3a..ab49b2776 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,38 +1,46 @@ repos: - repo: https://github.com/PyCQA/isort - rev: 5.5.4 + rev: 5.10.1 hooks: - id: isort - repo: https://github.com/psf/black - rev: 22.3.0 + rev: 22.8.0 hooks: - id: black - language_version: python3 + language: python_venv + language_version: python39 - repo: https://gitlab.com/pycqa/flake8 - rev: 3.9.0 + rev: 5.0.4 hooks: - id: flake8 name: flake8 entry: flake8 --config tox.ini language: python_venv - additional_dependencies: [ flake8-comprehensions, flake8-import-order ] + language_version: python39 + additional_dependencies: + - flake8-comprehensions ~= 3.10 types: [ python ] - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v3.2.0 + rev: v4.3.0 hooks: - id: check-added-large-files - id: check-json + - id: check-xml - id: check-yaml + language: python_venv + exclude: condabuild/meta.yaml - repo: https://github.com/pre-commit/mirrors-mypy - rev: v0.931 + rev: v0.971 hooks: - id: mypy - files: src/ + files: src|sphinx|test + language: python_venv + language_version: python39 additional_dependencies: - - numpy>=1.22 - - gamma-pytools>=2.0.dev8,<3a - - sklearndf>=2.0.dev3,<3a + - numpy~=1.22 + - gamma-pytools~=2.0,!=2.0.0 + - sklearndf~=2.0 diff --git a/README.rst b/README.rst index e33382290..0af1508a7 100644 --- a/README.rst +++ b/README.rst @@ -1,4 +1,4 @@ -.. image:: sphinx/source/_static/Gamma_Facet_Logo_RGB_LB.svg +.. image:: sphinx/source/_images/Gamma_Facet_Logo_RGB_LB.svg | @@ -103,13 +103,13 @@ In this quickstart we will train a Random Forest regressor using 10 repeated *sklearndf* we can create a *pandas* DataFrame compatible workflow. However, FACET provides additional enhancements to keep track of our feature matrix and target vector using a sample object (`Sample`) and easily compare -hyperparameter configurations and even multiple learners with the `LearnerRanker`. +hyperparameter configurations and even multiple learners with the `LearnerSelector`. .. code-block:: Python # standard imports import pandas as pd - from sklearn.model_selection import RepeatedKFold + from sklearn.model_selection import RepeatedKFold, GridSearchCV # some helpful imports from sklearndf from sklearndf.pipeline import RegressorPipelineDF @@ -117,7 +117,7 @@ hyperparameter configurations and even multiple learners with the `LearnerRanker # relevant FACET imports from facet.data import Sample - from facet.selection import LearnerRanker, LearnerGrid + from facet.selection import LearnerSelector, ParameterSpace # declaring url with data data_url = 'https://web.stanford.edu/~hastie/Papers/LARS/diabetes.data' @@ -144,29 +144,27 @@ hyperparameter configurations and even multiple learners with the `LearnerRanker regressor=RandomForestRegressorDF(n_estimators=200, random_state=42) ) - # define grid of models which are "competing" against each other - rnd_forest_grid = [ - LearnerGrid( - pipeline=rnd_forest_reg, - learner_parameters={ - "min_samples_leaf": [8, 11, 15], - "max_depth": [4, 5, 6], - } - ), - ] + # define parameter space for models which are "competing" against each other + rnd_forest_ps = ParameterSpace(rnd_forest_reg) + rnd_forest_ps.regressor.min_samples_leaf = [8, 11, 15] + rnd_forest_ps.regressor.max_depth = [4, 5, 6] # create repeated k-fold CV iterator rkf_cv = RepeatedKFold(n_splits=5, n_repeats=10, random_state=42) - # rank your candidate models by performance (default is mean CV score - 2*SD) - ranker = LearnerRanker( - grids=rnd_forest_grid, cv=rkf_cv, n_jobs=-3 + # rank your candidate models by performance + selector = LearnerSelector( + searcher_type=GridSearchCV, + parameter_space=rnd_forest_ps, + cv=rkf_cv, + n_jobs=-3, + scoring="r2" ).fit(sample=diabetes_sample) # get summary report - ranker.summary_report() + selector.summary_report() -.. image:: sphinx/source/_static/ranker_summary.png +.. image:: sphinx/source/_images/ranker_summary.png :width: 600 We can see based on this minimal workflow that a value of 11 for minimum @@ -233,8 +231,10 @@ The key global metrics for each pair of features in a model are: # fit the model inspector from facet.inspection import LearnerInspector - inspector = LearnerInspector(n_jobs=-3) - inspector.fit(crossfit=ranker.best_model_crossfit_) + inspector = LearnerInspector( + pipeline=selector.best_estimator_, + n_jobs=-3 + ).fit(sample=diabetes_sample) **Synergy** @@ -245,7 +245,7 @@ The key global metrics for each pair of features in a model are: synergy_matrix = inspector.feature_synergy_matrix() MatrixDrawer(style="matplot%").draw(synergy_matrix, title="Synergy Matrix") -.. image:: sphinx/source/_static/synergy_matrix.png +.. image:: sphinx/source/_images/synergy_matrix.png :width: 600 For any feature pair (A, B), the first feature (A) is the row, and the second @@ -273,7 +273,7 @@ to 27% synergy of `LDL` with `LTG` for predicting progression after one year. redundancy_matrix = inspector.feature_redundancy_matrix() MatrixDrawer(style="matplot%").draw(redundancy_matrix, title="Redundancy Matrix") -.. image:: sphinx/source/_static/redundancy_matrix.png +.. image:: sphinx/source/_images/redundancy_matrix.png :width: 600 @@ -312,7 +312,7 @@ Let's look at the example for redundancy. redundancy = inspector.feature_redundancy_linkage() DendrogramDrawer().draw(data=redundancy, title="Redundancy Dendrogram") -.. image:: sphinx/source/_static/redundancy_dendrogram.png +.. image:: sphinx/source/_images/redundancy_dendrogram.png :width: 600 Based on the dendrogram we can see that the feature pairs (`LDL`, `TC`) @@ -337,22 +337,17 @@ we do the following for the simulation: of that partition. - For each partition, the simulator creates an artificial copy of the original sample assuming the variable to be simulated has the same value across all observations – - which is the value representing the partition. Using the best `LearnerCrossfit` - acquired from the ranker, the simulator now re-predicts all targets using the models - trained for all folds and determines the average uplift of the target variable + which is the value representing the partition. Using the best estimator + acquired from the selector, the simulator now re-predicts all targets using the models + trained for full sample and determines the uplift of the target variable resulting from this. - The FACET `SimulationDrawer` allows us to visualise the result; both in a *matplotlib* and a plain-text style. -Finally, because FACET can use bootstrap cross validation, we can create a crossfit -from our previous `LearnerRanker` best model to perform the simulation, so we can -quantify the uncertainty by using bootstrap confidence intervals. - .. code-block:: Python # FACET imports from facet.validation import BootstrapCV - from facet.crossfit import LearnerCrossfit from facet.simulation import UnivariateUpliftSimulator from facet.data.partition import ContinuousRangePartitioner from facet.simulation.viz import SimulationDrawer @@ -360,16 +355,12 @@ quantify the uncertainty by using bootstrap confidence intervals. # create bootstrap CV iterator bscv = BootstrapCV(n_splits=1000, random_state=42) - # create a bootstrap CV crossfit for simulation using best model - boot_crossfit = LearnerCrossfit( - pipeline=ranker.best_model_, - cv=bscv, - n_jobs=-3, - verbose=False, - ).fit(sample=diabetes_sample) - SIM_FEAT = "BMI" - simulator = UnivariateUpliftSimulator(crossfit=boot_crossfit, n_jobs=-3) + simulator = UnivariateUpliftSimulator( + model=selector.best_estimator_, + sample=diabetes_sample, + n_jobs=-3 + ) # split the simulation range into equal sized partitions partitioner = ContinuousRangePartitioner() @@ -380,7 +371,7 @@ quantify the uncertainty by using bootstrap confidence intervals. # visualise results SimulationDrawer().draw(data=simulation, title=SIM_FEAT) -.. image:: sphinx/source/_static/simulation_output.png +.. image:: sphinx/source/_images/simulation_output.png We would conclude from the figure that higher values of `BMI` are associated with an increase in disease progression after one year, and that for a `BMI` of 28 @@ -436,15 +427,15 @@ BCG GAMMA team. If you would like to know more you can find out about or have a look at `career opportunities `_. -.. |pipe| image:: sphinx/source/_static/icons/pipe_icon.png +.. |pipe| image:: sphinx/source/_images/icons/pipe_icon.png :width: 100px :class: facet_icon -.. |inspect| image:: sphinx/source/_static/icons/inspect_icon.png +.. |inspect| image:: sphinx/source/_images/icons/inspect_icon.png :width: 100px :class: facet_icon -.. |sim| image:: sphinx/source/_static/icons/sim_icon.png +.. |sim| image:: sphinx/source/_images/icons/sim_icon.png :width: 100px :class: facet_icon diff --git a/RELEASE_NOTES.rst b/RELEASE_NOTES.rst index fc7cdab41..d85ae144f 100644 --- a/RELEASE_NOTES.rst +++ b/RELEASE_NOTES.rst @@ -1,15 +1,27 @@ Release Notes ============= +.. |mypy| replace:: :external+mypy:doc:`mypy ` +.. |shap| replace:: :external+shap:doc:`shap ` +.. |nbsp| unicode:: 0xA0 + :trim: + FACET 2.0 --------- +FACET |nbsp| 2.0 brings numerous API enhancements and improvements, accelerates model +inspection by factor |nbsp| 50 in many practical settings, makes major improvements to +visualizations, and is now fully type-checked by |mypy|. + + 2.0.0 ~~~~~ ``facet.data`` ^^^^^^^^^^^^^^ +- API: class :class:`.Sample` raises an exception if the name of any used column is not + a string - API: class :class:`.RangePartitioner` supports new optional arguments ``lower_bound`` and ``upper_bound`` in method :meth:`~.RangePartitioner.fit` and no longer accepts them in the class initializer @@ -19,28 +31,30 @@ FACET 2.0 - API: :class:`.LearnerInspector` no longer uses learner crossfits and instead inspects models using a single pass of SHAP calculations, usually leading to performance gains - of up to a factor of 50 -- API: return :class:`.LearnerInspector` matrix outputs as :class:`.Matrix` instances + of up to a factor of |nbsp| 50 +- API: return :class:`.LearnerInspector` matrix outputs as :class:`~pytools.data.Matrix` + instances - API: diagonals of feature synergy, redundancy, and association matrices are now - ``nan`` instead of 1.0 -- API: the leaf order of :class:`.LinkageTree` objects generated by + ``nan`` instead of |nbsp| 1.0 +- API: the leaf order of :class:`~pytools.data.LinkageTree` objects generated by ``feature_…_linkage`` methods of :class:`.LearnerInspector` is now the same as the - row and column order of :class:`.Matrix` objects returned by the corresponding - ``feature_…_matrix`` methods of :class:`.LearnerInspector`, minimizing the distance - between adjacent leaves - The old sorting behaviour of FACET 1 can be restored using method - :meth:`.LinkageTree.sort_by_weight` + row and column order of :class:`~pytools.data.Matrix` objects returned by the + corresponding ``feature_…_matrix`` methods of :class:`.LearnerInspector`, minimizing + the distance between adjacent leaves. + The old sorting behaviour of FACET |nbsp| 1.x can be restored using method + :meth:`~pytools.data.LinkageTree.sort_by_weight` ``facet.selection`` ^^^^^^^^^^^^^^^^^^^ -- API: :class:`.ModelSelector` replaces FACET 1 class ``LearnerRanker``, and now - supports any CV searcher that supports `scikit-learn`'s CV search API, including - `scikit-learn`'s native searchers such as :class:`.GridSearchCV` or - :class:`.RandomizedSearchCV` -- API: new classes :class:`.ParameterSpace` and :class:`MultiParameterSpace` offer an - a more convenient and robust mechanism for declaring options or distributions for - hyperparameter tuning +- API: :class:`.LearnerSelector` replaces FACET |nbsp| 1.x class ``LearnerRanker``, and + now supports any CV searcher that supports `scikit-learn`'s CV search API, including + `scikit-learn`'s native searchers such as + :class:`~sklearn.model_selection.GridSearchCV` or + :class:`~sklearn.model_selection.RandomizedSearchCV` +- API: new classes :class:`.ParameterSpace` and :class:`.MultiEstimatorParameterSpace` + offer a more convenient and robust mechanism for declaring options or distributions + for hyperparameter tuning ``facet.simulation`` ^^^^^^^^^^^^^^^^^^^^ @@ -54,18 +68,19 @@ FACET 2.0 ``facet.validation`` ^^^^^^^^^^^^^^^^^^^^ -- API: remove class ``FullSampleValidator`` +- API: removed class ``FullSampleValidator`` Other ^^^^^ -- API: class ``LearnerCrossfit`` is no longer used in FACET 2 and has been removed +- API: class ``LearnerCrossfit`` is no longer needed in FACET |nbsp| 2.0 and has been + removed FACET 1.2 --------- -FACET 1.2 adds support for *sklearndf* 1.2 and *scikit-learn* 0.24. +FACET |nbsp| 1.2 adds support for *sklearndf* |nbsp| 1.2 and *scikit-learn* |nbsp| 0.24. It also introduces the ability to run simulations on a subsample of the data used to fit the underlying crossfit. One example where this can be useful is to use only a recent period of a time series as @@ -75,21 +90,21 @@ the baseline of a simulation. 1.2.2 ~~~~~ -- catch up with FACET 1.1.2 +- catch up with FACET |nbsp| 1.1.2 1.2.1 ~~~~~ - FIX: fix a bug in :class:`.UnivariateProbabilitySimulator` that was introduced in - FACET 1.2.0 -- catch up with FACET 1.1.1 + FACET |nbsp| 1.2.0 +- catch up with FACET |nbsp| 1.1.1 1.2.0 ~~~~~ -- BUILD: added support for *sklearndf* 1.2 and *scikit-learn* 0.24 +- BUILD: added support for *sklearndf* |nbsp| 1.2 and *scikit-learn* |nbsp| 0.24 - API: new optional parameter ``subsample`` in method :meth:`.BaseUnivariateSimulator.simulate_feature` can be used to specify a subsample to be used in the simulation (but simulating using a crossfit based on the full @@ -99,18 +114,20 @@ the baseline of a simulation. FACET 1.1 --------- -FACET 1.1 refines and enhances the association/synergy/redundancy calculations provided -by the :class:`.LearnerInspector`. +FACET |nbsp| 1.1 refines and enhances the association/synergy/redundancy calculations +provided by the :class:`.LearnerInspector`. 1.1.2 ~~~~~ - DOC: use a downloadable dataset in the `getting started` notebook -- FIX: import :mod:`catboost` if present, else create a local module mockup +- FIX: import `catboost `_ if present, else create a local + module mockup - FIX: correctly identify if ``sample_weights`` is undefined when re-fitting a model - on the full dataset in a :class:`.LearnerCrossfit` -- BUILD: relax package dependencies to support any `numpy` version 1.`x` from 1.16 + on the full dataset in a ``LearnerCrossfit`` +- BUILD: relax package dependencies to support any `numpy` version |nbsp| 1.`x` from + |nbsp| 1.16 1.1.1 @@ -134,9 +151,9 @@ by the :class:`.LearnerInspector`. across matrices as an indication of confidence for each calculated value. - API: Method :meth:`.LearnerInspector.shap_plot_data` now returns SHAP values for the positive class of binary classifiers. -- API: Increase efficiency of :class:`.ModelSelector` parallelization by adopting the +- API: Increase efficiency of ``ModelSelector`` parallelization by adopting the new :class:`pytools.parallelization.JobRunner` API provided by :mod:`pytools` -- BUILD: add support for :mod:`shap` 0.38 and 0.39 +- BUILD: add support for :mod:`shap` |nbsp| 0.38 and |nbsp| 0.39 FACET 1.0 @@ -145,8 +162,9 @@ FACET 1.0 1.0.3 ~~~~~ -- FIX: restrict package requirements to *gamma-pytools* 1.0.* and *sklearndf* 1.0.x, - since FACET 1.0 is not compatible with *gamma-pytools* 1.1.* +- FIX: restrict package requirements to *gamma-pytools* |nbsp| 1.0.* and + *sklearndf* |nbsp| 1.0.x, since FACET |nbsp| 1.0 is not compatible with + *gamma-pytools* |nbsp| 1.1.* 1.0.2 ~~~~~ @@ -154,10 +172,10 @@ FACET 1.0 This is a maintenance release focusing on enhancements to the CI/CD pipeline and bug fixes. -- API: add support for :mod:`shap` 0.36 and 0.37 via a new :class:`.BaseExplainer` - stub class +- API: add support for |shap| |nbsp| 0.36 and |nbsp| 0.37 via a new + :class:`.BaseExplainer` stub class - FIX: apply color scheme to the histogram section in :class:`.SimulationMatplotStyle` -- BUILD: add support for :mod:`numpy` 1.20 +- BUILD: add support for :mod:`numpy` |nbsp| 1.20 - BUILD: updates and changes to the CI/CD pipeline diff --git a/azure-pipelines.yml b/azure-pipelines.yml index dfc04378f..727f31eb0 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -7,18 +7,18 @@ pr: - release/* pool: - vmImage: 'Ubuntu-latest' + vmImage: 'Ubuntu-latest' # set the build name name: $[ variables['branchName'] ] # run tests and full conda/tox build matrix every night at 4am schedules: -- cron: "0 4 * * 1-5" - displayName: Nightly full build - branches: - include: - - 1.2.x + - cron: "0 4 * * 1-5" + displayName: Nightly full build + branches: + include: + - 1.2.x resources: repositories: @@ -51,10 +51,10 @@ stages: steps: - task: UsePythonVersion@0 inputs: - versionSpec: '3.8.*' - displayName: 'use Python 3.8' + versionSpec: '3.9' + displayName: 'use Python 3.9' - script: | - python -m pip install isort==5.5.4 + python -m pip install isort~=5.10 python -m isort --check --diff . displayName: 'Run isort' - job: @@ -62,10 +62,10 @@ stages: steps: - task: UsePythonVersion@0 inputs: - versionSpec: '3.8.*' - displayName: 'use Python 3.8' + versionSpec: '3.9' + displayName: 'use Python 3.9' - script: | - python -m pip install black==22.3 + python -m pip install black~=22.8 python -m black --check . displayName: 'Run black' - job: @@ -73,10 +73,10 @@ stages: steps: - task: UsePythonVersion@0 inputs: - versionSpec: '3.8.*' - displayName: 'use Python 3.8' + versionSpec: '3.9' + displayName: 'use Python 3.9' - script: | - python -m pip install flake8==3.9.0 flake8-comprehensions flake8-import-order + python -m pip install flake8~=5.0 flake8-comprehensions~=3.10 python -m flake8 --config tox.ini -v . displayName: 'Run flake8' - job: @@ -84,10 +84,10 @@ stages: steps: - task: UsePythonVersion@0 inputs: - versionSpec: '3.8.*' - displayName: 'use Python 3.8' + versionSpec: '3.9' + displayName: 'use Python 3.9' - script: | - python -m pip install mypy~=0.931 numpy~=1.21 "gamma-pytools>=2.0.dev8,<3a" "sklearndf>=2.0.dev3,<3a" + python -m pip install mypy~=0.971 numpy~=1.22 gamma-pytools~=2.0,!=2.0.0 sklearndf~=2.0 python -m mypy src displayName: 'Run mypy' @@ -124,7 +124,6 @@ stages: set +x echo "##vso[task.setvariable variable=conda_build_config_changed;isOutput=true]$build_changed" - - stage: displayName: 'Unit tests' dependsOn: 'detect_build_config_changes' @@ -132,53 +131,56 @@ stages: conda_build_config_changed: $[ stageDependencies.detect_build_config_changes.checkout_and_diff.outputs['diff.conda_build_config_changed'] ] jobs: - - job: - displayName: 'pytest @ develop environment' - condition: ne(variables.source_is_release_branch, 'True') + - job: + displayName: 'pytest @ develop environment' + condition: ne(variables.source_is_release_branch, 'True') - pool: + pool: vmImage: 'ubuntu-latest' - steps: - - task: UsePythonVersion@0 - inputs: - versionSpec: '3.8.*' - displayName: 'use Python 3.8' + steps: + - task: UsePythonVersion@0 + inputs: + versionSpec: '3.8' + displayName: 'use Python 3.8' + + - checkout: self - - checkout: self + - script: dir $(Build.SourcesDirectory) - - script: dir $(Build.SourcesDirectory) + - task: Bash@3 + inputs: + targetType: 'inline' + script: | + set -eux + eval "$(conda shell.bash hook)" - - task: Bash@3 - inputs: - targetType: 'inline' - script: | - set -eux - eval "$(conda shell.bash hook)" - export PYTHONPATH=$(System.DefaultWorkingDirectory)/src/ - export RUN_PACKAGE_VERSION_TEST=$(project_name) - conda env create - conda activate $(project_name)-develop - pytest \ - --cov $(project_name) \ - --cov-config "tox.ini" \ - --cov-report=xml:coverage.xml --cov-report=html:htmlcov \ - --junitxml pytest.xml \ - . -s - displayName: 'pytest' - - - task: PublishTestResults@2 - condition: succeededOrFailed() - inputs: - testResultsFiles: '$(System.DefaultWorkingDirectory)/*.xml' - searchFolder: '$(System.DefaultWorkingDirectory)/' - testRunTitle: 'Publish test results' - - - task: PublishCodeCoverageResults@1 - inputs: - codeCoverageTool: Cobertura - summaryFileLocation: '$(System.DefaultWorkingDirectory)/coverage.xml' - reportDirectory: '$(System.DefaultWorkingDirectory)/htmlcov' + conda env create + conda activate $(project_name)-develop + + export PYTHONPATH=$(System.DefaultWorkingDirectory)/src/ + export RUN_PACKAGE_VERSION_TEST=$(project_name) + + pytest \ + --cov $(project_name) \ + --cov-config "tox.ini" \ + --cov-report=xml:coverage.xml --cov-report=html:htmlcov \ + --junitxml pytest.xml \ + . -s + displayName: 'pytest' + + - task: PublishTestResults@2 + condition: succeededOrFailed() + inputs: + testResultsFiles: '$(System.DefaultWorkingDirectory)/*.xml' + searchFolder: '$(System.DefaultWorkingDirectory)/' + testRunTitle: 'Publish test results' + + - task: PublishCodeCoverageResults@1 + inputs: + codeCoverageTool: Cobertura + summaryFileLocation: '$(System.DefaultWorkingDirectory)/coverage.xml' + reportDirectory: '$(System.DefaultWorkingDirectory)/htmlcov' # conda env & tox build test # testing matrix of python & sklearn versions @@ -191,170 +193,170 @@ stages: conda_build_config_changed: $[ stageDependencies.detect_build_config_changes.checkout_and_diff.outputs['diff.conda_build_config_changed'] ] jobs: - - job: - displayName: 'essential' - condition: > - and( - ne(variables.source_is_release_branch, 'True'), - ne(variables.source_is_develop_branch, 'True'), - ne(variables.is_scheduled, 'True'), - ne(stageDependencies.detect_build_config_changes.checkout_and_diff.outputs['diff.conda_build_config_changed'], '0') - ) - - pool: + - job: + displayName: 'essential' + condition: > + and( + ne(variables.source_is_release_branch, 'True'), + ne(variables.source_is_develop_branch, 'True'), + ne(variables.is_scheduled, 'True'), + ne(stageDependencies.detect_build_config_changes.checkout_and_diff.outputs['diff.conda_build_config_changed'], '0') + ) + + pool: vmImage: 'ubuntu-latest' - strategy: - matrix: - # We run three tests to cover conda/tox and maximum/minimum. - # This comprises only one minimum dependencies test for tox, - # which is usually faster than conda. - maximum_dependencies_conda: - FACET_V_PYTHON_BUILD: '=3.9.*' - BUILD_SYSTEM: 'conda' - PKG_DEPENDENCIES: 'max' - minimum_dependencies_tox: - FACET_V_PYTHON_BUILD: '=3.7.*' - BUILD_SYSTEM: 'tox' - PKG_DEPENDENCIES: 'min' - maximum_dependencies_tox: - FACET_V_PYTHON_BUILD: '=3.9.*' - BUILD_SYSTEM: 'tox' - PKG_DEPENDENCIES: 'max' - - steps: - - task: UsePythonVersion@0 - inputs: - versionSpec: '$(FACET_V_PYTHON_BUILD)' - displayName: 'Use Python $(FACET_V_PYTHON_BUILD)' - - - checkout: pytools - - checkout: self - - - script: dir $(Build.SourcesDirectory) - - - script: | - conda install -y -c anaconda conda-build~=3.21 conda-verify toml=0.10.* flit=3.0.* packaging~=20.9 - displayName: 'Install conda-build, flit, toml' - condition: eq(variables['BUILD_SYSTEM'], 'conda') - - - script: | - python -m pip install "toml==0.10.*" - python -m pip install "flit==3.0.*" - flit --version - python -m pip install "tox==3.20.*" - tox --version - displayName: 'Install tox, flit & toml' - condition: eq(variables['BUILD_SYSTEM'], 'tox') - - - task: Bash@3 - inputs: - targetType: 'inline' - script: | - set -eux - if [ "$BUILD_SYSTEM" = "conda" ] ; then eval "$(conda shell.bash hook)" ; fi - export RUN_PACKAGE_VERSION_TEST=$(project_name) - - cd $(Build.SourcesDirectory)/$(project_root) - ./make.py $(project_name) $(BUILD_SYSTEM) $(PKG_DEPENDENCIES) - displayName: "build & test" - - - task: CopyFiles@2 - inputs: - sourceFolder: $(System.DefaultWorkingDirectory)/$(project_root)/dist - targetFolder: $(Build.ArtifactStagingDirectory) - - - task: PublishBuildArtifacts@1 - inputs: - pathtoPublish: $(Build.ArtifactStagingDirectory) - artifactName: $(BUILD_SYSTEM)_$(PKG_DEPENDENCIES) - publishLocation: Container - - - job: - displayName: 'matrix' - condition: > - or( - eq(variables.source_is_develop_branch, 'True'), - eq(variables.source_is_release_branch, 'True'), - eq(variables.is_scheduled, 'True') - ) - - pool: - vmImage: 'ubuntu-latest' - strategy: - matrix: - default_dependencies_conda: - FACET_V_PYTHON_BUILD: '=3.8.*' - BUILD_SYSTEM: 'conda' - PKG_DEPENDENCIES: 'default' - minimum_dependencies_conda: - FACET_V_PYTHON_BUILD: '=3.7.*' - BUILD_SYSTEM: 'conda' - PKG_DEPENDENCIES: 'min' - maximum_dependencies_conda: - FACET_V_PYTHON_BUILD: '=3.9.*' - BUILD_SYSTEM: 'conda' - PKG_DEPENDENCIES: 'max' - default_dependencies_tox: - FACET_V_PYTHON_BUILD: '=3.8.*' - BUILD_SYSTEM: 'tox' - PKG_DEPENDENCIES: 'default' - minimum_dependencies_tox: - FACET_V_PYTHON_BUILD: '=3.7.*' - BUILD_SYSTEM: 'tox' - PKG_DEPENDENCIES: 'min' - maximum_dependencies_tox: - FACET_V_PYTHON_BUILD: '=3.9.*' - BUILD_SYSTEM: 'tox' - PKG_DEPENDENCIES: 'max' - - - steps: - - task: UsePythonVersion@0 - inputs: - versionSpec: '$(FACET_V_PYTHON_BUILD)' - displayName: 'Use Python $(FACET_V_PYTHON_BUILD)' - - - checkout: pytools - - checkout: self - - - script: dir $(Build.SourcesDirectory) - - - script: | - conda install -y -c anaconda conda-build~=3.21 conda-verify toml=0.10.* flit=3.0.* packaging~=20.9 - displayName: 'Install conda-build, flit, toml' - condition: eq(variables['BUILD_SYSTEM'], 'conda') - - - script: | - python -m pip install "toml==0.10.*" - python -m pip install "flit==3.0.*" - flit --version - python -m pip install "tox==3.20.*" - tox --version - displayName: 'Install tox, flit & toml' - condition: eq(variables['BUILD_SYSTEM'], 'tox') - - - task: Bash@3 - inputs: - targetType: 'inline' - script: | - set -eux - if [ "$BUILD_SYSTEM" = "conda" ] ; then eval "$(conda shell.bash hook)" ; fi - export RUN_PACKAGE_VERSION_TEST=$(project_name) + strategy: + matrix: + # We run three tests to cover conda/tox and maximum/minimum. + # This comprises only one minimum dependencies test for tox, + # which is usually faster than conda. + maximum_dependencies_conda: + FACET_V_PYTHON_BUILD: '=3.9' + BUILD_SYSTEM: 'conda' + PKG_DEPENDENCIES: 'max' + minimum_dependencies_tox: + FACET_V_PYTHON_BUILD: '=3.7' + BUILD_SYSTEM: 'tox' + PKG_DEPENDENCIES: 'min' + maximum_dependencies_tox: + FACET_V_PYTHON_BUILD: '=3.9' + BUILD_SYSTEM: 'tox' + PKG_DEPENDENCIES: 'max' + + steps: + - task: UsePythonVersion@0 + inputs: + versionSpec: '$(FACET_V_PYTHON_BUILD)' + displayName: 'Use Python $(FACET_V_PYTHON_BUILD)' - cd $(Build.SourcesDirectory)/$(project_root) - ./make.py $(project_name) $(BUILD_SYSTEM) $(PKG_DEPENDENCIES) - displayName: "build & test" + - checkout: pytools + - checkout: self - - task: CopyFiles@2 - inputs: - sourceFolder: $(System.DefaultWorkingDirectory)/$(project_root)/dist - targetFolder: $(Build.ArtifactStagingDirectory) + - script: dir $(Build.SourcesDirectory) + + - script: | + conda install -y -c anaconda conda-build~=3.21 conda-verify~=3.4 toml~=0.10 flit~=3.6 packaging~=20.9 + displayName: 'Install conda-build, flit, toml' + condition: eq(variables['BUILD_SYSTEM'], 'conda') - - task: PublishBuildArtifacts@1 - inputs: - pathtoPublish: $(Build.ArtifactStagingDirectory) - artifactName: $(BUILD_SYSTEM)_$(PKG_DEPENDENCIES) - publishLocation: Container + - script: | + python -m pip install "toml~=0.10" + python -m pip install "flit~=3.7" + flit --version + python -m pip install "tox~=3.25" + tox --version + displayName: 'Install tox, flit & toml' + condition: eq(variables['BUILD_SYSTEM'], 'tox') + + - task: Bash@3 + inputs: + targetType: 'inline' + script: | + set -eux + if [ "$BUILD_SYSTEM" = "conda" ] ; then eval "$(conda shell.bash hook)" ; fi + export RUN_PACKAGE_VERSION_TEST=$(project_name) + + cd $(Build.SourcesDirectory)/$(project_root) + ./make.py $(project_name) $(BUILD_SYSTEM) $(PKG_DEPENDENCIES) + displayName: "build & test" + + - task: CopyFiles@2 + inputs: + sourceFolder: $(System.DefaultWorkingDirectory)/$(project_root)/dist + targetFolder: $(Build.ArtifactStagingDirectory) + + - task: PublishBuildArtifacts@1 + inputs: + pathtoPublish: $(Build.ArtifactStagingDirectory) + artifactName: $(BUILD_SYSTEM)_$(PKG_DEPENDENCIES) + publishLocation: Container + + - job: + displayName: 'matrix' + condition: > + or( + eq(variables.source_is_develop_branch, 'True'), + eq(variables.source_is_release_branch, 'True'), + eq(variables.is_scheduled, 'True') + ) + + pool: + vmImage: 'ubuntu-latest' + strategy: + matrix: + default_dependencies_conda: + FACET_V_PYTHON_BUILD: '=3.8' + BUILD_SYSTEM: 'conda' + PKG_DEPENDENCIES: 'default' + minimum_dependencies_conda: + FACET_V_PYTHON_BUILD: '=3.7' + BUILD_SYSTEM: 'conda' + PKG_DEPENDENCIES: 'min' + maximum_dependencies_conda: + FACET_V_PYTHON_BUILD: '=3.9' + BUILD_SYSTEM: 'conda' + PKG_DEPENDENCIES: 'max' + default_dependencies_tox: + FACET_V_PYTHON_BUILD: '=3.8' + BUILD_SYSTEM: 'tox' + PKG_DEPENDENCIES: 'default' + minimum_dependencies_tox: + FACET_V_PYTHON_BUILD: '=3.7' + BUILD_SYSTEM: 'tox' + PKG_DEPENDENCIES: 'min' + maximum_dependencies_tox: + FACET_V_PYTHON_BUILD: '=3.9' + BUILD_SYSTEM: 'tox' + PKG_DEPENDENCIES: 'max' + + + steps: + - task: UsePythonVersion@0 + inputs: + versionSpec: '$(FACET_V_PYTHON_BUILD)' + displayName: 'Use Python $(FACET_V_PYTHON_BUILD)' + + - checkout: pytools + - checkout: self + + - script: dir $(Build.SourcesDirectory) + + - script: | + conda install -y -c anaconda conda-build~=3.21 conda-verify~=3.4 toml~=0.10 flit~=3.6 packaging~=20.9 + displayName: 'Install conda-build, flit, toml' + condition: eq(variables['BUILD_SYSTEM'], 'conda') + + - script: | + python -m pip install "toml==0.10.*" + python -m pip install "flit==3.0.*" + flit --version + python -m pip install "tox==3.20.*" + tox --version + displayName: 'Install tox, flit & toml' + condition: eq(variables['BUILD_SYSTEM'], 'tox') + + - task: Bash@3 + inputs: + targetType: 'inline' + script: | + set -eux + if [ "$BUILD_SYSTEM" = "conda" ] ; then eval "$(conda shell.bash hook)" ; fi + export RUN_PACKAGE_VERSION_TEST=$(project_name) + + cd $(Build.SourcesDirectory)/$(project_root) + ./make.py $(project_name) $(BUILD_SYSTEM) $(PKG_DEPENDENCIES) + displayName: "build & test" + + - task: CopyFiles@2 + inputs: + sourceFolder: $(System.DefaultWorkingDirectory)/$(project_root)/dist + targetFolder: $(Build.ArtifactStagingDirectory) + + - task: PublishBuildArtifacts@1 + inputs: + pathtoPublish: $(Build.ArtifactStagingDirectory) + artifactName: $(BUILD_SYSTEM)_$(PKG_DEPENDENCIES) + publishLocation: Container # apply veracode static code analysis during nightly build - stage: veracode_check @@ -407,7 +409,7 @@ stages: steps: - task: UsePythonVersion@0 inputs: - versionSpec: '3.8.*' + versionSpec: '3.8' displayName: 'use Python 3.8' - checkout: pytools @@ -458,7 +460,7 @@ stages: displayName: 'Release' dependsOn: check_release variables: - - group: artifact_publication + - group: artifact_publication jobs: - job: @@ -472,7 +474,7 @@ stages: steps: - task: UsePythonVersion@0 inputs: - versionSpec: '3.8.*' + versionSpec: '3.8' displayName: 'use Python 3.8' - checkout: pytools @@ -537,8 +539,8 @@ stages: displayName: 'Publish to PyPi' condition: eq(variables['source_is_release_branch'], 'True') env: - FLIT_PASSWORD: $(pypi_pw) - FLIT_USERNAME: $(pypi_user) + FLIT_PASSWORD: $(pypi_facet_uploads) + FLIT_USERNAME: __token__ - task: GitHubRelease@1 condition: > @@ -581,112 +583,146 @@ stages: displayName: 'Docs' variables: - - group: github_ssh + - group: github_ssh jobs: - - job: - displayName: 'Build and publish docs' - condition: > - or( - eq(variables.source_is_release_branch, 'True'), - eq(variables.source_is_develop_branch, 'True') - ) - + - job: + displayName: 'Build and publish docs' + condition: > + or( + eq(variables.source_is_release_branch, 'True'), + eq(variables.source_is_develop_branch, 'True'), + eq(variables['Build.Reason'], 'Manual') + ) - pool: + pool: vmImage: 'ubuntu-latest' - steps: - - task: UsePythonVersion@0 - inputs: - versionSpec: '3.8.*' - displayName: 'use Python 3.8' + steps: + - task: UsePythonVersion@0 + inputs: + versionSpec: '3.8' + displayName: 'use Python 3.8' - - task: InstallSSHKey@0 - inputs: - knownHostsEntry: $(knownHostsEntry) - sshPublicKey: $(sshPublicKey) - sshKeySecureFile: 'deploy_docs_$(project_name)' - displayName: 'Install the deploy SSH key' + - task: InstallSSHKey@0 + inputs: + knownHostsEntry: $(knownHostsEntry) + sshPublicKey: $(sshPublicKey) + sshKeySecureFile: 'deploy_docs_$(project_name)' + displayName: 'Install the deploy SSH key' - - checkout: pytools - - checkout: self + - checkout: pytools + - checkout: self - - script: dir $(Build.SourcesDirectory) + - script: dir $(Build.SourcesDirectory) - - task: Bash@3 - inputs: - targetType: 'inline' - script: | - set -eux - cd $(System.DefaultWorkingDirectory)/$(project_root) - git checkout --track origin/github-pages - mkdir -p docs - sudo apt-get install tree - echo "Current docs contents:" - tree docs - mkdir $(Build.ArtifactStagingDirectory)/old_docs - cp -r docs $(Build.ArtifactStagingDirectory)/old_docs - displayName: 'Save current docs version' - - - task: Bash@3 - inputs: - targetType: 'inline' - script: | - set -eux - eval "$(conda shell.bash hook)" - cd $(System.DefaultWorkingDirectory)/$(project_root) - echo "Checking out $(branchName)" - git checkout $(branchName) - git status - export PYTHONPATH=$(System.DefaultWorkingDirectory)/$(project_root)/src/ - conda env create -f environment.yml - conda activate $(project_name)-develop - cd $(System.DefaultWorkingDirectory)/$(project_root)/ - python sphinx/make.py html - echo "Current docs contents:" - tree docs - displayName: 'Build new docs version' - - - task: Bash@3 - inputs: - targetType: 'inline' - script: | - set -eux - eval "$(conda shell.bash hook)" - cp -r $(Build.ArtifactStagingDirectory)/old_docs/docs . - echo "Current docs contents:" - tree docs - mkdir -p $(System.DefaultWorkingDirectory)/$(project_root)/sphinx/build/ - cp -R docs/docs-version $(System.DefaultWorkingDirectory)/$(project_root)/sphinx/build/ - echo "Building sphinx docs" - conda activate $(project_name)-develop - cd $(System.DefaultWorkingDirectory)/$(project_root) - python sphinx/make.py prepare_docs_deployment - echo "Current docs contents:" - tree docs - mkdir $(Build.ArtifactStagingDirectory)/new_docs - mv docs $(Build.ArtifactStagingDirectory)/new_docs - displayName: 'Update saved docs' - - - task: Bash@3 - condition: eq(variables['source_is_release_branch'], 'True') - inputs: - targetType: 'inline' - script: | - set -eux - cd $(System.DefaultWorkingDirectory)/$(project_root) - echo "Adjusting git credentials" - git config --global user.name "Azure Pipelines" - git config --global user.email "azuredevops@microsoft.com" - git config --global url.ssh://git@github.com/.insteadOf https://github.com/ - git checkout github-pages - cp -r $(Build.ArtifactStagingDirectory)/new_docs/docs . - git status - git add docs - echo "Staged docs HTML build" - git status - git commit -m "Publish GitHub Pages [skip ci]" - echo "Committed to local branch github-pages" - git push --set-upstream origin github-pages - displayName: 'Publish docs' + - task: Bash@3 + inputs: + targetType: 'inline' + script: | + set -eux + + cd $(System.DefaultWorkingDirectory)/$(project_root) + echo "Checking out github-pages" + git checkout --track origin/github-pages + + # make sure we have a docs directory + mkdir -p docs/docs-version + + echo "Current documentation contents:" + ls docs/docs-version + + # copy the current documentation versions to the staging area + cp -r docs/docs-version $(Build.ArtifactStagingDirectory)/docs-version.bak + + displayName: 'Retrieve current documentation versions from github-pages' + + - task: Bash@3 + inputs: + targetType: 'inline' + script: | + set -eux + eval "$(conda shell.bash hook)" + + cd $(System.DefaultWorkingDirectory)/$(project_root) + echo "Checking out $(branchName)" + git checkout $(branchName) + + conda env create + conda activate $(project_name)-develop + + export PYTHONPATH=$(System.DefaultWorkingDirectory)/$(project_root)/src/ + + python sphinx/make.py html + + displayName: 'Build latest documentation' + + - task: Bash@3 + inputs: + targetType: 'inline' + script: | + set -eux + eval "$(conda shell.bash hook)" + + # install the tree utility + sudo apt-get install tree + + cd $(System.DefaultWorkingDirectory)/$(project_root) + + echo "Restoring previous documentation to the docs directory" + mkdir -p docs + mv $(Build.ArtifactStagingDirectory)/docs-version.bak docs/docs-version + ls docs/docs-version + + mkdir -p $(System.DefaultWorkingDirectory)/$(project_root)/sphinx/build/ + + conda activate $(project_name)-develop + python sphinx/make.py prepare_docs_deployment + + echo "Current docs contents:" + tree docs + mv docs $(Build.ArtifactStagingDirectory)/docs + displayName: 'Merge previous and latest docs' + + - task: ArchiveFiles@2 + inputs: + rootFolderOrFile: $(Build.ArtifactStagingDirectory)/docs + includeRootFolder: false + archiveType: 'zip' # Options: zip, 7z, tar, wim + archiveFile: $(Build.ArtifactStagingDirectory)/docs.zip + replaceExistingArchive: true + verbose: false + quiet: false + + - task: PublishBuildArtifacts@1 + inputs: + pathtoPublish: $(Build.ArtifactStagingDirectory)/docs.zip + artifactName: $(project_name)_docs + publishLocation: Container + + displayName: 'Publish docs artifact' + + - task: Bash@3 + condition: eq(variables['source_is_release_branch'], 'True') + inputs: + targetType: 'inline' + script: | + set -eux + cd $(System.DefaultWorkingDirectory)/$(project_root) + + echo "Adjusting git credentials" + git config --global user.name "Azure Pipelines" + git config --global user.email "azuredevops@microsoft.com" + git config --global url.ssh://git@github.com/.insteadOf https://github.com/ + git checkout github-pages + + rm -rf docs + mv $(Build.ArtifactStagingDirectory)/docs . + git add docs + + git status + git commit -m "Publish GitHub Pages [skip ci]" + + git push --set-upstream origin github-pages + + displayName: 'Publish docs to branch github-pages' diff --git a/condabuild/meta.yaml b/condabuild/meta.yaml index c6edcb483..39aecf0b5 100644 --- a/condabuild/meta.yaml +++ b/condabuild/meta.yaml @@ -35,7 +35,7 @@ test: - facet.validation - facet.simulation requires: - - pytest=5.2.* + - pytest ~= 7.1 # additional requirements of sklearndf - boruta_py {{ environ.get('FACET_V_BORUTA') }} - lightgbm {{ environ.get('FACET_V_LIGHTGBM') }} @@ -45,6 +45,7 @@ test: - typing_inspect {{ environ.get('FACET_V_TYPING_INSPECT') }} # additional requirements of shap - ipython {{ environ.get('FACET_V_IPYTHON') }} + - numba {{ environ.get('FACET_V_NUMBA') }} commands: - conda list - python -c 'import facet; diff --git a/environment.yml b/environment.yml index 84fa9318d..6bb97c2f9 100644 --- a/environment.yml +++ b/environment.yml @@ -5,42 +5,41 @@ channels: dependencies: # run - boruta_py ~= 0.3 - - gamma-pytools >= 2.0.dev8, <3a + - gamma-pytools ~= 2.0, != 2.0.0 - joblib ~= 1.1 - lightgbm ~= 3.3 - matplotlib ~= 3.5 - numpy ~= 1.22 - pandas ~= 1.4 - - python ~= 3.8.12 - - scikit-learn ~= 1.0.2 + - python ~= 3.9 + - scikit-learn ~= 1.1 - scipy ~= 1.8 - - shap ~=0.40 - - sklearndf >= 2.0.dev3, < 3a + - shap ~= 0.41 + - sklearndf ~= 2.0 # build/test - - black ~= 22.3 - conda-build ~= 3.21 - conda-verify ~= 3.1 - docutils ~= 0.17 - flit ~= 3.0 - - isort ~= 5.10 - jinja2 ~= 2.11 - - markupsafe < 2.1 # markupsafe 2.1 breaks support for jinja2 + - markupsafe ~= 2.0.1 # markupsafe 2.1 breaks support for jinja2 - m2r ~= 0.2 - - mypy ~= 0.931 - pluggy ~= 0.13 - - pre-commit ~= 2.17 - - pydata-sphinx-theme ~= 0.7 - - pytest ~= 5.4 + - pre-commit ~= 2.20 + - pytest ~= 7.1 - pytest-cov ~= 2.12 - pyyaml ~= 5.4 - - sphinx ~= 4.4 - - sphinx-autodoc-typehints ~= 1.12 - toml ~= 0.10 - tox ~= 3.24 - yaml ~= 0.2 + # sphinx + - nbsphinx ~= 0.8.9 + - sphinx ~= 4.5 + - sphinx-autodoc-typehints ~= 1.19 + - pydata-sphinx-theme ~= 0.8.1 # notebooks + - ipywidgets ~= 8.0 - jupyterlab ~= 3.2 - - nbclassic ~= 0.3 - - nbsphinx ~= 0.8.8 - openpyxl ~= 3.0 - seaborn ~= 0.11 + - tableone ~= 0.7 diff --git a/mypy.ini b/mypy.ini index 1674727fc..3dddd5145 100644 --- a/mypy.ini +++ b/mypy.ini @@ -1,6 +1,6 @@ [mypy] show_error_codes = True -allow_redefinition = True +strict = True [mypy-catboost.*] ; TODO remove once PEP 561 is supported diff --git a/pyproject.toml b/pyproject.toml index cef47700c..32194be6b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,5 @@ [build-system] -requires = [ - "flit_core >=2,<4", -] +requires = ["flit_core >=2,<4"] build-backend = "flit_core.buildapi" [tool.flit.sdist] @@ -16,15 +14,14 @@ dist-name = "gamma-facet" license = "Apache Software License v2.0" requires = [ - # direct requirements of gamma-facet - "gamma-pytools ~=2.0.dev8", + "gamma-pytools ~=2.0", "matplotlib ~=3.0", - "numpy >=1.17,<2a", + "numpy >=1.21,<2a", # cannot use ~= due to conda bug "packaging >=20", - "pandas >=0.24,<2a", + "pandas ~=1.0", "scipy ~=1.2", - "shap >=0.34,<0.41a", - "sklearndf ~=2.0.dev3", + "shap >=0.34,<0.42a", + "sklearndf ~=2.0", ] requires-python = ">=3.7,<4a" @@ -47,19 +44,20 @@ classifiers = [ [tool.flit.metadata.requires-extra] testing = [ - "pytest ~= 5.4", + "pytest ~= 7.1", "pytest-cov ~= 2.12", "flake8 ~= 3.8", "flake8-comprehensions ~= 3.2", "isort ~= 5.10", "lightgbm ~=3.0.0", + "xgboost ~= 1.5", ] docs = [ - "sphinx ~= 4.4", - "sphinx-autodoc-typehints ~= 1.12", - "pydata-sphinx-theme ~= 0.7", + "sphinx ~= 4.5", + "sphinx-autodoc-typehints ~= 1.19", + "pydata-sphinx-theme ~= 0.8.1", "jinja2 ~= 2.11", - "nbsphinx ~= 0.8.8", + "nbsphinx ~= 0.8.9", "jupyter == 1", "docutils ~= 0.17", "xlrd ~= 1.2", @@ -70,47 +68,55 @@ docs = [ Documentation = "https://bcg-gamma.github.io/facet/" Repository = "https://github.com/BCG-Gamma/facet" +[build] +# comma-separated list of packages to be built from source in pip min builds +no-binary.min = ["matplotlib", "shap"] + [build.matrix.min] # direct requirements of gamma-facet -gamma-pytools = "~=2.0.dev8" +gamma-pytools = "~=2.0.4" matplotlib = "~=3.0.3" -numpy = "==1.17.5" +numpy = "==1.21.6" # cannot use ~= due to conda bug packaging = "~=20.9" -pandas = "~=0.24.2" +pandas = "~=1.0.5" python = ">=3.7.12,<3.8a" # cannot use ~= due to conda bug -scipy = "~=1.2.1" +scipy = "~=1.4.1" shap = "~=0.34.0" -sklearndf = "~=2.0.dev3" +sklearndf = "~=2.0.1" # additional minimum requirements of sklearndf boruta = "~=0.3.0" lightgbm = "~=3.0.0" scikit-learn = "~=0.21.3" +xgboost = "~=1.5" # additional minimum requirements of gamma-pytools joblib = "~=0.14.1" typing_inspect = "~=0.4.0" # additional minimum requirements of shap ipython = "==7.0" +numba = "~=0.55" # required to support numpy 1.21 [build.matrix.max] # direct requirements of gamma-facet -gamma-pytools = ">=2.0.dev8,<3a" +gamma-pytools = "~=2.0" matplotlib = "~=3.5" -numpy = ">=1.22,<2a" -packaging = ">=20.9" +numpy = ">=1.22,<2a" # cannot use ~= due to conda bug +packaging = ">=20" pandas = "~=1.4" -python = ">=3.8.10,<4a" # cannot use ~= due to conda bug +python = ">=3.9,<4a" # cannot use ~= due to conda bug scipy = "~=1.8" -shap = "~=0.40.0" -sklearndf = ">=2.0.dev3,<3a" +shap = "~=0.41" +sklearndf = "~=2.1" # additional maximum requirements of sklearndf boruta = "~=0.3" lightgbm = "~=3.3" -scikit-learn = "~=1.0.2" +scikit-learn = "~=1.1" +xgboost = "~=1.5" # additional maximum requirements of gamma-pytools joblib = "~=1.1" typing_inspect = "~=0.7" # additional maximum requirements of shap ipython = ">=7" +numba = ">=0.55.2" # required to support numpy 1.22 [tool.black] # quiet = "True" diff --git a/sphinx/.gitignore b/sphinx/.gitignore new file mode 100644 index 000000000..7003f63aa --- /dev/null +++ b/sphinx/.gitignore @@ -0,0 +1,3 @@ +base +source/_generated +source/apidoc diff --git a/sphinx/auxiliary/Diabetes_getting_started_example.ipynb b/sphinx/auxiliary/Diabetes_getting_started_example.ipynb index 0c2bc317b..9881faa04 100644 --- a/sphinx/auxiliary/Diabetes_getting_started_example.ipynb +++ b/sphinx/auxiliary/Diabetes_getting_started_example.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "" + "" ] }, { @@ -62,20 +62,24 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "# Pipelining & Model Ranking\n", "\n", "To demonstrate the model inspection capability of FACET, we first create a pipeline to fit a learner. In this simple example we use the [diabetes dataset](https://web.stanford.edu/~hastie/Papers/LARS/diabetes.data) which contains age, sex, BMI and blood pressure along with 6 blood serum measurements as features. This dataset was used in this\n", "[publication](https://statweb.stanford.edu/~tibs/ftp/lars.pdf). A transformed version of this dataset is also available on scikit-learn [here](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset).\n", "\n", - "In this quickstart we will train a Random Forest regressor using 10 repeated 5-fold CV to predict disease progression after one year. With the use of *sklearndf* we can create a *pandas* DataFrame compatible workflow. However, FACET provides additional enhancements to keep track of our feature matrix and target vector using a sample object (`Sample`) and easily compare hyperparameter configurations and even multiple learners with the `LearnerRanker`." + "In this quickstart we will train a Random Forest regressor using 10 repeated 5-fold CV to predict disease progression after one year. With the use of *sklearndf* we can create a *pandas* DataFrame compatible workflow. However, FACET provides additional enhancements to keep track of our feature matrix and target vector using a sample object (`Sample`) and easily compare hyperparameter configurations and even multiple learners with the `LearnerSelector`." ] }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, + "execution_count": 3, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -93,216 +97,193 @@ " .dataframe thead tr th {\n", " text-align: left;\n", " }\n", - "\n", - " .dataframe thead tr:last-of-type th {\n", - " text-align: right;\n", - " }\n", "\n", "\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
ranking_scorer2_scoreregressorscoreparamtime
testregressorfitscore
rankmeanstdtypemin_samples_leafmax_depth
rankmin_samples_leafmeanstdmeanstd
00.3153740.4432880.063957RandomForestRegressorDF115
10.315305710.4436650.064180RandomForestRegressorDF110.0635356111.4186090.1102160.0870160.016462
20.3134330.4417630.064165RandomForestRegressorDF420.4432880.06331451141.5232470.1331200.0914950.015715
30.313297630.4422630.064483RandomForestRegressorDF80.063835681.4055020.0936910.0827700.011123
40.3131860.4420630.064439RandomForestRegressorDF840.4421840.06391961551.4680610.1518610.0846170.017360
50.3130480.4421840.064568RandomForestRegressorDF50.4420630.06379151561.2832160.0477140.0793140.007728
60.312822360.4418130.064495RandomForestRegressorDF80.063847581.3624110.0730240.0807900.013636
70.3125390.4409570.064209RandomForestRegressorDF15170.4417630.0635204111.2847280.1031980.0841570.016332
80.311975080.4412380.064631RandomForestRegressorDF0.063982481.3573040.0770020.0824230.007797
290.4409570.0635644151.2558230.0573880.0789600.010096
\n", "" ], "text/plain": [ - " ranking_score r2_score regressor \\\n", - " mean std type \n", - "rank \n", - "0 0.315374 0.443288 0.063957 RandomForestRegressorDF \n", - "1 0.315305 0.443665 0.064180 RandomForestRegressorDF \n", - "2 0.313433 0.441763 0.064165 RandomForestRegressorDF \n", - "3 0.313297 0.442263 0.064483 RandomForestRegressorDF \n", - "4 0.313186 0.442063 0.064439 RandomForestRegressorDF \n", - "5 0.313048 0.442184 0.064568 RandomForestRegressorDF \n", - "6 0.312822 0.441813 0.064495 RandomForestRegressorDF \n", - "7 0.312539 0.440957 0.064209 RandomForestRegressorDF \n", - "8 0.311975 0.441238 0.064631 RandomForestRegressorDF \n", + " score param time \\\n", + " test regressor fit \n", + " rank mean std max_depth min_samples_leaf mean std \n", + "7 1 0.443665 0.063535 6 11 1.418609 0.110216 \n", + "4 2 0.443288 0.063314 5 11 1.523247 0.133120 \n", + "6 3 0.442263 0.063835 6 8 1.405502 0.093691 \n", + "8 4 0.442184 0.063919 6 15 1.468061 0.151861 \n", + "5 5 0.442063 0.063791 5 15 1.283216 0.047714 \n", + "3 6 0.441813 0.063847 5 8 1.362411 0.073024 \n", + "1 7 0.441763 0.063520 4 11 1.284728 0.103198 \n", + "0 8 0.441238 0.063982 4 8 1.357304 0.077002 \n", + "2 9 0.440957 0.063564 4 15 1.255823 0.057388 \n", "\n", - " \n", - " min_samples_leaf max_depth \n", - "rank \n", - "0 11 5 \n", - "1 11 6 \n", - "2 11 4 \n", - "3 8 6 \n", - "4 15 5 \n", - "5 15 6 \n", - "6 8 5 \n", - "7 15 4 \n", - "8 8 4 " + " \n", + " score \n", + " mean std \n", + "7 0.087016 0.016462 \n", + "4 0.091495 0.015715 \n", + "6 0.082770 0.011123 \n", + "8 0.084617 0.017360 \n", + "5 0.079314 0.007728 \n", + "3 0.080790 0.013636 \n", + "1 0.084157 0.016332 \n", + "0 0.082423 0.007797 \n", + "2 0.078960 0.010096 " ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# standard imports\n", - "import pandas as pd\n", - "from sklearn.model_selection import RepeatedKFold\n", - "\n", - "# some helpful imports from sklearndf\n", - "from sklearndf.pipeline import RegressorPipelineDF\n", - "from sklearndf.regression import RandomForestRegressorDF\n", - "\n", - "# relevant FACET imports\n", - "from facet.data import Sample\n", - "from facet.selection import LearnerRanker, LearnerGrid\n", - "\n", - "# declaring url with data\n", - "data_url = 'https://web.stanford.edu/~hastie/Papers/LARS/diabetes.data'\n", - "\n", - "#importing data from url\n", - "diabetes_df = pd.read_csv(data_url, delimiter='\\t').rename(\n", - " # renaming columns for better readability\n", - " columns={\n", - " 'S1': 'TC', # total serum cholesterol\n", - " 'S2': 'LDL', # low-density lipoproteins\n", - " 'S3': 'HDL', # high-density lipoproteins\n", - " 'S4': 'TCH', # total cholesterol/ HDL\n", - " 'S5': 'LTG', # lamotrigine level\n", - " 'S6': 'GLU', # blood sugar level\n", - " 'Y': 'Disease_progression' # measure of progress since 1yr of baseline\n", - " }\n", - ")\n", - "\n", - "# create FACET sample object\n", - "diabetes_sample = Sample(observations=diabetes_df, target_name=\"Disease_progression\")\n", - "\n", - "# create a (trivial) pipeline for a random forest regressor\n", - "rnd_forest_reg = RegressorPipelineDF(\n", - " regressor=RandomForestRegressorDF(n_estimators=200, random_state=42)\n", - ")\n", - "\n", - "# define grid of models which are \"competing\" against each other\n", - "rnd_forest_grid = [\n", - " LearnerGrid(\n", - " pipeline=rnd_forest_reg,\n", - " learner_parameters={\n", - " \"min_samples_leaf\": [8, 11, 15],\n", - " \"max_depth\": [4, 5, 6],\n", - " }\n", - " ),\n", - "]\n", - "\n", - "# create repeated k-fold CV iterator\n", - "rkf_cv = RepeatedKFold(n_splits=5, n_repeats=10, random_state=42)\n", - "\n", - "# rank your candidate models by performance (default is mean CV score - 2*SD)\n", - "ranker = LearnerRanker(\n", - " grids=rnd_forest_grid, cv=rkf_cv, n_jobs=-3\n", + "# rank your candidate models by performance\n", + "selector = LearnerSelector(\n", + " searcher_type=GridSearchCV,\n", + " parameter_space=rnd_forest_ps, \n", + " cv=rkf_cv, \n", + " n_jobs=-3,\n", + " scoring=\"r2\"\n", ").fit(sample=diabetes_sample)\n", "\n", "# get summary report\n", - "ranker.summary_report()" + "selector.summary_report()" ] }, { @@ -374,25 +355,16 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# fit the model inspector\n", "from facet.inspection import LearnerInspector\n", - "inspector = LearnerInspector(n_jobs=-3)\n", - "inspector.fit(crossfit=ranker.best_model_crossfit_)" + "inspector = LearnerInspector(\n", + " pipeline=selector.best_estimator_,\n", + " n_jobs=-3\n", + ").fit(sample=diabetes_sample)" ] }, { @@ -404,12 +376,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -425,8 +397,9 @@ "MatrixDrawer(style=\"matplot%\").draw(synergy_matrix, title=\"Synergy Matrix\")\n", "\n", "# save copy of plot to _static directory for documentation\n", + "MatrixDrawer(style=\"matplot%\").draw(synergy_matrix, title=\"Synergy Matrix\")\n", "plt.savefig(\n", - " \"../source/_static/synergy_matrix.png\", bbox_inches=\"tight\", pad_inches=0\n", + " \"../source/_images/synergy_matrix.png\", bbox_inches=\"tight\", pad_inches=0\n", ")" ] }, @@ -461,12 +434,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -481,8 +454,9 @@ "MatrixDrawer(style=\"matplot%\").draw(redundancy_matrix, title=\"Redundancy Matrix\")\n", "\n", "# save copy of plot to _static directory for documentation\n", + "MatrixDrawer(style=\"matplot%\").draw(redundancy_matrix, title=\"Redundancy Matrix\")\n", "plt.savefig(\n", - " \"../source/_static/redundancy_matrix.png\",\n", + " \"../source/_images/redundancy_matrix.png\",\n", " bbox_inches=\"tight\",\n", " pad_inches=0,\n", ")" @@ -527,12 +501,14 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": 7, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -549,7 +525,7 @@ "\n", "# save copy of plot to _static directories for documentation\n", "plt.savefig(\n", - " \"../source/_static/redundancy_dendrogram.png\",\n", + " \"../source/_images/redundancy_dendrogram.png\",\n", " bbox_inches=\"tight\",\n", " pad_inches=0,\n", ")" @@ -580,28 +556,22 @@ " of that partition.\n", "- For each partition, the simulator creates an artificial copy of the original sample\n", " assuming the variable to be simulated has the same value across all observations –\n", - " which is the value representing the partition. Using the best `LearnerCrossfit`\n", - " acquired from the ranker, the simulator now re-predicts all targets using the models\n", - " trained for all folds and determines the average uplift of the target variable\n", + " which is the value representing the partition. Using the best estimator\n", + " acquired from the selector, the simulator now re-predicts all targets using the models\n", + " trained on full sample and determines the average uplift of the target variable\n", " resulting from this.\n", "- The FACET `SimulationDrawer` allows us to visualise the result; both in a\n", - " *matplotlib* and a plain-text style.\n", - "\n", - "Finally, because FACET can use bootstrap cross validation, we can create a crossfit\n", - "from our previous `LearnerRanker` best model to perform the simulation, so we can\n", - "quantify the uncertainty by using bootstrap confidence intervals." + " *matplotlib* and a plain-text style." ] }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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" ] @@ -613,7 +583,6 @@ "source": [ "# FACET imports\n", "from facet.validation import BootstrapCV\n", - "from facet.crossfit import LearnerCrossfit\n", "from facet.simulation import UnivariateUpliftSimulator\n", "from facet.data.partition import ContinuousRangePartitioner\n", "from facet.simulation.viz import SimulationDrawer\n", @@ -621,16 +590,12 @@ "# create bootstrap CV iterator\n", "bscv = BootstrapCV(n_splits=1000, random_state=42)\n", "\n", - "# create a bootstrap CV crossfit for simulation using best model\n", - "boot_crossfit = LearnerCrossfit(\n", - " pipeline=ranker.best_model_,\n", - " cv=bscv,\n", - " n_jobs=-3,\n", - " verbose=False,\n", - ").fit(sample=diabetes_sample)\n", - "\n", "SIM_FEAT = \"BMI\"\n", - "simulator = UnivariateUpliftSimulator(crossfit=boot_crossfit, n_jobs=-3)\n", + "simulator = UnivariateUpliftSimulator(\n", + " model=selector.best_estimator_,\n", + " sample=diabetes_sample,\n", + " n_jobs=-3\n", + ")\n", "\n", "# split the simulation range into equal sized partitions\n", "partitioner = ContinuousRangePartitioner()\n", @@ -643,7 +608,7 @@ "\n", "# save copy of plot to _static directory for documentation\n", "plt.savefig(\n", - " \"../source/_static/simulation_output.png\",\n", + " \"../source/_images/simulation_output.png\",\n", " bbox_inches=\"tight\",\n", " pad_inches=0,\n", ")" @@ -662,9 +627,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "facet-develop", "language": "python", - "name": "python3" + "name": "facet-develop" }, "language_info": { "codemirror_mode": { @@ -676,7 +641,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.8.12" }, "toc": { "base_numbering": 1, diff --git a/sphinx/auxiliary/Facet_sphinx_tutorial_template.ipynb b/sphinx/auxiliary/Facet_sphinx_tutorial_template.ipynb index 518f3ca19..20a171afd 100644 --- a/sphinx/auxiliary/Facet_sphinx_tutorial_template.ipynb +++ b/sphinx/auxiliary/Facet_sphinx_tutorial_template.ipynb @@ -20,7 +20,7 @@ "raw_mimetype": "text/html" }, "source": [ - "" + "" ] }, { @@ -29,8 +29,6 @@ "source": [ "# *Name of tutorial* with FACET\n", "\n", - "***\n", - "\n", "FACET is composed of the following key components:\n", "\n", "- **Model Inspection**\n", diff --git a/sphinx/make.py b/sphinx/make.py index c17c2aac4..4673dfb0d 100755 --- a/sphinx/make.py +++ b/sphinx/make.py @@ -1,30 +1,24 @@ #!/usr/bin/env python3 """ -Make sphinx documentation using the makefile in pytools +Make sphinx documentation using the pytools make utility """ - import os -import sys +from urllib import request +BRANCH = "2.0.x" -def make() -> None: - """ - Run the common make file available in the pytools repo - """ - cwd = os.path.dirname(os.path.realpath(__file__)) - os.chdir(cwd) - sys.path.insert( - 0, - os.path.normpath( - os.path.join(cwd, os.pardir, os.pardir, "pytools", "sphinx", "base") - ), - ) - # noinspection PyUnresolvedReferences - from make_base import make +if __name__ == "__main__": - make(modules=["pytools", "sklearndf", "facet"]) + # noinspection PyUnusedLocal + def run_make(branch: str, working_directory: str) -> None: + """Stub, overwritten by bootstrap.py""" + # run the common make file available in the pytools repo + with request.urlopen( + f"https://raw.githubusercontent.com/BCG-Gamma/pytools/{BRANCH}" + f"/sphinx/base/bootstrap.py" + ) as response: + exec(response.read().decode("utf-8"), globals()) -if __name__ == "__main__": - make() + run_make(branch=BRANCH, working_directory=os.path.dirname(__file__)) diff --git a/sphinx/source/.gitignore b/sphinx/source/.gitignore deleted file mode 100644 index b8b42e866..000000000 --- a/sphinx/source/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -/apidoc/ -/getting_started/ -release_notes.rst diff --git a/sphinx/source/_static/Facet-Simulator-BlockDiagram.png b/sphinx/source/_images/Facet-Simulator-BlockDiagram.png similarity index 100% rename from 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a/sphinx/source/_static/css/facet.css +++ b/sphinx/source/_static/css/facet.css @@ -2,10 +2,7 @@ img.padded-logo {padding-top: 20pt; padding-bottom: 20pt} img.facet_icon { height: 100px; - padding-top: 5pt; - padding-bottom: 5pt; - padding-left: 5pt; - padding-right: 5pt; + padding: 5pt; } .hello { diff --git a/sphinx/source/_static/redundancy_dendrogram.png b/sphinx/source/_static/redundancy_dendrogram.png deleted file mode 100644 index 7aa7ef32d..000000000 Binary files a/sphinx/source/_static/redundancy_dendrogram.png and /dev/null differ diff --git a/sphinx/source/_static/redundancy_matrix.png b/sphinx/source/_static/redundancy_matrix.png deleted file mode 100644 index 6f33cfc44..000000000 Binary files a/sphinx/source/_static/redundancy_matrix.png and /dev/null differ diff --git a/sphinx/source/_static/simulation_output.png b/sphinx/source/_static/simulation_output.png deleted file mode 100644 index 9d190732e..000000000 Binary files a/sphinx/source/_static/simulation_output.png and /dev/null differ diff --git a/sphinx/source/_static/synergy_matrix.png b/sphinx/source/_static/synergy_matrix.png deleted file mode 100644 index 9b34ade48..000000000 Binary files a/sphinx/source/_static/synergy_matrix.png and /dev/null differ diff --git a/sphinx/source/_templates/autosummary.rst b/sphinx/source/_templates/autosummary.rst deleted file mode 100644 index 1c0533b7c..000000000 --- a/sphinx/source/_templates/autosummary.rst +++ /dev/null @@ -1,6 +0,0 @@ -.. autosummary:: - :toctree: ../apidoc - :template: custom-module-template.rst - :recursive: - - facet diff --git a/sphinx/source/about_us.rst b/sphinx/source/about_us.rst index b6d55a194..6df92050f 100644 --- a/sphinx/source/about_us.rst +++ b/sphinx/source/about_us.rst @@ -48,28 +48,36 @@ okay that was the last geometry pun, we promise. | |SithanK| | |RicardoK| | |FlorentM| | |JoergS| | | Sithan Kanna | Ricardo Kennedy | Florent Martin | Jörg Schneider | +-------------------+-------------------+-------------------+-------------------+ +| |AndyS| | |MateuszS| | | | +| Andy Shora | Mateusz Sokół | | | ++-------------------+-------------------+-------------------+-------------------+ + +.. |JasonB| image:: /_images/team_contributors/Jason_Bentley.jpg + :class: team_pic -.. |JasonB| image:: _static/team_contributors/Jason_Bentley.jpg +.. |MaloG| image:: /_images/team_contributors/Malo_Grisard.jpg :class: team_pic -.. |MaloG| image:: _static/team_contributors/Malo_Grisard.jpg +.. |KonstantinH| image:: /_images/team_contributors/Konstantin_Hemker.jpg :class: team_pic -.. |KonstantinH| image:: _static/team_contributors/Konstantin_Hemker.jpg +.. |JanI| image:: /_images/team_contributors/Jan_Ittner.jpg :class: team_pic -.. |JanI| image:: _static/team_contributors/Jan_Ittner.jpg +.. |SithanK| image:: /_images/team_contributors/Sithan_Kanna.jpg :class: team_pic -.. |SithanK| image:: _static/team_contributors/Sithan_Kanna.jpg +.. |RicardoK| image:: /_images/team_contributors/Ricardo_Kennedy.jpg :class: team_pic -.. |RicardoK| image:: _static/team_contributors/Ricardo_Kennedy.jpg +.. |FlorentM| image:: /_images/team_contributors/Florent_Martin.jpg :class: team_pic -.. |FlorentM| image:: _static/team_contributors/Florent_Martin.jpg +.. |JoergS| image:: /_images/team_contributors/Joerg_Schneider.jpg :class: team_pic -.. |JoergS| image:: _static/team_contributors/Joerg_Schneider.jpg +.. |AndyS| image:: /_images/team_contributors/Andy_Shora.jpg :class: team_pic +.. |MateuszS| image:: /_images/team_contributors/Mateusz_Sokol.jpg + :class: team_pic diff --git a/sphinx/source/_templates/api_landing.rst b/sphinx/source/api_landing.rst similarity index 90% rename from sphinx/source/_templates/api_landing.rst rename to sphinx/source/api_landing.rst index 7b14f4518..52dd7b17c 100644 --- a/sphinx/source/_templates/api_landing.rst +++ b/sphinx/source/api_landing.rst @@ -1,7 +1,7 @@ The figure below provides a high level overview of the workflow when using FACET, and for each step in the workflow, a brief description. -.. image:: ../_static/Facet_flow.svg +.. image:: /_images/Facet_flow.svg :width: 550 Please refer to the :ref:`tutorials` for examples of using FACET classes diff --git a/sphinx/source/conf.py b/sphinx/source/conf.py index d192149c6..84d594f12 100644 --- a/sphinx/source/conf.py +++ b/sphinx/source/conf.py @@ -1,34 +1,27 @@ """ Configuration file for the Sphinx documentation builder. -Receives majority of configuration from pytools conf_base.py +Receives the majority of the configuration from pytools conf_base.py """ import os import sys -sys.path.insert( - 0, - os.path.abspath( - os.path.join( - os.path.dirname(__file__), - os.pardir, - os.pardir, - os.pardir, - "pytools", - "sphinx", - "base", - ) - ), -) +_dir_base = os.path.join(os.path.dirname(os.path.dirname(__file__)), "base") +sys.path.insert(0, _dir_base) from conf_base import set_config -# ----- custom configuration ----- +# ----- set custom configuration ----- set_config( globals(), project="facet", - modules=["facet", "pytools", "sklearndf"], - html_logo="_static/Gamma_Facet_Logo_RGB_LB.svg", + html_logo=os.path.join("_images", "Gamma_Facet_Logo_RGB_LB.svg"), + intersphinx_mapping={ + "pytools": ("https://bcg-gamma.github.io/pytools/", None), + "shap": ("https://shap.readthedocs.io/en/stable", None), + "sklearn": ("https://scikit-learn.org/stable", None), + "sklearndf": ("https://bcg-gamma.github.io/sklearndf/", None), + }, ) diff --git a/sphinx/source/contribution_guide.rst b/sphinx/source/contribution_guide.rst index ee783c413..1f6467dde 100644 --- a/sphinx/source/contribution_guide.rst +++ b/sphinx/source/contribution_guide.rst @@ -1,72 +1,70 @@ .. _contribution-guide: Development Guidelines -====================================== +====================== Setup ------------------------ +----- Python environment -~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~ There is an ``environment.yml`` provided in the repository root, which installs all required development dependencies in the ``facet-develop`` environment. .. code-block:: sh - conda env create -f environment.yml - conda activate facet-develop + conda env create -f environment.yml + conda activate facet-develop Pre-commit hooks -~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~ This project uses a number of pre-commit hooks such as black and flake8 to enforce uniform coding standards in all commits. Before committing code, please run .. code-block:: sh - pre-commit install + pre-commit install You can use ``pre-commit run`` to manually run the pre-commit hooks from the command line. Pytest -~~~~~~~~~~~~~~~ -Run ``pytest tests/`` from the facet root folder or use the PyCharm test runner. To -measure coverage, use ``pytest --cov=src/facet tests/``. Note that the code coverage +~~~~~~ +Run ``pytest test/`` from the facet root folder or use the PyCharm test runner. To +measure coverage, use ``pytest --cov=src/facet test/``. Note that the code coverage reports are also generated in the Azure Pipelines (see CI/CD section). Note that you will need to set the PYTHONPATH to the ``src/`` directory by running ``export PYTHONPATH=./src/`` from the repository root. - - Git Guidelines --------------------- +-------------- For commits to GitHub, phrase commit comments as the completion of the sentence *This commit will …*, e.g. .. code-block:: RST - add method foo to class Bar + add method foo to class Bar but not .. code-block:: RST - added method foo to class Bar + added method foo to class Bar Documentation ---------------------------- +------------- This section provides a general guide to the documentation of FACET, including docstrings, Sphinx, the README and tutorial notebooks. Docstrings -~~~~~~~~~~~ +~~~~~~~~~~ The API documentation is generated from docstrings in the source code. Before writing your own, take some time to study the existing code documentation and emulate the same @@ -110,7 +108,7 @@ explain usage patterns. which is too wordy and not imperative. -- Write docstrings for modules, classes, modules, and attributes starting with a +- Write docstrings for modules, classes, modules, and attributes starting with a descriptive phrase (as you would expect in a dictionary entry). Be concise and avoid unnecessary or redundant phrases. For example: @@ -122,6 +120,7 @@ explain usage patterns. Explains the inner workings of a predictive model using the SHAP approach. The inspector offers the following analyses: + - ... - ... @@ -143,7 +142,7 @@ explain usage patterns. @property def children(self) -> Foo: - """The child nodes of the tree""" + """The child nodes of the tree.""" pass but not @@ -155,7 +154,7 @@ explain usage patterns. """:return: the foo object""" pass -- Start full sentences and phrases with a capitalised word and end each sentence with +- Start full sentences and phrases with a capitalised word and end each sentence with punctuation, e.g., .. code-block:: python @@ -196,32 +195,34 @@ explain usage patterns. :param sample: training sample""" -- For method arguments, return value, and class parameters, one must hint the type using +- For method arguments, return value, and class parameters, one must hint the type using the typing module. Do not specify the parameter types in the docstrings, e.g., .. code-block:: python def f(x: int) -> float: - """ - Do something. + """ + Do something. - :param x: input value - :return: output value + :param x: input value + :return: output value + """ but not .. code-block:: python def f(x: int) -> float: - """ - Do something. + """ + Do something. - :param int x: input value - :return float: output value + :param int x: input value + :return float: output value + """ Sphinx Build -~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~ Documentation for FACET is built using `sphinx `_. Before building the documentation ensure the ``facet-develop`` environment is active as @@ -247,85 +248,72 @@ building and releasing FACET. The ``sphinx`` folder in the root directory contains the following: -- a ``make.py`` script for executing the documentation build via python. +- a ``make.py`` script for executing the documentation build via python + +- a ``source`` directory containing predefined .rst files for the documentation build + and other required elements (see below for more details) -- a ``source`` directory containing predefined ``.rst`` files for the documentation - build and other required elements, see below for more details. +- a ``base`` folder which contains -- an ``auxiliary`` directory which contains the notebook used in the quickstart as well - as a template notebook to be used when generating new tutorials to be added to the - documentation. Note this is kept separate as it is used to generate the example for - the repository `README.rst`, which is the included in the documentation build. + * the ``make_base.py`` and ``conf_base.py`` scripts with nearly all configuration for + ``make.py`` and ``conf.py`` + * ``_static`` directory, containing logos, icons, javascript and css used for + *pytools* and other packages documentation builds + * ``_templates`` directory, containing *autodoc* templates used in generating and + formatting the modules and classes for the API documentation The ``sphinx/source`` folder contains: -- a ``conf.py`` script that is the `build configuration file - `_ needed to customize the - input and output behavior of the Sphinx documentation build (see below for further - details). +- a ``conf.py`` script that is the + `build configuration file `_ + needed to customize the input and output behavior of the Sphinx documentation build + (see below for further details) - a ``tutorials`` directory that contains all the notebooks (and supporting data) used in the documentation build. Note that as some notebooks take a little while to generate, the notebooks are currently committed with cell output. This may change in the future where - notebooks are run as part of the sphinx build. - -- the base ``.rst`` files used for the documentation build, which are: + notebooks are run as part of the sphinx build - * ``index.rst``: definition of the high-level documentation structure which mainly - references the other rst files in this directory. +- the essential ``.rst`` files used for the documentation build, which are: - * ``tutorials.rst``: a tutorial overview that incorporates the tutorial notebooks - from the ``tutorials`` directory. + * ``index.rst``: definition of the high-level documentation structure which mainly + references the other ``.rst`` files in this directory - * ``contribution_guide.rst``: detailed information on building and releasing FACET. + * ``contribution_guide.rst``: detailed information on building and releasing + FACET. - * ``faqs.rst``: contains guidance on bug reports/feature requests, how to contribute - and answers to frequently asked questions including small code snippets. + * ``faqs.rst``: contains guidance on bug reports/feature requests, how to contribute + and answers to frequently asked questions including small code snippets - * ``about_us.rst``: description of the team behind open-sourcing FACET. + * ``api_landing.rst``: for placing any API landing page preamble for documentation + as needed. This information will appear on the API landing page in the + documentation build after the short description in ``src/__init__.py``. This file + is included in the documentation build via the ``custom-module-template.rst`` - * ``api_landing.rst``: for placing any API landing page preamble for documentation - as needed. This information will appear on the API landing page in the - documentation build after the short description in ``src/__init__.py``. This file - is included in the documentation build via the ``custom-module-template.rst``. +- ``_static`` contains additional material used in the documentation build, in this + case, logos and icons -- ``_static`` contains additional material used in the documentation build (mainly - figures) but also some formatting control: - * ``team_contributors``: contains photos for the FACET team. - - * ``icons``: contains the icons used in describing the main elements of FACET - in the documentation getting started page. - - * ``css/facet.css``: contains additional customization for the display of HTML - elements in the documentation build. - -- ``_templates`` contains the ``autosummary.rst`` which relies on the - ``custom-module-template.rst`` and ``custom-class-template.rst`` from - ``pytools/tree/develop/sphinx/source/_templates`` which is used in - generating/formatting the modules and classes for the API documentation. - -The two key scripts are ``make.py`` and ``conf.py``. The base configuration for these -scripts can be found in -`pytools/sphinx `_. -The reason for this was to minimise code given the standardization of the documentation +The two key scripts are ``make.py`` and ``conf.py``. The base configuration for the +these scripts can be found in `pytools/sphinx `_. +The reason for this is to minimise code given the standardization of the documentation build across multiple packages. **make.py**: All base configuration comes from ``pytools/sphinx/base/make_base.py`` and this script includes defined commands for key steps in the documentation build. Briefly, the key steps for the documentation build are: -- **Clean**: remove the existing documentation build. +- **Clean**: remove the existing documentation build -- **FetchPkgVersions**: fetch the available package versions with documentation. +- **FetchPkgVersions**: fetch the available package versions with documentation -- **ApiDoc**: generate API documentation from sources. +- **ApiDoc**: generate API documentation from sources -- **Html**: run Sphinx build to generate HTMl documentation. +- **Html**: run Sphinx build to generate HTMl documentation The two other commands are **Help** and **PrepareDocsDeployment**, the latter of which -is covered below under Building and releasing FACET. +is covered below under *Building and releasing FACET*. **conf.py**: All base configuration comes from ``pytools/sphinx/base/conf_base.py``. This `build configuration file `_ @@ -333,35 +321,33 @@ is a requirement of Sphinx and is needed to customize the input and output behav the documentation build. In particular, this file highlights key extensions needed in the build process, of which some key ones are as follows: -- `intersphinx `_ - (external links to other documentations built with Sphinx: scikit-learn, numpy...). +- `intersphinx `_ + (external links to other documentations built with Sphinx: matplotlib, numpy, ...) -- `viewcode `_ to - include source code in the documentation, and links to the source code from the objects - documentation. +- `viewcode `_ + to include source code in the documentation, and links to the source code from the objects documentation -- `imgmath `_ to render - math expressions in doc strings. Note that a local latex installation is required (e.g., - `MiKTeX `_ for Windows). +- `imgmath `_ + to render math expressions in doc strings. Note that a local latex installation is + required (e.g., `MiKTeX `_ for Windows) README -~~~~~~~ +~~~~~~ The README file for the repo is .rst format instead of the perhaps more traditional markdown format. The reason for this is the ``README.rst`` is included as the quick start guide in the documentation build. This helped minimize code duplication. However, there are a few key points to be aware of: -- The README has links to figures, logos and icons located in the ``sphinx/source/_static`` - folder. To ensure these links are correct when the documentation is built, they are - altered and then the contents of the ``README.rst`` is incorporated into the - ``getting_started.rst`` which is generated during the build and can be found in - ``sphinx/source/getting_started``. +- The README has links to figures, logos and icons located in the ``sphinx/source/_static`` folder. + To ensure these links are correct when the documentation is built, they are altered and then the + contents of the ``README.rst`` is incorporated into the ``getting_started.rst`` which is generated + during the build and can be found in ``sphinx/source/getting_started``. -- The quick start guide based on the ``Boston_getting_started_example.ipynb`` notebook in - the ``sphinx/auxiliary`` folder is not automatically included (unlike all the other - tutorials). For this reason any updates to this example in the README need to be +- The quick start guide based on the ``Diabetes_getting_started_example.ipynb`` notebook in + the ``sphinx/auxiliary`` folder is not automatically included (unlike all the other + tutorials). For this reason any updates to this example in the README need to be reflected in the source notebook and vice-versa. @@ -374,13 +360,13 @@ created for documentation need to be placed in ``sphinx/source/tutorial`` folder If you intend to create a notebook for inclusion in the documentation please note the following: -- The notebook should conform to the standard format employed for all notebooks included in - the documentation. This template (``Facet_sphinx_tutorial_template.ipynb``) can be found +- The notebook should conform to the standard format employed for all notebooks included in + the documentation. This template (``Facet_sphinx_tutorial_template.ipynb``) can be found in ``sphinx/auxiliary``. -- When creating/revising a tutorial notebook with the development environment the following - code should be added to a cell at the start of the notebook. This will ensure your local - clones (and any changes) are used when running the notebook. The jupyter notebook should +- When creating/revising a tutorial notebook with the development environment the following + code should be added to a cell at the start of the notebook. This will ensure your local + clones (and any changes) are used when running the notebook. The jupyter notebook should also be instigated from within the ``facet-develop`` environment. .. code-block:: python @@ -412,10 +398,10 @@ following: -- If you have a notebook cell you wish to be excluded from the generated documentation, add - ``"nbsphinx": "hidden"`` to the metadata of the cell. To change the metadata of a cell, - in the main menu of the jupyter notebook server, click on *View -> CellToolbar -> edit - metadata*, then click on edit Metadata in the top right part of the cell. The modified +- If you have a notebook cell you wish to be excluded from the generated documentation, add + ``"nbsphinx": "hidden"`` to the metadata of the cell. To change the metadata of a cell, + in the main menu of the jupyter notebook server, click on *View -> CellToolbar -> edit + metadata*, then click on edit Metadata in the top right part of the cell. The modified metadata would then look something like: .. code-block:: json @@ -424,11 +410,11 @@ following: "nbsphinx": "hidden" } -- To interpret a notebook cell as reStructuredText by nbsphinx, make a Raw NBConvert cell, - then click on the jupyter notebook main menu to *View -> CellToolbar -> Raw Cell Format*, +- To interpret a notebook cell as reStructuredText by nbsphinx, make a Raw NBConvert cell, + then click on the jupyter notebook main menu to *View -> CellToolbar -> Raw Cell Format*, then choose ReST in the dropdown in the top right part of the cell. -- The notebook should be referenced in the ``tutorials.rst`` file with a section structure +- The notebook should be referenced in the ``tutorials.rst`` file with a section structure as follows: .. code-block:: RST @@ -449,18 +435,18 @@ following: tutorial/name_of_new_tutorial_nb -- The source data used for the notebook should also be added to the tutorial folder unless +- The source data used for the notebook should also be added to the tutorial folder unless the file is extremely large and/or can be accessed reliably another way. -- For notebooks involving simulation studies, or very long run times consider saving - intermediary outputs to make the notebook more user-friendly. Code the produces the - output should be included as a markdown cell with code designated as python to ensure +- For notebooks involving simulation studies, or very long run times consider saving + intermediary outputs to make the notebook more user-friendly. Code the produces the + output should be included as a markdown cell with code designated as python to ensure appropriate formatting, while preventing the cell from executing should the user run all cells. Package builds --------------------------------- +-------------- The build process for the PyPI and conda distributions uses the following key files: @@ -468,14 +454,14 @@ files: - ``make.py``: generic Python script for package builds. Most configuration is imported from pytools `make.py `__ which is a build script that wraps the package build, as well as exposing the matrix - dependency definitions specified in the ``pyproject.toml`` as environment variables. -- ``pyproject.toml``: metadata for PyPI, build settings and package dependencies. -- ``tox.ini``: contains configurations for tox, testenv, flake8, isort, coverage report, - and pytest. -- ``condabuild/meta.yml``: metadata for conda, build settings and package dependencies. + dependency definitions specified in the ``pyproject.toml`` as environment variables +- ``pyproject.toml``: metadata for PyPI, build settings and package dependencies +- ``tox.ini``: contains configurations for tox, testenv, flake8, isort, coverage report, + and pytest +- ``condabuild/meta.yml``: metadata for conda, build settings and package dependencies Versioning -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~ FACET version numbering follows the `semantic versioning `_ approach, with the pattern ``MAJOR.MINOR.PATCH``. @@ -483,7 +469,7 @@ The version can be bumped in the ``src/__init__.py`` by updating the ``__version__`` string accordingly. PyPI -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~ PyPI project metadata, build settings and package dependencies are obtained from ``pyproject.toml``. To build and then publish the package to PyPI, @@ -491,7 +477,7 @@ use the following commands: .. code-block:: sh - python make.py gamma-facet tox default + python make.py facet tox default flit publish Please note the following: @@ -516,7 +502,7 @@ Please note the following: * Build output will be stored in the ``dist/`` directory. Conda -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~ conda build metadata, build settings and package dependencies are obtained from ``meta.yml``. To build and then publish the package to conda, @@ -524,7 +510,7 @@ use the following commands: .. code-block:: sh - python make.py gamma-facet conda default + python make.py facet conda default anaconda upload --user BCG_Gamma dist/conda/noarch/<*package.tar.gz*> Please note the following: @@ -532,28 +518,28 @@ Please note the following: - Build output will be stored in the ``dist/`` directory. - Some useful references for conda builds: - - `Conda build tutorial - `_ - - `Conda build metadata reference - `_ + - `Conda build tutorial + `_ + - `Conda build metadata reference + `_ Azure DevOps CI/CD --------------------- +------------------ This project uses `Azure DevOps `_ for CI/CD pipelines. The pipelines are defined in the ``azure-pipelines.yml`` file and are divided into the following stages: -* **code_quality_checks**: perform code quality checks for isort, black and flake8. -* **detect_build_config_changes**: detect whether the build configuration as specified - in the ``pyproject.yml`` has been modified. If it has, then a build test is run. -* **Unit tests**: runs all unit tests and then publishes test results and coverage. -* **conda_tox_build**: build the PyPI and conda distribution artifacts. -* **Release**: see release process below for more detail. -* **Docs**: build and publish documentation to GitHub Pages. +* **code_quality_checks**: perform code quality checks for isort, black and flake8. +* **detect_build_config_changes**: detect whether the build configuration as specified + in the ``pyproject.yml`` has been modified. If it has, then a build test is run. +* **Unit tests**: runs all unit tests and then publishes test results and coverage. +* **conda_tox_build**: build the PyPI and conda distribution artifacts. +* **Release**: see release process below for more detail. +* **Docs**: build and publish documentation to GitHub Pages. Release process -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~ Before initiating the release process, please ensure the version number in ``src/__init__.py`` is correct and the format conforms to semantic @@ -562,28 +548,35 @@ change and merge into develop before going any further. The release process has the following key steps: -* Create a new release branch from develop and open a PR to master. -* Opening the PR to master will automatically run all conda/pip build tests via - Azure Pipelines, triggering automatic upload of artifacts (conda and pip - packages) to Azure DevOps. At this stage, it is recommended that the pip package +- Create a new release branch from the tag of the latest release named + ``release/`` where ```` is the version number of the new release +- Create a new branch from the baseline branch (e.g., ``2.0.x``) named + ``dev/`` where ```` is the version number of the new release +- Opening a PR to merge ``dev/`` onto ``release/``. + This will automatically run all conda/pip build tests via + Azure Pipelines prior to allowing to merge the PR. + This will trigger automatic upload of artifacts (conda and pip + packages) from Azure DevOps. At this stage, it is recommended that the pip package build is checked using `PyPI test `__ to ensure all metadata presents correctly. This is important as package versions in PyPI proper are immutable. -* If everything passes and looks okay, merge the PR into master, this will - trigger the release pipeline which will: +- If everything passes and looks okay, merge the PR using a *merge commit* + (not squashing). + This will trigger the release pipeline which will: - * Tag the release commit with version number as specified in ``src/__init__.py``. - * Create a release on GitHub for the new version, please check the `documentation + * Tag the release commit with version number as specified in ``src/__init__.py`` + * Create a release on GitHub for the new version, please check the `documentation `__ - for details. - * Pre-fill the GitHub release title and description, including the changelog based on + for details + * Pre-fill the GitHub release title and description, including the changelog based on commits since the last release. Please note this can be manually edited to be more - succinct afterwards. - * Attach build artifacts (conda and pip packages) to GitHub release. - -* Manually upload build artifacts to conda/PyPI using ``anaconda upload`` and - ``flit publish``, respectively (see relevant sections under Package builds above) - This may be automated in the future. -* Remove any test versions for pip from PyPI test. -* Merge any changes from release branch also back to develop. -* Bump up version in ``src/__init__.py`` on develop to start work towards next release. + succinct afterwards + * Attach build artifacts (conda and pip packages) to GitHub release + * Upload build artifacts to conda/PyPI using ``anaconda upload`` and + ``flit publish``, respectively + +- Remove any test versions for pip from PyPI test +- Merge ``release/`` back onto the baseline branch from which + ``dev/`` was branched +- Bump up version in ``src/__init__.py`` on the baseline branch to start work towards + the next release \ No newline at end of file diff --git a/sphinx/source/faqs.rst b/sphinx/source/faqs.rst index 36ae36214..fb62b4255 100644 --- a/sphinx/source/faqs.rst +++ b/sphinx/source/faqs.rst @@ -41,9 +41,10 @@ on `stackoverflow `_. # run inspector inspector = LearnerInspector( + pipeline=clf_selector.best_estimator_, n_jobs=-3, verbose=False, - ).fit(crossfit=ranker.best_model_crossfit) + ).fit(sample=sample) # get shap values and associated data shap_data = inspector.shap_plot_data() @@ -51,21 +52,21 @@ on `stackoverflow `_. -5. **How can I extract CV performance from the LearnerRanker to create my +5. **How can I extract CV performance from the LearnerSelector to create my own summaries or figures?** You can extract the desired information as a data frame from the fitted - LearnerRanker object. + LearnerSelector object. .. code-block:: Python - # after fitting a ranker - cv_result_df = ranker.summary_report() + # after fitting a selector + cv_result_df = selector.summary_report() -6. **Can I use a custom scoring function with the LearnerRanker?** +6. **Can I use a custom scoring function with the LearnerSelector?** - The LearnerRanker works in a similar fashion to *scikit-learn*'s + The LearnerSelector works in a similar fashion to *scikit-learn*'s `gridsearchCV `_ so much of the functionality is equivalent. You can pass a custom scoring function much as you would for gridsearchCV. @@ -84,11 +85,16 @@ on `stackoverflow `_. my_score = make_scorer(huber_loss, greater_is_better=False) - # use the LearnerRanker with custom scorer and get summary report - ranker = LearnerRanker(grids=FACET_regressor_grid, cv=cv_iterator, scoring=my_score).fit( + # use the LearnerSelector with custom scorer and get summary report + selector = LearnerSelector( + searcher_type=GridSearchCV, + parameter_space=ps, + cv=cv_iterator, + scoring=my_score + ).fit( sample=FACET_sample_object ) - ranker.summary_report() + selector.summary_report() You can see more information on custom scoring with *scikit-learn* `here `__. @@ -97,20 +103,25 @@ on `stackoverflow `_. 7. **How can I generate standard** *scikit-learn* **summaries for classifiers, such as a classification report, confusion matrix or ROC curve?** - You can extract the fitted best scored model from the LearnerRanker and + You can extract the fitted best scored model from the LearnerSelector and then generate these summaries as you normally would in your *scikit-learn* workflow. .. code-block:: Python # get your ranking object - ranker = LearnerRanker(grids=FACET_classifier_grid, cv=cv_iterator).fit( + selector = LearnerSelector( + searcher_type=GridSearchCV, + parameter_space=ps, + cv=cv_iterator, + scoring="accuracy" + ).fit( sample=FACET_sample ) # obtain required quantities - y_pred = ranker.best_model_.predict(FACET_sample.features) - y_prob = ranker.best_model_.predict_proba(FACET_sample.features)[1] + y_pred = selector.best_estimator_.predict(FACET_sample.features) + y_prob = selector.best_estimator_.predict_proba(FACET_sample.features)[1] y_true = FACET_sample.target # generate outputs of interest @@ -139,14 +150,6 @@ on `stackoverflow `_. ax.set_title('ROC') ax.legend(loc='lower right') - - For practical examples see - :ref:`Standard Scikit-learn Classification Summary with - FACET`, - which also covers using the fit for each cross-validation - fold (the FACET crossfit object) to generate summaries of mean performance with - assessments of variability. - Citation -------- If you use FACET in your work we would appreciate if you cite the package. diff --git a/sphinx/source/index.rst b/sphinx/source/index.rst index db55dabf8..38e27bf73 100644 --- a/sphinx/source/index.rst +++ b/sphinx/source/index.rst @@ -1,4 +1,4 @@ -.. image:: _static/Gamma_Facet_Logo_RGB_LB.svg +.. image:: /_images/Gamma_Facet_Logo_RGB_LB.svg | @@ -9,10 +9,10 @@ Table of contents :maxdepth: 1 :titlesonly: - Getting started + Getting started <_generated/getting_started> API reference tutorials contribution_guide faqs about_us - release_notes + _generated/release_notes diff --git a/sphinx/source/tutorial/Classification_with_Facet.ipynb b/sphinx/source/tutorial/Classification_with_Facet.ipynb index 28887433f..768c87f36 100644 --- a/sphinx/source/tutorial/Classification_with_Facet.ipynb +++ b/sphinx/source/tutorial/Classification_with_Facet.ipynb @@ -6,17 +6,17 @@ "raw_mimetype": "text/html" }, "source": [ - "" + "" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "# Classification with FACET: Prediabetes Study\n", "\n", - "***\n", - "\n", "FACET is composed of the following key components:\n", "\n", "- **Model Inspection**\n", @@ -37,7 +37,7 @@ "\n", "**Context**\n", "\n", - "Prediabetes is a treatable condition that leads to many health complications and eventually type 2 diabetes. Identification of individuals at risk of prediabetes can improve early intervention and provide insights into those interventions that work best.\n", + "Prediabetes is a treatable condition that leads to many health complications and eventually type 2 diabetes. Identification of individuals at risk of prediabetes can improve early intervention and provide insights into those interventions that work best.\n", "Using a cohort of healthy (*n*=2847) and prediabetic (*n*=1509) patients derived \n", "from the [NHANES 2013-14 U.S. cross-sectional survey](https://wwwn.cdc.gov/nchs/nhanes/Search/DataPage.aspx?Component=Examination&CycleBeginYear=2013) we aim to create a classifier for prediabetes. For further details on data sources, definitions and the study cohort please see the Appendix ([Data source and study cohort](#Data-source-and-study-cohort)).\n", "\n", @@ -52,7 +52,7 @@ "\n", "1. [Required imports](#Required-imports)\n", "2. [Preprocessing and initial feature selection](#Preprocessing-and-initial-feature-selection)\n", - "3. [Selecting a learner using FACET ranker](#Selecting-a-learner-using-FACET-ranker)\n", + "3. [Selecting a learner using FACET selector](#Selecting-a-learner-using-FACET-selector)\n", "4. [Using FACET for advanced model inspection](#Using-FACET-for-advanced-model-inspection)\n", "5. [FACET univariate simulator: the impact of waist to height ratio](#FACET-univariate-simulator:-the-impact-of-waist-to-height-ratio)\n", "6. [Summary](#Summary)\n", @@ -81,6 +81,7 @@ "warnings.filterwarnings(\"ignore\", category=UserWarning, message=r\".*Xcode_8\\.3\\.3\")\n", "warnings.filterwarnings(\"ignore\", message=r\".*`should_run_async` will not call `transform_cell`\")\n", "warnings.filterwarnings(\"ignore\", message=r\".*`np\\..*` is a deprecated alias\")\n", + "warnings.filterwarnings(\"ignore\", message=r\"Importing display from IPython.core.display is deprecated.*\")\n", "\n", "\n", "# set global options for matplotlib\n", @@ -88,8 +89,8 @@ "import matplotlib\n", "import matplotlib.pyplot as plt\n", "\n", - "matplotlib.rcParams[\"figure.figsize\"] = (16.0, 8.0)\n", - "matplotlib.rcParams[\"figure.dpi\"] = 72" + "matplotlib.rcParams[\"figure.figsize\"] = (12.0, 6.0)\n", + "matplotlib.rcParams[\"figure.dpi\"] = 96" ] }, { @@ -126,11 +127,11 @@ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", + "from scipy import stats\n", "import shap\n", "import seaborn as sns\n", - "import tableone\n", "from sklearn.compose import make_column_selector\n", - "from sklearn.model_selection import RepeatedKFold" + "from sklearn.model_selection import RepeatedKFold, RandomizedSearchCV" ] }, { @@ -148,12 +149,11 @@ "source": [ "from facet.data import Sample\n", "from facet.inspection import LearnerInspector\n", - "from facet.selection import LearnerRanker, LearnerGrid\n", + "from facet.selection import LearnerSelector, ParameterSpace\n", "from facet.validation import BootstrapCV\n", "from facet.data.partition import ContinuousRangePartitioner\n", "from facet.simulation import UnivariateProbabilitySimulator\n", - "from facet.simulation.viz import SimulationDrawer\n", - "from facet.crossfit import LearnerCrossfit" + "from facet.simulation.viz import SimulationDrawer" ] }, { @@ -247,192 +247,422 @@ " \n", " \n", " \n", - " Age\n", - " Gender\n", - " Waist_Circumference\n", - " Weight\n", - " Standing_Height\n", - " BMI\n", - " Average_SBP\n", - " Average_DBP\n", - " HDL_Cholesterol\n", - " Total_Cholesterol\n", - " ...\n", - " Osmolality\n", - " Sodium\n", - " Potassium\n", - " Gamma_glutamyl_transferase\n", - " Calcium\n", - " Alanine_aminotransferase\n", - " Aspartate_aminotransferase\n", - " Pre_diab\n", - " SBP_to_DBP\n", - " Waist_to_hgt\n", + " 0\n", + " 1\n", + " 2\n", + " 3\n", + " 4\n", " \n", " \n", " \n", " \n", - " 0\n", + " Age\n", " 73.0\n", + " 56.0\n", + " 61.0\n", + " 56.0\n", + " 65.0\n", + " \n", + " \n", + " Gender\n", + " 2.0\n", + " 1.0\n", + " 2.0\n", " 2.0\n", + " 1.0\n", + " \n", + " \n", + " Waist_Circumference\n", " NaN\n", + " 123.1\n", + " 110.8\n", + " 85.5\n", + " 93.7\n", + " \n", + " \n", + " Weight\n", " 52.0\n", + " 105.0\n", + " 93.4\n", + " 61.8\n", + " 65.3\n", + " \n", + " \n", + " Standing_Height\n", " 162.4\n", + " 158.7\n", + " 161.8\n", + " 152.8\n", + " 172.4\n", + " \n", + " \n", + " BMI\n", " 19.7\n", + " 41.7\n", + " 35.7\n", + " 26.5\n", + " 22.0\n", + " \n", + " \n", + " Average_SBP\n", " 137.333333\n", + " 157.333333\n", + " 122.666667\n", + " 122.0\n", + " 141.333333\n", + " \n", + " \n", + " Average_DBP\n", " 86.666667\n", + " 82.0\n", + " 80.666667\n", + " 72.666667\n", + " 77.333333\n", + " \n", + " \n", + " HDL_Cholesterol\n", " 85.0\n", + " 38.0\n", + " 58.0\n", + " 59.0\n", + " 79.0\n", + " \n", + " \n", + " Total_Cholesterol\n", " 201.0\n", - " ...\n", - " 290.0\n", - " 142.0\n", - " 4.1\n", - " 31.0\n", - " 10.0\n", - " 28.0\n", - " 36.0\n", - " 1\n", - " 1.584615\n", - " NaN\n", + " 226.0\n", + " 168.0\n", + " 278.0\n", + " 173.0\n", " \n", " \n", - " 1\n", - " 56.0\n", + " High_BP\n", " 1.0\n", - " 123.1\n", - " 105.0\n", - " 158.7\n", - " 41.7\n", - " 157.333333\n", - " 82.000000\n", - " 38.0\n", - " 226.0\n", - " ...\n", - " 287.0\n", - " 143.0\n", - " 3.3\n", - " 22.0\n", - " 9.3\n", - " 16.0\n", - " 24.0\n", + " 1.0\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", + " \n", + " \n", + " Sleep_hours\n", + " 9.0\n", + " 5.0\n", + " 9.0\n", + " 6.0\n", + " 7.0\n", + " \n", + " \n", + " Trouble_sleeping\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 1.0\n", + " 0.0\n", + " \n", + " \n", + " Sleep_disorder\n", + " 0.0\n", + " 1.0\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", + " \n", + " \n", + " Told_overweight\n", + " 0.0\n", + " 1.0\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", + " \n", + " \n", + " General_health\n", + " 5.0\n", + " 5.0\n", + " 3.0\n", + " 3.0\n", + " 3.0\n", + " \n", + " \n", + " Family_hist_diab\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " 0\n", - " 1.918699\n", - " 0.775677\n", " \n", " \n", - " 2\n", - " 61.0\n", - " 2.0\n", - " 110.8\n", - " 93.4\n", - " 161.8\n", - " 35.7\n", - " 122.666667\n", - " 80.666667\n", - " 58.0\n", - " 168.0\n", - " ...\n", - " 281.0\n", - " 140.0\n", - " 3.9\n", - " 17.0\n", - " 9.9\n", - " 21.0\n", - " 20.0\n", - " 1\n", - " 1.520661\n", - " 0.684796\n", + " Feel_at_risk_diab\n", + " 0.0\n", + " 1.0\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", " \n", " \n", - " 3\n", - " 56.0\n", + " Vigorous_work_activity\n", + " 0.0\n", + " 1.0\n", + " 0.0\n", + " 1.0\n", + " 1.0\n", + " \n", + " \n", + " Moderate_work_activity\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", + " 1.0\n", + " 0.0\n", + " \n", + " \n", + " Walk_or_bicycle\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " \n", + " \n", + " Vigorous_rec_activity\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " \n", + " \n", + " Moderate_rec_activity\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " \n", + " \n", + " Tried_weight_loss_past_year\n", + " 0.0\n", + " 1.0\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", + " \n", + " \n", + " Healthy_diet\n", " 2.0\n", - " 85.5\n", - " 61.8\n", - " 152.8\n", - " 26.5\n", - " 122.000000\n", - " 72.666667\n", - " 59.0\n", - " 278.0\n", - " ...\n", + " 5.0\n", + " 4.0\n", + " 2.0\n", + " 3.0\n", + " \n", + " \n", + " WBC_count\n", + " 6.6\n", + " 9.4\n", + " 5.2\n", + " 9.5\n", + " 6.3\n", + " \n", + " \n", + " RBC_count\n", + " 4.72\n", + " 4.93\n", + " 4.66\n", + " 4.43\n", + " 4.35\n", + " \n", + " \n", + " Hematocrit\n", + " 43.8\n", + " 41.5\n", + " 39.8\n", + " 41.4\n", + " 43.1\n", + " \n", + " \n", + " Triglycerides\n", + " 88.0\n", + " 327.0\n", + " 68.0\n", + " 262.0\n", + " 39.0\n", + " \n", + " \n", + " Uric_acid\n", + " 4.2\n", + " 9.1\n", + " 5.1\n", + " 3.5\n", + " 6.3\n", + " \n", + " \n", + " Osmolality\n", + " 290.0\n", + " 287.0\n", + " 281.0\n", " 277.0\n", + " 281.0\n", + " \n", + " \n", + " Sodium\n", + " 142.0\n", + " 143.0\n", + " 140.0\n", " 139.0\n", + " 140.0\n", + " \n", + " \n", + " Potassium\n", + " 4.1\n", + " 3.3\n", + " 3.9\n", " 4.0\n", + " 4.8\n", + " \n", + " \n", + " Gamma_glutamyl_transferase\n", + " 31.0\n", + " 22.0\n", + " 17.0\n", " 21.0\n", + " 24.0\n", + " \n", + " \n", + " Calcium\n", + " 10.0\n", + " 9.3\n", + " 9.9\n", " 9.5\n", + " 9.5\n", + " \n", + " \n", + " Alanine_aminotransferase\n", + " 28.0\n", + " 16.0\n", + " 21.0\n", " 24.0\n", - " 23.0\n", - " 0\n", - " 1.678899\n", - " 0.559555\n", + " 20.0\n", " \n", " \n", - " 4\n", - " 65.0\n", - " 1.0\n", - " 93.7\n", - " 65.3\n", - " 172.4\n", - " 22.0\n", - " 141.333333\n", - " 77.333333\n", - " 79.0\n", - " 173.0\n", - " ...\n", - " 281.0\n", - " 140.0\n", - " 4.8\n", + " Aspartate_aminotransferase\n", + " 36.0\n", " 24.0\n", - " 9.5\n", " 20.0\n", + " 23.0\n", " 29.0\n", + " \n", + " \n", + " Pre_diab\n", + " 1\n", + " 0\n", + " 1\n", + " 0\n", " 0\n", + " \n", + " \n", + " SBP_to_DBP\n", + " 1.584615\n", + " 1.918699\n", + " 1.520661\n", + " 1.678899\n", " 1.827586\n", + " \n", + " \n", + " Waist_to_hgt\n", + " NaN\n", + " 0.775677\n", + " 0.684796\n", + " 0.559555\n", " 0.543503\n", " \n", " \n", "\n", - "

5 rows × 40 columns

\n", "" ], "text/plain": [ - " Age Gender Waist_Circumference Weight Standing_Height BMI \\\n", - "0 73.0 2.0 NaN 52.0 162.4 19.7 \n", - "1 56.0 1.0 123.1 105.0 158.7 41.7 \n", - "2 61.0 2.0 110.8 93.4 161.8 35.7 \n", - "3 56.0 2.0 85.5 61.8 152.8 26.5 \n", - "4 65.0 1.0 93.7 65.3 172.4 22.0 \n", - "\n", - " Average_SBP Average_DBP HDL_Cholesterol Total_Cholesterol ... \\\n", - "0 137.333333 86.666667 85.0 201.0 ... \n", - "1 157.333333 82.000000 38.0 226.0 ... \n", - "2 122.666667 80.666667 58.0 168.0 ... \n", - "3 122.000000 72.666667 59.0 278.0 ... \n", - "4 141.333333 77.333333 79.0 173.0 ... \n", - "\n", - " Osmolality Sodium Potassium Gamma_glutamyl_transferase Calcium \\\n", - "0 290.0 142.0 4.1 31.0 10.0 \n", - "1 287.0 143.0 3.3 22.0 9.3 \n", - "2 281.0 140.0 3.9 17.0 9.9 \n", - "3 277.0 139.0 4.0 21.0 9.5 \n", - "4 281.0 140.0 4.8 24.0 9.5 \n", - "\n", - " Alanine_aminotransferase Aspartate_aminotransferase Pre_diab SBP_to_DBP \\\n", - "0 28.0 36.0 1 1.584615 \n", - "1 16.0 24.0 0 1.918699 \n", - "2 21.0 20.0 1 1.520661 \n", - "3 24.0 23.0 0 1.678899 \n", - "4 20.0 29.0 0 1.827586 \n", - "\n", - " Waist_to_hgt \n", - "0 NaN \n", - "1 0.775677 \n", - "2 0.684796 \n", - "3 0.559555 \n", - "4 0.543503 \n", + " 0 1 2 3 \\\n", + "Age 73.0 56.0 61.0 56.0 \n", + "Gender 2.0 1.0 2.0 2.0 \n", + "Waist_Circumference NaN 123.1 110.8 85.5 \n", + "Weight 52.0 105.0 93.4 61.8 \n", + "Standing_Height 162.4 158.7 161.8 152.8 \n", + "BMI 19.7 41.7 35.7 26.5 \n", + "Average_SBP 137.333333 157.333333 122.666667 122.0 \n", + "Average_DBP 86.666667 82.0 80.666667 72.666667 \n", + "HDL_Cholesterol 85.0 38.0 58.0 59.0 \n", + "Total_Cholesterol 201.0 226.0 168.0 278.0 \n", + "High_BP 1.0 1.0 1.0 0.0 \n", + "Sleep_hours 9.0 5.0 9.0 6.0 \n", + "Trouble_sleeping 0.0 0.0 0.0 1.0 \n", + "Sleep_disorder 0.0 1.0 1.0 0.0 \n", + "Told_overweight 0.0 1.0 1.0 0.0 \n", + "General_health 5.0 5.0 3.0 3.0 \n", + "Family_hist_diab 0 0 0 0 \n", + "Feel_at_risk_diab 0.0 1.0 1.0 0.0 \n", + "Vigorous_work_activity 0.0 1.0 0.0 1.0 \n", + "Moderate_work_activity 1.0 0.0 0.0 1.0 \n", + "Walk_or_bicycle 0.0 0.0 0.0 0.0 \n", + "Vigorous_rec_activity 0.0 0.0 0.0 0.0 \n", + "Moderate_rec_activity 0.0 0.0 0.0 0.0 \n", + "Tried_weight_loss_past_year 0.0 1.0 1.0 0.0 \n", + "Healthy_diet 2.0 5.0 4.0 2.0 \n", + "WBC_count 6.6 9.4 5.2 9.5 \n", + "RBC_count 4.72 4.93 4.66 4.43 \n", + "Hematocrit 43.8 41.5 39.8 41.4 \n", + "Triglycerides 88.0 327.0 68.0 262.0 \n", + "Uric_acid 4.2 9.1 5.1 3.5 \n", + "Osmolality 290.0 287.0 281.0 277.0 \n", + "Sodium 142.0 143.0 140.0 139.0 \n", + "Potassium 4.1 3.3 3.9 4.0 \n", + "Gamma_glutamyl_transferase 31.0 22.0 17.0 21.0 \n", + "Calcium 10.0 9.3 9.9 9.5 \n", + "Alanine_aminotransferase 28.0 16.0 21.0 24.0 \n", + "Aspartate_aminotransferase 36.0 24.0 20.0 23.0 \n", + "Pre_diab 1 0 1 0 \n", + "SBP_to_DBP 1.584615 1.918699 1.520661 1.678899 \n", + "Waist_to_hgt NaN 0.775677 0.684796 0.559555 \n", "\n", - "[5 rows x 40 columns]" + " 4 \n", + "Age 65.0 \n", + "Gender 1.0 \n", + "Waist_Circumference 93.7 \n", + "Weight 65.3 \n", + "Standing_Height 172.4 \n", + "BMI 22.0 \n", + "Average_SBP 141.333333 \n", + "Average_DBP 77.333333 \n", + "HDL_Cholesterol 79.0 \n", + "Total_Cholesterol 173.0 \n", + "High_BP 0.0 \n", + "Sleep_hours 7.0 \n", + "Trouble_sleeping 0.0 \n", + "Sleep_disorder 0.0 \n", + "Told_overweight 0.0 \n", + "General_health 3.0 \n", + "Family_hist_diab 0 \n", + "Feel_at_risk_diab 0.0 \n", + "Vigorous_work_activity 1.0 \n", + "Moderate_work_activity 0.0 \n", + "Walk_or_bicycle 0.0 \n", + "Vigorous_rec_activity 0.0 \n", + "Moderate_rec_activity 0.0 \n", + "Tried_weight_loss_past_year 0.0 \n", + "Healthy_diet 3.0 \n", + "WBC_count 6.3 \n", + "RBC_count 4.35 \n", + "Hematocrit 43.1 \n", + "Triglycerides 39.0 \n", + "Uric_acid 6.3 \n", + "Osmolality 281.0 \n", + "Sodium 140.0 \n", + "Potassium 4.8 \n", + "Gamma_glutamyl_transferase 24.0 \n", + "Calcium 9.5 \n", + "Alanine_aminotransferase 20.0 \n", + "Aspartate_aminotransferase 29.0 \n", + "Pre_diab 0 \n", + "SBP_to_DBP 1.827586 \n", + "Waist_to_hgt 0.543503 " ] }, "execution_count": 6, @@ -455,7 +685,7 @@ "prediab_df[\"Healthy_diet\"] = prediab_df[\"Healthy_diet\"].astype(\"object\")\n", "\n", "# have a look\n", - "prediab_df.head()" + "prediab_df.head().T" ] }, { @@ -539,7 +769,8 @@ " preprocessing_numerical,\n", " make_column_selector(dtype_include=np.number),\n", " ),\n", - " ]\n", + " ],\n", + " verbose_feature_names_out=False,\n", ")" ] }, @@ -558,7 +789,8 @@ "cell_type": "code", "execution_count": 10, "metadata": { - "scrolled": true + "scrolled": true, + "tags": [] }, "outputs": [ { @@ -620,30 +852,33 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "# Selecting a learner using FACET ranker\n", + "# Selecting a learner using FACET\n", "\n", "FACET implements several additional useful wrappers which further simplify comparing and tuning a larger number of models and configurations: \n", "\n", - "- `LearnerGrid`: allows you to pass a learner pipeline (i.e., classifier + any preprocessing) and a set of hyperparameters\n", - "- `LearnerRanker`: multiple LearnerGrids can be passed into this class as a list - this allows tuning hyperparameters both across different types of learners in a single step and ranks the resulting models accordingly\n", + "- `ParameterSpace`: allows you to pass a learner pipeline (i.e., classifier + any preprocessing) and a set of hyperparameters\n", + "- `LearnerSelector`: one or more ParameterSpaces can be passed into this class - this allows tuning hyperparameters across different types of learners in a single step and ranks the resulting models accordingly\n", "\n", "The following learners and hyperparameter ranges will be assessed using 10 repeated 5-fold cross-validation:\n", "\n", "\n", "1. **Random forest**: with hyperparameters\n", - " - max_depth: [4, 5, 6]\n", - " - min_samples_leaf: [8, 11, 15] \n", - " \n", + " - max_depth: [4..7]\n", + " - min_samples_leaf: [8..19]; smaller ints are more frequent (zipfian distribution)\n", + " - n_estimators: [20..300]; smaller ints are more frequent (zipfian distribution)\n", " \n", "2. **Light gradient boosting**: with hyperparameters\n", - " - max_depth: [4, 5, 6]\n", - " - min_samples_leaf: [8, 11, 15] \n", + " - max_depth: [4..7]\n", + " - min_child_samples: [8..19]; smaller ints are more frequent (zipfian distribution) \n", + " - n_estimators: [20..300]; smaller ints are more frequent (zipfian distribution)\n", "\n", - "Note if you want to see a list of hyperparameters you can use `classifier_name().get_params().keys()` where `classifier_name` could be for example `RandomForestClassifierDF` and if you want to see the default values, just use `classifier_name().get_params()`.\n", + "Note if you want to see a list of hyperparameter names you can use `().get_params().keys()` where `` could be for example `RandomForestClassifierDF` and if you want to see the default values, just use `().get_params()`.\n", "\n", - "Finally, for this exercise we will use AUC as the performance metric for scoring and ranking our classifiers (the default is accuracy). Note that ranking uses the average performance minus two times the standard deviation, so that we consider both the average performance and variability when selecting a classifier." + "Finally, for this exercise we will use accuracy as the default performance metric for scoring and ranking our classifiers." ] }, { @@ -676,7 +911,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Then we create a list of learner grids where each learner grid is created using `LearnerGrid` and allows us to associate a `ClassifierPipelineDF` with a specified set of hyperparameter via the `learner_parameters` argument. Note this structure allows us to easily include additional classifiers and hyperparameters." + "Then we create parameter spaces with `ParameterSpace` for each classifier and specify set of hyperparameters for each one of them. Contrary to standard `sklearn` workflow, in this approach setting wrong hyperparameter will throw an exception as setting an attribute comes with a proper check. " ] }, { @@ -685,52 +920,49 @@ "metadata": {}, "outputs": [], "source": [ - "classifier_grid = [\n", - " LearnerGrid(\n", - " pipeline=rforest_clf,\n", - " learner_parameters={\n", - " \"max_depth\": [4, 5, 6], \n", - " \"min_samples_leaf\": [8, 11, 15],\n", - " },\n", - " ),\n", - " LearnerGrid(\n", - " pipeline=lgbm_clf,\n", - " learner_parameters={\n", - " \"max_depth\": [4, 5, 6],\n", - " \"min_samples_leaf\": [8, 11, 15],\n", - " },\n", - " ),\n", - "]" + "rforest_ps = ParameterSpace(rforest_clf)\n", + "\n", + "# random ints 4 <= x <= 7\n", + "rforest_ps.classifier.max_depth = stats.randint(4, 8)\n", + "# random ints 8 <= x <= 19; smaller ints are more frequent (zipfian distribution)\n", + "rforest_ps.classifier.min_samples_leaf = stats.zipfian(a=1, n=12, loc=7)\n", + "# random ints 20 <= x < 300; smaller ints are more frequent (zipfian distribution)\n", + "rforest_ps.classifier.n_estimators = stats.zipfian(a=1/2, n=380, loc=20)\n", + "\n", + "lgbm_ps = ParameterSpace(lgbm_clf)\n", + "\n", + "# random ints 4 <= x <= 7\n", + "lgbm_ps.classifier.max_depth = stats.randint(4, 8)\n", + "# random ints 8 <= x <= 19; smaller ints are more frequent (zipfian distribution)\n", + "lgbm_ps.classifier.min_child_samples = stats.zipfian(a=1, n=12, loc=7)\n", + "# random ints 20 <= x < 300; smaller ints are more frequent (zipfian distribution)\n", + "lgbm_ps.classifier.n_estimators = stats.zipfian(a=1/2, n=380, loc=20)\n", + "lgbm_ps.classifier.subsample = stats.uniform(0.8, 0.2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We now fit the grid defined above using the `LeanerRanker`, which will run a gridsearch (or random search if defined) using 10 repeated 5-fold cross-validation on our selected set of features from Boruta." + "We now fit a `LearnerSelector` using the parameter spaces defined above, running a random search using 10 repeated 5-fold cross-validation on our selected set of features from Boruta (`LearnerSelector` supports any CV approach implementing class `BaseSearchCV`)." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { - "scrolled": true + "scrolled": true, + "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[LightGBM] [Warning] Unknown parameter: min_samples_leaf\n" - ] - } - ], + "outputs": [], "source": [ - "clf_ranker = LearnerRanker(\n", - " grids=classifier_grid,\n", + "clf_selector = LearnerSelector(\n", + " searcher_type=RandomizedSearchCV,\n", + " parameter_space=[rforest_ps, lgbm_ps],\n", " cv=RepeatedKFold(n_splits=5, n_repeats=10, random_state=42),\n", " n_jobs=-3,\n", " scoring=\"roc_auc\",\n", + " random_state=42,\n", ").fit(prediab_initial_features)" ] }, @@ -738,7 +970,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can see how each model scored using the `summary_report()` method of the `LearnerRanker`." + "We can see how each model scored using the `summary_report()` method of the `LearnerSelector`." ] }, { @@ -762,249 +994,248 @@ " .dataframe thead tr th {\n", " text-align: left;\n", " }\n", - "\n", - " .dataframe thead tr:last-of-type th {\n", - " text-align: right;\n", - " }\n", "\n", "\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
ranking_scoreroc_aucclassifierscorecandidateparamtime
test-classifierfitscore
rankmeanstdtypemin_samples_leaf-max_depth
rankmin_samples_leafn_estimatorsmin_child_samplessubsamplemeanstdmeanstd
00.6601560.7239640.031904RandomForestClassifierDF114
10.6593070.7250830.032888RandomForestClassifierDF155
20.6589190.7251810.033131RandomForestClassifierDF115
30.6577120.7243850.033337RandomForestClassifierDF154
40.6575740.7234210.03292410.7251290.033742RandomForestClassifierDF11615164NaNNaN0.1359290.0029420.0084520.000312
50.6571300.7243060.033588920.7249970.032834RandomForestClassifierDF156518376NaNNaN0.3042410.0171140.0161900.000974
60.6567100.7231350.033213030.7248420.032747RandomForestClassifierDF84718230NaNNaN0.1941920.0119510.0112180.000494
70.6562190.7230380.03341040.7231240.033567RandomForestClassifierDF85
80.6558220.7234470.033813RandomForestClassifierDF86
90.6554510.7120170.028283LGBMClassifierDF84
100.6554510.7120170.028283LGBMClassifierDF114128NaNNaN0.1073440.0054420.0070330.000470
110.6554510.7120170.028283LGBMClassifierDF15150.7185290.034161RandomForestClassifierDF4
120.6509800.7068870.027953LGBMClassifierDF8533NaNNaN0.0282770.0008110.0034790.000201
130.6509800.7068870.027953560.7106670.031914LGBMClassifierDF1154NaN5890.9223710.0115550.0003900.0023860.000164
140.6509800.7068870.027953470.7092030.027709LGBMClassifierDF155NaN3780.8608480.0117610.0007730.0024610.000338
150.6467040.7035950.028446680.6983650.030459LGBMClassifierDF7NaN78860.8912140.0234140.0009380.0028750.000229
160.6467040.7035950.028446890.6925350.029564LGBMClassifierDF1164NaN16780.8341050.0237390.0011520.0029160.000286
170.6467040.7035950.0284463100.6855100.028177LGBMClassifierDF156NaN37980.9664890.0784970.0022830.0049910.000202
\n", "" ], "text/plain": [ - " ranking_score roc_auc classifier \\\n", - " mean std type \n", - "rank \n", - "0 0.660156 0.723964 0.031904 RandomForestClassifierDF \n", - "1 0.659307 0.725083 0.032888 RandomForestClassifierDF \n", - "2 0.658919 0.725181 0.033131 RandomForestClassifierDF \n", - "3 0.657712 0.724385 0.033337 RandomForestClassifierDF \n", - "4 0.657574 0.723421 0.032924 RandomForestClassifierDF \n", - "5 0.657130 0.724306 0.033588 RandomForestClassifierDF \n", - "6 0.656710 0.723135 0.033213 RandomForestClassifierDF \n", - "7 0.656219 0.723038 0.033410 RandomForestClassifierDF \n", - "8 0.655822 0.723447 0.033813 RandomForestClassifierDF \n", - "9 0.655451 0.712017 0.028283 LGBMClassifierDF \n", - "10 0.655451 0.712017 0.028283 LGBMClassifierDF \n", - "11 0.655451 0.712017 0.028283 LGBMClassifierDF \n", - "12 0.650980 0.706887 0.027953 LGBMClassifierDF \n", - "13 0.650980 0.706887 0.027953 LGBMClassifierDF \n", - "14 0.650980 0.706887 0.027953 LGBMClassifierDF \n", - "15 0.646704 0.703595 0.028446 LGBMClassifierDF \n", - "16 0.646704 0.703595 0.028446 LGBMClassifierDF \n", - "17 0.646704 0.703595 0.028446 LGBMClassifierDF \n", + " score candidate param \\\n", + " test - classifier \n", + " rank mean std - max_depth \n", + "2 1 0.725129 0.033742 RandomForestClassifierDF 6 \n", + "9 2 0.724997 0.032834 RandomForestClassifierDF 5 \n", + "0 3 0.724842 0.032747 RandomForestClassifierDF 7 \n", + "7 4 0.723124 0.033567 RandomForestClassifierDF 5 \n", + "1 5 0.718529 0.034161 RandomForestClassifierDF 4 \n", + "5 6 0.710667 0.031914 LGBMClassifierDF 4 \n", + "4 7 0.709203 0.027709 LGBMClassifierDF 5 \n", + "6 8 0.698365 0.030459 LGBMClassifierDF 7 \n", + "8 9 0.692535 0.029564 LGBMClassifierDF 4 \n", + "3 10 0.685510 0.028177 LGBMClassifierDF 6 \n", + "\n", + " time \\\n", + " fit \n", + " min_samples_leaf n_estimators min_child_samples subsample mean \n", + "2 15 164 NaN NaN 0.135929 \n", + "9 18 376 NaN NaN 0.304241 \n", + "0 18 230 NaN NaN 0.194192 \n", + "7 8 128 NaN NaN 0.107344 \n", + "1 8 33 NaN NaN 0.028277 \n", + "5 NaN 58 9 0.922371 0.011555 \n", + "4 NaN 37 8 0.860848 0.011761 \n", + "6 NaN 78 8 0.891214 0.023414 \n", + "8 NaN 167 8 0.834105 0.023739 \n", + "3 NaN 379 8 0.966489 0.078497 \n", "\n", " \n", - " min_samples_leaf max_depth \n", - "rank \n", - "0 11 4 \n", - "1 15 5 \n", - "2 11 5 \n", - "3 15 4 \n", - "4 11 6 \n", - "5 15 6 \n", - "6 8 4 \n", - "7 8 5 \n", - "8 8 6 \n", - "9 8 4 \n", - "10 11 4 \n", - "11 15 4 \n", - "12 8 5 \n", - "13 11 5 \n", - "14 15 5 \n", - "15 8 6 \n", - "16 11 6 \n", - "17 15 6 " + " score \n", + " std mean std \n", + "2 0.002942 0.008452 0.000312 \n", + "9 0.017114 0.016190 0.000974 \n", + "0 0.011951 0.011218 0.000494 \n", + "7 0.005442 0.007033 0.000470 \n", + "1 0.000811 0.003479 0.000201 \n", + "5 0.000390 0.002386 0.000164 \n", + "4 0.000773 0.002461 0.000338 \n", + "6 0.000938 0.002875 0.000229 \n", + "8 0.001152 0.002916 0.000286 \n", + "3 0.002283 0.004991 0.000202 " ] }, "execution_count": 15, @@ -1014,25 +1245,27 @@ ], "source": [ "# let's look at performance for the top ranked classifiers\n", - "clf_ranker.summary_report()" + "clf_selector.summary_report()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can see based on our learner ranker, we have selected a Random Forest algorithm that achieved a mean ROC AUC of 0.72 with a SD of 0.03." + "We can see based on our `LearnerSelector`, we have selected a Random Forest algorithm that achieved a mean accuracy of 0.725 with a SD of 0.034." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "# Using FACET for advanced model inspection\n", "\n", "The [SHAP approach](http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions) has become the standard method for model inspection. SHAP values are used to explain the additive contribution of each feature to the prediction for each observation (i.e., explain **individual** predictions).\n", "\n", - "The FACET `LearnerInspector` computes SHAP values for each crossfit (i.e., a CV fold or bootstrap resample) using the best model identified by the `LearnerRanker`. The FACET `LearnerInspector` then provides advanced model inspection through new SHAP-based summary metrics for understanding pairwise feature redundancy and synergy. Redundancy and synergy are calculated using a new algorithm to understand model predictions from a **global perspective** to complement local SHAP.\n", + "The FACET `LearnerInspector` computes SHAP values for each observation using the best model identified by the `LearnerSelector`. The FACET `LearnerInspector` then provides advanced model inspection through new SHAP-based summary metrics for understanding pairwise feature redundancy and synergy. Redundancy and synergy are calculated using a new algorithm to understand model predictions from a **global perspective** to complement local SHAP.\n", "\n", "The definitions of synergy and redundancy are as follows:\n", "\n", @@ -1097,14 +1330,17 @@ "source": [ "# run inspector\n", "clf_inspector = LearnerInspector(\n", + " pipeline=clf_selector.best_estimator_,\n", " n_jobs=-3,\n", " verbose=False,\n", - ").fit(crossfit=clf_ranker.best_model_crossfit_)" + ").fit(sample=prediab_initial_features)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "## Feature importance\n", "\n", @@ -1118,14 +1354,12 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -1184,9 +1418,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To interpret the synergy matrix, the first feature in a pair is the row (\"perspective from\"), and the second feature the column. For example, for (`SBP_to_DBP`, `RBC_count`) from the perspective of `SBP_to_DBP` we find that 4.4% of the information is combined with `RBC_count` to predict prediabetes risk. This represents an example of little synergy between the feature pair from the perspective of `SBP_to_DBP`.\n", + "To interpret pairs of features in the synergy matrix, rows represent the *primary feature* (\"perspective from\"), and the columns represent *associated features*. Column and row widths indicate relative feature importance.\n", "\n", - "One interesting observation is that looking at the column for `Age`, we see from the perspective of other features such as `BMI`, `RBC count` and `SBP_to_DBP`, we see values ranging up to 8.4% suggesting a small amount of synergy. This is consistent with the idea that `Age` is a strong independent feature (it has the highest feature importance) and that contributions of `BMI`, `RBC count` and `SBP/DBP` to predicting prediabetes are partly enabled by `Age`.\n", + "For example, for (`SBP_to_DBP`, `RBC_count`) from the perspective of primary feature `SBP_to_DBP` we find that 9.5% of its contribution to predict prediabetes risk is enabled by context from associated feature `RBC_count`, whereas `RBC_count`'s dependence on context from `SBP_to_DBP` is negligible.\n", + "\n", + "Another interesting observation is that looking at the column for `Age` from the perspective of other features such as `BMI`, `RBC count` and `SBP_to_DBP`, we see values ranging up to 15% suggesting that contributions of `BMI`, `RBC count` and `SBP/DBP` to predicting prediabetes are partly enabled by `Age`. By contrast, `Age` is a strong independent feature. It has the highest feature importance, as indicated by the column and row width of the matrix.\n", "\n", "We can inspect synergistic pairs of features more deeply using the *SHAP dependence plots* offered by the SHAP package. The `LearnerInspector` provides all data required for creating these and other plots through method\n", "`shap_plot_data()`:" @@ -1199,26 +1435,22 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1230,7 +1462,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "## Redundancy" ] @@ -1242,7 +1476,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1261,18 +1495,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As with synergy, the matrix row is the \"perspective from\" feature in the row-column feature pair. Let's take `Hematocrit` and `RBC count` as our features of interest. We can see that from the perspective of `Hematocrit` 35% of the information is duplicated with `RBC count` to predict prediabetes, and vice versa.\n", + "As with synergy, the matrix row is the primary feature (\"perspective from\") in the row-column feature pair. Let's take `Hematocrit` and `RBC count` as our features of interest. We can see that from the perspective of `Hematocrit` 35% of the information is duplicated with `RBC count` to predict prediabetes, and vice versa.\n", "\n", - "A second interesting and perhaps expected attribute of the heatmap is the apparent clustering of `BMI`, `Waist Circumference` and `Waist/Height`. Intuitively it makes sense that these features would have varying degrees of redundancy among them given they are physically related and the relationships with prediabetes risk." + "A second interesting and perhaps expected attribute of the heatmap is the apparent clustering of `BMI`, `Waist Circumference` and `Waist/Height`. Intuitively it makes sense that these features would have varying degrees of redundancy among them, given they are physically related." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "## Feature clustering\n", "\n", - "As detailed above redundancy and synergy for a feature pair is from the \"perspective\" of one of the features in the pair, and so yields two distinct values. However, a symmetric version can also be computed that provides not only a simplified perspective but allows the use of (1 - metric) as a feature distance. With this distance hierarchical, single linkage clustering is applied to create a dendrogram visualization. This helps to identify groups of low distance, features which activate \"in tandem\" to predict the outcome. Such information can then be used to either reduce clusters of highly redundant features to a subset or highlight clusters of highly synergistic features that should always be considered together.\n", + "As detailed above redundancy and synergy for a feature pair is from the \"perspective\" of one of the features in the pair, and so yields two distinct values. However, a symmetric version can also be computed that provides not only a simplified perspective but allows the use of (1 - metric) as a feature distance. With this distance, hierarchical, single linkage clustering is applied to create a dendrogram visualization. This helps to identify groups of low distance features which work \"in tandem\" to predict the outcome. Such information can then be used to either reduce clusters of highly redundant features to a subset or highlight clusters of highly synergistic features that should always be considered together.\n", "\n", "For this example, let's apply clustering to redundancy to see how the apparent grouping observed in the heatmap appears in the dendrogram. Ideally, we want to see features only start to cluster as close to the right-hand side of the dendrogram as possible. This implies all features in the model are contributing uniquely to our predictions." ] @@ -1284,7 +1520,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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YuXKlMWmyf/9+/Pz8jPU///wza9aswcfHp1B9SkoK3bp1Y+DAgTz11FPEx8dTv359HB0dcXBwKHYcSnaIiIiIiIiIiFn06NGDBx980LhzypYtW7C3tzcmNmrXrk2rVq3Yv38/AKdOneLzzz/nySef5OLFizz++OOMHTvWOIKjUqVKxMbGkp+fz6VLl4odh6axiIiIiIiIiJQxBoMVBoPV9RuWoL/ieuGFFzhy5IjxPCAgAIAmTZowc+ZMIiIi6Nq1K9bW1ri5ubFs2TKT61euXElYWBipqakYDAbmzJmDu7s7+/bt480336R169bGtr1796ZXr158+OGHDBgwoNgxWhkMBkOxW4uIiIiIiIiIxWRmZuLk5ES71EXYmHGB0rzMy+x0HcClS5duaM2O24WmsYiIiIiIiIiUNYabcFxHZmYmQ4YMoVWrVrRu3Zq+ffuSlJRk0mbr1q00bdqUZs2a0blzZ06dOgVATEwMbdq0oVWrVkyaNMnkmpycHObMmVOq13A1msYiInKH8Bs01dIhGEXNHWfpEERERETEzEaNGkX16tWN28MuXbqU0NBQNm7cCEBiYiLDhg1j69ateHl5sWXLFkJDQ9m+fTurVq0iPDyc3r1706BBA8aPHw8UJFBCQkLo37+/WWPVyA4RERERERGRMsfqJhzX9u233zJmzBjjeb9+/Th58iSpqakArF27lqCgILy8vABo1qwZO3bsICYmBhsbG9LT08nLyyM3NxeA1NRUevbsSXh4ON26dbvB92FKIztEhNOnTxcafiZyI6KioiwdgphBdnY2KSkptGzZskRbvYmIiEjZ5e/vj7W1NQMHDmTgwIEmdTk5OeTl5WFr+79UQnZ2tvE8KirKuFjpiRMnCA8Px9/fn6ioKPr160dYWBjLly9nxIgRJCYmEhQUxKRJk/D39zf7cyjZIXKXO336NLVq1+FSRrqlQ5Eb9ODAKQDY2drw3/90oFbVyjja23ImKY1JK7/mQlpGsfrxf/Bewh5tZVLm5GBP2qUsnp+9ipkDe1DByYGUS1mMmvc5ufn5xnZ92j/Mim2/Uq9ePfM9mFjctm3bjP9wERERkduEwargMGd/wK5du666QGm3bt0YOnQos2fPxsbGhokTJ+Lj44OTkxMA6enpuLm5sXPnTqZPn86SJUuIjIwkLS0NT09P1q9fD0B8fDw9evRg8ODBTJw4EYDIyEhq165ttsdRskPkFujfvz8HDhygQoUK5ObmYmtry3vvvYefnx9WVlZs376dRx55xNh+0KBBXL58mcWLFwMwbNgwDh06xO7du2nevDlVq1ZlxYoVZoktKSmJSxnpVO8Ugp1rJbP0KZY1+PHWnE9JZ8on3wIQ9mhLXgt5lOEfrinW9buO/MWuI3+ZlA17sh0xCRd4oIYXSWmXePHDNbwe0oUHanhxKOa08b72djYA3PefYWZ8IrGUS6f/5OyPm4xDU0VEROQ2UsxFRUvU33W8/fbbTJo0iXbt2lG1alUOHjzIxx9/bKx3dnZm5syZuLq6smbNGhwdHUlOTsbFxcXYJjo6mtDQUBYsWMC0adOYMWMGABMnTjTbZxxQskPklomMjDQOz/r555955pln+PXXX3FycmLRokXGZMelS5dYu3YtXbt2NV47e/ZsAGrVqsX27dtvSnx2rpVwqHjPTelbbq2o2DP8fCzGeL7zYDRdm5V+pIWHa3n8/e7jvQ3f8UCNe3AqZweAUzk78g0FozrG9ArkQloGH2z6AUA/S3eIyykXLB2CiIiI3Ebs7e2ZNGkSkyZNIjIykvr169O4cWNjvZ+fH7NnzyY/Px8rq4KRIvv372f06NEAHDp0iLCwMFauXImvry/x8fHUr18fg8FAfHy8WWNVskPEAlq0aGH8ptTb25vz58+TnJyMm5sba9asueHh4jk5OcZFf66wtbXFzs7uhvqVsmHr/uPGX9tYW9H7kSZs/OVwqft7/vHWLPjqR/INBqJiz5J6KYslI/vwR/w5jp1MYGLoY/x+6hwrt+8xR/giIiIiUgwGrDAUY1HRkvRXXLt372b9+vV88803JuU9evQgIiKChIQE424s9vb2+Pj4sHfvXsLDw1m9ejU1atQAoFKlSsTGxmIwGPDw8DDbs4CSHSIWsXLlSjp16mQ8DwkJ4eOPP2bw4MF89NFHhIeHs3bt2lL3P2XKFCZMmGBS9vrrr/PGG2+Uuk8pexa/FEI1Dzf+SrjA+198X6o+qnm48UCNe5j88f/+R3ZleoydjTXTn32SfdGnaHCvNx0b92Hp5t1sP/iHWeIXERERkdvPhQsXGDhwIBs3bsTGxsakztPTk4iICLp27Yq1tTVubm4sW7YMKFiwdP369cadWqBgK9vg4GAAIiIizBqnkh0it8jQoUNxdXUlPj4eFxcX4x96gP/7v/+jW7dudOrUCU9PTypWrHhD93rllVdMtoQCTFZMlrtD/3dWAtCkVnXmD3+Kp95cwuXcvBL1MeSJtswpIlHiYGfL2wN7sHF3FE7l7Dj412k+W/4lS0aGKNkhIiIicitYYM0OKBiNceDAgavWBwYGsmdP4RG/vXr1KlTWokULdu/eXewQS8L6pvQqIoVERkayY8cO/vjjDzZs2MDQoUM5cuQIAI6OjtSpU4fhw4cTFhZ2w/eys7PD0dHR5NAUlrtHm3r3m5zv/eMkcYnJ3Fel8NDAK+tvFKV2VU/ucXMptFips0M53h8SxGff7+erPUeo7O7CH/Hnyc7JJTunZMkUEREREZGbQV/1ilhAtWrV6NGjB19//bWx7LnnnmPo0KG0bduWXbt23fKYclK1EGFZd2VR0AGdWmBrY832AwUjLO5xc8GnckVOnk8yaf9yUEeeeqQxfaYv43DsmUL9De/+CJHrd5qUVXBy4L0XevH+F98bF0FNSc/Eu5IrVlbgaF/wv5WsiwnmfjyxgNyMZEuHICIiIldzk7aevZbk5GSGDRvGmTNnsLa2xtramtdee42WLVsa22zdupUxY8ZgbW2Nu7s7CxcupFq1asTExBAaGkpeXh5dunRh/PjxxmtycnKYP38+4eHhZnscJTtELCAzM5MvvviCV155xVjWsGFDvv++dOsq3Ah3d3ecyjtz8tuVt/zeYl4PDpwCwOgF63g5uCNhj7YkNy+fzOwcxi7eQEbWZZP2SemXSM3IIvNyTqG+Gt9fDWsrK/b9ecqk3LtSBWat3W5SvunXI8wa2IOgto1Zu+sgACc+m23uxxMLcnV1tXQIIiIiUojV34c5+7u2adOm0bhxY1588UUATp48SYcOHTh+vGCB/MTERIYNG8bWrVuNC5SGhoayfft2Vq1aRXh4OL1796ZBgwbGZEdmZiYhISH079/fjM+iZIfILTN06FAqVKhAfn4+OTk5DBgwwLjd7PUMGzaMQ4cOER8fT0BAAFWrVjXbHtTe3t78cfx3kpKSrt9YbmtBs9cDkJiawaj5667bfu6Xu5j7ZdGjiPb9eYrB731aqPzoycIjNlIyMhkw6yOTssOHS7/7i9w+oqOj6d69O5UrV7Z0KCIiInIb8PHx4eTJk+Tl5WFjY8O5c+eoUqWKsX7t2rUEBQUZFyFt1qwZO3bsICYmBhsbG9LT08nLyzPuHJmamkpwcDAjR44kMDDQrLEq2SFyCyxevPiqdX/8UXgxR39/f/z9/Y3ns2ff3G/Jvb298fb2vqn3kFthvaUDMPLz87N0CCIiIiJ3NIOh4DBnf9czePBgnn/+eby8vPDw8CA3N5ctW7YY66OioggICAAKdl8JDw/H39+fqKgo+vXrR1hYGMuXL2fEiBEkJiYSFBTEpEmTTD77mIsWKBURERERERERoOCL16ZNmzJv3rxCdW+++Sb29vbEx8dz9OhRIiMjeeqpp4wjNdLT03Fzc2Pnzp0MGTKEJUuW0K5dO9LS0vD09GT9+vV89913dOnShR49ehAWFsbEiRPp3LmzcSqMuWhkh4jIHSJq7jhLhyAiIiIit8pNWqB0165dODo6Ftlk/vz5/P7779jaFqQSHn30UVavXs2OHTsIDAzE2dmZmTNn4urqypo1a3B0dCQ5ORkXFxdjH9HR0YSGhrJgwQKmTZvGjBkzAJg4caLZpuqDkh0iIiIiIiIiUgy5ubnGRMcVDg4OZGZmAgVTmWfPnk1+fj5WVgXJk/379zN69GgADh06RFhYGCtXrsTX15f4+Hjq16+PwWAgPj7erLFqGouIiIiIiIiIXFfnzp2NIzGgIHmxZcsW4zodPXr04MEHHyQhoWBR+y1btmBvb4+Pjw979+4lLCyM1atX4+vrC0ClSpWIjY0lNjYWDw8Ps8aqkR0iIiIiIiIiZY0BM09juX6TiIgIXn75ZZo3b46trS0uLi6sWbMGZ2dnADw9PYmIiKBr165YW1vj5ubGsmXLgIIFS9evX2/cqQVg1KhRBAcHG/s2JyuDwZzrt4qIiMidIioqinr16nH48GHtsCMiInKbyMzMxMnJiTbnVmDjWM5s/eZlZvN95T5cunTpqmt2lCUa2SEiIiIiIiJS1tykBUrvFFqzQ0RERERERETuKBrZISIiIiIiIlLGGCjWMhsl6u9OopEdIiIiIiIiImXNlWks5jyuo3fv3gQEBJgcHh4e/PDDDwBs3bqVpk2b0qxZMzp37sypU6cAiImJoU2bNrRq1YpJkyaZ9JmTk8OcOXPM/no0skNERESuKTo62tIhiIjIbSA7O5uUlBRatmyJg4ODpcMRC/j4449Nzi9cuEDnzp1p0aIFiYmJDBs2jK1bt+Ll5cWWLVsIDQ1l+/btrFq1ivDwcHr37k2DBg0YP348ULDYakhICP379zd7rEp2iIiISJHOnTsHQPfu3S0biIiI3Fa2bdtGQECApcOQ22Aey9SpUxk5ciS2trasXbuWoKAg49ayzZo1Y8eOHcTExGBjY0N6ejp5eXnk5uYCkJqaSnBwMCNHjiQwMNCMD1JAyQ4REREpUmpqKgBVR4zFpXFzC0cjIiKWlrZvN/Gzphn//yB3t5MnT/L9998zY8YMoGDL+itJsBMnThAeHo6/vz9RUVH069ePsLAwli9fzogRI0hMTCQoKIhJkybh7+9/U+JTskNERESuyb5yFRzv87V0GCIiYmHZp+IsHYKYsPr7MGd/4O/vj7W1NQMHDmTgwIFXbT1hwgTGjx+PtXXBUqDp6em4ubmxc+dOpk+fzpIlS4iMjCQtLQ1PT0/Wr18PQHx8PD169GDw4MFMnDgRgMjISGrXrm3GZ1GyQ0RERERERET+tmvXLhwdHa/Z5tixYxw7dowFCxYYy5ydnZk5cyaurq6sWbMGR0dHkpOTcXFxMbaJjo4mNDSUBQsWMG3aNOOokIkTJ7JixQqzPoeSHSIiIiIiIiJljQXX7Hj11VeNozKu8PPzY/bs2eTn52NlVTBKZP/+/YwePRqAQ4cOERYWxsqVK/H19SU+Pp769etjMBiIj48322Ncoa1nRURERERERMoYg8HK7Edx/Prrr6SkpNC+fXuT8h49evDggw+SkJAAwJYtW7C3t8fHx4e9e/cSFhbG6tWr8fUtmBpbqVIlYmNjiY2NxcPDw7wvB43sEBEREREREZFiGjduHJMnTy5U7unpSUREBF27dsXa2ho3NzeWLVsGFCxYun79euNOLQCjRo0iODgYgIiICLPHqWSHiIiIiIiISJlzcxYovZ7NmzdftS4wMJA9e/YUKu/Vq1ehshYtWrB79+7ih1dCSnaICKdPnyYpKcnSYYjccbKzs0lJSaFly5Y4ODhYOhwRERGRu4aSHSJ3udOnT1O7Th0y0tMtHYrIHWvbtm3GfedFREREzMKCC5SWBUp2iNzlkpKSyEhPp+bQ1yl3j7elwxG5o6Qd2c+Zjz4gNTXV0qGIiIiI3FWU7BARAMrd441DVR9LhyFyR8lOOG3pEEREROROZbAqOMzZ3x1EyQ4RERERERGRMkazWK7N2tIBiIiIiIiIiEjZkZWVRYsWLdi7d69J+datW2natCnNmjWjc+fOnDp1CoCYmBjatGlDq1atmDRpksk1OTk5zJkzx+wxKtkhIiIiIiIiUtZcmcZizqOYRo8ezbPPPkuTJk2MZYmJiQwbNoyNGzfyyy+/MHr0aEJDQwFYtWoV4eHh/Pjjj3z22WfGazIzMwkODqZ69ermey9/U7JDbgufffYZx48ft3QYIiIiIiIicg0bNmwgIyOD5557zqR87dq1BAUF4eXlBUCzZs3YsWMHMTEx2NjYkJ6eTl5eHrm5uQCkpqbSs2dPwsPD6datm9njVLJDbgtDhgxh0aJFlg7jpli6dClTpkyxdBhSBuSkXCTuw2kc7N+5UF1+zmUS1q/g0HNdST920Fh+OfEs0VNfInryiyRsWGlyjSE3l8RtX9z0uEVERETEAm7SyA5/f3+aNm3KvHnzCt3y4sWL/Pe//8Xa2ppevXoxdOhQLl68CEBUVBQNGjQA4MSJEwQFBeHv709UVBT9+vVj48aNBAQEMGLECBITE+nevTuvvvoqgYGBN+X1KNkhpVK3bt2r1j3++OMlHqWxceNGRo4cWep4IiMjS30twM6dOzl06NAN9XGzxcbG8sUX+uB6p7r4/Tf8NXMcFZq2KVSX8edRoie/iJWNLY4+tUzqknfvpFL7bvi++i4pe743ludfzib2g6nYV/S86bGLiIiIyJ1j165d7Nmzh4EDBxaqe/fdd6lXrx7vv/8+q1evxt/fnyeeeAKA9PR03Nzc2LlzJ0OGDGHJkiW0a9eOtLQ0PD09Wb9+Pd999x1dunShR48ehIWFMXHiRDp37nxTRvkr2SGl4uPjQ3x8fJF1MTEx+Pr6lqi/pk2b4ulZ+g9ls2fPLvW1ADt27ODgwYPXb2hBMTExbNiwwdJhyE1iyM/D99V3qdC0daG63OSL+Ax9jcpdn8LK1nQTLStra/KzMzHk52HIywMgLzOD2PcmUql9V1wbtrgl8YuIiIjInW/Tpk3MnTuXcuXKAfDUU09RoUIFDh06hLOzMzNnzmTu3LmsWbMGLy8vkpOTcXFxMV4fHR3Nf/7zHz788EO+/PJLZsyYwYwZM5g4caLZY9XWs1IqDRo0ICoqiqpVqzJjxgx27drFunXrjNk8a+uCPNrq1auZO3cueXl52NjY8OGHH5okQgICAoCCD/KTJ08mJCTE5D4XLlwgLCyM5ORksrOz6dq1K+PGjTPWBwUFcf78eeLj44191alThw8//LBYz3HlD1hMTAyOjo4sWLAAgGnTptGiRcGHxPj4eIYMGUJCQgI5OTm0bduWN998Ezs7u2K/r6SkJPr06cPJkydJS0tj4sSJdO3a1Vi/aNEiIiIicHV1pWbNmlSqVIlmzZoREhLC6dOnCQkJITk5mYSEBONzBgcH8/zzzxd5v5ycHONcuCtsbW1LFLPcWpXaPXbVugpN/K9a5+7fkVOLZ5H841Y8OvUkNy2F2DlT8OrZj/K1/G5GqFIKcXFxREVFWTqMEouLi7N0CCIiInI1JVxUtFj9XUdOTk6hzxT29vbk5+fj5+fH7Nmzyc/Px8qqoK/9+/czevRoAA4dOkRYWBgrV67E19eX+Ph46tevj8FguOoX6TdCyQ4plYYNGxIVFUWnTp348ccfycjIIC8vjyNHjhjnaZ09e5bly5ezadMmypUrx7fffsuLL77Ixo0bjf1s374dgAkTJhR5n7feeouuXbvy7LPPAjBy5EgOHz5MvXr1APj0008BqFWrlrGvknjsscd47LHHmDBhAr6+voWSLQC9e/fm5Zdf5rHHHsNgMDBixAjeeustXnnllWLf5/PPP2fnzp1Uq1aNM2fO0LJlSx577DGsra357bffeOedd/juu++oWLEiMTExtGrVimbNmgHg7e3N9u3b2blzJytWrGD+/PnXvd+UKVMKvdPXX3+dN954o9gxS9lg6+qGz/CC3+ucpERiIidQKaArCesL1u+o2ieccl7VLBniXS0vIxWAYcOGWTgSEbmaM/PfI23PT2BlhZ3nPVQfNR7bCm4AJG/fzLlPloC1DVa2tlQdOhqn2g+QfSae2IljIT8P1xZt8Or/vy8fDLk5JG5Yg2fPpyzzQCIiN1GvXr0YNWoUc+fOxcrKis2bNxMTE0P9+vXx9vYmIiKChIQEvLy82LJlC/b29vj4+LB3717Cw8NZvXo1NWrUAKBSpUrExsZiMBjw8PAwe6xKdkipNGjQgK1bt5KTk4PBYKBNmzbs2bOHI0eO0LBhQwC8vLxMpl106NCBIUOGlOg+3t7eHD58mIyMDMqXL8/bb79tzse4rqSkJJKTk3nssYJv3a2srHj55Zfp2rVriZId3bt3p1q1gg+cVapUoVq1apw9exZvb282b97Ms88+S8WKFYGCKUI9evS4obhfeeUVxowZY1Jma6s/7ney7IR4Ts6fQbX+L3Ju4yqqBIcBkLB+JTUGjbnO1XKz5GVlAVD1hZdxbtjMwtGUXNLWTZz7ZKGlwxC5aRJWLgIrqD23IEF8cfOXnJo1FZ833iLj8AESN67BN3Ix1vb2ZMXF8NfYYdRd9jnJW7/Go+dTVOz4GEf79TQmO/Kzs4h5YwyVHr+x/4+LiBSHwVBwmLO/6xk3bhyvvfYazZs3x8HBARcXF9auXYu1tTWenp5ERETQtWtXrK2tcXNzY9myZUDBgqXr16837tQCMGrUKIKDgwGIiIgw34P8TZ9+pFTq1KnDiRMn2L17Ny1atKBNmzZs3bqVixcvEhQUBEBubi7jxo3jl19+MQ5jMpTwT+OwYcNYsWIFzzzzDFlZWTz55JOEhYWZ/XmuJjU1lcqVK5uUeXl5kZKSUqJ+riQyrnB0dCQnJwco2Fva29vbpP5G1i8BsLOz05SVu0jmyb84tWQWNQaOodw9VclJTsSh2r1gMJCTnGjp8ASw8/TC0adkaxndDtIqmv9bFpHbiZWNDR7dg43nbu0CSVhasPtAbmoK9zw9AGt7ewAcavhgXd6FvLRUsLYmP/NSwVpJuX+vl5SRzl+vjaLyU/1wfbjlrX8YEZFbwMbGhilTplx1t8nAwED27NlTqLxXr16Fylq0aMHu3bvNHuMVSnZIqdjY2GBlZcWWLVvo2rUrDRs2ZMaMGeTm5hqnmCxfvpzs7Gy2b9+OlZUVBoOB2rVrl+g+VlZWhIaGEhoaSm5uLgMHDsTV1dWYULmipEmUohTVh4uLC+fOnTMpS0hIwNXV9Ybvd4WjoyPnz583KTt//jy1atUq1NYczyl3lksxfxC/PJKa4a9iX6kgMWfr7ErOhQQwgK1zBQtHKCJy+6r8VD+T87Q9P+PcoDEAFVq1NanLSboA+XnYurlTscuTnHzzdS5+/QWewaHkJifx1/iRVHluCM4PNb5l8YvIXc4Ca3aUJUp2SKndd999bNy4kddeew1ra2tsbGxISUnByckJKBjZUb58eeOojhUrVhh/XVxDhgyhZ8+etG/fHltbW1xdXcnMzCzULjc3l/Pnz5d6RISnpycHDhygT58+JuUVK1bE1dWVL7/80rhmx5tvvkn37t1LdZ+iBAYG0rdvX/r160fFihWJi4vj888/p1WrVoViPHz4MPn5+cYFYOXOEb/8PbJOxxrP/5xesJCTY81alKtSjeSfC9akyTh2kBPHRlO+7kNYWVtT8ZHH8Rn2BnYV/jd6yOPR/xD7wVQAvJ8efAufQkSk7Lp89gwJy+dz3/T3CtXl5+QQO/kVqr04FgA794rcN71g2/vL5xM4MXY4Hj2CObu4YIH0aiPG4VDD55bFLiJ3K6u/D3P2d+dQskNKrWHDhly8eNH4wbt169b88ssvxvq+ffvyzDPP0KpVK+zt7QkNDaV8+fImfQQGBpKXl2eyG0rDhg2ZNWsWACNGjCA8PJwJEyaQn59P48aNCyUkAGbMmEGnTp1wcXHBz8+PDz74oETPEhoaSt++fWnevDlOTk4mu7F88sknhIeHM3nyZC5fvoy/vz8vv/xyifq/lsaNGzNs2DDatWuHm5sb1apVIzg4GBsbG5N2Dz74IO3bt6dJkyZUqFCB3r17M2jQILPFIZZVNfTa69lca7eWfyt//wPUGn9j2zGLiNxNcpIuEPPGaGqMnYitq+mIOENeHrETX6bSY90pX6+BSV32qThiJo6lxssTSFg+n6ovjATg7OIP8Hl9+i2LX0REClOyQ0rthRde4IUXXjCejxgxwqS+XLlyfPzxxyZlV3ZVuWLLli3XvMf999/PN998c91YevXqVeQ8sOJycXHh888/L7KuWrVqJgutllS/fv0KlW3evNn467i4OJycnDh06BBQMEqlS5cuRSYypk6dytSpU0sdi4iIiJjKS08j5tWRVBv+Mg417zOpMxgMxL31Bi5NW+De4VGTusw/jxP35uv4vD6dctVqkHP+HA731y5YL+m86RRYEZGbwvD3Yc7+7iBKdsgdLSAgoMjyf44euREHDhzgxRdfLLLumWeeKTLR8W9Vq1bl0KFDNG3aFCcnJ/Ly8hg0aBB169a94fhERETk6vKzMvnrlRFUeW4ITg/UK1QfP/stHGrci8eTpl+oXDoWxcm3J3PvpHew96oCgG0FNy6fPQ0GA7Zu7rckfhERuTolO+SOtn379pvaf4MGDW74HjY2NkyfrqGuIiIit9rJWVPJPPEHZxa8b1Lu++48krZ8ReK6VZT3a0DqT98b67xfeInLZ05z35uR2FX6345FlXs/Q8xrowCoNtx8011FRK5KC5Rek5IdIiIiInJXqjl20lXrKj76BBUffaLIuvIP1C9cVq8BdeZ/XERrERGxBG3pICIiIiIiIlLGGG7CUVyrV6+mdu3aBAQEGI+ZM2ca67du3UrTpk1p1qwZnTt35tSpUwDExMTQpk0bWrVqxaRJpgnnnJwc5syZU9LXcFUa2SEiAGQnnLZ0CCJ3nJzki5YOQURERO5UFpzGcubMGd544w2efvrpQnWJiYkMGzaMrVu34uXlxZYtWwgNDWX79u2sWrWK8PBwevfuTYMGDRg/fjwAmZmZhISE0L9/f7M9jpIdInc5d3d3yjs7Exs5wdKhiIiIiIhIGXDmzBn8/PyKrFu7di1BQUF4eXkB0KxZM3bs2EFMTAw2Njakp6eTl5dHbm4uAKmpqQQHBzNy5EgCAwPNFqOSHSJ3OW9vb47//jtJSUmWDkXkjrNt2zaGDRtm6TBERETkTnSTRnZkZmaaFNva2mJnZ2dSdubMGXbv3s0777xDUlIS/v7+TJgwAScnJ6Kiooy7Yp44cYLw8HD8/f2JioqiX79+hIWFsXz5ckaMGEFiYiJBQUFMmjQJf39/8z0LSnaICAUJD29vb0uHIXLHiY6OtnQIIiIiIiVSqVIlk/PXX3+dN954w6Ts0qVLWFtbs379emxtbYmIiGDAgAF88sknpKen4+bmxs6dO5k+fTpLliwhMjKStLQ0PD09Wb9+PQDx8fH06NGDwYMHM3HiRAAiIyOpXbu2WZ5DyQ4REZHbUM7FROI/nEHy9q9ouPmgsdxgMJCw4kPSD+4BaxswGHBv/ziVHu1O9tl44qaPw5Cfj+vDrfHqM+h/1+XmcOHLNXg88ZQlHkdERETKiAsXLuDo6Gg8t7UtnDb49NNPTc6HDx/OBx98QEZGBs7OzsycORNXV1fWrFmDo6MjycnJuLi4GNtHR0cTGhrKggULmDZtGjNmzABg4sSJrFixwizPod1YREREbjMXvl7Hny8Pwq1Nx0J1ab/+QOafx7l/+nx8p8/jvsnvkfjFKrLj40je8Q0e3YKpHbGc5O83G6/Jz84iZvJ/sfP0upWPISIiImWQo6OjyfHvKSwAH374IQaD6f4t9vb25OXl4efnx6ZNm1i5cqUxabJ//37jGh+HDh0iJCSE5cuX4+fnR3x8PPXr16devXrEx8eb7TmU7BAREbnd5OdSK2I5bm0KL9Jl53EP+dmZ5GdnAZCXkY6VtRU25Z2xsrEmP+sShrw8yMsz1v81YQQeTwRToeUjt/IpRERE5CYyYIXBYMaD4q//8cMPP5hsE7tx40a8vLxwdXWlR48ePPjggyQkJACwZcsW7O3t8fHxYe/evYSFhbF69Wp8fX2BgmkzsbGxxMbG4uHhYbb3o2ksIiIit5lKj/3nqnWO99XGvUNXop7qgL1XVXLOnaHmuLewdatIxY5PEPfOG1zcshHPnn3ITUkiZvIovPoNwbleo1v4BCIiInLTGf4+zNlfMc2fP58XXniBJk2a4OTkRM2aNfnoo48A8PT0JCIigq5du2JtbY2bmxvLli0DChYsXb9+vXGnFoBRo0YRHBwMQEREhNkeR8kOERGRMiT90F4ufrueB5ZsxM69ElmnYoidNpZy1WpSrko17ps4G4DLiQn89caLeHQLImHFXBKAqkNexqGaj0XjFxERkbLP0dGRRYsWXbU+MDCQPXv2FCrv1atXobIWLVqwe/dus8YHmsYiIiJSpiSu/4QqzwzBzr1gpXSHaj54dn+ai19/bmyTHR9HzMSRVBs+ntRffsB74Et4D3yJhBVzLRW2iIiImJ3VTTjuHBrZISIiUobkX87Gyr6cSZm1fTnys7MByPzrOCffeYOaL79Juao1yEk8h8O9tcBgICfxnCVCFhEREbnlNLJDRESkDHF/pDMJK+eSf7kguZGbmsK5z5bi1rYjl44f4eQ7b+Az/m3KVa0BgI1rBS4nnOZywmlsXN0sGLmIiIiYlcHK/McdRCM7REREbjOnZk8hK+6E8Tx61LMAONZ6gKqDRpGXnkb0yAFY2dpCvoHKQf0p/2ADknd+y70TZmNX8X8rmVf+Tz9ip4wGoOrgMbf2QUREROTmseACpWWBkh0iIiK3mWrDXrlmvccTT+HxxFOFyt3adSpUVv7BBtSO/MhssYmIiMTFxREVFXXV+uzsbFJSUmjZsiUODg63MDKR/1GyQ0RERERERK4rNzUFgGHDhhWr/bZt2wgICLiZId3VDFhhMOOioubs63agZIeIiIiIiIhcV37mJQDumzyICq0bXrVdyg/7OfHqXFJTU29RZCKFKdkhIiIiIiIixWZftTLl69S8an3mX6dvYTR3Ma3ZcU1KdoiIiMg1XT53hswT0ZYOQ0RELCznwnlLhyBSbEp2iIiISJFsHJ0AiJ81zcKRiIiISCHm3i5WW8+KiIjI3cDGpQIAs2fPpn379haORkRELG3btm3FXpxUbgWrvw9z9nfnULJDRERErqlGjRr4+flZOgwREbGw6GhNaZSyw9rSAYiIiIiIiIhICRluwlFMly9fplGjRiQmJpqUb926laZNm9KsWTM6d+7MqVOnAIiJiaFNmza0atWKSZMmmVyTk5PDnDlzSvToxaGRHSLC6dOnSUpKsnQYInecuLg4S4cgIiIiYnbz5s0jKCgIDw8PY1liYiLDhg1j69ateHl5sWXLFkJDQ9m+fTurVq0iPDyc3r1706BBA8aPHw9AZmYmISEh9O/f3+wxKtkhcpc7ffo0tevUISM93dKhiIiIiIhIcRmsMFhggdKMjAwWL17MDz/8YFK+du1agoKC8PLyAqBZs2bs2LGDmJgYbGxsSE9PJy8vj9zcXABSU1MJDg5m5MiRBAYGmu85/qZkh8hdLikpiYz0dGo8MxJ7j3ssHc4dzcGruqVDMJF19qSlQ7jjJe39gQvbN1g6DBEREZFi8/f3x9ramoEDBzJw4MBC9bNmzeKFF17A0dGR/v37ExYWhr+/P1FRUQQEBABw4sQJwsPDjeX9+vUjLCyM5cuXM2LECBITEwkKCmLSpEn4+/vflOdQskNEALD3uOe2+zAuN5d+v28+O1c3S4cgIiIiUiK7du3C0dGxyLoLFy7w1Vdf8d133xWqS09Px83NjZ07dzJ9+nSWLFlCZGQkaWlpeHp6sn79egDi4+Pp0aMHgwcPZuLEiQBERkZSu3Ztsz6Hkh1lwGeffcZDDz1k9t98c/r1119JTk6mY8eOlg5FRERERETkzmewKvbUk2L3dx3Tpk1j3Lhx2NjYFKpzdnZm5syZuLq6smbNGhwdHUlOTsbFxcXYJjo6mtDQUBYsWMC0adOYMWMGABMnTmTFihXmexaU7CgThgwZwjPPPMObb75p6VCu6u233yYmJkbJDpFimv54SzycHEzKanm6MXz99/wWn3iVqwrrWf8+qrqWJ3LXIQBsra2Y0bUVFRzKkZKVzeiNP5Kb/7+ltUMa1Wblb8fN8xAiIiIiclfZtm0be/bsYebMmQAcO3aMAwcO0L59e/z8/Jg9ezb5+flYWRUkTvbv38/o0aMBOHToEGFhYaxcuRJfX1/i4+OpX78+BoOB+Ph4s8eqZMdNULduXY4dO1Zk3eOPP86sWbNKNEpj48aN1KxZs9TxREZGMnTo0FJdu3//fl5++WWysrK4fPkyrVu3ZvLkydjb25u0mz59OhkZGaWO8VZYuHAhc+fOxcnJCYAlS5bg4+Nj2aDkrjVm008m5xUc7Jn7f49w8PSFYvdhb2PNUw1r0X/VVmPZA5XdSbqUzYgNu3gtsCkPVHbn0NmLADzfwg9728JZeLn5cs6fJTMm2tJhlFjO+bOWDkFERESupoTbxRarv+vYt2+fyfk/1+w4f/48ERERJCQkGHdjsbe3x8fHh7179xIeHs7q1aupUaMGAJUqVSI2NhaDwWCyq4u5KNlxE/j4+BAfH0/VqlUL1cXExODr61ui/po2bXpD8cyePbtUyY4LFy7Qr18/1q1bx7333kt+fj4vv/wyEyZMYMqUKSZtbyQZc6tMnz6dffv24ezsbOlQRAp5rvmDLN17jDxD8f+P1btRLdYdPkHG5VxjWZ7BgJN9wV/tTva2xv7++0gjLlzK4sMfoswbuFyTdbmC0Tvx79++I/OKw9XV1dIhiIiIyG3O09OTiIgIunbtirW1NW5ubixbtgwoWLB0/fr1xp1aAEaNGkVwcDAAERERZo9HyY6boEGDBkRFRVG1alVmzJjBrl27WLdunXHBFmtrawBWr17N3LlzycvLw8bGhg8//NAkEXJlJduYmBgmT55MSEiIyX0uXLhAWFgYycnJZGdn07VrV8aNG2esDwoK4vz588THxxv7qlOnDh9++GGxnmP58uUMGDCAe++9FwBra2vGjx9PWFiYsc2IESPYv38/ycnJNG3alPnz55v0MWbMGL777jucnZ0JDg5m4cKFAIwePZqePXsCBVsUvfPOO9ja2mJlZcVbb73Fww8/DEBsbCxhYWFs3rzZ2OeUKVOoVq0a/fr1Y8KECaSmprJ3716qVatG3bp1Wb9+PQ0bNjTGMmzYMA4dOsSpU6fo1q2bsZ/FixcbR3Z8+umnvPPOO9jY2GBvb897772Hn58fAEePHiU8PJxjx47x3nvvsXz5chITE3F2dubrr78GCnY0CQ8PJyYmhtzcXB577DEmTJhgvFf//v3p3LkzK1asICUlBYPBwKJFi0xG+FzrPQAcPHiQF198kezsbHJzc/nvf//L//3f/xXr91Jub/e4ONG4qidv79xf7Guc7e3oXLsGfT/ZShUXJyZ0bsbAz3ZwJCGJ1KzLLA5qzx+Jyfx+LpkJnZrx+/kkPvrtj5v3EFIkG6eC5Ors2bNp3769haMpuezsbFJSUmjZsqWlQxEREZFCrP4+zNlfySxevNjkPDAwkD179hRq16tXr0JlLVq0YPfu3SW+Z3Ep2XETNGzYkKioKDp16sSPP/5IRkYGeXl5HDlyhAYNGgBw9uxZli9fzqZNmyhXrhzffvstL774Ihs3bjT2s337dgCTD83/9NZbb9G1a1eeffZZAEaOHMnhw4epV68eUPABHqBWrVrGvkoiOjqaJ5980qTMxcWFVatWGc9nzZoFwM6dO4tcUGb69OnExsbyyCOPsH//fnbt2mVM9gD8/vvvTJkyhW3btlGhQgXi4uJ4/PHH2bdvH3Z2dsWK087Ojh07dtCxY0datGjBL7/8Qp06dUhJSaFChQrMnj37mu/hyJEjTJ06lR07duDm5sbBgwcJCQlh//79ADzwwANs376d/v37M336dBYtWmR8x1cMGTKEDh06EBYWRn5+PqGhoXzyySc89dRTxjbr1q1j9erVODo6snHjRsaMGcPnn39erPeQm5tLr169+Oyzz6hfvz7Jyck88sgjPPzww8ZhYP+Uk5Nj3L/6Cltb22K/U7m1Brf0Y+7PUSUahdj/4bos3XuM3Pz8QnVTtxUML7S1tubNx1vyW/x5HqpSiY61qrN07zF2/HnaTJFLcdWoUcOYQBURERGRm8/6+k2kpK6M7MjJycFgMNCmTRv27NlDVFQUDRs2BMDLy4sNGzZQrlw5ADp06MDx4yVbNNDb25vDhw8b18p4++23C30IvxEZGRnG9S1uVFJSEm+99ZZJogNgy5YthISEUKFCBaDgA8GOHTsKtbuWKx8gatasiZ+fH1ZWVtSoUYPk5ORiXf/111/Tt29f3NzcAHjooYfw9vYu8vfjySefLPId79y50zjixdramuHDh7NhwwaTNiEhIcYtnDp37syRI0eMddd7DwcPHqROnTrUr18fADc3N55++mnjyJJ/mzJlCk5OTibHv6ceye3h3oou3FvRlZ0nip+AqORUjobeHnzz+8mrtnGwteHdJ/zZ+scpsnLzOHjmAs+v3clzzfWBW0REROROYDBYmf24k2hkx01Qp04dTpw4we7du2nRogVt2rRh69atXLx4kaCgIAByc3MZN24cv/zyi3GlWkMJ5upDwfSMFStW8Mwzz5CVlcWTTz5pMsXkRjk5OXHp0iWz9FW3bt0iEyeJiYncf//9JmWVKlUqUd9XEgJWVlYmvy6utLQ0PvnkE7744gtj2fnz50lLSyvUtkmTJkX2ceHCBeNUISgYWXFl+s8VFStWNP7azs6O/H98I3+995CWlsavv/5qco+0tDT69+9fZDyvvPIKY8aMMSmztdUf99vREP+HeP/vnVSK4mhnS2aO6SidVj5VcHWwZ2Gvgp+HcrY2+Li7sLBXANO27eNs2iVmPdGalb/9zo4/T/N8Sz/2njpPdm4el3PzburziIiIiMgtYoEFSssSffq5CWxsbLCysmLLli107dqVhg0bMmPGDHJzc42jApYvX052djbbt2/HysoKg8FQoh1aoOADfWhoKKGhoeTm5jJw4EBcXV2NCZUrSppEucLX15ejR4+abCebnZ3NpEmTmDx5con6+ufeyv/k4eHBuXPnTMouXLiAm5sbNjY22NrakpWVZVJ/8eJFqlWrVqL7X4unpyfh4eHFWsT1as9Rs2bNUk0VuuJ678HT05NWrVqxZs2aYvVnZ2enKStlgN89FXEpZ8cvJ88VWT8moBHBDWoR+vEWohIuGsu/OBLDF0dijOf/XLPD1cGe93u25YMfD/NzXAIAKZmX8XYtjxXgYKfdWERERETkzqdpLDfJfffdx8aNG2ncuDG2trbY2NiYTAvJzc2lfPnyxhEIK1asKNFoBChYJ2Lbtm1Awbf2rq6uZGZmFmqXm5vL+fPnS/wMffr0YdGiRcTExBjL5syZQ3p6eon7upoOHTrw0UcfkZqaCsCpU6d45JFHjKMeqlSpQlxcHAkJCcb6r776ymz3vxLDsmXLjNNesrKyeP7554t8l1fTrFkzk8V5Nm3aZFx5uLgxXOs91KpViz/++IPDhw8br5k6dSoHDhwo9j3k9jO8zUNE/nDwqvVJl7JJzb5M1r/WX7mWqq7leff7A8ZEB8CXx2Lp9qAPy3sH8vnhv24oZhERERG5XVjdhOPOoZEdN0nDhg25ePGicVpF69at+eWXX4z1ffv25ZlnnqFVq1bY29sTGhpK+fLlTfoIDAwkLy+PmJgYHB0dWbBgAQ0bNjQuCjpixAjCw8OZMGEC+fn5NG7cmD59+hSKZcaMGXTq1AkXFxf8/Pz44IMPivUMnp6eLFy4kEGDBpGdnU12djbNmzdn+vTpxjajRo1i7969JCcnk5CQYJxmcWWUw/vvv8/MmTOJiYkhICAAR0dHvvzyS+P1devWZdy4cTz22GPGXUgWLVpkHJVgbW3Nu+++S+fOnXFzc+P+++/n6aefLlb8xVWnTh3Gjh3L448/jq2tLbm5uYwdO9a4vkZGRgZdu3Zlx44drFixgtatWzN48GCTETTvvvsuw4YNY/HixeTl5VGnTh3j71NxXO892NnZsXLlSl566SWys7PJysqie/fuxgVvpWwa+NmOa9bP232EebuPXLMNwJm0S8a+jp5LKlSfknWZsNWlH3kkIiIiIlLWWBlKO8dBRO4IUVFR1KtXD99Rb+HgVd3S4YjcUVIO/8rJJe+wbt26QrtbiYiIlDXr16+ne/fu1F08Ho9Hr74teeLXP3Gs/yT9/+8myczMxMnJiYf3bcXaoZzZ+s3PyubXxh24dOmS8YvfskwjO+5i/1zs8p/+OXpEREREREREbj/m3kFFu7HIHeNGFtSUO8/lxITrNxKREslJumDpEERERETuSkp2iNzl3N3dKe/sTNySty0disgdy9XV1dIhiIiIyB3H3IuKamSHiNxBvL29Of777yQlFV7YUkRuTHZ2NikpKbRsefV5zSIiIiJifkp2iAje3t54e3tbOgwRERERESkuw9+HOfu7g1hbOgAREREREREREXPSyA4RERERERGRssYAmHMHFY3sEBERERERERG5fWlkh4iIiIiIiJhdXFwcUVFRlg4DKNiBUGvU3V2U7BARERERERGzyU1OA2DYsGEWjuR/yrs4c/zY73dUwsNgsMJgxmks5uzrdqBkh4iIiIiIiJhNXkYmALXf7YN7+wctHA1kRidwsGcESUlJd1SyQ65NyQ4RERERERExu3I1KuHsV83SYdy5DFZmXqD0+n1lZ2czcuRIDh06xKVLl6hevToffPAB99xzj7HN1q1bGTNmDNbW1ri7u7Nw4UKqVatGTEwMoaGh5OXl0aVLF8aPH2+8Jicnh/nz5xMeHm62x9ECpSIiIiIiIiJyXRMnTqRKlSrs3LmTX3/9lYcffpjnn3/eWJ+YmMiwYcPYuHEjv/zyC6NHjyY0NBSAVatWER4ezo8//shnn31mvCYzM5Pg4GCqV69u1liV7BARERERERGR62ratClDhw41nnft2pU//vjDeL527VqCgoLw8vICoFmzZuzYsYOYmBhsbGxIT08nLy+P3NxcAFJTU+nZsyfh4eF069bNrLEq2SEiIiIiIiJS1lyZxmLOA/D396dp06bMmzev0C179OiBq6srALm5ubz//vuEhIQY66OiomjQoAEAJ06cICgoCH9/f6KioujXrx8bN24kICCAESNGkJiYSPfu3Xn11VcJDAw0++vRmh0iIiIiIiIiAsCuXbtwdHS8ZpuAgABOnDhBnTp1mDBhgrE8PT0dNzc3du7cyfTp01myZAmRkZGkpaXh6enJ+vXrAYiPj6dHjx4MHjyYiRMnAhAZGUnt2rXN9hxKdoiIiIiIiIiUNYa/D3P2V0zbt28H4LvvvqNTp07s3r0bBwcHnJ2dmTlzJq6urqxZswZHR0eSk5NxcXExXhsdHU1oaCgLFixg2rRpzJgxAyhYD2TFihVmexxNYxERERERERGR6/ryyy9Nztu2bcv999/P0aNHAfDz82PTpk2sXLnSODpk//79+Pn5AXDo0CFCQkJYvnw5fn5+xMfHU79+ferVq0d8fLxZY1WyQ0RERERERKSMMWBl9uN6ZsyYYZyKAnDq1CmOHz/O/fffDxSs6fHggw+SkJAAwJYtW7C3t8fHx4e9e/cSFhbG6tWr8fX1BaBSpUrExsYSGxuLh4eHWd+PprGIiIiIiIiIlDX/WFTUbP1dx8cff8zw4cN58803sbOzo3z58ixbtsy4aKmnpycRERF07doVa2tr3NzcWLZsGVCwYOn69euNO7UAjBo1iuDgYAAiIiLM9ywo2SEiIiIiIiIixeDl5cWqVauu2SYwMJA9e/YUKu/Vq1ehshYtWrB7926zxfdPSnaIiIiIiIiIlDUWXKC0LNCaHSIiIiIiIiJyR9HIDhHh9OnTJCUlWToMEREREbGA7OxsUlJSaNmyJQ4ODpYOR4rN6u/DnP3dOZTsELnLnT59mtp16pCRnm7pUERERETEgrZt20ZAQIClwxAxCyU7RO5ySUlJZKSnU6PvCOwreV3/AhGR24iDVzVLh2Ai6+wpS4cgIlJi6X8e5uyG5aSmplo6FCkBg6HgMGd/dxIlO0QEAPtKXrfdhwYRkbJGf4+KSFmUnXjW0iFIaVhg69myRMmOm6RPnz7Ex8dz9uxZ+vTpwyuvvGLpkEpsy5YtTJkyBYA9e/aQlpZm4YhERESubnq3VniUdzQpq+VRgeGff89v8eeL1UfnOjX4b4fGrN4fzYc/HgbA1tqKGU+0poKDPSlZlxm94Qdy8//39VdIkzqs3Pu7+R5EREREbphFkh25ublMmjSJ7du3Y21dsCHMq6++SmBgoCXCuSlWrFgBwNKlSzl1qvRDWpcuXUrPnj1xcXExV2jFFhgYaPw9qVWrVqn7iY2N5eDBg3Tr1s1cod0UCxcuZO7cuTg5OQGwZMkSfHx8LBuUiIgU25gvfjQ5r+Bgz9ygAA6eTrzutR7lHZjUpQUXMrLYdtz0/9sP3FORpEtZjFj3Pa91epgH7qnIoTMXAHi+VT3sbWzM9xAiIiLFpgVKr8UiyY433ngDa2trdu7ciZWVFadPn6ZTp0589dVXVK9e3RIh3baWLFlCYGCgRZId5hITE8OGDRtu+2TH9OnT2bdvH87OzpYORUREzOC5ln4s/fUYecWYhFzZ2ZEFP0ex99R5nm9Vz6Quz2DAyd4OACd7O2N//23fmAsZWcYRICIiInL7sEiy49NPP+XIkSNYWRVkjry9vXn99ddJTEykevXqrF69mrlz55KXl4eNjQ0ffvghvr6+AAQEBPDoo4/y0UcfERYWxs8//8xvv/3GqFGjGDBgABMmTCA1NZW9e/dSrVo16taty/r162nYsCHz588HIC8vjwkTJvDzzz+TmZlJzZo1mT9/Po6OjleN+d8WLVpEREQErq6u1KxZk0qVKtGsWTNCQkKKdX3//v0JCwvD39/fWFarVi3++OMPAKZOncrmzZvZv38/vXr1oly5cgB89dVXxu2gDhw4wNixY7l8+TIZGRmMHz+exx57DKBY78Hf359ly5Zx//33A5Cfn4+fnx979uyhfPnyxX4XV3P69GlCQkJITk4mISHBuLJzcHAwzz//PABjxozhu+++w9nZmeDgYBYuXAjA6NGj6dmzJ8A1fx5iY2MZPnw4TZo0YefOnVy4cIEePXrw2muvGeO4cOECYWFhJCcnk52dTdeuXRk3bpyxftiwYRw6dIhTp06ZJGQWL15sHNnx6aef8s4772BjY4O9vT3vvfcefn5+ABw9epTw8HCOHTvGe++9x/Lly0lMTMTZ2Zmvv/7aGMOLL75IQkICKSkp9OzZkzFjxhjvZTAYGDNmDLt27cLW1pYaNWrw4Ycfmvw+XCsGERExdY+LE42rVebt7b8Vq/2RhKtvv33k7EVSsy6zuHcH/jifwu8JSUx4tDm/n0vio33HzRWyiIhIyRj+PszZ3x3klic7EhMT8fT0xNbW9Na9evUC4OzZsyxfvpxNmzZRrlw5vv32W1588UU2btxobNugQQOGDx9O5cqV2bVrF+7u7nTr1o0BAwYAYGdnx44dO+jYsSMtWrTgl19+oU6dOqSkpFChQgW+/PJL8vPz+fbbbwEYN24c77//PqNGjSrWM/z222+88847fPfdd1SsWJGYmBhatWpFs2bNzPGKjDGNGzeOgIAAVqxYQdWqVYtsM3fuXKpXr05CQgLNmzfnzz//xObv4bTXew8DBw5k2bJlTJgwAShYo6Ndu3ZmSXRAQRJr+/bt7Ny5kxUrVhiTLP80ffp0YmNjeeSRR9i/fz+7du0yTm2C4v08/PDDD7zwwguMHz+enJwc2rZty5NPPkmDBg0AeOutt+jatSvPPvssACNHjuTw4cPUq1fwzd3s2bOBgmTT9u3bC8V45MgRpk6dyo4dO3Bzc+PgwYOEhISwf/9+AB544AG2b99O//79mT59OosWLTL2fcWsWbPo1asXTzzxBLm5ubRv354OHTrQtGlTAL7++mvOnDnDrl27AFi5ciWfffYZ/fr1K1YM/5aTk0Nubq5Jma2tLXZ2dkW2FxG50wz2r8fcHw+b7d9tU7fsAcDW2po3u7Xit1Pnecjbg451qrP012PsiI43051ERETEHKyv38S8MjIyjGsiADz33HMEBATQqFEj1qxZg5eXFxs2bDCOZOjQoQPHj5t+a+Ln54eDgwOVK1emXr16VKtWzWTxzCvfdtesWRM/Pz+srKyoUaMGycnJAHTr1o3Jkycb2xd1j2vZvHkzzz77LBUrVgTAx8eHHj16lOxFmMGmTZuM037uueceY9Ljiuu9h169erFhwwYMfw/HXbp0KWFhYbf2If6WlJTEW2+9ZZLoAIr181CzZk06duwIFCR4OnToQFRUlLHe29ubw4cPk5GRAcDbb79dKBlxLV9//TV9+/bFzc0NgIceeghvb+8if2aefPLJIvuePHkyTzzxBFCQdGjXrp3J9V5eXpw6dYozZ84AEBISYkx0lDQGgClTpuDk5GRyXFlsVkTkTndvRVfurejKzj/Nm4BwsLXh3R5t2Hr8FFm5eRw8ncjzq3fwXAuNshMREQu4shuLOY87yC0f2eHk5MSlS5eM51e+7Z8yZQrp6enk5uYybtw4fvnlF+M0F8O/5tpe+UBsZWVlbHPlv/+u/+evrzhz5gyjRo3i9OnTACQnJxu/YS+OzMxMvL29Tco8PT2Lfb25LF26lKVLl5Kfn4+VlRWHDx82eVfXew9OTk74+/vz3Xff0ahRI/76668SvQdzqlu3rkkS7Iri/DxcSTpd4ejoSE5OjvF82LBhrFixgmeeeYasrCyefPLJEiV10tLS+OSTT/jiiy+MZefPny9yd5omTZoU2cdPP/3EpEmTyMzMBArWMflnwq1Ro0ZMnDiRcePGkZCQQJMmTXj55ZeNo2xKEgPAK6+8YjJNBig0mkpE5E41pM1DvP/DoavWO9rZkpmTe9X6ojjb2zGrRxtW7v2dHdHxPN+qHntPnSM7N4/LeXk3GrKIiEiJaRbLtd3yTz+enp6cO3eO3Nxckw9f58+fp27duixfvpzs7Gy2b9+OlZUVBoOB2rVrmzWGl19+mZ49e/J///d/AGzfvp2PPvqo2Nc7Ojpy/rzpFnbnz58v0Y4ltra2ZGVlGc/T0tIKTTu44t8f7gH+/PNP5syZw86dO41reLRr167Y97/iueeeIyIigujo6GKvN1IaRT3DP11tAVZz/DxYWVkRGhpKaGgoubm5DBw4EFdXV4KCgop1vaenJ+Hh4QwdOvS6ba/2HAMGDOCbb76hRo0aALz++uuF2rRp04Y2bdoABWvCjB49mjlz5pQ4BigY4aIpKyJyN/LzqohLOTt+iUsosn5M+yYEN6pF6MpviTp7sVh9ujrYE9mzLR/sOsTPsQX9pmRextu1PFaAg5LJIiIit51bPo0FCqZPTJ061Xh+4sQJvv76a9q3b09ubi7ly5c3fou/YsUKk9EI5pCbm2v8UJqXl8enn35aousDAwNZuHAhFy8W/CMpLi6Ozz//vER9PPDAA3zzzTfG8zlz5hT54dTT05MDBw4UKs/Pz8fOzg57e3ugYLHSf07dKK4GDRoQFxfH0qVLb1qyw9PTk8OHD5Ofn1/ia83x8zBkyBC2bdsGFCSZXF1djSMsiqNDhw4sW7bMOP0nKyuL559/vkR95OfnG0dpXLhwweT3HgpG6UyfPt14XrFiRZP+zRGDiMjdYHjbBkR+f/Cq9UmZWaRmXSYrx3Q0hp9XRRY+1YGFT3XghdYP8ULrh4zntTwq8O7OA8ZEB8CXR2Po5ncvy/t04vNDf9605xEREbkqTWO5Jot8FTFhwgQmTJhA27ZtMRgMlCtXjhUrVuDu7k7fvn155plnaNWqFfb29oSGhpptwcwrJk6cyLPPPsvkyZOxt7enR48eV13osSiNGzdm2LBhtGvXDjc3N6pVq0ZwcLBxYVCAfv36ERcXx9mzZ8nOzmbLli14enoaEysDBw4kNDSUFi1aUKFCBcLDw4t8ztdee42BAwcybdo07OzsjLux1KpVi65du/Lwww9Tvnx5mjRpQvPmzUv1Prp168bevXuN60FcsX37diZOnAhAfHy8cTeV0aNHG3d9KY4HH3yQ9u3b06RJEypUqEDv3r0ZNGgQAO+//z4zZ84kJiaGgIAAHB0d+fLLL43XmuPnYcSIEYSHhzNhwgTy8/Np3Lgxffr0Kfb1derUYezYsTz++OPY2tqSm5vL2LFjjbv3ZGRk0LVrV3bs2MGKFSto3bo1gwcPNhk58u6779KlSxfjWjP/HoUTFBTECy+8QMuWLXFwcMDV1ZW5c+cWOwYRESkw8NPCC03/07yfopj3U+EvB6LOXuTZT7YW+z4pWZcJW7WtxPGJiIjIrWFluN78AikkLi6O7777zviBOTc3ly5duhAZGUndunUtHF3JPfbYY7z++uulTpZI2RYVFUW9evXwHTEdB69qlg5HRERERG6xlMN7OLl8FuvWrePJJ5+8arv169fTvXt36i4ej8ejLa/aLn7hev56dS711w6n8pNFr2l3K6VHnWL3Q+M4fPiwcROHsiwzMxMnJycabv8F67+XNDCH/Kws9gc049KlS3fEl6qaZFqEKyMY/q1hw4bMmjWLqlWrcujQIZo2bYqTkxN5eXkMGjSozCU6jh8/zjPPPEPbtm1LlegYMWLEVUfEFLWFq4iIiIiIiMitoGRHEa73Qd3GxsZkfYWyqnbt2vz444+lvn7WrFlmjEYs7fKFs5YOQUREREQsICcl0dIhiJidkh0idzl3d3fKOzsTt0zJKxEREZG7maurq6VDkBIx96KiWqBURO4g3t7eHP/9d5KSkiwdioiIiIhYQHR0NN27d6dy5cqWDkXEbJTsEBG8vb3x9va2dBgiIiIiIlJMBkPBYc7+7iTWlg5ARERERERERMScNLJDREREREREpMyxwrzrbGjNDhERERERERGxJMPfhzn7u4NoGouIiIiIiIiI3FE0skNERERERESkrDGYeetZs25ja3ka2SEiIiIiIiIidxSN7BAREREREREpYwxYYTDjoqLm7Ot2oJEdIiIiIiIiIlIir732Gq1atcLf35/g4GAuXLhgrNu6dStNmzalWbNmdO7cmVOnTgEQExNDmzZtaNWqFZMmTTLpLycnhzlz5pgtPiU7RERERERERMoaw004imnGjBlYWVnx448/smvXLrp3787QoUMBSExMZNiwYWzcuJFffvmF0aNHExoaCsCqVasIDw/nxx9/5LPPPjP2l5mZSXBwMNWrVy/16/g3JTtEREREREREyporC5Sa86Ag8fDPIycnp9CtbWxseP75543nPXv25ODBgwCsXbuWoKAgvLy8AGjWrBk7duwgJiYGGxsb0tPTycvLIzc3F4DU1FR69uxJeHg43bp1M9vrUbJDRERERERERACoVKkSTk5OxmPKlCmF2rz00ktUqVLFeL5161batGkDQFRUFA0aNADgxIkTBAUF4e/vT1RUFP369WPjxo0EBAQwYsQIEhMT6d69O6+++iqBgYFmfQ4tUCoiIiIiIiJS1hgMBYc5+wMuXLiAo6OjsdjW9tppg7i4OKZOncqGDRsASE9Px83NjZ07dzJ9+nSWLFlCZGQkaWlpeHp6sn79egDi4+Pp0aMHgwcPZuLEiQBERkZSu3ZtszyOkh0iIiIiIiIiAoCjo6NJsuNazp07R+/evVm4cCEVK1YEwNnZmZkzZ+Lq6sqaNWtwdHQkOTkZFxcX43XR0dGEhoayYMECpk2bxowZMwCYOHEiK1asMMtzKNkhIiIiIiIiREdHX7M+Li7uFkUixXKTRnYUV0pKCr169SIiIoK6desay/38/Jg9ezb5+flYWRWsA7J//35Gjx4NwKFDhwgLC2PlypX4+voSHx9P/fr1MRgMxMfHm+1xlOwQERERERG5i507dw6A7t27WzYQKTMuXbrEf/7zHyZNmkTTpk1N6nr06EFERAQJCQl4eXmxZcsW7O3t8fHxYe/evYSHh7N69Wpq1KgBFKwREhsbi8FgwMPDw2wxKtkhIiIiIiJyF0tNTQWgxtgRuDZvctV2iRu/4eyC5bcqLLmeEm4XW6z+imno0KEcOnSI119/3aT822+/xdPTk4iICLp27Yq1tTVubm4sW7YMKFiwdP369cadWgBGjRpFcHAwABERETf+HH9TskNERERERESwr3IPTr73XbXeztN837qLGVhwGsvChQuvWR8YGMiePXsKlffq1atQWYsWLdi9e3ex711c2npWRERERERERO4oGtkhIpw+fZqkpCRLhyEiIiIixZSdnU1KSgotW7bEwcHB0uGIJVh4gdLbnZIdIne506dPU7tOHTLS0y0dioiIiIiU0LZt2wgICLB0GCK3HSU7RO5ySUlJZKSnU2PAaOw9vK5/gYjILeJQpbqlQzDKOnPS0iGIiJhI/+MwZz9fbFxcVO5CGtlxTUp2iAgA9h5et9UHCxGR24n+fhSR20124llLhyByW9MCpXLX+Oyzzzh+/Lilw7imX3/9lc2bN1s6DBERERERud1dGdlhzuMOopEdctcYMmQIzzzzDG+++aalQ7mqt99+m5iYGDp27Hjdtr6+vkRHR9+CqERELGt6l+Z4OJkuvlfLowLDv9jFb6cvXPf6ZtU8CW/hR57BgJ2NNcv2HWdLdDy21lbMeKwFFRzsScm6zOgvfyY3/3//0Atp6MvK/fp7VkREbk8GgwGDGRMU5uzrdqBkh9zWYmNjCQsLKzTaYcKECfj6+hISElLsvjZu3EjNmjXNHaJZTZ8+nYyMDEuHISJyWxnz1W6T8woO9szt0YaDZy5e99oqLk6MadeQweu+51xGFi7l7JjXoy0J6ZkAJGVmM2LjT7zWoTEPVHbn0NmCPp9v/gD2NjbmfxgRERG5JZTskLtG06ZNLR3Cdd3uyRgRkdvBcw/XZem+4+QV4xuo9vd7s+bwX5zLyAIgLTuHBb8eo3Otanz5+0mc7Ar+KeRkZ0ve36M6/tu2ARcys/lw9+Gb9xAiIiI3zPD3Yc7+7hxas0PKtI8//piAgABcXFz46aefeOSRR2jTpg3jxo0ztgkICCAgIIB7772XlStXFurj4sWLhIaG8sgjj9CyZUtGjx5Nfn5+ieJYvXo1gYGBBAQEEBgYWGh6yaFDh3j00UeN91i+fLlJ/YgRIwgICKBRo0Y899xzhfrPyMigX79+NGrUiEceeYSIiIgSxScicqe4x9mRxlU9+fr34u2OYmVlhb2t6T93HGxtyM03cORcEqlZOSz+zyOkZuXw+/lkJgQ25VRqBgt/PXYzwhcREZFbRCM7pEzr3bs3vXv3xtfXl3fffZd169bh5uZm0mb79u1AwdSXorz44osEBAQwYMAAAF5++WXee+89hg0bVqwYzp49y/Lly9m0aRPlypXj22+/5cUXX2Tjxo0A5OTk0Lt3b1atWoWfnx9ZWVl06dKFhx56iAYNGgAwa9YsAHbu3MmKFSsK3eOVV16hevXqLF26FIC33nrrmjHl5OSQm5trUmZra4udnV2xnklE5HY1uMWDzN19pNjfPW2NjmfRfx7h57hzHDufTDXX8gxpVY8xX/0MwNQdvwFga23Fm12a81t8Ig9VqUjH/zzC0n2/s+PEmZv0JCIiIjdIW89ek0Z2yB0hLy+PYcOGFUp0FMeuXbuMiQ6ASZMmERoaWuzrvby82LBhA+XKlQOgQ4cOJru+HD9+nPvuuw8/Pz8AHBwc+OKLL6hVq1ax77F582ZGjhxpPB8xYsQ1ExdTpkzBycnJ5JgyZUqx7ycicju6192Fe91d2flX8RMQZ9Iu8d+vfmZYq3rM7dGG0e0acPRcEgfP/m+9DwdbG97t2oqt0fFk5eZx8OxFnv/8O55r9sDNeAwRERHzMNyE4w6ikR1y2ytqSkl+fj5WVlYmZY0bNy5V///ux87ODnd392Jfn5uby7hx4/jll1+Mff1zJePExEQqV65sco2zs3OJYszOzjaJyc7OjgoVKly1/SuvvMKYMWNMymxt9cddRMq2Ia3q8f5PV19Hw9HOhsycvELlh85eJHz9D7iUs+P9J1vz4hc/Guuc7W2Z1bUVK/f/wY4TZ3i++YPsjT9Pdl4+l3NLNqVRREREbh/69CO3tSpVqnD69OlC5bGxsQQGBhrPbW1tcXR0LNU9/r3FUk5ODunp6cVOeCxfvpzs7Gy2b9+OlZUVBoOB2rVrG+s9PDw4d+6cyTXp6elYW1vj5ORUrHvY29uTnJxsHLmSk5NDSkrKVdvb2dlpyoqI3FH87nHHxd6OX06dL7J+TLuGBD90P6GfbiMqIanINpM6PsyHu49wMTMbANdydkQ+0ZoPfo7i55MFf0+nZGXj7eqEFQUjPkRERG5bmsZyTZrGIrc1e3t7HnzwQRYvXmws27t3L3v37jXb7ir+/v7GtTAAXn/99UILiF5Lbm4u5cuXN47qWLFihclokdq1a3PixAmOHj0KFIzSePLJJ/njjz+KfY+OHTsyc+ZM4/ns2bNLvIiqiEhZNrxVfSKvMaojKTOb1OzLZBUxsgPgmSa1OZmSzo+xCcayqq7leXfXQWOiA+DL30/S7QEflge35/Mjf5nvAUREROSW0sgOue3Nnz+fUaNGsXjxYqytrXFxceGzzz7D0dGRXbt28eqrrxIdHU1AQAAAc+fONRlZERgYSF5eHjExMTg6OrJgwQIaNmxoXBT03XffZfjw4SxevJjs7Gxat27NkCFDih1f3759eeaZZ2jVqhX29vaEhoZSvnx5Y72dnR0ff/wxI0aMIDs7m6ysLMLDw42LkwKMGjWKvXv3kpycTEJCgvFZriyuOnnyZAYNGkTDhg2pWLEi/fv3x9PTs/QvVUSkjBn4+XfXrJ/3y1Hm/XL0qvVL9h4vVHb0fHKhspSsy4St2Vni+ERERG45jey4JiU75LZXsWJFFi1aVGSdv7+/MSFwNVu2bLlu/yUZyfFv5cqV4+OPPzYpe/bZZ03O69evz9dff33VPv45aqMoLi4ufPTRRyZlJVlEVURERERE5G6iZIfINRw4cIAXX3yxyLpnnnmGfv363dqAREREREREQCM7rkPJDpFraNCgwXVHjtwpLieetXQIIiIiIlJMOUmJlg5BLE3JjmtSskPkLufu7k55Z2fiFs2wdCgiIiIiUkKurq6WDkHktqRkh8hdztvbm+O//05SUtFbNYqIiIjI7Sc6Opru3btTuXJlS4cilqKRHdekZIeI4O3tjbe3t6XDEBERERERMQslO0RERERERETKGo3suCYlO0RERERERMqo6OjoG+4jLi7ODJGI3F6U7BARERERESljzp07B0D37t0tG4hYjuHvw5z93UGU7BARERERESljUlNTAag+6r+4NG12Q31d/PpLzi5ZZI6w5FbSNJZrUrJDRERERESkjLK7pwqO999/Q33YVvIwUzQitw8lO0RERERERETKGgNmHtlhvq5uB9aWDkBERERERERExJw0skNERERERESkrNGaHdekkR0iIiIiIiIickfRyA4RERERERGRskYjO65JyQ4RERERERGRMsZgMGAwY4LCnH3dDjSNRURERERERETuKBrZISIiIiIiIlLmmHkayx2292ypR3acOnWKBQsW8MEHHwBw6NAhswUlIiIiIiIiIlJapUp2rF27lieeeIKkpCQWL14MwMaNG5k+fbpZgxMREREREZHby+X4c2T8HnvVIyfhoqVDvDtcWaDUnMcdpFTTWGbMmMGOHTtwdXXlq6++AmDs2LG0bNmSMWPGmDVAERERERERsTwbJ0cATrw618KRCFAw60SzWK6qVMkOGxsbXF1dAbCysjKWOzg4mCcqERERERERua3YVij4DDh79mzat29/1Xbbtm1j2LBhtyoskSKVKtlRrlw5jhw5woMPPmgsi4qKwt7e3myBiYiIiIiIyO2nRo0a+Pn5XbU+Ojr6FkZzFzP31BNNY4FZs2bRs2dP6taty9GjR+nVqxd//PEHK1euNHd8IiIiIiIiIiIlUqpkx0MPPURUVBQHDx4kLS0NT09PatWqha2tdrIVKYtOnz5NUlKSpcMQERG5rWRnZ5OSkkLLli01XVtEbj8a2XFNpc5O2NjY0KhRI3PGIiIWcPr0aWrXqUtGepqlQxEREbktbdu2jYCAAEuHISIiJVCqZMerr77K5MmTzR2LiFhAUlISGelp1Bw5mXL3VLV0OCIiIreNtKh9nFkaSWpqqqVDEREpTCM7rqlUyY7vv/++yPLo6Gh8fX1vKCARsYxy91TFofq9lg5DRETktpF9Nt7SIYiIXJ2SHddkXaqLrK2LzHAPGjTohgMSEREREREREbkRpUp2TJs2jUGDBvHzzz+TnZ1t7phERERERERE5FqujOww53EHKdU0lk6dOnHPPffw9NNPY2Vlha2tLQaDgT///NPc8YmIiIiIiIiIlEipkh1NmzZl27Zthcrbt29/wwGJiIiIiIiIyHVozY5rKtU0loEDBxZZrh1aRERERERERMTSSjWy46mnniqyvFWrVjcUjIiIyJ0qJ/kCZ5bPIfnHrTz08Q6TupTdOzm/6VOwtsbKxhbvPuE43luLy+fPEPf+VMjPx6Vhc+7p2dd4jSE3lwvbNuLRqfutfRARERG5PWhkxzWVamSHiIiIFN/FHV/x19TRVGjerlBdxh9RXNy+ifvGz8L3jUiqhb1E3PuTMeTnkfzTdip17I7vxPdJ2b3TeE3+5WxiZ0/AvlLlW/kYIiIicjsx3ITjDlKqkR21a9fGysrKeJ6bm4udnR3ly5dn7969ZgtOJCAggOzsbMqVK4fBYMDKyoo33niDdu3asXTpUiZMmEDNmjXJzc0lOzubsWPH0qNHD+P1+/fv5+WXXyYrK4vLly/TunVrJk+ejL29vQWfqnQiIyMZOnSopcMQkVIw5OXhO/F9rB0cC9Xlpafh2a031nYFfy+Vq1IdG6fy5KWnYWVtTX5WJob8PAx5eQXtL2UQN3sCHo8H4VK/6S19DpG7VVxcHFFRUZYOQ8REXFycpUMQua2VKtlx/Phxk/O0tDQ++OADHnjgAbMEJfJPq1evpmrVqgCcPXuW9u3b8/PPPwPw7LPP8sorrwCQmJhIy5Ytadu2LZUqVeLChQv069ePdevWce+995Kfn8/LL7/MhAkTmDJlisWep7Rmz56tZIdIGVWpQ9er1rk2amFynpuShCE/H1tXN9zbPsqpeTNI/uFbPB77D7mpycRGTMAraADl69S/2WGL3PXy0lMBGDZsmIUjESl7OuQts3QIRs5+1Swdws2haSzXVKpkx7+5uLjw3//+ly5dutCtWzdzdClSJC8vLx588EFOnDhRqM7Dw4MGDRpw5swZKlWqxPLlyxkwYAD33nsvANbW1owfP56wsLBi389gMDBx4kQ2b96MjY0NlSpVYu7cuXh6ehrbzJs3j4ULF2JnZ4e1tTXTp0+nZcuWAMTGxhIWFsbmzZuN7adMmUK1atXo168fAI0aNeL1119nzpw5pKamUr16dZYtW4ajY8E3wEFBQZw/f574+HgCAgIAqFOnDh9++OFV487JySE3N9ekzNbWFjs7u2I/u4jcevm5OZz8YBre/QoSm7aubviMKkjO5lw8T8w746nU8UkS1hb8A7LqM8MoV6W6xeIVudPlZWUCUPWFl3Fu2MzC0YiYStq6iXOfLLR0GCK3LbMkO65ITk42Z3cihRw8eJDo6Gjq1q3LgQMHCtUlJydTt25dAKKjo3nyySdN2ri4uLBq1api32/x4sX89ddffPfdd1hbW/PVV18xaNAg1q5dC8DWrVv5+OOP2b59O05OTvz55588+uij7Nu3DxcXl2LdIyMjg99//51vv/0WgFGjRjFv3jyGDx8OwKeffgpArVq12L59e7H6nDJlChMmTDApe/3113njjTeKdb2I3HqG/DxOvj8V93ZdKF/Lz6Qu++wpTs6ZSrXnRnNu/UqqPP08AAlrllJjyKuWCFfkrmLn6YWjj6+lwxAxkVbRw9IhlEj22WT+GPkRCZ/8XGjUx8WtUUSPXYWVtTW2bk48sCAMh2oVjfWZMec5NnARuWmZGPIN1HqrN+7t6pKfk8vhp94nJykDO/fy1PvkBazt/vcRN272N9QY1vmWPeMtp5Ed11SqZMdPP/1kcp6VlcXXX39NlSpVzBKUyD/16tWLcuXKce7cOVxcXPjss89wcHAAYOHChWzZsoXk5GTOnj3Lhx9+iK1twY91RkYGTk5ON3Tvr776irFjx2JtXbCWb5cuXWjcuLGxfsOGDQwdOtR4n/vvv58OHTqwc+dOuna9+rD1f8rJySE8PNx4/uijjxoTHKX1yiuvMGbMGJOyK+9FRG4/BoOBU/PfxrleY9xaBpjUZcad4NS8GdQY8grlvKqRk5SIQ437wGAgJynRQhGLiIgU3+nF33Ey4hvufa07CZ/8bFJ3OTGN4y+uoNHmMZTzcuPilsNE9ZtLk61jjW2i+n7I/ZN74d62LlmnLvLbo2/R9IfxXPr9LHaerjy0ZjhHn19M2r5YKjS/H4ATk9aRn5VzS5/zbnP27FleeuklPv74Ywz/SpRs3bqVMWPGYG1tjbu7OwsXLqRatWrExMQQGhpKXl4eXbp0Yfz48cZrcnJymD9/vslnoxtRqk8/8+fPN+3E1pYHHniA1157zSxBifzTlTU7oqOj+b//+z98ff/3zco/1+xIT0/npZdeIiUlhb59++Lk5MSlS5du6N6JiYlUrmy628E999xj/HVqamqhei8vL1JSUop9D1tbW5NRII6OjuTk3NhfzHZ2dpqyIlKGnFn+PuW8a1Cpg+lU0Esnfid+0bvUfPEN7D0K/u6xdXYlJ/EsGMDWpYIlwhURESkRQ24eTXe9hk35coXqzq/dQ+VezSjn5QaAa7P7Sd5xlMyY8zj6eHLp+Bms7Gxxb1swertcVXcuHT3N+XX7cK5Xlbz0LADy0rOwsrHCYDDwx8iPsK/syn3ju9+qR7QIg8FQKMlwo/0V1+LFi5k1axZvvPEGH3/8sUldYmIiw4YNY+vWrXh5ebFlyxZCQ0PZvn07q1atIjw8nN69e9OgQQNjsiMzM5OQkBD69+9vtucpVbJj0aJFZgtApLh8fX3x8fFh8+bNdOzYsVC9s7MzQ4YM4dVXX6Vv3774+vpy9OhRk7bZ2dlMmjSJyZMnF+ueHh4enDt3jmrV/reoUUJCgjHh4eLiwrlz50yuSUhIoEmTJkBBIiMrK8uk/uLFiyb9FZc5/yITkVsrftG7ZMXHGM//nPQiAI731saxpi8XtqzHqZYfafv/921Xlaef5/L5s/iMmoydWyVjuUfXYGIjJgLg3W/ILYlfRETkRlR9LuCqdelH4qn4SMFGF5knznHshaVU8K9FRlQ8jj6epEfF4/JQwfpUeRnZHB20CM8nm5Bx5BRV+rXGzr08ewOm4FyvOs4Na3L0uYW4NKhB9aGdbsmzWZQFp7Hk5uby008/Ub58+UJ1a9euJSgoCC8vLwCaNWvGjh07iImJwcbGhvT0dPLy8oxrDKamphIcHMzIkSMJDAw0z7Ng5jU7RG62ESNG8NZbbxWZ7DAYDHz66afGREOfPn3o2LEjTzzxBD4+PgDMmTOH9PT0Yt+vS5cuvP/++8yfPx9ra2u+/fZbPvzwQ+OaHd26dWPq1Kk8+uijxjU7Nm/ezPTp0wGoUqUKcXFxxgTJqVOn+Oqrr3jooYdK/Oy5ubmcP3/eZHFUESkbqg548Zr17m2K/geZ0/11C5WVr+VHrckfmCMsERERi8tLz8LWzYmknceIfWsjDy5+jlPvbSY37cqIjWxs3ZzIOnWRo88t5L7XupObns35tb9iZWVFnci+AORfziWqzwe4ta5Nys9/cm7tFGqM6ILnE42vdXspQmZmpsl5URsdPPfcc1e9PioqyrixwokTJwgPD8ff35+oqCj69etHWFgYy5cvZ8SIESQmJhIUFMSkSZPw9/c363OUKtkxb948Bg4cWKh8/vz513xokRv1yCOP8NJLL3Hs2DHgf2t25Ofnk52djb+/v3E6laenJwsXLmTQoEFkZ2eTnZ1N8+bNjYmI4ujfvz9xcXG0adMGW1tb424sV3Ts2JHo6GjatWuHvb09VlZWLF26FFdXV6BgB5h3332Xzp074+bmxv3338/TTz9dqmefMWMGnTp1wsXFBT8/Pz74QB92RERERKRss3F2IPbtL7F1daT+Z8OwcbQnJ/kSti4Of9eX48K3h0jZ9Qd15w3A0ceThNW/YPN3PUDepWwOBb+HV4g/eelZVGh+P95hz7Kv/dQ7O9lxk0Z2VKpUyaS4pBsdpKen4+bmxs6dO5k+fTpLliwhMjKStLQ0PD09Wb9+PQDx8fH06NGDwYMHM3FiwajVyMhIateubZbHKVWyY9WqVUUmOz7++GMlO8Ssitp9ZN++fQDUrVvXuH3r1TRp0oRvvvmm1Pe3srLijTfeuOYf7sGDBzN48OCr1vfo0YMePXpctf6PP/4wOff39y8yq9mrVy969ep1/aBFRERERMoI5werciryW9rnLsXKygqA9ANx1Bz1WEG9X1VSf/6TdklzsXV1/Ls+lvIPFkwLz025xMH/zKb60E54PtGYExM/x61NHWwc7bF20Bp2pXHhwgUcHR2N5yXd6MDZ2ZmZM2fi6urKmjVrcHR0JDk52WSdwujoaEJDQ1mwYAHTpk1jxowZAEycOJEVK1aY5TnMNo3FYDBo61kpU5YuXcqSJUuKrHv33Xdp0KDBrQ1IREREROQu49mjCScjv+VyQopxNxYre1scfQqmbjvVrkKFVrVI2x9r3I3l3Lq9NP1+PDkX0znQfRb3vdaDioH1ALCr5ExW7AUM+fnkXbpsyUe7+W7SyA5HR0eTZEdJ+fn5MXv2bPLz840JrP379zN69GgADh06RFhYGCtXrsTX15f4+Hjq16+PwWAgPj7+xp/jbyVKdmzcuJENGzbw+++/M2jQIGO5wWDg8OHDPPbYY2YLTORm69ev33VHhoiIiIiIyI05NmQpGUdPG8/3dpgGgGtjH2rN6E3tWSEceOIdrKytsa3ghN8S01kEfsuf5+jAhUSnZmEwGKj7Xj/s3MuTui8G36lBuLWuY2zr9VQLDgW/R/y8bXg/0/bWPKClWHCB0mvp0aMHERERJCQkGHdjsbe3x8fHh7179xIeHs7q1aupUaMGUDBtJjY2FoPBgIeHh1ligBImOx566CFcXFzYs2cPISEhJnWenp488MADZgtMREREREREyr667137C8aKgfVo9vfIjKI4+njS+NuXC5W7NvYpVGZXyYXGW8aWOEYpmRdeeIEjR44Yz68sSNqkSRNmzpxJREQEXbt2xdraGjc3N5YtWwYULFi6fv16404tAKNGjSI4OBiAiIgIs8VYomRHjRo1qFGjBm5ubrRte4dnyURERERERERuV4a/D3P2V0zvv//+NesDAwPZs2dPofKi1iBs0aIFu3fvLv7Ni8m6NBetXLnS3HGIiIiIiIiIiJhFqZIdVapUMXccIiIiIiIicofYatOXc+v3WjoMANKjTlk6hJvEcBOOa8vMzGTIkCG0atWK1q1b07dvX5KSkgD48ssvad68Oc2bN+fLL780ue706dOsXr36hp+4JEq9G4vBYCAhIQHDvxYxUSJEpGzKTjDfysciIiJ3gpzkC5YOoczIuZhI/IczSN7+FQ03HzSWGwwGElZ8SPrBPWBtAwYD7u0fp9Kj3ck+G0/c9HEY8vNxfbg1Xn3+sQFCbg4XvlyDxxNPWeJxROQqRo0aRfXq1XnvvfeAgh0uQ0ND2bhxI5GRkaxbtw6AAQMGGDcw+euvv3j22WeZN2/eLY21VMmOr776ivDwcKpXr05UVBT169cnKiqKFi1a8MUXX5g7RhG5idzd3Snv7ELs269aOhQREREpgy58vY7za5fjFTqY5O1fmdSl/foDmX8e5/7p87Gytib/cjZ/jHgG5/qNSf5+Cx7dgnFv/xjHBv3HmOzIz84idtpYKnZ+0hKPc1e7fCaBS9EnrlkvtxEL7Mby7bffcvz4ceN5v379eOedd0hNTcXGxob09HQAbGxsAIiKiuKFF15g2bJlxt1XbpVSJTsmT57ML7/8gqenJ+3bt2fbtm0cOHDAuMKqiJQd3t7eHP/9mHH4mYiIiBTYtm0bw4YNs3QYt7/8XGpFLMfG0alQlZ3HPeRnZ5KfnYWNoxN5GelYWVthU94ZKxtr8rMuYcjLg7w8APIy0omZMprK/+mHS+MWt/pJ7lrWf//exU2bVaz2rq6uNzMcKa6blOzw9/fH2tqagQMHMnCg6TbAOTk55OXlYWv7v1RCdnY2tra2vPrqqwwaVJC0nDp1Kr/++iujR4/mk08+Mdl95VYpVbLDyckJT09Pk7IGDRqwd+/tMSdLRErG29sbb29vS4chIiJyW4mOjrZ0CGVCpcf+c9U6x/tq496hK1FPdcDeqyo5585Qc9xb2LpVpGLHJ4h75w0ubtmIZ88+5KYkETN5FF79huBcr9EtfAKxrVABgNmzZ9O+ffurtsvOziYlJYWWLVveqtDEAnbt2oWjo2ORdd26dWPo0KHMnj0bGxsbJk6ciI+PD05OTrRo0YJt27YBsGPHDiZMmEDfvn0JDg6mWrVqzJ8/HyenwknRm6VUyQ57e3tOnz6Nt7c3VlZW5OTkYGtraxyyIiIiIiIikn5oLxe/Xc8DSzZi516JrFMxxE4bS7lqNSlXpRr3TZwNwOXEBP5640U8ugWRsGIuCUDVIS/jUM3HovHfbWrUqIGfn5+lw5DissA0lrfffptJkybRrl07qlatysGDB/n4449N2mzcuJGIiAjWr19PYGAg3333HfPmzeOjjz4iLCzMfPFeR6l2Y5k6dapxeErfvn1p06YN7dq1K3LPXBERERERuTslrv+EKs8Mwc69EgAO1Xzw7P40F7/+3NgmOz6OmIkjqTZ8PKm//ID3wJfwHvgSCSvmWipsEbkKe3t7Jk2axI8//kjbtm0JCQmhcePGxvqPP/6YOXPmsH79elxdXXF0dMTBwYF69eoRH39rN0Qo1ciOBg0aGBci7devH23atCE/Px9fX1+zBiciIiIiImVX/uVsrOzLmZRZ25cjPzsbgMy/jnPynTeo+fKblKtag5zEczjcWwsMBnISz1kiZJGywwIjO67YvXs369ev55tvvjGWLViwgI0bN/L5559TrlzBn/tLly6Rn59PbGwsHh4e5ou1GEq99ew/3XfffeboRkRERERE7iDuj3QmYeVcao59E2v7cuSmpnDus6VUHfxfLh0/wqnIyfiMfxv7ylUAsHGtwOWE02AwYOPqZtngRaRIFy5cYODAgWzcuNG46woULF762WefmSxeOmDAAFq1aoWTkxOrV6++pXHeULIjOjqatLQ0GjVqRHZ2tjF7IyIiIiIid4dTs6eQFfe/7UqjRz0LgGOtB6g6aBR56WlEjxyAla0t5BuoHNSf8g82IHnnt9w7YTZ2Ff/3bW/l//QjdspoAKoOHnNrH0SkrLHQyI5KlSpx4MCBQuWDBw8usqyo8luhVMmOI0eOEBISgre3NydPnuTgwYO8+OKLdOvWjccee8zcMYqIiIiIyG2q2rBXrlnv8cRTeDzxVKFyt3adCpWVf7ABtSM/MltsInc0C05jKQtKtUDp0KFDWb58OZs2bTLOu5k1axbTpk0za3AiIiIiIiIicntITk6mb9++dOzYkc6dO9OlSxd++uknAL788kuaN29O8+bN+fLLL02uO336dNmYxpKdnU29evUAsLKyAsDBwcFkbo6IiIiIiIiI3CSGvw9z9ncd06ZNo3Hjxrz44osAnDx5kg4dOnD8+HEiIyNZt24dULBWx5VZH3/99RfPPvss8+bNM2Ow11eq7EReXh5paWm4uLgYy1JSUsjJyTFbYCIiIiIiInJtOQlnyPzzzxvuQ6Q4fHx8OHnyJHl5edjY2HDu3DmqVPl7gWEbG9LT042/BoiKiuKFF15g2bJl1KhR45bGWqpkx4gRI2jXrh19+/blzJkzvP/++yxbtoyxY8eaOz4RERERERH5FxtHJwBOznzLbH26urqarS+5BW7Smh3+/v5YW1szcOBABg4caNJk8ODBPP/883h5eeHh4UFubi5btmwB4NVXX2XQoEEATJ06lV9//ZXRo0fzySef4OXlZb44i6lUyY6goCDq16/P119/zVNPPYW1tTXLly+ndu3a5o5PRERERERE/sXGpQIAs2fPpn379jfUV3Z2NikpKbRs2dIcocktUpDrMF+y40pXu3btwtHRscg2b775Jvb29sTHx2Nvb2/MCXz//fe0aNGCbdu2AbBjxw4mTJhA3759CQ4Oplq1asyfPx8nJyezxXs9JUp2xMXFUaNGDeLi4njggQd44IEHblZcIiIiIiIich01atTAz8/P0mHIXWL+/7d353FR1fsfx98DDDKjuCSakWllaCmmXi0ESxn3Si0t10wTzfVaptmmaXSVfl2rq5nWRUuxFBS7ghlX0xQ1yq5abpRrLinuIoosMjC/P8ypiUXQgQF8PR+P83hwvuec7/mceZxs+PD5fr9z5mjPnj32+To7d+6s6OhoxcfHq3379pKkFStWaMaMGYqNjVX79u21YcMGhYeHa9GiRRoyZEiJxVqk1VieffZZSdKgQYOKIxYAAAAAAFAYV4exOHO7BqvVmmthEi8vL6Wnp0uSIiMjNXv2bMXGxqpy5coymUzy8vKSv7+/jh07ViwfQ36ua+lZZ5bKAAAAAACA0q9Tp06aNm2afX/nzp1as2aNLBaL5s6dq8WLF2vZsmX24SppaWnKycnR4cOH5ePjU6KxFmkYy6VLl5SWlmZfbhYAAAAAALiCkycoLcTaszNmzNCrr76qgIAAeXh4yNvbW1988YUqVaqkrKwsLV261KHyIyQkREFBQTKbzYqOjnZirNdWpGTH0KFD1axZMx07dkwNGjRwOGaz2WQwGLRnzx6nBggAAAAAAFzPZDJpxowZeR4bMWJEnm15tZeEIiU7Bg8erMGDB6tt27b2WVYBAAAAAEAJK6alZ8uL61p6tnHjxs6OAwAAAAAAFBbJjgJdV7Ijv7IVAGVTUlKSkpOTXR0GUOb1XvK9q0OwW9wr0NUhAGXekSNHXB0CAOA6XVeyA0D5kZSUJL8GDZSWmurqUIAyr9GkOa4Owc7f39/VIQDlRtbpE0o/tN/VYQAOsk6fcHUIcDUXVHb07dtXJ044vns7d+7U888/r6+++kqSNHnyZD366KP240lJSUpISFDPnj2dF2shkOwASokTJ04oLi5OISEhJXrf5ORkpaWm6o7eo+RZrUaJ3htA8ak3/E1XhwCUeRd2/6TT8bE6Nuv/XB0KkK/KlSu7OgTcRCIjIx32z549q06dOun7779XTEyMpCsrsFxNdhw8eFCDBw9WeHh4SYdKsgMoLWJiYjRq1Cj17t1bFStWLPH7e1arIa+at5f4fYHyqGPDu/R8uxY6dfGSvW3D3t80/7udhbq+/X13ql9AQ2Xn2GSz2bTn5DnNWLNZkvRuz3aqYqqglPRMvRT9jaw5f/wVpn/LRvp8U6Ik8d8z4ASXz52SJH3wwQdq27ati6MBHGVmZiolJUWBgQxbvGnZVJjVYovWXxGFhYVp3LhxWrhwoVJ/rxR3d3eXJCUmJmrUqFFasGCB6tSp48RAC4dkB1zKz89P+/btc2gbNGiQhgwZolatWpVYHDNnztTo0aNL7H556d+/vxo2bJhvoiMiIkI9evSQt7d3CUcGoKhqeJs1O/5Hxe08UORrK1Uwakz7B9QnPEapmVmSpNcfDVS3pn7adzJZ59LSNWbxGk3q2kr33eajncdOS5KGt2mmCh7uTn0OAFfUqVNHjRo1cnUYAFAiWrVqJTc3Nw0dOlRDhw7N97zffvtNGzdu1LRp03TXXXdp2LBhkq4kQDZv3qzx48crKipKtWrVKqnQHbi55K5AKfPBBx+4OgRVqlRJrVu3zvf4/PnzdeHChRKMCMD1quFt0tnU9Ou6NsOardTMy6piqiBJMrq7qbKpgk6kXFKOzSazp1GSZPY0Kuf3sbUvdw6QNSdHM77Z4pwHAAAApd/VOTucuUlKSEjQli1bCkx0SFJoaKjeeOMNubm5qWXLllq7dq3Wrl2rjIwMvfzyyxowYIB69+6tp59+WmlpaSXxiTigsgOl2pIlS/T+++/L3d1dnp6e+vDDD+1/WbFYLOrcubMWLVqkIUOGaNOmTfrpp5/00ksv2ee9OHv2rMaMGaOTJ08qJSVFPXr00CuvvGLvv1evXjp9+rSOHTsmi8UiSWrQoIE+/vhj+znh4eH65JNPZDQa5ebmpnfeecehXHDnzp0aP368MjIylJmZqZEjR+qZZ56xH+/UqZOOHDmiPn36KDk5Wf/73/+Unp6uuLg43XbbbYqIiND8+fMlSUePHs1V6RIWFqbVq1dr27Zt6tmzpypUuPIL0H//+195eXnl+bllZWXJarU6tHl4eMhoNBbp8wdwfXwqmXV/7RoaGOSvyl4V9NNvJzVr3VZlZGVf81prdo4mx27UgsFdlZKeKd8qlfRpwg59d+CYJOlCeqbmDXpM+06e0+4TZ/XW4w9rz4lzWvhDYnE/FgAAKE1cuPTs7t27tXv3bs2dO9ehfcWKFZoxY4ZiY2PVvn17bdiwQeHh4fbf2UoSyQ6UWj///LPCwsIUHx+vqlWraseOHXr66ae1bds2+zlNmjTRCy+8oJo1ayohIUHVqlVT165d7cmOf/3rX+rZs6e6desmq9Wqtm3bql27dmrRooWkK8kU6cpwmnXr1uWK4ZtvvlFkZKTWrVsns9msAwcOqHPnzvrxxx/l7e2trKws9e3bV4sXL1ajRo2UkZGhRx55RPfff7+aNGkiSVq1apUiIiIUGhqqsLAwTZ8+3eEeAwcO1MCBA+1x/NXrr7+u119/XRaLRZ9//rluv/3a4/CnTp2q0NBQh7bJkyfrzTffvOa1AG6cl9FDOTabRkeuVnaOTf1bNtI/Hm+t8Utz/zvzV1VMFfTWE601cuEq7TlxTpUqGDW1ext1aHinVv98SGFxV5a39XB30ztPWvTjkRO6v3ZNzWv4mCK+26n4PSyVCQAAitfEiRP11ltvObRFRkbqs88+U2xsrMxms0wmk7y8vOTv76+NGzeWeIwkO+BS2dnZ9oqKq3bv3q0hQ4Zo5cqVGjBggKpWrSpJuv/+++Xr66u9e/eqfv36kqRGjRrJy8tLNWvWlL+/vwwGgy5evGjva8qUKfafPTw81KZNG+3du9ee7LiW5cuXa/To0TKbzZKkevXqqV27dlq/fr26dOmivXv36u6777ZXm3h5eenLL7+Um1vuEWL16tVTnz59Cv/h3IAJEyY4VLBIV54fQMl4KXqtw/7nmxLV6+/3yWT0UHqWNZ+rrujsf7e+TvxVe06ckySlZmbp7bjv9c+nLFr98yFJkpfRXe/1bKcVO/bL7GnUzqOnNXnrRs0b9BjJDgAAbhYuquzYvHmzUlJSHCZunjt3rlasWKFly5bZK9HT0tKUk5Ojw4cPy8fHx3lxFhK//cCl3N3dc1VUDBo0SJJ08eJFRUVF6csvv7QfO336tEMy42pSwWAwyGAw2H++6vvvv9c//vEPpadfGTt/6NAhhwTItVy4cEE1a9Z0aKtVq5ZSUlIkSWfOnMl1vFKlSnn21bx580Lf90YZjUaGrAAu1KvFvVqyZbdDmzU7R25uBoc2k6eH0i87Jj88PdyVaXUc7pJpzVYF45X/ZVeqYNT0Pu31+aZExe85ohHBzbTl0AllWrNzXQcAAOBsr7/+eq7fqbKysrR06VKHP7CGhIQoKChIZrNZ0dHRJR0myQ6UXjVq1NDIkSNvaJWUkJAQrVq1yr7U0eTJk/M8z5ZPFtPb21unTp1yaDt58qQ9ceHj45PreGpqqtzc3OzVIH/u60blFyeA0uVvdWrJzc2gqP/9IklqU/8OnUlN06XfV1eRpFcfaaneDzTUM3OXa1fSGXt7/O7DmtGng1YlHtTpi2lydzNodLvmWrnrgCqbKujDvh00O/5Hbfo1SZJ0Pi1TvlUryWCQTEb+tw4AwE3DRZUdq1evztU2YsSIPNvyai8prMaCUqtdu3ZasGCBzp8/L0nKyMjQ8OHD7VUahZGTk2NfyvXs2bNatWpVnudZrVadPn06V3vXrl01c+ZM++zBBw4c0OrVq9WmTRtJUv369fXrr7/ql1+u/EKTmZmpxx9/PNcko85Qo0YNbd++3en9AnC+ycs3qtFtPlo87AlFhHTRI/719PIXjlVs5y5l6EJ6Zq5hLb8lX9Tb//1e/3zSogUhXfT54K46kXJJ87/bqdurVtK/1my2JzokKW7nAXVr4qfPB3fTsp/2lsjzAQAAlHb8CQilVoMGDfTaa6/psccek4eHh6xWq1577TWZTKZC9zF9+nQ98sgj9nk9riYp/mratGnq2LGjvL291ahRI3300UeSpA4dOmj//v1q06aNPD09ZTAYFBERocqVK0u6MlwkMjJSL774ojIzM5WRkaGRI0faJyeVpOeee84+S/GaNWsUGBiosLAw+/HPP/9cn3zyiSQ5rAozffp0h34mTZqkoUOH6u2335bRaCxwNRYArpVpzdYbsQVPxBW+YZvCN2zL89jmQ8c1aP5Xudp/OX42V1tKeqYGR8RdV5wAAKAMc+FqLGWBwUZdPHBTS0xMlL+/v+7oPUqe1Wq4OhygTPOqee3VkkpKxqljrg4BKPMuHdqtEyujFBMTo8cff9zV4QBlRmxsrJ544gk1/s8Lqvl4yc1bl5/UxKP64f7XtWvXLvvCAmVZenq6zGazGk76t9yMnk7rNyfrsn5+a5jS0tKK9Afm0orKDuAmV61aNZkrVdJvi2e5OhSgzGs0aY6rQ7A78PGbrg4BKDeuVnQCAMoOkh3ATc7X11f79uxRcnKyq0MB4Ey9drk6AqDMy8zMVEpKigIDA10dCgDkZvt9c2Z/5QjJDgDy9fWVr6+vq8MAAAAAUAZkZGQoODhYs2bNUvPmzRUXF6fQ0FBJV1bAfPTRR+3nJiUlKSEhQT179izRGFmNBQAAAACAMsZmszl9K6zx48dr8ODBat78ypwsM2fOVExMjGJiYjRz5kz7eQcPHlT//v3VrFkzpz//tVDZAQAAAABAWeOi1ViWL1+uS5cu6bnnnrO3ubu7KzU11f6zdGUhhFGjRmnBggWqU6eO8+IsJCo7AAAAAACAJKlVq1Zq0aKFwsPDcx07d+6cXn75Zbm5ualnz54aPXq0zp07p4kTJ2rYsGEaNmyYJk6cqM2bN2vUqFGKiopySaJDorIDAAAAAICyp5gqOxISEvJdenb69Ony9/fXrFmzVKFCBUVFRalbt2769ttvtXbtWklSfHy8QkNDNWDAAPXu3Vu1a9fWnDlzZDabnRdrIVDZAQAAAAAArumrr77Sv//9b1WoUEGS1KdPH1WpUkU7d+6UJK1YsUJTp05VbGysPv74Y61atUoBAQFatGhRicdKsgMAAAAAgLLmamWHM7dryMrKktFodGjz9PRUTk6OIiMjNXv2bMXGxqpy5coymUzy8vKSv7+/jh07VlyfQr5IdgAAAAAAgGvq2bOnXnrpJfvKLatXr9ahQ4f0ww8/aPHixVq2bJl9uEpaWppycnJ0+PBh+fj4lHiszNkBAAAAAEBZ44LVWF5//XVNmjRJAQEB8vLykre3t/7zn/9o5cqVWrp0qTw8/kgxhISEKCgoSGazWdHR0c6Ls5BIdgAAAAAAUNa4INnh7u6uqVOnaurUqQ7tI0aMyHXuiBEj8mwvKQxjAQAAAAAA5QqVHQAAAAAAlDUuqOwoS6jsAAAAAAAA5QqVHQAAAAAAlDW23zdn9leOUNkBAAAAAADKFSo7AAAAAAAoa5izo0AkOwAAAAAAKGtIdhSIYSwAAAAAAKDQoqOjVb9+fVksFvv27rvvKi4uTgEBAQoICFBcXJzDNUlJSYqOji6xGKnsAAAAAACgrHFhZcfx48f15ptvql+/fg7tjzzyiGJiYiRJISEhevTRRyVJBw8e1ODBgxUeHu60cK+FZAcAJSUlKTk52dVhAAAAwMUyMzOVkpKiwMBAeXl5uToclFLHjx9Xo0aNcrW7u7srNTXV/rMkJSYmatSoUVqwYIHq1KlTYjGS7ABucklJSap/7726dPGiq0MBAABAKbF27VpZLBZXh4ECFc/as+np6Q6tHh4eMhqNDm3Hjx/XDz/8oPfff1/Jyclq1aqVQkNDNXHiRA0bNkySFBYWps2bN2v8+PGKiopSrVq1nBjrtZHsAG5yycnJunTxou6Z/k953XGHq8MBAACAC6X8b7N+e+d9XbhwwdWhwEWqV6/usD958mS9+eabDm1paWlyc3NTbGysPDw8NGPGDIWEhCgqKkpr166VJMXHxys0NFQDBgxQ7969Vbt2bc2ZM0dms7lEnoNkBwBJktcdd8h0z92uDgMAAAAulHHkN1eHgMIqpjk7zp49K5PJZG/28MidNliyZInD/gsvvKCPPvpIly5dUsWKFbVixQrNmDFDsbGxat++vTZs2KDw8HAtWrRIQ4YMcV7MBWA1FgAAAAAAypqryQ5nbpJMJpPD9tchLJL08ccfy/aXRIunp6eys7MVGRmp2bNnKzY2VpUrV5bJZJKXl5f8/f117NixEvloJCo7AAAAAABAEXz77bfKzs7WqFGjJEkrVqxQrVq1tGTJEq1YsULLli1ThQoVJF0Z8pKTk6PDhw/Lx8enxGIk2QEAAAAAQFnjwqVn58yZo1GjRql58+Yym82qW7euFi1apOjoaC1dutRh6EtISIiCgoJkNpsVHR3tvHivgWQHAAAAAAAoNJPJpE8//TRX+4gRI/Jsy6u9uJHsAAAAAACgjHFhYUeZQLIDAAAAAICyhmxHgViNBQAAAAAAlCtUdgAAAAAAUOY4ubJDVHYAAAAAAACUWlR2AAAAAABQ1jBnR4Go7AAAAAAAAOUKlR0AAAAAAJQ1Njm5ssN5XZUGJDsAAAAAAChrGMZSIIaxAAAAAACAcoXKDgAAAAAAyhoqOwpEZQcAAAAAAChXSHYAAAAAAFDWXK3scOZWSJcvX1azZs105swZSVJcXJwCAgIUEBCguLg4h3OTkpIUHR3t1EcvDJIdAAAAAACg0MLDw9WrVy/5+PhIkmbOnKmYmBjFxMRo5syZ9vMOHjyo/v37q1mzZiUeI3N2AAAAAAAcHDlyRImJidd9LUqATc5dLraQfV26dEnz5s3Tt99+a29zd3dXamqq/WdJSkxM1KhRo7RgwQLVqVPHiYEWDskOAAAAAIAkyZqSIkl6/vnnXRwJrqmYJiht1aqV3NzcNHToUA0dOjTXaf/61780atQomUwmDRo0SEOGDNHEiRM1bNgwSVJYWJg2b96s8ePHKyoqSrVq1XJejEVAsgMAAAAAIEnKTkuXJN315ihVCbq+oQenY9bo2OwoZ4aFEpSQkCCTyZTnsbNnz+q///2vNmzY4NDesmVLrV27VpIUHx+v0NBQDRgwQL1791bt2rU1Z84cmc3mYo/9z0h2AAAAAAAcePrWlLn+ndd1rbFmdecGg7y5YOnZt99+W6+//rp9qMpfrVixQjNmzFBsbKzat2+vDRs2KDw8XIsWLdKQIUOcF2shkOwAAAAAAADXtHbtWm3ZskXvvvuuJGn37t3avn272rZtq+bNm+uzzz5TbGyszGazTCaTvLy85O/vr40bN5Z4rCQ7AAAAAAAoa1xQ2fHjjz867F+ds+OXX37R4sWLtWzZMlWoUEGSlJaWppycHB0+fNi+aktJItkBAAAAAACuW1ZWlpYuXSoPjz9SDCEhIQoKCpLZbFZ0dHSJx0SyAwAAlFs5ly/rt3dnKH3/AWVnZMjz1lt158SXZaz+x3jyCz9s1m/TP5TB4Cb3yt66K3SCPG+9VZnHkvTrhDdly8lR1VaB8h02+I9+s6w6858Y1ez9lCseCwAAl1R2/NW8efMkXVnB5a9GjBihESNG3HBY18vNZXcGbkJTpkxRx44dXR0GANw0kv79iYw1fHTvpx+r0aL5quh/nw794x378azk8zr8f++p/sz31XDRPNV6tr9+ff1NSdK5VWtUs/eTarhgrs6tWWu/JicjQwdevpIQAQDAZa4mO5y5lSMkO4AStG7dOlWsWFGnTp1ydSgAcFOo2PA+3dq3p32/auuHlHnkN/t+8jfrdEun9jL6XKn0qOTfUBe3/KjMY0mSu5ty0tJly86WzZotScpOTdX+sa+qZu8nVTX44ZJ9GAAAUGgkO4ASsmvXLvn5+alHjx5aunSpvT07O1tjxoxR48aNZbFY9MYbb+ihhx7SsWPH7Ofs2LFDbdu2VatWrRQQEKAvvvjCFY8AAGVOtXbBcq9USZJks1p1Kmqpbnmsk/14+oFfZa7vJ0nKOHpM+8dPUKWm9yv9wK/y6fqYzm/4VruHjFStZ/oqK/m89o15Wbc9N0hVWj7okucBAOAqm83m9K08Yc4OoIRERUWpR48eevDBB/XUU09p5MiRkqQPP/xQZ86c0bZt2+Tu7q4vv/xS77zzR4m11WpVz549tXTpUjVu3Fjnz59XcHCwHnjgAdWpUyfPe2VlZclqtTq0eXh4yGg0Ft8DAkAptnvwCGUeS5JX3Tq6feRz9vactHS5e1fShS0/6sS8Bbr7H5N0MnKJsi+lyXhLNfnNuLK03uWTp7T/xZdVs9eTSvr3J5Kkuq++JK878/53GAAAuBaVHUAJ+frrr2WxWFS1alV5eXnp6NGjkqRVq1Zp3Lhxcnd3lyR17dpVfn5+9ut27NihBg0aqHHjxpKkqlWrql+/flq5cmW+95o6darMZrPDNnXq1GJ8OgAo3e795CM1WRkr36Eh2jP8eeVkZkqS3MwmnYhYqNNLl+me9/5PRp/qyr54Ue4VzfZrM478pv0vvaY7J76q8xu/0x1jR+uOsaN17PekBwAALsGcHQWisgMoAVu2bNHmzZvtk5PGx8crMjJS48ePV3p6umrUqOFw/p/3L168qM2bN8tisTi0DRo0KN/7TZgwQa+88opD25+XgQKAm8X5jQmq+vAfM8R7N2+mCrVrK/3XQ6p4XwOZ6t2tU4uWqMW2TTIYDJKktD37VGtg/ys/79uvQ2+G6e63Q+VV5w5lnTolk989ks2mLOZfAgC4ku33zZn9lSP89gOUgKioKMXGxqpbt26SpDNnzqhr164aP368TCaTTp8+rdq1a9vPP336tP3nGjVqKCgoqEjzdBiNRoasAICkE/M/l81qVTVLG0nS5ZMnlXn4iLzuuF2SVK1tsE4uXCzr2XMy+lRXyqb/yeDhoQq3++rSz7/o8NRpqvdumCrcVkuS5FG1ii4nHZdskke1qq56LAAAcA0kO4BiZrPZFBcXpylTptjbfHx8ZDabdeDAAXXo0EHvvfeeIiIi5O7urri4OO3du9d+rp+fn/bt26ddu3bJ399fkhQWFqbHHntMTZo0KfHnAYCypN47U3T4nfd1/NMFMnh4yN1k0l1TJ9snLTXeUk11XhmrvaPHymBwk7t3Jd019U1JUubRJPnNmGZfqUWSag18WgdenihJqvPKiyX+PAAA2Dl76AnDWAAURUJCgu6//355eXk5tD/11FOKiorSK6+8ohdffFFNmzZV9erVFRgYqKCgIPscHkajUQsXLtTYsWOVmZmpjIwMPfHEEyQ6AKAQjD7Vdc+0gucsqtLywTxXV7mlY7tcbZXub6yGCz91WnwAAKB4kOwAitlDDz2khx56KFf7iBEjJEkbNmxQr169NHPmTEnSqVOntHLlStWsWdN+buPGjfX111+XTMAAAAAASj8qOwpEsgNwsXvvvVejR4/WuHHjZDKZ5OHhofDwcLm5sVgSAAAAAFwPkh2Ai9WsWVOLFy92dRgAAAAAyhIqOwpEsgMAAAAAgLKGZEeBqJMHAAAAAADlCpUdAAAAAACUNVR2FIhkBwAAAADA6TKPnFVq4lFXh6H0/SddHQJcgGQHAAAAAMBp3M0mSdLeMZ+7OJI/VPSupGrVqrk6DCez/b45s7/yg2QHAAAAAMBpPKp6S5I++OADtW3b1sXRXFGtWjX5+vq6OowyLzMzU+PGjdPOnTuVlpamO+64Qx999JFuvfVWxcXFKTQ0VJI0efJkPfroo/brkpKSlJCQoJ49e5ZYrCQ7AAAAAABOV6dOHTVq1MjVYZRbBptNBifOs1GYvt566y3ddttt+vDDDyVJb7/9toYPH65ly5Zp5syZiomJkSSFhITYkx0HDx7U4MGDFR4e7rRYC4NkBwAAAAAAZY0LJiht0aKF2rVrZ9/v0qWLFi5cKElyd3dXamqq/WdJSkxM1KhRo7RgwQLVqVPHebEWAskOAAAAAABwTd27d7f/bLVaNWvWLD399NOSpIkTJ2rYsGGSpLCwMG3evFnjx49XVFSUatWqVeKxupX4HQEAAAAAwA2yFcMmtWrVSi1atChw2InFYlG9evX066+/KiQkRJLUsmVLrV27VmvXrlVGRoZefvllDRgwQL1799bTTz+ttLQ0p38CBaGyAwAAAAAASJISEhJkMpkKPGfdunWSpA0bNqhjx4764Ycf5OXlJUlasWKFZsyYodjYWLVv314bNmxQeHi4Fi1apCFDhhR7/FdR2QEAAAAAQFlzdc4OZ27XEBcX57DfunVr1atXT7/88oskKTIyUrNnz1ZsbKwqV64sk8kkLy8v+fv769ixY8XyMeSHyg4AAAAAAHBN06ZNU1ZWlh5//HFJ0tGjR7V3717Vq1dPc+fO1YoVK7Rs2TJVqFBBkpSWlqacnBwdPnxYPj4+JRoryQ4AAAAAAMoYg2wyyIlLzxair8jISL3wwgv6v//7PxmNRlWsWFELFixQ5cqVlZWVpaVLl8rD4480Q0hIiIKCgmQ2mxUdHe20WAuDZAcAAAAAAGWNC5aerVWrlhYvXpznsREjRuTZlld7SWDODgAAAAAAUK5Q2QEAAAAAQJnzx3Kxzuuv/KCyAwAAAAAAlCtUdgAAAAAAUMYYbDYZnDhnhzP7Kg1IdgAAAAAAUNYwiqVADGMBAAAAAADlCpUdAAAAAACUOZR2FITKDgAAAAAAUK5Q2QEAAAAAQBnDBKUFI9kBQJKU8dtvrg4BAAAALpZ15oyrQwCcgmQHcJOrVq2aKnp7a/+Yl10dCgAAAIBCY86OgpDsAG5yvr6+2rt7t5KTk10dCgAAAFxs7dq1ev75510dBgrDJsmZQ0/KV66DZAeAKwkPX19fV4cBAAAAF9u/f7+rQwCcgmQHAAAAAABljEE2GZxYjuHMvkoDlp4FAAAAAADlCpUdAAAAAACUOTbnztlBZQcAAAAAAEDpRWUHAAAAAABlDkvPFoRkBwAAAAAAZYzBZpPBicNYnNlXacAwFgAAAAAAUK5Q2QEAAAAAQFljc/IEpVR2AAAAAAAAlF5UdgAAAAAAUOYwQWlBqOwAAAAAAADlCpUdAAAAAACUMazGUjCSHQAAAAAAlDkMYykIw1gAAAAAAEC5QmUHAAAAAABlDUvPFojKDgAAAAAAUK6Q7AAAAAAAoMyxFcNWeJMmTVJQUJBatWql3r176+zZs4qLi1NAQIACAgIUFxfncH5SUpKio6Ov92GLjGEsAAAAAACUMa5cjWXatGkyGAz67rvvJEmRkZEaPXq0kpOTFRMTI0kKCQnRo48+Kkk6ePCgBg8erPDwcKfFey0kOwAoKSlJycnJrg4DAAAATpCZmamUlBQFBgbKy8vL1eGgHHJ3d9fw4cPt+z169NDUqVN15513KjU11X6OJCUmJmrUqFFasGCB6tSpU2IxkuwAbnJJSUmq36CBLv3+jxIAAADKh7Vr18pisbg6DBSXYpqgND093aHZw8NDRqPRoW3s2LEO+998840efvhhDRw4UMOGDZMkhYWFafPmzRo/fryioqJUq1Yt58VaCCQ7gJtccnKyLqWmqu7IifKseZurwwEAAMANSt29Q8ej/q0LFy64OhSUQdWrV3fYnzx5st588818zz9y5IjCwsK0fPly3XLLLVq7dq0kKT4+XqGhoRowYIB69+6t2rVra86cOTKbzcUZvh3JDgCSJM+at8nLt66rwwAAAMANyjyV5OoQUCKKPqnotfuTzp49K5PJZG/18Mg/bXDq1Cn17dtXn3zyiW655RZ7+4oVKzRjxgzFxsaqffv22rBhg8LDw7Vo0SINGTLEiTHnj2QHAAAAAACQJJlMJodkR35SUlLUs2dPzZgxQ/fee6+9PTIyUp999pliY2NlNptlMpnk5eUlf39/bdy4sThDd0CyAwAAAACAMsaVq7GkpaXpqaee0j/+8Q+1aNHC3j537lytWLFCy5YtU4UKFezn5uTk6PDhw/Lx8XFavNdCsgMAAAAAgDKneIaxFMbo0aO1c+dOTZ482aG9R48eWrp0qcPQl5CQEAUFBclsNis6Otpp0V4LyQ4AAAAAAFBon3zySaHPHTFihEaMGFGM0eSNZAcAACgzKri7aWzAfbrPp4pybDYdu5im//suURcvWwt1fVBtH4U0ucehraLRXRcvWzXyv//TP9v9TVUqGJWSmaWXv/lR1j+V9D7d6E4tTDzkzMcBAOD6FdPSs+UFyQ6UiClTpmjDhg36+uuvXR1KiUtPT9fYsWO1fft2eXh4yNPTU2+//bYeeOABSVJERIRCQ0NVt25dWa1WZWZm6rXXXlP37t0lSYcPH1azZs3UpEkTZWdn68KFC+revXuukjEAuBmMC7hPJy5l6O3vEiVJ3fxuV5ilqUav2lKo6787ekbfHT3j0Da6RQMdSknVfT5VlJyRqbFrtuqNh/x1n08V7Tx9XpI0rNk9quDu7tRnAQAAxcfN1QHg5rBu3TpVrFhRp06dcnUoJe6NN95Q/fr19d1332nDhg2aO3euBgwYoLS0NPs5gwcP1rp167Rx40bFxcXp5Zdf1tmzZ+3HmzdvrnXr1mnDhg3avHmz9u3bp/nz57vgaQDAtQJr19Cn2w/Y95fvO6ZaFU2qaLy+v99UN1VQUG0ffbX/mLJtNpl/78ds9FD273/hGt/yPllzbPpgy54bfwAAAJzEIJvTt/KEZAeK3a5du+Tn52efrOaqiRMnKiIiwuHcp59+WmvXrrXv79ixQ23btlWrVq0UEBCgL774wuH8Tp066b777lNoaKjGjBmjoKAgNWvWTMePH5ckZWdna9KkSerYsaMefvhh9e/fX+np6fbrs7OzNWbMGDVu3FgWi0VvvPGGHnroIR07dqzQMVzLnj171LFjR/v+nXfeqc8++0zZ2dl5nu/j46MmTZrYn+GvjEajxo4dq9jY2HzvmZWVpfT0dIctKyurSHEDQGnk4WaQu8Hg0Obp7qbsnJzr6m9Ys3v0ybYDyrFJP59J0YXMLH3apaUuZGZpz9kLerP1/Tp2MV2f/CnBAgAASj+SHSh2UVFR6tGjh7p27ar//Oc/9vaBAwdq0aJF9v20tDRt27ZNwcHBkiSr1WpftzkhIUGrVq3SP/7xDx05csR+zapVq/Tqq68qIiJCLVu21HfffaeffvpJt912myQpLi5OOTk5+vrrr7Vx40bVqVNHs2bNsl//4Ycf6syZM9q2bZvWrVunBx98UP/73//sxwsTw7X07dtXI0eOVEJCgnJ+/zLeokULeXt753n+jh07dP78eYe1qv/q8uXL9qWc8jJ16lSZzWaHberUqYWOGQBKq/VHTum1oEbyMBhkkDTib35KupimjOyiJztu9zbpPp8qWnPohL3t7e8SFbJik6Zt+llvW5pqz9kLalyzqj7t0lLBdWo68UkAoPgdOXJEiYmJRdqK8j0XLnZ1zg5nbuUIc3ag2H399deaPHmyjEajvLy8dPToUdWuXVt+fn7KyMjQqVOnVLNmTS1fvlxPPvmk3Nyu5OB27NihBg0aqHHjxpKkqlWrql+/flq5cqWGDh3qcI969eqpT58+ue7dtWtXde3a1b7frl07LV682L6/atUqTZ06Ve6/j8Pu2rWr/Pz87MeLEkN++vXrp7vuuktz587V0KFD1bRpU02YMEENGza0n/PJJ59ozZo1On/+vE6cOKGPP/7YYbmmP0tJSVFoaGiBMxpPmDBBr7zyikNbfv0BQFny7qafNayZnz7tGqhTlzJU/xZvvbL2p+vqa1TzBvrox7252r3c3TSt/d/01b5jMhs9tPPUeb25+4g+7dJS8UduvuGYAMqe7NSLkqTnn3/exZGgeLlu6dmygN9+UKy2bNmizZs324dxxMfHKzIyUuPHj5ckPfPMM1qyZIn+/ve/KzIyUu+995792osXL2rz5s2yWCwObYMGDcp1n+bNm+d5/+PHj+ull15SUlKSJOn8+fNq0aKF/Xh6erpq1KjhcM2f94sSQ0ECAwMVGBgom82m+Ph4PfHEE4qPj5evr6+kK3N2TJgwQZKUmpqqsWPHKiUlRQMGDJAkbd26VRaLRdnZ2crJydFzzz2nbt265Xs/o9Eoo9FYpBgBoCyw5tg0a+tezdq6V30b3al95y7ol7MX8jzX5OGudGveQwb9bvHWrRW9ck1WWsnooX91aK6Fuw4q/sgpDf+bn7YeP6fM7BxlXkf1CAC4QvblDEmS79Bx8r6/xTXOdnQufqVOL4249olAKUeyA8UqKipKsbGx9l/Mz5w5o65du9qTHb169VKPHj309NNP69KlS7rnnj+WA6xRo4aCgoIKNUdGfkNCXn31VfXo0UNPPvmkpCsTpf556IzJZNLp06dVu3Zte9vp06evK4b8REVF2atODAaDLBaLunTpoh9++MG+4sqfVapUSX//+981ceJEe7KjefPmWr169XXHAADlTeMaVWWpe6uG//eHPI+/EthQvRrW1YDY75R4JiXX8ecfaKAP/zLhaOUKRn3QsYU+2rpXPyRdmST6fMZl3VbJJIMkLw9WYwFQtnjWuFVedesV6RrjLdWLKRo4m8Fmk8GJQ0+c2VdpwJwdKDY2m01xcXEOk3P6+PjIbDbrwIErE71VrlxZtWrV0rRp09SvXz+H6/38/LRv3z7t2rXL3hYWFqbt27cXOgar1WpPhGRnZ2vJkiUOxzt06KD33nvPPlloXFyc9u79o6TZGTFER0crKirKvn/27FmtWbPGPjTmr2w2m5YsWZJvtQoA3OyqVDBq0sON9cb67crJ53vZuYzLupiZpYw8JoNudms1ucmgn04mO7TfXsmkGf/bbU90SNJ/DySpW/3b9Vm3IMXs+c2pzwEAAIoPlR0oNgkJCbr//vvl5eXl0P7UU08pKirKPmzj2WefVffu3e1DTa4yGo1auHChxo4dq8zMTGVkZOiJJ55QkyZN7Oc899xzmjt3riRpzZo1CgwMVFhYmP34W2+9pcGDB2vKlCny9PRU9+7dtW3bNvvx0aNH68UXX1TTpk1VvXp1BQYGKigoyD6HR2FiuJY5c+bopZde0qxZs2T4fQWBsLAwhyqWq3N25OTkKDMzU61atdKkSZMKfQ8AuJmkZGap5382FnjOnJ/2a85P+/M89tPJZI1atTlXe17DYVIyszTkq7yrRwAAcClnTypazio7DDZbOXsioAg2bNggg8Gghx9+WJJ06tQpderUSVu3brVPlFreJSYmyt/fX35vzpKXb11XhwMAAIAbdHrtcp2ICtedE/6pKi2Di3btl1FKCn9fDcJDVb1jq+u6/9mvE7Rn6GTFxMTo8ccfv64+kL/09HSZzWb9rVNvubk7r34hJ9uqH1ctVlpamkwmk9P6dRUqO3BTu/feezV69GiNGzdOJpNJHh4eCg8PL3SiIyIiQvPnz8/z2PTp04tUAQIAAAAAcA6SHbip1axZ02Ep2qIaOHCgBg4c6MSIAAAAAODamKC0YDdHnT4AAAAAALhpUNkBQJJ0+dRxV4cAAAAAJ7CmJF/7JJQDtt83Z/ZXfpDsAG5y1apVU8VKlXR49hRXhwIAAAAATkGyA7jJ+fr6au+ePUpO5i8AAAAA5cHatWv1/PPPuzoMFDeWni0QyQ4A8vX1la+vr6vDAAAAgBPs37/f1SGgRDCMpSBMUAoAAAAAAMoVKjsAAAAAAChjWHq2YFR2AAAAAACAcoXKDgAAAAAAyhqbnDxBqfO6Kg2o7AAAAAAAAOUKlR0AAAAAAJQ5rMZSEJIdAAAAAACUMUxQWjCGsQAAAAAAgHKFyg4AAAAAgIPLSaeUtvfQdV+LksAwloKQ7AAAAAAASJLcK5gkSQffnHXDfVWuXPmG+wCuF8kOAAAAAIAkyb1yFUnSBx98oLZt215XH5mZmUpJSVFgYKAzQ8NfUdhRIJIdAAAAAAAHderUUaNGjVwdBnDdSHYAAAAAAFDGGGSTwYnlGM7sqzQg2QEAAAAAQFljs13ZnNlfOcLSswAAAAAAoFyhsgMAAAAAgLKGyo4CUdkBAAAAAADKFSo7AAAAAAAoc5w7QWl5W3uWZAcAJSUlKTk52dVhAEC+ekcluDoEB4v7tHJ1CACQryNHjrg6BJQEhrEUiGQHcJNLSkqSX4MGSktNdXUoAJCvRhP/7eoQHPj7+7s6BAAAUACSHcBNLjk5WWmpqbqj5wh53lLT1eEAQJlQb9hkV4cAAPk6v/MHnf1upS6fPqmMwweKdO3l0yeLKSo4n03OHXpCZQeAcsjzlpryquHr6jAAoEAdG92l59s/oFMX0uxtG/Ye0fyEHUXq55HG9dS+4V0at3iNJMnDzaB3e7dXFVMFpaRn6qXFa2TN+eNLX/+W/vp80y77Pv9eAijNrv4BKyn8vevuo3Llys4KB3AJkh0AAKDMqOFt1ux1WxW3o2h/qfyz26t6a0BQYw2e95W97T5fH527lKExkas1qdvDus/XRzuPnpYkDQ/+myp4uN9w7ABQUjxMFSVJH3zwgdq2bVukazMzM5WSkqLAwMDiCA1OZLDZZHDiPBvO7Ks0INkBl0pPT9fYsWO1fft2eXh4yNPTU2+//bYeeOABRUREKDQ0VHXr1pXValVmZqZee+01de/eXZJ0+PBhNWvWTE2aNFF2drYuXLig7t27a/LkopcWz5w5U6NHj3b24znEmJmZqbS0NHXr1k1vvPGGjEajJGnQoEHavn27qlSpIqvVKg8PD3344Ydq1KiRJDl8DpcvX1ZmZqZef/119ejRw+nxAkBpV8PbrP2nrn9CZQ83g0KfaK2Jy9Yr7XKWvT0nxyaz55WvRWZPD+X8XtXx8iOBOncpXR+v+fHGAgcAF6hTp479OyVwsyHZAZd64403VL9+fX300UeSpEOHDumRRx7R1q1bJUmDBw/WhAkTJElnzpxRYGCgWrdurerVq0uSmjdvrtWrV0uSsrKyNGjQIM2fP1/PPvtskeL44IMPiiXZITnGePnyZY0dO1YjR47UnDlz7OfMnDlTrVpdmdl/06ZNevbZZ7V582b78b9+Du3bt1fjxo3l5+eX5z2zsrJktVod2jw8POwJFgAoq3wqmXV/7ZoaGHS/Kps89dORk5q1dosysrILdX3Iw01lMEjPtrpflb08FfPTXq3bfViJSWd0If2y5oV00b6Tydp94qzeeqK19pw4q4WbEov5qQAAuB7M2VEQN1cHgJvbnj171LFjR/v+nXfeqc8++0zZ2bm/tPr4+KhJkyY6fvx4nn0ZjUaNHTtWsbGxhb5/r169ZLFYdOzYMVksFlksFg0fPtzhnPDwcAUEBOihhx5S69at9f333xe6/7/y9PTU9OnTtX79ep06dSrPc1q2bKkLFy7k24ePj48GDBiguLi4fM+ZOnWqzGazwzZ16tTrjhsASgsvTw/l2GwavWiV+s9ZrpMpl/SP7sGFurZSBaP6BjTSR+t+1BvL1mvCf+I16KEmesjvDklS2FcJGvTpCv1z5fd656m22n38rBrXrql5IV0UfG/d4nsoAACux9WlZ525lSNUdsCl+vbtq5EjRyosLEyBgYFyc3NTixYt8jx3x44dOn/+vO699958+7t8+bIqVKhQ6PsvWbJEkuTn56d169blOv7NN98oMjJS69atk9ls1oEDB9S5c2f9+OOP8vb2LvR9/szDw0OtWrXSjz/+qM6dO+c6vnDhQocEUF4uX76sqlWr5nt8woQJeuWVV3LdFwDKupcWf+Ow//mmXer1YEOZjB5Kz7Lmc9UVLe7y1de7ftWWQ1eS5qmZWfrnf7/X4Ieb6tt9v0mSvIzueq93e63Yvl9mTw/tPHpKk7fs1ryQrorffbh4HgoAADgdv/3Apfr166e77rpLc+fO1dChQ9W0aVNNmDBBDRs2lCR98sknWrNmjc6fP68TJ07o448/zveX9pSUFIWGhmrEiBFOi2/58uUaPXq0zGazJKlevXpq166d1q9fry5dulx3v7Vq1VJKSop9f/To0apcubKOHTsmb29vLViwIN9r9+3bp88//1yrVq3K9xyj0ciQFQDlUq8H7tOSzb84tFmzs+XmZnBoM3l6KP3yX4bzuRlkzclxaMvKztbVSytVMGp63476fNMuxe8+rBGWv2nLoePKtGYr01pwIgUAgJLHMJaCkOyAywUGBiowMFA2m03x8fF64oknFB8fL8lxrorU1FSNHTtWKSkpGjBggCRp69atslgsys7OVk5Ojp577jl169bNabFduHBBNWvWdGj7a6Liepw4cUJVqlSx7/95zo6jR4/qmWee0axZs3IlfbKysuTt7a25c+fq9ttvv6EYAKAs+lvdWnIzGBT1v58lSW3q19GZ1HRdyvxjstFXHw1U7wcb6Zk5sdp17LS9/YdfkzSmw4Navm2v9pw4J6O7m17o8KC+3LZPlU0V9OHTnTR77VZt+vWYJOl8WqZ8q3rLYDguk5GvTAAAlCX8nxsuFRUVpT59+kiSDAaDLBaLunTpoh9++CHXuZUqVdLf//53TZw40Z7s+PPknzfCls/4NG9v71xza5w8eVLNmze/7ntZrVZ99913euedd/I8Xrt2bXXv3l0rV660Jzv+nPQBgJvZ5JgNmtjlIS0e3l0ZWVYdT0nVy9GOQ1vOXcrQhfTMXMNaLmZc1vgl3+iVR4JkMEgVPDwUt2O//rvzgO67rbr+9fUP+unISfv5cTv26/0+7dXrgfu07Mc9JfJ8AAAUFkvPFoxkB1wqOjpakuwJj7Nnz2rNmjUaOXKkEhISHM612WxasmTJDSUa8mO1WnX69GnVqFHDob1r164KCwtT586d7XN2rF69Ot9ExbVcvnxZ48ePV+vWrXNVjFyVnp6uL7/8kuQGAOQh05qtN2LWF3hO+PqfFL7+pzyP/XL8rELmrciz/a9S0jM1eN5X1xcoAABwKZIdcKk5c+bopZde0qxZs2QwXBk0HRYWpnvuuUcJCQn24Rs5OTnKzMxUq1atNGnSJKfHMW3aNHXs2FHe3t5q1KiRfSncDh06aP/+/WrTpo08PT1lMBgUERGhypUrF7rvq0NtMjMzlZaWpm7duundd991OGf06NGqUqWKcnJylJWVpZCQEAUHBzvzEa/p8rm8V4cBgNLAq4avq0NwkHE6ydUhAEC+slLOuToElARnr6BSzio7DLb86vcB3BSSkpLk16CB0lJTXR0KAOSr0cR/uzoEB4lThrk6BAC4prVr18pisbg6DDhZenq6zGazWga1lbu7u9P6zc7O1qbv1iotLU0mk8lp/boKlR0otyIiIjR//vw8j02fPl1NmjS54Xu8+OKL2rZtW57H8lrKtjTy9fXVvj17lJyc7OpQAKDs6LPL1REAQL4yMzOVkpKiwMBAV4cCuAyVHQAAAAAAlBFXKzsCAy1Or+z4/vt15aayw83VAQAAAAAAADgTw1gAAAAAAChzbL9vzuyv/KCyAwAAAAAAlCtUdgAAAAAAUNaw9GyBqOwAAAAAAKCMMcjm9K0oTpw4oX79+slgMNjb4uLiFBAQoICAAMXFxTmcn5SUpOjoaKc8e2GQ7AAAAAAAAIU2b948dezYUU899ZRD+8yZMxUTE6OYmBjNnDnT3n7w4EH1799fzZo1K7EYGcYCAAAAAEBZ48JhLFarVd9//70qVqzo0O7u7q7U1FT7z5KUmJioUaNGacGCBapTp47z4r0Gg81WzgbmAMjFZrMpIyPD1WEAAAAApZKXl5fDcIzSLD09XWazWS0faCU3N+cN1sjJydGmzQk6e/asTCaTvd3Dw0NGozHf6wwGg66mFTZt2qTXX39dkhQWFiZ3d3eNHz9eUVFRqlWrltNiLQySHcBN4Oo/iAAAAAByS0tLc/gFvzTLysrSnXfeqaSkJKf37e3trYsXLzq0TZ48WW+++Wa+1/w52fFn8fHxCg0N1TPPPKOIiAjVrl1bc+bMKbHfS0h2ADeBgio70tPTVb169VwZXCAvvC8oCt4XFAXvC4qC9wVFUZj3pSxVdkhXEh5Wq9Xp/dpstlyfQ1EqO65asWKFZsyYoS+++ELt27fXhg0bFB4eLrPZrCFDhjg97rwwZwdwEzAYDNf8ImAymfiygELjfUFR8L6gKHhfUBS8LyiK8vS+GI3GAhMQrhQZGanPPvtMsbGxMpvNMplM8vLykr+/vzZu3FhicZDsAAAAAAAAN2zu3LlasWKFli1bpgoVKki6MkQoJydHhw8flo+PT4nFQrIDAAAAAAAU2qhRo/Tzzz/b9y0WiyRpx44dOnnypDw8/kg1hISEKCgoSGazWdHR0SUWI8kO4Cbn4eGhyZMnO/yDBOSH9wVFwfuCouB9QVHwvqAoeF+cb9asWYU+d8SIERoxYkQxRpM3JigFAAAAAADlivMW5QUAAAAAACgFSHYAAAAAAIByhWQHAAAAAAAoV0h2AOXY+++/r6ZNm6p58+YaOnSosrKyCn3toUOH1KFDBwUEBOiBBx7Q+vXrizFSlAY38r788ssvslgsat26tVq2bKnIyMhijBSlwY28L1fNnTtXEyZMKIboUNo4432JiopSz549iyE6lDY38r785z//kcViUYcOHdSxY0eNHz/+ut43lB0nTpxQv379ZDAYinwt33fLN5IdQDm1atUqrV69Wlu2bNHWrVt16623aurUqYW+vn///nrjjTf0ww8/aNmyZRoxYoTOnz9ffAHDpW7kfbl06ZL69OmjRYsWacOGDfrmm2/0/vvv65dffinmqOEqN/rviyRlZGRo1qxZeuWVV4opSpQWznhfDh48qPfff1+ffvppMUWJ0uJG3peUlBS9+uqriomJ0erVq/X1118rPT1dCxYsKOao4Srz5s1Tx44d9dRTT13X9XzfLd9IdgDlVEREhF599VX7EltdunRRaGhooa7du3evjEajWrduLUm6/fbb9csvvygmJqa4woWL3cj7cvLkSQ0fPly33XabJKlixYpq3769EhMTiy1euNaNvC9Xffjhhxo0aJAqV65cHCGiFLnR9yUrK0tDhgzRvHnz5O3tXVxhopS4kffFZDKpSpUqOnfunCQpMzNTycnJql27drHFC9eyWq36/vvv1aNHjyJfy/fd8o9kB1BOJSYmqkmTJpKklStX6u2331atWrWUmppapGsvXbqk/v3764knnuCX13LsRt6Xu+++22HtdJvNpoSEBAUEBBRbvHCtG3lfpCt/fV2yZImGDx+uw4cPq0OHDsUZLlzsRt+Xd955RzabTe+++666d++u2NjY4gwXLnYj74unp6fmzp2rhx56SI0bN1aNGjXUsGFDderUqbjDhos899xzqlix4nVdy/fd8s/D1QEAKB6XLl1SlSpVNHPmTO3cuVPR0dEKDg5WamqqKlWqVOC1qampqlq1qo4ePaohQ4Zo8uTJSk1N1RdffFFC0aOk3cj78lcTJ05Ujx49dMcddxRTtHC1G31fpk2bpnHjxsnT07MEooWr3cj7kpKSolmzZikqKkpt2rRRSkqKHnvsMXl6euqRRx4poSdASbqR9+Xs2bMKCQnRV199paZNmyolJUUDBw7U0qVLr3uYA8ovvu+Wf1R2AOWUp6en+vXrp7S0NIWHh8toNOr8+fOF+kWkUqVKWrVqlQYNGqSPP/5YgYGBOn/+POXD5diNvC9/NmPGDKWkpGjMmDHFEyhKhRt5X06ePKmEhAT16tWrBCJFaXAj78v69evVs2dPtWnTRpJUpUoV/etf/9LcuXOLO2y4yI28L0uWLFHPnj3VtGlTSbInTaZPn168QaNM4vtu+UdlB1BO5eTkqGbNmvbJ/y5evKicnJxcXxby+ktJo0aNtGnTJqWkpNjH02/btk2NGjUqmeBR4m7kfblq/vz52rp1qyIiIoo9XrjWjbwvq1atUnJystq2bSvpykSle/bskcVi0cyZM+Xv718yD4EScyPvi9Vqtc/dcJWnp6dycnKKN2i4zI28LxkZGfLy8nJo8/LyUnp6evEGjVKP77s3Jyo7gHJq+vTp2rt3r6xWqyTpn//8p3r37u1wzgsvvKBq1app8+bNDu3169dXUFCQtm3bJkk6evSoli1bpscff7xEYkfJu5H3Rbqy1N+XX36pTz/99LqWfkPZciPvy4ABA7Rt2zatW7dO69atU1RUlJo3b65169aR6CinbuR9adu2rVasWKHt27dLujLh5GuvvZbrepQfN/K+dOvWTZ9++qmSkpIkXUmWTZw4UX369CmZ4FEq8X335kVlB1BOde7cWT///LOaN28ud3d3NW/eXLNnz3Y4p0aNGqpWrZrMZnOu6xcuXKghQ4bowoULstlsmj17tqpVq1ZS4aOE3cj7sn//fvXt21fNmjVzmGiyX79+eu6550okfpSsG/33BTeXG3lfqlatqqioKI0ZM0Y5OTnKyMhQv379+OW1HLuR96VevXr64IMP1K9fP1mtVl2+fFndunXTuHHjSvIRUIJGjRqln3/+2b5vsVgkSc2bN9e7774rie+7NzODzWazuToIAAAAAAAAZ2EYCwAAAAAAKFdIdgAAAAAAgHKFZAcAAAAAAChXSHYAAAAAAIByhWQHAAAAAAAoV0h2AAAAAACAcoVkBwAAQCmWnZ2tWbNmKTU11dWhAABQZpDsAAAAKCXuueeeXG379u3T3//+d3377bcuiAgAgLKJZAcAAHCJTz75RA8++KCCg4MVHBysQ4cOObX/w4cP68svv3Rqn65w7733avXq1WrXrt11XV9ePgcAAIrCw9UBAACAm9M777yjH3/8UZUqVSqW/g8dOqTly5era9euxdJ/SWrfvv11X1uePgcAAAqLyg4AAFCinn/+eVksFh09elRdu3aVxWKRxWJxqOxYsmSJWrZsqVatWslisSgxMdGhj+3bt+vRRx9V+/btFRgYqLi4OPuxpKQkWSwWjRkzRl999ZW9/48//th+zqBBg5SQkODQp5+fn/3nyMhIWSwWeXt76/vvv1dwcLAefvhhvf766/ZzduzYobZt26pVq1YKCAjQF198UaTP4dKlSxo4cKCaNWum4OBgzZgxI9czXo29WrVqOnbsWK4+tmzZIovForZt2+rhhx/WihUrivQ5ZGdna9KkSerYsaMefvhh9e/fX+np6fbjERERmjJligYNGqQ2bdqoWbNmDveQpJ07d6pz584KDg5WYGCgPvvsM4fjycnJ6tu3rwIDA/XAAw9o8uTJRfqcAAC4LjYAAAAXuOeee/JsT0xMtDVp0sSWnJxss9lstu3bt9uaNGnicM6jjz5qO3LkiM1ms9lOnDhhq1u3rs1qtTqcEx8fbxsyZEie93j22Wdt33777TXjqVevnq1Xr172WK7Kysqy1a9f37Zjxw6bzWazJScn25o0aWI7fPhwnvfLywsvvGCbMGGCff+dd96x1atXL89zg4ODbUePHs3V/uCDD9r27dtns9lsttTUVFvPnj1tWVlZDucU9DksX77cIYbXXnvNNm3aNPv+/PnzbQ0bNrT99ttvNpvNZktKSrLVrVvXlp2dbbPZbLbLly/bGjVqZNu1a5fNZrPZ0tPTbcHBwbZt27bZ++jXr59tzpw5NpvNZsvOzrb169fPFhkZmc+nAgCAc1DZAQAASpWVK1dqwIABqlq1qiTp/vvvl6+vr/bu3Ws/56uvvtIdd9whSbr11lt1xx136OTJk06PJTs7W88//7w9lqt27NihBg0aqHHjxpKkqlWrql+/flq5cmWh+169erXGjRtn33/xxRdlNBqLFJ+vr6+2bt2q7OxsVaxYUUuWLJGHR+FHKXft2lVTpkyx77dr187hc5akzp07q3bt2pKk2267TbVr19aJEyckSXv37tXdd9+tRo0aSZK8vLz05ZdfOlTJrF+/XkOGDJEkubm56YUXXtDy5cuL9JwAABQVc3YAAIBS5eLFi4qKinKYVPP06dO6ePGifT8iIkIRERHKycmRwWDQrl27ZLPZiiWev/3tb3nGuHnzZlksFoe2QYMGFbrfzMxMVatWzb5vNBpVpUqVIsX22WefaebMmerevbu8vLw0cuRIBQcHF/r648eP66WXXlJSUpIk6fz582rRooXDObfccovDvslkUlZWliTpzJkzqlmzpsPxv87BcvbsWYfPKSsrS3fddVehYwQA4HqQ7AAAAKVKjRo1NHLkSI0ePTrP4wcOHNDs2bO1fv16eXl5SZLatGmT57n5JUA8PDyUkZFh37948aKsVmue55lMpjxjDAoKKvI8HX/m6emp8+fP26tGsrKylJKSUqQ+KlWqpNdee03SlbkxOnbsqJiYGN1+++0O5+X3Obz66qvq0aOHnnzySUnSunXrtGjRokLf38fHR6dOnXJoS01NlZubm8xmsySpbt26WrduXaH7BADAGRjGAgAASpV27dppwYIFOn/+vCQpIyNDw4cPt0+cmZOTI6PRKE9PT0lXJvL86wSm0pWExK5du5STk5Pr2H333adVq1bZ92fPnl2kISR+fn7at2+fdu3aZW8LCwvT9u3bC91Hhw4d9O6779r3P/jggzxjzU92drY6dOhgH77j7e0tg8Fgr7q4qqDPwWq1ytvb297fkiVLCn1/Sapfv75+/fVX/fLLL5KuVKs8/vjj2rdvn/2cBx98UPPmzbPvf/XVV1qwYEGR7gMAQFGR7AAAAKVKgwYN9Nprr+mxxx5TmzZt1K5dO3Xp0sVeYeHn56cuXbrogQceUOvWrTV//nwFBATk6qdhw4Zq27atmjdvruDgYP373/+2Hxs6dKj27dunli1bqlOnTrr33ntVsWJF+/GEhARZLBbt37/fvorJn+eyMBqNWrhwocaOHas2bdooICBABoNBTZo0KfRzTpkyRb/++quaNm2qtm3bqmbNmqpRo4b9+K5du+z33rZtm3r27CmLxaKIiAhJkru7u8aNG6fu3bvLYrGoTZs2Gjp0qO68885Cfw5vvfWWwsLC1Lp1a3Xq1En+/v6Fjv/q5xAZGakXX3xRFotFwcHBevbZZx0+h+nTp+ubb75R69at1apVK33xxRd6/PHHi3QfAACKymArrgGuAAAAAAAALkBlBwAAAAAAKFdIdgAAAAAAgHKFZAcAAAAAAChXSHYAAAAAAIByhWQHAAAAAAAoV0h2AAAAAACAcoVkBwAAAAAAKFdIdgAAAAAAgHKFZAcAAAAAAChXSHYAAAAAAIBy5f8BcIKgnV6BBy0AAAAASUVORK5CYII=\n", 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" ] @@ -1333,18 +1569,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "****************************** Redundancy linkage ******************************\n", - " \n", - "Age 31% -------------------------------------------------------\\ \n", - "Average_SBP 10% -----------------------------------------------------\\_/-\\ \n", - "SBP_to_DBP 6% -----------------------------------------------------/ | \n", - "RBC_count 12% --------------------------------------\\_______________ | \n", - "Hematocrit 5% --------------------------------------/ \\\\ |_ \n", - "Uric_acid 8% ------------------------------------------------------/|\\| \n", - "Gamma_glutamyl_ 6% -------------------------------------------------------/|/ \n", - "Waist_to_hgt 11% --------------------\\____ | \n", - "Waist_Circumfer 8% --------------------/ \\------------------------------/ \n", - "BMI 4% -------------------------/ \n" + "============================== Redundancy linkage ==============================\n", + "\n", + "BMI 4% ----------------------\\\n", + "Waist_to_hgt 11% ------------------\\___/---------------------------------\\\n", + "Waist_Circumfer 7% ------------------/ |\n", + "Gamma_glutamyl_ 7% -------------------------------------------------------\\\\\n", + "Uric_acid 8% -----------------------------------------------------\\ ||__\n", + "RBC_count 12% ---------------------------------------\\_____________/-/|\n", + "Hematocrit 6% ---------------------------------------/ |\n", + "Age 30% -------------------------------------------------------\\|\n", + "Average_SBP 10% ------------------------------------------------------\\//\n", + "SBP_to_DBP 6% ------------------------------------------------------/\n", + "\n" ] } ], @@ -1396,252 +1633,251 @@ " vertical-align: top;\n", " }\n", "\n", - " .dataframe thead tr th {\n", - " text-align: left;\n", - " }\n", - "\n", - " .dataframe thead tr:last-of-type th {\n", - " text-align: right;\n", + " .dataframe thead tr th {\n", + " text-align: left;\n", " }\n", "\n", "\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
ranking_scoreroc_aucclassifierscorecandidateparamtime
test-classifierfitscore
rankmeanstdtypemin_samples_leaf-max_depth
rankmin_samples_leafn_estimatorsmin_child_samplessubsamplemeanstdmeanstd
00.6656170.7299850.03218410.7306490.033252RandomForestClassifierDF116718230NaNNaN0.1632550.0110570.0118160.005582
10.6639380.7291440.032603920.7297620.033654RandomForestClassifierDF15518376NaNNaN0.2676290.0194980.0179500.007206
20.6637620.7292720.032755RandomForestClassifierDF84
30.6629510.7283670.032708RandomForestClassifierDF115
40.6627360.7281340.03269930.7291910.032447RandomForestClassifierDF156
50.6626330.7290570.033212RandomForestClassifierDF86
60.6622040.7282170.033007RandomForestClassifierDF11415164NaNNaN0.1165020.0032900.0083890.000300
70.6606930.7282140.03376040.7288810.033787RandomForestClassifierDF858128NaNNaN0.0973720.0100870.0085200.003782
80.6604540.7269670.033257150.7239970.034597RandomForestClassifierDF154
90.6561070.7182610.031077LGBMClassifierDF84
100.6561070.7182610.031077LGBMClassifierDF11433NaNNaN0.0251000.0009130.0034050.000173
110.6561070.7182610.031077560.7176300.029876LGBMClassifierDF154NaN5890.9223710.0104590.0004630.0023280.000127
120.6524910.7133090.030409LGBMClassifierDF85
130.6524910.7133090.030409LGBMClassifierDF115
140.6524910.7133090.030409470.7142310.031225LGBMClassifierDF155NaN3780.8608480.0102390.0003860.0022520.000156
150.6468270.7096440.031408680.7002940.031576LGBMClassifierDF7NaN78860.8912140.0205120.0009150.0028450.000243
160.6468270.7096440.031408890.6965270.032738LGBMClassifierDF1164NaN16780.8341050.0227420.0040560.0031370.001046
170.6468270.7096440.0314083100.6849730.032143LGBMClassifierDF156NaN37980.9664890.0678320.0019110.0049450.000251
\n", "" ], "text/plain": [ - " ranking_score roc_auc classifier \\\n", - " mean std type \n", - "rank \n", - "0 0.665617 0.729985 0.032184 RandomForestClassifierDF \n", - "1 0.663938 0.729144 0.032603 RandomForestClassifierDF \n", - "2 0.663762 0.729272 0.032755 RandomForestClassifierDF \n", - "3 0.662951 0.728367 0.032708 RandomForestClassifierDF \n", - "4 0.662736 0.728134 0.032699 RandomForestClassifierDF \n", - "5 0.662633 0.729057 0.033212 RandomForestClassifierDF \n", - "6 0.662204 0.728217 0.033007 RandomForestClassifierDF \n", - "7 0.660693 0.728214 0.033760 RandomForestClassifierDF \n", - "8 0.660454 0.726967 0.033257 RandomForestClassifierDF \n", - "9 0.656107 0.718261 0.031077 LGBMClassifierDF \n", - "10 0.656107 0.718261 0.031077 LGBMClassifierDF \n", - "11 0.656107 0.718261 0.031077 LGBMClassifierDF \n", - "12 0.652491 0.713309 0.030409 LGBMClassifierDF \n", - "13 0.652491 0.713309 0.030409 LGBMClassifierDF \n", - "14 0.652491 0.713309 0.030409 LGBMClassifierDF \n", - "15 0.646827 0.709644 0.031408 LGBMClassifierDF \n", - "16 0.646827 0.709644 0.031408 LGBMClassifierDF \n", - "17 0.646827 0.709644 0.031408 LGBMClassifierDF \n", + " score candidate param \\\n", + " test - classifier \n", + " rank mean std - max_depth \n", + "0 1 0.730649 0.033252 RandomForestClassifierDF 7 \n", + "9 2 0.729762 0.033654 RandomForestClassifierDF 5 \n", + "2 3 0.729191 0.032447 RandomForestClassifierDF 6 \n", + "7 4 0.728881 0.033787 RandomForestClassifierDF 5 \n", + "1 5 0.723997 0.034597 RandomForestClassifierDF 4 \n", + "5 6 0.717630 0.029876 LGBMClassifierDF 4 \n", + "4 7 0.714231 0.031225 LGBMClassifierDF 5 \n", + "6 8 0.700294 0.031576 LGBMClassifierDF 7 \n", + "8 9 0.696527 0.032738 LGBMClassifierDF 4 \n", + "3 10 0.684973 0.032143 LGBMClassifierDF 6 \n", + "\n", + " time \\\n", + " fit \n", + " min_samples_leaf n_estimators min_child_samples subsample mean \n", + "0 18 230 NaN NaN 0.163255 \n", + "9 18 376 NaN NaN 0.267629 \n", + "2 15 164 NaN NaN 0.116502 \n", + "7 8 128 NaN NaN 0.097372 \n", + "1 8 33 NaN NaN 0.025100 \n", + "5 NaN 58 9 0.922371 0.010459 \n", + "4 NaN 37 8 0.860848 0.010239 \n", + "6 NaN 78 8 0.891214 0.020512 \n", + "8 NaN 167 8 0.834105 0.022742 \n", + "3 NaN 379 8 0.966489 0.067832 \n", "\n", " \n", - " min_samples_leaf max_depth \n", - "rank \n", - "0 11 6 \n", - "1 15 5 \n", - "2 8 4 \n", - "3 11 5 \n", - "4 15 6 \n", - "5 8 6 \n", - "6 11 4 \n", - "7 8 5 \n", - "8 15 4 \n", - "9 8 4 \n", - "10 11 4 \n", - "11 15 4 \n", - "12 8 5 \n", - "13 11 5 \n", - "14 15 5 \n", - "15 8 6 \n", - "16 11 6 \n", - "17 15 6 " + " score \n", + " std mean std \n", + "0 0.011057 0.011816 0.005582 \n", + "9 0.019498 0.017950 0.007206 \n", + "2 0.003290 0.008389 0.000300 \n", + "7 0.010087 0.008520 0.003782 \n", + "1 0.000913 0.003405 0.000173 \n", + "5 0.000463 0.002328 0.000127 \n", + "4 0.000386 0.002252 0.000156 \n", + "6 0.000915 0.002845 0.000243 \n", + "8 0.004056 0.003137 0.001046 \n", + "3 0.001911 0.004945 0.000251 " ] }, "execution_count": 24, @@ -1650,30 +1886,30 @@ } ], "source": [ - "# re-run ranker without redundant features\n", - "clf_ranker = LearnerRanker(\n", - " grids=classifier_grid,\n", + "clf_selector = LearnerSelector(\n", + " searcher_type=RandomizedSearchCV,\n", + " parameter_space=[rforest_ps, lgbm_ps],\n", " cv=RepeatedKFold(n_splits=5, n_repeats=10, random_state=42),\n", " n_jobs=-3,\n", " scoring=\"roc_auc\",\n", + " random_state=42,\n", ").fit(prediab_no_redundant_feat)\n", "\n", - "clf_ranker.summary_report()" + "clf_selector.summary_report()" ] }, { "cell_type": "code", "execution_count": 25, - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [], "source": [ "# run inspector\n", "inspector_no_redun = LearnerInspector(\n", + " pipeline=clf_selector.best_estimator_,\n", " n_jobs=-3,\n", " verbose=False,\n", - ").fit(crossfit=clf_ranker.best_model_crossfit_)" + ").fit(sample=prediab_no_redundant_feat)" ] }, { @@ -1683,7 +1919,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1719,8 +1955,15 @@ "outputs": [ { "data": { + "text/html": [ + "
RandomForestClassifierDF(\n",
+       "    max_depth=7, min_samples_leaf=18, n_estimators=230, random_state=42\n",
+       ")
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], "text/plain": [ - "RandomForestClassifierDF(max_depth=6, min_samples_leaf=11, random_state=42)" + "RandomForestClassifierDF(max_depth=7, min_samples_leaf=18, n_estimators=230, random_state=42)" ] }, "execution_count": 27, @@ -1729,7 +1972,7 @@ } ], "source": [ - "clf_ranker.best_model_.classifier" + "clf_selector.best_estimator_.classifier" ] }, { @@ -1744,10 +1987,8 @@ "As the basis for the simulation, we divide the feature into relevant partitions: \n", "\n", "- We use FACET's `ContinuousRangePartitioner` to split the range of observed values of waist to height ratio into intervals of equal size. Each partition is represented by the central value of that partition. \n", - "- For each partition, the simulator creates an artificial copy of the original sample assuming the variable to be simulated has the same value across all observations - which is the value representing the partition. Using the best `LearnerCrossfit` acquired from the ranker, the simulator now re-predicts all targets using the models trained for all folds and determines the average value of the target variable resulting from this.\n", - "- The FACET `SimulationDrawer` allows us to visualise the result; both in a matplotlib and a plain-text style\n", - "\n", - "Finally, because FACET can use bootstrap cross validation, we can create a crossfit from our previous `LearnerRanker` best model to perform the simulation so we can quantify the uncertainty by using bootstrap confidence intervals." + "- For each partition, the simulator creates an artificial copy of the original sample assuming the variable to be simulated has the same value across all observations - which is the value representing the partition. Using the best estimator acquired from the selector, the simulator now re-predicts all targets using the models trained on full sample and determines the average value of the target variable resulting from this.\n", + "- The FACET `SimulationDrawer` allows us to visualise the result; both in a matplotlib and a plain-text style" ] }, { @@ -1755,28 +1996,14 @@ "execution_count": 28, "metadata": {}, "outputs": [], - "source": [ - "# create a bootstrap CV crossfit for simulation using best model\n", - "boot_crossfit = LearnerCrossfit(\n", - " pipeline=clf_ranker.best_model_,\n", - " cv=BootstrapCV(n_splits=1000, random_state=42),\n", - " n_jobs=-3,\n", - " verbose=False,\n", - ").fit(sample=prediab_no_redundant_feat)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], "source": [ "# set up and run a simulation\n", "sim_feature = \"Waist_to_hgt\"\n", "\n", "waist_to_hgt_simulator = UnivariateProbabilitySimulator(\n", - " crossfit=boot_crossfit,\n", - " n_jobs=-1\n", + " model=clf_selector.best_estimator_,\n", + " sample=prediab_no_redundant_feat,\n", + " n_jobs=-1,\n", ")\n", "\n", "waist_to_hgt_partitions = ContinuousRangePartitioner()\n", @@ -1789,12 +2016,12 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1817,64 +2044,45 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "********************************* Waist_to_hgt *********************************\n", + "================================= Waist_to_hgt =================================\n", + "\n", "\n", "probability(1):\n", "\n", "Baseline = 0.342\n", "\n", - "Partition 2.5th percentile Median 97.5th percentile\n", + "Partition 2.5th percentile Mean 97.5th percentile\n", "========= ================ ========= =================\n", - "0.42 0.261 0.302 0.34 \n", - "0.44 0.259 0.298 0.335 \n", - "0.46 0.26 0.297 0.333 \n", - "0.48 0.261 0.297 0.332 \n", - "0.5 0.26 0.297 0.332 \n", - "0.52 0.261 0.296 0.33 \n", - "0.54 0.27 0.307 0.343 \n", - "0.56 0.27 0.308 0.346 \n", - "0.58 0.32 0.362 0.408 \n", - "0.6 0.317 0.356 0.396 \n", - "0.62 0.323 0.363 0.402 \n", - "0.64 0.322 0.363 0.405 \n", - "0.66 0.334 0.373 0.417 \n", - "0.68 0.353 0.396 0.445 \n", - "0.7 0.362 0.41 0.466 \n", - "0.72 0.366 0.415 0.475 \n", - "0.74 0.366 0.415 0.476 \n", - "0.76 0.373 0.428 0.494 \n", - "0.78 0.376 0.431 0.5 \n", + "0.4 0.289 0.297 0.306 \n", + "0.45 0.287 0.296 0.305 \n", + "0.5 0.286 0.295 0.304 \n", + "0.55 0.295 0.304 0.313 \n", + "0.6 0.352 0.361 0.37 \n", + "0.65 0.366 0.375 0.383 \n", + "0.7 0.401 0.409 0.418 \n", + "0.75 0.409 0.417 0.426 \n", + "0.8 0.416 0.424 0.432 \n", "\n", "Observed frequencies:\n", "\n", "Partition Frequency\n", "========= =========\n", - "0.42 21\n", - "0.44 42\n", - "0.46 52\n", - "0.48 68\n", - "0.5 47\n", - "0.52 79\n", - "0.54 82\n", - "0.56 89\n", - "0.58 86\n", - "0.6 75\n", - "0.62 61\n", - "0.64 59\n", - "0.66 48\n", - "0.68 37\n", - "0.7 22\n", - "0.72 21\n", - "0.74 23\n", - "0.76 14\n", - "0.78 11\n", + "0.4 22\n", + "0.45 118\n", + "0.5 159\n", + "0.55 214\n", + "0.6 186\n", + "0.65 127\n", + "0.7 69\n", + "0.75 46\n", + "0.8 18\n", "\n" ] } @@ -2005,7 +2213,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -2217,7 +2425,7 @@ "[5 rows x 38 columns]" ] }, - "execution_count": 32, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -2237,7 +2445,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -2449,7 +2657,7 @@ "[5 rows x 40 columns]" ] }, - "execution_count": 33, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -2464,7 +2672,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -3029,7 +3237,7 @@ "Waist_to_hgt 0.514059 0.571075 0.635716 1.063995 " ] }, - "execution_count": 34, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -3041,7 +3249,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -3050,20 +3258,18 @@ "" ] }, - "execution_count": 35, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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3n7GMRZIkSZKkZeHBnpIkSZIkaUIwiSFJkiRJkiYEt5NohTN/4SKmzpg13mFI0oS0YOZe4x2CJEnScrNSrsRIckyS93R9Pj/JSV2f/zPJ+wbp+7Ekewwx9t5JNhtGDIcnuTXJTUluTPLGVn7ScPqPlSQbJLk2yQ1Jdnm85pUkSZIkaaytlEkM4CpgJ4D2us/1gc276ncCrhyoY1V9tKp+NMTYewNDJiGSvA14GbB9VW0B7Aqkjf/mqrplgD6ThhpzGbwUuLWqtq6qy4fTYTnGIkmSJEnSqK2sSYwraUkMOsmLm4D7kjw5yRrAC4C/T3JdWylxYpIAJDk1yX7temaSW5LMS/LZJDsBrwaOTjI3ycaDzP8h4F+r6g8AVbWoqr7WxpydpKddL24rP64FdkzyxjbXjUm+0T+evj7t925JLk3ynSS3tVgPSvLjJPOTbJxkOvAfwCtavGsl2TPJ1UmuT3JGkrXbeAuSfDTJFcBrl9LuqFY+P8mmrXztJKe0snlJ9m3lA47TX5LDkvQm6V3ywKIR/bElSZIkSauGlTKJUVW/AR5J8iw6yYyrgWuBHYEeYB5wfFVt11ZKrAW8snuMJE8B9gE2r6otgU9U1VXAucAHqmp6Vf2i/9xJ1gHWGahuAE8CbqqqHYB7gQ8DL6mqrYB3D6N/X7tpwBuA51XV9sBJwDurai7wUeD0qpre5jsC2KOqtgF6ge5tNX+sqp2BHy2l3d2t/EvA4a3sI8CiqprWntfFSdZfyjh/VlUnVlVPVfVMmjxlGLcuSZIkSVrVrMwHe/atxtgJ+BywUbteRGe7ye5JPghMBp4C3Ax8v6v/H4A/AiclmQX8YJjzBqhhtl0CnNmuXwJ8t6ruBqiq3w2j/3VVdSdAkl8AF7Ty+cDuA7R/IZ2tMFe2hSdPpJPg6XP6MNud1X7PAf6xXe8BHNDXoKruTfLKpYwjSZIkSdKwrcxJjL5zMabR2U7yK+D9dJITJ9NZrdBTVb9KciSwZnfnqnokyfZ0zpQ4AHgHnUTDkKrqD0nuT/Kcqrp9Kc3/WFVL2vVgyY9HaCtm2paXJ3bVPdR1/WjX50cZ+G8b4MKqOnCQeO4fZru+eZZ0zTNQ/EsbR5IkSZKkYVuZkxhX0kla3N4SBb9Lsh6dMzLe0trc3c5o2A/4bnfnVj65qn6Y5Brg563qPmCdpcz9aeCEJPu3pMa6wAFVdeIQfS4Czk5yTFXdk+QpbTXGAmBb4DvAa4DVh3Pzg7imxfXcqvp5ksnAM6rqtlG263YBnUTPewCSPHmU4zBtoyn0+opASZIkSVI/K+WZGM18Om8luaZf2aK2ZeOr7fM5wHUD9F8H+EGSecClwHtb+beBD7RXlg52sOeXgEuA65Lc1Po/MFSwVXUz8Eng0iQ30tkCQ4vzxUl+DOzAX1ZLjFhV3QUcAnyr3dc1wKajbdfPJ4Ant4NSbwR2H+U4kiRJkiQNKFXDPb5Benz09PRUb2/veIchSZIkSRpjSeZUVc9o+6/MKzEkSZIkSdJKZGU+E2O5S3IC8KJ+xV+oqlPGIx5JkiRJklZmJjGWQVW9fbxjkCRJkiRpVeF2EkmSJEmSNCGYxJAkSZIkSROC20m0wpm/cBFTZ8wa7zAkAQtm7jXeIUiSJEl/5kqMcZBknySVZNPxjmU4kixJMjfJzUluTPK+JE9odbslWdTq5yX5UZK/aXWHJLmr1d2S5C3jeyeSJEmSpInMJMb4OBC4AjhgWQdKMmnZw1mqB6tqelVtDrwMeAXw7131l7f6LYHrgO4DT0+vqunAbsCnkjztcYhXkiRJkrQSMonxOEuyNp3Xsv4zcECSlyf5Tlf9bkm+3673THJ1kuuTnNH6kmRBko8muQJ4bZK3JLmurZI4M8nk1m7jJNe0uo8lWdw1zwda+bwkRw03/qr6LXAY8I4k6XdvAdYB7h2k3y+AZw/yXA5L0pukd8kDi4YbjiRJkiRpFWIS4/G3N3BeVd0G/A64B3hhkie1+v2B05OsDxwB7FFV2wC9wPu6xvljVe1cVd8Gzqqq7apqK+AndBIkAF8AvlBV2wG/6euYZE9gE2B7YDqwbZJdh3sDVXU7nX87f9OKdkkyF/gfYA/g5P59kjwHeA7w80HGPLGqeqqqZ9LkKcMNRZIkSZK0CjGJ8fg7EPh2u/428FrgPOBVSVYD9gK+B7wQ2Ay4siUIDuaxqxhO77reIsnlSeYDBwGbt/IdgTPa9X91td+z/dwAXA9sSiepMRLdqzD6tpM8EzgF+I+uuv1b/N8C3lpVvxvhPJIkSZIkAb6d5HGV5KnAS+gkHQqYBBRwKJ1zJH4HXFdV97WtGRdW1YGDDHd/1/WpwN5VdWOSQ+icPzFkKMCnq+oro7yP5wBLgN8CL+hXfS5wZtfn06vqHaOZR5IkSZKkbiYxHl/7AV+vqrf2FSS5FHgE2AZ4C39ZYXENcEKS51bVz9s5F89o21D6Wwe4M8nqdFZiLOwaY982ZvchoucDH09yWlUtTrIR8HA7t2JISTYAvgwcX1XV71gMgJ3pnH0xatM2mkKvr3WUJEmSJPVjEuPxdSAws1/ZmXQSDD8ADqGzbYSququtqvhWkjVa2yOAgZIYHwGuBX4JzKeT1AB4D/DNJO8HZgGL2tgXJHkBcHVLQiwGXk9nZcVA1mpbQlank3D5BvC5rvq+MzHS5njzoE9AkiRJkqRRSlWNdwxaTtrqjQfbiokDgAOr6jXjHdfS9PT0VG9v73iHIUmSJEkaY0nmVFXPaPu7EmPlti1wfDtf4/fAm8Y3HEmSJEmSRs8kxkqsqi4HthpO23bo6EUDVL20qu4Z08AkSZIkSRoFkxgCoCUqpo93HJIkSZIkDeYJ4x2AJEmSJEnScJjEkCRJkiRJE4LbSbTCmb9wEVNnzBrvMCQBC2buNd4hSJIkSX/mSowVTJIPJ7k5ybwkc5PskGR2kp+2zz9JclhX+wVJ5ie5MckFSf52iLE/tAxxLWnz39zmel+SJ7S63ZIsavXzkvwoyd+0ukOS3NXqbknyltHGIEmSJElatZnEWIEk2RF4JbBNVW0J7AH8qlUfVFXTgRcBn0nyxK6uu1fVVkAvMFSiYtRJDODBqppeVZsDLwNeAfx7V/3lrX5L4Drg7V11p7fYdwM+leRpyxCHJEmSJGkVZRJjxbIhcHdVPQRQVXdX1W/6tVkbuB9YMkD/y4DnDjRwkpnAWm1FxGmt7H1Jbmo/7xlukFX1W+Aw4B1J0m+eAOsA9w7S7xfAsweI77AkvUl6lzywaLihSJIkSZJWISYxViwXAM9McluSLyZ5cVfdaUnmAT8FPl5VAyUxXgnMH2jgqprBX1ZTHJRkW+BQYAfghcBbkmw93ECr6nY6/37+phXtkmQu8D90VpCc3L9PkucAzwF+PsB4J1ZVT1X1TJo8ZbhhSJIkSZJWISYxViBVtRjYls4qh7uA05Mc0qoPals1ngUcnqR7NcMlLYGwLvDpYU63M3B2Vd3f5j0L2GWEIXevwujbTvJM4BTgP7rq9m/xfQt4a1X9boTzSJIkSZLk20lWNG2FxWxgdpL5wMH96u9Kcj2dFRS/bMW7V9XdI5wqS28yROfOqoolwG+BF/SrPhc4s+vz6VX1jmWZT5IkSZIkkxgrkCTPBx6tqp+1oul0EhVbdLWZDGzNY1c6DNfDSVavqofpnJ9xajsrI8A+wBuGGecGwJeB46uq+h2LAZ1VHr8YRXwATNtoCr2+1lGSJEmS1I9JjBXL2sBxSdYDHqFzdsRhwHfpnInxILAGcGpVzRnF+CcC85Jc387FOBX4cas7qapuGKLvWm1LyOottm8An+uq7zsTI8Ai4M2jiE+SJEmSpEGlqsY7Bukxenp6qre3d7zDkCRJkiSNsSRzqqpntP092FOSJEmSJE0IbidZCSW5ls62k25vqKoBX7/a1e+pwEUDVL20qu4Zq/gkSZIkSRoNkxgroaraYZT97qFzmKgkSZIkSSsct5NIkiRJkqQJwSSGJEmSJEmaENxOohXO/IWLmDpj1niHIa2UFszca7xDkCRJkkbNlRjjKMk+SSrJpuMdy3Ak+XCSm5PMSzI3yQ6tfHaSn7aynyQ5rKvPgiTzk9yY5IIkfzt+dyBJkiRJmshMYoyvA4ErgAOWdaAkk5Y9nCHH3xF4JbBNVW0J7AH8qqvJQVU1HXgR8JkkT+yq272qtgJ6gQ8tzzglSZIkSSsvkxjjJMnadL7w/zNwQJKXJ/lOV/1uSb7frvdMcnWS65Oc0fr2rXL4aJIrgNcmeUuS69qqhzOTTG7tNk5yTav7WJLFXfN8oJXPS3LUECFvCNxdVQ8BVNXdVfWbAdqtDdwPLBmg7jLguYM8j8OS9CbpXfLAoiHCkCRJkiStqkxijJ+9gfOq6jbgd8A9wAuTPKnV7w+cnmR94Ahgj6rahs5qhvd1jfPHqtq5qr4NnFVV27VVDz+hkyAB+ALwharaDvhz4iHJnsAmwPZ0Xq26bZJdB4n3AuCZSW5L8sUkL+5Xf1qSecBPgY9X1UBJjFcC8wcavKpOrKqequqZNHnKICFIkiRJklZlJjHGz4HAt9v1t4HXAucBr0qyGrAX8D3ghcBmwJVJ5gIHA8/uGuf0rustklyeZD5wELB5K98ROKNd/1dX+z3bzw3A9cCmdJIaf6WqFgPbAocBd9FJsBzS1eSgts3kWcDhSbpjvKTFvi7w6YEfhyRJkiRJQ/PtJOMgyVOBl9BJOhQwCSjgUODtdFZmXFdV9yUJcGFVHTjIcPd3XZ8K7F1VN7YEw25LCwX4dFV9ZThxt9UVs4HZLVFycJuzu81dSa4HdgB+2Yp3r6q7hzOHJEmSJEmDMYkxPvYDvl5Vb+0rSHIp8AiwDfAW/rLC4hrghCTPraqft3MuntG2ofS3DnBnktXprMRY2DXGvm3M7kNEzwc+nuS0qlqcZCPg4ar6bf+BkzwfeLSqftaKpvOXJEV3u8nA1sB/DOM5DGjaRlPo9TWQkiRJkqR+TGKMjwOBmf3KzqSTYPgBcAidVQ59KxsOAb6VZI3W9ghgoCTGR4Br6SQX5tNJagC8B/hmkvcDs4BFbewLkrwAuLqz4IPFwOuBv0pi0Dmw87gk69FJtvycztaSPqcleRBYAzi1quYs5RlIkiRJkjQiqarxjkHLWVsd8WBVVZIDgAOr6jXjHddgenp6qre3d7zDkCRJkiSNsSRzqqpntP1dibFq2BY4vp2v8XvgTeMbjiRJkiRJI2cSYxVQVZcDWw2nbTt09KIBql5aVfeMaWCSJEmSJI2ASQw9RktUTB/vOCRJkiRJ6u8J4x2AJEmSJEnScJjEkCRJkiRJE4LbSVZiSf4W+DywHfAQsAB4T1UN9HpWkiyuqrWHGO9jwGVV9aOxj/Yv5i9cxNQZs5bnFHocLJi513iHIEmSJGklYxJjJdXeRHI28LWqOqCVTQeeBgyYxFiaqvromAUoSZIkSdIIuZ1k5bU78HBVfbmvoKrmAjckuSjJ9UnmJ3nNQJ2TfLDV35hkZis7Ncl+7XpBkvXbdU+S2e36yCRfS3JBa/OPSf6jjXVektWX721LkiRJklZWrsRYeW0BzBmg/I/APlX1h5aEuCbJuVVVfQ2SvBzYG9ihqh5I8pQRzr0xnSTKZsDVwL5V9cEkZwN7Aef075DkMOAwgEnrbjDC6SRJkiRJqwJXYqx6AnwqyTzgR8BGdLaYdNsDOKWqHgCoqt+NcI7/rqqHgfnAJOC8Vj4fmDpQh6o6sap6qqpn0uQpI5xOkiRJkrQqMImx8roZ2HaA8oOADYBtq2o68H/Amv3aBCiG9gh/+ffTv/9DAFX1KJ0tLX1jPYqrfyRJkiRJo2QSY+V1MbBGkrf0FSTZDng28NuqejjJ7u1zfxcAb0oyufUbaDvJAv6SJNl3LAOXJEmSJGkg/q/4SqqqKsk+wOeTzKBzFsYC4Ejg2CS9wFzg1gH6ntfeZNKb5E/AD4EP9Wt2FPD/JfkQcO1Yxj5toyn0+npOSZIkSVI/6TrPUVoh9PT0VG9v73iHIUmSJEkaY0nmVFXPaPu7nUSSJEmSJE0IJjEkSZIkSdKEYBJDkiRJkiRNCCYxJEmSJEnShGASQ5IkSZIkTQgmMSRJkiRJ0oSw2ngHsKpK8lTgovbxb4ElwF3t8/ZV9ackrwY2q6qZQ4wzFfhBVW2xHGP9GHBZVf2oX/luwOFV9cqxnG/+wkVMnTFrLIdc5S2Yudd4hyBJkiRJy8wkxjipqnuA6QBJjgQWV9Vn++qTrFZV5wLnjkuAf4ljUlV9dDxjkCRJkiQJ3E6yQklyapLPJbkE+EySQ5Ic3+o2TnJNkuuSfCzJ4gH6X55ketfnK5NsmWTtJKckmZ9kXpJ9W/2eSa5Ocn2SM5Ks3coXJPlokiuA17a49mt1/5Dk1lb3j11zPSnJyS2+G5K8ppVvnuTHSea2uTdZfk9QkiRJkrQyM4mx4nkesEdVvb9f+ReAL1TVdsBvBul7EnAIQJLnAWtU1TzgI8CiqppWVVsCFydZHziizbUN0Au8r2usP1bVzlX17b6CJGsCXwVeBexCZxtMnw8DF7f4dgeOTvIk4G0t7ulAD/DrgQJPcliS3iS9Sx5YNMTjkSRJkiStqkxirHjOqKolA5TvCJzRrv9rsL7AK5OsDrwJOLWV7wGc0Neoqu4FXghsBlyZZC5wMPDsrrFOH2D8TYE7qupnVVXAN7vq9gRmtLFmA2sCzwKuBj6U5N+AZ1fVgwMFXlUnVlVPVfVMmjxlkNuTJEmSJK3KPBNjxXP/aDtW1QNJLgReA/wTnZUPAAGqX/MAF1bVgSOMo/843ePtW1U/7Vf+kyTXAnsB5yd5c1VdPNR9SJIkSZI0EFdiTBzXAPu26wOGaHcScCxwXVX9rpVdALyjr0GSJ7fxXpTkua1sctuCMpRbgb9LsnH73J0AOR94Z5K08bZuv58D3F5Vx9I5pHTLpcwhSZIkSdKAXIkxcbwH+GaS9wOzgAEPjqiqOUn+AJzSVfwJ4IQkN9F5letRVXVWkkOAbyVZo7U7ArhtsACq6o9JDgNmJbkbuALoe7Xrx4HPA/NaImMB8Epgf+D1SR4G/hf42NJudNpGU+j1laCSJEmSpH7SOdpAK7okk4EHq6qSHAAcWFWvGaDd0+mcSbFpVT36OIc5Jnp6eqq3t3e8w5AkSZIkjbEkc6qqZ+ktB+ZKjIljW+D4tsrh93QO7nyMJG8EPgm8b6ImMCRJkiRJGoxJjAmiqi4HtlpKm68DX398IpIkSZIk6fHlwZ6SJEmSJGlCMIkhSZIkSZImBJMYkiRJkiRpQjCJIUmSJEmSJgQP9tQKZ/7CRUydMWu8w1gpLJi513iHIEmSJEljZlxWYiTZJ0kl2bR9nprkpmUY76Qkm41dhMsmyceS7DHKvrsl2WmsYxpgnk2TzE1yQ5KNl/d8kiRJkiQtq/HaTnIgcAVwwFgMVlVvrqpbxmKssVBVH62qH42y+27AgEmMJGO5cmZv4HtVtXVV/WI4HZJMGsP5JUmSJEkakcc9iZFkbeBFwD8zQBKjrcq4PMn17WenVr5bktlJvpvk1iSnJUmrm52kp10vTvLJJDcmuSbJ01r5BknOTHJd+3nREDFun+SqtkrhqiTPb+WHJDknyfeT3JHkHUne19pdk+Qprd2pSfZr1wuSHNXuZX7X6pOntLHmtb5bJpkKvA14b1slsUsb63NJLgE+s5TYzkpyXpKfJfmPVj6pjXFTm/+9SV4BvAd4cxuXJK9P8uM271f6EhbteX4sybXAjkk+2p7fTUlO7PobvCvJLe1+vt3KnpTk5Nb+hiSvGeKZH5akN0nvkgcWLfXfkSRJkiRp1TMeKzH2Bs6rqtuA3yXZpl/9b4GXVdU2wP7AsV11W9P58r0Z8Bw6yZD+ngRcU1VbAZcBb2nlXwCOqartgH2Bk4aI8VZg16raGvgo8Kmuui2A1wHbA58EHmjtrgbeOMh4d7f7+RJweCs7CrihqrYEPgR8vaoWAF9ucU6vqstb2+cBe1TV+5cS23Q6z2wasH+SZ7ayjapqi6qaBpxSVT/smmf3JC9o/V5UVdOBJcBBXc/zpqraoaquAI6vqu2qagtgLeCVrd0MYOt2P29rZR8GLm7PfHfg6CRPGugBVdWJVdVTVT2TJk8Z5DFKkiRJklZl43Gw54HA59v1t9vnE7rqVweOTzKdzpfp53XV/biqfg2QZC4wlc62lG5/An7QrucAL2vXewCbtYUDAOsmWaeq7hsgxinA15JsAlSLqc8lrc99SRYB32/l84EtB7nns7ri+cd2vTOdZApVdXGSpyYZ7Nv7GVW1ZBixXVRViwCS3AI8G7gZeE6S44BZwAUDjP9SYFvguvZ81qKTTILO3+DMrra7J/kgMBl4Shv/+8A84LQk5wDntLZ7Aq9O0pe4WRN4FvCTQe5TkiRJkqRBPa5JjCRPBV4CbJGkgEl0voh/savZe4H/A7ais1Lkj111D3VdL2Hg+B+uqhqgzROAHavqwWGE+nE6yYp92haP2YPE8GjX50cHiae7T3c8GaBdDVAGcP8oYlsCrFZV9ybZCvh74O3APwFv6jd+gK9V1f8bYO4/9iVQkqxJ52/VU1W/SnIkncQEwF7ArsCrgY8k2byNu29V/XSQ+5IkSZIkadge75UY+9HZNvHWvoIklwLP6GozBfh1VT2a5GA6iY6xcAHwDuDoNu/0qpo7SNspwMJ2fcgYzd/fZXS2bHw8yW50tpz8Icl9wLpD9BtRbEnWB/5UVWcm+QVw6gDNLgK+l+SYqvptO9tjnar6Zb92fQmLu9M522Q/4LtJngA8s6ouSXIFne02awPnA+9M8s6qqiRbV9UNS4t52kZT6PXVoJIkSZKkfh7vMzEOBM7uV3YmnTMh+nwRODjJNXS2ktzP2HgX0NMOnryFv5zbMJD/AD6d5ErGLonS35F98QAzgYNb+feBffoO9hyD2DYCZrftN6cCf7Xaor3Z5QjgghbPhcCGA7T7PfBVOltnzgGua1WTgG8mmQ/cQOesjd/TWTWyOjAvnVfofnwY8UqSJEmSNKD8ZeeFtGLo6emp3t7e8Q5DkiRJkjTGksypqp7R9h+Pt5NIkiRJkiSN2Hi8nWSFkeRQ4N39iq+sqrePRzySJEmSJGlwq3QSo6pOAU4Z7zgkSZIkSdLSuZ1EkiRJkiRNCCYxJEmSJEnShLBKbyfRimn+wkVMnTFrvMNYKSyYudd4hyBJkiRJY2bMVmIk2SdJJdl0rMYcZJ7dkuw0Vu2WhyQfS7LHKPs+LnEn2TTJ3CQ3JNl4ec8nSZIkSdKyGsvtJAcCVwAHjOGYj5FkNWA3YDhf8ofbbsxV1Uer6kej7L4bg8Td7n+s7A18r6q2rqpfDKdDkkljOL8kSZIkSSMyJl+Kk6wNvAjYHTgXODLJhsDpwLptnn+pqsuTLAa+0treCxxQVXcleQtwGPBE4OfAG6rqgSSnAr8Dtm6/XwQsSfJ64J3AesARrd89wEHAWsDb+rW7Ffgy8KwW9nuq6spB7md74PNtnAeBQ6vqp0kOofPlfxKwBfCfbd43AA8Br6iq37WYf1BV302yAPga8CpgdeC1VXVrkqcAJwPPAR5o9/6HAeL+5677vz7J6UPE9mpgMrAxcHZVfbAlHv4/oAeoNudPgfe0eXatqt3bfO9q93Mt8K9VtaT9vT4H/D3w/iQvafeyFnAV8NaqqiTvarE/AtxSVQckeRJwHDCt/Rs4sqq+N8gzP6w9Ayatu8FATSRJkiRJq7ixWomxN3BeVd0G/C7JNsDrgPOrajqwFTC3tX0ScH1VbQNcCvx7Kz+rqrarqq2An9D58t7necAeVbUvnUTEMVU1vaoup7P644VVtTXwbeCDVbVggHZfaJ+3A/YFThrifm4Fdm1jfhT4VFfdFu3etgc+CTzQ2l0NvHGQ8e5u9/sl4PBWdhRwQ1VtCXwI+PogcXff//uXEtt0YH86SYP9kzyzlW1UVVtU1TTglKr6Ydc8uyd5Qev3ovb3WkInGQSdv9dNVbVDVV0BHN/+TlvQSWS8srWbAWzd7udtrezDwMXtme8OHN0SG3+lqk6sqp6q6pk0ecogj1GSJEmStCobq+0JB9JZHQCdRMKBwPeBk5OsDpxTVXNb/aN0VmgAfBM4q11vkeQTdFZWrA2c3zX+GVW1ZJC5nwGc3lZ+PBG4Y5B2ewCbJen7vG6SdarqvgHaTgG+lmQTOqsXVu+qu6T1uS/JonafAPOBLQeZu+8e5wD/2K53ppNMoaouTvLUJIN9e+++/6Fiu6iqFgEkuQV4NnAz8JwkxwGzgAsGGP+lwLbAde35rAX8ttUtAc7sart7kg/SWfHxlDb+94F5wGlJzgHOaW33BF6dpC9xsyadlTA/GeQ+JUmSJEka1DInMZI8FXgJnSRE0dlqUcAHgV2BvYBvJDm6qr4+wBDVfp8K7F1VN7atEbt1tbl/iBCOAz5XVecm2Q04cpB2TwB2rKoHl35XfJxOsmKfJFOB2V11D3VdP9r1+VEGf559bZZ0tckA7WqAMnjs/Q83tiXAalV1b5Kt6GwHeTvwT8Cb+o0f4GtV9f8GmPuPfQmUJGsCXwR6qupXSY6kk5iAzt95VzpbWj6SZPM27r5V9dNB7kuSJEmSpGEbi5UY+9HZCvHWvoIkl9L5QntlVX21bSHYBvg6nWTCfnRWbLyOznYQgHWAO9vKjYOAhYPMdx+dczb6TOlqe/AQ7S4A3gEc3WKc3rU6pL/uMQ8ZpM2yuozOfX68JV/urqo/JOkf9zLFlmR94E9VdWaSX9BJFvV3EfC9JMdU1W/beR3rVNUv+7XrS1jc3c5B2Q/4bpInAM+sqkuSXEHn79q3muadSd7Zzs3YuqpuWFrM0zaaQq+vBpUkSZIk9TMWZ2IcCJzdr+xMOl+W5ya5gc62iS+0uvuBzZPMobOC42Ot/CN0DpS8kM65D4P5PrBPez3oLnRWXpyR5HLg7iHavQvoSTKvbbV4W/+Bu/wH8OkkV9JZWbI8HNkXDzCTvyRg+se9rLFtBMxOMpfO3+SvVltU1S10Dke9oMVzIbDhAO1+D3yVztaZc4DrWtUk4JtJ5gM30Dlr4/d0Vo2sDsxLclP7LEmSJEnSqKRqsB0My2nCZHFVrf24TqoJpaenp3p7e8c7DEmSJEnSGEsyp6p6Rtt/rN5OIkmSJEmStFyN1dtJhm1FWoWR5FDg3f2Kr6yqt49HPJIkSZIkaXCPexJjRVJVpwCnjHcckiRJkiRp6dxOIkmSJEmSJgSTGJIkSZIkaUJYpbeTaMU0f+Eips6YNd5hTGgLZu413iFIkiRJ0pgb1kqMJE9L8l9Jbk8yJ8nVSfZZ3sGNpSSHJDl+KW2mJnnd4xDLqUn2G6L+PUkmPw5xvCvJT5KctrznkiRJkiRpWS01iZEkwDnAZVX1nKraFjgAeMZyjm08TAWWexJjGN4DDJjESDJpDOf5V+AVVXXQcBonceWOJEmSJGncDGclxkuAP1XVl/sKquqXVXVcW7lweZLr289OAEl2S3Jpku8kuS3JzCQHJflxkvlJNm7tTk3ypSSXtFUeL05yclsdcGrffK1Nb5Kbkxw1VLBJXpHk1iRXJDk2yQ8GaPOYlRBJFrfLmcAuSeYmee+y3F+SdZLckWT11mfdJAv6Pg8R/7uApwOXJLmkL74kH0tyLbBjko8muS7JTUlObIkmksxO8pkWx21Jdmnlm7eyuUnmJdkkyZeB5wDntnt9Unv21yW5IclrWt9DkpyR5PvABUnWTnJRex7zu9o9KcmsJDe2uPZv5du2ZzUnyflJNhzkvg9rf+PeJQ8sGuoRSZIkSZJWUcNJYmwOXD9I3W+Bl1XVNsD+wLFddVsB7wamAW8AnldV2wMnAe/savdkOomS9wLfB45pc05LMr21+XBV9QBbAi9OsuVAwSRZE/gK8PKq2hnYYBj3120GcHlVTa+qY5bl/qrqPmA20Hc4wQHAmVX18FABVNWxwG+A3atq91b8JOCmqtqhqq4Ajq+q7apqC2At4JVdQ6zW4ngP8O+t7G3AF6pqOtAD/Lqq3tY1zzHAh4GLq2o7YHfg6CRPav13BA6uqpcAfwT2ac9kd+A/WxLlH4DfVNVWLa7zWsLmOGC/toLnZOCTg9z3iVXVU1U9kyZPGeoRSZIkSZJWUSN+O0mSE9r/tl8HrA58Ncl84Axgs66m11XVnVX1EPAL4IJWPp/Oto0+36+qauX/V1Xzq+pR4Oaudv+U5HrgBjoJju55um0K3F5Vd7TP3xrp/fWzrPd3EnBouz4UOGWUcSwBzuz6vHuSa1tcL6HzTPqc1X7P6YrjauBDSf4NeHZVPTjAHHsCM5LMpZN8WRN4Vqu7sKp+164DfCrJPOBHwEbA0+jc9x5tJcguVbUIeD6wBXBhG/cIVs5tSJIkSZKkx8Fwzji4Gdi370NVvT3J+kAvndUT/0dnVcIT6PwvfZ+Huq4f7fr8aL95HxqgzZ/bJfk74HBgu6q6t20zWXOQWDOM+wF4pMXbd+bHEwdpt0z3V1VXti0pLwYmVdVNw4yvvz9W1ZIW75rAF4GeqvpVkiN57PPoi2NJVxz/1bai7AWcn+TNVXVxvzkC7FtVP31MYbIDcH9X0UF0VrhsW1UPJ1kArFlVtyXZFngF8OkkFwBnAzdX1Y6jvG9JkiRJkv5sOEmMi+n8z/u/VNWXWlnfoZNT6GxNeDTJwcBYHjrZZ106X6IXJXka8HI6KwUGcivwnCRTq2oBnS0gA1kAbAt8B3gNnRUXAPcB63S1G4v7+zqdFSEfH0GfvjjuHqCuL2Fxd5K1gf2A7w41WJLn0Fmhcmy73pLO37Xb+cA7k7yzqirJ1lV1wwDDTQF+2xIYuwPPbnM8HfhdVX0znTNGDqFzxsgGSXasqqvb9pLnVdXNQ8U7baMp9PqKUEmSJElSP0vdTtK2euxN5yyKO5L8GPga8G90VgQcnOQa4Hk89n/sx0RV3UhnG8nNdM5UuHKItg/SeePGeUmuoLOKYqBTIr9K535+DHSvNJgHPNK2y7yXsbm/0+ic+zGSrS0nAv/dd7Bnt6r6fYt/Pp23xlw3jPH2B25qWzo2pZNY6e/jdJI585LcxOBJl9OAniS9dFZl3NrKpwE/bnN8GPhEVf2JTpLlM0luBOYCOw0jXkmSJEmS/ko6OYqVR5K1q2px2yZyAvCzdnDleMWzH/CaqnrDeMUw0fT09FRvb+94hyFJkiRJGmNJ5rQXd4zKcLaTTDRvaVs/nkhnBcdXxiuQJMfR2f7yivGKQZIkSZKklcWETWIkORv4u37F/9ZWXYzbyotuVfXOoeqHuIfzl19UkiRJkiRNTBM2iVFV+4x3DMtqZbgHSZIkSZIeL0s92FOSJEmSJGlFYBJDkiRJkiRNCBN2O8mqKskSOq9XXQ34CXBwVT0wSNvdgD9V1VVjOP8Pgde1V70uF/MXLmLqjFnLa/hVwoKZe413CJIkSZI05lyJMfE8WFXTq2oL4E/A24Zouxuw01hOXlWvWJ4JDEmSJEmSBmMSY2K7HHhukqckOSfJvCTXJNkyyVQ6CY73JpmbZJckr0pybZIbkvwoydMAkry4tZnb6tZJsmGSy1rZTUl2aW0XJFk/ydQkN/UFkuTwJEe269lJjmn9f5JkuyRnJflZkk887k9JkiRJkrRScDvJBJVkNeDlwHnAUcANVbV3kpcAX6+q6Um+DCyuqs+2Pk8GXlhVleTNwAeB9wOHA2+vqiuTrA38ETgMOL+qPplkEjB5hCH+qap2TfJu4HvAtsDvgF8kOaaq7ul3P4e1OZm07gajeCKSJEmSpJWdSYyJZ60kc9v15cD/B1wL7AtQVRcneWqSKQP0fQZwepINgScCd7TyK4HPJTkNOKuqfp3kOuDkJKsD51TV3AHGG8q57fd84OaquhMgye3AM4HHJDGq6kTgRIA1NtykRjiXJEmSJGkV4HaSiafvTIzpVfXOqvoTkAHaDZQIOA44vqqmAW8F1gSoqpnAm4G1gGuSbFpVlwG7AguBbyR5Y7+xHuGx/37W7Ff/UPv9aNd132eTZ5IkSZKkETOJsXK4DDgI/vxGkrur6g/AfcA6Xe2m0ElKABzcV5hk46qaX1WfAXqBTZM8G/htVX2VzmqPbfrN+X/A37RVH2sArxzzu5IkSZIkqYv/I75yOBI4Jck84AH+kqD4PvDdJK8B3tnanZFkIXAN8Het3XuS7A4sAW4B/hs4APhAkoeBxcBjVmJU1cNJPkZnK8sdwK1jdTPTNppCr68IlSRJkiT1kyqPH9CKpaenp3p7e8c7DEmSJEnSGEsyp6p6Rtvf7SSSJEmSJGlCMIkhSZIkSZImBJMYkiRJkiRpQjCJIUmSJEmSJgSTGJIkSZIkaUIwiSFJkiRJkiaE1cY7AKm/+QsXMXXGrPEOY0JbMHOv8Q5BkiRJksacKzFWAkmmJrmpX9mRSQ4foO3bkrxxOccz4BwDxSlJkiRJ0nC5EmMVkmS1qvry8p7n8ZhDkiRJkrTqMYmxkksyG7gKeBFwbpJ1gMVV9dkkzwW+DGwALAFeW1W/GGCMtYHvAU8GVgeOqKrvtbo3AocDBcyrqjckObJrjm2Bk4EHgCuGiPMw4DCASetuMBa3LkmSJElayZjEWDWsV1Uvhs42k67y04CZVXV2kjUZfHvRH4F9quoPSdYHrklyLrAZ8GHgRVV1d5KnDND3FOCdVXVpkqMHC7CqTgROBFhjw01qhPcnSZIkSVoFeCbGymGwL/195af3r2grMjaqqrMBquqPVfXAIOME+FSSecCPgI2ApwEvAb5bVXe3MX7Xb44pdBIol7aibwz/liRJkiRJeixXYqwc7qGz1aPbU4A72vX9A/TJCMY/iM6Wk22r6uEkC4A12xhDrZpYWr0kSZIkScNmEmMlUFWLk9yZ5KVVdVHb1vEPwBeAQwfp84ckv06yd1Wdk2QNYNIgqzGmAL9tCYzdgWe38ouAs5McU1X3JHlK92qMqvp9kkVJdq6qK+gkQ5Zq2kZT6PUVoZIkSZKkftxOsvJ4I3BEkrnAxcBRAx3S2c8bgHe1bSJXAX87SLvTgJ4kvXQSEbcCVNXNwCeBS5PcCHxugL6HAickuRp4cGS3JEmSJEnSX6TK1f5asfT09FRvb+94hyFJkiRJGmNJ5lRVz2j7uxJDkiRJkiRNCJ6JoT9LMo2/foPIQ1W1w3jEI0mSJElSN5MY+rOqmg9MH+84JEmSJEkaiNtJJEmSJEnShGASQ5IkSZIkTQhuJ1lFJPkw8DpgCfAo8NaqunYY/XYDDq+qVyZ5NbBZVc1cnrHOX7iIqTNmLc8pVmoLZu413iFIkiRJ0nJhEmMVkGRH4JXANlX1UJL1gSeOdJyqOhc4d6zjkyRJkiRpONxOsmrYELi7qh4CqKq7q+o3SV6a5IYk85OcnGQNgCT/kOTWJFcA/9g3SJJDkhzfrk9Nsl9X3eL2e7cklyb5TpLbksxMclCSH7d5Nn48b1ySJEmStPIwibFquAB4ZksqfDHJi5OsCZwK7F9V0+isyvmXVv5V4FXALsDfjmK+rYB3A9OANwDPq6rtgZOAdw7UIclhSXqT9C55YNEoppQkSZIkrexMYqwCqmoxsC1wGHAXcDrwVuCOqrqtNfsasCuwaSv/WVUV8M1RTHldVd3ZVn78gk4SBWA+MHWQGE+sqp6q6pk0ecooppQkSZIkrew8E2MVUVVLgNnA7CTzgYOHaj6MIR+hJcGShMeesfFQ1/WjXZ8fxX9zkiRJkqRRciXGKiDJ85Ns0lU0Hfg/YGqS57ayNwCXArcCf9d1dsWBgwy7gM7qDoDXAKuPZcySJEmSJPXn/4qvGtYGjkuyHp0VFD+ns7XkW8AZSVYDrgO+3N5echgwK8ndwBXAFgOM+VXge0l+DFwE3D9WwU7baAq9viZUkiRJktRPOsceSCuOnp6e6u3tHe8wJEmSJEljLMmcquoZbX+3k0iSJEmSpAnBJIYkSZIkSZoQTGJIkiRJkqQJwSSGJEmSJEmaEExiSJIkSZKkCcEkhiRJkiRJmhBWG+8ANDxJngGcAGxGJ/n0A+ADVfWn5TDXIUBPVb1jOG2SvA14oKq+3sovqKrfjHb++QsXMXXGrNF2X+UtmLnXeIcgSZIkScuFKzEmgCQBzgLOqapNgOcBawOfHNfAmqr6clV9vX08BHj6OIYjSZIkSVpJmcSYGF4C/LGqTgGoqiXAe4E3Jdk8yY+TzE0yL8kmSaYmuTXJSUluSnJakj2SXJnkZ0m2B0jylCTntH7XJNmy/8RJXpXk2iQ3JPlRkqcN0ObIJIcn2Q/oAU5r8eyV5Oyudi9LctZyekaSJEmSpJWcSYyJYXNgTndBVf0B+B/geOALVTWdTgLh163Jc4EvAFsCmwKvA3YGDgc+1NocBdxQVVu2sr7VFN2uAF5YVVsD3wY+OFiQVfVdoBc4qMXzQ+AFSTZoTQ4FThmob5LDkvQm6V3ywKLBppAkSZIkrcI8E2NiCFCDlM8GPtTOzDirqn7W2X3CHVU1HyDJzcBFVVVJ5gNTW/+dgX0BquriJE9NMqXfHM8ATk+yIfBE4I7hBt3m+wbw+iSnADsCbxyk7YnAiQBrbLjJQPcqSZIkSVrFuRJjYriZziqLP0uyLvBM4Gjg1cCDwPlJXtKaPNTV/NGuz4/yl+RVBpirfwLhOOD4qpoGvBVYc4SxnwK8HjgQOKOqHhlhf0mSJEmSAJMYE8VFwOQkbwRIMgn4T+BU4G+B26vqWOBcOttHhusy4KA25m7A3W2bSrcpwMJ2ffAwxrwPWKfvQ3tLyW+AI1q8kiRJkiSNittJJoC2LWMf4ItJPkIn+fRDOudYvI/Odo2Hgf8FPgasO8yhjwROSTIPeICBkxRHAmckWQhcA/zdUsY8FfhykgeBHavqQeA0YIOqumU4QU3baAq9viZUkiRJktRPqjx+QMtXkuPpHCD6/w2nfU9PT/X29i7nqCRJkiRJj7ckc6qqZ+ktB+ZKDC1XSeYA9wPvH+9YJEmSJEkTm0kMLVdVte14xyBJkiRJWjl4sKckSZIkSZoQTGJIkiRJkqQJwSSGJEmSJEmaEExiSJIkSZKkCcGDPbXCmb9wEVNnzBrvMCaMBTP3Gu8QJEmSJOlx4UqMYUqyJMncrp+poxjjyCSHj6Lf3kk2G2GftyV543KIZWqSm9p1T5Jjl9L+kCTHj3QeSZIkSZL6cyXG8D1YVdPHae69gR8AtwyncZLVqurLyzUioKp6gd7lPY8kSZIkSeBKjGWSZNsklyaZk+T8JBu28o2TnNfKL0+y6TDHe0uS65LcmOTMJJOT7AS8Gji6rQDZeJC+s5N8KsmlwLu7V1okeVeSW5LMS/LtQeb97yRrDXGfNya5Gnh7V/luSX7QrrdPclWSG9rv53cN8cz2PH6a5N8HmeOwJL1Jepc8sGg4j0uSJEmStIoxiTF8a3VtJTk7yerAccB+VbUtcDLwydb2ROCdrfxw4IvDnOOsqtquqrYCfgL8c1VdBZwLfKCqplfVL4bov15Vvbiq/rNf+Qxg66raEnhbd0WSdwCvAvauqgcHGfcU4F1VteMQc98K7FpVWwMfBT7VVbc9cBAwHXhtkp7+navqxKrqqaqeSZOnDDGNJEmSJGlV5XaS4XvMdpIkWwBbABcmAZgE3JlkbWAn4IxWDrDGMOfYIskngPWAtYHzRxjj6YOUzwNOS3IOcE5X+RuAX9NJYDw8UMckU+gkRy5tRd8AXj5A0ynA15JsAhSwelfdhVV1TxvvLGBn3IYiSZIkSRohkxijF+Dm/qsTkqwL/H6U52ecSiehcGOSQ4DdRtj//kHK9wJ2pbMt5SNJNm/lN9FZHfEM4I5B+oZOUmJpPg5cUlX7tENPZ3fV9e8/nPEkSZIkSXoMkxij91NggyQ7VtXVbXvJ86rq5iR3JHltVZ2RznKMLavqxmGMuQ6d1Ryr09l+sbCV39fqRizJE4BnVtUlSa4AXkdnlQfADcCXgHOT/H1V/aZ//6r6fZJFSXauqitaXAOZ0hXvIf3qXpbkKcCDdA4pfdNQMU/baAq9vjZUkiRJktSPZ2KMUlX9CdgP+EySG4G5dLaRQOeL/j+38puB1wxz2I8A1wIX0jljos+3gQ+0QzMHPNhzCJOAbyaZTydpcUxV/b7rPq6gc27HrCTrDzLGocAJ7WDPwc7N+A/g00mubHN2u4LONpS5wJntrSaSJEmSJI1IqlzZrxVLT09P9faa55AkSZKklU2SOVX1Vy97GC5XYkiSJEmSpAnBMzHGQZITgBf1K/5CVZ2yPPuO59iSJEmSJC0rkxjjoKrePh59x3NsSZIkSZKWldtJJEmSJEnShGASQ5IkSZIkTQhuJ9GIJFkCzAcCLAHeUVVXJZkK3AF8oqo+0tquD9wJfKWq3pHkSGBxVX12qDnmL1zE1BmzluNdTFwLZu413iFIkiRJ0rhxJYZG6sGqml5VWwH/D/h0V93twCu7Pr8WuPnxDE6SJEmStPIyiaFlsS5wb9fnB4GfJOl75+/+wHce96gkSZIkSSslt5NopNZKMhdYE9gQeEm/+m8DByT5XzrbTX4DPH1pgyY5DDgMYNK6G4xlvJIkSZKklYQrMTRSfdtJNgX+Afh6knTVnwe8DDgQOH24g1bViVXVU1U9kyZPGduIJUmSJEkrBZMYGrWquhpYH9igq+xPwBzg/cCZ4xSaJEmSJGkl5HYSjVqSTYFJwD3A5K6q/wQurap7HrtIQ5IkSZKk0TOJoZHqOxMDOq9ZPbiqlnQnK6rqZpbhrSTTNppCr68SlSRJkiT1YxJDI1JVkwYpXwBsMUD5qcCp7frI5ReZJEmSJGll55kYkiRJkiRpQjCJIUmSJEmSJgSTGJIkSZIkaUIwiSFJkiRJkiYEkxiSJEmSJGlCMIkhSZIkSZImBF+xqhXO/IWLmDpj1niHMW4WzNxrvEOQJEmSpBWSKzGGIclTk8xtP/+bZGHX5yf2a/ueJJOHMebsJD1D1K+d5CtJfpHk5iSXJdkhydQkN40w/lOT7DeSPq3fbkl2Gmm/IcY7MsnhYzWeJEmSJGnV4kqMYaiqe4Dp0PkiDiyuqs8O0vw9wDeBB5Zx2pOAO4BNqurRJM8BXgD83zKOOxK7AYuBq4bbIclqVfXIcotIkiRJkrTKciXGKCV5aZIbksxPcnKSNZK8C3g6cEmSS1q7LyXpbaspjhrm2BsDOwBHVNWjAFV1e1X17bGYlOSrbcwLkqzV+k1Pck2SeUnOTvLkAcbeNsmlSeYkOT/Jhq38XUluaX2/nWQq8DbgvW3FyS5JNkhyZpLr2s+LWt8jk5yY5ALg60meneSiNtZFSZ41jHs+rD2n3iUPLBrOY5IkSZIkrWJMYozOmsCpwP5VNY3OipZ/qapjgd8Au1fV7q3th6uqB9gSeHGSLYcx/ubA3KpaMkj9JsAJVbU58Htg31b+deDfqmpLYD7w792dkqwOHAfsV1XbAicDn2zVM4CtW9+3VdUC4MvAMVU1vaouB77QPm/X5jypa/htgddU1euA44Gvt7FOA45d2g1X1YlV1VNVPZMmT1lac0mSJEnSKsjtJKMzCbijqm5rn78GvB34/ABt/ynJYXSe9YbAZsC8ZZz/jqqa267nAFOTTAHWq6pLu2I6o1+/5wNbABcm6buPO1vdPOC0JOcA5wwy7x7AZq0vwLpJ1mnX51bVg+16R+Af2/U3gP8Yyc1JkiRJkjQQkxijc/9wGiX5O+BwYLuqujfJqXRWcSzNzcBWSZ7Qt52kn4e6rpcAaw0nHiDAzVW14wB1ewG7Aq8GPpJk8wHaPAHYsStZ0Rm0k9QY6pnUMOOTJEmSJGlQJjFGZ006qx+eW1U/B94A9K2AuA9YB7gbWJfOl/tFSZ4GvByYvbTBq+oXSXqBo5J8tKoqySZ0VnHcOEifRUnuTbJL2/rRHVOfnwIbJNmxqq5u20ueB/wEeGZVXZLkCuB1wNrtXtbt6n8B8A7gaOicwdG1IqTbVcABdFZhHARcsbR77jZtoyn0+ppRSZIkSVI/JjFG54/AocAZSVYDrqNzfgTAicB/J7mzqnZPcgOdlRW3A1eOYI43A/8J/DzJA8A9wAeW0udg4MvtFa+3txj/rKr+1F61emzbfrIanS0wtwHfbGWhc+7F75N8H/huktcA7wTeBZyQZF7rexmdwz/7exdwcpIPAHf1j0OSJEmSpNFIlSv9tWLp6emp3t7e8Q5DkiRJkjTGksxpL78YFd9OIkmSJEmSJgS3k4yzJNcCa/QrfkNVzR+PeCRJkiRJWlGZxBhnVbXDeMcgSZIkSdJE4HYSSZIkSZI0IZjEkCRJkiRJE4LbSbTCmb9wEVNnzBrvMMbNgpl7jXcIkiRJkrRCciXGKCRZ3O/zIUmOb9dHJlmYZG6SnyU5K8lmXW1nJxnW62SSbJ/ksiQ/TXJrkpOSTG5zHL4sMY+g33uSTB5N30HGW5Bk/bEaT5IkSZK06jCJsXwcU1XTq2oT4HTg4iQbjGSAJE8DzgD+raqeD7wAOA9YZ8yjHdp7gBElMZJMWj6hSJIkSZJWZSYxlrOqOh24AHjdCLu+HfhaVV3dxqmq+m5V/V+r36yt6rg9ybv6OiV5X5Kb2s97Bho4yQeSXJdkXpKjWtmTksxKcmPru38b9+nAJUkuae32THJ1kuuTnJFk7Va+IMlHk1wBvDbJgUnmt7E+s7SbTXJYkt4kvUseWDTCRyVJkiRJWhV4JsborJVkbtfnpwDnDtH+emDTEc6xBfC1Ieo3BXanszLjp0m+BGwJHArsAAS4NsmlVXVDX6ckewKbANu3Nucm2RXYAPhNVe3V2k2pqkVJ3gfsXlV3t20gRwB7VNX9Sf4NeB/wsTb8H6tq5yRPB64BtgXuBS5IsndVnTPYzVTVicCJAGtsuEkN/zFJkiRJklYVJjFG58Gqmt73IckhwFDnXGQ5xDCrqh4CHkryW+BpwM7A2VV1f4vrLGAX4Iaufnu2n76ytekkNS4HPttWTfygqi4fYM4XApsBVyYBeCJwdVf96e33dsDsqrqrxXEasCtwzrLcsCRJkiRp1WYS4/GxNdA7wj4301nJ8L1B6h/qul5C5285nGRJgE9X1Vf+qiLZFngF8OkkF1TVxwboe2FVHTjI2Pd3tZMkSZIkaUyZxFjOkuxLZ+XD+0fY9Xjgx0lmVdW1bazXAz8aos9lwKlJZtJJJOwDvKFfm/OBjyc5raoWJ9kIeJjOv4XfVdU325tMDmnt76OzZeVuOltETkjy3Kr6eXtryTOq6rZ+c1wLfKFtP7kXOBA4brg3Pm2jKfT6mlFJkiRJUj8mMZaP97aEw5OAm4CX9G2taGYlebhdX11Vr+0/QFX9X5ID6Gzx+BvgUTpJirMGm7Sqrk9yKvDjVnRS93kYrc0FSV4AXN22hCwGXg88Fzg6yaN0khr/0rqcCPx3kjurave2deZbSdZo9UcAt/Wb484k/w+4hE4y5YdVNdiKEkmSJEmShiVVnqGoFUtPT0/19o50940kSZIkaUWXZE5VDXWm5JB8xaokSZIkSZoQ3E4yzpL8PfCZfsV3VNU+4xGPJEmSJEkrKpMY46yqzqdz2KYkSZIkSRqC20kkSZIkSdKEYBJDkiRJkiRNCG4n0Qpn/sJFTJ0xa7zDGDcLZu413iFIkiRJ0grJlRgjlOTDSW5OMi/J3CQ7JHlPksljOMeCJOu366vGatw23iFJju9XNjvJkK+4SXJSks2W0ubUJPsNUD41yetGF7EkSZIkSR0mMUYgyY7AK4FtqmpLYA/gV8B7gDFLYnSrqp2Wx7gjVVVvrqpbRtl9KmASQ5IkSZK0TExijMyGwN1V9RBAVd0N7Ac8HbgkySUASb6UpLet2Diqr3NbYXFUkuuTzE+yaSt/apILktyQ5CtAuvosbr93aysmvpvk1iSnJUmre0UruyLJsUl+MNobTLJnkqtbjGckWbuV/3m1RpJ/TnJbK/tqv5Uduya5KsntXasyZgK7tJUr7x1k3sPaM+td8sCi0YYvSZIkSVqJmcQYmQuAZ7Yv8F9M8uKqOhb4DbB7Ve3e2n24qnqALYEXJ9mya4y7q2ob4EvA4a3s34Erqmpr4FzgWYPMvzWdVR+bAc8BXpRkTeArwMuramdgg2Hcx/4toTA3yVygLzmxPnAEsEeLsRd4X3fHJE8HPgK8EHgZsGm/sTcEdqazYmVmK5sBXF5V06vqmIECqqoTq6qnqnomTZ4yjFuQJEmSJK1qTGKMQFUtBrYFDgPuAk5PcsgATf8pyfXADcDmdJIOfc5qv+fQ2WYBsCvwzTbHLODeQUL4cVX9uqoeBea2/psCt1fVHa3Nt4ZxK6e3hML0qppOJ1kBncTEZsCVLblxMPDsfn23By6tqt9V1cPAGf3qz6mqR9vWk6cNIxZJkiRJkobFt5OMUFUtAWYDs5PMp/NF/8+S/B2dFRbbVdW9SU4F1uxq8lD7vYTHPv8axvQPdV339c8gbUcjwIVVdeBS2gylO8axjE2SJEmStIoziTECSZ4PPFpVP2tF04Ff0lkRsQ5wN7AucD+wKMnTgJfTSXoM5TLgIOATSV4OPHkEYd0KPCfJ1KpaAOw/gr79XQOckOS5VfXz9saVZ1TVbV1tfgwck+TJwH3AvsD8pYx7H53nMyzTNppCr68ZlSRJkiT1YxJjZNYGjkuyHvAI8HM6W0sOBP47yZ1VtXuSG4CbgduBK4cx7lHAt9oWlEuB/xluQFX1YJJ/Bc5LcjedJMOoVNVdbXvMt5Ks0YqPAG7rarMwyaeAa+mcBXILsLSTOOcBjyS5ETh1sHMxJEmSJEkaSqqGs4tBK7Ika1fV4va2khOAny3PREHXfKsBZwMnV9XZYzV+T09P9fb2Lr2hJEmSJGlCSTKnvQhjVDzYc+XwlnYQ583AFDpvK1mejmzz3QTcAZyznOeTJEmSJMntJCuDturiMSsvkhwKvLtf0yur6u1jMN/hS28lSZIkSdLYMomxkqqqU4BTxjsOSZIkSZLGittJJEmSJEnShGASQ5IkSZIkTQhuJ1mFJDkG+GVVfb59Ph/4VVW9uX3+T2BhVX1ugL4fAy6rqh8NMf6RwOKq+my/8vWA11XVF4cT5/yFi5g6Y9aw7mmiWzBzr/EOQZIkSZImDFdirFquAnYCSPIEYH1g8676nYArB+pYVR8dKoGxFOsB/zrKvpIkSZIkASYxVjVX0pIYdJIXNwH3JXlykjWAFwAkuTTJnCTnJ9mwlZ2aZL92/Yoktya5IsmxSX7QNcdmSWYnuT3Ju1rZTGDjJHOTHP143KgkSZIkaeXjdpJVSFX9JskjSZ5FJ5lxNbARsCOwCPgJnVe1vqaq7kqyP/BJ4E19YyRZE/gKsGtV3ZHkW/2m2RTYHVgH+GmSLwEzgC2qavpgsSU5DDgMYNK6G4zF7UqSJEmSVjImMVY9fasxdgI+RyeJsROdJMZCYE/gwiQAk4A7+/XfFLi9qu5on79FSz40s6rqIeChJL8FnjacoKrqROBEgDU23KRGfluSJEmSpJWdSYxVT9+5GNPobCf5FfB+4A/AxcBGVbXjEP2zlPEf6rpegv/GJEmSJEljxDMxVj1XAq8EfldVS6rqd3QO3twROB3YIMmOAElWT7J5v/63As9JMrV93n8Yc95HZ3uJJEmSJEmj5v+Sr3rm03kryX/1K1u7qn7bDu88NskUOv8+Pg/c3Newqh5M8q/AeUnuBn68tAmr6p4kVya5CfjvqvrAUO2nbTSFXl89KkmSJEnqJ1UeP6CRSbJ2VS1O5+CME4CfVdUxYzV+T09P9fb2jtVwkiRJkqQVRJI5VdUz2v5uJ9FovCXJXDorNKbQeVuJJEmSJEnLldtJNGJt1cWYrbyQJEmSJGk4XIkhSZIkSZImBJMYkiRJkiRpQjCJIUmSJEmSJgSTGJIkSZIkaULwYE+tcOYvXMTUGbOWaYwFM/cao2gkSZIkSSsKV2IsB0k+nOTmJPOSzE2yQ5LZSUb9LtxliGVBkvXb9VXLYfxDkhw/1uNKkiRJktSfKzHGWJIdgVcC21TVQy2B8MRxDguAqtppWcdIMqmqlixD/9Wq6pFljUOSJEmStOpxJcbY2xC4u6oeAqiqu6vqN90NkuyZ5Ook1yc5I8narXzbJJcmmZPk/CQbtvLZST6f5KokNyXZfrDJkzw1yQVJbkjyFSBddYvb7w2TXNZWidyUZJdWfmCS+a3sM939knwsybXAjkkOTXJbkkuBF3W12yDJmUmuaz8vauVHJjkxyQXA1weJ+7AkvUl6lzywaEQPXJIkSZK0ajCJMfYuAJ7ZvuR/McmLuyvbyowjgD2qahugF3hfktWB44D9qmpb4GTgk11dn9RWUvxrqxvMvwNXVNXWwLnAswZo8zrg/KqaDmwFzE3ydOAzwEuA6cB2Sfbumxu4qap2AH4BHEUnefEyYLOucb8AHFNV2wH7Aid11W0LvKaqXjdQ0FV1YlX1VFXPpMlThrg9SZIkSdKqyu0kY6yqFifZFtgF2B04PcmMriYvpPPF/8ok0NlqcjXwfGAL4MJWPgm4s6vft9r4lyVZN8l6VfX7AULYFfjH1nZWknsHaHMdcHJLnJxTVXOTvASYXVV3ASQ5rY11DrAEOLP13aFfu9OB57W6PYDNWvwA6yZZp12fW1UPDvzUJEmSJElaOpMYy0E7M2I2MDvJfODgruoAF1bVgd19kkwDbq6qHQcbdimfh1vXlwjZFdgL+EaSo4E/DNHlj/3OwRhs/CcAO/ZPVrSkxv1DxSRJkiRJ0tKYxBhjSZ4PPFpVP2tF04Ff0lllAXANcEKS51bVz5NMBp4B/BTYIMmOVXV1WyXxvKq6ufXbH7gkyc7Aoqoa7OCIy4CDgE8keTnw5AFifDawsKq+muRJwDZ0tpJ8oW13uRc4kM72lv6ube2eSifx8VrgxlZ3AfAO4Og2z/SqmjvU8xrItI2m0OsrUiVJkiRJ/ZjEGHtrA8clWQ94BPg5cBjwXYCquivJIcC3kqzR+hxRVbcl2Q84NskUOn+bzwN9SYx72ytS1wXeNMT8R7WxrwcuBf5ngDa7AR9I8jCwGHhjVd2Z5P8Bl9BZLfLDqvpe/46t3ZF0tsDcCVxPZ+sLwLvoJGjmtfgvA942RKySJEmSJA1bqobceaAVQJLZwOFV1TvesTweenp6qrd3lbhVSZIkSVqlJJlTVT2j7e/bSSRJkiRJ0oTgdpIJoKp261+W5FDg3f2Kr6yqtz8uQUmSJEmS9DgziTFBVdUpwCnjHYckSZIkSY8Xt5NIkiRJkqQJwSSGJEmSJEmaENxOohXO/IWLmDpj1l+VL5i51zhEI0mSJElaUbgSYwWR5MNJbk4yL8ncJDskmZ1k1K+eGcHcRyY5fHnPI0mSJEnSsnAlxgogyY7AK4FtquqhJOsDTxznsJZZktWq6pHxjkOSJEmStHJwJcaKYUPg7qp6CKCq7q6q33Q3SLJnkquTXJ/kjCRrt/Jtk1yaZE6S85Ns2MpnJ/l8kquS3JRk+6XEsFnrc3uSd3XN+77W/6Yk72llU5Pc1NXm8CRHds37qSSXAu9O8trW98Yklw02eZLDkvQm6V3ywKIRPDpJkiRJ0qrClRgrhguAjya5DfgRcHpVXdpX2VZmHAHsUVX3J/k34H1JPg0cB7ymqu5Ksj/wSeBNreuTqmqnJLsCJwNbDBHDpsDuwDrAT5N8CdgSOBTYAQhwbUtO3LuU+1mvql7cYp8P/H1VLUyy3mAdqupE4ESANTbcpJYyviRJkiRpFWQSYwVQVYuTbAvsQieRcHqSGV1NXghsBlyZBDpbTa4Gnk8nMXFhK58E3NnV71tt/MuSrJtkvar6/SBhzGorQR5K8lvgacDOwNlVdT9AkrNajOcu5ZZO77q+Ejg1yXeAs5bST5IkSZKkQZnEWEFU1RJgNjC7rV44uKs6wIVVdWB3nyTTgJurasfBhl3K524PdV0vofNvI4O0fYTHbkVas1/9/X+esOptSXYA9gLmJpleVfcMEYckSZIkSQMyibECSPJ84NGq+lkrmg78kr9s/7gGOCHJc6vq50kmA88AfgpskGTHqro6yerA86rq5tZvf+CSJDsDi6pqpIdNXEZnFcVMOgmNfYA3AP8H/E2SpwKL6RxKet4g97ZxVV1LZyvKq4BnAkMmMaZtNIVeX6cqSZIkSerHJMaKYW3guHZmxCPAz4HDgO8CtPMuDgG+lWSN1ueIqrotyX7AsUmm0Pl7fh7oS2Lcm+QqYF3+ck7GsFXV9UlOBX7cik6qqhsAknwMuBa4A7h1iGGOTrIJnSTIRcCNI41DkiRJkiSAVHmG4sooyWzg8KrqHe9YRqqnp6d6eydc2JIkSZKkpUgyp6p6RtvfV6xKkiRJkqQJwe0kK6mq2q1/WZJDgXf3K76yqt7+uAQlSZIkSdIyMImxCqmqU4BTxjsOSZIkSZJGw+0kkiRJkiRpQjCJIUmSJEmSJgSTGFrhzF+4iKkzZjF1xqzxDkWSJEmStAIxiTFMSZ6aZG77+d8kC7s+P3GUYx6S5PhB6hYvW8SPGWt2klG/wmaQMV+dZMZYjilJkiRJ0lA82HOYquoeYDpAkiOBxVX12b76JKtV1SPjE93jr6rOBc4d7zgkSZIkSasOV2IsgySnJvlckkuAzySZnuSaJPOSnJ3kya3dn1dCJFk/yYKuYZ6Z5LwkP03y74PM84Ek17VxjxoiniclmZXkxiQ3Jdl/gDZ7Jrk6yfVJzkiydivfNsmlSeYkOT/Jhl2xfz7JVW3M7Vv5n1eRtOdwbGtze5L9WvkTknwxyc1JfpDkh311A8R1WJLeJL1LHli01GcvSZIkSVr1mMRYds8D9qiq9wNfB/6tqrYE5gMDJiX62R44iM4qj9f23/aRZE9gk9ZuOrBtkl0HGesfgN9U1VZVtQVwXr+x1geOaPFuA/QC70uyOnAcsF9VbQucDHyyq+uTqmon4F9b3UA2BHYGXgnMbGX/CEwFpgFvBnYc7CFU1YlV1VNVPZMmTxmsmSRJkiRpFeZ2kmV3RlUtSTIFWK+qLm3lXwPOGEb/C9tWFZKcRScR0NtVv2f7uaF9XptOUuOyAcaaD3w2yWeAH1TV5f3qXwhsBlyZBOCJwNXA84EtgAtb+STgzq5+3wKoqsuSrJtkvQHmPqeqHgVuSfK0VrYznefzKPC/bcWKJEmSJEmjYhJj2d0/jDaP8JdVL2v2q6ulfA7w6ar6ytImqarbkmwLvAL4dJILqupj/ca6sKoOfMwEyTTg5qoabKXE0mIEeKjfPN2/JUmSJElaZm4nGSNVtQi4N8kuregNQN+qjAXAtu26/5kQL0vylCRrAXsDV/arPx94U9fZFRsl+ZuBYkjydOCBqvom8Flgm35NrgFelOS5rf3kJM8DfgpskGTHVr56ks27+u3fyncGFrV7HY4rgH3b2RhPA3YbTqdpG01hwcy9WDBzr2FOI0mSJElaFbgSY2wdDHw5yWTgduDQVv5Z4DtJ3gBc3K/PFcA3gOcC/1VV3VtJqKoLkrwAuLpt9VgMvB747QDzTwOOTvIo8DDwL/3GuivJIcC3kqzRio9oKzj2A45t22JWAz4P3Nza3JvkKmBd4E3DfRjAmcBLgZuA24BrAU/tlCRJkiSNSqoG2hkgdSSZDRzeP7kygv5rV9XiJE8Ffgy8qKr+d6g+PT091ds7qukkSZIkSSuwJHOqqmfpLQfmSgwtbz9oB4E+Efj40hIYkiRJkiQNxiTGBNRWNVw0QNVL+950Mlaqarfx7C9JkiRJUh+TGBNQS1RMH+84JEmSJEl6PPl2EkmSJEmSNCGYxJAkSZIkSROCSQytcOYvXMTUGbOYOmPWeIciSZIkSVqBrPBJjCSV5Btdn1dLcleSH4xwnAVJ1h+jmA5J8vSxGGsZYjgyyeHLYdz1kvxr1+enJ/nuUvq8OsmMdr13ks3GOi5JkiRJklb4JAZwP7BFkrXa55cBC5f3pEkmDVF9CDBuSYwky/NA1vWAPycxquo3VbXfUB2q6tyqmtk+7g2YxJAkSZIkjbmJkMQA+G9gr3Z9IPCtvookT0lyTpJ5Sa5JsmUrf2qSC5LckOQrQLr6vD7Jj5PMTfKVvoRFksVJPpbkWmDHJB9Ncl2Sm5KcmI79gB7gtNZ/rSTbJrk0yZwk5yfZcKCbSPI3Sea0663aKpNntc+/SDI5ybOTXNTu56Ku+lOTfC7JJcBn+o37liT/3ZXo6T/vW9p93JjkzCSTW/nTkpzdym9MshMwE9i43dvRSaYmuam1vzbJ5l3jzm73fkiS41v/VwNHt/4bJ7m+q/0mffc/QIyHJelN0rvkgUUDNZEkSZIkreImShLj28ABSdYEtgSu7ao7CrihqrYEPgR8vZX/O3BFVW0NnAv0JQNeAOwPvKiqpgNLgINanycBN1XVDlV1BXB8VW1XVVsAawGvrKrvAr3AQa3/I8BxwH5VtS1wMvDJgW6iqn4LrJlkXWCXNs4uSZ4N/LaqHgCOB77e7uc04NiuIZ4H7FFV7+8rSPIO4FXA3lX14CDP76x2H1sBPwH+uZUfC1zayrcBbgZmAL+oqulV9YF+43wb+Kc274bA06vqz0mJqrqqPesPtP6/ABYlmd6aHAqcOsizObGqeqqqZ9LkKYPchiRJkiRpVbY8tyWMmaqal2QqnVUYP+xXvTOwb2t3cVuBMQXYFfjHVj4ryb2t/UuBbYHrkkAnOfHbVrcEOLNr7N2TfBCYDDyFzpf87/eb//nAFsCFbbxJwJ1D3M5VwItafJ8C/oHOKpHLW/2OfXED3wD+o6vvGVW1pOvzG4Bf00lgPDzEnFsk+QSdrSJrA+e38pcAbwRo4y5K8uQhxvkOcCGdBNE/AWcM0bbPScChSd5HJ3m0/TD6SJIkSZL0VyZEEqM5F/gssBvw1K7yDNC2+v3uFuBrVfX/Bqj7Y1+SoK36+CLQU1W/SnIksOYg491cVTsO5yboJCt2AZ4NfA/4txbnYAeVdt/D/f3qbgKmA88A7hhizlPpJDpuTHIInWc4YlW1MMk9bcvO/sBbh9HtTDpJj4uBOVV1z2jmliRJkiRpomwngc42jY9V1fx+5ZfRtoMk2Q24u6r+0K/85UDfCoOLgP2S/E2re0rbztFfX8Li7iRrA92HW94HrNOufwpskGTHNt7q3edGDOAy4PXAz6rqUeB3wCuAK1v9VcAB7fog4IohxrqBTiLh3Az9tpR1gDuTrM5fts5A51n8S4t7Utvm0n1vA/k28EFgygB/C/r3r6o/0ln58SXglCHG/bNpG01hwcy9WDBzr6U3liRJkiStMiZMEqOqfl1VXxig6kigJ8k8OodSHtzKjwJ2bQdL7gn8TxvnFuAI4ILW50Lgrw7irKrfA18F5gPnANd1VZ8KfDnJXDrbR/YDPpPkRmAusNMQ97GgXV7Wfl8B/L6q+ra7vIvO9ot5dLaLvHuwsdp4VwCHA7My+CtkP0LnHJELgVu7yt9NZ8vMfGAOsHlbKXFlO8z06AHG+i6dJMt3Bpnr28AH0jlQdeNWdhqdFSUXDHUvkiRJkiQNJVUD7biQxk6Sw+ms3PjIcNr39PRUb2/vco5KkiRJkvR4SzKnqnpG238inYmhCSjJ2cDGdA4RlSRJkiRp1ExiLCdJTqDzFpJuX6iqYZ0LMVHmXJqq2me85pYkSZIkrVxMYiwnVfX2VWFOSZIkSZIeLxPmYE9JkiRJkrRqM4khSZIkSZImBJMYo5DkqUnmtp//TbKw6/MT+7WdneSvTl5NckiS4x+/qJdNkqcn+e4w2i0epHzvJJsNZ675CxcxdcYsps6YNdIwJUmSJEkrMc/EGIWqugeYDpDkSGBxVX12PGMajiSrVdUjo+lbVb8B9luG6fcGfgDcsgxjSJIkSZJWYa7EGCNJXprkhiTzk5ycZI0B2hya5LYkl/LXbxHp3/bZSS5KMq/9flaSKUkWJHlCazM5ya+SrJ5k4yTnJZmT5PIkm7Y2pyb5XJJLgM+0+NZLxz1J3tjafSPJHkkmJTk6yXVt7re2+qlJbuqa9zut/vQk13avNknyySQ3JrkmydOS7AS8Gji6rVbZeGyeuiRJkiRpVWISY2ysCZwK7F9V0+iscPmX7gZJNgSOopO8eBmwtK0VxwNfr6otgdOAY6tqEXAj8OLW5lXA+VX1MHAi8M6q2hY4HPhi11jPA/ao+v/bu/dgu8ryjuPfX0+oEBCoVVsIShChNhKJeFBUYESdVtQKVoU4iIU6UG8FrOBgrR3bTistjlMQxSKKlwKJAW0z1oodBAlEAidckihS5aKIKFQh3AQ0Pv1jr4Obk3PL2ftcdvb3M8Octdd633c9e/PMPivPed+16r3AVU0MzwVuBQ5q2hwAXA28DdhYVfsD+wPHJdljRGzvBO5tYvsH4AVtx7YHrq6qfYErgOOqajWwEjilqpZU1S0j32yS45MMJRna9PDGCT4aSZIkSVI/sojRHQPAbVX1v83rzwEHj2jzIuDyqrqnqh4Dlk8w5ouBC5rtLwAHNtvLgSOb7aXA8iQ7AC8BViS5Afg3YJe2sVZU1aZme1UT28HA2cDiJAuAn1fVg8AfAW9txlkD/C6w14jYDgSWAVTVBmBd27HHaC0bAVgLLJzgfdKMc05VDVbV4MD8nSbTRZIkSZLUZ7wnRnc8NMl21cE5hvuuBD6c5Cm0ZkB8g9bsh/uqaskk4rsCeBfwTOADwOtp3etiVXM8tGZ0XNI+QJKF7S/HifOXVTUc6ybMMUmSJElSlzgTozu2BRYmeXbz+mjgmyParAFe1jzZZBvgTROMuZrWTAuAo4ArAZrZEtcAZwBfqapNVXU/cFuSNwE097vYd7RBq+oO4KnAXlV1azPuyfymiHEJ8I4mRpLsnWT7EcNcCRzRHF8ELJ7gvQA8ADx5Eu0kSZIkSRqVfyXvjkeAY2kt55gHXAt8sr1BVd3VPMnkW8BdwHW0lqGM5QTgM0lOAe5pxh+2HFgBvKxt31HA2Un+BtiG1nKPG8cYe03buVcBH6YpkgDn0loCcl2SNOc+fET/TwCfS7IOuJ7WcpKJbmSxDPhUkhOAN452X4xhixfsxNBpr5lgOEmSJElSv8lvZv5Lk5NkANimqh5pnjRyKbB3c6+Pjg0ODtbQ0FA3hpIkSZIkzSFJ1lbV4MQtR+dMDE3FfOCyZslJgHd0q4AhSZIkSdJYLGLMsiQfYPP7Y6yoqn+cjXgmo6oeAKZcOZMkSZIkaSosYsyyplgxZwsWkiRJkiTNFT6dRJIkSZIk9QSLGJIkSZIkqSdYxJAkSZIkST3BIsZWKsmDI14fk+SsZvvtSd46Qf/H20/yfLcnWZ/khubnYW3HNjX7NyRZkWT+eGOtv3PjZE8rSZIkSeojFjH6UFV9sqo+Pw1DH1JVS4A3Ame27f9FVS2pqn2Ax4C3T8O5JUmSJElbOYsYfSjJh5Kc3Gzvn2Rdkm8lOT3Jhramuyb5WpLvJfmXLTjFjsC9YxxbBTx7iqFLkiRJkvqYj1jdem2X5Ia2108BVo7S7jzg+KpaneS0EceWAM8HHgVuTvKxqrpjnHNeliTAs4AjRh5MMg84FPjaKMeOB44HGNjxaeOcQpIkSZLUr5yJsfUaXsKxpFni8bcjGyTZGXhyVa1udl0wosmlVbWxqh4BvgPsPsE5D2mWjCwGzkqyQ7N/uKAyBPwQ+PTIjlV1TlUNVtXgwPydJvcOJUmSJEl9xZkY/S0THH+0bXsTk8yXqrolyU+BRcA1NAWVKUUoSZIkSVLDmRh9rKruBR5IckCza2k3xk3ydGAP4AfdGE+SJEmSJHAmhuBtwKeSPARcDnTyfNPLkmwCtgFOraqfTmWQxQtcTiJJkiRJ2lyqarZj0CxKskNVPdhsnwrsUlUnzmZMg4ODNTQ0NJshSJIkSZKmQZK1VTU41f7OxNBrkryfVi78ADhmdsORJEmSJGl0FjH6XFUtB5ZPtn2SNcCTRuw+uqrWdzUwSZIkSZJGsIihLVJVL5rtGCRJkiRJ/cmnk0iSJEmSpJ5gEUOSJEmSJPUEixiSJEmSJKkn9FQRI8nlSf54xL6TktzaPB60LyT5UJKTp2HcnZO8s+31rkkumqDP64Y/+ySHJ1nUaRzr79zY6RCSJEmSpK1QTxUxgAuBpSP2LQX+rKpO63TwJAOdjjHdkkznzVh3Bh4vYlTVj6vqjeN1qKqVbZ/94UDHRQxJkiRJkkbTa0WMi4DXJnkSQJKFwK7As5Oc1ezbM8nVSa5N8vdJHmz2J8npSTYkWZ/kyGb/y5JcluQCYH2SbZOc17S5PskhTbtjhs/RvP5K03cgyWfbxn3PaIEneXqStc32vkkqyTOb17ckmZ9k9ySXJlnX/Bw+/tkkH01yGfDPI8Y9Lsl/J9lujPMe13wWNya5OMn8Zv/vJflys//GJC8BTgP2THJD81ktTLKhab8myXPbxr08yQuGP5em/+uA05v+eya5rq39XsPvX5IkSZKkqeipIkZV/Qy4BnhVs2spsByotmZnAGdU1f7Aj9v2/ymwBNgXeCWtf2zv0hx7IfCBqloEvKs512LgzcDnkmw7TlhLgAVVtU/T57wxYr8b2DbJjsBBwBBwUJLdgbur6mHgLODzVfU84HzgzLYh9gZeWVXvHd6R5N3AnwCHV9UvxojvS1W1f1XtC9wEvK3ZfybwzWb/fsC3gVOBW6pqSVWdMmKcZcARzXl3AXatqseLElW1GlgJnNL0vwXYmGRJ0+RY4LNjxEiS45MMJRna9LDLSSRJkiRJm+upIkajfUnJ0uZ1uxcDK5rtC9r2HwhcWFWbquqnwDeB/Ztj11TVbW3tvgBQVd8FfkCrgDCWW4FnJflYklcB94/TdjXwUuBg4J+anwcBq9piH475C00sw1ZU1aa210cDhwJvqKpHxznnPklWJVkPHAUMz6Z4OXB28z43VdVElYMvAm9qto/gN5/xeM4Fjm2W6RzJE/9/PEFVnVNVg1U1ODB/p0kMLUmSJEnqN71YxPgP4BVJ9gO2q6rrJmg/LOMce2gS7X7FEz+vbQGq6l5aszsupzWL49xxzrOKVtFid+A/m34HAleM0b59hslDI45tABYCu41zPmjNfnh3M0vk74bj3lJVdSfwsyTPo1WQWDaJbhfTKrS8FljbzKSRJEmSJGlKeq6IUVUP0ioYfIbNZ2EAXA28odluvwnoFcCRzT0snkZrFsQ1o/S/gtaMBZLsDTwTuBm4HViS5LeSPIPWEhSSPBX4raq6GPggraUZY7kCeAvwvar6NfBz4NXAVc3x1W0xHwVcOc5Y1wN/AaxMsus47Z4M3JVkm+H31bgUeEfzHgaaZS4PNO3Hsgx4H7BTVa0f5fgT+lfVI8AltGZ8jLrMRpIkSZKkyeq5IkbjQlqzGEabDXAS8FdJrgF2AYaXSXwZWAfcCHwDeF9V/WSU/p8ABprlF8uBY5rlGlcBtwHrgY8AwzNAFgCXJ7mB1qyH948VdFXd3mwOz7y4Erivmc0BcAKt5RfraC0XOXGssZrxrgROBv6rKaaM5oPAGuB/gO+27T8ROKR5n2uB5zYzJa5qblJ6+ihjXUSryPLFMc61DDiluSHqns2+82nNKPn6eO+l3eIFLieRJEmSJG0uVTVxqx7SPH3jF1VVSZYCb66qw2Y7rn6V5GRaMzc+ONk+g4ODNTQ0NI1RSZIkSZJmQ5K1VTU41f7zuhnMHPEC4KwkAe4D/nx2w+lfSb4M7EnrJqKSJEmSJHVkqytiVNUqWktNZk2Sj9N6Ckm7M6pq2u4LMRvnnEhVvX62zi1JkiRJ2vpsdctJ1PuSPEDrZqpSNzwV+L/ZDkJbDfNJ3WZOqZvMJ3WbOaVuGs6n3avqaVMdZKubiaGtws2drJGS2iUZMp/ULeaTus2cUjeZT+o2c0rd1K186tWnk0iSJEmSpD5jEUOSJEmSJPUEixiai86Z7QC0VTGf1E3mk7rNnFI3mU/qNnNK3dSVfPLGnpIkSZIkqSc4E0OSJEmSJPUEixiaMUleleTmJN9Pcuoox5PkzOb4uiT7Tbav+s9U8ynJM5JcluSmJN9OcuLMR6+5qJPvqOb4QJLrk3xl5qLWXNXh77ydk1yU5LvNd9WLZzZ6zUUd5tR7mt95G5JcmGTbmY1ec80k8uk5Sb6V5NEkJ29JX/WnqebUVK7NLWJoRiQZAD4OHAosAt6cZNGIZocCezX/HQ+cvQV91Uc6ySfgV8B7q+oPgQOAd5lP6jCnhp0I3DTNoaoHdCGfzgC+VlXPAfbFvOp7HV5HLQBOAAarah9gAFg6Q6FrDppkPv2cVt58ZAp91Wc6ySmmcG1uEUMz5YXA96vq1qp6DFgGHDaizWHA56vlamDnJLtMsq/6y5TzqaruqqrrAKrqAVr/OFgwk8FrTurkO4okuwGvAc6dyaA1Z005n5LsCBwMfBqgqh6rqvtmMHbNTR19RwHzgO2SzAPmAz+eqcA1J02YT1V1d1VdC/xyS/uqL005p6ZybW4RQzNlAXBH2+sfsXlyjtVmMn3VXzrJp8clWQg8H1jT/RDVYzrNqX8F3gf8epriU2/pJJ+eBdwDnNcsTzo3yfbTGax6wpRzqqrupPWXzx8CdwEbq+rr0xir5r5Orq29LtdoupIXk702t4ihmZJR9o18NM5YbSbTV/2lk3xqHUx2AC4GTqqq+7sYm3rTlHMqyWuBu6tqbffDUo/q5DtqHrAfcHZVPR94CHDNuTr5jvodWn8R3QPYFdg+yVu6HJ96SyfX1l6XazQd58WWXJtbxNBM+RHwjLbXu7H5VMax2kymr/pLJ/lEkm1ofUmeX1VfmsY41Ts6yamXAq9Lcjut6ZMvT/Lv0xeqekCnv/N+VFXDf4W6iFZRQ/2tk5x6JXBbVd1TVb8EvgS8ZBpj1dzXybW11+UaTUd5saXX5hYxNFOuBfZKskeS36Z1Q6mVI9qsBN7a3F37AFrTHe+aZF/1lynnU5LQWmt+U1V9dGbD1hw25ZyqqvdX1W5VtbDp942q8q+c/a2TfPoJcEeSP2javQL4zoxFrrmqk+uoHwIHJJnf/A58Bd4stt91cm3tdblGM+W8mMq1+bwphyltgar6VZJ3A5fQuiv2Z6rq20ne3hz/JPBV4NXA94GHgWPH6zsLb0NzRCf5ROuv5kcD65Pc0Oz766r66gy+Bc0xHeaU9ARdyKe/BM5vLgRvxVzrex1eR61JchFwHa2nAFwPnDPz70JzxWTyKcnvA0PAjsCvk5wELKqq+70u10id5BTwPLbw2jxVLmGSJEmSJElzn8tJJEmSJElST7CIIUmSJEmSeoJFDEmSJEmS1BMsYkiSJEmSpJ5gEUOSJEmSJPUEixiSJEmSJKknWMSQJEmSJEk9wSKGJEmSJEnqCf8Pf1m3eyjiOEgAAAAASUVORK5CYII=\n", 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -3076,7 +3282,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -3096,7 +3302,7 @@ "dtype: float64" ] }, - "execution_count": 36, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -3108,7 +3314,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -3117,20 +3323,18 @@ "" ] }, - "execution_count": 37, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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+ "image/png": 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -3142,60 +3346,254 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": { - "scrolled": false - }, + "execution_count": 37, + "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'TableOne' is not defined", - "output_type": "error", - "traceback": [ - "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[0;31mNameError\u001B[0m Traceback (most recent call last)", - "\u001B[0;32m\u001B[0m in \u001B[0;36m\u001B[0;34m\u001B[0m\n\u001B[1;32m 18\u001B[0m ]\n\u001B[1;32m 19\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 20\u001B[0;31m mytable = TableOne(\n\u001B[0m\u001B[1;32m 21\u001B[0m \u001B[0mprediab_eda\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 22\u001B[0m \u001B[0mcolumns\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mprediab_eda\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcolumns\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdrop\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m\"Pre_diab\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mto_list\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n", - "\u001B[0;31mNameError\u001B[0m: name 'TableOne' is not defined" - ] + "data": { + "text/html": [ + "
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Grouped by Pre_diab
Missing01P-Value
n28471509
Age, mean (SD)043.4 (16.4)54.0 (16.4)<0.001
Gender, n (%)1.001330 (46.7)755 (50.0)0.040
2.01517 (53.3)754 (50.0)
Waist_Circumference, mean (SD)19894.7 (15.3)101.9 (15.7)<0.001
..................
Calcium, mean (SD)679.5 (0.4)9.4 (0.4)0.129
Alanine_aminotransferase, mean (SD)4924.0 (19.1)26.4 (20.2)<0.001
Aspartate_aminotransferase, mean (SD)4924.7 (14.1)26.5 (27.1)0.018
SBP_to_DBP, mean (SD)1341.7 (0.3)1.8 (0.4)<0.001
Waist_to_hgt, mean (SD)2040.6 (0.1)0.6 (0.1)<0.001
\n", + "

61 rows × 4 columns

\n", + "

" + ], + "text/plain": [ + " Grouped by Pre_diab \n", + " Missing 0 1 P-Value\n", + "n 2847 1509 \n", + "Age, mean (SD) 0 43.4 (16.4) 54.0 (16.4) <0.001\n", + "Gender, n (%) 1.0 0 1330 (46.7) 755 (50.0) 0.040\n", + " 2.0 1517 (53.3) 754 (50.0) \n", + "Waist_Circumference, mean (SD) 198 94.7 (15.3) 101.9 (15.7) <0.001\n", + "Weight, mean (SD) 35 77.8 (20.2) 83.9 (22.2) <0.001\n", + "Standing_Height, mean (SD) 38 167.6 (10.1) 166.8 (10.2) 0.014\n", + "BMI, mean (SD) 42 27.6 (6.4) 30.0 (7.2) <0.001\n", + "Average_SBP, mean (SD) 127 119.6 (16.3) 126.3 (18.1) <0.001\n", + "Average_DBP, mean (SD) 134 70.0 (10.7) 70.7 (11.7) 0.057\n", + "HDL_Cholesterol, mean (SD) 38 54.9 (16.6) 51.5 (15.2) <0.001\n", + "Total_Cholesterol, mean (SD) 38 187.2 (38.0) 196.3 (41.8) <0.001\n", + "High_BP, n (%) 0.0 1 2113 (74.2) 847 (56.2) <0.001\n", + " 1.0 734 (25.8) 661 (43.8) \n", + "Sleep_hours, mean (SD) 4 6.8 (1.4) 6.9 (1.5) 0.024\n", + "Trouble_sleeping, n (%) 0.0 2 2161 (75.9) 1107 (73.4) 0.073\n", + " 1.0 685 (24.1) 401 (26.6) \n", + "Sleep_disorder, n (%) 0.0 10 2624 (92.4) 1349 (89.6) 0.003\n", + " 1.0 217 (7.6) 156 (10.4) \n", + "Told_overweight, n (%) 0.0 2 2057 (72.3) 943 (62.6) <0.001\n", + " 1.0 790 (27.7) 564 (37.4) \n", + "General_health, n (%) 1.0 340 317 (12.1) 94 (6.7) <0.001\n", + " 2.0 799 (30.5) 340 (24.3) \n", + " 3.0 1038 (39.6) 623 (44.6) \n", + " 4.0 403 (15.4) 293 (21.0) \n", + " 5.0 62 (2.4) 47 (3.4) \n", + "Family_hist_diab, n (%) 0 0 2254 (79.2) 1147 (76.0) 0.018\n", + " 1 593 (20.8) 362 (24.0) \n", + "Feel_at_risk_diab, n (%) 0.0 45 2066 (73.3) 1001 (67.0) <0.001\n", + " 1.0 752 (26.7) 492 (33.0) \n", + "Vigorous_work_activity, n (%) 0.0 1 2287 (80.3) 1229 (81.5) 0.374\n", + " 1.0 560 (19.7) 279 (18.5) \n", + "Moderate_work_activity, n (%) 0.0 2 1857 (65.2) 1022 (67.8) 0.101\n", + " 1.0 989 (34.8) 486 (32.2) \n", + "Walk_or_bicycle, n (%) 0.0 0 2088 (73.3) 1158 (76.7) 0.016\n", + " 1.0 759 (26.7) 351 (23.3) \n", + "Vigorous_rec_activity, n (%) 0.0 0 2033 (71.4) 1251 (82.9) <0.001\n", + " 1.0 814 (28.6) 258 (17.1) \n", + "Moderate_rec_activity, n (%) 0.0 0 1569 (55.1) 904 (59.9) 0.003\n", + " 1.0 1278 (44.9) 605 (40.1) \n", + "Tried_weight_loss_past_year, n (%) 0.0 499 1616 (64.1) 839 (62.9) 0.499\n", + " 1.0 907 (35.9) 495 (37.1) \n", + "Healthy_diet, n (%) 1.0 0 245 (8.6) 139 (9.2) 0.642\n", + " 2.0 628 (22.1) 312 (20.7) \n", + " 3.0 1191 (41.8) 658 (43.6) \n", + " 4.0 631 (22.2) 326 (21.6) \n", + " 5.0 152 (5.3) 74 (4.9) \n", + "WBC_count, mean (SD) 0 7.2 (2.2) 7.3 (2.5) 0.727\n", + "RBC_count, mean (SD) 0 4.6 (0.5) 4.7 (0.5) <0.001\n", + "Hematocrit, mean (SD) 0 41.5 (4.0) 41.7 (4.1) 0.048\n", + "Triglycerides, mean (SD) 50 137.8 (97.5) 154.0 (117.5) <0.001\n", + "Uric_acid, mean (SD) 48 5.2 (1.3) 5.7 (1.4) <0.001\n", + "Osmolality, mean (SD) 47 278.6 (4.7) 279.9 (5.0) <0.001\n", + "Sodium, mean (SD) 47 139.8 (2.2) 140.0 (2.2) 0.023\n", + "Potassium, mean (SD) 48 4.0 (0.3) 4.0 (0.4) <0.001\n", + "Gamma_glutamyl_transferase, mean (SD) 48 26.5 (52.1) 29.3 (34.8) 0.036\n", + "Calcium, mean (SD) 67 9.5 (0.4) 9.4 (0.4) 0.129\n", + "Alanine_aminotransferase, mean (SD) 49 24.0 (19.1) 26.4 (20.2) <0.001\n", + "Aspartate_aminotransferase, mean (SD) 49 24.7 (14.1) 26.5 (27.1) 0.018\n", + "SBP_to_DBP, mean (SD) 134 1.7 (0.3) 1.8 (0.4) <0.001\n", + "Waist_to_hgt, mean (SD) 204 0.6 (0.1) 0.6 (0.1) <0.001" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "# let's do a table comparing features by the target\n", - "categorical = [\n", - " \"Gender\",\n", - " \"High_BP\",\n", - " \"Trouble_sleeping\",\n", - " \"Sleep_disorder\",\n", - " \"Told_overweight\",\n", - " \"General_health\",\n", - " \"Family_hist_diab\",\n", - " \"Feel_at_risk_diab\",\n", - " \"Vigorous_work_activity\",\n", - " \"Moderate_work_activity\",\n", - " \"Walk_or_bicycle\",\n", - " \"Vigorous_rec_activity\",\n", - " \"Moderate_rec_activity\",\n", - " \"Tried_weight_loss_past_year\",\n", - " \"Healthy_diet\",\n", - "]\n", "\n", - "mytable = TableOne(\n", + "from tableone import TableOne\n", + "\n", + "TableOne(\n", " prediab_eda,\n", " columns=prediab_eda.columns.drop(\"Pre_diab\").to_list(),\n", - " categorical=categorical,\n", + " categorical=[\n", + " \"Gender\",\n", + " \"High_BP\",\n", + " \"Trouble_sleeping\",\n", + " \"Sleep_disorder\",\n", + " \"Told_overweight\",\n", + " \"General_health\",\n", + " \"Family_hist_diab\",\n", + " \"Feel_at_risk_diab\",\n", + " \"Vigorous_work_activity\",\n", + " \"Moderate_work_activity\",\n", + " \"Walk_or_bicycle\",\n", + " \"Vigorous_rec_activity\",\n", + " \"Moderate_rec_activity\",\n", + " \"Tried_weight_loss_past_year\",\n", + " \"Healthy_diet\",\n", + " ],\n", " groupby=\"Pre_diab\",\n", " pval=True,\n", " remarks=False,\n", " overall=False,\n", - ")\n", - "print(mytable)" + ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# KDE plots by prediabetes status as well for those continuous features\n", "distn_vars = [\n", @@ -3229,7 +3627,7 @@ "g = sns.FacetGrid(\n", " df_kde, col=\"variable\", hue=\"Pre_diab\", col_wrap=5, sharex=False, sharey=False\n", ")\n", - "g.map(sns.kdeplot, \"value\", shade=True)\n", + "g.map(sns.kdeplot, \"value\", fill=True)\n", "plt.show()" ] }, @@ -3262,7 +3660,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -3276,7 +3674,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.13" }, "toc": { "base_numbering": 1, @@ -3294,4 +3692,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/sphinx/source/tutorial/Model_simulation_deep_dive.ipynb b/sphinx/source/tutorial/Model_simulation_deep_dive.ipynb index a5513859e..6b10d66f7 100644 --- a/sphinx/source/tutorial/Model_simulation_deep_dive.ipynb +++ b/sphinx/source/tutorial/Model_simulation_deep_dive.ipynb @@ -6,16 +6,25 @@ "raw_mimetype": "text/html" }, "source": [ - "" + "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Bootstrap simulation with FACET\n", + "# Work in progress\n", "\n", - "***\n", + "\n", + "FACET 2 no longer relies on bootstrapping in simulations, and instead calculates confidence intervals based on the standard error of mean predictions. This notebook will be updated accordingly for the release of FACET 2.0.\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Simulation with FACET\n", "\n", "FACET is composed of the following key components:\n", "\n", @@ -79,7 +88,9 @@ "import warnings\n", "\n", "warnings.filterwarnings(\"ignore\", category=UserWarning, message=r\".*Xcode_8\\.3\\.3\")\n", + "warnings.filterwarnings(\"ignore\", message=r\".*`should_run_async` will not call `transform_cell`\")\n", "warnings.filterwarnings(\"ignore\", message=r\".*`np\\..*` is a deprecated alias\")\n", + "warnings.filterwarnings(\"ignore\", message=r\"Importing display from IPython.core.display is deprecated.*\")\n", "\n", "\n", "# set global options for matplotlib\n", @@ -87,8 +98,8 @@ "import matplotlib\n", "import matplotlib.pyplot as plt\n", "\n", - "matplotlib.rcParams[\"figure.figsize\"] = (16.0, 8.0)\n", - "matplotlib.rcParams[\"figure.dpi\"] = 72" + "matplotlib.rcParams[\"figure.figsize\"] = (12.0, 6.0)\n", + "matplotlib.rcParams[\"figure.dpi\"] = 96" ] }, { @@ -122,8 +133,6 @@ "metadata": {}, "outputs": [], "source": [ - "# list your usual imports here such as pandas, numpy and others \n", - "# not covered by FACET, sklearndf or pytools\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns" @@ -142,7 +151,6 @@ "metadata": {}, "outputs": [], "source": [ - "from facet.crossfit import LearnerCrossfit\n", "from facet.data import Sample\n", "from facet.data.partition import (\n", " ContinuousRangePartitioner,\n", @@ -160,7 +168,7 @@ "source": [ "**sklearndf imports**\n", "\n", - "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n", + "Instead of using the \"regular\" *scikit-learn* package, we are going to use *sklearndf* (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). *sklearndf* is an open source library designed to address a common issue with *scikit-learn*: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. *sklearndf* retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard *scikit-learn* import:\n", "\n", "`from sklearn.pipeline import Pipeline`\n", "\n", @@ -352,7 +360,7 @@ "\n", "The figure above provides and overview of the simulation process for a single bootstrap split. This process is repeated many time to create a distribution of predicted impact for each value of a feature.\n", "\n", - "\n" + "\n" ] }, { @@ -378,7 +386,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -448,7 +456,7 @@ { "data": { "text/plain": [ - "" + "Text(0, 0.5, 'frequency')" ] }, "execution_count": 9, @@ -457,26 +465,24 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.bar(\n", " x=rop_bins.partitions_,\n", - " height=rop_bins.frequencies_/drilling_obs.features.shape[0],\n", + " height=rop_bins.frequencies_ / sum(rop_bins.frequencies_),\n", " width=rop_bins.partition_width_,\n", - " edgecolor=\"k\"\n", + " edgecolor=\"w\",\n", ")\n", - "\n", - "sns.kdeplot(data=drilling_obs.features[SIM_FEATURE])" + "plt.xlabel(\"partition\")\n", + "plt.ylabel(\"frequency\")" ] }, { @@ -494,25 +500,19 @@ { "data": { "text/plain": [ - "[(17.0, (16.5, 17.5)),\n", - " (18.0, (17.5, 18.5)),\n", - " (19.0, (18.5, 19.5)),\n", - " (20.0, (19.5, 20.5)),\n", - " (21.0, (20.5, 21.5)),\n", - " (22.0, (21.5, 22.5)),\n", - " (23.0, (22.5, 23.5)),\n", - " (24.0, (23.5, 24.5)),\n", - " (25.0, (24.5, 25.5)),\n", - " (26.0, (25.5, 26.5)),\n", - " (27.0, (26.5, 27.5)),\n", - " (28.0, (27.5, 28.5)),\n", - " (29.0, (28.5, 29.5)),\n", - " (30.0, (29.5, 30.5)),\n", - " (31.0, (30.5, 31.5)),\n", - " (32.0, (31.5, 32.5)),\n", - " (33.0, (32.5, 33.5)),\n", - " (34.0, (33.5, 34.5)),\n", - " (35.0, (34.5, 35.5))]" + "{16.0: (15.0, 17.0),\n", + " 18.0: (17.0, 19.0),\n", + " 20.0: (19.0, 21.0),\n", + " 22.0: (21.0, 23.0),\n", + " 24.0: (23.0, 25.0),\n", + " 26.0: (25.0, 27.0),\n", + " 28.0: (27.0, 29.0),\n", + " 30.0: (29.0, 31.0),\n", + " 32.0: (31.0, 33.0),\n", + " 34.0: (33.0, 35.0),\n", + " 36.0: (35.0, 37.0),\n", + " 38.0: (37.0, 39.0),\n", + " 40.0: (39.0, 41.0)}" ] }, "execution_count": 10, @@ -521,7 +521,7 @@ } ], "source": [ - "list(zip(rop_bins.partitions_, rop_bins.partition_bounds_))" + "dict(zip(rop_bins.partitions_, rop_bins.partition_bounds_))" ] }, { @@ -698,7 +698,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -738,22 +738,17 @@ "execution_count": 14, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-3)]: Using backend LokyBackend with 10 concurrent workers.\n", - "[Parallel(n_jobs=-3)]: Done 30 tasks | elapsed: 3.3s\n", - "[Parallel(n_jobs=-3)]: Done 180 tasks | elapsed: 5.9s\n", - "[Parallel(n_jobs=-3)]: Done 430 tasks | elapsed: 10.5s\n", - "[Parallel(n_jobs=-3)]: Done 780 tasks | elapsed: 17.7s\n", - "[Parallel(n_jobs=-3)]: Done 1000 out of 1000 | elapsed: 22.6s finished\n" - ] - }, { "data": { + "text/html": [ + "
ClassifierPipelineDF(\n",
+       "    classifier=LGBMClassifierDF(min_child_samples=15, random_state=42)\n",
+       ")
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], "text/plain": [ - "" + "ClassifierPipelineDF(classifier=LGBMClassifierDF(min_child_samples=15, random_state=42))" ] }, "execution_count": 14, @@ -767,16 +762,7 @@ " classifier=LGBMClassifierDF(random_state=42, min_child_samples=15),\n", ")\n", "\n", - "# instantiate the crossfit\n", - "boot_crossfit = LearnerCrossfit(\n", - " pipeline=lgbm_clf,\n", - " cv=boot_cv,\n", - " n_jobs=-3,\n", - " verbose=1,\n", - ")\n", - "\n", - "# fit the model\n", - "boot_crossfit.fit(sample=drilling_obs, verbose=-1)" + "lgbm_clf.fit(drilling_obs.features, drilling_obs.target)" ] }, { @@ -790,21 +776,10 @@ "cell_type": "code", "execution_count": 15, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "model_split_1 = next(boot_crossfit.models())\n", - "model_split_1.is_fitted" + "# model_split_1 = next(boot_crossfit.models())\n", + "# model_split_1.is_fitted" ] }, { @@ -820,144 +795,25 @@ "metadata": {}, "outputs": [], "source": [ - "single_test_obs = drilling_obs.features.loc[2:2]\n", - "rows = [model.predict_proba(X=single_test_obs)[1].ravel() for model in boot_crossfit.models()]\n", - "pred_df = pd.DataFrame(rows, columns=['pred_prob'])" + "# single_test_obs = drilling_obs.features.loc[2:2]\n", + "# rows = [model.predict_proba(X=single_test_obs)[1].ravel() for model in boot_crossfit.models()]\n", + "# pred_df = pd.DataFrame(rows, columns=['pred_prob'])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 1000.000000\n", - "mean 0.187645\n", - "std 0.293122\n", - "min 0.000658\n", - "25% 0.006171\n", - "50% 0.011662\n", - "75% 0.279029\n", - "max 0.996891\n", - "Name: pred_prob, dtype: float64" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pred_df['pred_prob'].describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case for the observation we choose, we see that the 1000 models produce an average predicted probability of the positive class of 0.03 with an IQR of 0.003 to 0.015." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulating actuals\n", - "\n", - "For each test split we can predict the outcome and look at the deviation between the average prediction for a test split and the actual average of the target in the original dataset. The spread and offset of these deviations can serve as an indication of how the bias of the model contributes to the uncertainty of simulations." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, "outputs": [], "source": [ - "# instantiate simulator\n", - "rop_simulator = UnivariateProbabilitySimulator(crossfit=boot_crossfit, n_jobs=-3)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# simulate average difference between \n", - "actual_distribution = rop_simulator.simulate_actuals()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Median: -0.000683\n", - "lower bound: -0.0558\n", - "upper bound: 0.0525\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ECDFDrawer().draw(actual_distribution)\n", - "\n", - "print(f\"Median: {np.median(actual_distribution):.3g}\")\n", - "print(f\"lower bound: {np.percentile(actual_distribution, 2.5):.3g}\")\n", - "print(f\"upper bound: {np.percentile(actual_distribution, 97.5):.3g}\")" + "# pred_df['pred_prob'].describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "In our case, the model's deviation does not exceed 6% of the target." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now to convince ourselves that this indeed works, lets grab the fitted model and the test set for the first split in our cross fit and see if we can re-produce the number for the first split." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "actual mean predicted uplift: -0.023030198104474375\n", - "reproduced actual mean predicted uplift: -0.023030198104474375\n" - ] - } - ], - "source": [ - "model_split_1 = next(boot_crossfit.models())\n", - "train_split_1, test_split_1 = next(boot_crossfit.splits())\n", - "\n", - "yhat_split_1 = model_split_1.predict_proba(X=drilling_obs.features.loc[test_split_1, :])[1]\n", - "\n", - "print(f\"actual mean predicted uplift: {actual_distribution[0]}\")\n", - "print(f\"reproduced actual mean predicted uplift: {yhat_split_1.mean() - drilling_obs.target.mean()}\")" + "In this case for the observation we choose, we see that the 1000 models produce an average predicted probability of the positive class of 0.03 with an IQR of 0.003 to 0.015." ] }, { @@ -971,22 +827,25 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ + "# create the simulator\n", + "rop_simulator = UnivariateProbabilitySimulator(model=lgbm_clf, sample=drilling_obs, n_jobs=-3)\n", + "\n", "# first run the simulator\n", "rop_simulation = rop_simulator.simulate_feature(feature_name=SIM_FEATURE, partitioner=rop_bins)" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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qvownxPPK2tqa3r17ExQUVNClCCGEEEII8cyplAJofc6fP5+ff/6ZcePG0blzZ5PDcJ06dZg4cSL16tXj1q1bNGvWjIMHD+Lq6pqn8zUaDfb29mRkZGBnZ2fcbjAY+OGHH7C3t6dJkya4ublhYSFPkouXj16vZ+/evVy7do0PP/xQOsRCCCGEEOKVUyCdYZ1Ox6FDh3BwcDD53MuXL2NlZUW9evUAKFy4MBcuXGDNmjUMGDDggedotVp0Op3xs0ajeeBxCQkJaDQa+vbti5+fn8m1CfEiqVevHhcuXCA5ORlPT8+CLkcIIYQQQohnqkDanm+88YZZQRjg3LlzlC9fHoD09HT69OlDhw4dOHfu3EPPmTBhAvb29sZ/PDw8HnicwWAAwNJSXqUWLz+1Wg38///3QgghhBBCvEpeuGeA09LScHV15datW3Tu3Jl33nmHYcOGkZqa+tBzPvvsMzIyMoz/xMfHP8OKhRBCCCGEEEI8b164MOzo6MjWrVsZOHAgs2bNombNmiQlJeHk5PTQc6ysrLCzs8v1jxAFRa/XM336dNLS0gq6FCGEEEIIIV5Zz/XzwGlpaTg6OubaVrZsWf7++2+Sk5NxdnYGICwsjLJlyxZEiQIYOHAgQ4YMoXbt2gVdygvhypUrvPPOOxQtWpQWLVoYt8fFxdG1a1cALl68yIoVK+SeCiGEEM+AoijsCbvM7PV7iUlMKehyhHghVCoeyI/DuhZ0GU/kuQ3DI0eOZMaMGRw8eJCqVasat5coUYJatWoRFhZmnE169erVHDhwoACrLVhffPEFO3bsQKVS4e/vz4wZMx76XvSDVKxY8b6ZuJcvX55rUqWIiAhOnz5N27Zt86vsl9bUqVMZMWLEQ/eXKlWK7du337ccmKenJ7t37wZyfsEghBBCiKfvzPXb/PjXNo5eDC/oUoR4oYTFJBV0CU+sQMLw8OHDOX/+vPFzw4YNAahcuTI//vgjAF5eXri5uWFvb3/f+YsXL2bIkCGkpKSgKAozZszAzc3t2RT/nPnhhx9QqVQcPHgQgCVLljBixAj+/PPPPI9hY2NjDGEPEx4ezrp16yQM58Evv/zyyDAM0KRJk2dUjRBCCCEe5GZMAlNW7GTT4bMAqGyssajwGupg/wKuTIgXgzUv/iSsBRKGp0+f/thjxo4dy9ixYx+4Lzg4mB07duR3WS8ktVrNW2+9ZfzcqVMnJkyYkOfzs7KysLa2fuj+O3fu0Lt3b5KSkoiOjjb+4qJ79+65vvfUqVN88803xvdg582bR/HixfNUQ0REBCNGjCAkJISwsDASExNp2bIl3377rXGd58TERIYNG0Z4eDg6nY5WrVoxfvx44xgff/wxe/fuxdHRke7du/Pbb78B8OGHH9KpUycAVq1axaRJk7C0tESlUvH999/neurg9OnTjBo1iqysLHQ6HR999BGdO3c27q9YsSJffvklM2bMICUlhYCAABYuXGh8B71bt27ExsZy+/Zt430qWbIks2bNMt6jUaNGATmP9p89e5bChQvn6R7943H3QQghhBCPlpSWwex1e1m84wg6vR6VWo1FmRKoy5fBcPsu2r2HAaWgyxTiuedc2Buo+tjjnmfP7WPSz5My/b98pt93fkHew83777+f6/POnTupW7duns+/e/culpaWDB8+nIsXL2Jpacknn3xiDHN+fn7s3r2b0NBQFi1axK+//vrAcbZu3cqqVauws7Njw4YNfPTRR6xevTrPdWzevJl169YxZcoUdDodffr0Yc6cOcbA/c4779C4cWOGDBmCwWCgb9++LF26lB49egDw3XffERERQYMGDQgLC+PAgQPGIA1w6dIlJkyYwK5du3BxcSEyMpLWrVtz4sQJrKys0Ol0dO3alRUrVlCuXDmSkpJo0KABVatWJTAwEMhZyuvSpUts27YNgA8++IA5c+YwcuRIAJYtWwZA8eLFH9hpL1++vHH7P/fXVI+7D0IIIYR4sKxsLYt3HGHO+r2kZGQCYFksGItKr6OyUKENPUSwXkufZtVRW6gKuFohnn/ers4FXcITkzD8EomMjGTixImsW7cuz+fExcWRlJTEqFGjKF68OLdu3aJdu3b8/vvvvP7663keZ8iQIcYOafPmzRk9erRJtZcpU4aWLVsCOes8jx49mm+++cYYhkNDQ1m8eDEAFhYWjBw5ksmTJ98XAhMTE/n+++9zBWGAHTt20Lt3b1xcXAAIDAxkz549xuNOnz5NyZIlKVeuHACurq706tWLLVu2MHToUAC0Wi3Dhg0zjtmiRQtjAH5W8nofhBBCCJHDYDCw8e+zTFm5kztxSQDY+xdCW/l1LDzd0Z+/jO7YKTrXfJ0xfVpib/PwJ+aEEC8XCcN5YEqntqDExMTQs2dPfvvtN9zd3fN8XpUqVThx4oTxs7+/P6NHj+bPP/80KQz/+zutrKwwGEx7h8DLy+u+zxkZGcbP8fHxubqpWq2WkJCQ+8YpVarUA98zj4uLo2jRorm2/XuSsdTUVI4ePZrrO1JTU3NNZGVpaZlrCS87Ozu0Wm1eLi/f5PU+CCGEEAIOX7jBD0u2cj7iLgAuXu6kVyyHzr8QVmnpaNZvxz4llW8Hd6B1zXIFXK0Q4lmTMPwSSE5OpmvXrkyZMoVSpUo99LgHLVV1+vRpUlNTcy3hY2dnR3Z29n3nK8rTe38mLi4u1+fY2NhcoTYoKOixk3wBD11v2tPTk5iYmFzb4uPjcXV1Ra1W4+XlRa1atVi5cqUZ1ef2NO9TXu+DEEII8Sq7ciuGScu2E3rqMgAuzo5YVCpHekggVioVulPnyQg7y2uBvvz4wVsE+uS9kSCEeHlYPP4Q8TzLyMigS5cufP3111SpUuWhx40cORI3NzeOHj2aa7uVlRXDhg0jNjYWyHkvdurUqcZJp/7h5eXF2bNnTe745tWFCxeM7+LqdDp++umnXGvwVqtWjfnz5xs/b9y4kYULF+Z5/MaNG/Pnn3+SkpKzduCtW7do0KCB8XqKFy/OlStXOHv2rPGciRMncurUKZOvRafTGe9nfnvS+yCEEEK8zGISU/hi3lo6jp1B6KnL2NlYU7JuFTQdWpJRNBjHjAwyV29Ge/w0A5rWYNHYwRKEhXiFSWf4BTdixAjOnDnDl1/mnuRr27ZtWFlZGT8/bKmq0qVL8+OPP9KyZUtsbW2BnEma6tSpk+u4MmXK0KhRIypXroyLiws9e/bkzTffzLfrqFOnDps2bWLChAkkJCTQrFmzXONPnjyZd999l/nz56PX6ylZsiQ///yzcf/06dP58ccfCQ8Pp2HDhtjZ2bFp0ybj/lKlSvHpp5/SqlUr42zS8+bNM94jKysrFi9ezPvvv09WVhaZmZl06NCB8uXLm3wtP/zwA82aNcPJyYmyZcsyc+ZMAM6ePWtcciksLIyuXbtiY2PDgAED6N+/P4mJicZfQly8eJFTp07h4uJC06ZN+fTTT/N0H4QQQohXUbomi3mbD/D75oNosrWoLVTUqFGeK8WLEW5ljQ1A2FkSTpzFzdGOie/3pn75EgVdthCigKmUp/lM53NKo9Fgb29PRkaGcdInyHnvdsaMGQwbNgxvb+8CrPDVEhERwZAhQ9i+fXtBl/JKkf/fhRBCvOh0ej0rQk8wfc0e4pNzlnesVb4EWZXKcdYiZyIsvywN4Wt3oKSmUb10CN+92Qlvtxd/FlwhxJOTzrAQQgghhHihKIrCrpOXmLRsOzfu5sw78npRf8o1rMEGlTWZBgVntQrHsxe5cfAEFioVIzo14o22dVFbyFuCQogcEoZFgQsKCpKusBBCCCHy5PS1W/ywdBvHL0cAEODtTo+29dnm4MKK9CxQFCqoFc4v30hsYgq+7s58/1YXqpQMKuDKhRDPGwnDQgghhBDiuXczJoHJK3ay+XDOZJeujva80bYeSUWDmX43GX16Fj7WlhSJjGTPxlAAGlYsyYQhHXB1vH/ZRSGEkDAshBBCCCGeW0lpGcxaG8qfO4+i0+uxtrKkX7MaVK5TmUm3E4m8m4wKaOliy9mVW9kTcQcrSzUf9mhG7ybVUalUBX0JQojnlIRhIYQQQgjx3MnK1rJo+2HmbNhHakYmKpWK9rXLM6h9A/5KzmbU1RgAithbUy8jhYXTVqDJ1hLk48FPw7tSJqhQAV+BEOJ5J2FYCCGEEEI8NwwGAxsOnWHKyp3cjU8GoGbZInzQvRl3HRwZdjWauGwdliroV8iV8B0HmH3oNADta5dnbN/WONjZFOQlCCFeEBKGhRBCCCHEc+HQuev8+Nc2LkTcBaCEvw8f9GhGieJBfHc9ml0RtwEo52RLLwcLfp79FzdjErCzseaLfq1pX6dCAVYvhHjRSBgWQgghhBAF6sqtaH78azv7Tl8BwMfNmRGdGtGu9uusj0nl0xM3SNUZsFdb8E6QB5qzl/ho2Q50ej2lAn2ZNLwrwb6eBXwVQogXjYRhIYQQQghRIKITUpi6ehdr9oVhUBQcbG0Y0qYO/ZrVINYAw87e5mhyBgC13RwYXsiZKQvXs/dUTmju07Q6H3RvhrWV/EgrhDCd/MnxggsNDaV169ZERUXh6Oho3F6qVCnGjBlD//79C7A6IYQQQoj7pWuy+G3Tfn7fcojMbC2Wagt6NKjK2x3q4+LkwB+3EpgdEUeWQcHVSs1HRX1wi4vjra9/JTYpFRcHO74Z0oHGlUoV9KUIIV5gEoZfEn/99ReDBw8G4MCBA9y4caOAKxJCCCGEyE2r07Mi9DjTV+8hITUdgKZVSvNe1yYE+3pyITWTd06GczEtC4DW3s6MCvZkyab9zFq3F0VRqFQikB/e6kIhD5eCvBQhxEtAwnAeVNx78Zl+38l6pv2Ws02bNrnC8Lx582jfvr1x/7Jly5g0aRJqtRpra2umTZtG2bJljftPnTrFmDFjyM7OJj09nc8//5xWrVoZ91esWJEvv/ySGTNmkJKSQkBAAAsXLsTOzu4Jr1QIIYQQrwJFUdh54iKTlm0nPCoegArFAvigezMqlQhEozcw5XoMf9xKQA8UsrHks+K+FFH0vPfTIo5fjkClUvF2+/q83b4+lmp1wV6QEOKlIGH4JeDk5ISjoyPnz58nMDCQiIgIGjZsCMD58+eZOHEie/bswdXVldOnT9O7d2/CwsKM53/66afMnj2bgIAAoqOjqV69OteuXUN97y+a9PR0Ll26xLZt2wD44IMPmDNnDiNHjnzm1yqEEEKIF8upqzf54a9tnLgcCUCgjzvvd21K0yqlUalUHElM55srUdzM1KICehd2Y1iwF3+fukynuWtITtfg5erE9291pnrpkIK9GCHES0XCcB6Y2qktCEOGDGHevHmUKVOGnj17EhUVBcCWLVvo168frq6uALz++uv4+flx+fJlSpQoAcDGjRuN4/j4+BhDsZ+fHwBarZZhw4YZj2nRogXLli17RlcmhBBCiBdRZHQCPy/fwdaj5wBwc7Ln7fb16dawCtaWlqRo9fx8I4Y1UTlrCRezt+GLEr6UtLPihyVbWLz9MAD1yhdn4pCOuDs7FNi1CCFeThKGXxI1atRgzJgxhIWFsWbNGqZMmQJAamoqS5cuZf369cZjY2NjSU1NNX5esGABCxYswGAwoFKpOHv2LIqiGPdbWlri5ORk/GxnZ4dWq30GVyWEEEKIF01iajoz14aydNcxdHo9NlaW9GtekyGt6+Bkb4uiKOyITeG7a9HEZeuxUqkYEujBwAAPbsfE0/PHFVyIuIulWs373ZrQr1kNLCwsCvqyhBAvIQnDL5Hu3btz7dq1XLNKe3l5MWzYMEaMGPHAc65du8aMGTMIDQ3F1tYWgPr16z+TeoUQQgjx8sjM1rJo+9/8umE/qRmZqFQqOtSpwIhOjYyTXcVkafnf1Wh2x6cBUMHZjs9L+FLE3oZ1B04xfsEGNFnZBHi58eOwrpQrUrggL0kI8ZKTMPwSeeutt+7b1rhxY/r06UPfvn1xdXUlMzOTUaNG8fPPP2NnZ4fBYMDKygpra2sgZzKtc+fOPevShRBCCPGCMhgMrD94mikrdxGVkPPIc+3XijK6ezNKBfrmHKMorI5KZvL1GNL0BhzUFrwb4kWXQq5osrIZM2cVaw+cAqBV9dcYN7Atjna2BXZNQohXg4Thl1zJkiUZM2YMrVu3xtLSEp1Ox5gxY4wzQRcvXpw2bdpQtWpVHBwcqFy5MtWrVy/gqoUQQgjxIjh47ho/Lt3GxcicuUpKBvjwQfdm1C5XzHhMREY2X1+5y/FkDQD13B0YU8wXX1srzkfcZfT05UREx2NrbcVnfVvRqW5FVCpVgVyPEOLVolL+/XLoK0Kj0WBvb09GRkau5YFiYmKYMWMGw4YNw9vbuwArFOLpk//fhRBCmOtSZBQ/LdvO/jNXAfB1d+bdzo1pW+t11Pfe79UaFBbeSmBORBzZioKblZqPi/rQzCtnHpLFOw7zw9JtaHV6Svj78NPwrhT18yqwaxJCvHqkMyyEEEIIIfIkOiGFX1btYs3+MBRFwcHWhjfa1KFf85rYWlsZjzuXquGry1FcTs8CoJ2PC+8V8cbVSk1SWgafzV3D7pOXAOjRqCof9Wye63whhHgWJAwLIYQQQohHStNkMnfjfhZu/ZvMbC2Wagu6N6rG2+3q51rySKM3MDMijsW3EjAAfjZWfF7ClxpuOcccuxTBR7NWEJWQgpO9LV8Pak+zqmUK6KqEEK86CcNCCCGEEOKBtDo9y/ccY8aaUBJS0wFoVqUMo7o2IdjXI9exfyemM+FKFLcytVgAfQq7MSzYCzu1BXqDgTnr9zJ99R4MikKFYgH88FZnCnu5FcBVCSFEDgnDQgghhBAiF0VR2HH8ApOW7SAiOh6AisUC+LBncyoUC8h1bLJWz8/XY1gbnTOTdHEHG74s4UtZp5x5WWISU/ho1kqOXAxHpVLxRpu6vNOxIVaW6md7UUII8R8ShoUQQgghhFHY1Zv8uHQbJ65EAhDk48H73ZrQpHLpXLM8K4rC9rhUvrsaTYJWj7VKxdAgD/r5e2BlkXNc6KnLfPrrahJTM/BwceS7oZ2o9VrRArkuIYT4LwnDQgghhBCCiOh4fl6+g21HzwPg7uTAsA716dqgyn1d3OgsLd9ejSY0Pg2Ais52fFHCl2B7GwCydTp+Xr6DBVsOATnrDn87tBOeLo7P8IqEEOLRJAwLIYQQQrzCElPTmbE2lL92HUWnN2BjZUn/FjUZ0roOjna2uY41KAor7ibxy41Y0vUGHNUWjAzxolMhVyzudY0joxP4YOZyzt64g6XagpGdGzOwZS0s7i25JIQQzwsJw0I8h1asWMHrr79OiRIlCroUIYQQLylNVjZ/bPubuRv3k6bJQqVS0bFuRUZ0aoivu8t9x9/IyOLry1GcTNEA0MDDkTHFfPC2+f8lkTYeOsO439eTnplFYU9Xfny7C+X/846xEEI8LyQMi6duwYIF3Lp1i88++6ygS3lhvPPOOwwYMID//e9/ubb36dOH27dvExUVRZ8+feSeCiGEMJlOr2fNvjCmrd5NTFIqAHXKFWN0t6aUDPS973itQWHBrXjmRMSjVRQ8rNR8XMyHJp5OxneIM7KymbhoE6v2ngSgWdUyfDWwHc4Ods/uwoQQwkQShl9wGo2GDz/8kBMnTmBhYUGRIkWYMmUKbm45SxV89913bNmyJdc5N2/eZMCAAYwdOzZP3zF8+HCOHj2Kg8P/ryP42Wef0aRJk1zHTZ06lREjRjzhFb38FixYQKdOnXBycnroMRs2bCAoKOi+7YsWLTKOcevWradWoxBCiJePoijsPHGRyct3cP1uHABlggrxfvem1Cr74EmtzqZo+OpKFFfSswBo7+PCe0W8cbH6/3eIL9+M5v0Zy7l+JxYbK0s+6d2Cbg2q5JpsSwghnkcShl9wH3zwAQEBAUybNg3ICUl9+/Zlw4YNAHz88cd8/PHHuc5p0KABPXr0yPN33L17lzVr1uDn5/fI43755RcJw3nw+++/06RJk0eG4SpVqjzDioQQQrzsjl+OYNJf2zl59SYAAV5ujOzSmBbVyj7wXV6N3sD08FiW3E7EAPjbWjG2uC/V3f7/F+OKovDX7mP8788tZGt1FPHzYtKwrpQI8HlWlyWEEE9EwvALbtu2bVy+fNn4uX///kyaNImUlBScnZ3vO379+vWULFmSYsWK5fk7oqKi8Pb2fuj+bt26ERsby+3bt2nYsCEAJUuWZNasWcZjEhMT6dOnDzdv3iQ1NZWvvvqKNm3a5LmGihUrMnz4cP744w/S09MJDAzk119/xcPDA8j5C/mLL75gy5YtWFpaEhwczKxZs3BxyXnnacmSJcyZM4djx46xbds2xowZg16vp27dukycOBGAM2fO8OGHH5KZmUlWVhbDhg2jb9++ua5h2LBhhIeHo9PpaNWqFePHjzfuHzhwIM2bN2fRokUkJyejKArz5s0zvvc7ceJEtm/fTlhYGF27dsXGJmfGzc2bN2NrmzNByT/3Lzw8nG+++YbevXvn+R7l5T4IIYR4tVy9HcPPy3ew++QlIGeG6Lfb16drw8pYWz74x8BDCel8cyWKO1laLID+/u68GeSJnfr/Q3NyuoYv561j27Gcmae71K/EmN4tsbOxfurXJIQQ+UXCcB6U6f/lM/2+8wvGP/6ge7RaLXq9Hst//YWWlZWV6/M/DAYDX3/9NatWrTKpnoyMDH7++Wd27NhBeno6ffv25c033zTuX7ZsGQDFixdn9+7dDxxj9erVhIaG4u/vz927d6lZsyatWrXK88ySycnJ3Lhxg9DQUACmTZvG8OHDWbp0KQBz584lIiKCw4cPY2FhwaxZs/j444+Ngbxnz5707NmTYsWKMXnyZNasWYOrq6txfK1WS8+ePfnrr78oW7YsmZmZtGzZktdff53y5csDOe/xNm7cmCFDhmAwGOjbty9Lly7N1WVfs2YNy5cvx87Ojg0bNvDxxx+zevVqAD799FM+/fRTGjZsyKJFiyhcuPB91/nP/ft3yDbF4+6DEEKIV0NUQjLTVu9mzb4wDIqCnY01A1rUZFDL2jjY2TzwnCStnp+uRbMhJgWAkg42fFGiEGWccs8oHXb1Jh/MXMGduCQcbG0YP7AtrWqUe+rXJIQQ+U3muH/BtW3blhEjRqDVajEYDIwbN47g4GDs7e3vO/bPP/+kbt26+Pv7m/QdycnJlCtXjq1bt7J9+3ZCQ0NNDlcdOnQwfm+hQoXw9/cnKioqz+crisIHH3xg/Dx8+HD27dtn/Lx+/XpGjRplDNdvvvkmO3fuvG8cvV7Pu+++mysIA1y+fJkiRYpQtmxZAGxtbVm/fj3Fixc3HhMaGsqQIUMAsLCwYOTIkaxbty7XOL1798bOLmeykObNm3P+/Pk8X2N+yOt9EEII8XJKTtcwadl2Wn70C6v2nsTCQkXPxlXZ8v27jOjU6IFBWFEUtsSk0OnYdTbEpGBjoeLdEC/+qBicKwgbDAZ+3bCPvhPmcScuiXIhhVn51VsShIUQLyzpDOeBKZ3aZ+2nn37i66+/pn79+hQuXJjTp0+zZMmS+47Lzs7mhx9+YMeOHSZ/x40bN4z/bmdnxw8//EDnzp1566238jyGu7t7rs92dnZotdo8n69Wq42TggGoVCpj6ARITU1l+PDhxseNgQd2xwEqVap037a4uLj7HgV3dHTM9Tk+Pt74GDPkdJNDQkJyHfPv67SyssJgMDzqsvKdKfdBCCHEyyMrW8viHUeYs2EfKek5Sx81r1qWkV0aE+zr8dDzojK1fHs1ir0J6QBUcbFnbAlfguxyP+4cm5TKJ3NWcejcdQAGtqzFyC6NH/qotRBCvAjkT7AXnLW1NV9//TVff/01U6dOpVy5cg8Me3PmzKFDhw54eXk9dKzU1NT7JnVKSkpi8+bN9OzZ07jNzs6O7Ozs+85XFOUJruTR9Ho9iYmJxkCsKAoajca438vLi88//5xy5R7922lLS8tcIfofnp6exMTE5NqWlpaGhYWFscseFBT00MfATfW07lVe74MQQoiXg95gYN2BU0xdtZuohGQAqpUK5v1uTXm96MOfBMsyGFh4M4F5N+PJNCg4qi14r4g3HXxdsPjPLNAHzlzlkzmriE9Jx83Jnm/f6ES98sUfMrIQQrw45DHpl8Thw4dZu3btA9edTU9PZ8aMGbz//vsPPX/VqlU4Ozvz888/59ru6OjI119/zdGjR4GcEPe///2Pzp073zeGTqcjNjb2Ca/k4aZMmWL89+nTp1OnTh3j5xYtWjB58mT0ej0AERERfPjhh3keu0SJEly/fp0LFy4AOe9dt2/fnitXrhiPqVatGvPnzzd+3rhxIwsXLjT5Ory8vDh16pTJ5+XFk94HIYQQLwZFUQgNu0ynz2fy2dw1RCUkUzLAh9mj+zD/kwEPDcKKorA7LpXOx24wIyKOTINCU08nVlYJoVMh11xBWKvTM2nZdt748Q/iU9KpXjqE1V+/LUFYCPHSkM7wSyA+Pp6hQ4eyYcMG1Gr1fft//vlnBgwY8MgZhZ2dnXFzc7vvcWZLS0s2bdrE4MGDjZ3Yxo0b88knn9w3xg8//ECzZs1wcnKibNmyzJw58wmvLHcdQUFB1K9fn/T0dAoXLszcuXON+wcOHEhkZCR169ZFrVbj6OjIpEmTjPsPHDjA2LFjuXr1qvFR59mzZxtneraysmLJkiW89957ZGVlkZmZybBhw4yTZwFMnjyZd999l/nz56PX6ylZsuR9vzzIiy+++IKhQ4fy7bffYmVllWs26SZNmqDX6wkPD8fOzo65c+dSoUIF4/f079+fyMhIoqKiyMrKYseOHXh5eRknMXvcfRBCCPHiO3X1Jj8t286xSxEAFPJwYWTnxrSpWe6RE1Nez8jih6vR/J2UAUAxexs+KuZNVVeH+469FZvIhzNXcOraLSxUKt7p2JA32tZFnceJL4UQ4kWgUp7ms63PKY1Gg729PRkZGbkemY2JiWHGjBkMGzbskUsJiWevePHiubq04snJ/+9CCPFiuXE3jikrdhqXM3JxsOOtdvXo0agqNtZWDz0vVadndkQcf91JRKeAs6UFbwd50cXPFcv/PBINsPXoOb6Yt47UjEx83Z354e0uVC4R9NSuSwghCop0hoUQQgghnmOxSalMX7OHlaEn0BsM2Fpb0bdZDYa0roOTve1DzzMoCuuik5l6I5YErR4V0KWQK8OCPXGzuv9HwMxsLf/7cwvLdh8DoFGlUnwzuD2ujvevUCGEEC8DCcPihSBdYSGEEK+a1IxM5m06wMKth9Bka7FQqehavzLDOjTAx935keeeTtHw3dVozqdlAlDR2Y6PivlQyvHB4fnq7RhGz1jOlVsxWFmq+ahHc3o1qYbqAZ1jIYR4WUgYFkIIIYR4jmRrdfy1+ygz1+4lKS3n/d4mlUszsktjivo9fFUIgNgsHVPDY1gfnQKAt7Ulo4p408LL6YHBVlEUVoaeYOLizWRmawn29eCnYV0pHVQo/y9MCCGeMxKGhRBCCCGeAwaDgY1/n+WXlTu5HZcEQKUSgXzQvRkVigU88lytQeHP2wn8GhlPut6AlUpFP393BgV6YK9+8KRXsUmpfPPHRrYfy1lJoUOdCnzWtxUOtjb5el1CCPG8kjAshBBCCFGAFEXh4Nlr/LRsOxcjowAo6ufF+92a0qBCicc+qrw/IY0fr8UQockGoL6HI6OLeBNgZ/3A4w0GA8v2HOfn5TtIzcjE3taaL/q1oV3t8g88XgghXlYShoUQQgghCsi5G3f4adl2/j5/HQBfd2fe6diI9nXKP3YZo0hNNj9di2ZvQjoAwXbWfFDUm9rujg8958qtGMbNX8fJqzcBqF++BJ/3b42fh2v+XJAQQrxACiwMT5o0iYULF6JWq6lcuTLTp0/HyurhywL826pVq5g6dSqWlpaoVCrKly/PxIkT83y+EEIIIURBioiO55eVu9h8+CwAzva2vNGmLr2bVsf2EcskAWToDcyNjGPRrUS0ioKD2oKhQR709HPHyuLBXeSsbC2z1+9l7sYD6PR6PF0c+bRPK5pXLSOTZAkhXlkFEoa3bt3K9u3bOXbsGJaWlnz++edMmDCBcePGPfbc5ORkPvnkE44ePYqLiwsA77zzDgsXLmTw4MFPuXJhLr1ez6xZs+jfvz+Ojg//jbUQQgjxMotPSWPmmlCW7TmGTm/A2sqSPk2rM6R1nccuYaQoCptjUph8I5bYbB0A7X1ceCfEC0/rh/9Id+TCDb6cv56I6HgAujWswvtdm+DsYJd/FyaEEC+gAgnDCxYs4JNPPsHSMufr27RpQ40aNfIUhu3s7HBxcSEhIQEXFxeysrJITEzE39//oedotVp0Op3xs0ajeeJrEDl+/PFHNm7cSGZmJo6Ojmzfvv2Bx125coV33nmHokWL0qJFi2dcpRBCCFGw0jVZ/L7lIPO3HCQjMxuVSkXHuhV5p2NDCnm4PPb8C6mZfHctmlMpOT/DvOZky0dFfSjn/PBAm5SWwQ9Lt7F630kAivh5MX5gWyqXCMqfixJCiBdcgYThc+fOUb58ziQNW7ZsYdasWfj6+pKWlvbYrqG1tTVz586lTp06uLu7ExERwccff0zz5s0fes6ECRMYP358vl7D82LgwIGcOnUKFxcXdDodlpaWTJs2jbJlyz6T7//ggw/44IMPiIiIYMiQIQ89rlSpUmzfvp369es/k7qEEEKI54FWp2dF6HFmrNlDfErOu70NKpTgva5NKO7v89jzE7J1zAiPY1VUEgrgbqVmZIgXbXxcsHjI482KorDx7zP8b/EWElLTsbJU81a7egxuVQdrK5kuRggh/lEgfyKmp6fj4uLC1KlTOXPmDMuXL6dBgwZ5CsPx8fEMGjSIjRs3UqFCBZKTk+nfvz8rVqygS5cuDzzns88+4+OPPzZ+1mg0eHh45Os1FaSpU6dSu3ZtAP7++28GDBjA0aNHC7iq+zVp0qSgSxBCCCGeCUVR2HLkHFNW7iQyOgGA8kX9Gd2tKVVKBT/2fJ2isPxOIjMj4kjVGbBUQc/C7rwR6IGTpfqh592MSeCrBRs4cPYaAFVLBTNuQFtCCnnmy3UJIcTLpEDCsLW1Nb169aJChQrMmTMHgKSkpDy9S7ps2TK6du1KhQoVAIyhumfPng8Nw1ZWVq/M5Fo1atQgJSXF+Hn58uXMnj0bvV6PWq1m1qxZFCtWzLj/2LFjfPjhh6hUKrRaLR9//DFt2rQx7k9MTGTYsGGEh4ej0+lo1aqVSV32U6dOMWrUKADCwsI4e/YshQsXNu6PiIhg5MiRVK5cmdDQUOLj4+nYsSNffPFFvtUghBBCPEuHL9zgp7+2cfbGHQCCfT14r2sTmlQunafJqg4npvPDtWiuZeQslVTTzYEPi3oTYv/w9X+1Oj0Ltx5i+po9ZGZrcXaw48MezehUt6JMkCWEEA9RIGHYYDDg7e1t7NampqZiMBjuC8MP6hRnZmZia2uba5utre1Tfw94puEYsWQ81e8A8MKety2qmH3+4sWLadasGQBRUVH88ccfbNy4ERsbG7Zt28aoUaPYsGGD8fjhw4ezePFiihUrRnp6OgMHDqRFixbG97nfeecdGjduzJAhQzAYDPTt25elS5fSo0ePPNVTvnx5du/eDUDDhg0feMz+/fsZPnw4n3/+OVqtlnr16tG+fXvjo/RPWoMQQgjxLFyMjGLSsu3sP3MVAC9XJ4Z3aECnehWxVD+8m/uPO5nZTLoey864VAD8ba0YXdSb+u6Ojwy0Z67f5ov567h0b43i1jXL8UmvFng4y4SVQgjxKAUShidPnsyUKVOM77h+//33dO/ePdcxI0eOZMaMGRw8eJCqVasat7dr146OHTvSrVs3/Pz80Ol0jB079qkHoycJqE/biBEjcHZ25vbt2zg5ObFw4UIAfH19WbdunfG4xo0b88477+Q618/Pj+PHjxMSEoKDgwPLli3LtT80NJTFixcDYGFhwciRI5k8eXK+3u+goCCaNm0K5HTxGzdunOu98mdRgxBCCGGu27GJ/LJqFxsOnUFRFBztbBjcug59m9XA3sb6sedr9AYW3Izn91sJZBkUbC1UDAn0oI+/OzaPWGs4XZPFlJU7WbzjCIqiUNjTlS8HtKVOuWIPPUcIIcT/K5Aw3KJFC86fP0/lypWN6wzPmDEj1zFeXl64ublhb597mYGiRYvyyy+/0KtXL3Q6HdnZ2bRr147Ro0c/y0t4rvz7neFbt27Rt29fpk+fTokSJfj00085cuSI8TfKiqLkOvePP/5g6tSpdOzYEVtbW4YNG0aDBg2M++Pj43N1dLVaLSEhIflav7u7e67PdnZ2aLXaZ1qDEEIIYarE1HRmr9vLkl1H0er0WKrV9GpclTfb1cPNyeGx5yuKwo64VCZdjyEqK2fVi5Zezows4oWPzaNf79p14iLf/LGRqIQU1BYW9G9Zi2EdGuQpfAshhMhRYFMKvv/++7z//vsP3T927FjGjh37wH0NGjRgz549T6myF5u/vz8dO3Zky5YtHD58mKysLHbv3o1KpUJRFEqUKJHreEdHR8aMGQPkvJvbrFkz1qxZY3yvNygoyPiYc0F5HmoQQggh/pGRlc0f2/7mt437SdNkoVKpaFvrdUZ0aoS/l1uexriSnskPV2M4mpzzClZJBxs+KuZDJZdHrzUck5jCxEWb2XbsPACvhfgxfmA7SgcVerKLEkKIV5DMr/+S0Wg0rF+/ns8++4wrV67g4OBg7AovWrQo1ztHer2eFi1asGjRInx8fHBycjJOpPWPatWqMX/+fAYOHAjAxo0biY+Pp1+/fs/smp6HGoQQQgidXs+qvSeZvmYPsUk57/XWKVeM97o2yXMYTdHqmRkRx/I7iegBV0s1w4M96VjIFfUj3gs2GAws232MSct3kKbJws7GmlFdGtGrSXXUj3iUWgghxMNJGH4JjBgxAhcXFwwGA1qtlkGDBtGgQQNq1qzJgAEDqFWrFtbW1vTt2xcHh/9/bEutVjN69Gg6duyIjY0N2dnZDB06lODgYOMxkydP5t1332X+/Pno9XpKlizJzz//bNz/888/s27dOjIzM7l06ZLxcebFixfj5+fH2bNnGTFiBJAzm3TXrl2xsbFhwIAB9O/fP0/X97gahBBCiKdJURR2Hr/Izyt2cONuHABlg/14v1tTapYtkqcx9IrCmqhkpt2IJUmnxwLo7ufK20FeuFg9enKtK7diGDd/HSev3gRy1ike2681fh6uT3JZQgjxylMp/32J9BWg0Wiwt7cnIyMDOzs74/aYmBhmzJjBsGHD8Pb2LsAKhXj65P93IYR4vGOXIvjpr22cunYLgABvd0Z1aUzzqmWwyGNH9mRyBt9fi+ZiWhYAVVzs+bCoNyUcbR95Xla2llnr9vLbpgPo9Hq8XJ34tE9LmlUpI8slCSFEPpDOsBBCCCHEf1y5Fc3Py3ewJ+wyAB7ODrzdvj5dGlTG2jJvPz7FZGmZfD2WzbEpAPjaWPJ+EW+aeDo9NswevnCDcfPXExEdD0D3hlV4r2sTnB3sHnmeEEKIvJMwLIQQQghxz934ZKat3s3a/WEYFAU7G2sGtazFgBa1cLCzydMYWQYDi24l8ltkHBqDgo2Fiv7+7gwI8MBO/ehuclJaBt8v2cqa/WEAFPXzYvzAdlQqEfiklyaEEOI/JAwLIYQQ4pWXnK7h1w37WLT9MNlaHZZqC3o0qMpb7evj6eKYpzEURWFvQho/XYvhZmbOZJSNPZ14v4gXfraPXvJIURTWHzzNd0u2kJiagbWVJW+1q8egVrXz3IkWQghhGvnT9V/+efdHp9MVcCVCPH16vR4gz++8CSHEyygzW8viHYf5df0+UjIyAWhZ/TXe7dyIIB+PPI8TnpHFD9diOJiYDkARe2s+KupDdbfHrzccGZ3AVws2cPDcNQCqlQpm3MC2BPt6mnFFQggh8krC8L+4u7tjZ2fHypUradKkCW5ubhIUxEtJr9ezd+9erK2tcXFxKehyhBDimdMbDKzdf4ppq3cRlZDzTm/1MiGM7taU10IK53mcNJ2eXyPj+fN2AjoFHNUWvB3sSddCblhZPPq9YK1Oz4KtB5m+eg9ZWh0uDnZ82KM5HetWkAmyhBDiGZDZpO1yT0SRnJzM+vXruXr1agFVJ8SzYW1tTe/evQkKCiroUoQQ4plRFIXQU5eZtGwHV2/HAFAq0JfR3ZpS67WieQ6hBkVhQ3QKv9yIIV6rRwV09HVheLAX7taP7zWcvnaLL+ev49LNaADa1Hydj3s1x8M5b49kCyGEeHIShu3un5VRURQ0Gg3p6em8grdHvAIsLCxwcXHBysqqoEsRQohnwmAwsOvEJeZs2MvZG3cAKOzpyrudG9O6xmsmPQl2NkXDd9eiOZua81j16852fFzUhzJOj14qCSBdk8XklTv5c8cRFEXB38uNL/q3oU65YuZdmBBCCLPJY9IPoFKpsLe3x97evqBLEUIIIcQT0On1bPr7LL9u2Me1O7FAzjJJb7SpS49GVbG2yvuPQvHZOqbeiGVtdDIAntaWjArxopW3c546yjtPXGTCHxuJSkhBbWHBgJa1GNahAXY2j55cSwghxNMhYVgIIYQQL52sbC1rDoTx28YD3IpNBMDX3YXBrWrTqV5FkwKo1qCw9E4icyLiSNMbsFRBH393hgR44GCpfuz50QkpTFy8ie3HLgBQLqQw4wa2pXRQIfMuTgghRL6QMCyEEEKIl0Z6ZhbL9xxn/uaDxCalAhDk48EbberSplY5k5cpOpSQzg/XormhyQagnrsD7xf1Icju8WHaYDDw1+5jTFq2g/TMLOxtrRnZuTG9mlRDLRN0CiFEgZMwLIQQQogXXnK6hj93HOaPbYdJSssAoGSgL0Pb1KVZ1TImh89bmmx+uh7Dnvg0AALtrPigqA913fM2wdWVW9F8OX89YVdvAtCwYknG9m1NIQ+ZwV8IIZ4XEoaFEEII8cKKS05j4dZDLNl5lPTMLAAqFAvgzbb1qFe+uMlLFGn0BuZFxrPwVgLZioK92oKhgR70Kuz+2KWSIGfd4lnrQpm36QA6vQEvVyfG9m1Fk8qlZbkkIYR4zkgYFkIIIcQL5058EvM2HWBl6AmytDoAapYtwptt61G1VLDJwVNRFLbGpvLz9RhisnPGa+PtzLsh3njZ5O3HpUPnrjN+wXoioxNQqVT0aFSV97o2wcn+8bNMCyGEePYkDAshhBDihXHjbhxzN+5n/cFT6PQGABpVKsXQNnV5vai/WWNeSsvku6vRnEzRAFDa0ZaPi/lQ3vn+5RcfJDE1nR+WbmPN/jAAihX2ZvzAtlQsHmhWPUIIIZ4NCcNCCCGEeO5diLjLrxv2sfXoeRRFwUKlonXNcgxtU5fi/j5mjZmo1TEjPI5Vd5MwAG5WakYEe9He1wWLPHSWFUVh/cHT/O/PLSSlZWBtZclb7eoxqFVtkyfqEkII8eyZ/Sd1RkYGGo0Gd3d3eQdGCCGEEE/FySuRzFm/j9BTlwGwVKvpWKcCg1vXIdDH3awxdYrCyrtJzAiPJUVnQA30LuzGm0GeOOVhqSSAyOgExi9Yz6Fz1wGoXjqELwe0JdjXw6yahBBCPHsmheHjx48zefJkDh06hK2tLY6OjkRHR+Pn50e3bt0YOnQodnZ5e6RICCGEEOJBFEXh0LnrzFm/lyMXwwGwtbaiW4PKDGhZC19382ZkVhSFvQlpTA+P40p6zmRb1V3t+bCoD0UdbPI0hlan5/ctB5mxZg9ZWh0uDnZ81LM5HepUkOaAEEK8YPIcht9//32uXLnC8OHDWbBgARb/WqLg9u3b/PnnnzRo0IC5c+dSrly5p1KsEEIIIV5eBoOBXScv8ev6fZy5cRsAJ3tbejWuRt9mNXB3djBvXEVhT3wav0bGcTEtJwT72VjxQVFvGng45jnEnrp6ky/nr+fyrWgA2tZ6nY97tjC7LiGEEAVLpSiKkpcDV65cSefOnR95TFxcHBcvXqROnTr5UtzTotFosLe3JyMjQzrZQgghRAHT6fVsPnyOXzfs4+rtGADcnRzo17wGPRtXM3s2ZoOisDMulV8j442dYE9rNQP8PehcyBVbdd7WHk7TZDJ5xU6W7DyKoigEeLnx5YC21HqtqFl1CSGEeD7kOQy/TCQMCyGEEAUvW6tjzf4wftu4n5uxiQD4ujszsGVtutSvhJ2NtVnj6hWF7bGp/BoZx/WMbAC8rS0ZGOBBB1+XPIdggB3HLzDhj01EJ6agtrBgYMtavN2+vtm1CSGEeH7IVIdCCCGEeKYysrJZvvsY8zcfJCYpFYBAH3feaF2HtrXLmz0Ts05R2BqTwtzIeMI1OSHY18aSQQEetPd1wdoi7yE4OiGFCYs2seP4BQDKFSnM+IHtKBXoa1ZtQgghnj8ShoUQQgjxTKSka/hzxxEWbvubpLQMAEr4+zC0bV2aVyuL2oSw+m9ag8LmmGTmRsZzM1ML5LwTPDjQg7Y+LlhZ5H1iK73BwF+7jvLz8p2kZ2Zhb2vNe12a0KNxVbPrE0II8XwyOQynpKSwZs0azp07R2pqKk5OTpQtW5b27dvj4mLe7I5CCCGEeHnFp6SxYMshluw8Snpmzru7rxf158229WhQoYTZszBrDQobopOZdzOeW/dCcIBtTghu5W1aCAa4fDOaL+av4/S1WwA0qlSKz/q0opCH/HwjhBAvI5PC8L59+3jrrbe4cOECHTt25O233yYpKYmwsDBq167NzJkzqVu37tOqVQghhBAvkLvxyczbfIAVe46TpdUBUKNMEd5sV49qpYLNDsHZBgPronJC8N2snHGD7KwZEuhBC29nLE0cNzNby8y1oczffACd3oC3qxNj+7amSZXSZtUnhBDixWDSBFrVq1dn9erVFCpUiL59+zJixAiqV68OwK1bt+jcuTOHDx9+asXmF5lASwghhHh6wqPimbtxH+sOnEan1wPQsGJJhratR/mi/maPm2UwsCYqmfmR8URn54TgEHtr3gj0pJmXE2ozwvXBc9cY//sGbsYkoFKp6NGoCqO6NDF7BmshhBAvDpM6wyqVCj8/PwBmz55Nly5dmD17NoGBgfj7m/+XmxBCCCFefJcio5izYR9bj5zDoChYqFS0rlGON9rUpUSAj9njZuoNrIpKYv7NBOLuheBi9jYMDfKgsacTFmaE4ISUdL5fspV1B08BUNzfm/ED21GhWIDZdQohhHixmBSGnZ2dOXDgALVr18bBwYG5c+cyaNAgVq1axYkTJ3B2dn5adQohhBDiOXXq6k1mr9/LnrDLAFiq1XSqU57BresQ5ONh9rgavYHldxNZeDOBeG1Oh7mEgw1Dgzxp6OFoVghWFIV1B07x3ZKtJKVlYG1lydvt6zOwZS2zZ7EWQgjxYjLpT/1ff/2VPn36YG1tTfny5XF1dSU1NRVnZ2dq1arFokWLnladQgghhHiOKIrC3+dvMGf9Xg5fuAGArbUVXRtUZmDLWvi6mz/pVLpOz7K7SSy8lUDSvRBc2tGWoUEe1Hd3NOtdY0VR2BN2mWmrd3Mh4i4A1cuE8GX/tgT7mh/YhRBCvLhMemf4H1euXOHcuXOkpaXh6OhI2bJlKV68+NOo76mQd4aFEEII8xgMBvaEXWb2+r2cuX4bAEc7G3o1qUa/ZjVxd3Ywe+w0nZ6/7iTyx60EknUGAF5zsmVokCd13BzMDsH7Tl9l2updnL1xBwAvVyfe69qE9rXLmz2JlxBCiBefWWH4RSdhWAghhDCNTq9n65HzzNmwlyu3YgBwc7KnX/Oa9Gpc7YkmnErV6fnzdiKLbyeQei8El3e2481AT2q42Zsdgg+evca01bs5dW+pJA8XR95oXYduDatga21ldr1CCCFeDvJyjBBCCCEeKlurY92BU/y6cT83YxIA8HFzZlCrWnSpXxk7G2uzx07W6ll8O4EltxNJ0+eE4EouOSG4qqv5Ifjv8zeYvno3J65EAuDu5MDg1rXp0ajqE9UrhBDi5SJhWAghhBD3ycjKZmXoceZtOkh0YgoAAd7uvNG6Du1ql8fayvwfIRK1OhbdSmTpnUQy7oXgaq72vBHoSRVXe7PHPXoxnKmrdnHsUgQAro72DG5Vm55NqmEvIVgIIcR/5FsYPn78OPb29pQuLQvUCyGEEC+qlHQNS3YdZeHWQySmZgA5yw4NbVOP5tXKYKlWmz12QraOP24l8NedRDSGnLe0aro58EagBxVdzA/Bxy9HMG31bg6fz5nIy9nBjoEta9GnSXUc7GzMHlcIIcTLLV/eGU5PT8fNzQ13d3eioqLyo66nSt4ZFkIIIXJLSEln4bZD/LnjCGmaLADKFSnMm23r0aBCCSwsLMweOzZLx8Jb8ay4m0TmvRBcx92BNwI9ed3Z/L+Hw67eZNqq3Rw8dw0AJ3tbBrSoSZ+mNZ7oHWYhhBCvhnzpDDs4OPD222/j7e2dH8MJIYQQ4hmJSkhm/uaDLN9znMxsLZCz5NDQNvWoUSbkiWZbjsnS8vvNBFZFJZF1LwTXc3dkaJAHZZ3MD8Fnrt9m2urd7Dt9BciZzbpvsxr0b14TZwf5JbcQQoi8kdmkpTMshBDiFRQRHc9vG/ezZv8pdPqctXwbVijJ0LZ1KV8s4InGvpup5feb8ayOSkZ778eMRh6OvBHkSSlH8zu258PvMG31bvaEXQbA3taavk1r0L9FTVwdzX/MWgghxKtJJtASQgghXiGXb0bz64Z9bD58FoOiYKFS0ar6a7zRpi4lA32faOw7mdnMi0xgbXQSOgVUQFNPJ4YEelDiCULwhYi7TF+zh10nLgJgZ21F76bVGdiyFm5O5q9rLIQQ4tUmYVgIIYR4BZy6dos56/ey++QlACzVajrWLs/g1nUI9vV4orFvarL5LTKejTHJxhDcwsuZIYEeFHUwfwKrK7eimb56D9uOnQfA1tqKno2rMqhVbTycHZ+oZiGEECJfw/BHH33E999/n59DCiGEEMIMmdlawq7e5OjFcA6du07Y1ZtATqDsUr8SA1vWppCHyxN9R0RGNr/djGNTdAp6wAJo7e3M4EAPQuzND8HX7sQyY80ethw5h6Io2FhZ0r1hFQa3roOXq9MT1SyEEEL8I1/D8PHjx/NzOCGEEELkkSYrm5NXb3LsYjhHLoZz5vpttDq9cb+jnQ09G1ejX/MaT9xVvZ6RxW+R8WyJScEAqIF2Pi4MCvQgyM789Xxv3I1jxto9bPr7LIqiYGWpplvDKrzRug7ebs5PVLMQQgjxX/KYtBBCCPECSs/MIuzKTY5eCufohXDO3LhjnAgLQKVSUTqoEFVLBlG1dAjVSwfjaPdkyw1dTc/i18g4tsemogCWKmjv48KgAA/8nyAER0THM3NNKBsOncagKFiq1XRtUIk32tTF1/3JutdCCCHEw5gUhi0sLB66xIKiKE+0/IIQQgghHi5dk8WJK5EcvRjO0YvhnAu/g05vMO63UKkoG+xHlVJBVCsVQqUSgbjk0zJDl9Iy+TUynp1xqUBOCO7g68rAAA/8bK3MHvdmTAKz1u1l3YFT6A0GLNUWdK5biTfb1cPPwzVfahdCCCEexqQw3LNnT7p27UqHDh0euL9Ro0b5UZMQQgjxykvTZHLi8k2OXrzBkYvhnA+/i96QO/yWCymcK/w62T9Z5/e/LqRm8mtkHLvj0wCwUqnoVMiFAf4e+D5BCL4dl8TsdaGs2R+GTm9AbWFB53o5Idjfyy2/yhdCCCEeyaQwPGnSJJo3b06LFi2wtb3/L1zpDAshhBDmSc3I5MTlSI5cvMGxixGcC7+D4d4avQBqCwteL+qf89hzqRAqlQh44seeH+ZsioZfI+PYm5AOgI2Fis6FXOnv7463jfkh+G58MnPW72Xl3pPo9HosVCo61KnAW+3qE+jjnl/lCyGEEHliUhj28fFh1KhRLF68mMGDB9+3v3Tp0vlWmBBCCPEyS0nXcPxf4fdCxN1c4ddSbUG54MJULRVMtdIhVCwWgIOd+TM058WpFA1zIuI4mJgTgm0tVHT1c6Ofvzue1uZPMxKdkMKvG/axPPQ4Wl1OCG5b63Xebl+fYF/P/CpfCCGEMIlKUf71N+8rQqPRYG9vT0ZGBnZ2+fM+lRBCCPEoSWkZHL8cydELNzh6KYKLkVEoucKvmnIhflQtHUzVksFUKB6Ag+3TDb//OJGcwZyIOA4nZQBgZ6Gie2E3+hZ2x/0JQnBsUipzN+7nr93HyNbqUKlUtKr+Gm+3r08RP6/8Kl8IIYQwi8wmLYQQQjwFSWkZHLsYwZGLOeH38s3o+8Lv60ULU61UMFVKBVOhWAD2NubPyGwqRVE4lpzBnIh4jiXnhGAHtQU9/dzo5e+Gm5X5PyLEJafx26b9/LXrGJnZWgCaVy3LsA4NKO7vnS/1CyGEEE9KwrAQQgiRDxJS0jl2KcL42PPlW9G59ltZqilf1J+qpYKpWiqY8kX9sXuG4fcfiqJwOCmnE3wyRQOAo9qCXoXd6FXYHRcrtdljJ6amM2/TAf7ccQTNvRDctEpphrVvQMlA33ypXwghhMgveQ7Dd+7cwc/P77HH3b17l0KFCj32uEmTJrFw4ULUajWVK1dm+vTpWFmZNinH0qVLWblyJcuXLzfpPCGEEOJJxaekcfRiBEcv3uDoxQiu3o7Jtd/aypIK/wq/rxf1x9ba/MmnnpSiKBxMTGdOZDyn74VgZ0sLehd2p2dhN5wszQ/BSWkZzN98kEXbD6PJygagYcWSDO/YkDJBj/+ZQAghhCgIeX5nuGHDhnTo0IEhQ4bg4OBw3/5Tp07x6aefMnToUNq3b//IsbZu3crkyZNZv349lpaWfP7556jVasaNG5fnwm/cuEH37t3ZuXMnTk5OeT4P5J1hIYQQpotNSs3p/N575/f6ndhc+22sLKlQPIBqpXLe+S1XpDA2BRh+/5Gs1XM4KZ2FtxI4l5oJgKulmj7+7nT3c8XxCUJwcrqGhVsPsXDr36RnZgFQv3wJhndswGshhfOjfCGEEOKpyXMY1mq1TJo0iblz5/L6668TEhKCg4MDUVFRHDlyBH9/f8aPH0+lSpUeO1avXr148803qV+/PgCHDx+mRo0a5HUuL61WS4sWLfjll18oW7Zsno7X6XTGzxqNBg8PDwnDQgghHiomMYWj9975PXYpght343Ltt7W2omLxAGPnt1xIYayf4D3b/JKq03M8OYNjSTn/XE7P4p+/Xd2s1PT3d6ernxv2agvzvyMjMycEb/ub1IycgF2nXDGGd2xI+aL++XAVQgghxNNn1mzSZ86c4fr162g0Gry8vKhcuTKurq55Pr98+fKEhobi6urKli1bmDVrFocPH+bKlSs4Ojo+9vxvvvmGXbt2ERQURFJSEgMGDHhkN3rcuHGMHz/+vu0ShoUQQvwjKiGZoxcjOHYxnCMXw4mIjs+1387aikolAqlSKphqpYIpG+KHteXzEX7DkjUcTc7gWFI6F9P+P/wCWKlUvO5sS0MPJzoVcsXuCUJwmiaTRdsO8/uWg6TcC8E1yxbhnY4NqVg88AmvRAghhHi2CmRppWLFinHlyhWmTZvGmTNnmD59Og0aNGDlypX4+j56go3k5GRKlSrF0qVLqV+/PsnJybRu3ZrPPvuMli1bPvAc6QwLIYT4r7vxyRy9GM7Re+H3ZkxCrv32ttZUKh6Ys85vqWDKBPth9QSPFOeXdJ2ekymanM5vcgYXUjMx/Gu/pQrKOdlRxdWeKi72vO5sh+0TBGCA9Mws/txxhHmbDpCcnvO+cbVSwbzTsSFVSgU/0dhCCCFEQSmQX2lbW1vTq1cvKlSowJw5cwBISkrKU1c4NDSUrl27Gh+xdnFx4eeff+Z///vfQ8OwlZWVyZNzCSGEeLncjksyht+jF8O5FZuYa7+DrQ2VSwZStWTOY89lggthqS748KvRG+51ftM5lpTB+dRM9P/abwy/LvZUcbWnvLPdE3V//y0jK5slO3NCcGJqzvJLlUoEMqJTI6qXDsmX7xBCCCEKSoGEYYPBgLe3Nx9//DEAqampGAyG+8JwWlrafdt0Oh2W/3kszdraGoPBgBBCCPGPW7GJHLkQzrFLOZ3fO3FJufY72tlQpWQQVUoGU610MKUCfZ+b8HvqX53fc6kadP96hksNlHOyNXZ+K7jYP9H7vw+Sma3lr11HmbtxP/Ep6QBUKBbAO50aUrNMEVQqVb5+nxBCCFEQzArDEydOZMCAAXlaaulBJk+ezJQpU4zB9vvvv6d79+65jhk5ciQzZszg4MGDVK1a1bi9UaNGfPLJJ/Tv35/y5cuTlZXFmDFj6Nevn1m1CCGEeLnciU9i3Pz17D9zNdd2Z3tbKpcMMj72XDLQF7VF/oZIc2TqDZxO0XDs3qRXZ/4Tfi2Ask62xs5vRWc7HJ7S49pZ2VqW7TnOrxv2EZecBkC5IoUZ0akRtV8rKiFYCCHES8WsMOzp6UmPHj1wc3NjyJAhtG7dGgsTfqBo0aIF58+fp3LlysZ1hmfMmJHrGC8vL9zc3LC3t8+13dXVlaVLlzJq1CgMBgOZmZn06tWLHj16mHMpQgghXhKKorAy9ATfLdlKemYWDrY21CgTYpztuUSAz3MRfrMMBs6kZHIsKZ1jyRmcTslE+6/pO1RAaUcbqrg6UMXFnooudk+0BnBeZGt1rNx7gjnr9xGdmAJA2WA/3unYkHrli0sIFkII8VJ6ogm0rl69yoIFC9i5cyeNGjVi0KBBFClSJD/reypknWEhhHi5RCUk88W8dcZucJPKpfmifxs8XR4/F8XTpjUonEnVGJc6Op2qIcuQO/yWcLDJeezZ1Z7KLvZPPfz+I1unY/W+k8xet4+ohGQASgb6MqJjQxpWLCkhWAghxEstX2aTDg8PZ/DgwZw+fZrq1aszadIkSpQokR/1PRUShoUQ4uWgKAqr9p3kuz+3kKbJwtnBjrF9W9G6RrkCC3Jag8K51P9/7PlUioZMQ+6/aos72Bgfe67sYo+L1bN9V1mr07PuQBgz1+01vktd3N+bdzo2pHGlUiY97SWEEEK8qMyeQCsjI4Ply5ezePFinJ2d+fDDD2nevDmHDx9m4MCBHDhwID/rFEIIIXKJSkjmy/nr2Xf6CgCNKpXiy/5t8HJ1eqZ16BSF86mZxgmvTiZn3Bd+i9pbGye8quxqj5tVwaxPrNPrWX/wNLPWhnLz3mzaRf28GN6xIc2qlJYQLIQQ4pVi1t/G/fv3JywsjJ49e7Jw4cJcawPXqFGDhg0b5luBQgghxL8pisKa/WH8788tpGZk4uxgx6e9W9K21uvPpBusUxQupWVy9N5jzydTNGToc69oEGKXE36r3uv8ulsXTPj9h95gYOOhM8xYu4fI6Jz1lEMKeTK8QwOaVyv7XLxLLYQQQjxrZj0mvWPHDpo0afI06nkm5DFpIYR4MUUnpDDu9/WEnroMQMMKJRk3sO1T7QbrFYVLaVkcu7fO78lkDWn/Cb9BdtbGx56ruNrjWcDhFyAmMYXDF8I5fOE6B89eN74THOjjzrD2DWhds5yEYCGEEK+0fHln+EUjYVgIIV4siqKw9sAp/rd4MykZmTjb2zKmd0va1S6f791gg6JwJT2Lo0kZHE3K4ERyxn3hN8DWKlfn19vGKl9rMEdiajpHLoZz+PwNDl+4wY27cbn2B3i58XaHBrSpWe65WE9ZCCGEKGhm/eo6Ozsba2vrPG8XQgghzBWTmNMN3hOW0w2uX74E4we2xdvNOV/GNygKV9OzjBNeHU/OIEWXO/wWtrX6/86viz2+tgUfflMzMjl2KYLD569z+MINLt2MzrXf3taayiWCqF46hBplQp6bdZWFEEKI54VZYbhly5bs3Lkzz9uFEEIIUymKwvqDp5m4aBMpGZk43esGt3/CbrCiKFzPyM555zc5g+NJGSTp9LmO8bWxpOq94FvF1QG/5yD8ZmRlc+JyJEcu3ODw+RucC7+D4V8Pd9lYWVKxeCDVSgdTo0wRygb7YfWMlmgSQgghXkRmheGHPVmdnZ39RMUIIYQQALFJqYz7fT27T14CoF754owf0A4fd9O7wYqicEOTbVzn91hyBona3OHX2/pe+HW1p6qLPX62VgW+xm5WtpZT125x+ELOY8+nr91Gp///ui3VFlQoGkD10iFULx1C+aL+2FgXfGgXQgghXhQmheH09HSSk5PJzs4mKirKGIoVRSEsLAyDwfCYEYQQQoiHUxSFDYfOMGHRJlLSNTja2fBJr5Z0rFvBpHCapNWzIzaFo/c6v/H/Cb+e1pZU/deEVwHPQfjV6vScC7/D3+evc+RCOCevRJKl1Rn3W6hUlAspTPUyIVQrHULF4gE42NoUYMVCCCHEi82kMLxy5Up+//13Lly4QO/evXN1iL29vZkzZ06+FyiEEOLVEJuUyvgFG9h14iIAdcoV46tB7fB1dzFpnO2xKUy8Gk3SvwKwh5Wayve6vlVdHQi0K/jwqzcYuBQZxd/nb3Dk4g2OXYogIzP3E1YlA3yoXjon/FYpGYSzg0z6KIQQQuQXs2aTbtSoEbt27Xoa9TwTMpu0EEI8PxRFYdPfZ/nmj40k3+sGf9yrBZ3qVjQpsMZn6/j2ajQ741IBqOhsRwtvZ6q42hNiZ13g4VdRFK7ejuXwhescPn+Do5ciSEnX5DompJBnzmPPZUKoWjIYd2eHAqpWCCGEePmZ9c5w06ZN87sOIYQQr6C45DS+WrCBHccvAFD7taJ8Nag9hTzy3g1WFIWtsal8dzWaJJ0ee7UFo0K86FzIFYsCDMCKohAZk2Bc6ujIhRvEp6TnOqawpyvVy4RQvXQRqpcOzrcZsoUQQgjxeLLOsHSGhRDimVMUhc2Hz/LNH5tISsvAwdaGj3s2p3P9SiZ1cOOydUy8EsXu+DQAqrva80WJQgU2+/Od+CSOXAg3LncUlZCSa7+Xq5Ox81u9dAj+Xm4FUqcQQgghzOwMP8yZM2coV65cfg4phBDiJROfksbXCzay7dh5AGqVLcpXg9vh5+Ga5zEURWFzTArfXYsmRWfAQW3B+0W86ejr8kwfh45LTjMudfT3hRvcjEnItd/NyZ6qpYKpUTqE6mWKEOzrUeCPawshhBAiR752hhs3bvxCrDMsnWEhhCgYOd3gjSSmZmBva81HPZrTtUFlkwJiTJaWCVei2ZuQ0w2u5ebA2OK+FHoG3eCktAyOXYzg73vv/V67E5trv6OdDVVLBhs7v8X9vbGwsHjqdQkhhBDCdHnuDGs0GgmOQgghzJKQks5XCzew7WhON7hGmSJ8Pbg9hT1d8zyGoihsiEnhh2vRpOoMOKotGF3Um/Y+T68bnKbJ5PilSONavxcjo3KtpGBnbUWlEoHG935LB/liqVY/lVqEEEIIkb/yFIbnz5/P0KFD2b17N3Xq1KFEiRL3/eChKAp37tx5KkUKIYR4cW05co6vF24wdoM/6N6M7g2rmNwN/uZKFPsSciagquOe0w32scnfbrAmK5uTV28aH30+e+MOeoPBuN/KUk3FYgFUu/feb7kihbG2zNc3joQQQgjxjOTpb/BWrVoxbdo0KlWqBIC/v/8Dl1Zq1KhR/lYnhBDihZWYms7XCzey5cg5AKqXCeGbQe0pbMKkUYqisC46mR+vxZCmz+kGf1jUh7Y+zvnSDc7W6Th97XZO+L1wg7CrN9Hq/n99YrWFBRWKBVCtdDDVSxehYvEAbK0LZnIuIYQQQuSvfF1n+EVZf/ifd4bHpm3Dys6moMsRQoiXTnRCCucj7pCt1aFWW1AywJcAbzcg7wE2U2/gQlomcdk6ALysLSntZIvNE7yDqygKKekaElLTSUhJJzE1I1fnF5UKZ3tb3J0dcHdywN3JHrU89iyEEEK8cL6wqPfYY8x6tmvAgAEP3D548GBzhiswn1rUwc5C3oMWQoj8kpiazoQ/NrH78FkAqpUK5pshHUxaQkhRFNZEJTPpek432NnSgo+K+tDK1fRusMFg4PKtGA6fv86RC+EcvRROmiYr1zHFCnvnvPNbKoQqpYJwdbQ36TuEEEII8WKSdYZlUjAhhMgXO45dYPyC9cSnpGNnbcXo7k3p0aiqSbMp383U8vWVKA4l5rwbXN/Dkc+K+eJlY9rvbg+du85fu49y5EI4SWkZufYF+rhTvXQINcoUoWqpYDxdHE0aWwghhBAvB5n1QwghxBNJSstgwqJNbDx0BoCqpYL5ZnB7Arzd8zyGoiisikrm5+sxpOsNuFha8HExH1p4mdYNTknX8MPSbazce8K4zdfdhRr3ljqqVjqEQh4ueb84IYQQQry08jUM9+jRg6VLl+bnkEIIIZ5jO09cZNzv64lPTsPO2or3uzWlZ2PTusF3MrP56nIUh5NyOriNPBwZU9wXT2vT/oraE3aJ8b9vIDoxBStLNW+2rUebmq8T4O321JZeEkIIIcSLK1/DcGxsbH4OJ4QQ4jmVlJbBxEWb2XDoNACVSwQxYUgHAn3y3g02KAor7iYx5UYsGXoDrpZqPinmQzMvJ5PCa1JaBt8u3sz6gzm1lC/qzzdDOlDUz8u0ixJCCCHEKyXPYfjbb7995H5FUYiIiHjigoQQQjzfdt3rBsclp2FrbcV7XZvQu0k1k7rBtzXZjL8cxdHknG5wU08nPinmg7uJ3eBtR8/z9cINxKekY2ttxcjOjejTrAbqJ5hxWgghhBCvhjz/1PHHH38wZswYHjXflo2NLFMkhBAvq+R0Dd8u2sy6g6cAqFQikAlDOhDk45HnMQyKwvK7SUy5HoPGoOBmpWZMMR+aejmbVEt8ShpfL9zItqPnAahSMoivB7c3qRYhhBBCvNryHIZ9fX3p27fvI4/5/fffn7QeIYQQz6E9YZf4cv56YpNSsbW2YlSXxvRuWt2kDuxNTTbjL9/leLIGgOZeTnxU1LRusKIobPz7DBMXbSYpLQM7G2s+6N6U7g2rmNSZFkIIIYTI808ghQsXfuwxgYGBT1SMEEKI50tKuob//bmFNfvDAKhUPJBvhnQg2Ne0bvDSO4lMvRFLpkHB3UrNp8V9aezpZFItMYkpjP99A7vDLgFQq2xRxg9sS2ET1jAWQgghhPiHrDMs6wwLIcQDhZ66zJfz1hGTlIqNlSUjuzSmr4nv40Zoshl/6S4nU3K6wS29nPmomA+uVuo8j6EoCqv3hfHdki2kZmTiaGfDxz2b06leJZklWgghhBBmk3WGhRBC5PLfbnCFYgFMGNKBkEKeeR5DrygsuZ3ItPBYsgwKntZqPi3mS0MTu8F34pP4ct46Dpy9BkD98iUYN6AtPu6mvWMshBBCCPFf+RqGR48ezU8//ZSfQwohhHiG9p66wpfz1xGdmIK1lSXvdmpE/xY1TeoGh2dk8eXlKE7f6wa39nbmw6I+uJjQDTYYDCzbc5wf/9pGRmY2Lg52fNqnFW1qlpNusBBCCCHyRb6G4bCwsPwcTgghxDOSmpHJd0u2sGrvSSBnrd4JQzpQxIS1evWKwuJbCcyIiLvXDbZkbHEf6nuY1g2OjE7gi3lrOXIxHIBmVcowtl9rPF0cTRpHCCGEEOJR8hSGr1y5wvz58xk9ejQeHh4sWbLkvmMURSE6OjrfCxRCCPF07T9zlS/mrSUqIacbPKJTQwa0qGVSN/h6RhbjLt3lTGomAO18XBhdxBtnE7rBeoOBP3ccZvLynWiytbg7OfB5/9Y0r1rW5GsSQgghhHicPIXh06dPM3/+fLp06YKHhwefffYZAwcOvG/NYY1G81SKFEIIkf/SNJl8v2QrK0JPAFCuSGEmvtGRoiZ0g3WKwh+3EpgVHke2ouBtbcnYEr7UdTeti3v9Tiyf/7aWk1dvAtCm5uuM6d0CNycHk8YRQgghhMgrs2aTbtSoEbt27bpve+PGjdm5c2e+FPY0yWzSQohX3cGz1xj721qiEpKxslQbu8GW6rx3cq+lZ/Hl5bucu9cNbu/jwuii3jhZ5n0MnV7P71sOMm31HrK1OrxcnfiyfxsaVSpl8jUJIYQQQpgiX98ZtjDhkTohhBDPXpomkx+WbmP5nuMAlAspzIQ3OlCssHeex9ApCgtuJjA7Ig6touBjbcnnJXypbWI3+PLNaMb+toazN+4A0KleRT7q0RxnB/klpRBCCCGePllnWDrDQohXxMFz1/j8t7Xcjc/pBr/TsSEDW5rWDb6SnsmXl6K4kJbTDe7s68qoIl44mtANztbpmLthP7PW7UWn11PIw4WvBrajdrliJl+TEEIIIYS5ZJ1hIYR4yaVrsvjhr20s230MgLLBfkx8owPF/X3yPIbWoPD7zXjmRMahU8DXxpIvSxSihptp7/SeD7/DZ3PXcOlmzoSLPRpV5f1uTXC0szVpHCGEEEKIJ2V2GL5w4QLbtm0jIyMDe3t7mjZtSpkyZfKzNiGEEE/o0LnrfD5vLXfikrBUqxnesQGDW9U2qRt8OS2TLy/f5WJaFgBdCrkyKsQLBxO6wVnZWmauC+W3jQfQGwwEeLnx9eD2VCsdYuolCSGEEELkC7PC8LJlyxg3bhyDBg3Cz8+PmJgYOnfuzNixY+ndu3d+1yiEEMJE6ZlZ/PTXdpbuOgqY3w2edzOeufe6wX42VnxZwpdqJnaDT129yWe/reX6nVhUKhX9mtXg3S6NsbexNmkcIYQQQoj8ZNY7w1WqVGHLli14enoat8XGxtK0aVPCwsLys76nQt4ZFkK8zA5fuMHYuWu4fa8bPKx9fQa3roOVCZ3cS2mZfHHpLpfTc7rB3f1ceTfEG3t13idK1GRlM3XVbhZuPYRBUQgp5Mk3g9tTsXigydckhBBCCJHfzOoM29vb5wrCAF5eXjg5OeVLUUIIIUyXnpnFz8t28OfOIwCUDirExCEdKBnom+cxtAaFuZFxzLsZj06BwrZWjCvhSxVX07rBxy6GM3beWiKjE7BQqRjSug7DOzTAxtrKpHGEEEIIIZ4Ws8Kwi4sLcXFxuQJxfHw8fn5++VaYEEKIvDEYDGw7doFJy7ZzKzYRS7UFb7Wrzxtt6prUDb6QmvNu8JV73eAefm68G+KFnQnd4P8G8hL+PnwzpD2vhRQ27aKEEEIIIZ6yPIfhiRMnolKpAPDz86NWrVr0798fLy8vYmNjWbx4MQMHDnxqhQohhMhNURT2nb7KlJU7uRBxF4BSgb5MfKMjpUzoBmcbDPwaEc/8m/HogQBbK74sUYjKrvYm1XPw3DW+mLfu3mRdFgxtW4+hbetibSkLFwghhBDi+ZPnd4YXLlyYpwH79ev3RAU9C/LOsBDiRXfsYjiTV+7kxOVIAHzcnHmrfT061a1kUjf4XKqGLy/d5VpGNiqgV2E3hgeb1g1Ozcjkh6VbWRF6AoAyQYX4ZkgHkwK5EEIIIcSzZtYEWi86CcNCiBfVuRt3mLJyJ/vPXAXA1dGeoW3r0qNRVWxNeB83y2BgdkQcC24mYAAC7awYX6IQFVxM6waHhl1m3O/riU5MwcpSzfAODRhk4tJNQgghhBAFQZ5dE0KIF8DV2zFMW7WbbcfOA+Bga8PAlrXo17wGjna2Jo11JkXDl5fvcuNeN7ivvztvB3ma1A1OSsvgf4u3sO7gKQDKF/XnmyEdKOrnZVItQgghhBAFxawwnJWVxfTp07l8+TL/bSzPnj07XwoTQggBt2ITmbFmD+sOnMKgKNhYWdK7aXWGtK6Dq6NpXdxMvYFZEXH8cSunGxxsZ824koUo72zaEzLbj53nq4UbiU9Ow8bKkpFdGtO3WQ3UFnkP00IIIYQQBc2sMPzGG28QGBhIt27d+PDDD/npp59YtmwZ5cqVy+/6hBDilRSblMrsdXtZtuc4Or0eS7UF3epX4a129fB2czZ5vFMpGsZduku4JhsLYIC/O28GeWJrQjc4PiWNCX9sYsuRcwBUKRnEV4PaE+zrYXI9QgghhBAFzax3huvVq8fevXsBaNSoEbt27UKv19OyZUu2bduW70XmN3lnWAjxvEpKy+C3jftZvOMImdlaVCoV7Wq9zrAODQjwdjd5PI3ewIzwWBbfTkQBQuytGV+iEOVM6AYrisKmv88yYdEmktIysLOx5oPuTenesAoW0g0WQgghxAvKrM7wvwPkP8stqdVqsrOz86cqIYR4xaRrsli47RDzNx8kTZOzzm/TKqV5p2Mjivt7mzXmyeQMxl2+S6RGiwUwMMCDoUEe2JgQYGMSU/hq4UZ2nbgIQM2yRfhqYDsKe7mZVZMQQgghxPPCrDBco0YNvvjiC7766isKFSrE6tWrcXJyMgbjvJg0aRILFy5ErVZTuXJlpk+fjpVV3mdCBZg7dy43btxgwoQJpl6CEEI8F7KytSzddZQ5G/aRmJoBQO3XivJu58aUK1LYrDE1egPTw2P58143uKi9NeNLFqKsk2nd4DX7w/juzy2kZGTiaGfDxz2b06leJZP+rBdCCCGEeF6Z9Zi0wWBg06ZNtGnThqioKD755BP0ej3jx4+nSJEijz1/69atTJ48mfXr12Npacnnn3+OWq1m3Lhxea4hMzOTmjVrEhoairOzae/PyWPSQoiCptXpWb3vJDPXhhKdmAJAxWIBjOzSmGqlQ8we93hSBuMv3+VmphY1Od3gN4I8sDahG3wnPolx89cbl2+qX74EXw5og6+7i9l1CSGEEEI8bwpkneFevXrx5ptvUr9+fQAOHz5MjRo17puZ+lF+/PFHrK2teffddx97rFarRafTGT9rNBo8PDwkDAshnjmDwcCmw2eZumo3N2MSACgZ6MuoLo2p93pxs7uuKVo9MyPiWHonEYDiDjaMK1GIMk55X3bJYDCwPPQ4Py7dTnpmFi4OdnzapxVtapaTbrAQQgghXjoFss7wuXPnKF++PABbtmxh1qxZ+Pr6kpaWhqOj42PPT05OZtmyZezfv5+IiAiGDBnC9u3bH3r8hAkTGD9+fL7VL4QQplIUhd0nL/HLyl1cvhUNQLCvByM6NaJ51TJmT0SVqtOz+FYCi28nkqY3YKmCQQEeDAn0xMoi7wH2ZkwCX8xbx+ELN4Cc95XH9m2Nl6uTWXUJIYQQQjzvzArDSUlJDBs2jNDQUBwcHMjIyKBu3bpMmzYND4/HL7GRnp6Oi4sLU6dO5cyZMyxfvpwGDRrkOQz/8MMPjB49Gmtr6zzV+9lnn/Hxxx8bP//TGRZCiGfh7/PXmbxiJ6ev3QLA192F4R0b0L52eSzVarPGTNPp+fN2IotuJ5CqMwBQ3dWe94p4U9LRtG7w4h2Hmbx8J5psLe5ODnzevzXNq5Y1qy4hhBBCiBeFWWH47bffpkaNGvz555/GbZMnT2bIkCGsXr36sedbW1vTq1cvKlSowJw5c4CcgJ2XIBwdHc2BAwf4+uuv81yvlZWVyZNzCSHEkzp17RZTVuzk7/PXAfBwdmBo23p0b1gFayvzHsxJ1+lZeieRhbcSSLkXgqu52vNWkCcVXexNGuvG3Tg+/20tJ65EAtC6Zjk+7d0SNycHs2oTQgghhHiRmPXT2LVr11iyZEmubaNGjWLRokV5Ot9gMODt7W3s1qampmIwGO4Lww/qFG/dupXExEQaNWoE5EykdenSJRo2bMjUqVN57bXXzLkkIYTIN5dvRjNl5U52n7wEgLO9LYNa1aZ30+o42NqYNWaG3sBfdxJZeDOBJJ0egEoudrwd5EkVV9PCq06vZ8GWQ0xdvZtsrQ4vVye+7N+GRpVKmVWbEEIIIcSLyKwwbG//4O5DXmd1njx5MlOmTEGn02Fpacn3339P9+7dcx0zcuRIZsyYwcGDB6latapxe79+/ejXr5/xc17eGRZCiGchIjqeaat3s+nvsyiKgp21FX2b12Bgy9q4OJg3WZ9Gb2D5nUTm30ogSZsTgis454Tgqq72Jk9sdeVWNGPnruXMjdsAdKxbkY97NsfZzPqEEEIIIV5UZoXh9u3bM2nSJN577z1UKhWKojB16lQGDhyYp/NbtGjB+fPnqVy5snGd4RkzZuQ6xsvLCzc3t4cGbyGEeF5EJSQzc20oq/aeRG8wYGWppnvDKgxtWw9Pl8e//vEgmXoDK+4mMf9mPAn3QnA5J1uGBXtR3YwQrNXpmbtxHzPX7kWn1+Pr7sJXg9pRp1wxs+oTQgghhHjR5XlppRIlShh/+FIUBZ1Oh42NDW5ubiQkJKDT6ShcuDChoaFPteD8IOsMCyHyQ0JKOr9u2MeSXUfJ1uqwUKnoULcCb7dvQGFPV7PGzDIYWHkvBMdl54Tgsk62vBXkSW03B7OWODofcZfP5q7hUmQUAD0aVeX9bk1wtMv7RFtCCCGEEC+bPHeGL1++/DTrEEKIF0ZqRia/bznIgq2HyMjMBqBFtbKM6NSIkEKeZo2ZbTCwJiqZ3yLjicnOWRe9tKMNbwV5UdfdvBCcrdUxc20oczfuR28wEODlxleD21O9dIhZNQohhBBCvEwKZJ1hIYR4EWmysvlzxxF+3biflHQNAPXLl2BE50aUCSpk1phag8LaqCR+uxlPVFZOCC7hYMNbQZ408HA0KwRDzkzWY+eu4dqdWFQqFf2a1eDdLo2xt8nbknRCCCGEEC87s8PwwYMHWbNmDcnJyTg7O9O+fXvq1KmTn7UJIcRzIVunY2XoCWauDSUuOQ2AKiWDGNWlCZVKBJo1ptagsD46mbmRcdy9F4KLO9jwZpAnDT0csTAzBGdma/ll5S4Wbj2EQVEIKeTJN4PbU7G4eXUKIYQQQryszArD8+fPZ968ebzzzjv4+voSHR3NmDFjGDhwIIMGDcrvGoUQokDoDQbWHzzN9NW7uR2XBEDZYD9GdWlMrdeKmtW11SkKG6OT+TUyntuZWgCK2FvzVpAnjT2dzA7BAMcuRfD5b2uJiI7HQqViSOs6DO/QABtrWWddCCGEEOK/8jyB1r/VqFGDnTt34uDw/2tbpqWl0bhxYw4fPpyvBT4NMoGWEOJRFEVh+7EL/LJqF9fvxAJQ1M+Ldzs3oknl0maH4M0xKfwaEcfNeyE4xM6aoUGeNPVyQv0EIfh8+B1mrg1l54mLABT392bCkA68FlLY7DGFEEIIIV52ZnWGra2tcwVhAEdHR2xsbPKlKCGEKAiKonDg7DWmrNjJufA7APh7ufFOx4a0rlkOtYWFyWPqFYWtMSnMjowjUpMTgoPsrBka6EFzb+cnCsGnrt1i1tpQQk/lTHBoY2XJoFa1ebNdPawtZUoIIYQQQohHMeunJZVKxa1bt/D39zdui4yMxIwmsxBCPBeOX45gyoqdHLsUAYCXqxNvt6tHp/qVzAqWBkVhW2wqcyLiuKHJmXE6wNaKN4I8aentjOUTPg49a20oB89dA8DO2ooejasyoEUtvFydzB5XCCGEEOJVYlYYnjBhAo0aNaJVq1Z4e3sTHR3N5s2bmTdvXn7XJ4QQT9X5iLtMWbGTfaevAODiYMeQNnXo1bgadmbMvGxQFHbGpTI7Io5rGTkh2M/GiqFBHrTydsHKwrwQrCgKhy/cYObaUI5eDAfAwdaG3k2r0b95TdycHB49gBBCCCGEyMWsd4YBEhMTCQ0NJSUlBScnJ+rXr4+7u3t+1/dUyDvDQojrd2KZumo3W4+eA8De1pr+zWsyoEUtnOxtTR7PoCjsjk9jdkQcV9KzAPC1seSNQE/a+jxZCN5/5iqz1oZy8upNAJztbenTrAZ9m9XAxUH+DBNCCCGEMIfZYfhFJmFYiFfX7bgkZqzZw9r9YRgUBWsrS3o1rsaQ1nVwdza9u6ooCnvuheBL90Kwj7UlQwI9aO/r+kQhePfJS8xet5czN24D4OpoT/8WNenVuJpZgV0IIYQQQvw/sx6THjt2LN98801+1yKEEE9NbFIqczbs469dx9Dp9ViqLehSrzJvtauHr7uLyeMpisK+hHRmRcRxIS0TAK97IbiDrwvWZky2BWAwGNhx/CKz1oVyMTIKAA9nBwa0rEWPRlVxsJWJCoUQQggh8oNZYXjfvn0P3H716lWKFSv2RAUJIUR+Sk7X8NvG/SzefhhNthaVSkWbmq8zvGMDgnw8TB5PURQOJOaE4HOpOSHY01rNwAAPOhdyxcbMEKw3GNh65Byz1u3l6u0YALxdnRjcujZd6lc26/1lIYQQQgjxcGaFYQsLC1JSUnB2ds61/c0332Tnzp35UpgQQjyJ9Mws/tj2N/M3HyQ1Iye0NqpUinc7NaJEgI/J4ymKwt+JGcyMiOXMvRDsbpUTgrsUcsVWbV4I1un1bDx0hjkb9nHjbhwAvu4uvNGmDp3qVsTG2sqscYUQQgghxKOZFYa//fZb3nzzTUaOHEnFihVlfWEhxHMjK1vLsj3HmL1uHwmp6QDULFuEdzs3pnxR/8ecfT9FUTialMHMiDjCUjQAuFqpGejvTlc/N+zMDMHZOh3r9p/i1w37uBmbCECAlxtvtKlLuzrlZZ1gIYQQQoinzKwJtJydnfHx8UGv16NSqbC0tERRFK5du4Zer38adeYrmUBLiJePTq9n7f4wpq8JJSohGYDyRf0Z2aUxNcoUMWvMY0kZzIqI5XhyTgh2sbSgf4AH3f3csDczBGdla1m17yRzN+7nbnxOncG+HrzZth6ta5bDUq02a1whhBBCCGEas1oPVapUYdeuXfdtb9So0RMXJIQQpjAYDGw5co6pq3YTER0PQAl/H0Z2aUyDCiVQqUyfzflkcgazIuI4kpQBgLOlBf383enh54aDpXlhVZOVzYrQ4/y28QAxSakAFPXz4q129WlRvSxqM981FkIIIYQQ5jErDA8dOvSB22WGaSHEs2IwGNgddplpq3Zx6WY0AIE+7ozo2IiW1ctiYUa4PJWiYVZ4LH/fC8GOagv6+rvTs7AbTmaG4PTMLP7adYz5mw8Qn5Lz2HbJQF/ealePppVLm1WnEEIIIYR4cmavM2wwGDhz5gwZGRnY29vz2muvoX5BHu+Tx6SFeHFla3WsP3iK+ZsPcv3ehFM+bs4M61CfDnUqYmVGaD2bomFmRBwHE3PCqqPagt6F3ejt7252CE7TZLJ4xxEWbDlEUlpOuH4txI+32tWnYcWSZnWshRBCCCFE/jGrMxwWFkbXrl0pVaoUnp6exMbGcvHiRZYvX07FihXzu0YhhCA5XcNfu46yaPth4pLTgJxZlwe0qEn3hlXMmnX5fGomsyJi2ZeQE4Lt1Rb0KuxGn8LuuFiZF4KT0zX8se1vFm37m5R7s1hXKBbA2+3rU6dcMQnBQgghhBDPCbPC8IgRI/jrr7+oVKmScdvx48cZPnw4Bw8ezLfihBDiTnwSC7ccYsXeE2RkZgNQMsCHgS1r07L6a2Z1gi+mZTIrIo7Q+JxQbWehomdhd/r6u+NqZghOTE1nwdZDLN5+hPTMLACqlgrmrXb1qVEmREKwEEIIIcRzxqwwbDAYcgVhgMqVK8sPe0KIfHMxMop5mw6w+fBZ9AYDkLNE0qCWtan1WlGz/ry5nJbJ7Ig4dt0LwbYWKnr4udHX3x13a/OWMopLTmP+5gMs3XUMTVZOWK9Vtihvta9PlZJBZo0phBBCCCGePrN++rOzsyM7Oxtra2vjtqysLGxtbfOtMCHEq0dRFA6du868TQc4eO4aAGoLC1rXKMfAVrUpE1TIrHGvpmcxOyKOHXE5szjbWKjoWsiVAQEeeJgZgqMTUpi3+QDLdh8jS6sDoF754rzdrj7liwWYNaYQQgghhHh2zPopsFu3brRr1463334bLy8v4uLimD17Nu3bt+fQoUMA1KxZM18LFUK8vLQ6PVuOnGP+5gNcjIwCwM7Gmi71K9GvWQ0Ke7mZNe71jJwQvD02FQWwVqno4ufKAH8PvGzMC8G345L4beN+Vu49gVaXs65640qleKtdfcqG+Jk1phBCCCGEePbMmk160KBBjx5UpeK3334zu6inTWaTFuL5kK7JYkXocRZu+5u78ckAeLg40qdpdbo3rIKro71Z44ZnZDEnIp4tsSkogJVKRadCLgwK8MDbxvSJtgAioxP4dcM+1h4IQ6c3oFKpaFalDG+1q0fJQF+zxhRCCCGEEAXH7KWVXmQShoUoWLFJqSzafpi/dh01zrgcUsiTAS1q0q5WebNmhgaI1GQzJyKOzTEpGABLFXT0dWVQgAe+tuaNeeNuHHPW72XDoTPoDQYsVCpa1SjH0LZ1KVbY26wxhRBCCCFEwTPvOUEhhDDD9TuxzN98kHUHTxkfMa5UPJCBrWrTsEIJLCwszBr3liabXyPj2RidjJ57IdjHlUGBHviZGYKv3Iph9rpQthw5h0FRUFtY0KFOBYa2rUewr4dZYwohhBBCiOeHhGEhxFOlKAonrkTyf+3deViVdf7/8Rc7yK64AqW5S5YIKO7gnjqNS1I645SV/ixrbGpq+lbTNtV0aU1jje1TjU1aubSpuSXgluYuI5qYqagIyiY7HM79+wM4ekTZCg5wno/r6ro69337mQ/vgU+++Cz3B6u3KXb/j5LKtlKMDOupu8cNUp9fcNjU2cJivX8qXd+kZstkSE6SJrbz1b3BrRTo4Vrtn7+awydT9M7Xm7V+d6IkydnJSVOG9NGsCUMUVMe9ywAAAGh8CMMA6kWp2axNe4/ogzXbdOCn05IkVxdnTRzcR3eNHaCO7QLq1K5hGNqTXaClZzMUdyFXZkmOkm5t66t7r2ul4DqG4ITjZ/T2V/GWwO7q4qzbhvbVPeMHq30r3zq1CQAAgMaLMAzgV1VYXKIvt+7XR2u361RqhiTJ19ND00ZEaPrI/grw9apbu6VmfZt2UUvPZiopr0hS2XLoca19dO/1Abq+jiF4X9IpvfVVvLYmHJMkubu66PbocM28ZaDa+PvUqU0AAAA0foRhAL+KrNx8Ldn4g5Zs/EEZOXmSpMAAP905doAmD+2rFm51C6sphSValpKplSlZyjaZJUmtXJx0W3s/TWnvX+dXJO06ckJvfhWnnYk/Syp7ldPvRvbTnWMHqJVP3QI7AAAAmg7CMIBf5PT5TH20drtWbt6nwuISSVJIxw66e9wgjQrvKWcnp1q3aRiG9mYX6NOzmYq9kKPS8ush3u6a3sFfI1t7y7UOh20ZhqHvDx3XW1/Fa8/Rk5IkLw83/X5Uf/1hzIA6v8oJAAAATQ9hGECd/O/nM/pgzTat35Uoc/kb2gb37qJ7xg9Wvx4d5eDgUOs2C0vNWnv+opaeydTRy5ZC3xLgo2mB/urtU7dXoRmGoc0HkvTW1/E6WL5/2cfTQ38YHanfj+ovH09esQYAAGBvCMMAaswwDG0+mKQP12zTD0dOSJKcnRz1m8ibNPOWQeoW3LZO7Z4rLNGylCytTMlSVvkrl1palkL7qY1b3V6PZDabtWnfj3r7q3glnkyRJPl7t9BdYwdq2ogIeXm416ldAAAANH2EYQDVKjaZtPr7BH347XYdO5MmSfJ0d1NMdJhmjI5Uu5a1P23ZMAztv1igpWcytemypdA9vdw1PdBfo+u4FFoqO8l6/a5EvfP1Zh09nSpJauXrpXvGDVJMdHid9y8DAACg+SAMA7imnPxCfR63W/9dv1OpmRclSW38vDVjTKRiosLl3aL2M6tFZrPWlZ8KfST30lLoMQHemhbYUjd5u9dpibUkmUpL9e3OQ3rn63gdT7kgSWrr76N7xg/SbcPC5O5atxlmAAAAND+EYQCVnMvI1sfrd+jz2D3KKywLrF0C22jmLQM1fkBvuTrXfuhIKyrRsrNZWn4uS1klZfPA/i5OmtLeT1N/wVJoSSoxleqb7Qf17qrNltc5dQjw06wJgzVpcKhcXRjqAAAAYI2/IQKwOJqcqg+/3abVOxJkKi17jVG/Hh01c9wgDb2pa61nbA3D0IGLBVp6NlPfnb+0FLqHl5umdWipMW285VbHpdCSVFxi0hdb9+n9VVt15kKWJCm4TUvN/s0Q3TrwZrk41/4kawAAANgHwjBg5wzD0A9HTuiDNdu05WCSJMnRwUFj+4Xo7nGDdGOnwFq3WbYUOkefns3Q4fKl0E6SRrf21rQO/rrZx6POS6El6cz5TC2L36OVm/fpQnauJOmG9gGa/ZuhGhd5Y51e5wQAAAD7QhgG7JSptFQbdh/WB2u26dCJs5Ikd1cXTR4SqjvHDlBwm5a1bjOtqOxU6BUpWcosXwrt51y+FLqDn9r+gqXQptJSxe07qs/jdmvb/36SUf46p25BbfX/bh2q0RG95PQLZpkBAABgXwjDgJ3JLyrWys17tXjdDp0+nymp7HVDvxvZX9NGRMjf27NW7RmGoYM5hVp6JkPfXciRqSyjqrunm6YF+mtMax+5O9U9pJ65kKUV8Xu0YvM+nc/KkSS5ujhrdHgvxUSHK6zbdb9olhkAAAD2iTAM2In0i7lasvEHLdn4g7LzCiRJ17VtqbvGDtTEwX1qfdJysdms9edztPRMphJzCyWVLYUeFeCtOwL9FfoLlkKbSksVfyBJy+J2a8vBY5ZZ4E7tAxQTFabfDu4jP68WdWobAAAAkAjDQLN34ly6Plq7XV9t3a+iEpMk6abOQbpn3CAN79uj1kuLzxeZtDwlU8tTspRx2VLoyeWnQrdzr/tS6JT0bC0v3wtc8SonF2cnjY7opdujwhXW/XpmgQEAAPCrIAwDzdT+Y8n6YM02fbf3iGVmNbpPd80cN6hOS4sTLhZo6ZlMbbhw0bIUuqunm6YH+mvsL1gKXWo2a3P5LPDmA0kyl/e1Y7tWmhoVpomD+9R66TYAAABQHcIw0IyYzWbF7j+qD9ds096kU5LKZlZvHXiz7rploDp3aF2r9krMhjacv6ilZzP1v5yypdCOkkYElJ0K3de37kuhz2Vka0X8Xq3YvFfnMspmgZ2dnDQ2vKdiosMV0aMjs8AAAACoN4RhoBkoKi7RN9sP6sO12/VzygVJkk8Ld90+PEK/H9Vfrf28a9XehWKTVqRkaXlKpi4Uly2F9nV21KT2fpra3l8d6rgUutRs1taDx/R57G7FHzhqmQW+rm1LTY0K06TBoWrpwywwAAAA6h9hGGjCsvMK9OmmXfrvhp1KL3/fbruWvrpzTKRuGxYmTw+3WrV3KKdsKfS689ZLoe/o4K9b2vjIo45LoVMzLmrllr1aHr9XKenZkspmgUeH9VBMdLj69egoR16LBAAAgAZEGAaaoDMXsrR43fdaHr9XBUXFkqTu17XT3bcM0th+IXJxdqpxWyVmQxsv5GjpmQwlXLYUeniAl6Z18FeYb4s6LVcuNZu1LeEnfR63W/H7j6rUbJYkBbf219ToME0aEqpWPl61bhcAAAD4NRCGgSbCMAwdPH5G/92wQ2t3HrKEy4EhnXX3uEEaEHJDrUJrerFJy1OytDwlSxeKy06Z9nF21OR2fpraoe5Loc9n5WhF/F4t37xXZy9kSZKcnRw1OqLsvcCRPTsxCwwAAACbIwwDjdzFvAJ9s/2glsfv0Y/JqZIkJ0dHjR/QWzNvGaRe17evVXsVS6HXn89RSfme3S4t3DQtsO5Loc1ms7YfOq7PY3crdt+PlqAe1Npftw3rq0lDQmu9bxkAAACoT4RhoBEyDEP7jyVrWdwerf3hkAqLSyRJ/t4tNHFwH00f2V+BAX41bq/EbOi7CzlaejZTBy8WSJIcJEW38tK0QH+F13Ep9PmsHK3csk/L4/boTPkssJOjo0aF91RMVLgGhNzALDAAAAAaJcIw0Ihk5xXo620HtCxuj46dSbNcj+x1g6ZGhWlE3x5ydan5j21G+anQy1KydL58KbS3s6MmtfNTTHs/BXq41rqPZrNZOxJ/1uexu7Vp3xGZSstmgTsE+Om2YX01ZWhfZoEBAADQ6NksDP/jH//Q4sWL5eTkpLCwMC1atEguLjXbo3j48GHdf//9Ki0tVXFxsebNm6dp06bVc4+B+mEYhvYcPaXlcXu0btchFZWUhdZWPp6aNCRUU4b11fVtW9WqzcM5hVp6NkNr0y4thb6hhaumdfDX+La+dVoKfSE7V1+UzwInn8+UVDYLPKJv2YnQA2/sLCdmgQEAANBE2CQMr1u3Ths2bNDu3bvl7Oysv/71r3rxxRf17LPPVvtn8/LydMcdd2jt2rVq37698vLyFBUVpT59+qhnz57133ngV5KVm6+vtu7Xsrg9Ol7+bmBJGnRjZ02NCldUaDe5Otf8R7TEbCg2PUdLz2Rq/2VLoYe1KjsVup9f7ZdCm81m7Tx8Qp/H7tZ3e4/IVFr2zuH2rXw1dViYJg8NVRt/n1q1CQAAADQGNgnD//nPf/T444/Lufwv+hMmTFBkZGSNwnBqaqrmzJmj9u3LDg3y9PTUyJEjdejQoWuG4ZKSEplMJsvngoKCX/5FAHVgGIZ2/3hSn8ft1vpdiSoxlYXLAF8vTR4aqilD+yq4TctatZlRbNIX57L0+dkspZUvhfZyctTEdr66vYO/guqwFDr9Yq6+3LJfy+L36FRqhiTJ0cFB0aHdFRMdrsG9uzALDAAAgCbNwTDK11A2oJtvvlnx8fHy8/PT2rVr9fbbb2vnzp1KSkqSl1ft3jtqGIaGDRumTz75RMHBwVd95tlnn9Vzzz1X6Xp+fr48PDzq9DUAtZFxMU9fbSubBT5xLl2S5ODgoEE3dlZMVLiG9elWq3cDS9KR3EJ9eiZT36ZdVHH5j3EnD1fdEeivCW191aKWS6ENw9APR8pmgTfsPmyZBW7X0le3DeuryUND1a6lb63aBAAAABorm4ThLl26KCkpSf/617+UkJCgRYsWKSoqSitWrFC7du1q1daTTz6p1q1b66GHHrrmM1ebGW7VqhVhGPXKbDbrhyMntCxuj1W4bOPnrSnD+mrykFAFtvavVZslZkNx5Uuh9122FHpIS09NC2yp/nVYCp2Zk6cvtpQF9ZOpZUHd0cFBw27upqnRYRpyU1dmgQEAANDs2GSZtKurq6ZPn64+ffro3XfflSRlZWXVelZ44cKFys7O1osvvljlcy4uLjU+nAv4pa61xHjYzd00NSpMQ2/uKmenms8CG4ahxNxCrUq9qHXnLyqzpCxUezk56rftfBXTwV/X1XIptGEY2lUxC7znsGW5dlt/H00pPxG6fStmgQEAANB82SQMm81mtWnTRn/5y18kSTk5OTKbzZXCcG5u7jUD8kcffaQ9e/boP//5T733F6hOxeuGlsXtsTpoql1LH00eWrbEuEMrv1q1ebawRGvSsrU69aJOFBRbrt/QwlUxHfw1oY2PPGu5tDorN19flh/a9XP5oV0OvyCoAwAAAE2VTcLwP//5Ty1cuFAmk0nOzs6aP3++br/9dqtn5s2bpzfffFPbt29XRESE1b2VK1fqm2++0WeffVbrJaHAr+l8Vk7Z64bi9+p0+euGKg6amhoVpsG9u9QqXOaYSrXxfI5WpWVrb/alg95aujjpljY+Gt/GVz283Gr1fW8Yhvb8eFKfXXFoV8Vy7SnD+tY6qAMAAABNnU3C8NixY5WYmKiwsDDLe4bffPNNq2dat24tf39/tWjRwur6sWPHNG3aNIWGhmrUqFGW69OnT9esWbMapP+wb2azWdsPHdeyuN2K3fejTKVmSWWvG7ptWF9NGlK7g6ZKzIa+z8zT6rRsxafnqshcto3fzdFBUa28NKGtryL9PeVcy1/8ZOXm6+ttB/R53B4dP3teUtks8JCbumpqVJii+nRjFhgAAAB2yyYHaNlaQUGBWrRowQFaqJW0zItauXmflm/eq7MXsiRJTo6OiurTTVOjwjWod+caHzR1rX3ADpLCfVtofFsfjQjwllctl0EbhqG9Saf0eexurduVqOKSsoPjAny9NGVoX902rG+tD+0CAAAAmiObzAwDTUWp2aytCce0LG6P4vcfVam5bBY4MMBPt0WFafKQULX2865xe2cLS/Rt+T7gn6/YBzy+ja/GtfFRO/faH/aWnVdQNgscu1s/XTYLPLh3lzq/ugkAAABozgjDwFWcy8gumwWO36tzGdmSJGcnR40O76WpUWEaEHKDHGs4C1yxD3h1Wrb2XLEPeGz5PuCetdwHLJXNAu8/lqzPY3dr7Q+HVFQ+C9zK10uTh4RqalSYgpgFBgAAAK6KMAyUM5WWasvBY1oWt1ubDyTJXL6DILi1v26LCtPEwX1qPAtc3T7g8W19NaAO+4Al6WJegb7ZflCfx+1W0uk0y/WBIZ0VEx2u6NDuzAIDAAAA1SAMw+6dTc/Sivi9Wrl5n1IzL0qSnJ2cNDqsh6ZGhat/z441mgWu2Ae8OvWi1l62D1iSIn7BPmBJyi8q1paDSVq/K1Gx+35UYXGJJKmVj6cmDQnVbcPCdF3blrVuFwAAALBXhGHYJVNpqeIPJGlZ3G5tOXhMFefIXde2paaWzwK38rn6O66vdK19wJ0u2wfcvg77gPMKi7T5QJLW7TqkLQeSVFAegCUpstcNiokO1/C+3eXqzI8xAAAAUFv8LRp25cz5TC2P36uVW/bpfFaOJMnF2UmjwnpqanS4+vXoWKO9u/W1Dzi3oFBx+49q/a5EbTmYZNkHLEk3dw7S6IheGh3eixOhAQAAgF+IMIxmr8RUqrj9P2pZ3B5t+99PllngTu0DNDUqTL8ddLP8vT2rb8dsaEdmnlZVsQ840s9TLo61C8A5+YWK3fej1u06pG3/+8nyOiRJCu0SrDH9QjQyvKc6tPKrVbsAAAAAro0wjGYrOS3DMgucnp0rSXJ1cdbo8F6KiQpTWPfrq525rWof8OXvA/au5T7g7LyCsgD8Q1kANpWWv2fYwUFh3a7XmH69NCqsl9q29KnlVw0AAACgJgjDaFaKTSZt2vujlsft0fZDP1mu39ChtW6PDtNvBt4sP68W1bZTH/uAs3Lz9d3eI1r3wyHtSDwuU2nZO4sdHRzUr0fHshngsJ61em8xAAAAgLohDKNZOJmaruVxe/TFlv3KyMmTJLm5OGtsvxBNjQpXaNfgameBc02l2nghR6tSf719wBkX8/Td3sNavytROxJ/Vqn5UgCO7HWDRkf00siwngrwrdlhXQAAAAB+HYRhNFnFJSZ9t/eIPo/brZ2JP1uudw1qo6lRZbPAvp4eVbZR1T7gYa28NKGNryL9a7cP+EJ2rjbuKQvAu46csARgJ0dHDbqxs0ZH9NKIvj3V0qf6fcoAAAAA6gdhGE3OiXMXtCxuj77cul+ZOfmSJHdXF93S/0bFRIXpps5BVc7e1sc+4PNZOdqwO1HrdyVq948nZS4/pMvZyVFDbupaHoB71GiJNgAAAID6RxhGk1BUXKINew5rWdwe7TpywnK9e3BbxUSHa3xkb/lUMwucUliiNVfbB+zhqvFta78PODXjotaXB+C9Sacsp1Q7OzlpyI2dNaZfiKJDu1c7Ow0AAACg4RGG0Widz8rRgWOntfPIz1q1/aCy88r28Xq4umhcZG9NjQpT7xsCq5wFrtgHvDr1onZn51uu+7s4aWxrH41v66NeXu413geckp6tDbsTte6HQ9p3LNly3dXFWYNu7KwxEWUB2LuFex2/agAAAAANgTCMRqHUbFbS6TTtTzqlfceStT8pWcnnM62e6Xl9e8VEh2l8ZG95eVw7bP7a+4DPnM/U+t2JWrcrUQd/Om257ubirCE3ddWYiBAN69O1yj4BAAAAaFwIw7CJi3kFOvDTae0/lqz9x5J14KfTyi8stnqmhburbrohSH26BGl43x66sVPgNdszDEOHc4u0KjW70j7gMF8PjW/jq5Gta74PODktQ+t3JWrdrkP6389nLdfdXV009OayADz05q7ydHer5VcOAAAAoDEgDKPeGYahk6np2pdUNuO771iyfjp73rLHtkJQa3/16RKs0K7B6tMlWF2D2sjZqerwWrYP+KJWp2Xr5/wr9wH76JY2vupQw33AJ86la/2uQ1q/K1GJJ1Ms1z3cXBXVp5tGR/TSkJu6qoWbay2+egAAAACNEWEYv7qComId+vmsZbnz/p+SLac+V3BxdlJIxw5W4be1n3eN2v819wH/nHJB6344pHW7E/XjqXOW657ubooK7aYx4SEafFMXubvW/GAtAAAAAI0fYRi/2LmM7LJZ3/Lwe/hUikylZqtnWvl6qW956A3tep16Xd9eri41//a71j5gVwcHRQV4aXwbXw2o4T7gY2fStO6HQ1q/O1FJp9Ms17083DQ8tIdGR/TSoBs7y40ADAAAADRbhGHUSompVD8mn7OE331JyTqXkW31jKODg3pc1658xvc6hXYNVmCAX41PbJakUsPQyYJiHc4p1MGLBdp4IUcZddwHbBiGkk6nad2uQ1q3K1HHz5633PNp4a7hfXtoTESIBoTcUKuADgAAAKDp4m/+qFJWbr4l9O4/lqyE42dUWFxi9Yx3C3fd3DnIEn5vuiFQnh41P1iq1DB0Ir9Yh3MLy/7JKdSR3EIVmK33FNdmH7BhGDpy6pzlEKwT59It93w9PTQirCwA9+/VSa7O/BgAAAAA9oYUAAuz2azjKRcsh1ztP5asn1MuVHquY7tWl+31vU6dOwTI0dGxRv8blwffxJyy8PvjVYKvJLVzc1ZPL3f19HLXwJae1e4DNgxDiSdStG5X2RLoU6kZlnv+3i00sm9PjekXoogeHeVSw1OlAQAAADRPhGE7lldYpITjZyzh98CxZF3ML7R6xs3FWb1vCLTs9e3TJUj+3p41ar8i+CaWz/ZWF3x7ebmrp7e7JQC3dK3+29MwDCUcP6P1uxO1fleiTl/2buJWPp4aGdZToyNCFNHj+mpPpgYAAABgPwjDdsIwDJ25kFW+3PmU9iUl62hyqsxXvN6oXUsfq1nf7te1rdEyYlNF8C0PvRXBt/Aqwbe9m7N6eruXhV8vd/WoYfCtYDablXD8jNaWvwYpJf3SnuUAXy+NDu+l0RG9FNb9ejnVcMYaAAAAgH0hDDdTxSUmJZ5M0b6kU5Y9vxeyc62ecXZyVMh1HdSn/JTnPl2C1b6Vb7VtmwxDP+cX6XBOYdmsb26Rjl4j+HZwc7HM9vbyclcPbzf51+GQKrPZrP3HTmvdrkPasDtR5zIuWu618fPW6IheGh0RotCuwQRgAAAAANUiDDcT57NydODYae07dkr7k5L1vxNnVWIqtXrGz6uFQrsEq0/XYIV2CVZIpw7ycHOtsl2TYeh4XpFltjcxp1BJeUU1Cr49vd3l51K3pck5+YU6diZNSafTdPhkimL3/ai0rBzL/XYtfTUmomwG+ObOQTXeswwAAAAAEmG4SSo1m5V0Ok37k06VHXSVlKzky/bKVugS2OZS+O0arOvbtqryAKoSs6Hj+UWWE50P5xbqaF6R5Z2+lwt0d7Hs7e3lXbbUuS7Bt7C4RMdTLujY6TQlnU5V0pk0HU1Oq/S6JknqEOCn0eG9NKZfiHp36kAABgAAAFBnhOEm4GJegQ4eP2NZ8nzgp9PKLyy2eqaFu6tuuqHi9UbBuqlzkHw9Pa7Z5uXBt2Kfb9I1gm9QRfC97HAr31oGX1NpqU6mZiipPPRWzPqeSs2otG9ZklxdnNW5Q2t1DWyjrkFt1K9nJ93YqUOt3lUMAAAAANdCGG5kDMPQydSM8n2+ZeH32JnzMq4IjEGt/S876CpYXYPaXPO05Irge/nhVkdzi1R8lRAa7O5iFXp7ernLpxbB12w2KyU9W0nlYTfpdJqSzqTp+NnzlZZtS5KTo6NuaB9gCb0V/wS3acneXwAAAAD1hjDcyJSUlmrSU2+qqMRkuebi7KSQjh2swm9rP++r/3mzoZ+uCL5J1QTfilOde3q7y7uG7981DEPpF/N09HRq+RLnstB77ExapVnrCoEBfuoa1EZdAtuoa1BbdQtqo07tA+RahwO1AAAAAOCXIIU0Mq7Ozhp2czcZMizv9u11ffurBsYSs6Fj5YdbVbzLNymvSCVXCb7XebhYHWzVw6vmwfdiXoGOnTmvpDOpSjqdVhZ+z6QpMyf/qs+38vVS18A26lY+y9slqK26dGgtTw+32hUDAAAAAOqJg3Hl+ls7UFBQoBYtWig/P18eHtfeV9uYlJgNJeVdOtwqMbdQx37l4FtYXKLjZ88r6XSajp4uD75n0qxeY3Q57xbu6hrYRl2C2liWOXcJbKOWPp6/+OsFAAAAgPrEzHAjVGw2lwXfy5c65xXJdJVfW1zv4Vq+xNlNvbzc1b0GwbfEVKqTqemWsFuxt/dUWkalvcmS5FZxmFVQWfDtFtRWXYPaqK2/DwdaAQAAAGiSCMONTInZUNT2JBVccaqzg6SOluBbtse3h5ebvKoIvmazWWfTsy0nOCedSVNScpqOp1yQqfTqh1ld3z7Asry5a2BbdQlqo+A2/hxmBQAAAKBZIQw3Mi6ODurYwk2FpWarU517eLnJ8xrB1zAMXcjOtQ69p9N07Mx5FRRd/TCroNb+l5Y4ly9z5jArAAAAAPaCPcONcM9widmQi+PVlx9n5xVYljZXHGSVdDpNWblXP8wqwNfLEna7BrdV18A26hzYWp7uHGYFAAAAwH4xDdgIuTg6qKCoWMfPXrA6yCrpdJpSM6s4zOqyg6y6BrVVl8DW8vfmMCsAAAAAuBJhuJEpNZt16xOLdOJc+lUPs3J3dbl0mFV58O0W1FZt/L05zAoAAAAAaogw3MhUHFTl6OCgju0DLGG3Ym9vUGsOswIAAACAX4o9w41wz/DZ9CwF+HhxmBUAAAAA1BPSViPUoZWfrbsAAAAAAM0a620BAAAAAHaHMAwAAAAAsDuEYQAAAACA3SEMAwAAAADsDmEYAAAAAGB3CMMAAAAAALtDGAYAAAAA2B3CMAAAAADA7hCGAQAAAAB2hzAMAAAAALA7zrbugC0YhiFJKigosHFPAAAAAAD1wd3dXQ4ODte8b5dhuLCwUJLUqlUrG/cEAAAAAFAf8vPz5eHhcc37DkbFNKkdMZvNysrKqvY3BbZSUFCgVq1aKT09vcr/8+wF9biEWlijHtaoxyXUwhr1sEY9LqEW1qjHJdTCGvWw1lTqwczwVTg6Oqply5a27ka1PDw8GvU3V0OjHpdQC2vUwxr1uIRaWKMe1qjHJdTCGvW4hFpYox7Wmno9OEALAAAAAGB3CMMAAAAAALtDGG6EnJ2d9cwzz8jZ2S5XsVdCPS6hFtaohzXqcQm1sEY9rFGPS6iFNepxCbWwRj2sNZd62OUBWgAAAAAA+8bMMAAAAADA7hCGAQAAAAB2hzAMAAAAALA7hGEAAAAAgN0hDNezc+fOafr06XJwcLjmM3PmzNGiRYtq3XZRUZHuvvtuhYWFqU+fPvrXv/71S7pa765VC8Mw9Nxzz2n48OEaNWqURo4cqQ8//LBWbTe1WkjS008/rYEDB2rQoEG6/fbblZ6ebrn33XffKTw8XP369dOYMWN0+vTpWrXd3OqxYsUKDRw4UEOGDNHw4cO1b9++WrXd1OpRVS0q/P73v9eGDRtq3XZTq4VUs3rYyzgqXbse9jaWFhUV6YEHHtCwYcMUERGhyZMnKzU11eoZexpLa1IPexlLa1KLCvYwltamHvYwllZXD3sbSyWpuLhYoaGhunDhgtV1uxhDDdSbDz74wOjdu7exYsUK41qlXrZsmTFt2rQ6tf/4448bL7zwgmEYhlFcXGwMHz7ciI2NrWt361VVtVizZo0xadIko7S01DAMwygoKDDCw8ONpKSkGrfflGphGIYxf/584+mnn7Z8XrJkieX74Pz580avXr2MlJQUwzAMY8OGDUZUVFSt2m9O9fj++++NsWPHGoWFhYZhGMaPP/5o9OzZ0zCZTDVuvynVo6paVDhw4IAxZsyYOrXflGphGDWrh72Mo4ZRdT3sbSx94oknLH01DMN46aWXjIkTJ1o+29tYWl097Gksra4WFexlLK1pPexlLK2uHvY2lhqGYbzxxhvGSy+9ZHXNXsZQwnA9evfdd43c3FzDMIyrhuGTJ08agwcPtjxTW127djXy8vIsn++77z4jJCSkbp2tZ1XVouI/Rjk5OYZhGEZqaqrRr18/Iy0trcbtN6VaGIZhvPrqq8bZs2ctnwsLCy39feedd4xnn33Wci87O9uQZPz88881br851WP16tXGpk2brJ6PjIw0zp8/X+P2m1I9qqpFhQkTJhj79u2rU/tNqRaGUX097GkcNYyq62FvY+nKlSuN7Oxsy+eDBw9a9dXextLq6mFPY2l1tahgL2NpTephT2NpdfWwt7E0NzfX6Nu3r5Gfn2913V7G0Kb9luRGbtasWdXeDw4O1uzZs2UYhv7v//5PvXv3rlHbmZmZatGihVq0aCFJ+vvf/66cnBy5urr+4n7Xh6pqcdNNN+n3v/+9OnTooE6dOunUqVP69NNP1bp16xq13dRqIUkPP/yw1efvvvtOQ4YMkSQdOnRI0dHRkqTjx4/r/vvv16BBg3To0CF17Nix2rabWz3GjRtndS8tLU2lpaUKCAioUdtNrR5V1UKStm7dKn9/f/Xp00f/+c9/dPr0aT355JM1arup1UKqvh72NI5KVdfD3sbSSZMmWf7dZDJp0aJF+t3vfme5Zm9jaXX1sKextLpaSPY1ltakHvY0llZXD3sbS1977TXNnTtXHh4emjlzpu69917LWGkPYyh7hm1k06ZNOnnypBYsWKBPPvlETz75pKZOnaqUlJQa/fnc3Fz5+fmpqKhIM2fOlL+/vz744APl5+fXc89/fVu2bNFHH32kpKQkHThwQDt37tRTTz2l48eP1+jPN/VanDp1Si+99JJefPFFSZe+nvj4eD3wwAP66KOPNGzYMOXk5NSoveZWj8sVFxfrzjvv1Ouvv17j9ppyPa5Wi2effVZ/+9vf6tReU66FVLke9j6OXlkPex1Lo6Oj1blzZx0/flx333235bq9jqXXqsfl7GUsraoW9jiWXqse9jqWXqse9jSWpqen69tvv9Wdd95Z6Z7djKE2npm2G1eW+k9/+pOxYsUKq2vz5883Xn/99Rq1l5GRYbRr18645ZZbjPXr1xuGYRhpaWlGeHj4r9PhenRlLWJiYozvv//e6trixYuNJ598skbtNeVapKamGgMHDjQOHz5sufbHP/7RGD9+vDFt2jTLkpX777/fWLVqVY3abG71qGAymYyYmBjjs88+q1WbTbUeV6vFN998Y/z5z3+2fP7oo4+s9j1Vp6nWwjCuXg97HkevVg97HksNwzDi4+ONm266ySgoKDAMw77HUsOoXI8K9jaWGkblWtjzWGoYlethz2OpYVSuhz2NpY888ojVmHjXXXcZW7duNQzDfsZQlknbiMlkkrOzdfldXV1lNpsrPZuTkyNvb2+ra/7+/jp37pz+9re/KSwsTJK0f/9+hYSE1F+n60lhYaHc3d2trrm7u6ugoKDSs82pFtnZ2Zo6daoWLlyoHj16WK6HhITo9ddfl9lstpy8vX//fj366KOV2rCHekhlJzvOnj1bI0aMUExMzDXbaC71uFYt1qxZo71791qWLZ07d05FRUWKjY3Vxo0brdpoLrWQrl0Pex1Hr1UPextL16xZY7X0d+jQoercubMOHz6s0NBQuxtLq6uHZD9jaXW1sLextLp62NtYWl097Gks3bRpk3bv3q1XXnlFknTkyBEdOHBAw4cPt58x1NZp3F5cWeq4uDgjIiLCyMzMNAyj7OCCHj16GMePH7d6ruL05X/84x+V2vzLX/5idULbiBEjGt0JbVdzZS0++eQTY/LkyZbfyKWnpxv9+/ev9Fu55lSLvLw8Y+TIkUZ8fHyle2lpaTU6vc9e6mEYhvHQQw8Z8+fPr7KN5lKP6mpxuWvNZjSXWhhG1fWwx3G0qnrY21gaFRVlfPnll5bPycnJRkhIiOVgHHsbS6urh2HYz1hak1pcrrmPpdXVw97G0urqYW9j6eUunxm2lzGUmeF6NHfuXCUmJlo+V/wGMiwsTK+88ooeeughjR07Vu7u7jKZTHrjjTfUqVMnqzZ8fHzk7++vli1bVmr/ueee03333aewsDCZTCbde++9ioqKqtevqa6qq0VWVpaioqLk4uIis9msxx57TJGRkVZtNJdaSNKDDz6ohIQEPfPMM1bX169fr9atW2vhwoWaMGGCHB0d5efnp8WLF1dqw17q8emnn+qtt97SgAEDtGbNGsu9+fPnKyIiwvK5udSjqlq4uLjUqI3mUgup6noMGzbMrsZRqep6TJ8+3a7G0qVLl2revHl6+eWX5eLiIk9PTy1evFg+Pj6SZHdjaXX1+Pjjj+1mLK2uFjXVHGohVV8PextLq6uHvY2l12IvY6iDYRiGrTsBAAAAAEBD4jRpAAAAAIDdIQwDAAAAAOwOYRgAAAAAYHcIwwAAAAAAu0MYBgAAAADYHV6tVAc7d+60dRfqRf/+/Wv9Z6iFteZYD2phjZ+TS/jesMb3xiXU4hJ+TqzxvXEJtbiEnxNrfG9cUtfvjZpiZhgAAAAAYHcIwwAAAAAAu0MYBgAAAADYHfYMAwAAAGhy6ns/aVNCLeqGmWEAAAAAgN0hDAMAAAAA7A7LpAEAAADgCkVFRXrkkUeUkJCg/Px8BQcH66233lLbtm1t3bUG11xrwcwwAAAAAFzh+eefV/v27RUfH69du3YpIiJCc+bMsXW3bKK51oIwDAAAAABXCA8P14MPPmj5PGHCBCUlJdmwR7bTXGvBMmkAAAAAuMKkSZMs/24ymbRo0SL97ne/s2GPbKe51oKZYQAAAAC4hujoaHXu3FnHjx/X3Xffbevu2FRzqwUzw3XAe7wAAAAA+xAbGytJ2rx5s0aPHq2dO3fK3d3dxr2yjeZWC8IwAAAA0AQwIdOw1qxZo3Hjxlk+Dx06VJ07d9bhw4cVGhpqw541vOZaC5ZJAwAAAMAVFixYoK+++sry+fTp0zp69Kg6d+5sw17ZRnOtBTPDAAAAAHCFpUuXat68eXr55Zfl4uIiT09PLV68WD4+PrbuWoNrrrUgDDcS586d08MPP6ylS5fKMAyre999953+8pe/yNHRUf7+/vr3v/+toKAgG/UUAAAAaP7atWunzz77zNbdaBSaay1YJt0IfPjhhxo9erRuu+22SvcuXLigP/7xj1q1apV++OEHPfroo5oxY4YNegkAAIDm5ty5c5o+fbocHBwq3SsqKtLzzz8vd3d3xcfH26B3QP0iDDcCJpNJ33//vSZPnlzp3sqVKxUTE6N27dpJkvr166e4uDidOHGigXsJAACA5qSqCZkdO3YoMjJSLi4uCgsLs0HvgPrHMulGYNasWde8d+jQIUVHR0uSjh8/rvvvv1+DBg3SoUOH1LFjxwbqIQAAAJqbigkZT0/PSvdSUlL0xRdfqGPHjlq/fr0NegfUP2aGG7nc3Fz5+fkpPj5eDzzwgD766CMNGzZMOTk5tu4aAAAAmrBZs2ZdNQhL0qRJk5h4QbPHzHAj5+XlpVdeeUU+Pj5asWKFPDw8lJWVJW9vb1t3DQAAAACaLGaGG7mQkBCtXr1an3zyiTw8PCRJ+/fvV0hIiI17BgAAAABNFzPDjdykSZO0cOFCpaamql27dtq4caNcXV0bzbKV/v3727oLAAAAAFBrhOFGYO7cuUpMTLR8rjgwKywsTK+88ooWLlyoCRMmyNHRUX5+flq8eLGtugoAAAAAzQJhuBFYtGhRlfdHjhyp3bt3N1BvAAAAYA+qmpDp3r27lixZIkmKi4tTVFSUoqKi5OTkpI0bN9qkv8CvjTAMAAAA2KHqJmSqev0n0BxwgBYanXPnzmn69OlycHCodO9f//qXBgwYoKioKI0aNUoJCQk26CEAAACApo4wjEblww8/1OjRo3XbbbdVurds2TKtX79e8fHxiouL04IFCxQTE6Pc3Fwb9BQAAABAU0YYRqNiMpn0/fffa/LkyZXuLVmyRC+++KJcXV0lSX369NGoUaMUGxvb0N0EAAAA0MSxZxiNSlV7UwzDUGFhodW1goICubi41He3AAAAADQzzAyjyZgxY4YefPBBpaenS5LWrFmj1atXa+jQoTbuGQAAAICmhplhNBlTpkxRfn6+Jk+eLB8fH6Wnp+vxxx9XixYtbN01AABQT/r372/rLgBoppgZRpMyY8YMxcfH65lnnpG/v78eeOABW3cJAAAAQBPEzDCanMzMTN1333366quv5OjI73MAAAAA1B5hGI3K3LlzlZiYaPkcHR0tSQoLC9Mrr7wiwzD0hz/8QU8//bQ6dOhgq24CAAAAaOIIw2hUFi1aVOV9BwcHffPNNw3UGwAA0Bw9/fTT2rhxoxwcHBQUFKQ333xTrVq1snpmzpw56t27t+bOnWujXgKob6wxBQAAgN1YsGCBHBwctH37dm3btk0TJ07Ugw8+aPXM8uXLdfHiRYIw0MwRhgEAAGA3nJycNGfOHMvnyZMn6+DBg5bPp06d0sKFC/Xee+/ZonsAGhBhGAAAAHbj4YcfVvv27S2fv/vuOw0ZMsTyedasWQoODtbs2bM1ffp0JSQk2KKbABoAe4YBAABgl06dOqWXXnpJX3/9tSRp06ZNOnnypD744AMFBgbq0KFDmjJlimJjY60CNIDmgZlhAAAA2J20tDRNmzZN77//vlq2bClJWrVqlV566SUFBgZKkkJCQnTPPfdo+fLltuwqgHpCGAYAAIBdyc7O1tSpU7Vw4UL16NHDct1kMsnZ2XrhpKurq8xmc0N3EUADIAwDAADAbuTn5+u2227T3/72N4WHh1vdmzJlil544QVlZWVJKltG/fbbb+vWW2+1QU8B1Df2DAO/ov79+9u6CwAAoAoPPvigEhIS9Mwzz1hdX79+vYYNG6aHHnpIY8eOlbu7u0wmk9544w116tTJRr0FUJ8IwwAAALAb//73v6u8P336dE2fPr2BegPAllgmDQAAAACwO4RhAAAAAIDdIQwDjVRRUZEeeOABDRs2TBEREZo8ebJSU1Ov+uycOXO0aNGiBu4hAAAA0HQRhoFG6vnnn1f79u0VHx+vXbt2KSIiQnPmzKn03PLly3Xx4kXNnTvXBr0EAAAAmiYO0AIaqfDwcI0YMcLyecKECfrkk0+snjl16pQWLlyotWvXNnT3AAAAgCaNmWGgkZo0aZJ8fHwkSSaTSYsWLdLvfvc7q2dmzZql4OBgzZ49W9OnT1dCQoItugoAAAA0OcwMA41cdHS0jh8/ru7du+u5556zXN+0aZNOnjypDz74QIGBgTp06JCmTJmi2NhYtW/f3oY9BgAAABo/ZoaBRi42NlYnT57UU089pdGjR6uwsFCStGrVKr300ksKDAyUJIWEhOiee+7R8uXLbdldAAAAoEkgDAON1Jo1a6w+Dx06VJ07d9bhw4cllS2ddna2Xtzh6uoqs9ncYH0EAAAAmiqWSQON1IIFC1RSUqLf/va3kqTTp0/r6NGj6ty5syRpypQpevTRRzV06FD5+fnp1KlTevvttyuFaFvp37+/rbsAAAAAXBNhGGikli5dqnnz5unll1+Wi4uLPD09tXjxYsuhWsOGDdNDDz2ksWPHyt3dXSaTSW+88YY6depk454DAAAAjR9hGGik2rVrp88++6zKZ6ZPn67p06c3UI8AAACA5oM9wwAAAAAAu8PMMIAmpbi4WP3799eGDRsUEBAgSTp//rxCQkIUEhJieS4wMFD//e9/bdVNAAAANHKEYQBNyrvvvquYmBhLEJaklJQUTZw4Ue+++64NewYAAICmhDAMoMnIy8vThx9+qK1bt1pdT0lJUdu2bW3UKwAAADRF7BkG0GS89tprmjt3rjw8PDRz5kxt27ZNUlkYTk9PV0xMjIYOHao77rhDJ06csG1nAQAA0KgxMwygSUhPT9e3336rzZs3V7qXnZ2tjIwMvffee/L19dX27ds1fvx47dmzR+7u7jboLQAAABo7wjCAJuHvf/+7nnjiCTk5OVW6N2/ePM2bN8/yeeDAgRo4cKDi4uI0duzYhuwmAPwq+vfvb+suAECzRxgG0CRs2rRJu3fv1iuvvCJJOnLkiA4cOKDhw4crKipKN998s4KDgy3Pe3h4qLi42FbdBQAAQCNHGAbQJOzdu9fq88yZM3Xvvfdq0KBBev/997V06VJ99NFHcnFx0c8//6wNGzbohRdesFFvAQAA0NgRhgE0effee6+ys7PVr18/eXt7y8PDQ0uWLJGPj4+tuwYANrds2TI9+eSTCgwMtFwbP368/vznP0uSVqxYoVdffVVOTk5ycXHRq6++qtDQUFt1FwAaDGEYQJP04YcfWn1+5JFH9Mgjj9ioNwDQeKWkpOjZZ5/V9OnTK93bsWOH3n//fcXGxsrNzU1Hjx7VxIkTlZCQcNUzGgCgOeHVSgAAAM1YVe9iz8jI0GOPPSY3NzdJUrdu3eTr66vMzMyG7CIA2ARhGAAAoBlLSUnRzp07NX78eA0cOFCPPvqo8vPzJUnjxo1TdHS05dm0tDSVlpYqICDAVt0FgAZDGAYAAGjG8vPz5ejoqK+++krbt29XUFCQ7r777krPFRcX684779Trr79ug14CQMNjzzAAAEAz9vnnn1t9njdvnt566y3l5eXJ09NTklRaWqoZM2Zo5syZioyMtEU3AaDBMTMMAADQjL399tsyDMPqmqurq0pLSyVJhmFo9uzZGjFihGJiYmzRRQCwCcIwAABAM7Z161a9+eabls+rVq1Su3btLK+fe/jhh9WjRw/Nnj3bVl0EAJtgmTQAAEAz9t5772nu3LkKCwtTixYtdP3112vJkiWSpI8//lhvvfWWBgwYoDVr1lj+zPz58xUREWGrLgNAgyAMAwAANGMeHh764IMPrnpvxowZmjFjRgP3CAAaB8IwAACwuf79+9u6CwAAO8OeYQAAAACA3SEMAwAAAADsDmEYAAAAAGB3CMMAAKDZKiwsVGRkpPbs2WN1/a233tLgwYM1ePBgDRo0SD/88IONeggAsBUO0AIAAM3Wo48+qnvuuUdhYWGWaxs2bNA333yj2NhYubi4aPv27br11luVnJwsFxcXG/YWANCQmBkGAADN0tdff628vDzNmjXL6rqvr69effVVS/AdOHCgSkpKlJ2dbYtuAgBshDAMAACanYyMDD322GNydHTU1KlT9eCDDyojI0OS1K9fP/Xs2dPy7Mcff6yIiAgFBATYqrsAABtgmTQAAGh2/vnPf+rGG2/UokWL5Obmpk8//VS33nqrtm7dannmT3/6kzZu3KjCwkItWbLEhr0FANgCM8MAAKDZWb16td555x25ublJku644w75+voqISHB8sxrr72mhIQEbdy4Uffff7/VPQBA80cYBgAAzU5JSUmlw7BcXV1lNpu1detW5eTkWK5ff/31mjFjhjZu3NjQ3QQA2BDLpAGgnvXv39/WXQDsztSpU/XnP/9Z77zzjhwcHLRhwwadOHFCvXv31vPPP68vv/xS8+fPl6OjowoLC7V69Wo99dRTtu42AKABEYYBAECz88QTT+jpp59W//795e7uLm9vb61cuVKOjo568skn9dRTTykyMlIeHh4yDEP33XefhgwZYutuAwAakINhGIatOwEAsB87d+60dRfqRV1WAFALAABshz3DAAAAAAC7wzJpAGhipk2bpnPnzlldS0hI0JdffqnBgwdrxYoVevXVV+Xk5CQXFxe9+uqrCg0NtVFvAQAAGifCMAA0MUuXLrX6nJ6erjFjxigyMlI7duzQ+++/r9jYWLm5ueno0aOaOHGiEhIS5OTkZKMeAwAAND6EYQBo4l566SU98sgjcnZ2VkZGhh577DHLu1W7desmX19fZWZmKiAgwMY9LcN+UgAA0BgQhgGgCUtOTtaWLVu0YMECSdK4ceOs7qelpam0tLTRBGEAAIDGggO0AKAJe+655/TXv/5Vjo6Vh/Pi4mLdeeedev31123QMwAAgMaNMAwATdSRI0d05MgR/eY3v6l0r7S0VDNmzNDMmTMVGRlpg94BAAA0biyTBoAm6qmnntLzzz9f6bphGJo9e7ZGjBihmJgYG/Ss4VR1sva2bdu0du1aq3vJycm666679NRTTzVkNwEAQCPkYBiGYetOAABqZ9euXXriiSe0YcOGSvf+9Kc/qUOHDnr00Udt0DPbqjhZe8eOHXJ2rvz73qioKL3//vvq0qWLDXoHAAAaE2aGAaAJeuKJJ/TCCy9Uuv7xxx/rrbfe0oABA7RmzRrL9fnz5ysiIqIhu2gTl5+sfaVvvvlG3bt3JwgDAABJzAwDAJqJ5ORkTZkyRTt27Kh0oJjZbFZkZKRWrlypoKAgG/UQAAA0JhygBQBoFqo6WXvJkiUaMmQIQRgAAFgQhgEATV5VJ2sXFxdrwYIFevzxx23Qs4aVlZWlP/zhDxo1apTGjBmjW265Rd9//73VM0VFRXr++efl7u6u+Ph4G/UUAADbIwwDAJq8a52sLUnvvvuuJk6cqNatWzdwrxre3//+d/Xt21cbNmzQunXr9O677+rOO++03N+xY4ciIyPl4uKisLAwG/YUAADb4wAtAECTtmvXLmVnZ2v48OGV7uXl5enNN9+sNDvaXHXs2FHJyckqLS2Vk5OT0tLS1L59e8v9lJQUffHFF+rYsaPWr19vw54CAGB7hGEAQJN2rZO1Jem1117TXXfdJV9f3wbulW3cd999mjNnjtq1a6eAgACZTCZt3LjRcn/SpEk27B0AAI0LYRgA0KRd7V3LFZ566qkG7Intvfzyy3J1ddWZM2fk6uqqtWvX6o477tCWLVuu+ropAADsGXuGAQBoJt577z394x//kKurqyRp7Nix6tWrl+Li4mzbMQAAGiHCMAAAzYTJZKo0A+zu7q6CggIb9QgAgMaLMAwAQDMxZswYLViwwPI5ISFBGzduVHR0tA17BQBA4+RgGIZh604AAIBfrqCgQI8//rh27NghZ2dneXt765VXXtGNN94oqWwZ9ZIlSyTJsnQ6KipKTk5OVgdtAQBgDwjDAAAAAAC7wzJpAAAAAIDdIQwDAAAAAOwOYRgAAAAAYHcIwwAAAAAAu0MYBgAAAADYHcIwAAAAAMDuEIYBAAAAAHaHMAwAAAAAsDuEYQAAAACA3SEMAwAAAADszv8HRRWHcq838NoAAAAASUVORK5CYII=\n", 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\n", - "
" - ], - "text/plain": [ - "partition 17.0 18.0 19.0 20.0 21.0 22.0 \\\n", - "split \n", - "0 0.049906 0.049906 0.166919 0.265232 0.312876 0.297162 \n", - "1 0.091842 0.091842 0.100337 0.193954 0.203056 0.252020 \n", - "2 0.009679 0.009679 0.024988 0.025052 0.123883 0.126408 \n", - "3 0.021690 0.122891 0.106262 0.106659 0.225007 0.267777 \n", - "4 0.021572 0.124259 0.124259 0.177597 0.263064 0.249085 \n", - "\n", - "partition 23.0 24.0 25.0 26.0 27.0 28.0 \\\n", - "split \n", - "0 0.299539 0.299052 0.335352 0.529027 0.538611 0.532143 \n", - "1 0.238959 0.263947 0.309629 0.498949 0.588171 0.559805 \n", - "2 0.209497 0.366908 0.366153 0.583650 0.593623 0.606231 \n", - "3 0.269421 0.423507 0.459069 0.656906 0.555915 0.561615 \n", - "4 0.223781 0.226017 0.413647 0.529550 0.569325 0.606052 \n", - "\n", - "partition 29.0 30.0 31.0 32.0 33.0 34.0 \\\n", - "split \n", - "0 0.586882 0.778060 0.750020 0.957752 0.994613 0.994652 \n", - "1 0.731027 0.679344 0.895259 0.897717 0.982798 0.982671 \n", - "2 0.619351 0.638837 0.691888 0.817875 0.994545 0.994545 \n", - "3 0.788436 0.826636 0.793199 0.986338 0.992405 0.992395 \n", - "4 0.714682 0.764587 0.771251 0.868054 0.987244 0.988662 \n", - "\n", - "partition 35.0 \n", - "split \n", - "0 0.994663 \n", - "1 0.982671 \n", - "2 0.994545 \n", - "3 0.992393 \n", - "4 0.988662 " - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "rop_simulation.outputs.head()" + "# rop_simulation.outputs.head()" ] }, { @@ -1243,23 +884,12 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 21, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "SIM_VALUE = 23.0\n", - "ECDFDrawer().draw(rop_simulation.outputs[SIM_VALUE].rename(f\"Output at {SIM_FEATURE}={SIM_VALUE}\"))" + "# SIM_VALUE = 23.0\n", + "# ECDFDrawer().draw(rop_simulation.outputs[SIM_VALUE].rename(f\"Output at {SIM_FEATURE}={SIM_VALUE}\"))" ] }, { @@ -1271,22 +901,11 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 22, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.29953861017735744" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "rop_simulation.outputs.loc[0, SIM_VALUE]" + "# rop_simulation.outputs.loc[0, SIM_VALUE]" ] }, { @@ -1298,26 +917,15 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 23, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.29953861017735744" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "model_split_1 = next(boot_crossfit.models())\n", - "(train_split_1, test_split_1) = next(boot_crossfit.splits())\n", - "test_split_1_modified = drilling_obs.features.loc[test_split_1]\n", - "test_split_1_modified[SIM_FEATURE] = SIM_VALUE\n", - "model_split_1.predict_proba(X=test_split_1_modified)[1].mean()" + "# model_split_1 = next(boot_crossfit.models())\n", + "# (train_split_1, test_split_1) = next(boot_crossfit.splits())\n", + "# test_split_1_modified = drilling_obs.features.loc[test_split_1]\n", + "# test_split_1_modified[SIM_FEATURE] = SIM_VALUE\n", + "# model_split_1.predict_proba(X=test_split_1_modified)[1].mean()" ] }, { @@ -1370,7 +978,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1384,7 +992,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.13" }, "toc": { "base_numbering": 1, diff --git a/sphinx/source/tutorial/Scikit-learn_classifier_summaries_using_FACET.ipynb b/sphinx/source/tutorial/Scikit-learn_classifier_summaries_using_FACET.ipynb index 4fa262f8b..00f821d32 100644 --- a/sphinx/source/tutorial/Scikit-learn_classifier_summaries_using_FACET.ipynb +++ b/sphinx/source/tutorial/Scikit-learn_classifier_summaries_using_FACET.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "" + "" ] }, { @@ -13,8 +13,6 @@ "source": [ "# Standard Scikit-learn Classification Summary with FACET\n", "\n", - "***\n", - "\n", "FACET is composed of the following key components:\n", "\n", "- **Model Inspection**\n", @@ -71,6 +69,7 @@ "warnings.filterwarnings(\"ignore\", category=UserWarning, message=r\".*Xcode_8\\.3\\.3\")\n", "warnings.filterwarnings(\"ignore\", message=r\".*`should_run_async` will not call `transform_cell`\")\n", "warnings.filterwarnings(\"ignore\", message=r\".*`np\\..*` is a deprecated alias\")\n", + "warnings.filterwarnings(\"ignore\", message=r\"Importing display from IPython.core.display is deprecated.*\")\n", "\n", "\n", "# set global options for matplotlib\n", @@ -78,8 +77,8 @@ "import matplotlib\n", "import matplotlib.pyplot as plt\n", "\n", - "matplotlib.rcParams[\"figure.figsize\"] = (16.0, 8.0)\n", - "matplotlib.rcParams[\"figure.dpi\"] = 72" + "matplotlib.rcParams[\"figure.figsize\"] = (12.0, 6.0)\n", + "matplotlib.rcParams[\"figure.dpi\"] = 96" ] }, { @@ -126,7 +125,7 @@ " PrecisionRecallDisplay,\n", ")\n", "from sklearn.compose import make_column_selector\n", - "from sklearn.model_selection import RepeatedKFold" + "from sklearn.model_selection import RepeatedKFold, GridSearchCV" ] }, { @@ -143,8 +142,7 @@ "outputs": [], "source": [ "from facet.data import Sample\n", - "from facet.selection import LearnerRanker, LearnerGrid\n", - "from facet.crossfit import LearnerCrossfit" + "from facet.selection import LearnerSelector, MultiEstimatorParameterSpace, ParameterSpace" ] }, { @@ -208,14 +206,11 @@ "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "'Churn'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "churn sample data loaded with 7043 observations\n" + ] } ], "source": [ @@ -231,7 +226,7 @@ "\n", "# To support preprocessing pipeline we will also convert SeniorCitizen to object type\n", "# only tenure, MonthlyCharges and TotalCharges are numeric\n", - "churn_df.SeniorCitizen = churn_df.SeniorCitizen.astype(str)\n", + "churn_df.SeniorCitizen = churn_df.SeniorCitizen.astype(\"category\")\n", "\n", "# Create a new 0/1 target where 1=churn\n", "churn_df.Churn = churn_df.Churn.map(dict(Yes=1, No=0))\n", @@ -243,8 +238,7 @@ " target_name=\"Churn\",\n", ")\n", "\n", - "# check target name\n", - "churn_sample.target_name" + "print(f\"churn sample data loaded with {len(churn_sample)} observations\")" ] }, { @@ -331,7 +325,8 @@ " preprocessing_numerical,\n", " make_column_selector(dtype_include=np.number),\n", " ),\n", - " ]\n", + " ],\n", + " verbose_feature_names_out=False,\n", ")" ] }, @@ -398,9 +393,9 @@ "\n", "FACET implements several additional useful wrappers which simplify comparing and tuning models:\n", "\n", - "- `LearnerGrid`: allows you to pass a learner pipeline (i.e., classifier + any preprocessing) and a set of hyperparameters\n", + "- `ParameterSpace`: allows you to pass a learner pipeline (i.e., classifier + any preprocessing) and set hyperparameters.\n", "\n", - "- `LearnerRanker`: multiple LearnerGrids can be passed into this class as a list - this allows tuning hyperparameters both across different types of learners in a single step and ranks the resulting models accordingly\n", + "- `LearnerSelector`: multiple LearnerGrids can be passed into this class as a list - this allows tuning hyperparameters both across different types of learners in a single step and ranks the resulting models accordingly\n", "\n", "For the purpose of this tutorial we will assess a Random Forest Classifier and hyperparameter ranges will be assessed using 10 repeated 5-fold cross-validation and be scored using AUC:\n", "\n", @@ -436,139 +431,132 @@ " .dataframe thead tr th {\n", " text-align: left;\n", " }\n", - "\n", - " .dataframe thead tr:last-of-type th {\n", - " text-align: right;\n", - " }\n", "\n", "\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
ranking_scoreroc_aucclassifierscoreparamtime
testclassifierfitscore
rankmeanstdtypen_estimatorsmax_depth
rankn_estimatorsmeanstdmeanstd
00.8255020.8460880.010293RandomForestClassifierDF5008
10.8254800.8459250.010223RandomForestClassifierDF310.8464670.010198720080.4288130.0056490.0300350.002274
20.8213490.8427050.010678RandomForestClassifierDF200420.8461760.01020771000.2250230.0029870.0180260.000395
30.8209690.8424840.010758RandomForestClassifierDF500130.8427050.01057042000.3307420.0042080.0229320.000563
40.8017120.8224120.010350RandomForestClassifierDF50016040.8420600.01070241000.1761520.0022480.0148630.000459
50.8009080.8215150.010304RandomForestClassifierDF50.8416390.01005010200160.5480960.0221820.0380360.003584
60.7947950.8161960.010701RandomForestClassifierDF50032
70.7942100.8154160.010603RandomForestClassifierDF20032460.8409890.010235101000.2881220.0198390.0230280.003089
\n", "" ], "text/plain": [ - " ranking_score roc_auc classifier \\\n", - " mean std type n_estimators \n", - "rank \n", - "0 0.825502 0.846088 0.010293 RandomForestClassifierDF 500 \n", - "1 0.825480 0.845925 0.010223 RandomForestClassifierDF 200 \n", - "2 0.821349 0.842705 0.010678 RandomForestClassifierDF 200 \n", - "3 0.820969 0.842484 0.010758 RandomForestClassifierDF 500 \n", - "4 0.801712 0.822412 0.010350 RandomForestClassifierDF 500 \n", - "5 0.800908 0.821515 0.010304 RandomForestClassifierDF 200 \n", - "6 0.794795 0.816196 0.010701 RandomForestClassifierDF 500 \n", - "7 0.794210 0.815416 0.010603 RandomForestClassifierDF 200 \n", + " score param time \\\n", + " test classifier fit \n", + " rank mean std max_depth n_estimators mean std \n", + "3 1 0.846467 0.010198 7 200 0.428813 0.005649 \n", + "2 2 0.846176 0.010207 7 100 0.225023 0.002987 \n", + "1 3 0.842705 0.010570 4 200 0.330742 0.004208 \n", + "0 4 0.842060 0.010702 4 100 0.176152 0.002248 \n", + "5 5 0.841639 0.010050 10 200 0.548096 0.022182 \n", + "4 6 0.840989 0.010235 10 100 0.288122 0.019839 \n", "\n", - " \n", - " max_depth \n", - "rank \n", - "0 8 \n", - "1 8 \n", - "2 4 \n", - "3 4 \n", - "4 16 \n", - "5 16 \n", - "6 32 \n", - "7 32 " + " \n", + " score \n", + " mean std \n", + "3 0.030035 0.002274 \n", + "2 0.018026 0.000395 \n", + "1 0.022932 0.000563 \n", + "0 0.014863 0.000459 \n", + "5 0.038036 0.003584 \n", + "4 0.023028 0.003089 " ] }, "execution_count": 9, @@ -586,24 +574,22 @@ " classifier=RandomForestClassifierDF(random_state=42),\n", ")\n", "\n", - "# set grid of hyper-parameters \n", - "classifier_grid = [\n", - " LearnerGrid(\n", - " pipeline=rforest_clf,\n", - " learner_parameters={\"max_depth\": [4, 8, 16, 32], \"n_estimators\": [200, 500]},\n", - " ),\n", - "]\n", - "\n", - "# run the learner ranker\n", - "clf_ranker = LearnerRanker(\n", - " grids=classifier_grid,\n", + "# set space of hyper-parameters \n", + "classifier_ps = ParameterSpace(rforest_clf)\n", + "classifier_ps.classifier.max_depth = [4, 7, 10]\n", + "classifier_ps.classifier.n_estimators = [100, 200]\n", + "\n", + "# run the learner selector\n", + "clf_selector = LearnerSelector(\n", + " searcher_type=GridSearchCV,\n", + " parameter_space=classifier_ps,\n", " cv=RepeatedKFold(n_splits=5, n_repeats=10, random_state=42),\n", " n_jobs=-3,\n", " scoring=\"roc_auc\",\n", ").fit(churn_sample_kept_features)\n", "\n", "# look at results\n", - "clf_ranker.summary_report()" + "clf_selector.summary_report()" ] }, { @@ -612,7 +598,7 @@ "source": [ "# Using the final fitted model\n", "\n", - "As part of the `clf_ranker` we can access a final model (`best_model_`) that represents the selected best model but re-fit using all available training data. With this model we can then predict either the class or the probability (score) and generate standard scikit-learn classifier performance summaries such as a classification report, confusion matrix or precision-recall curve." + "As part of the `clf_selector` we can access a final model (`best_estimator_`) that represents the selected best model but re-fit using all available training data. With this model we can then predict either the class or the probability (score) and generate standard scikit-learn classifier performance summaries such as a classification report, confusion matrix or precision-recall curve." ] }, { @@ -622,8 +608,8 @@ "outputs": [], "source": [ "# obtain required quantities\n", - "y_pred = clf_ranker.best_model_.predict(churn_sample_kept_features.features)\n", - "y_prob = clf_ranker.best_model_.predict_proba(churn_sample_kept_features.features)[1]\n", + "y_pred = clf_selector.best_estimator_.predict(churn_sample_kept_features.features)\n", + "y_prob = clf_selector.best_estimator_.predict_proba(churn_sample_kept_features.features)[1]\n", "y_true = churn_sample_kept_features.target" ] }, @@ -647,12 +633,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.85 0.92 0.89 5174\n", - " 1 0.72 0.57 0.63 1869\n", + " 0 0.84 0.92 0.88 5174\n", + " 1 0.70 0.53 0.61 1869\n", "\n", - " accuracy 0.83 7043\n", - " macro avg 0.79 0.74 0.76 7043\n", - "weighted avg 0.82 0.83 0.82 7043\n", + " accuracy 0.82 7043\n", + " macro avg 0.77 0.73 0.74 7043\n", + "weighted avg 0.81 0.82 0.81 7043\n", "\n" ] } @@ -678,7 +664,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -687,14 +673,12 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -705,7 +689,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "## Precision-recall curve\n", "\n", @@ -720,7 +706,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -729,14 +715,12 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -747,24 +731,13 @@ }, { "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using the crossfit for the best model\n", - "\n", - "The `clf_ranker` also contains a crossfit object (`best_model_crossfit_`) which allows us to access the test training splits from the cross-validation via `.splits()` and the best model fit on each splits training set via `.models()`.\n", - "\n", - "With this information we can iterate over the cross-validation splits and generate performance metrics (or any other summary) for each trained model, providing a set of results. This is often useful when we want to get some sense of the variability in performance across the cross-validation folds. This is also exactly what the `LearnerRanker` is doing for the specified performance metric to get obtain average performance - 2 standard deviations.\n", - "\n", - "Here we give two examples, the first is summarising the mean performance and standard deviation for a selection of common classifier performance metrics, and the second, is creating an average ROC curve with an assessment of variability across folds by plotting the RCO curve $\\pm$ 1 standard deviation." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "## Panel of metrics with error from cross-validation\n", + "## Panel of metrics\n", "\n", - "As cross-validation is intended to inform us about average performance on a test-set, it can be helpful to collate statistics about the variability of performance as well. Below we demonstrate how to use the best model crossfit object to obtain both the mean and standard deviation for a set of common classification metrics: Accuracy, F1, Precision, Recall and AUC. This approach can of course be adapted to any metric and any summary thereof.\n", + "Below we demonstrate how to use the best estimator results to obtain a set of common classification metrics: Accuracy, F1, Precision, Recall and AUC. This approach can of course be adapted to any metric and any summary thereof.\n", "\n", "For more information about classifier metrics in scikit-learn please see [classification-metrics](https://scikit-learn.org/stable/modules/model_evaluation.html#classification-metrics)." ] @@ -786,36 +759,26 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "metrics = []\n", "\n", - "# use established crossfit which contains all splits and fitted models\n", - "best_crossfit = clf_ranker.best_model_crossfit_\n", - "for (train_index, test_index), model in zip(best_crossfit.splits(), best_crossfit.models()):\n", - " \n", - " # get required target outputs\n", - " y_true = churn_sample_kept_features.target[test_index]\n", - " y_prob = model.predict_proba(churn_sample_kept_features.features.iloc[test_index])[1]\n", - " y_pred = model.predict(churn_sample_kept_features.features.iloc[test_index])\n", - " \n", - " # calculate metrics\n", - " metrics.append(pd.Series({\n", - " 'Accuracy': accuracy_score(y_true, y_pred),\n", - " 'F1': f1_score(y_true, y_pred),\n", - " 'Precision': precision_score(y_true, y_pred),\n", - " 'Recall': recall_score(y_true, y_pred),\n", - " 'AUC': roc_auc_score(y_true, y_prob)}))\n", + "# calculate metrics\n", + "metrics.append(pd.Series({\n", + " 'Accuracy': accuracy_score(y_true, y_pred),\n", + " 'F1': f1_score(y_true, y_pred),\n", + " 'Precision': precision_score(y_true, y_pred),\n", + " 'Recall': recall_score(y_true, y_pred),\n", + " 'AUC': roc_auc_score(y_true, y_prob)})\n", + ")\n", " \n", "# collect required summaries and plot\n", "metrics_df = pd.DataFrame(metrics)\n", @@ -823,93 +786,19 @@ "ax.bar(\n", " metrics_df.columns,\n", " metrics_df.mean(),\n", - " yerr=metrics_df.std(),\n", " align='center',\n", " ecolor='lime',\n", " capsize=10,\n", " color='lightgrey'\n", ")\n", - "ax.set_ylabel('Metric +/- SD')\n", + "ax.set_ylabel('Metric')\n", "ax.set_title('Summary of model performance')" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ROC Curve with error based on cross-validation\n", - "\n", - "Another common performance summary for a classifier is the ROC curve which shows the trade of between the False Positive Rate and the True Positive Rate at all classification thresholds. While we can plot one curve for the best model, using the crossfit object we can get an assessment of the standard deviation at each threshold and in turn capture some information about the variability of the ROC curve.\n", - "\n", - "For more information about cross-validation ROC curves in scikit-learn please see the basis for this example here: [plot_roc_crossval](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc_crossval.html#sphx-glr-auto-examples-model-selection-plot-roc-crossval-py)." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# define plotting function\n", - "def roc_plot(fpr, tpr, tpr_std, auc, auc_std):\n", - "\n", - " fig, ax = plt.subplots()\n", - " ax.plot([0, 1], [0, 1], linestyle='--', lw=2, color='k', alpha=.8)\n", - " ax.plot(fpr, tpr, color='lime', label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (auc, auc_std), lw=2, alpha=.8)\n", - " tpr_upper = np.minimum(tpr + tpr_std, 1)\n", - " tpr_lower = np.maximum(tpr - tpr_std, 0)\n", - " ax.fill_between(fpr, tpr_lower, tpr_upper, color='grey', alpha=.2, label=r'$\\pm$ 1 SD')\n", - " ax.set_xlabel('False Positive Rate')\n", - " ax.set_ylabel('True Positive Rate')\n", - " ax.set_title('ROC')\n", - " ax.legend(loc='lower right')\n", - " plt.show()\n", - " \n", - "# some set-up\n", - "tprs = []\n", - "aucs = []\n", - "mean_fpr = np.linspace(0, 1, 100)\n", - "\n", - "# use established crossfit which contains all splits and fitted models\n", - "best_crossfit = clf_ranker.best_model_crossfit_\n", - "for (train_index, test_index), model in zip(best_crossfit.splits(), best_crossfit.models()):\n", - " \n", - " # predict probability for a splits test set using the fitted model\n", - " prediction = model.predict_proba(churn_sample_kept_features.features.iloc[test_index])\n", - " \n", - " # calculate roc curve and interpolate true positive rate\n", - " fpr, tpr, t = roc_curve(churn_sample_kept_features.target[test_index], prediction[1])\n", - " tprs.append(interp(mean_fpr, fpr, tpr))\n", - " \n", - " # calculate AUC\n", - " roc_auc = auc(fpr, tpr)\n", - " aucs.append(roc_auc)\n", - "\n", - "# collect required summaries\n", - "auc_mean, auc_std = np.mean(aucs), np.std(aucs)\n", - "mean_tpr, std_tpr = np.mean(tprs, axis=0), np.std(tprs, axis=0)\n", - "\n", - "# create plot\n", - "roc_plot(mean_fpr, mean_tpr, std_tpr, auc_mean, auc_std)" - ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -923,7 +812,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.9.13" }, "toc": { "base_numbering": 1, diff --git a/sphinx/source/tutorial/Water_Drilling_Incident_Classification_with_Facet.ipynb b/sphinx/source/tutorial/Water_Drilling_Incident_Classification_with_Facet.ipynb index 766761806..6289d3e08 100644 --- a/sphinx/source/tutorial/Water_Drilling_Incident_Classification_with_Facet.ipynb +++ b/sphinx/source/tutorial/Water_Drilling_Incident_Classification_with_Facet.ipynb @@ -6,7 +6,7 @@ "raw_mimetype": "text/html" }, "source": [ - "" + "" ] }, { @@ -15,8 +15,6 @@ "source": [ "# Introduction to FACET\n", "\n", - "***\n", - "\n", "FACET is composed of the following key components:\n", "\n", "- **Model Inspection**\n", @@ -50,7 +48,7 @@ "\n", "1. [Required imports](#Required-imports)\n", "2. [Data and initial feature selection](#Data-and-initial-feature-selection)\n", - "3. [Selecting a learner using FACET ranker](#Selecting-a-learner-using-FACET-ranker)\n", + "3. [Selecting a learner using FACET selector](#Selecting-a-learner-using-FACET-selector)\n", "4. [Using FACET for advanced model inspection](#Using-FACET-for-advanced-model-inspection)\n", "5. [FACET univariate simulator: the impact of rate of penetration](#FACET-univariate-simulator:-the-impact-of-rate-of-penetration)\n", "6. [Appendix: generating the dataset](#Appendix:-generating-the-dataset)" @@ -80,6 +78,7 @@ "warnings.filterwarnings(\"ignore\", category=UserWarning, message=r\".*Xcode_8\\.3\\.3\")\n", "warnings.filterwarnings(\"ignore\", message=r\".*`should_run_async` will not call `transform_cell`\")\n", "warnings.filterwarnings(\"ignore\", message=r\".*`np\\..*` is a deprecated alias\")\n", + "warnings.filterwarnings(\"ignore\", message=r\"Importing display from IPython.core.display is deprecated.*\")\n", "\n", "\n", "# set global options for matplotlib\n", @@ -87,8 +86,8 @@ "import matplotlib\n", "import matplotlib.pyplot as plt\n", "\n", - "matplotlib.rcParams[\"figure.figsize\"] = (16.0, 8.0)\n", - "matplotlib.rcParams[\"figure.dpi\"] = 72" + "matplotlib.rcParams[\"figure.figsize\"] = (12.0, 6.0)\n", + "matplotlib.rcParams[\"figure.dpi\"] = 96" ] }, { @@ -97,7 +96,8 @@ "ExecuteTime": { "end_time": "2020-08-28T17:21:45.452088Z", "start_time": "2020-08-28T17:21:45.450036Z" - } + }, + "tags": [] }, "source": [ "# Required imports" @@ -139,7 +139,8 @@ "source": [ "import pandas as pd\n", "import numpy as np\n", - "from sklearn.model_selection import RepeatedKFold" + "from scipy import stats\n", + "from sklearn.model_selection import RandomizedSearchCV, RepeatedKFold" ] }, { @@ -169,12 +170,11 @@ "source": [ "from facet.data import Sample\n", "from facet.inspection import LearnerInspector\n", - "from facet.selection import LearnerRanker, LearnerGrid\n", + "from facet.selection import LearnerSelector, ParameterSpace\n", "from facet.validation import BootstrapCV\n", "from facet.data.partition import ContinuousRangePartitioner\n", "from facet.simulation import UnivariateProbabilitySimulator\n", - "from facet.simulation.viz import SimulationDrawer\n", - "from facet.crossfit import LearnerCrossfit" + "from facet.simulation.viz import SimulationDrawer" ] }, { @@ -297,82 +297,92 @@ " \n", " \n", " \n", - " Weight on bit (kg)\n", - " Rotation speed (rpm)\n", - " Depth of operation (m)\n", - " Mud density (kg/L)\n", - " Rate of Penetration (ft/h)\n", - " Temperature (C)\n", - " Mud Flow in (m3/s)\n", - " Hole diameter (m)\n", - " Incident\n", - " Inverse Rate of Penetration (h/ft)\n", + " 0\n", + " 1\n", + " 2\n", + " 3\n", + " 4\n", " \n", " \n", " \n", " \n", - " 0\n", + " Weight on bit (kg)\n", " 289.201651\n", - " 10594.222670\n", - " 790.947541\n", - " 2.898840\n", - " 28.403279\n", - " 39.539919\n", - " 50.299606\n", - " 5.369813\n", - " 0.0\n", - " 0.035207\n", + " 341.949835\n", + " 266.831213\n", + " 267.340585\n", + " 305.977342\n", " \n", " \n", - " 1\n", - " 341.949835\n", + " Rotation speed (rpm)\n", + " 10594.222670\n", " 6962.659505\n", - " 811.833996\n", - " 1.677378\n", - " 27.066685\n", - " 74.050548\n", - " 72.140061\n", - " 5.580490\n", - " 1.0\n", - " 0.036946\n", + " 11065.697315\n", + " 7890.678632\n", + " 12017.344224\n", " \n", " \n", - " 2\n", - " 266.831213\n", - " 11065.697315\n", + " Depth of operation (m)\n", + " 790.947541\n", + " 811.833996\n", " 619.497649\n", - " 2.213403\n", - " 30.556081\n", - " 45.194728\n", - " 10.908230\n", - " 4.374240\n", - " 0.0\n", - " 0.032727\n", + " 1048.481202\n", + " 613.434303\n", " \n", " \n", - " 3\n", - " 267.340585\n", - " 7890.678632\n", - " 1048.481202\n", + " Mud density (kg/L)\n", + " 2.898840\n", + " 1.677378\n", + " 2.213403\n", " 2.683010\n", - " 23.735377\n", - " 55.135234\n", - " 51.029350\n", - " 6.981177\n", - " 0.0\n", - " 0.042131\n", + " 2.360972\n", " \n", " \n", - " 4\n", - " 305.977342\n", - " 12017.344224\n", - " 613.434303\n", - " 2.360972\n", + " Rate of Penetration (ft/h)\n", + " 28.403279\n", + " 27.066685\n", + " 30.556081\n", + " 23.735377\n", " 28.502248\n", + " \n", + " \n", + " Temperature (C)\n", + " 39.539919\n", + " 74.050548\n", + " 45.194728\n", + " 55.135234\n", " 60.585239\n", + " \n", + " \n", + " Mud Flow in (m3/s)\n", + " 50.299606\n", + " 72.140061\n", + " 10.908230\n", + " 51.029350\n", " 44.159394\n", + " \n", + " \n", + " Hole diameter (m)\n", + " 5.369813\n", + " 5.580490\n", + " 4.374240\n", + " 6.981177\n", " 4.217036\n", - " 1.0\n", + " \n", + " \n", + " Incident\n", + " 0.000000\n", + " 1.000000\n", + " 0.000000\n", + " 0.000000\n", + " 1.000000\n", + " \n", + " \n", + " Inverse Rate of Penetration (h/ft)\n", + " 0.035207\n", + " 0.036946\n", + " 0.032727\n", + " 0.042131\n", " 0.035085\n", " \n", " \n", @@ -380,33 +390,29 @@ "" ], "text/plain": [ - " Weight on bit (kg) Rotation speed (rpm) Depth of operation (m) \\\n", - "0 289.201651 10594.222670 790.947541 \n", - "1 341.949835 6962.659505 811.833996 \n", - "2 266.831213 11065.697315 619.497649 \n", - "3 267.340585 7890.678632 1048.481202 \n", - "4 305.977342 12017.344224 613.434303 \n", - "\n", - " Mud density (kg/L) Rate of Penetration (ft/h) Temperature (C) \\\n", - "0 2.898840 28.403279 39.539919 \n", - "1 1.677378 27.066685 74.050548 \n", - "2 2.213403 30.556081 45.194728 \n", - "3 2.683010 23.735377 55.135234 \n", - "4 2.360972 28.502248 60.585239 \n", + " 0 1 2 \\\n", + "Weight on bit (kg) 289.201651 341.949835 266.831213 \n", + "Rotation speed (rpm) 10594.222670 6962.659505 11065.697315 \n", + "Depth of operation (m) 790.947541 811.833996 619.497649 \n", + "Mud density (kg/L) 2.898840 1.677378 2.213403 \n", + "Rate of Penetration (ft/h) 28.403279 27.066685 30.556081 \n", + "Temperature (C) 39.539919 74.050548 45.194728 \n", + "Mud Flow in (m3/s) 50.299606 72.140061 10.908230 \n", + "Hole diameter (m) 5.369813 5.580490 4.374240 \n", + "Incident 0.000000 1.000000 0.000000 \n", + "Inverse Rate of Penetration (h/ft) 0.035207 0.036946 0.032727 \n", "\n", - " Mud Flow in (m3/s) Hole diameter (m) Incident \\\n", - "0 50.299606 5.369813 0.0 \n", - "1 72.140061 5.580490 1.0 \n", - "2 10.908230 4.374240 0.0 \n", - "3 51.029350 6.981177 0.0 \n", - "4 44.159394 4.217036 1.0 \n", - "\n", - " Inverse Rate of Penetration (h/ft) \n", - "0 0.035207 \n", - "1 0.036946 \n", - "2 0.032727 \n", - "3 0.042131 \n", - "4 0.035085 " + " 3 4 \n", + "Weight on bit (kg) 267.340585 305.977342 \n", + "Rotation speed (rpm) 7890.678632 12017.344224 \n", + "Depth of operation (m) 1048.481202 613.434303 \n", + "Mud density (kg/L) 2.683010 2.360972 \n", + "Rate of Penetration (ft/h) 23.735377 28.502248 \n", + "Temperature (C) 55.135234 60.585239 \n", + "Mud Flow in (m3/s) 51.029350 44.159394 \n", + "Hole diameter (m) 6.981177 4.217036 \n", + "Incident 0.000000 1.000000 \n", + "Inverse Rate of Penetration (h/ft) 0.042131 0.035085 " ] }, "execution_count": 6, @@ -423,7 +429,7 @@ ")\n", "\n", "# quick look\n", - "df.head()" + "df.head().T" ] }, { @@ -584,8 +590,8 @@ "outputs": [], "source": [ "# create a FACET sample object with features selected by Boruta\n", - "drilling_obs_reduced_featset = drilling_obs.keep(feature_names=\n", - " feature_preprocessing.feature_names_original_.unique()\n", + "drilling_obs_reduced_featset = drilling_obs.keep(\n", + " feature_names=feature_preprocessing.feature_names_original_.unique()\n", ")" ] }, @@ -600,12 +606,12 @@ } }, "source": [ - "# Selecting a learner using FACET ranker\n", + "# Selecting a learner using FACET selector\n", "\n", "FACET implements several additional useful wrappers which further simplify comparing and tuning a larger number of models and configurations: \n", "\n", - "- `LearnerGrid`: allows you to pass a learner pipeline (i.e., classifier + any preprocessing) and a set of hyperparameters\n", - "- `LearnerRanker`: multiple LearnerGrids can be passed into this class as a list - this allows tuning hyperparameters both across different types of learners in a single step and ranks the resulting models accordingly\n", + "- `ParameterSpace`: allows you to pass a learner pipeline (i.e., classifier + any preprocessing) and a set of hyperparameters\n", + "- `LearnerSelector`: multiple ParameterSpaces can be passed into this class as MultiEstimatorClassifierParameterSpace - this allows tuning hyperparameters both across different types of learners in a single step and ranks the resulting models accordingly\n", "\n", "The following learners and hyperparameter ranges will be assessed using 5 repeated 5-fold cross-validation:\n", "\n", @@ -615,11 +621,11 @@ "\n", " \n", "2. **Light gradient boosting**: with hyperparameters\n", - " - min_samples_leaf: [8, 11, 15]\n", + " - min_child_samples: [8, 11, 15]\n", "\n", "Note if you want to see a list of hyperparameters you can use `classifier_name().get_params().keys()` where `classifier_name` could be for example `RandomForestClassifierDF` and if you want to see the default values, just use `classifier_name().get_params()`.\n", "\n", - "Note that ranking uses the average performance minus two times the standard deviation, so that we consider both the average performance and variability when selecting a classifier. The default scoring metric for classification is accuracy.\n", + "Finally, for this exercise we will use accuracy which is the default performance metric for scoring and ranking our classifiers.\n", "\n", "First, we specify the classifiers we want to train using `ClassifierPipelineDF` from sklearndf. Note here we also include feature preprocessing steps." ] @@ -657,7 +663,7 @@ } }, "source": [ - "Then we create a list of learner grids where each learner grid is created using `LearnerGrid` and allows us to associate a `ClassifierPipelineDF` with a specified set of hyperparameter via the `learner_parameters` argument. Note this structure allows us to easily include additional classifiers and hyperparameters." + "Then we create parameter spaces with `ParameterSpace` for each classifier and specify set of hyperparameters for each one of them. Contrary to standard `sklearn` workflow, in this approach setting wrong hyperparameter will throw an exception as setting an attribute comes with a proper check." ] }, { @@ -670,15 +676,15 @@ }, "outputs": [], "source": [ - "# define learner grid\n", - "clf_grid = [\n", - " LearnerGrid(\n", - " pipeline=rforest_clf, learner_parameters={\"min_samples_leaf\": [8, 11, 15]}\n", - " ),\n", - " LearnerGrid(\n", - " pipeline=lgbm_clf, learner_parameters={\"min_data_in_leaf\": [8, 11, 15]}\n", - " ),\n", - "]" + "rforest_ps = ParameterSpace(rforest_clf)\n", + "\n", + "# random ints 8 <= x <= 19; smaller ints are more frequent (zipfian distribution)\n", + "rforest_ps.classifier.min_samples_leaf = stats.zipfian(a=1, n=12, loc=7)\n", + "\n", + "lgbm_ps = ParameterSpace(lgbm_clf)\n", + "\n", + "# random ints 8 <= x <= 19; smaller ints are more frequent (zipfian distribution)\n", + "lgbm_ps.classifier.min_child_samples = stats.zipfian(a=1, n=12, loc=7)" ] }, { @@ -689,7 +695,7 @@ } }, "source": [ - "We now fit the grid defined above using the `LeanerRanker`, which will run a gridsearch (or random search if defined) using 5 repeated 5-fold cross-validation." + "We now the `LearnerSelector` using the parameter spaces defined above, which will run a gridsearch using 10 repeated 5-fold cross-validation on our selected set of features from Boruta." ] }, { @@ -704,22 +710,21 @@ "name": "#%%\n" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[LightGBM] [Warning] min_data_in_leaf is set=8, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=8\n" - ] - } - ], + "outputs": [], "source": [ "# create cv iterator 5 repeated 5-fold\n", "cv_approach = RepeatedKFold(n_splits=5, n_repeats=5, random_state=42)\n", "\n", - "# fit ranker\n", - "model_ranker = LearnerRanker(grids=clf_grid, cv=cv_approach, n_jobs=-3).fit(\n", - " sample=drilling_obs_reduced_featset\n", + "# fit selector\n", + "model_selector = LearnerSelector(\n", + " searcher_type=RandomizedSearchCV,\n", + " parameter_space=[rforest_ps, lgbm_ps],\n", + " cv=cv_approach,\n", + " n_jobs=-3,\n", + " scoring=\"accuracy\",\n", + " random_state=42,\n", + ").fit(\n", + " drilling_obs_reduced_featset\n", ")" ] }, @@ -731,27 +736,25 @@ } }, "source": [ - "To see the configuration of the best selected model, we can access the `best_model_` property of the fitted `LearnerRanker` object." + "To see the configuration of the best selected model, we can access the `best_estimator_` property of the fitted `LearnerSelector` object." ] }, { "cell_type": "code", "execution_count": 15, - "metadata": { - "ExecuteTime": { - "end_time": "2020-08-31T08:34:04.806643Z", - "start_time": "2020-08-31T08:34:04.799183Z" - }, - "pycharm": { - "name": "#%%\n" - } - }, + "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
ClassifierPipelineDF(\n",
+       "    classifier=LGBMClassifierDF(min_child_samples=17, random_state=42)\n",
+       ")
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" + ], "text/plain": [ - "ClassifierPipelineDF(classifier=LGBMClassifierDF(min_data_in_leaf=15,\n", - " random_state=42))" + "ClassifierPipelineDF(classifier=LGBMClassifierDF(min_child_samples=17, random_state=42))" ] }, "execution_count": 15, @@ -760,7 +763,7 @@ } ], "source": [ - "model_ranker.best_model_" + "model_selector.best_estimator_" ] }, { @@ -771,21 +774,13 @@ } }, "source": [ - "We can see how each model scored using the `summary_report()` method of the `LearnerRanker`." + "We can see how each model scored using the `summary_report()` method of the `LearnerSelector`." ] }, { "cell_type": "code", "execution_count": 16, - "metadata": { - "ExecuteTime": { - "end_time": "2020-08-31T08:34:04.813316Z", - "start_time": "2020-08-31T08:34:04.809195Z" - }, - "pycharm": { - "name": "#%%\n" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -803,117 +798,201 @@ " .dataframe thead tr th {\n", " text-align: left;\n", " }\n", - "\n", - " .dataframe thead tr:last-of-type th {\n", - " text-align: right;\n", - " }\n", "\n", "\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
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rankmeanstdtype-min_samples_leafmin_data_in_leaf
rankmin_child_samplesmeanstdmeanstd
00.7663650.85200.042817810.85440.036341LGBMClassifierDFNaN15.0170.0150630.0006350.0011860.000078
10.753891620.84520.0456540.044732LGBMClassifierDFNaN11.0110.0216190.0011110.0013930.000095
20.739825330.83160.0458880.044960LGBMClassifierDFNaN8.080.0237060.0010440.0015070.000059
30.7032980.77120.033951RandomForestClassifierDF8.0930.83160.044960LGBMClassifierDFNaN80.0228720.0002840.0014270.000067
40.68541050.76480.032634RandomForestClassifierDF9NaN0.2811480.0124090.0167390.000678
260.75520.0348950.034190RandomForestClassifierDF11.011NaN0.2805170.0131830.0172940.001392
50.6778730.74640.03426460.75520.034190RandomForestClassifierDF11NaN0.2753850.0123400.0165890.000584
180.75080.032486RandomForestClassifierDF12NaN0.2646020.0055800.0160630.000214
090.74880.035589RandomForestClassifierDF15.014NaN0.2683460.0118110.0164400.000448
7100.74040.035268RandomForestClassifierDF18NaN0.2668380.0126620.0160580.000363
\n", "" ], "text/plain": [ - " ranking_score accuracy_score classifier \\\n", - " mean std type \n", - "rank \n", - "0 0.766365 0.8520 0.042817 LGBMClassifierDF \n", - "1 0.753891 0.8452 0.045654 LGBMClassifierDF \n", - "2 0.739825 0.8316 0.045888 LGBMClassifierDF \n", - "3 0.703298 0.7712 0.033951 RandomForestClassifierDF \n", - "4 0.685410 0.7552 0.034895 RandomForestClassifierDF \n", - "5 0.677873 0.7464 0.034264 RandomForestClassifierDF \n", + " score candidate param \\\n", + " test - classifier \n", + " rank mean std - min_samples_leaf \n", + "8 1 0.8544 0.036341 LGBMClassifierDF NaN \n", + "6 2 0.8452 0.044732 LGBMClassifierDF NaN \n", + "3 3 0.8316 0.044960 LGBMClassifierDF NaN \n", + "9 3 0.8316 0.044960 LGBMClassifierDF NaN \n", + "4 5 0.7648 0.032634 RandomForestClassifierDF 9 \n", + "2 6 0.7552 0.034190 RandomForestClassifierDF 11 \n", + "5 6 0.7552 0.034190 RandomForestClassifierDF 11 \n", + "1 8 0.7508 0.032486 RandomForestClassifierDF 12 \n", + "0 9 0.7488 0.035589 RandomForestClassifierDF 14 \n", + "7 10 0.7404 0.035268 RandomForestClassifierDF 18 \n", "\n", - " \n", - " min_samples_leaf min_data_in_leaf \n", - "rank \n", - "0 NaN 15.0 \n", - "1 NaN 11.0 \n", - "2 NaN 8.0 \n", - "3 8.0 NaN \n", - "4 11.0 NaN \n", - "5 15.0 NaN " + " time \n", + " fit score \n", + " min_child_samples mean std mean std \n", + "8 17 0.015063 0.000635 0.001186 0.000078 \n", + "6 11 0.021619 0.001111 0.001393 0.000095 \n", + "3 8 0.023706 0.001044 0.001507 0.000059 \n", + "9 8 0.022872 0.000284 0.001427 0.000067 \n", + "4 NaN 0.281148 0.012409 0.016739 0.000678 \n", + "2 NaN 0.280517 0.013183 0.017294 0.001392 \n", + "5 NaN 0.275385 0.012340 0.016589 0.000584 \n", + "1 NaN 0.264602 0.005580 0.016063 0.000214 \n", + "0 NaN 0.268346 0.011811 0.016440 0.000448 \n", + "7 NaN 0.266838 0.012662 0.016058 0.000363 " ] }, "execution_count": 16, @@ -923,7 +1002,7 @@ ], "source": [ "# let's look at performance for the top ranked classifiers\n", - "model_ranker.summary_report()" + "model_selector.summary_report()" ] }, { @@ -942,7 +1021,7 @@ "\n", "The [SHAP approach](http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions) has become the standard method for model inspection. SHAP values are used to explain the additive contribution of each feature to the prediction for each observation (i.e., explain **individual** predictions).\n", "\n", - "The FACET `LearnerInspector` computes SHAP values for each crossfit (i.e., a CV fold or bootstrap resample) using the best model identified by the `LearnerRanker`. The FACET `LearnerInspector` then provides advanced model inspection through new SHAP-based summary metrics for understanding pairwise feature redundancy and synergy. Redundancy and synergy are calculated using a new algorithm to understand model predictions from a **global perspective** to complement local SHAP.\n", + "The FACET `LearnerInspector` computes SHAP values using the best model identified by the `LearnerSelector`. The FACET `LearnerInspector` then provides advanced model inspection through new SHAP-based summary metrics for understanding pairwise feature redundancy and synergy. Redundancy and synergy are calculated using a new algorithm to understand model predictions from a **global perspective** to complement local SHAP.\n", "\n", "The definitions of synergy and redundancy are as follows:\n", "\n", @@ -1010,21 +1089,14 @@ "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "model_inspector = LearnerInspector(n_jobs=-3)\n", - "model_inspector.fit(crossfit=model_ranker.best_model_crossfit_)" + "model_inspector = LearnerInspector(\n", + " pipeline=model_selector.best_estimator_,\n", + " n_jobs=-3\n", + ").fit(\n", + " drilling_obs_reduced_featset\n", + ")" ] }, { @@ -1036,13 +1108,13 @@ "data": { "text/plain": [ "feature\n", - "Rate of Penetration (ft/h) 0.181858\n", - "Inverse Rate of Penetration (h/ft) 0.180741\n", - "Weight on bit (kg) 0.155553\n", - "Mud density (kg/L) 0.150201\n", - "Rotation speed (rpm) 0.141218\n", - "Depth of operation (m) 0.100157\n", - "Hole diameter (m) 0.090272\n", + "Rate of Penetration (ft/h) 0.184227\n", + "Weight on bit (kg) 0.164185\n", + "Inverse Rate of Penetration (h/ft) 0.163715\n", + "Rotation speed (rpm) 0.147971\n", + "Mud density (kg/L) 0.145815\n", + "Hole diameter (m) 0.104211\n", + "Depth of operation (m) 0.089876\n", "Name: 0.0, dtype: float64" ] }, @@ -1074,7 +1146,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1084,7 +1156,7 @@ } ], "source": [ - "synergy_matrix = model_inspector.feature_synergy_matrix(clustered=False)\n", + "synergy_matrix = model_inspector.feature_synergy_matrix()\n", "MatrixDrawer(style=\"matplot%\").draw(synergy_matrix, title=\"Synergy\")" ] }, @@ -1092,7 +1164,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To interpret the synergy matrix, the first feature in a pair is the row (\"perspective from\"), and the second feature the column. For example, let's take the highest synergy value of 78% for the feature pair rotation speed and weight on the bit. From the perspective of rotation speed we find that 78% of the information is combined with weight on the bit to predict failure. This seems sensible in context, as drilling with both a high bit weight and a high rotation can have a disproportionately large impact on the wear of the equipment, and so drastically increase the likelihood of failure. It is understandable that the synergy is also high from the perspective of weight on the bit (68%). This also means if we want to reduce the impact of either of these factors on the likelihood of failure, we should consider them both together and not independently." + "To interpret the synergy matrix, the first feature in a pair is the row (\"perspective from\"), and the second feature the column. For example, let's take the highest synergy value of 81% for the feature pair rotation speed and weight on the bit. From the perspective of rotation speed we find that 81% of the information is combined with weight on the bit to predict failure. This seems sensible in context, as drilling with both a high bit weight and a high rotation can have a disproportionately large impact on the wear of the equipment, and so drastically increase the likelihood of failure. It is understandable that the synergy is also high from the perspective of weight on the bit (70%). This also means if we want to reduce the impact of either of these factors on the likelihood of failure, we should consider them both together and not independently." ] }, { @@ -1109,7 +1181,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1119,7 +1191,7 @@ } ], "source": [ - "redundancy_matrix = model_inspector.feature_redundancy_matrix(clustered=False)\n", + "redundancy_matrix = model_inspector.feature_redundancy_matrix()\n", "MatrixDrawer(style=\"matplot%\").draw(redundancy_matrix, title=\"Redundancy\")" ] }, @@ -1127,7 +1199,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As with synergy, the matrix row is the \"perspective from\" feature in the row-column feature pair. First let's consider the feature pair (ROP, Inverse ROP). The redundancy here is similar from the perspective of either (73%) and this is because one feature is the inverse of the other and so can substitute one another in the model for predicting failure. Next let's consider the feature pair (depth of the operation, hole diameter) which have the highest redundancies after (ROP, Inverse ROP). From the perspective of hole diameter 50% of the information is duplicated with depth of the operation to predict failure. Intuitively, we can see why, as the depth of operation and the hole diameter are highly connected as drillers use thinner drilling bits as they drill deeper into the earth." + "As with synergy, the matrix row is the \"perspective from\" feature in the row-column feature pair. First let's consider the feature pair (ROP, Inverse ROP). The redundancy here is similar from the perspective of either (85%) and this is because one feature is the inverse of the other and so can substitute one another in the model for predicting failure. Next let's consider the feature pair (depth of the operation, hole diameter) which have the highest redundancies after (ROP, Inverse ROP). From the perspective of hole diameter 51% of the information is duplicated with depth of the operation to predict failure. Intuitively, we can see why, as the depth of operation and the hole diameter are highly connected as drillers use thinner drilling bits as they drill deeper into the earth." ] }, { @@ -1144,15 +1216,11 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -1188,43 +1256,38 @@ "cell_type": "code", "execution_count": 22, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# remove redundant feature Inverse ROP\n", "redundant_features = [\"Inverse Rate of Penetration (h/ft)\"]\n", "drilling_obs_not_redundant = drilling_obs_reduced_featset.drop(feature_names=redundant_features)\n", "\n", - "model_ranker = LearnerRanker(grids=clf_grid, cv=cv_approach, n_jobs=-3).fit(\n", - " sample=drilling_obs_not_redundant\n", + "model_selector_2 = LearnerSelector(\n", + " searcher_type=RandomizedSearchCV,\n", + " parameter_space=[rforest_ps, lgbm_ps],\n", + " cv=cv_approach, \n", + " n_jobs=-3,\n", + " scoring=\"accuracy\"\n", + ").fit(\n", + " drilling_obs_not_redundant\n", ")\n", "\n", - "model_inspector = LearnerInspector(n_jobs=-3)\n", - "model_inspector.fit(crossfit=model_ranker.best_model_crossfit_)" + "model_inspector_2 = LearnerInspector(\n", + " pipeline=model_selector_2.best_estimator_,\n", + " n_jobs=-3\n", + ").fit(\n", + " drilling_obs_not_redundant\n", + ")" ] }, { "cell_type": "code", "execution_count": 23, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1234,7 +1297,7 @@ } ], "source": [ - "redundancy = model_inspector.feature_redundancy_linkage()\n", + "redundancy = model_inspector_2.feature_redundancy_linkage()\n", "DendrogramDrawer().draw(title=\"Redundancy linkage\", data=redundancy)" ] }, @@ -1266,11 +1329,8 @@ "As the basis for the simulation, we divide the feature into relevant partitions: \n", "\n", "- We use FACET's `ContinuousRangePartitioner` to split the range of observed values of ROP into intervals of equal size. Each partition is represented by the central value of that partition. \n", - "- For each partition, the simulator creates an artificial copy of the original sample assuming the variable to be simulated has the same value across all observations - which is the value representing the partition. Using the best `LearnerCrossfit` acquired from the ranker, the simulator now re-predicts all targets using the models trained for all folds and determines the average predicted probability of the target variable resulting from this.\n", - "- The FACET `SimulationDrawer` allows us to visualise the result; both in a matplotlib and a plain-text style\n", - "\n", - "\n", - "Finally, because FACET can use bootstrap cross validation, we can create a crossfit from our previous `LearnerRanker` best model to perform the simulation so we can quantify the uncertainty by using bootstrap confidence intervals." + "- For each partition, the simulator creates an artificial copy of the original sample assuming the variable to be simulated has the same value across all observations - which is the value representing the partition. Using the best estimator acquired from the selector, the simulator now re-predicts all targets using the models trained on full sample and determines the mean predicted probability of the target variable resulting from this, as well as a confidence interval derived from the standard error of the mean predicted probability.\n", + "- The FACET `SimulationDrawer` allows us to visualise the result; both in a matplotlib and a plain-text style" ] }, { @@ -1283,33 +1343,25 @@ }, "outputs": [], "source": [ - "# create a bootstrap CV crossfit for simulation using best model\n", - "boot_crossfit = LearnerCrossfit(\n", - " pipeline=model_ranker.best_model_,\n", - " cv=BootstrapCV(n_splits=1000, random_state=42),\n", - " n_jobs=-3,\n", - " verbose=0,\n", - ").fit(sample=drilling_obs_not_redundant)\n", - "\n", "# set-up and run a simulation\n", "SIM_FEATURE = \"Rate of Penetration (ft/h)\"\n", "rop_bins = ContinuousRangePartitioner()\n", - "rop_simulator = UnivariateProbabilitySimulator(crossfit=boot_crossfit, n_jobs=-3)\n", + "rop_simulator = UnivariateProbabilitySimulator(\n", + " model=model_selector_2.best_estimator_,\n", + " sample=drilling_obs_not_redundant,\n", + " n_jobs=-3\n", + ")\n", "rop_simulation = rop_simulator.simulate_feature(feature_name=SIM_FEATURE, partitioner=rop_bins)" ] }, { "cell_type": "code", "execution_count": 25, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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vT8WKFUt7KCIiIiIi152DUQpTn9OnT+ezzz7j7bffpnfv3oUOhlu2bMkHH3xA69atiYqKonPnzmzZsgV/f3+7zjeZTHh6epKZmYmHh4ftuNVq5aOPPsLT05OOHTsSEBCAo6MyyeXWY7FY2LRpE8ePH+fll1/WDLGIiIiI3HZKZWbYbDazdetWvLy8Cn3ukSNHcHFxoXXr1gCUK1eOgwcPsnjxYgYPHlyscSUmJmIymRg4cCDh4eHF6kvkRte6dWsOHjxISkoKwcHBpT0cEREREZHrqlSC4SeeeKLI5+7fv5969eoBkJGRwfDhw+nVqxf79++/7Dm5ubmYzWbba5PJdMl2VqsVAGdnLaWWW5+TkxPwv3/3IiIiIiK3k5suBzg9PR1/f3+ioqLo3bs3zz77LCNGjCAtLe2y57z//vt4enra/gQFBV3HEYuIiIiIiMiN5qYLhr29vVm1ahVDhgxh0qRJNG/enOTkZHx8fC57zuuvv05mZqbtT0JCwnUcsUh+FouF8ePHk56eXtpDERERERG5bd3Q+cDp6el4e3vnO1anTh1+//13UlJS8PX1BWDXrl3UqVPnsv24uLhog6BraMiQIQwbNox77rmntIdyUzh69CjPPvssVatWpWvXrrbj8fHx9OnTB4BDhw6xYMECPVMREZHrwGyxsOL3fcxZ8zvxKfpltYg9GlavwMcj+pT2MIrlhg2GR44cyYQJE9iyZQtNmjSxHa9RowYtWrRg165dtt2kFy1axObNm0txtKXrzTffZO3atTg4OBAREcGECRMKlQreoEGDAjtxz58/P9+mSpGRkezZs4eePXuW1LBvWV9++SXPPffcZd+vVasWa9asKVAOLDg4mF9++QXI+wWDiIiIXFs5uWZ+2ryLqct+40xcUmkPR+Smcig+pbSHUGylEgw/88wzHDhwwPa6Xbt2ADRq1IiPP/4YgJCQEAICAvD09Cxw/ty5cxk2bBipqakYhsGECRMICAi4PoO/wXz00Uc4ODiwZcsWAL777juee+45vv32W7v7cHNzswVhl3Pq1CmWLFmiYNgOX3zxxRWDYYCOHTtep9GIiIjIP2Xl5LJg4w6mrdhMdGIqAE6+PjjUuwPH8LBSHp3IzcHHy620h1BspRIMjx8//qptxowZw5gxYy75XqVKlVi7dm1JD+um5OTkxFNPPWV7/eCDD/L+++/bfX52djaurq6Xff/cuXP079+f5ORkYmJibL+4ePjhh/Ndd/fu3bz33nu2dbDTpk2jevXqdo0hMjKS5557jsqVK7Nr1y6SkpLo1q0bH374oa3Oc1JSEiNGjODUqVOYzWbuvfde3nnnHVsfr7zyCps2bcLb25uHH36Yb775BoCXX36ZBx98EICFCxfy6aef4uzsjIODA//973/zZR3s2bOHF154gezsbMxmM//3f/9H7969be83aNCAt956iwkTJpCamkr58uWZNWuWrVZ13759iYuL4+zZs7bnVLNmTSZNmmR7Ri+88AKQl9q/b98+ypUrZ9czuuhqz0FEREQuL8OUzbxf/mTGyq0kXEiHdgv0x3JXbRwrV8CIT8Sy/0gpj1Lk5pDl7w1t7iztYRTLDZsmfSO5Y9Bb1/V6B2baH9y8+OKL+V6vW7eOVq1a2X3++fPncXZ25plnnuHQoUM4Ozvz6quv2oK58PBwfvnlFzZu3MicOXP4+uuvL9nPqlWrWLhwIR4eHixbtoz/+7//Y9GiRXaP4+eff2bJkiV8/vnnmM1mBgwYwJQpU2wB97PPPkuHDh0YNmwYVquVgQMHMm/ePB555BEA/vOf/xAZGUnbtm3ZtWsXmzdvtgXSAIcPH+b9999n/fr1+Pn5cfr0abp3787OnTtxcXHBbDbTp08fFixYQN26dUlOTqZt27Y0adKEChUqAHmlvA4fPszq1asBeOmll5gyZQojR44E4IcffgCgevXql5xpr1evnu34xedbWFd7DiIiIlJQaoaJuWu3MWvV76Rk5JXY9AkLwnRnbYyKEbiYssjeuBVOnuGOSmVxcHAo5RGL3PjqhvmW9hCKTcHwLeT06dN88MEHLFmyxO5z4uPjSU5O5oUXXqB69epERUVx3333MWPGDO666y67+xk2bJhthrRLly6MGjWqUGO/44476NatG5BX53nUqFG89957tmB448aNzJ07FwBHR0dGjhzJuHHjCgSBSUlJ/Pe//80XCAOsXbuW/v374+fnB0CFChXYsGGDrd2ePXuoWbMmdevWBcDf359HH32UlStXMnz4cCCvXvWIESNsfXbt2tUWAF8v9j4HERERgcTUDGat3sq3a/8g3ZQNQEhEGVLvqEl2RFlcrVayt+8mZ/8RapYL4b23hlOncngpj1pErhcFw3YozExtaYmNjaVfv3588803BAYG2n1e48aN2blzp+11REQEo0aN4ttvvy1UMPz3a7q4uGC1Wu0+F/LWiP/zdWZmpu11QkJCvtnU3NxcKleuXKCfWrVqXXKdeXx8PFWrVs137O+bjKWlpfHnn3/mu0ZaWlq+jaycnZ3zlfDy8PAgNzfXntsrMfY+BxERkdtZbFIq01du4Yf12zHl5P2srlS1PIl31CQlJBhHB3A+HknG73/hajHz/IPtGNy1BS7OTqU8chG5nhQM3wJSUlLo06cPn3/+ObVq1bpsu0uVqtqzZw9paWn5Svh4eHiQk5NT4HzDMEpu0P8QHx+f73VcXFy+oLZixYpX3eQLuGy96eDgYGJjY/MdS0hIwN/fHycnJ0JCQmjRogU//vhjEUaf37V8TvY+BxERkdvR2fhkvln+Gwt//YucXDMAdWpXIfmOmpy/UDnDPzWVuLW/YSSl0LhmRd4deh+VygRfoVcRuVU5Xr2J3MgyMzN56KGHGDt2LI0bN75su5EjRxIQEMCff/6Z77iLiwsjRowgLi4OyFsX++WXX9o2nbooJCSEffv2FXrG114HDx60rcU1m8188skn+Wrw3n333UyfPt32evny5cyaNcvu/jt06MC3335LamrejpFRUVG0bdvWdj/Vq1fn6NGj7Nu3z3bOBx98wO7duwt9L2az2fY8S1pxn4OIiMit6FR0Aq9PXUy3//uceev/JCfXTLP6NblrwP0ca9GUeH9/Ag0Ljr9sJnb+cryysnh7cE9mvDpYgbDIbUwzwze55557jr179/LWW/k3+Vq9ejUuLi6215crVVW7dm0+/vhjunXrhru7O5C3SVPLli3ztbvjjjto3749jRo1ws/Pj379+vHkk0+W2H20bNmSFStW8P7775OYmEjnzp3z9T9u3Dief/55pk+fjsVioWbNmnz22We298ePH8/HH3/MqVOnaNeuHR4eHqxYscL2fq1atXjttde49957bbtJT5s2zfaMXFxcmDt3Li+++CLZ2dlkZWXRq1cv6tWrV+h7+eijj+jcuTM+Pj7UqVOHiRMnArBv3z5byaVdu3bRp08f3NzcGDx4MIMGDSIpKcn2S4hDhw6xe/du/Pz86NSpE6+99ppdz0FEROR2cjQqhilLf+XnbfuwGgaODg50bHonDnfV5lerMwbg4+iA95HjnNr0BxgG7RvW4o2B3QkLvPk3/xGR4nEwrmVO5w3KZDLh6elJZmambdMnyFt3O2HCBEaMGEFoaGgpjvD2EhkZybBhw1izZk1pD+W2on/vIiJys9p/8hyTl25i7Y6DADg7OdK9RT38GtThJ5OVLKuBswPUycpg18I15GSaCPLzZszAe+nc+A7tFi0igGaGRUREROQm8dfR00xasolf9xwFwNXFmd6tG1CxST3mpGQTl5G3TriJhzPnVv/KH0dOAfBg6wa89HBn/L0LbrIpIrcvBcNS6ipWrKhZYREREbkkwzDYdvAkk5dsYtvBkwB4uLnySPvGNGjegGkJGSyMywCglpcb5U5FsnLpBqyGQfmQAN4ech/N61QpxTsQkRuVgmERERERueEYhsGm3UeZvHQTu46dAcDbw40BnZrSrnUjZsSm83JkIgChrs50dTVY8e1P7I5LwtHBgSHdWvDsA+3wcHMtzdsQkRuYgmERERERuWFYrVbW7TzEpCWbOBh5HgB/b08e69KMnm0a8118OkMPRWM2wN3RgX5hvpzb9Adfb9oJQM3yYYx9/H7urFyuNG9DRG4CCoZFREREpNSZLRZ+3rafKUs3cfxcXonCID9vhnZrwYNtGrEiKZP++8+SYrbiANwX5kud5CS+/HIOCSnpuLo4M+L+Ngzpdg8uzk6lezMiclNQMCwiIiIipSbHbGbp5t1MWfYbZ2Lz0p7LBPoxrPs9PNCqAX+m5zB4/zlOmXIAaOznyeBgL75bsJq3dx4CoFGNirw79D4ql1XNYBGxn4JhEREREbnusnJyWbhpJ1OXbyY6MQWA8qGBDO/Rkp731ONklpkXDkfzR3ImABU8XBhZKYTYfYf518Q1pJuy8fZwY9TDnejTphGOjo6leTsichNSMCwiIiIi101GVjY//LKd6T9vIT4lHYCq4SEM79mabk3rkGwx+PBEHD9Fp2AAvs6OPFkxmCaOFsZ+vYDthyMBaNegJm8+1oOwQN9SvBsRuZkpGBYRERGRay41w8S36/5g1qrfSU7Pm+2tXbEsT93Xmg4Na5FjwIyziUw7nYDJauDsAH3DAxgSHsDCtb/T56eN5OSaCfL14vWB3enS5A4cHBxK+a5E5GamYPgmt3HjRrp37050dDTe3t6247Vq1WL06NEMGjSoFEcnIiIit7uktAxmr/6dOWu2kW7KBqBe1Qieur8Nre+qjgGsikvli5NxRGebAWgb5M0LlUNJi4njiQ++4fDpaAB6tazP//Xrgr+3Z2ndjojcQhQM3yK+//57Hn/8cQA2b97MyZMnS3lEIiIicjuLS05jxsotzFu/HVN23uZXTWtX5sn7WtO0dmUcHBzYlZLJJydi2ZeWBUBNLzderBJKXU8Xvlr0CzNXbsVqGESEBPD2kJ60qFO1NG9JRG4xCobt0GDToet6vb9a1ypU+x49euQLhqdNm8b9999ve/+HH37g008/xcnJCVdXV7766ivq1Klje3/37t2MHj2anJwcMjIyeOONN7j33ntt7zdo0IC33nqLCRMmkJqaSvny5Zk1axYeHh7FvFMRERG51ZxLSGba8s0s2LSTnNy8md5Wd1XnyZ6taVijAgBnTTl8cSqO1XFpAAS7OvFspRB6hPnxx4GT9Jq+hDNxSTg6ODCoa3Oee7A9nm6upXZPInJrUjB8C/Dx8cHb25sDBw5QoUIFIiMjadeuHQAHDhzggw8+YMOGDfj7+7Nnzx769+/Prl27bOe/9tprTJ48mfLlyxMTE0PTpk05fvw4Tk55NfoyMjI4fPgwq1evBuCll15iypQpjBw58rrfq4iIiNyYImMSmLrsN37avAuzxQpAx0a1ebJna+pUDgcgzWxh2ukE5p5NItcwcHd0YGBEIIPLB5Gblc1b035i4aa/AKhZPox3h95P3SrlSu2eROTWpmDYDoWdqS0Nw4YNY9q0adxxxx3069eP6Oi8tTUrV67ksccew9/fH4C77rqL8PBwjhw5Qo0aNQBYvny5rZ+wsDBbUBwenveDKzc3lxEjRtjadO3alR9++OE63ZmIiIjcyI6djWXK0l9Z8fterIaBo4MD3ZvVZXjPVlSPCAPAbBgsOp/MxMh4knItAHQP9eXZSiGEuTmzevsB3pu9goSUdFycnXj6/jY8fm9LXJydSvPWROQWp2D4FtGsWTNGjx7Nrl27WLx4MZ9//jkAaWlpzJs3j6VLl9raxsXFkZaWZns9c+ZMZs6cidVqxcHBgX379mEYhu19Z2dnfHx8bK89PDzIzc29DnclIiIiN6oDkeeZsmQTq7cfAMDZyZFe99RnWPdWVCoTZGu3OTGdT0/EciIzb91wA18PRlUNpY6PB7FJqTw/eTnrduYtSWtYowLvDrmPKuEh1/+GROS2o2D4FvLwww9z/PjxfLtKh4SEMGLECJ577rlLnnP8+HEmTJjAxo0bcXd3B6BNmzbXZbwiIiJy89l17AyTl2xi4+4jALg4O/FQm4YMvbcl5YL9be2OZ2Tz6YlYtiRlABDh7sLIyiF0CPbBMAx++GU7H3+/mnRTNl7ubozq25G+7Rrj6OhYGrclIrchBcO3kKeeeqrAsQ4dOjBgwAAGDhyIv78/WVlZvPDCC3z22Wd4eHhgtVpxcXHB1TVvU4rdu3ezf//+6z10ERERuYEZhsGfh04xackmfj9wAgAPVxf6tm/MkK4tCA3wtbVNzDEzMTKeheeTsQLeTo48USGIR8oF4OroyKnoBN6avoQ/D50CoF39mrwxqDtlAv1K4c5E5HamYPgWV7NmTUaPHk337t1xdnbGbDYzevRo207Q1atXp0ePHjRp0gQvLy8aNWpE06ZNS3nUIiIiciMwDINf9xxj8pKN/HXsDABe7m7073Q3j3VuTqCvl61tttXKd2eT+OZ0AukWK05A37L+PFkxmEBXZ3LNFr5e8SvjF28gJ9dMoI8Xrw+8l65318HBwaGU7lBEbmcOxt8Xh94mTCYTnp6eZGZm5isPFBsby4QJExgxYgShoaGlOEKRa0//3kVE5HKsVivrdx5m0pKNHIg8D4CflwePdWnGox2b4uf1v+9PhmGwJj6Nz0/EcS47b0+RloFevFA5lKpebgAcOHWOMd/8xKHTeRt89mpZn//r1wV/b8/rfGciIv+jmWERERERAcBitbJy234mL93EsbOxAAT5ejG4WwseadcELw+3fO33pZr4+EQsu1NNAFT1dGVUlTCaB+bNGJuycxi/eAMzV27FYrVSLtiftwf35J661a7vjYmIXIKCYREREZHbXK7ZwtIte/h62a9ExiQAUCbQl8fvbUnvNg1xd3XJ1/58Vi5fnozj57hUAAJdnBhRKYT7y/jhfCHl+fcDJ3hr+lLOxCbi6ODAoC7Nea53ezzdXK/vzYmIXIaCYREREZHbVHZOLot+/YupKzZzLj4ZgPIhATzRoxX3tayHq3P+r4oZZgszziQy+2wi2VYDVwcH+kcEMLR8EN4XagKnZJj4eN5qfty0E4DqEaGMHXo/d1WNuK73JiJyNQqGRURERG5DW/ef4O0ZeTO3AFXKBjO8Z2vubXYnzk5O+dpaDIMl0SlMiIwjPscCQJcQH56vHEK4+/9melf/eYD3Zi8nPiUdF2cnnr6vDUO731MgqBYRuRHok0lERETkNpKcnsl/v1vF4t92AVA1PIRnH2xHx0a1cbpEjd9tSRl8ciKWoxnZANT1cWdU1TDq+f5tE9KkVN6bvYK1Ow4C0LB6Bd4Zeh9Vw0Ou/Q2JiBSRgmERERGR24BhGCz/fS//nruSxLQMXF2cefr+Ngztdg8uzk4F2p/MzGbciVg2JWYAUMbNmZGVQ+kS4mMrhWQYBgs27uDj79eQlpmFp7sro/p24uF2jXG8RGAtInIjUTAsIiIicos7G5/MuzOX8eueowDcXasSbw/pSaUywQXaJudamBwZz4LzSZgN8HRyZGj5IPqXC8Dd6X8BbmRMAm9NW8Ifh04B0KZeDd4c1IOyQX7X5Z5ERIpLwbDIDWjBggXcdddd1KhRo7SHIiIiNzGL1crcNdv4/Mf1mLJz8PV05+VHOvNg64a22d2Lcq0G359LYsrpeNLMVhyBB8v4MaJSCEGu//vKaLZYmLFyC+MXbSA710ygjxevDehGt6Z3FuhTRORGpmBYrrmZM2cSFRXF66+/XtpDuWk8++yzDB48mH//+9/5jg8YMICzZ88SHR3NgAED9ExFROSyDp2O5q1pS9h78iwAXe+uw+j+3Qjx98nXzjAMfklIZ9yJWM5k5QLQ1N+TF6uEUsPbPV/bA5HneeObnzgYeR6A+++pxyuPdsXf2/M63JGISMlSMHyTM5lMvPzyy+zcuRNHR0eqVKnC559/TkBAAAD/+c9/WLlyZb5zzpw5w+DBgxkzZoxd13jmmWf4888/8fLysh17/fXX6dixY752X375Jc8991wx7+jWN3PmTB588EF8fHwu22bZsmVUrFixwPE5c+bY+oiKirpmYxQRkZtXVk4uExZvYPrPW7BYrZQJ9OWNx3rQrkHNAm0Pp2fx0fEYdqSYAKjs4cq/qobSMsAr3yxvVk4u4xf9woyVW7FYrYQH+/P24J60rFvtut2XiEhJUzB8k3vppZcoX748X331FZAXJA0cOJBly5YB8Morr/DKK6/kO6dt27Y88sgjdl/j/PnzLF68mPDw8Cu2++KLLxQM22HGjBl07NjxisFw48aNr+OIRETkVvH7gbxySadjEnFwcKB/p6a80LsDXh5u+dqZDYOZZxKYFBmP2QB/ZyeeqhTMg2X8cXHMn+r8x8GTvDl9ia3Pxzo347ne7fFyz9+niMjNRsHwTW716tUcOXLE9nrQoEF8+umnpKam4uvrW6D90qVLqVmzJtWq2f+b3OjoaEJDQy/7ft++fYmLi+Ps2bO0a9cOgJo1azJp0iRbm6SkJAYMGMCZM2dIS0vj3XffpUePHnaPoUGDBjzzzDPMnj2bjIwMKlSowNdff01QUBCQl+L15ptvsnLlSpydnalUqRKTJk3Czy9vE4/vvvuOKVOmsH37dlavXs3o0aOxWCy0atWKDz74AIC9e/fy8ssvk5WVRXZ2NiNGjGDgwIH57mHEiBGcOnUKs9nMvffeyzvvvGN7f8iQIXTp0oU5c+aQkpKCYRhMmzbNtu73gw8+YM2aNezatYs+ffrg5pb3JeLnn3/G3T0vDe3i8zt16hTvvfce/fv3t/sZ2fMcRETk1pScnslH81az6Ne/AKhWLpSxQ++jXrXyBdqeMeUw5vB59qTmzQb3LevPs5VD8PnHjtKpGSY++X4N8zfu+F+fj99PvaoR1/huRESuDwXDdrhj0FvX9XoHZr5z9UYX5ObmYrFYcP5bMfvs7Ox8ry+yWq2MHTuWhQsXFmo8mZmZfPbZZ6xdu5aMjAwGDhzIk08+aXv/hx9+AKB69er88ssvl+xj0aJFbNy4kYiICM6fP0/z5s2599577S67kJKSwsmTJ9m4cSMAX331Fc888wzz5s0DYOrUqURGRrJt2zYcHR2ZNGkSr7zyii0g79evH/369aNatWqMGzeOxYsX4+/vb+s/NzeXfv368f3331OnTh2ysrLo1q0bd911F/Xq1QPy1vF26NCBYcOGYbVaGThwIPPmzcs3y7548WLmz5+Ph4cHy5Yt45VXXmHRokUAvPbaa7z22mu0a9eOOXPmUK5cuQL3efH5/T3ILoyrPQcREbm1GIbBz9v28eHcn0lIzcDF2Ymn72vD0O734PqP7wKGYbAoOoWPj8dgshqEuDrzTo2yNA/0KtDv2u0HGTt7OXHJaTg7OfHUfa0Z1qNlgT5FRG5mKgB3k+vZsyfPPfccubm5WK1W3n77bSpVqoSnZ8GNLL799ltatWpFREThfqObkpJC3bp1WbVqFWvWrGHjxo2FDq569eplu27ZsmWJiIggOjra7vMNw+Cll16yvX7mmWf49ddfba+XLl3KCy+8YAuun3zySdatW1egH4vFwvPPP58vEAY4cuQIVapUoU6dOgC4u7uzdOlSqlevbmuzceNGhg0bBoCjoyMjR45kyZIl+frp378/Hh4eAHTp0oUDBw7YfY8lwd7nICIiN79zCck8/dlcXpq4gITUDBrXrMji90bw1P1tCgSt8TlmXtgfxdij0ZisBp1DfJjfqHKBQDguOY0Xvvye57+cR1xyGvWrlWfh2KcY0autAmERueXoU80OhZmpvd4++eQTxo4dS5s2bShXrhx79uzhu+++K9AuJyeHjz76iLVr1xb6GidPnrT9t4eHBx999BG9e/fmqaeesruPwMDAfK89PDzIzc21+3wnJyfbpmAADg4OtqATIC0tjWeeecaWbgxccnYcoGHDhgWOxcfHF0gF9/b2zvc6ISHBlsYMebPJlStXztfm7/fp4uKC1Wq90m2VuMI8BxERuTlZrFa+XbuNcQvyyiX5eLrz0sOd6N264SUzrtbHpzH2aDTJuRZ8nB0ZXa0M3ULzL6UyDIOFm3by0bzVpGZm4enuyr8e6ki/Dk3szuISEbnZ6FvyTc7V1ZWxY8cyduxYvvzyS+rWrXvJYG/KlCn06tWLkJCQy/aVlpZWYFOn5ORkfv75Z/r162c75uHhQU5OToHzDcMoxp1cmcViISkpyRYQG4aByWSyvR8SEsIbb7xB3bp1r9iPs7NzviD6ouDgYGJjY/MdS09Px9HR0TbLXrFixcumgRfWtXpW9j4HERG5OR0+Hc2b05ew90ReuaTOTe7g9QH3FiiXBJButvDR8ViWxKQAcLe/J+/WLEuYm0u+dqdjEnlrxhK2Hcj75Xeru6rz1uAehAf5X9ubEREpZfpV3y1i27Zt/PTTT5esO5uRkcGECRN48cUXL3v+woUL8fX15bPPPst33Nvbm7Fjx/Lnn38CeUHcv//9b3r37l2gD7PZTFxcXDHv5PI+//xz23+PHz+eli1b2l537dqVcePGYbFYAIiMjOTll1+2u+8aNWpw4sQJDh48COStu77//vs5evSorc3dd9/N9OnTba+XL1/OrFmzCn0fISEh7N69u9Dn2aO4z0FERG5MWTm5fDZ/LX3enszeE2cJC/Dlq5H9GPfsw5cMhLcnZ9J3x0mWxKTg5ujAy1VDmVi3fL5A2DAMZq/eSq8xE9h24CQBPp7896neTHqxvwJhEbktaGb4FpCQkMDw4cNZtmwZTk5OBd7/7LPPGDx48BV3FPb19SUgIKBAOrOzszMrVqzg8ccft83EdujQgVdffbVAHx999BGdO3fGx8eHOnXqMHHixGLeWf5xVKxYkTZt2pCRkUG5cuWYOnWq7f0hQ4Zw+vRpWrVqhZOTE97e3nz66ae29zdv3syYMWM4duyYLdV58uTJtp2eXVxc+O677/jXv/5FdnY2WVlZjBgxwrZ5FsC4ceN4/vnnmT59OhaLhZo1axb45YE93nzzTYYPH86HH36Ii4tLvt2kO3bsiMVi4dSpU3h4eDB16lTq169vu86gQYM4ffo00dHRZGdns3btWkJCQmybmF3tOYiIyM1n28GTvPW30kaPdribF/p0wNvDvUDbHKuV8afimR2ViAHU9nbnvVplqeKZvwxScnomr09dzC9/HQagZ4u7ePXRrgT4FNxMS0TkVuVgXMvc1huUyWTC09OTzMzMfCmzsbGxTJgwgREjRlyxlJBcf9WrV883SyvFp3/vIiI3tuT0TD7+fjULN/2vXNK7Q++j/iXKJQEcSc9izOHzHM3IxhF4vEIQT1QILlA3ePfxKEZNmM+5+GR8Pd157/FedGxc+1rfjojIDUczwyIiIiI3EMMwWPnHfj6Ys8JWLump+1rzePdLlzayGAazoxIZfyoOswHl3V14r1Y4d/l6FOh31qqtfPLDGswWK3Url+OTZ/oQERJQoE8RkduBgmG5KWhWWEREbgfnEpIZO3M5G3cfAaBRjYq8M6QnVcIvvQHmWVMObxw+z1+peUuZHirrz4tVQvFwyr8tTEqGide+XmRLi36sczNefLiTyiWJyG1Nn4AiIiIipSyvXNIffP7jOjKz8soljerbiYfaXLpckmEYLIlJ4b/HY8m0WAl2deLNGmVpFehdoG2BtOhhvejYSGnRIiIKhkVERERK0ZEzMbw5fQl7jkcB0LnxHbw+8NLlkgASc8yMPRrNhoR0ADoE+/B69TACXPJ/rVNatIjIlSkYFhERESkF2Tm5TFyykWkrNmO2WAkL8GXMY93p0LDWZc/ZmJDGu0eiScy14O3kyCvVwuge6ouDQ/5NspQWLSJydfpEFBEREbnO/jh4kremLyUyJgEHBwf6dWjCv/p0vGS5JIAMs4VPTsSyKDoFgMZ+nrxbsyxl3V0KtP17WrSPpzvvKy1aROSSSi0Y/vTTT5k1axZOTk40atSI8ePH4+JS8AP9UhYuXMiXX36Js7MzDg4O1KtXjw8++MDu80VERERKQ0qGiY/nrebHTTsBqBoewrtD76NB9QqXPWdXSiZjDp/nbFYurg4OPFc5hEfLBeD4j9lgwzCYvfp3Pv5+DWaLRWnRIiJXUXBHhutg1apVrFmzhu3bt7Njxw7CwsJ4//337To3JSWFV199lcWLF7NmzRpWr16NyWRi1qxZ13jUUhwWi4Xx48eTnp5e2kMRERG57gzD4Odt++gx+it+3LQTF2cnnn2gHT+++9RlA+Fcq8EXJ2N5fPdpzmblUsPLjbkNKzEgIrBAIJySYeK5L+bx729XYrZYeKxzM2aPGapAWETkCkplZnjmzJm8+uqrOF9Yt9KjRw+aNWvG22+/fdVzPTw88PPzIzExET8/P7Kzs0lKSiIiIuKy5+Tm5mI2m22vTSZTse9B8nz88ccsX76crKwsvL29WbNmzSXbHT16lGeffZaqVavStWvX6zxKERGR0nM+IYWxs5axYVdeuaSGNSrwzpD7qHqZckkAxzKyGXPoHIczsnEEhpYP4qmKwbg4OhRoq7RoEZGiKZVgeP/+/dSrVw+AlStXMmnSJMqUKUN6ejre3gVLAvydq6srU6dOpWXLlgQGBhIZGckrr7xCly5dLnvO+++/zzvvvFOi93CjGDJkCLt378bPzw+z2YyzszNfffUVderUuS7Xf+mll3jppZeIjIxk2LBhl21Xq1Yt1qxZQ5s2ba7LuEREREqbxWrlu3V/MG5BXrkkbw83Rj3ciT5tGl2yXBKA1TCYezaJr07GkWMYRLi7MLZmWer7eRZoq7RoEZHiKZVgOCMjAz8/P7788kv27t3L/Pnzadu2rV3BcEJCAkOHDmX58uXUr1+flJQUBg0axIIFC3jooYcuec7rr7/OK6+8YnttMpkICgoq0XsqTV9++SX33HMPAL///juDBw/mzz//LOVRFdSxY8fSHoKIiMh1cTQqhjenLWH3hXJJnRrX5vUB9xIa4HvZc85n5fLW4fP8mZIJwANl/BhVJRQvZ6cCbVMyTLw+dTHrdx4CtFu0iEhRlMonpqurK48++ij169dnypQpACQnJ181EAb44Ycf6NOnD/Xr1wewBdX9+vW7bDDs4uJy22yu1axZM1JTU22v58+fz+TJk7FYLDg5OTFp0iSqVatme3/79u28/PLLODg4kJubyyuvvEKPHj1s7yclJTFixAhOnTqF2Wzm3nvvLdQs++7du3nhhRcA2LVrF/v27aNcuXK29yMjIxk5ciSNGjVi48aNJCQk8MADD/Dmm2+W2BhERESul+ycXCYt2cQ3K37DbLES6u/DmMe6XzFt2TAMlsem8p9jMaRbrAS6OPFGjTK0Dbp0neE9x6N4UWnRIiLFVirBsNVqJTQ01DZbm5aWhtVqLRAMX2qmOCsrC3f3/GUH3N3dr/k64InW7cSReU2vARCCJ087Ni7y+XPnzqVz584AREdHM3v2bJYvX46bmxurV6/mhRdeYNmyZbb2zzzzDHPnzqVatWpkZGQwZMgQunbtalvP/eyzz9KhQweGDRuG1Wpl4MCBzJs3j0ceecSu8dSrV49ffvkFgHbt2l2yzW+//cYzzzzDG2+8QW5uLq1bt+b++++3pdIXdwwiIiLXw5+HTvHW9CWcik4A4JH2eeWSfDwvXS4JICnXzPtHY1gXnwZA2yBv3qhehkDXgl/RlBYtIlKySiUYHjduHJ9//rltjet///tfHn744XxtRo4cyYQJE9iyZQtNmjSxHb/vvvt44IEH6Nu3L+Hh4ZjNZsaMGXPNA6PiBKjX2nPPPYevry9nz57Fx8fHtrN2mTJlWLJkia1dhw4dePbZZ/OdGx4ezo4dO6hcuTJeXl788MMP+d7fuHEjc+fOBcDR0ZGRI0cybty4En3eFStWpFOnTkDeLH6HDh3yrSu/HmMQEREpqtQME598v4b5G3cAUCU8hHeH3EfDGpcvlwTwW2I67xw5T3yOBS8nR16uGsp9YX44OBTcJOufadEDOzdjlNKiRUSKpVQ+Qbt27cqBAwdo1KiRrc7whAkT8rUJCQkhICAAT8/8G0ZUrVqVL774gkcffRSz2UxOTg733Xcfo0aNup63cEP5+5rhqKgoBg4cyPjx46lRowavvfYaf/zxh+0Hq2EY+c6dPXs2X375JQ888ADu7u6MGDGCtm3b2t5PSEjIN6Obm5tL5cqVS3T8gYGB+V57eHiQm5t7XccgIiJSWIZhsHr7Ad6fvYL4lHScnZx4smcrnujRCleXy3/FMlmsfHoilgXnkwFo4OvB2JplKefhesn2BdKiH+9Fx8ZKixYRKa5S+3Xiiy++yIsvvnjZ98eMGcOYMWMu+V7btm3ZsGHDNRrZzS0iIoIHHniAlStXsm3bNrKzs/nll19wcHDAMAxq1KiRr723tzejR48G8tbmdu7cmcWLF9vW9VasWNGW5lxaboQxiIiI/F10YgpjZy3nl78OA9CwegXeGXrlckkAe1JNjDl0jjNZuTg7wDOVQhgYEYjTJWaDlRYtInJtXXpff7lpmUwmli5dSsOGDTGbzXh5edlmhefMmZMv9cpisdCpUydiYmIA8PHxsW2kddHdd9/N9OnTba+XL19uS8O+Xm6EMYiIiEDeviffrt1Gz9Hj+eWvw3h7uPHmoB7Mem3IFQPhXKvBhFNxDNkVyZmsXKp7uTGnQSUGlw+6ZCCckmHi+S/m8e9vV2K2WBjYuRmzxwxVICwiUoK00OQW8Nxzz+Hn54fVaiU3N5ehQ4fStm1bmjdvzuDBg2nRogWurq4MHDgQLy8v23lOTk6MGjWKBx54ADc3N3Jychg+fDiVKlWytRk3bhzPP/8806dPx2KxULNmTT777DPb+5999hlLliwhKyuLw4cP29KZ586dS3h4OPv27eO5554D8naT7tOnD25ubgwePJhBgwbZdX9XG4OIiMj1cDQqlremL2HXsTMAdGyUVy4pLPDy5ZIATmRmM+bQeQ6mZ+EADIoIZESlYFwvU2tYadEiIteHg/HPRaS3AZPJhKenJ5mZmXh4eNiOx8bGMmHCBEaMGEFoaGgpjlDk2tO/dxER+2Tn5DJl2a98vew3zBYLIf4+jBl4L50a33HF86yGwbxzSXxxMo5sq0FZN2ferRlOY3/PS7b/Z1r0nZXD+fSZvpoNFhG5RjQzLCIiInIZ2w9H8tb0JZw8Hw9A33aNebFPR3y9PK54Xkx2Lm8dPs+25LyyjPeF+fFy1VC8nZ0u2T4lw8SYqYtZ9/fdovt2uuJGXCIiUjz6hBURERH5h9QME5/8sIb5Gy6USyobzNtD7qNxzYpXPffn2FQ+PBZNmtmKv4sTb1QvQ/tgn8u233M8ilET5nNWadEiIteVgmERERGRCwzDYM32g7w/ZwVxyWk4OznxRI+WPNmz9VVnaVNyLXxwLJrVcWkAtA704o0aZQl2vfR5SosWESldCob/xvHCRhZms7mURyJy7VksFuB//+5FRG53MYmpjJ29nPUXUpUbVCvP20Puo3rE1fdV2JqYwVtHzhOXY8bD0YGXqobxQBm/fFUc/k5p0SIipU+fuH8TGBiIh4cHP/74Ix07diQgIECBgtySLBYLmzZtwtXVFT8/v9IejohIqbJarXz/y3Y+/WEtGVnZeLm78WLfjjzcrvFVvweYLFY+PxnL9+eSAbjL14P3apalvIfrZc/5Z1r0e4/ff9XNuEREpORpN2mP/BtgpKSksHTpUo4dO1ZKoxO5PlxdXenfvz8VK159/ZuIyK3q2NlY3pq2hL8ulEtq37AWYwbeS5nAq/+icH+aiTGHznPKlIOzAzxVMZhB5YNwvsxssGEYzFnzOx/NU1q0iMiNQMGwR8HdIA3DwGQykZGRwW34eOQ24OjoiJ+fHy4uLqU9FBGRUpGTa2by0k22cknBft6MGdidTo1rXza1+SKzYfDN6QS+jozHAlTxdOW9muHU9nG/7DlKixYRufHoE/gSHBwc8PT0xNPz0nUARURE5Oa140gkb01bwokL5ZL6tG3EqL6drlouCSAyM4cxh8+xLy0LgP7lAni2UgjuTpdPp1ZatIjIjUnBsIiIiNzyDMPgj0OnmPHzFjbuPgJA5bLBvDO4J41rVbLr/Pnnk/nsRCxZVoMybs68U6Msdwd4XfGcf6ZFfzKiD+VDA0vqtkREpBgUDIuIiMgtK8dsZuW2/cxYuYVDp6MBcHNxZui99zC8RyvcXK++XCQ2O5d3jkSzJSkDgO6hvrxSLQwfZ6fLnvPPtOgBnZry0sOdlRYtInID0SeyiIiI3HKS0zOZv2EHc9dsIzY5r+5vkK8Xj3a8m0faNyHA5/Izun+3Ji6V949Gk2K24ufsyOvVy9ApxPeK5ygtWkTk5qBgWERERG4Zp2MSmbV6K4s2/YUpJxeAauVCGdS1OT2a1bVrJhggzWzh38diWBGbCkCLAC/eqlGGULfLn6+0aBGRm4uCYREREbmpGYbBzqOnmblyK+t2HrJVgrjnzqoM7tqCFndWveoO0X/3R1IGbx4+T0yOGXdHB/5VJZQ+Zf2v2IfSokVEbj76hBYREZGbktliYc32g8xYuYW9J84C4OLsRM8WdzGoS3OqR4QVqr8si5WvTsUx92wSAHf6uPNezXAqerpe8by9J87y4vgflBYtInKTUTAsIiIiN5W0zCx+3LST2at/53xCCgD+3p7069CER9o3IcTfp9B9HkrP4vVD5ziRmYMT8ETFYB6vEITzFWaD89Kit/HRvNVKixYRuQkVORjOzMzEZDIRGBhYqNQjERERkaI4G5/MnDW/s2DDTjKysgGoVCaIQV2bc1+Leni4XXkG91LMhsHMMwlMiozHbEAlD1feq1WWOj5XrjmcmmFizDc/sXbHQUBp0SIiN6NCfWLv2LGDcePGsXXrVtzd3fH29iYmJobw8HD69u3L8OHD8fC4esF6EREREXvtOR7FjJVbWLP9IBarFYCmtSszqGtzWt9VHUdHxyL1e8aUw5jD59mTagLg4XB/RlYOxcPpyv0pLVpE5NZgdzD84osvcvToUZ555hlmzpyZ7wfP2bNn+fbbb2nbti1Tp06lbt2612SwIiIicnuwWK2s33mImSu3svPoaQCcnRxt64HvqBRe5L4Nw2BhdAqfHI/BZDUIcXXm3ZplaRZw5XJLSosWEbm1OBgXt1y8ih9//JHevXtfsU18fDyHDh2iZcuWJTK4a8VkMuHp6UlmZqZmskVERG4gGVnZLPr1L2av+p0zcXkbWfl6utO3XWMe7Xg3ZQL9itV/ZGYOn5yI4dfEDAC6hPgwuloZ/Fycrnie0qJFRG49dgfDtxIFwyIiIjeWmMRU5q7dxg+/bCc1MwuA8iEBDOzSjAdaNcDL3a1Y/e9ONTHrTAK/JKRjAD7OjoyuVoZuob5XPXfvibOMmjCfqLgkpUWLiNxC9OtMERERKTUHIs8zc+UWft62D7Mlbz1ww+oVGNS1Oe0b1sKpiOuBAayGwabEdGadSeSvC+uCXRwc6BHmy5MVgwlzc7ni+f9Mi65TKZxPn1FatIjIrULBsIiIiFxXVquVTXuOMnPlVrYdPAmAo4MDXe+uw6CuLahXNaJY/edYrSyPSWV2VCInTTlA3kxwn7IBPBIeQIjb1b/+/DMtun+nprystGgRkVtKoT/RU1NTWbx4Mfv37yctLQ0fHx/q1KnD/fffj59f8dbxiIiIyK3LlJ3Dki27mbXqd06ejwfAy92Nh9o0ZECnppQLCShW/2lmC/PPJfPduSTic8wAlHFzpn+5QB4o44eX85XXBV/097Robw833nu8F52bKC1aRORWU6hg+Ndff+Wpp57i4MGDPPDAAzz99NMkJyeza9cu7rnnHiZOnEirVq2u1VhFRETkJhSXnMa89X/y3bo/SU7PBKBMoB8DOzfloTaN8PF0L1b/Mdm5zD2bxI/nk8m8kGpd3cuNQRGBdA7xxcXRwa5+DMNg7tpt/Pc7pUWLiNwOCrWBVtOmTVm0aBFly5Zl4MCBPPfcczRt2hSAqKgoevfuzbZt267ZYEuKNtASERG59o5GxTBj5VaWbd1DrtkCwJ2VwxnctQWdGt+Bi50ztZftPyOLWWcSWRmXivnCt5m7/T0ZFBFI8wAvHBzsC4IhLy36jWk/sWa70qJFRG4XhfqEd3BwIDw8r67f5MmTeeihh5g8eTIVKlQgIqJ463tERETk5mcYBlv2HWfGyi1s3nccyPv+0KFhLQZ3a0HD6hUKFaReqv/tKZnMPJPI5qS88kiO5JVIGhQRRG2fws8yKy1aROT2VKhg2NfXl82bN3PPPffg5eXF1KlTGTp0KAsXLmTnzp34+l69PIGIiIjcenJyzSzbupeZq7ZwNCoWAA9XFx5o1YCBXZpRMSyoWP2bDYP18WnMPJPIgfS80kvujg7cX8aPgeUCKefhWug+L5UW/cmIPlQIU1q0iMjtoFDB8Ndff82AAQNwdXWlXr16+Pv7k5aWhq+vLy1atGDOnDnXapwiIiJyA0pKy+D79duZu+4PElLSAQjx96F/x7vp264x/t6exerfZLGyJCaFOVGJRGXlAuDv4kS/8AD6hgfg71K0VGulRYuISKHWDF909OhR9u/fT3p6Ot7e3tSpU4fq1atfi/FdE1ozLCIiUjwnz8czc9VWfvptF9m5eTs316xQhsFdmtOt2Z24OhcvqEzKNfP92SS+P5dM8oX1xuXdXRgYEUjPMD/cnYpef3jfybO8OF5p0SIit7siBcM3OwXDIiIihWcYBn8cOsXMlVvYsOuI7XjretUZ3LUFTWtXLtZ6YIAzphzmRCWyJCaFLGveV5Q6Pu4MigikfbAPTsXo/2xcEtNXbuGHX3YoLVpERApfZ1hERERuLzlmMyu37Wfmqq0cjDwPgJuLM/fdU4/HujSnanhIsa+xP83EzDOJrItPw3rhWKtALx6LCKKRn0exguyjUbF8s/w3lv++F4s1r/dHO97N/z3SRWnRIiK3Mf0EEBERkUtKyTAxf8N25q75g5ikVAACfbx4tOPdPNK+CYG+XsXq3zAMNidlMOtMIn+m5NUfdnaAHqF+PBYRSFUvt2L1v/vYGaYs+5Vf/joMgJOjIz1b3MXj97akRvmwYvUtIiI3vxILhnfs2IGnpye1a9cuqS5FRESkFJyOSWT2mt9ZuOkvTNk5AFQND2Fw1+b0aH4Xbq4uxeo/12qwMi6V2VGJHM3IBsDLyZGHyvrzaLkAQt2K3r9hGGzed5yvl/3Kn4dOAXmz2L1bN2RwtxZEhAQUa+wiInLrKJE1wxkZGQQEBBAYGEh0dHRJjOua0pphERGR/AzD4K+jZ5ixcgvrdh7i4teDFnWqMqhrc1rWrVbs9cDpZgsLo5P5NiqJmJy8TbdCXJ3pXy6AB8v64+NctJ2hASxWK6v/PMDU5b/ZUrm9Pdzo1+FuBnZuRrCfd7HGLiIit54SmRn28vLi6aefJjQ0tCS6ExERkevEbLGwZvtBZqzcwt4TZwFwdnKiR/O6DOrSnJoVyhT7GnHZZr49l8iCc8mkW/LW7FbxdGVQRCDdQv1wcSx6kJ2Ta2bJ5t18s2IzkTEJAAT5eTOoSzMebtcEH0/3Yo9fRERuTdpNWjPDIiJyG0o3ZfHjxp3MXrONc/HJAPh5efBI+yY82vFuQvx9in2NE5nZzDqTyIrYVHIvfN1o6OfB4Igg7gn0wrEYM80Zpmx+2LCdmSu3EpucBkBESABDu7WgV6sGuBczlVtERG592kBLRETkNnIuIZnZq39nwYadZGTlrdetGBbEoC7NuK9lfTzdXIvVv2EY7Eo1MeNMIpsS0wFwADoE+zAoIpC6vsX7JXRSWgZz1mxj7to/SM0wAVAjIoxhPVrS9e46ODsVPdVaRERuLwqGRUREbgN7T5xlxsotrP7zgK28UJNalRjUpTlt69fA0dGxWP1bDIONCenMOJPA3rQsAFwdHLivjB8DIgKp6FG8IPtcQjIzft7Cgo07ycrJBaBh9Qo80aMVretVL/Z6ZhERuf2UaDD8f//3f/z3v/8tyS5FRESkEAzDIC45jaNRsRyJiuFoVCwHT0dz+HTeBpdOjo50v7Ae+M7K5Yp9vWyrlWUxeTtDR5rydp72c3akb3gAj4QHEOhavK8ax8/F8c3y31i2dQ/mC+uNW9erzhM9WtGoRsVij19ERG5fJRoM79ixoyS7ExERkStIyTBx7GwsR8/EcPRsLEej8v6kXEgf/jsfT3f6tG1E/45NKRvkV/xr51qYfz6JeWeTSMi1AFDWzZmBEYH0KuOPh1PxZpr3njjL18t+te1s7ejgQPdmdXm8e0tqlcCmXiIiIkqTFhERucFl5eRy4lwcR6JiOXphtvdoVCwxSamXbO/r6U71iDBqlA+lerlQqkWEcUelssVeDwxwLiuXuWcTWXQ+GZM1b1OsWt5uDIoIomOID87FSFc2DIOtB04wddlv/H7gBAAuzk480KoBQ7vdQ4WwwGKPX0RE5KJCBcOOjo6XXZNjGIbW64iIiBSD2WLhdEyiLeg9djaWI2diORObiPUSxR/cXV2oGh5C9YjQC3/CqBERSoi/T4n/TD6cnsXMM4msjkvFcuFY8wAvBkUEcre/Z7GuZ7VaWbfzEF8v+5V9J88B4OXuxiPtG/NYl+YlsrO1iIjIPxUqGO7Xrx99+vShV69el3y/ffv2JTEmERGRW5phGJxPTLkww/u/md4T5+PJyTUXaO/k6EiVssHUuBj0lgujevlQIkICcCrmxldXG+e25Exmnkng9+TMvLEA94b68lhEIDW9i1fDN8dsZtmWvXyz4jdOno8HINDHi4FdmtGvfRN8vVT+UERErp1CBcOffvopXbp0oWvXrri7F/wBqJlhERGR/JLSMvJtZnUsKpajZ2NJN2Vfsn14sD81IkKpVu5/M72Vywbj6nL9VjaZDYM1cWnMPJPA4Yy8cXo4OvBgWX8eLRdIuHvxavhmZuewYMMOZqzcQnRiXqp32SA/hna7hwdbN8CjBNK5RURErqZQP1nDwsJ44YUXmDt3Lo8//niB92vXrl1iAxMREbmZZGRlc/xs3P9mes/GciQqloSU9Eu2D/DxpEZEmC3FuUZEGFXLheDtUbzZ1uIwWawsik5mblQS57LzyhcFuTjRr1wAfcoG4OtSvBq+yemZzF2zjTlrttk2+aoaHsITPVrRremduDirRrCIiFw/DoZxiUVItziTyYSnpyeZmZl4eCgFS0RE7JdjNhMZnZBvtvdoVCxRcUmXbO/p7kq1cqG2FOdq5fI2tgry9b7OI7+8xBwz351L4odzSaSa88oXVfRw5bGIQLqH+eJWzFTsmMRUZqzawg+/7MCUnVd+qV7VCJ7o0apEahyLiIgUhXaTFhERuQSr1crZ+GRb0Hsxvfnk+Xhbvdu/c3ZyokrZYKpf2MH5Yopz2SC/GzbYizTlMDsqkaXRKeRc+N34Xb4eDI4IpE2QN47FXP50Kjqeb5Zv5qfNuzFb8rbdalm3Gk/0aEXjmhW1vEpEREpVqQXDn376KbNmzcLJyYlGjRoxfvx4XFwKtwZp3rx5/Pjjj8yfP/8ajVJERG51hmEQn5KeV6f3b/V6j52Ns81i/p2DgwPlQwP/t5nVhVTnimFBN02a795UEzOiEvglPp2L6WFtgrwZHBFIfT/PYve//+Q5vl7+K2u2H7RVm+jSpA5P9GjJHZXCi92/iIhISbA7TfrcuXOEh1/9B9j58+cpW7bsFdusWrWKcePGsXTpUpydnXnjjTdwcnLi7bfftmvQACdPnuThhx9m3bp1+PgUruSC0qRFRG5PaZlZeeWK/raZ1ZGoWJLTMy/ZPsTfJ2+Wt3zeDs41yodSJTykROr1Xm9Ww+C3xAxmnEngr9S89bouDg50D/NlYEQgVTzditW/YRj8cegUXy/9lS37jwN5s+W9WtZj6L0tqVQmqNj3ICIiUpLsDobbtWtHr169GDZsGF5eXgXe3717N6+99hrDhw/n/vvvv2Jfjz76KE8++SRt2rQBYNu2bTRr1gx7ly/n5ubStWtXvvjiC+rUqWNXe7P5f6UqTCYTQUFBCoZFRG5RVquVw2di/le66MJs7/mElEu29/F0v5DanH+219+7+LOkpSnNbOFoRjaH0rP48XwyJzLzZrq9nRzpE+5Pv/BAQtyKlyRmtVpZ/9dhpi7/jT3HowDwcHPl4XaNGdSlOWGBvsW+DxERkWvB7p+Aq1ev5tNPP6V+/frcddddVK5cGS8vL6Kjo/njjz+IiIhg7NixNGzY8Kp97d+/n3r16gGwcuVKJk2aRJkyZUhPT8fb++obivznP//BMAw+/vhjkpOTGTx48BUD8Pfff5933nnH3lsVEZGb2Nm4JF6auIDdFwKzv3N1caZqeEhe6aKI/832hgX43tTrV3OsVk5m5nAsI5tjGdkcvfB3TE7+msVhrs70jwjkwTJ+eBUzpTvXbGHF73uZuvw3jp+LA8Df25MBnZryaMe7b/pfJIiIyK2vSLtJ7927lxMnTmAymQgJCaFRo0b4+/vbfX61atU4evQoX331FXv37mX8+PG0bduWH3/8kTJlylzx3JSUFGrVqsW8efNo06YNKSkpdO/enddff51u3bpd8hzNDIuI3B7WbD/AmG9+Ii0ziyBfLxrVrPi/zazKh1I+NBCnG3QzK3tYDYPzWbkczcwf9EZm5mC5RHs3RweqeLpR3cuNu/096Rzii4tj8YJ+U3YOP27ayfSft9hm2ssE+jKkWwt6t2l0U6aQi4jI7alIuVF169albt26Rb6oq6srjz76KPXr12fKlCkAJCcn2zUrvHHjRvr06WNLsfbz8+Ozzz7j3//+92WDYRcXl0JvziUiIjeP7JxcPpq3mm/X/QFA+4a1eO/x+2/q2cmkXHO+gPdYRjbHM3PIvMRO1g5ABQ8Xqnm5Ud3TLe9vL3ciPFxwKqEZ75QME9+t+4M5q7eRmJYBQJWywTzevSXdm9fF1VkFKkRE5OZSKj+5rFYroaGhvPLKKwCkpaVhtVoLBMOXSps2m804/+MHrqurK1ZrwS8HIiJy6zsVHc+L4+dz6HQ0Ls5OvPxIZ/p3bHrTpD1nWaycyMzhWEbW/wLfzGzicy411wtBLk5U87oY8Ob9XcXTDQ+nazPjHZecxsxVW/l+/XYysrIBqFu5HE/0aEX7hjVv2LJRIiIiV1MqwfC4ceP4/PPPbYHtf//7Xx5++OF8bUaOHMmECRPYsmULTZo0sR1v3749r776KoMGDaJevXpkZ2czevRoHnvsset9GyIiUsqWbd3D2zOWkpmVQ/nQQD4d0Yc6lW/M0j0WwyAqK7fAut4zphwu9etcD0cHqnm5UfVi0HthxjfQ9fr86I6MSWDais0s/m0Xuea8wLx5nSoM696KZndUvml+2SAiInI5RfqJ+sEHHzB48GC7Si1dSteuXTlw4ACNGjWy1RmeMGFCvjYhISEEBATg6Zk/xc3f35958+bxwgsvYLVaycrK4tFHH+WRRx4p0lhEROTmk5mdwwdzVrBw018A3Nv0Tt4e0hNvD/dSHlmehBxzvvTmoxnZnMjMJstacJsOJ6Cyp2u+gLe6lxvh7i44lkLAeTDyPFOX/8aqP/ZjvVAjuFPj2gzr3oq6Vcpd9/GIiIhcK0XaQGvKlCnMmTOHgIAAhg0bRvfu3W+qNCnVGRYRuXkdjYrlxfE/cPxcHG4uzrw+4F56t2lYKjOVmRYrx/+xrvdYZjZJuZdOcQ51dc6X3lzdy41Knq64lfLPUMMw2HE4kq+X/8ave44C4OzkSM8W9Xj83nuoEh5SquMTERG5FooUDF907NgxZs6cybp162jfvj1Dhw6lSpUqJTm+a0LBsIjIzccwDBZu2sn7c34mKyeXKuEhfDqiDzXKh13za5sNg9OmnAIbWkVl5V6yvbeTI1X/vq73woyvn0vxyhmVNMMw2Lj7CF8v/ZW/jp0BwMPVhYfaNmJw1xaUDfIr5RGKiIhcO8UKhi86deoUjz/+OHv27KFp06Z8+umn1KhRoyTGd00oGBYRubmkm7J4Z8Yylv++F4AHWjXg9YH3lngZH8MwiPtbivPFv09m5pBziR+Xzg5Q2TN/enNVLzfKujnf0GtqzRYLP2/bz9Tlv3I0KhYAXy8P+ne8mwGdmhLg41XKIxQREbn2irwLR2ZmJvPnz2fu3Ln4+vry8ssv06VLF7Zt28aQIUPYvHlzSY5TRERuUwdOnePFCfM5HZOIh5srbw3qwX331Ct2v+lmS77U5ouBb6r50tUJyro5X0hvdrcFvhU9XItdt/d6ysrJZdGvfzFtxWbOxicDEOrvw+BuLejTthFe7m6lO0AREZHrqEjB8KBBg9i1axf9+vVj1qxZlClTxvZes2bNaNeuXYkNUEREbk+GYfDt2j/477xV5Jot1KxQhk9H9KFy2eBC9ZNrNThlyi6Q4nw+23zJ9r7OjgXSm6t5ueHtfGOlOBdGaoaJeev/ZPbq30lIzasRXDEsiGHd76Fni3q4uqhGsIiI3H6KlCa9du1aOnbseC3Gc10oTVpE5MaWkmHijW9+Yu2OgwD069CE/3ukC26uLnad/2dyBovOp3A0I5tTpmzMl/hJ5+rgQGVP1wIbWoW43tgpzlditVo5E5fEkTMxeX+iYjhyJpbTsYlc/HFfu2JZnujRik6Na+N0E21+KSIiUtJKZM3wzUbBsIjIjWv3sTOMmriAc/HJeHu4Mfbx++nSpI5d5+ZaDSZGxjHjTCIXf7g5ABHuLrYZ3ovreit4uOJ8kwa9AMnpmRw5E8PhMzEcjcr7+1hULKacgpt6OTs50qhmRYbd25IWd1a9aYN9ERGRklSkvKicnBxcXQtuWnK54yIiIldjtVqZvnILny9Yh9lipW7lcnw84iHKhwbadf65rBxePXiOvWlZOAJDywfRNsibql5ueDjdvDOgOblmTpyP/9tMb96f2OS0S7YPC/ClekQoNcuHUT0ijJrlw6gcHoyrs1KhRURE/q5IPxm7devGunXr7D4uIiJyJYmpGYz+epGtxu2grs35V5+Odgdwa+NSeedINOkWK2GuznxQO5yGfp7XcsglzjAMohNTL8z2RnM0KpbDZ2I4FR2P2VJwUy8PVxeqlw+jRkQoNcqHUSMijBrlw/D3vrnuW0REpLQUKRi+XGZ1Tk5OsQYjIiK3nz8PneLliQuITU7Dz8uDD4c/QNv6Ne06N8ti5ZMTsSw4nwxA2yBv3qpRFv8brJ7vP6WbsjgaFfu3md68/07LzCrQ1sHBgYphQRdmekOpWb4MNcqHERHij6PW/IqIiBRZoYLhjIwMUlJSyMnJITo62hYUG4bBrl27sFovXY5CRETknyxWK5OXbGLC4g1YDYOGNSrw8dMPUSbQz67zj2dk8+rBcxzLzMbFwYEXq4TycLj/DbUe1myxcDomMd+63qNRsUTFJV2yvb+3JzXLh12Y6Q2lRvkyVC0XUuL1lEVERKSQwfCPP/7IjBkzOHjwIP379883QxwaGsqUKVNKfIAiInLriUtO4/8m/ci2gydxcHDgyZ6teeaBtjg7XX1G1zAMFkWn8NHxGLKsBhU9XPlP7XBqertf+4FfQXxKer41vUfOxHD8XBzZuQVLOLk4O1E1PMSW2nwxAA72876hgnkREZFbWZF2k27fvj3r16+/FuO5LrSbtIhI6flt7zFenbyQxLQMgvy8+c+TD9KiTlW7zk0zW3jvaDSr4/I2j+oZ5sur1crgeR03yMrKyeX42Thb4Htx1vdi/d5/Khvk97fZ3ry/K4YF4XIT1y0WERG5FRRpzXCnTp1KehwiInKLyzVb+HLheqYu/w2AZndU4T9PPkiIv49d5+9PM/HqwXNEZeXi4ejAa9XL0CPMvpTqorBarZxLSOHwmRiOnIm2reuNjE7AeonfI3u5u/1tXW8Y1cuHUb1cKL5e+qWriIjIjUh1hjUzLCJyzZ1LSOblCQv469gZHB0cePbBdjzRoxVOdmwAZTUM5kQl8uWpOMwG1PJ249+1ylHRs+TW0aZkmDh6oXTRxXW9R6JiyMwquDGkk6MjFcv8Y0OriFDCg2+s9coiIiJyZSVadHDv3r3UrVu3JLsUEZGb3Lqdh3h96mJSM0yEBfjy0dMP0bhmRbvOTcwx88bh82xJyktBfrRcACMrh+BaxF2Uc80WTkXHX5jtvVi3N5boxJRLtg/287alNl/8U7VsMG6uLkW6voiIiNw4SnRmuEOHDjdFnWHNDIuIXHs5uWY++WENs1f/DkCbejX44IleBPh42XX+H0kZvH74PPE5ZvycHXm7ZlnaBtmXUg0Qm5T6j6A3huPn4jFbLAXaurk4Uz0ilOoRYfnW9wb62jdWERERufnYPTNsMpkUOIqIiF1OxyQyasJ89p86h7OTIy/27cSgLs3tSiM2GwaTT8XzzZkEDKChnwcf1AonzM2+2dj4lHTe+OYnNu4+csn3y4cE5JvprVk+jPKhgXalbIuIiMitw65gePr06QwfPpxffvmFli1bUqNGjQJfaAzD4Ny5c9dkkCIicvP4eds+3py2hIysbCJCAvj46Ye4q2qEXeeez8rltUPn2JVqwhF4smIwwyoE4WTnWtzNe4/x6teLSEhJx8PNlTqVyv6vdFFEGNXKheLl4VaMuxMREZFbhV1p0jExMSxevJiBAwfi6el52dJKN0vJpYtp0mPSV+OiL0UiIiXCarVy8HQ0UbGJAIQF+nFnpXCc7SwhFJudy/70LMxWA3dHR+70dSfAxb4EJqth5VhULCfPxwMQ6OtF3SoRuGttr4iIyG3pTcfWV21j17eMsLAwnnzyyWIP6EbzmmNLPByV+i0iUlzHz8Uxavx8jkTF4OrizKv9uvBw0yZ2pUVnW618diKWzeeSAWgd6MXbNcvaHQhHxiTw8sQF7Dt5DidHR555oC1P3G3fTtUiIiJy+yrSbtKDBw++5PHHH3+8OGMREZGb0OLfdjF25jJMOblUKhPEJyP6ULtiWbvOPZmZzSsHz3E0IxtnB/hXlVD6hQfYXaJoyebdvDtrGZlZOYQH+/PRU71pUL1CcW5HREREbhOqM6xNwUREiiQjK5uxM5ezZMtuAHq2uIs3B/XAy/3qy08Mw2BJTAr/PhZDltWgvLsL/65djjt83O27timbsbP+d+0uTerwzpCe+HrpM11ERETsU6J1hkVE5PZw6HQ0oybM5+T5eDxcXRjzWHd6taxv14xuhtnC+0dj+DkuFYDuob6MrhaGl51ri/edPMtLExdwOiYRd1cXXuvfjd5tGto9mywiIiICJRwMP/LII8ybN68kuxQRkRuIYRh8/8t2/v3tSnJyzVSPCOWTEX2oVi7UrvMPpGXx6sGznMnKxd3RgdHVynBfGT+7zrVarcxYuZVxC9ZhtlioWT6Mj0f0oWp4SHFuSURERG5TJRoMx8XFlWR3IiJyA0nLzOLNaUtY9ed+APq0bcTo/t3s2rHZMAzmnk3i85OxmA2o4eXGf2qHU8nTvh3945LTeO3rRWzedxyA/p2a8lLfTrhpt2gREREpIruD4Q8//PCK7xuGQWRkZLEHJCIiN569J84yasJ8ouKS8HJ3450hPbm3WV27zk3MMfP2kfP8mpgBwCPhAbxQJQQ3O3d7/m3vMUZPWUhCagb+3p68P6wX7RrULPK9iIiIiEAhguHZs2czevRorrTflpubavaKiNxKDMNg1qqtfPLDWswWC3dULMsnz/ShYliQXedvT87gtUPnicsx4+vsyNs1ytIu2Meuc3PMZj5fsI7pP28BoGntyvx7+IOEBfoW+X5ERERELrI7GC5TpgwDBw68YpsZM2YUdzwiInKDSE7P5LWvF7Fh1xEABnRqyksPd8bVjvq/ZsPg68h4vj6dgAE08PXgg1rhlHG3L635VHRe7eD9p/JqBz/7YDuGdW+p2sEiIiJSYuwOhsuVK3fVNhUqqLajiMitYMeRSF6euIDoxFR8Pd15b1gvOjaqbde50Vm5vHboHH+lmnAAnqgQxPCKwTgXsXbwx08/RP1q5YtxNyIiIiIFqc6w6gyLiNhYrVa+Xv4bXy38BYvVSv1q5fno6YcoF+xv1/kbEtJ4+/B5UsxWgl2d+aBWWZr4e9l1bropi7GzlrN0yx4Aut5dh7cHq3awiIiIXBuqMywiIgDEp6Tz6uSFbNmft2PzsO4tee7B9rjYUf83x2rlsxNxzDuXBEDLQC/eqVGWQFf7fszsPZFXO/hMbCIeri68NvBeHmzVQLWDRURE5Jop0WB41KhRfPLJJyXZpYiIXAdb9h/nlckLSUhJJ9DHiw+HP0Cru6rbdW5kZg6vHjrLofRsnB1gZOVQHi0XgKMdgazVamX6yi18vmAdZouVmhXK8MnTD1FFtYNFRETkGivRYHjXrl0l2Z2IiFxjZouFCYs3MHnprxiGwd21KvHfp3oTGmDfjs1LY1L48Gg0JqtBeXcXPqwdTh0f+9Ka45LTGD1lkW0memDnZrzYp6NqB4uIiMh1YVcwfPToUaZPn86oUaMICgriu+++K9DGMAxiYmJKfIAiInJtRCem8PLEH9lxJBJHBwdG9GrLU/e3sWvH5gyzhQ+PxbA8NhWAriG+vF49DG87UqoBNu0+ymtfLyIxLYMAn7zawW3rq3awiIiIXD92BcN79uxh+vTpPPTQQwQFBfH6668zZMiQAjWHTSbTNRmkiIiUrI27jjD660Ukp2cS4u/DR0/15u7ale0691B6Fq8cPMtpUy7ujg68Wi2M+8L87Frfm5Nr5rMFa5m5cisATe+ozH+GP2j3TLSIiIhISSnSbtLt27dn/fr1BY536NCBdevWlcjAriXtJi0it6scs5lx89cxY+UWAFrWrcaHwx8gyNf7qucahsF355IYdyKOXMOgupcb/64dThVPN7uufSo6npcmLOBA5HmcHB15vnd7ht57j2oHi4iISKko0TXDjvpCIyJyw4qKS2LUhPnsPXEWJ0dHXnioA0O6tbDrszs518Jbh8+zKTEdgL5l/flXlVDcna5+rmEY/LR5N2NnLceUnUO5C7WD66l2sIiIiJQi1RnWzLCI3AZW/3mAN6b9RFpmFmWD/PhkRB/q2xmM7kjO5LVD54jNMePj7MhbNcrSIdjHrnPTTVm8M3MZy7fuBeDepnfy1uCe+Hi6F/leREREREqC6gyLiNzCsnNy+e+8VXy37k8AOjSsxdjH78ff2/Oq51oMg6mnE5gSGY8VqOfrwQe1wgl3t2+35z3Ho3h54gLOxCXh4erCmMe606tlfdUOFhERkRtCkYPhgwcPsnr1ajIzM/H09KRTp07ccccdJTk2EREphlPR8fxr/HwOn47GxdmJlx/pTP+OTe0KRmOzc3nt0Dl2pJhwAIaVD+LJSsE421s7+OctfP5jXu3gWhXK8MmIPlQuG1wCdyUiIiJSMoq0yPeHH36gd+/e5ObmEh4eTk5ODr1792bu3LklPT4RESmCJZt30/vNyRw+HU350EC+HTOMAZ2a2RUIb0pI5+Edp9iRYiLY1YmJdcvzTOUQuwLhuOQ0nvh4Np/8sAazxcpjnZsx780nFAiLiIjIDadIa4YbN27MypUrCQ7+35ebuLg4OnXqxK5du0pyfNeE1gyLyK0qMzuH92evYNGvfwF5a3TfHtITb4+rr9HNsVr54mQcc88mAdAiwIuxNcsS6GpfEtHG3Ud47etFJKVlEuDjyQfDHqBN/RpFvxkRERGRa6hIadKenp75AmGAkJAQfHzs21BFRERK3tGoGP41fj4nzsXh5uLM6wPupXebhnbNBp825fDqwXMcTM/C2QGeqxTCgIhAHO2tHTx/LTNX5dUObnZHFf7z5IOE+OtngoiIiNy4ihQM+/n5ER8fny8gTkhIIDw8vMQGJiIi9knNMDHt583MXLmV7FwzVcJD+OyZPlSPCLPr/BUxKbx/LIZMi5Vy7i78u1Y4d/ralzVzKjqeURMWcDDyPM5OF2oHd7tHpfZERETkhmd3MPzBBx/YZhfCw8Np0aIFgwYNIiQkhLi4OObOncuQIUOu2UBFRCQ/U3YOc9duY+ryzaRmmAB4oFUDXh94L55urlc9P9Ni5T/HYlgSkwJA5xAfxlQvg4+z01XPNQyDxb/t4r3ZKzBl5xAREsBHTz9EvaoRxbspERERkevE7jXDs2bNsqvDxx57rFgDuh60ZlhEbma5Zgs/btrJxJ82EpecBkDT2pV5oU9Hu4PRw+lZvHrwHKdMObg7OvBy1TAeKONnV0p1WmYW785cxvLf82oHd29Wl7cG97BrXbKIiIjIjaJIG2jd7BQMi8jNyGq1smLbPr5c+AtnYhMBqFMpnH/16UjzOlXsCmQNw+CH88l8ejyWHMOgqqcr/6ldjqpebnaNYfeF2sFRcUl4uLnyxmPduf+eeqodLCIiIjedItcZFhGR68MwDDbtPsq4BWs5fCYGgCplg3m+dwc6Na5tdyCakmvhnSPn+SUhHYCHyvozqkoo7k5XX99rtVr5ZsVmvly4HrPFSu2KZflkxENUKqOSSSIiInJzKlIwnJ2dzfjx4zly5Aj/nFiePHlyiQxMRERg++FIxs1fy86jpwEoE+jHMw+05f576uHsdPW1vRf9lZLJa4fOEZ1txtvJkTdrlKFTiK9d58YmpfLqlEX8fuAEAIO6NudfD3XE1UW/TxUREZGbV5G+yTzxxBNUqFCBvn378vLLL/PJJ5/www8/ULdu3ZIen4jIbelg5HnGLVjHr3uOAhDg48mTPVvzcLvGuLm62N2PxTCYdjqBSZHxWIG6Pu78u3Y44e5X32ALYOOuI7w2Na92cKCPFx888QCt61Uvyi2JiIiI3FCKtGa4devWbNq0CYD27duzfv16LBYL3bp1Y/Xq1Xb18emnnzJr1iycnJxo1KgR48ePx8XF/i94AFOnTuXkyZO8//77hTpPa4ZF5EZ1KjqBrxauZ8W2fQB4ubsxuGtzBndtgZeHfet6L4rNzuWNw+f5IzkTgCHlA3m6YggujvbVDv7khzXMXv07AC3qVOXD4Q+odrCIiIjcMoo0M/z3APLiWjUnJydycnLsOn/VqlWsWbOG7du34+zszBtvvMH777/P22+/bfcYsrKyGD9+PBs3bizU2EVEbkQxialM+GkDCzf9hcVqxdXFmX4dmjC8RysCfLwK3d9viem8cfg8ybkWAl2ceK9mOM0D7evn5Pl4Rk2Yz6HT0Tg7OTLyoQ4M6dpCtYNFRETkllKkYLhZs2a8+eabvPvuu5QtW5ZFixbh4+Nj9yYuM2fO5NVXX8XZOe/yPXr0oFmzZoUKhr/66iuGDBmCr+/V17zl5uZiNpttr00mk93XERG5lpLTM5m6/DfmrtlGdq4ZRwcHerduyIhebSkb5Ffo/nKtBl+eimN2VN5u0838PRlbK5xg16t/3BuGwcJf/+KD2Ssw5eRSPjSQj57qzV2qHSwiIiK3oCIFw2+99RYrVqwA4OOPP+bVV1/FYrHwzTff2HX+/v37qVevHgArV65k0qRJlClThvT0dLy9va96fkpKCj/88AO//fYbkZGRDBs2jDVr1ly2/fvvv88777xj19hERK6HjKxsZq/6nWk/bybdlA1AlyZ1eL53eyqXLdoOzWdMObx68BwH0rNwdoBnKoXwWEQgjnbWDn57xlJ+vpCe3aP5Xbw5qLtqB4uIiMgtq1TqDFerVo2jR4/y1VdfsXfvXsaPH0/btm358ccfKVOmzFXPHzNmDHXr1uXhhx+2Kxi+1MxwUFCQ1gyLyHWXk2vmhw3bmbxkEwmpGQDcc2dVXnioI3Uqhxepzwyzhe/OJTHjTCIZFivhbi58WDucu3zt+3zbfewML01cwNn4ZDzcXHlrUA/uu6dekcYiIiIicrMolboYrq6uPProo9SvX58pU6YAkJycbNescExMDJs3b2bs2LF2X8/FxaXQm3OJiJQki9XKsi17+HLRL5yLTwagXtUI/tWnI3fXrlykPk0WKz9cCIKTzRYAOgb78GaNMvg4X73sksVq5Zvlv/Hlwl+wWK3cUbEsH4/oQ6UyQUUaj4iIiMjNpEjBcHJyMiNGjGDjxo14eXmRmZlJq1at+OqrrwgKuvqXKKvVSmhoKK+88goAaWlpWK3WAsHwpdKmV61aRVJSEu3btwfyNtI6fPgw7dq148svv+TOO+8syi2JiFwThmGwbuchvvhxPcfOxgJQrVwoIx/qQPsGNe3ea+Hvsq1WFpxLZvqZBBJy84Lg+r4ejKgUTBN/+zbJik1K5ZUpC9l24CQAg7u24IU+HXB1Vu1gERERuT0UKU26X79+NGvWjJEjR9qOjRs3jo0bN7Jo0aKrnr9y5Uo+//xzli5dattN2snJKd8GWiNHjmTChAls2bKFJk2aXLYve9Kk/0mllUTketh28CSfzV/LnuNRAJQL9ue5B9vTvXldnIqwM3OO1cri6BSmnk4gLidv6UcdH3dGVAymeYCX3YH1hl2Hee3rxSSnZxLk68WHwx+kZd1qhR6PiIiIyM2sSFMAx48f57vvvst37IUXXmDOnDl2nd+1a1cOHDhAo0aNbHWGJ0yYkK9NSEgIAQEBeHp6FmWIIiKlZt/Js4ybv44t+48DEOTnzVM9W9OnXaMizbzmWg2WxqTw9el4orPzguCaXm48XSmY1oHedgfB2Tm5fPLDGuas2QbkrVX+4AnVDhYREZHbU5Fmhtu2bcuGDRsKHG/fvj3r168viXFdU5oZFpFr4cS5OL74cT2rtx8AwNvDjaH33sPAzs3wcncrdH8Ww2BFbCpTIuOJysoFoKqnK09VDKZ9sI9du0T/fWyjJi7g8OlonJ2ceOGhDgzu2ly1g0VEROS2VaRg+LPPPsMwDP71r3/h4OCAYRh8+eWXBAQEMHDgwGsxzhKlYFhEStK5hGQmLN7A4l93YTUM3FycGdCpKY93b4m/d+GzW6yGweq4NCZHxnPKlANARQ9XnqwYTOcQH5wKEQQbhsHCTTv5YM7PmHJyqRAWyMdPP8SdlcsVelwiIiIitxK7g+EaNWrYUvEMw8BsNuPm5kZAQACJiYmYzWbKlSvHxo0br+mAS4KCYREpCYmpGUxZuonv1v9JrtmCk6Mjvds05On72hAW6Fvo/gzDYH1COpNOxXMsM6/2cIS7C8MrBtMt1BfnQm62lZph4u0ZS1n5x34A7mtRjzce646XR+FnqUVERERuNXYvXjty5Mi1HIeIyE0j3ZTFjJVbmbFyC5lZeTO33ZvV5dkH21ExrPBliQzDYFNiOpMi4zmUnhcEl3Fz5okKwfQM88PFsfA7Tv919DQvT/qRc/HJeLq78uZjqh0sIiIi8neqoSEiYqfsnFzmrf+TyUt/JTk9E4DW9aozsncHalcsW+j+DMNga1IGEyPj2ZeWBUCIqzPDKgTRq4wfrkVYz2uxWvl62a+MX7QBi9XKnZXD+ejph4oUpIuIiIjcyoocDG/ZsoXFixeTkpKCr68v999/Py1btizJsYmI3BDMFgs//baL8Ys3Ep2YAkDDGhV44aGONK5ZsUh9/pmcwcRT8fyVagIg0MWJoeWD6F3WH3enom1qFZOYyqtTFrLtYF7t4KH33sPzvdurdrCIiIjIJRTpG9L06dOZNm0azz77LGXKlCEmJobRo0czZMgQhg4dWtJjFBEpFYZhsHr7Ab74cT0nz8cDULN8GC/06Ujru6rbXdLo73alZDLhVDx/puTNLPs7OzGofCAPhwfgUcQg2JSdw/e/bGfykk2kZJgI8vPm3088wD2qHSwiIiJyWUXaTbpZs2asW7cOLy8v27H09HQ6dOjAtm3bSnSA14I20BKRKzEMgy37jjNuwTr2nzoHQPnQQJ5/sD3dmtYpUjmifakmJkbGsyUpAwAfZ0cGlgvk0XIBeDk7FWmcmdk5fLfuD2b8vIWE1Lx+W9atxgdPPECwn3eR+hQRERG5XRRpZtjV1TVfIAzg7e2Nm5t2KBWRm9vuY2f4bP5a/jh0CoAQfx9G9GrDg60a4lKEoPVwehYTTsWzKTEdAC8nRx4tF8DAiEB8ihgEZ5iy+W79H0z/eQtJaXkzzHdWDmdEr7a0qVejSDPWIiIiIrebIgXDDg4OREVFERERYTt2+vRpijDJLCJyQzgaFcPnP65n/c5DAPh6efBE95Y82vFuPNxcC93f8YxsJkbGsy4+DQB3RwceKRfAoIgg/F2KFgSnm7KYuzZvJjglI2+t8V1VIxhxf1ta3VVNQbCIiIhIIRQpGH7//fdp37499957L6GhocTExPDzzz8zbdq0kh6fiMg1FRWXxFcLf2Hp1j0YhoGHqwsDuzRjaLd78PUq/DKKU5nZTI5MYFVcKgbg5uhAn7L+DC4fRJBr0TaySsvMYs6a35m56ndSLwTBDaqVZ0SvtrS4s6qCYBEREZEiKNKaYYCkpCQ2btxIamoqPj4+tGnThsDAwJIe3zWhNcMiEp+SzqQlG/nhlx2YLRacnZzo264RT/ZsTYi/T6H7O2PKYUpkPCtiU7ECLg4OPFjWj6Hlgwh1cynSGFMyTMxe/TuzV/9OWmZe6aVGNSoyoldbmt1RWUGwiIiISDEUORi+mSkYFrl9pWaYmP7zFmat2oopJxcHBwd6Nr+LZx9sR0RIQKH7O5eVy9TT8SyNScFsgLMD3B/mz+MVgijrXrQgODk9k1mrtjJnzTbSTdkA3F2rEk/3asvdtSopCBYREREpAUXK2RszZgzvvfdeSY9FROSaMWXn8O3aP/h6+W+2VOP2DWsxsnd7qkeEFbq/2OxcvjmdwMLoZMwGOAL3hfnxRIUgIjwKv8YYICktgxkrtzJ37TYys3IAaHZHFUbc34bGtSoVqU8RERERubQiBcO//vrrJY8fO3aMatVU11JEbhy5ZgsLf93JhMUbiUvO28zq7lqV+FefjtSrVr7Q/SXkmJl+JoH555LJMQwcgG4hvjxZMZiKnkULghNTM5ixcgtz1/6BKTsvCG5RpyojerWlYY0KRepTRERERK6sSMGwo6Mjqamp+Pr65jv+5JNPsm7duhIZmIhIcVitVn7etp8vF63ndEwiAHdULMu/+nQs0qZTSblmZp1JZN65JLKseatLOgb78FTFYKp6Fa2sXHxKOtN/3sy8dX9iyskF8uoEj+jVlvpFCNRFRERExH5FCoY//PBDnnzySUaOHEmDBg1UX1hEbhiGYbBpz1HGLVjH4dPRAFQqE8TzvTvQuXFtHB0dC9Vfaq6F2VGJfHsuiUyLFYA2Qd48XTGYmt7uRRpjXHIa01Zs5vtftpN1IQhuU68GT9/fhruqRlzlbBEREREpCUXaQMvX15ewsDAsFgsODg44OztjGAbHjx/HYrFci3GWKG2gJXJr2nEkks/mr2XnkdMAlAn05Zlebbm/ZX2cnQpX2zfdbGHu2STmRCWSfiEIbhHgxYhKwdTxKdrnRmxSKlOX/8b8DTvIzjUD0K5BTUbc35Y6lcOL1KeIiIiIFE2RZoYbN27M+vXrCxxv3759sQckIlJYh05HM27BWjbtPgqAv7cnw3u2ol/7Jri5Fm5HZ5PFyndnk5gVlUCKOS8Ivtvfk6crBlPfz7NI44tOTGHqst9YsGknOReC4I6NavPU/W24o2LZIvUpIiIiIsVTpGB4+PDhlzyuHaZF5Ho6FR3P+EUbWP77XgA83V0Z3LUFg7s2x9ujcCnMWRYr888nM/1MAkm5eRkuDXw9GFEpmMb+XkUa37mEZKYu+40fN+0k15zXZ+cmd/DUfW2oVaFMkfoUERERkZJR5DrDVquVvXv3kpmZiaenJ3feeSdOhUxDLC1Kkxa5eeXkmlm38xDzN+zg9wMnAHBxdqJf+yYM79maQN/CBa45VisLz6fwzZkE4nPyZm3r+rgzolIITf09i1TT92xcElOW/cqiX3dhvrCcpOvddXjqvtZFKuMkIiIiIiWvSDPDu3btok+fPtSqVYvg4GDi4uI4dOgQ8+fPp0GDBiU9RhERTp6PZ/6GHfy0eRdJaZkAuLu60KN5XZ66vw3hQf6F6i/XarAkJoWpp+OJzs4Lgmt5uzGiYggtA72KFASfiU1kytJf+WnzLswWKw4ODnRvVpcn72tNtXKhhe5PRERERK6dIgXDzz33HN9//z0NGza0HduxYwfPPPMMW7ZsKbHBicjtLSsnl9V/HmDBxh1sPxxpO16zfBh92jaiR/O78PUqXHaH2TBYEZPClNMJnM3K28m5mqcbT1cKpl2Qd5GC4MiYBCYv2cTSLXuwWK04OjjQs8VdPNmzNVXCQwrdn4iIiIhce0UKhq1Wa75AGKBRo0ZF+hIpIvJPR6NimL9hB0u27CE1wwSAh5sr3ZvdyUNtGlG3SrlCf95YDINVsalMPh3PaVNeEFzZw5UnKwbTKcQHxyJ8fp2Kjmfykk0s27oXi9WKk6MjvVrWZ3jP1lQqE1To/kRERETk+ilSMOzh4UFOTg6urq62Y9nZ2bi7F63mpoiIKTuHlX/sZ/6GHew6dsZ2vE6lcPq0bcS9ze4s9KZYAFbDYF18GpMi4zmRmQNAeXcXhlcMpluoL05FCIKPn4tj8pJNrPh9L1bDwNnJkQdbN2B4j9ZUCAssdH8iIiIicv0VKRju27cv9913H08//TQhISHEx8czefJk7r//frZu3QpA8+bNS3SgInJrOhh5ngUbd7Bs617SMrMA8HJ3o0fzuvRp24g7KhWt/q5hGGxISGdSZDxHMrIBKOvmzBMVgukR5oeLY+GD4GNnY5n000Z+/mM/xoUguHfLhjzRsxURIQFFGqeIiIiIlI4i7SY9dOjQK3fq4MA333xT5EFda9pNWqR0ZZiyWbFtLws27GTvybO24/WqRvBQ20Z0a3onnm6uV+jh8gzDYHNSBhNPxXMgPS+4DnV15vEKQTxQxr9IQfCRMzFM/Gkjq7cfuBAEO9G7dQOG9WhFuWD/Io1TREREREpXkUsr3cwUDItcf4ZhsP/UOeb/soPl2/aSmZWXsuzr6U7Pe+rRp00japQvetkhwzD4IzmTCZHx7EnNW2cc5OLE0ApB9C7rj5ujY6H7PHQ6mok/bWDN9oNAXgmnh9o0ZFj3VpQN8ivyWEVERESk9BUpTVpExF5pmVks37qHHzbs4NDpaNvxhjUq0LdtYzo3uQN3V5diXWNnSiYTTsWxIyUvCPZ3cWJIRCB9wgPwcCp8EHzg1Dkm/LSR9TsPAeDq4kyfto0Ydm9LwgJ9izVWEREREbkxKBgWkRJnGAa7j0cxf8MOVm7bhyknb/dmf29P7r+nHg+1bUTVYpYcMgyDnSkmpp6O5/fkvLrDPs6ODIoI5JHwALycnQrd576TZ5m4eCO/7DoMgJuLMw+3a8zQe+8hNEBBsIiIiMitRMGwiJSYlAwTSzbvZsHGHRyNirUdb1q7Mg+1aUTHRrVwK+Ys8LmsXJbHpLA0JoUzF+oEezs50r9cAP0jAvEpQhC8+3gUE3/awKbdRwFwd3XhkfZNGNKtBSH+PsUar4iIiIjcmBQMi0ixGIbBjiOnmb9hO6v/PEB2rhmAQB8vHmhVn95tGhW75m6mxcq6+DSWRqfwZ0qm7XiIqzP3l/FjQLlA/FwKHwTvOnaGCYs38NveY0BeLeNHOzRhcLcWBPl6F2vMIiIiInJjUzAsIkWSlJbB4t928ePGnZw4H2873qJOVfq0a0S7BjVxdS76R4zVMNiZksmSmBTWxqVhsubt9efm6EDbIG/uC/OjaYBXkeoE7zgSyYTFG9i6/wQAnu6uPNrhboZ0a0GAj1eRxywiIiIiNw8FwyJiN6vVyh+HTjF/ww7W7jhIrtkCQIi/T94scOuGlA8NLNY1zphyWBqTwrKYFM5nm23H6/l6cF+YH51CfIqUCg3w56FTTFi8gW0HTwJ59YwHdGrKoK7N8ff2LNa4RUREROTmomBYRK4qPiWdxb/9xfwNOzkTmwjk1RNvXa86fdo2pk296jg7FS1ABUgzW1gTl8aymBT+ulAWCaCMmzM9wvzoEeZHRY+i1x3edvAkExZvYPvhSAB8PN0Z0Kkpj3Vpjp+XyquJiIiI3I4UDIvIJVmtVrbsP8GCDTtY/9chzBYrAGUCfXmwdUMebN2A8CD/IvdvMQz+SMpkaUwK6xPSyL6QBu3u6EDHYB96hvnR2N8TxyKkQUNeELz1wAkmLN7AziOngbyaxo91ac6ATk3xVRAsIiIicltTMCwi+cQmpbJw01/8uGknZ+OTAXBydKRdg5r0bduYlndVw8mx8LV7LzqZmc3SmBSWx6QSm/O/NOjGfp70DPOlQ7BPkcoiXWQYBpv3HWfC4g3sOnYGAD8vDwZ1bU7/jk3x8XQvct8iIiIicutQMCwiWKxWfttzjB82bGfT7qNYrHmzwOHB/jzUpiEPtmpQrDq7qbkWVsalsjQmhX1pWbbjEe4ueWnQob6UK2Ia9EWGYbBpz1Em/rSRPcejgLy6xkO6teDRDnfj5eFWrP5FRERE5NaiYFjkNnYuIZmFm/5i4aadRCemAuDs5EjnxnfQp20jmtepgmMRZ4HNhsHWxAyWxKSwMSGdXCMvDdrLyZFOIXlp0A18PXAoYhr0RYZhsGHXESb+tIF9J88BeWWdhnRrwSMdmuDlriBYRET+v707j466uv8//soyWckGhLBFQTYlBAghJCJLgiIIoWURVFpqtcpB0WK1Lj+lblXrAZeiRa1a169YRdQKRAQ0iaAkLBKMYReBoCSBbGRPJvP5/ZEwMIZsLJnMzPNxjuf4uZ/P3Lx5O9z2nXs/9wJAQxTDgIupMdcqdcdefZS6TRu+3y+jvkgN79JRM+OHaeqoKHUOOvszdveVVeqznGJ9nndC+TV1u027SYoL9tOUsCAldA6Qr8fZL7M+qbK6Rsnb9+j11Ru169BRSVKnQH/dPOkKXTcuRn7e5zbTDAAAAOdGMQy4iCPHCvVR6jZ9siFDx4pKJEkmTw+Nj75M18ZHa8Slvc56Frig2mxdBr27tMra3svXq3436ECFeZvO+c9QY67VpqwDSkrL1Jff7VZZZd3P6hzUQbdMHqWZ8dHypQgGAABAC1AMA06s2mxW8vY9Wp68Td9m/Wht792ts2bGR+u3VwxRSID/WfVdYzG0oaBUK3OLtbGgVOa6CWYFeLprQmigfhMWpEEBPue8DNpisei7fdlKSsvUF1uyVFhSbr0X2buHfjtqiKaPGSYfr3MvtgEAAOA6KIYBJ3QwJ18r6meBC0rKJEleJk9NiBmomfHDFd3/orMqUg3D0K7Sut2g1+SdUJG5bhm0h6TRHf01JSxIYzp1kPc57DZt/TmHjmp1WqY+T89STkGx9d4l3UM1OW6QJsVF6uKwTuf0cwAAAOC6KIYBJ1FVXaP123ZrecpWbd590Nrer2cXXTs2WlNGDlZwB7+z6vtYlVmf5xVrZe4J7S8/tQy6r5+3pnQN1KQuQersde7DycGc41q9KVNJ6T/op6PHre3dOwdrUmxdATwgPOycZ5sBAAAAimHAwf34yzF9lLJNn27MUHFZhSTJx8uka2IHaWZ8tIb06XlWxWOVxaLU/FJ9llOsTYVlstS3B5s8dE1ooKaEBenSDt7nXJgezS/W5+k/KCktUzvrN8KS6naEnjgiQpMvj9SQPj3P+n1mAAAA4EwohgEHVFldoy82Z2l56jZ9t/ewtf3Si7pqVny0Jl8+WAF+Pq3u1zAMZZZU6rPcYq09dkIl5roS2NNNGtuxg6aEBWlUxw4yuZ9bAVxYUqYvtuzU6k2Z2rb3kLW9g6+3roq+TJPjIhU7sLc8PTzO6ecAAAAAjaEYBhzIviO5+jBlm1Z+s0MnyislSX4+XpocG6mZCdGK6NX9rGZqcyprtLp+GfShimpr+2UdvJUYFqSJoYHqeI7LoMsqqvTld7u1Oi1Tm7J+lLm2rtD2Nnlq7ND+SowbrNGD+8qbjbAAAADQBiiGgXausrpGazZn6cPkrcrYn21tj+zdQ9fGD9Ok2Ej5+3q3ut+KWou+Ol6ilbnF2lxUrvrNoNXJ5KFJYUGaEhaofv6tn10+XVV1jTZ8v1+r0r5XasZeVdWYJUke7u4aPbifJsdFatywAerge24/BwAAAGgtimGgndr/c54+TN6qz06bBfb38daUkYM1Mz5al13crdV9Goah74ortDK3WOuPl6isfnbW5Oam+E4dNKVrkC4P8ZfnObwHbK6tVfrOn7Q6LVPrt+1SacWpDbei+1+syXGDdHVMhDoGnt2RTgAAAMD5QDEMtCNV1TX6YstOfZiy1eZd4MjePTQzPlrXxA2Sv0/rZ4F/rqjWqrwTWpVbrCOVNaf6DfDRlLAgTQgNVKDp7N/PNQxDGfuztTotU19szlL+iTLrvYEXd9OkuEhdEztI3ToFnfXPAAAAAM4nimGgHTjwyzEt/9WO0H4+XpocF6nrEoZrYK/ure6zzFyr9fXLoLcVV1jbw7w8NTksSIlhgert1/rC+iTDMLQnO1dJaXVHIf1yvMh6r1fXTpoUF6nJcZHq3a3zWf8MAAAA4EKxWzH83HPP6Z133pGHh4eio6O1dOlSmUwt2zhn165duv3221VbW6vq6motWLBAN9xwwwWOGDi/qmvMWrd1lz5M2aotp50LPPDibpqVMFyT41r/LrDFMLSlqFwrc4v15fESVVrq3gT2cXfTuM4BmhIWpJhgP3mcwzLow7kFSkrL1Oq0TP34yzFre9eOgbomdpAmx0Xqsou7cRYwAAAA2jW7FMNffPGF1q1bp61bt8rT01N/+9vf9OSTT+rRRx9t9rNlZWW6/vrrtWbNGnXr1k1lZWWKj4/X0KFDddlll1344IFzdDAnX8tTturTjRkqLCmXJPl6mTT58kjNShiuQb17tLrPQxXVWpVTrFV5xcqpMlvbowJ9NSUsSONDA9TB8+yXQecVnqg/C/gHZf70s7U9uIOfJsQM1OS4SA3rfxFnAQMAAMBh2KUYfvvtt/XAAw/I07PuxycmJiouLq5FxXBubq7mzZunbt3qNg/y9/fXVVddpaysLIphtFvVZrO+3LZbH6ZsVfrOn6ztAy7qquvio5U4cnCrd1QuMddq7bESfZZbrO9PnFoG3d3bpMSwQCWGBSnc1+usYy4qLde6rTuVlPaDNu8+KMOom2X28/HSVcMu06S4SF0ecYlM51BkAwAAAPZil2I4KytLQ4YMkSStWbNGr7zyirp27arS0lJ16NChyc9ecskluu2226zXhmHom2++0e23397oZ2pqamQ2n5otq6ioaPRZ4Hw6nFug5Slb9cmGDBWU1G0q5eNl0jWxgzQrYbgGX9KjVcuJzYah9MIyfZZbrJTjpaquL1B93d00PjRQU8KCNCzIV+5nuUS5rLJKydv3KCktUxszf5S5tlaS5GXy1JjB/TQpLlLxQ/vLh7OAAQAA4ODsUgyXlZUpKChIL774ojIzM7V8+XLFx8e3qBj+tYULF2r69OkKDw9v9Jknn3xSjz322LmGDbRIjblWX23frQ+Tt2pT1gFre7+eXTQrfrimjBysQH/fVvW5r6xSq3JPKCnvhI5X1/1ix03SiGA/TQkL0pWdA+TrcXZLlKtrzNqYuV9JaZlK3r5HFdV1u027u7npikF9NCkuUldFX6YAP84CBgAAgPNwM06ufWxDAwcO1JAhQzR06FDdf//9kqSIiAilp6e3qhhesmSJ9u3bp3/9619NPnemmeFOnTqpvLxcvr6tK0qAxhw5VqjlKdv08Ybtyi8ulSR5mzw1cUSEZiUM19C+4a2aBc6vNuvz+uOQ9pSdOqv3Il+TpoQFaXKXIHXzObsZ2lqLRZt3HVRSWqbWbd1pPcdYkqL6hmtSXKQmjIhQ56DW/XIKAAAAcBR2mRm2WCzq0qWLtRAuKSmRxWJpUAg3NVP81ltvadu2bXr77beb/Xkmk6nFO1UDrVFjrlVqxl59mLJV3/zwo/W92ku6h+q6hGj95oqhCmrFLHCVxaLU/FKtyi3WtwVlqq1vD/R014TQuveAIwN8zmqnZsMw9P2Bn5WUlqnP03/Q8fqCXZIGhIdp8uWRumbEIPUIDWl13wAAAICjsUsx/M9//lNLliyR2WyWp6enFi1apOuuu87mmQULFuill17St99+q5iYGJt7H3/8sVauXKkPPviA41tgFz8fL9KK1G1a8fV2HSsqkVT3Xu3VwwdqVsJwRfe/qMXfTcMwlHGiQqtyi7X2WIlKay2SJE83aWzHDkrsEqgxnTrI6yx3at53JFer0zL1edoPyj5WaG0P79JRk+MGaVJcpPr26HJWfQMAAACOyi7F8MSJE7Vz505FR0dbzxl+6aWXbJ4JDQ1VSEiI/Pz8bNr379+vG264QVFRURo/fry1ffbs2br11lvbJH64JnNtrVJ37NPylK3a8P1+6yxw726dNSs+Wr8dNVTBHfya6eWUIxXVWl2/DPpIZY21fWAHHyWGBWpCaKA6ep3dX9EjxwqVlJappLQftPdIrrU9NDhAk2LrCuBBvbvzyyQAAAC4LLu8M2xvFRUV8vPz451htMjR/GJ9lLpNH3+9XbmFJyRJJk8P6yzw8AEXt7ioLDHXat2xEq3KLdb2045D6uLlqUlhgUrsEqQ+/t5nFeexohKt2ZylpLRM7fjxiLU90N9XE4YP1KTLIzV8wMXy4CxgAAAAwD4zw0B7V2uxaMP3+/Rh8lZ9vWOfLPW/M7o4rJNmxkdr6qih6hjo36K+zIahTQVlWpVnexySj7ubruwcoMSwIMUE+8njLGZpT5RVaN22XUpKy1T6zp+scfp6mTQu+lJNjovUyEF95OXJX3UAAADgdPw/ZOA0uQUntOLr7/RR6nfKKSiWJHl6eOjq6Es1K2G4Yi/r3aJZYMMwtLesSitzi/V53gkV1NRthXXyOKTE+uOQ/M7iOKSKqmqlZOzV6rRMbfh+n2rMtdY4xw7uq8lxkYqPGiA/b69W9w0AAAC4CophuLxai0XfZP6oD1O2KjVjr2otdRtYhYeGaGZCtKaNjlKnwJYdMZRXVaM1eSe0Ku+E9p12HFIvXy8lhgVpUpfAszoOqbK6Rhu/368vtmTpq+17VFFVLUlyc3NT3MBLNDlukK4aPrBVO1cDAAAAroxiGC7rWFGJdRb4l+NFkiRPD3fru8BxA3vLvQXv11bUWpSSX6JVuSeUVlgmS317sKeHJnQJ0JSwIA3s0PrjkE4vgJMz9qi8stp6b3CfnpocN0gTRwxSaHBAq/oFAAAAQDEMF2OxWLQp64A+TNmq5O17ZK4/xqhH52BdGx+t6aOjWlRcWgxD3xXXHYe0/niJyk47Dim+YwclhgVpVMcOMrmfvwJ4UO/umhAToatjBiq8S8dW9QsAAADAFsUwXMLx4lJ9smG7PkrZZj1r18PdXVcOq3sX+IpBfVo0C3yovFqr8oq1OrdYR6vM1vbIAB8lhgXp6tBABZs8WhUbBTAAAADQ9iiG4bQsFovSdx3Uh8lb9eV3u6yzwN06BenascM0Y8wwdQkJbLaf4pparT1W9x7w96cdh9TV21OTuwRpcligevu17jgkCmAAAADAviiG4XQKTpTpk43btTxlmw7nFkiS3N3clBA1QLMShmtUZN9mz9qtsRj6prBUq3JP6Ov8UtXUH1nk5+GuqzoHKDEsUNFBfnJvxXvAFMAAAABA+0ExDKdgGIY2766bBV63dZfMtXXHDXXtGKgZY4Zpxthh6toxqNk+dpVWaVVusT4/dkJFpx2HFFd/HNK4zgHybcVxSBTAAAAAQPtEMQyHVlhSpk83Zmh5yjYdzMmXVHfc0Ngh/TUrYbhGD+4rT4+m3+HNrapRUt4Jrcot1oHyU8XqJX5emlJ/HFIX75Yfh0QBDAAAALR/FMNwOIZhaNueQ/ogZavWbtmpGnPdDG6X4ADNGFs3C9y9U3CTfVTUWvTl8RKtyi3W5qJyGfXtwSYPXRMaqClhQbq0g3eLj0OqrK7Rxsz9+mIzBTAAAADgCCiG4TCKSsv12Tc79GHKNh345ZikulngUZF9NSthuOKH9m9yFthiGNpaVK5VecVaf6xEFZa6Etjk5qaxnTooMSxQI0NafhwSBTAAAADguCiG0a7VWizavi9bH6Vs0xdbslRVU3ecUeegDpo+Jkozx0arR2hIk338VF6lVbknlJRXrJzTjkMaEuirxC6Bujo0UIEtPA6JAhgAAABwDhTDaHfKKqr0bdaPSt6+R1/v2KeCkjLrvZERfTQrYbgSogbI5Nl4AVtUU6svjtW9B/xDSaW1vbu3SZPDAjU5LEgX+3q1KB4KYAAAAMD5UAyjXfj5eJFStu9RSsYebd590PoesCT16Bysa2IH6dqx0boorPGCs8ZiaENBqVblFmtDQanM9S8C+3u4a3xogBK7BCkqyLdFxyFRAAMAAADOjWIYdlFrsSjzwM9K3r5HqRl7tfdIrvWem5ubovqGKz5qgOKH9lffHl0a3cjKMAxllVRqVV6x1uSdULHZIklylzQyxF+JYUGK79ShRcchUQADAAAAroNiGG2mrKJK3/zwo1IyGi5/9vPx0qhBfRUfNUBjBvdTx0D/Jvs6WlmjpLxirco9oYMVp4rWfv7eSgwL1DWhQQr1bv7r3VQBHNGruyaOoAAGAAAAnBHFMC6on48VKiVjb6PLn+OjBihh6AANH3CxvExNfx3LzLX68njdMuitxaeOQ+po8tA1XeqOQxrQwafZmKqqa7SBAhgAAABwaRTDOK9OX/6ckrFH+47kWe/ZLn8eoL49Qps9x7ey1qJtxeX6PO+Evjxeosr645C83NwU37mDEsOCdHmIvzyb6cdaAG/JUvJ2CmAAAADA1VEM45w1tfzZ38dbV0T2UcLQARrdguXPtYah3aWVSiss1+aiMmUUV6jaMKz3owJ9lRgWpPGhAQpoYjdpiQIYAAAAQOMohnFWfj5WqOSMPUrZvlebdx+UubaR5c+XXiwvz8a/ZoZh6FBFtdKLyrW5sExbistVUr8J1kkD/L0V36mDJocFKbyZ45AogAEAAAC0BMUwWqTWYtH3Px6p2/15x95zWv6cV1WjzUV1M7/pheXKqzbb3O/pY1JssL9GhPhpeJCfOno1/TWlAAYAAADQWhTDaNTpy59Td+xVYUm59d7py5/HDOmnkIDGlz+XmGu1tahcm4vKlV5Upp/Kq23uh5g8NCLYTyOC/RUb7Kcezcz+ShTAAAAAAM4NxTBsNLX8uWdoiOKH9ld8M8ufqy0W7ThRoc2FdcVvVkmlTl/47OvupujTit++/t5yb2YDLIkCGAAAAMD5QzHs4k5f/pySsVf7fz61/NndzU3D+l2ksfUFcGPLn2sNQ3tKq+qXPZcp40SFdddnSfJ0k4YE+GpEsJ9iQ/w1KMBXJvfmi19JKjhRpq17Dmndtp0UwAAAAADOG4phF1RaUVm3/Hn7Hn39/b5WL382DEOHK2u0ubBM6UXl2lpUpuJfbXrVz9/bWvwOC/SVfzM7P0uSxWLRj78c0/Z92crYn63t+7J1KDff5hkKYAAAAADnA8WwizhyrLBu86uMJpY/Rw3Q8AFnXv58vNqsLfUbXqUXlSmnynbTq27enooN8a/b+Cq4+U2vJKmsskrf//izMvYf1vZ92drx4xGVlFfaPOPrZVLkJT00anBfTYiJoAAGAAAAcF5QDDuplix/jo/qr7FDzrz8ucxcq23FFUovKtPmwnLtL6+yuR/s6aGY+pnfEcF+6uljanIHacMw9Et+sbbvO6yMfdnavj9bew7nyHLaGcKS1K1TkKL6hmtov3BF9b1IAy4Kk6dH87PKAAAAANAaFMNOpLnlz6Mi+yp+aP8zLn+usRj6/kR98VtUrh9OVKj2tPs+7m4aFuSn2GA/jQjxV/9mNr2qNpu1+1COtu87rO37s5WxL1t5RSU2z3h6uCviou4a2i9cQ/uGK6pfuLp2DDovuQAAAACAplAMO7iTy59TMvZoy+5DLV7+bDEM7SurUnr9e7/fFZfbbHrlIWlwoG9d8Rvsr8GBPvJyd280jsKSMmXsP1JX/O7L1g8//ayqGtul1EH+voqyFr4XaVDv7vL1bv4YJQAAAAA43yiGHUytxaId+48oJaPp5c/xQweoT3fb5c9HKqqVXlSuzfWzv0U1tTZ99/Hzsi57jg7yU4dGNr2yWCw6cPS4MvZl67t9h5WxP1sHc/IbPHdJt86K6ndR/ZLncPXq2knuTRTUAAAAANBWKIYdQGlFpTZm/qjUjMaXPydEDdDowX1tlj8XVJu1ub74TS8s1y9VNTb9dvX2tG54FRPsr1DvM38dyquqlXngZ+us74792Trxq42ufOo3uoqqf9d3SN+eCu7gdx6zAAAAAADnD8VwO5WdV6CUjL1nXP4cHhqi+KgBGju0v83y5/JaizYUlFqPPNpXZrvpVaCnu2Lqi9/YEH9d1MimV7/kF1k3udq+97D2ZOeq1mJ7dFJYSGBd4dvvIkX1C9eA8K4yteD4JAAAAABoDyiG25nyqmpd/9hrLVr+XGMx9ENJhTYXFSm9sEyZJRUyn7Y5s7e7m6ICfTWi/sijAR285fGr4rfGXKvdh3Osxxtl7M9WTsEJm2c83N0V0atuo6th/S7S0L7h6taJja4AAAAAOC6K4XbGz9tLHh7uZ1z+bNRvevXez4VKLyrTd8UVKq89NWPrLikywEcjgv0VG+KnwYG+8v7VO7pFpeXK2J9tLXwzD/ysymrb5dOBfj7WHZ6H9btIgy7pIT82ugIAAADgRNwM41cHvbqAiooK+fn5qby8XL6+vvYOp4EjxwrVJSRAXp6e+qWyWumF5UovKtOWonIV/GrTq95+XnXLnoP9NTzYTwGnLVW2WCw6mJNvc7zRgaPHG/y83t061+3w3DdcUf0vUm82ugIAAADg5JgZbmdqLIZ2yUNv/XRcmwvLlF1pO2vbxcvT+s7viGA/dfE2We9VVFVry/76HZ7rZ36LyypsPu9t8lTkJT2sxxsN7duzwZnDAAAAAODsKIbbGTc36fG9OSqtX/7cwcNdMacVv718vaybXuUUFOvzjGxtrz/eaPfhHJlrbTe66hIcYN3kamjfcF16cVeb84YBAAAAwBVRFbUznm5umt0jRF7ubooN9telAT7ydHNTjblWe7Nz9V79Rlfb92Urp6DY5rPubm667OJu1uONhvYLV/dOQWfcMRoAAAAAXBnvDLfDd4aluo2udvx4pO6Io32HlXngZ1X8aqOrAD8fDenTs26H537hirykh/x9vO0UMQAAAAA4DmaG25lqs1nXPvxvm6OVTro4rFP92b7hGtr3IvXp3pmNrgAAAADgLFAMtzMn3+f1MnlqUO/udTs815/t2zGQja4AAAAA4HxgmXQ7XCb987FChdYfrQQAAAAAOP+ottqhHqEh9g4BAAAAAJwaL5wCAAAAAFwOxTAAAAAAwOVQDAMAAAAAXA7FMAAAAADA5VAMAwAAAABcDsUwAAAAAMDlUAwDAAAAAFyO3Yrh5557TkOHDlV0dLTmzp2rmpqaFn/24MGDGj9+vGJjYxUTE6PU1NQLGCkAAAAAwNnYpRj+4osvtG7dOm3dulXbtm1TWFiYnnzyyRZ//ve//73+9re/KT09XZ988oluu+02FRUVXbiAAQAAAABOxS7F8Ntvv60HHnhAnp6ekqTExEQ99thjLfrs3r17ZTKZNGbMGElSjx49tGvXLn366aeNfqampkYVFRU2/wAAAAAAXJenPX5oVlaWhgwZIklas2aNXnnlFXXt2lWlpaXq0KFDiz9bVlamuXPnaurUqcrKymr0M08++eQZi22KYgAAAABwTj4+PnJzc2v0vl2K4bKyMgUFBenFF19UZmamli9frvj4+BYVw6WlpQoODtaRI0d0yy236JFHHlFpaalWrFjR6Gceeugh3X///dbrgoIC9ezZU506dTpvfyYAAAAAQPtRXl4uX1/fRu/bpRj28vLS7NmzNXToUL366quSpKKiomYLYUnq0KGDvvjiC33zzTd67bXX1KtXLy1fvlwBAQGNfsZkMslkMlmvu3Xrpvz8/GZ/U2AvFRUV6tSpk/Lz85v8j+cqyMcp5MIW+bBFPk4hF7bIhy3ycQq5sEU+TiEXtsiHLUfJh4+PT5P37VIMWywWdenSxTpbW1JSIovF0qAYPtNMcUREhNLS0lRcXKzAwEBJUkZGhiIiIlr8893d3dWxY8dz/FNceL6+vu36y9XWyMcp5MIW+bBFPk4hF7bIhy3ycQq5sEU+TiEXtsiHLUfPh1020PrnP/+pvXv3ymw2S5IWLVqk6667zuaZBQsWKCQkRFu2bLFp79+/v0aOHKmMjAxJ0pEjR/TJJ5/ot7/9bZvEDgAAAABwfHaZGZ44caJ27typ6OhoeXh4KDo6Wi+99JLNM6GhoQoJCZGfn1+Dz7/33nu65ZZbdOLECRmGoZdeekkhISFtFT4AAAAAwMHZpRiWpLvvvlt33313o/cXLlyohQsXnvFer169tH79+gsVmt15enrqkUcesR495erIxynkwhb5sEU+TiEXtsiHLfJxCrmwRT5OIRe2yIctZ8mHm2EYhr2DAAAAAACgLdnlnWEAAAAAAOyJYhgAAAAA4HIohgEAAAAALodiGAAAAADgciiGL7CcnBzNnj1bbm5ujT4zb948LV26tNV9V1VV6eabb1Z0dLSGDh2qf/3rX+cS6gXXWC4Mw9Bjjz2mcePGafz48brqqqv05ptvtqpvR8uFJD388MMaOXKkrrjiCl133XXKz8+33vvyyy81fPhwjRgxQhMmTNCRI0da1bez5WPFihUaOXKkRo8erXHjxmn79u2t6tvR8tFULk76/e9/r3Xr1rW6b0fLhdSyfLjKOCo1ng9XG0urqqp0xx13aOzYsYqJidH06dOVm5tr84wrjaUtyYerjKUtycVJrjCWtiYfrjCWNpcPVxtLJam6ulpRUVE6fvy4TbtLjKEGLpg33njDiIyMNFasWGE0lurly5cbN9xww1n1/8ADDxhPPPGEYRiGUV1dbYwbN85ITk4+23AvqKZykZSUZEybNs2ora01DMMwKioqjOHDhxv79u1rcf+OlAvDMIxFixYZDz/8sPV62bJl1u/BsWPHjIEDBxpHjx41DMMw1q1bZ8THx7eqf2fKx6ZNm4yJEycalZWVhmEYxp49e4zLLrvMMJvNLe7fkfLRVC5O2rFjhzFhwoSz6t+RcmEYLcuHq4yjhtF0PlxtLH3wwQetsRqGYTz11FPG1KlTrdeuNpY2lw9XGkuby8VJrjKWtjQfrjKWNpcPVxtLDcMwXnzxReOpp56yaXOVMZRi+AJ69dVXjdLSUsMwjDMWw4cOHTJGjRplfaa1+vXrZ5SVlVmvb7vtNiMiIuLsgr3AmsrFyf8xKikpMQzDMHJzc40RI0YYeXl5Le7fkXJhGIbx7LPPGr/88ov1urKy0hrvv//9b+PRRx+13isuLjYkGT/99FOL+3emfKxevdr46quvbJ6Pi4szjh071uL+HSkfTeXipMTERGP79u1n1b8j5cIwms+HK42jhtF0PlxtLP3444+N4uJi6/X3339vE6urjaXN5cOVxtLmcnGSq4ylLcmHK42lzeXD1cbS0tJSY9iwYUZ5eblNu6uMoY59SnI7d+uttzZ7Pzw8XHPnzpVhGPp//+//KTIyskV9FxYWys/PT35+fpKkf/zjHyopKZGXl9c5x30hNJWLwYMH6/e//726d++u3r176/Dhw/rvf/+r0NDQFvXtaLmQpLvvvtvm+ssvv9To0aMlSVlZWUpISJAkHThwQLfffruuuOIKZWVlqVevXs327Wz5mDRpks29vLw81dbWqnPnzi3q29Hy0VQuJGnjxo0KCQnR0KFD9fbbb+vIkSN66KGHWtS3o+VCaj4frjSOSk3nw9XG0mnTpln/3Ww2a+nSpfrd735nbXO1sbS5fLjSWNpcLiTXGktbkg9XGkuby4erjaXPP/+85s+fL19fX91000265ZZbrGOlK4yhvDNsJ1999ZUOHTqkxYsX67333tNDDz2kmTNn6ujRoy36fGlpqYKDg1VVVaWbbrpJISEheuONN1ReXn6BIz//NmzYoLfeekv79u3Tjh07lJ6eroULF+rAgQMt+ryj5+Lw4cN66qmn9OSTT0o69edJTU3VHXfcobfeektjx45VSUlJi/pztnycrrq6WjfeeKNeeOGFFvfnyPk4Uy4effRR/f3vfz+r/hw5F1LDfLj6OPrrfLjqWJqQkKA+ffrowIEDuvnmm63trjqWNpaP07nKWNpULlxxLG0sH646ljaWD1caS/Pz8/X555/rxhtvbHDPZcZQO89Mu4xfp/ovf/mLsWLFCpu2RYsWGS+88EKL+isoKDC6du1qXHPNNcbatWsNwzCMvLw8Y/jw4ecn4Avo17mYNWuWsWnTJpu2d955x3jooYda1J8j5yI3N9cYOXKksWvXLmvbn//8Z2Py5MnGDTfcYF2ycvvttxurVq1qUZ/Olo+TzGazMWvWLOODDz5oVZ+Omo8z5WLlypXGX//6V+v1W2+9ZfPeU3McNReGceZ8uPI4eqZ8uPJYahiGkZqaagwePNioqKgwDMO1x1LDaJiPk1xtLDWMhrlw5bHUMBrmw5XHUsNomA9XGkvvuecemzHxj3/8o7Fx40bDMFxnDGWZtJ2YzWZ5etqm38vLSxaLpcGzJSUlCggIsGkLCQlRTk6O/v73vys6OlqSlJGRoYiIiAsX9AVSWVkpHx8fmzYfHx9VVFQ0eNaZclFcXKyZM2dqyZIluvTSS63tEREReuGFF2SxWKw7b2dkZOjee+9t0Icr5EOq29lx7ty5uvLKKzVr1qxG+3CWfDSWi6SkJH333XfWZUs5OTmqqqpScnKy1q9fb9OHs+RCajwfrjqONpYPVxtLk5KSbJb+jhkzRn369NGuXbsUFRXlcmNpc/mQXGcsbS4XrjaWNpcPVxtLm8uHK42lX331lbZu3apnnnlGkrR7927t2LFD48aNc50x1N7VuKv4dapTUlKMmJgYo7Cw0DCMuo0LLr30UuPAgQM2z53cffm5555r0Of9999vs0PblVde2e52aDuTX+fivffeM6ZPn279jVx+fr4RGxvb4LdyzpSLsrIy46qrrjJSU1Mb3MvLy2vR7n2ukg/DMIy77rrLWLRoUZN9OEs+msvF6RqbzXCWXBhG0/lwxXG0qXy42lgaHx9vfPrpp9br7OxsIyIiwroxjquNpc3lwzBcZyxtSS5O5+xjaXP5cLWxtLl8uNpYerrTZ4ZdZQxlZvgCmj9/vnbu3Gm9PvkbyOjoaD3zzDO66667NHHiRPn4+MhsNuvFF19U7969bfoIDAxUSEiIOnbs2KD/xx57TLfddpuio6NlNpt1yy23KD4+/oL+mc5Wc7koKipSfHy8TCaTLBaL7rvvPsXFxdn04Sy5kKQ777xTmZmZeuSRR2za165dq9DQUC1ZskSJiYlyd3dXcHCw3nnnnQZ9uEo+/vvf/+rll1/W5ZdfrqSkJOu9RYsWKSYmxnrtLPloKhcmk6lFfThLLqSm8zF27FiXGkelpvMxe/ZslxpL33//fS1YsEBPP/20TCaT/P399c477ygwMFCSXG4sbS4f7777rsuMpc3loqWcIRdS8/lwtbG0uXy42ljaGFcZQ90MwzDsHQQAAAAAAG2J3aQBAAAAAC6HYhgAAAAA4HIohgEAAAAALodiGAAAAADgciiGAQAAAAAuh6OVzkJ6erq9Q7ggYmNjW/0ZcmHLGfNBLmzx9+QUvhu2+G6cQi5O4e+JLb4bp5CLU/h7Yovvxiln+91oKWaGAQAAAAAuh2IYAAAAAOByKIYBAAAAAC6Hd4YBAAAAOJwL/T6pIyEXZ4eZYQAAAACAy6EYBgAAAAC4HJZJAwAAAMCvVFVV6Z577lFmZqbKy8sVHh6ul19+WWFhYfYOrc05ay6YGQYAAACAX3n88cfVrVs3paamasuWLYqJidG8efPsHZZdOGsuKIYBAAAA4FeGDx+uO++803qdmJioffv22TEi+3HWXLBMGgAAAAB+Zdq0adZ/N5vNWrp0qX73u9/ZMSL7cdZcMDMMAAAAAI1ISEhQnz59dODAAd188832DseunC0XzAyfBc7xAgAAAFxDcnKyJOnrr7/W1VdfrfT0dPn4+Ng5KvtwtlxQDAMAAAAOgAmZtpWUlKRJkyZZr8eMGaM+ffpo165dioqKsmNkbc9Zc8EyaQAAAAD4lcWLF+t///uf9frIkSPau3ev+vTpY8eo7MNZc8HMMAAAAAD8yvvvv68FCxbo6aeflslkkr+/v9555x0FBgbaO7Q256y5oBhuJ3JycnT33Xfr/fffl2EYNve+/PJL3X///XJ3d1dISIj+85//qGfPnnaKFAAAAHB+Xbt21QcffGDvMNoFZ80Fy6TbgTfffFNXX321rr322gb3jh8/rj//+c9atWqVNm/erHvvvVdz5syxQ5QAAABwNjk5OZo9e7bc3Nwa3KuqqtLjjz8uHx8fpaam2iE64MKiGG4HzGazNm3apOnTpze49/HHH2vWrFnq2rWrJGnEiBFKSUnRwYMH2zhKAAAAOJOmJmTS0tIUFxcnk8mk6OhoO0QHXHgsk24Hbr311kbvZWVlKSEhQZJ04MAB3X777briiiuUlZWlXr16tVGEAAAAcDYnJ2T8/f0b3Dt69Kg++eQT9erVS2vXrrVDdMCFx8xwO1daWqrg4GClpqbqjjvu0FtvvaWxY8eqpKTE3qEBAADAgd16661nLIQladq0aUy8wOkxM9zOdejQQc8884wCAwO1YsUK+fr6qqioSAEBAfYODQAAAAAcFjPD7VxERIRWr16t9957T76+vpKkjIwMRURE2DkyAAAAAHBczAy3c9OmTdOSJUuUm5urrl27av369fLy8mo3y1ZiY2PtHQIAAAAAtBrFcDswf/587dy503p9csOs6OhoPfPMM1qyZIkSExPl7u6u4OBgvfPOO/YKFQAAAACcAsVwO7B06dIm71911VXaunVrG0UDAAAAV9DUhMyAAQO0bNkySVJKSori4+MVHx8vDw8PrV+/3i7xAucbxTAAAADggpqbkGnq+E/AGbCBFtqdnJwczZ49W25ubg3u/etf/9Lll1+u+Ph4jR8/XpmZmXaIEAAAAICjoxhGu/Lmm2/q6quv1rXXXtvg3vLly7V27VqlpqYqJSVFixcv1qxZs1RaWmqHSAEAAAA4MophtCtms1mbNm3S9OnTG9xbtmyZnnzySXl5eUmShg4dqvHjxys5ObmtwwQAAADg4HhnGO1KU++mGIahyspKm7aKigqZTKYLHRYAAAAAJ8PMMBzGnDlzdOeddyo/P1+SlJSUpNWrV2vMmDF2jgwAAACAo2FmGA5jxowZKi8v1/Tp0xUYGKj8/Hw98MAD8vPzs3doAADgAomNjbV3CACcFDPDcChz5sxRamqqHnnkEYWEhOiOO+6wd0gAAAAAHBAzw3A4hYWFuu222/S///1P7u78PgcAAABA61EMo12ZP3++du7cab1OSEiQJEVHR+uZZ56RYRj6wx/+oIcffljdu3e3V5gAAAAAHBzFMNqVpUuXNnnfzc1NK1eubKNoAACAM3r44Ye1fv16ubm5qWfPnnrppZfUqVMnm2fmzZunyMhIzZ8/305RArjQWGMKAAAAl7F48WK5ubnp22+/1TfffKOpU6fqzjvvtHnmo48+0okTJyiEASdHMQwAAACX4eHhoXnz5lmvp0+fru+//956ffjwYS1ZskSvvfaaPcID0IYohgEAAOAy7r77bnXr1s16/eWXX2r06NHW61tvvVXh4eGaO3euZs+erczMTHuECaAN8M4wAAAAXNLhw4f11FNP6bPPPpMkffXVVzp06JDeeOMN9ejRQ1lZWZoxY4aSk5NtCmgAzoGZYQAAALicvLw83XDDDXr99dfVsWNHSdKqVav01FNPqUePHpKkiIgI/elPf9JHH31kz1ABXCAUwwAAAHApxcXFmjlzppYsWaJLL73U2m42m+Xpabtw0svLSxaLpa1DBNAGKIYBAADgMsrLy3Xttdfq73//u4YPH25zb8aMGXriiSdUVFQkqW4Z9SuvvKLf/OY3dogUwIXGO8PAeRQbG2vvEAAAQBPuvPNOZWZm6pFHHrFpX7t2rcaOHau77rpLEydOlI+Pj8xms1588UX17t3bTtECuJAohgEAAOAy/vOf/zR5f/bs2Zo9e3YbRQPAnlgmDQAAAABwORTDAAAAAACXQzEMtFNVVVW64447NHbsWMXExGj69OnKzc0947Pz5s3T0qVL2zhCAAAAwHFRDAPt1OOPP65u3bopNTVVW7ZsUUxMjObNm9fguY8++kgnTpzQ/Pnz7RAlAAAA4JjYQAtop4YPH64rr7zSep2YmKj33nvP5pnDhw9ryZIlWrNmTVuHBwAAADg0ZoaBdmratGkKDAyUJJnNZi1dulS/+93vbJ659dZbFR4errlz52r27NnKzMy0R6gAAACAw2FmGGjnEhISdODAAQ0YMECPPfaYtf2rr77SoUOH9MYbb6hHjx7KysrSjBkzlJycrG7dutkxYgAAAKD9Y2YYaOeSk5N16NAhLVy4UFdffbUqKyslSatWrdJTTz2lHj16SJIiIiL0pz/9SR999JE9wwUAAAAcAsUw0E4lJSXZXI8ZM0Z9+vTRrl27JNUtnfb0tF3c4eXlJYvF0mYxAgAAAI6KZdJAO7V48WLV1NTot7/9rSTpyJEj2rt3r/r06SNJmjFjhu69916NGTNGwcHBOnz4sF555ZUGRbS9xMbG2jsEAAAAoFEUw0A79f7772vBggV6+umnZTKZ5O/vr3feece6qdbYsWN11113aeLEifLx8ZHZbNaLL76o3r172zlyAAAAoP2jGAbaqa5du+qDDz5o8pnZs2dr9uzZbRQRAAAA4Dx4ZxgAAAAA4HKYGQbgUKqrqxUbG6t169apc+fOkqRjx44pIiJCERER1ud69Oih//u//7NXmAAAAGjnKIYBOJRXX31Vs2bNshbCknT06FFNnTpVr776qh0jAwAAgCOhGAbgMMrKyvTmm29q48aNNu1Hjx5VWFiYnaICAACAI+KdYQAO4/nnn9f8+fPl6+urm266Sd98842kumI4Pz9fs2bN0pgxY3T99dfr4MGD9g0WAAAA7RozwwAcQn5+vj7//HN9/fXXDe4VFxeroKBAr732moKCgvTtt99q8uTJ2rZtm3x8fOwQLQAAANo7imEADuEf//iHHnzwQXl4eDS4t2DBAi1YsMB6PXLkSI0cOVIpKSmaOHFiW4YJAOdFbGysvUMAAKdHMQzAIXz11VfaunWrnnnmGUnS7t27tWPHDo0bN07x8fEaMmSIwsPDrc/7+vqqurraXuECAACgnaMYBuAQvvvuO5vrm266SbfccouuuOIKvf7663r//ff11ltvyWQy6aefftK6dev0xBNP2ClaAAAAtHcUwwAc3i233KLi4mKNGDFCAQEB8vX11bJlyxQYGGjv0ADA7pYvX66HHnpIPXr0sLZNnjxZf/3rXyVJK1as0LPPPisPDw+ZTCY9++yzioqKsle4ANBmKIYBOKQ333zT5vqee+7RPffcY6doAKD9Onr0qB599FHNnj27wb20tDS9/vrrSk5Olre3t/bu3aupU6cqMzPzjHs0AIAz4WglAAAAJ9bUWewFBQW677775O3tLUnq37+/goKCVFhY2JYhAoBdUAwDAAA4saNHjyo9PV2TJ0/WyJEjde+996q8vFySNGnSJCUkJFifzcvLU21trTp37myvcAGgzVAMAwAAOLHy8nK5u7vrf//7n7799lv17NlTN998c4PnqqurdeONN+qFF16wQ5QA0PZ4ZxgAAMCJffjhhzbXCxYs0Msvv6yysjL5+/tLkmprazVnzhzddNNNiouLs0eYANDmmBkGAABwYq+88ooMw7Bp8/LyUm1trSTJMAzNnTtXV155pWbNmmWPEAHALiiGAQAAnNjGjRv10ksvWa9XrVqlrl27Wo+fu/vuu3XppZdq7ty59goRAOyCZdIAAABO7LXXXtP8+fMVHR0tPz8/XXzxxVq2bJkk6d1339XLL7+syy+/XElJSdbPLFq0SDExMfYKGQDaBMUwAACAE/P19dUbb7xxxntz5szRnDlz2jgiAGgfKIYBAIDdxcbG2jsEAICL4Z1hAAAAAIDLoRgGAAAAALgcimEAAAAAgMuhGAYAAE6rsrJScXFx2rZtm037yy+/rFGjRmnUqFG64oortHnzZjtFCACwFzbQAgAATuvee+/Vn/70J0VHR1vb1q1bp5UrVyo5OVkmk0nffvutfvOb3yg7O1smk8mO0QIA2hIzwwAAwCl99tlnKisr06233mrTHhQUpGeffdZa+I4cOVI1NTUqLi62R5gAADuhGAYAAE6noKBA9913n9zd3TVz5kzdeeedKigokCSNGDFCl112mfXZd999VzExMercubO9wgUA2AHLpAEAgNP55z//qUGDBmnp0qXy9vbWf//7X/3mN7/Rxo0brc/85S9/0fr161VZWally5bZMVoAgD0wMwwAAJzO6tWr9e9//1ve3t6SpOuvv15BQUHKzMy0PvP8888rMzNT69ev1+23325zDwDg/CiGAQCA06mpqWmwGZaXl5csFos2btyokpISa/vFF1+sOXPmaP369W0dJgDAjlgmDQAXWGxsrL1DAFzOzJkz9de//lX//ve/5ebmpnXr1ungwYOKjIzU448/rk8//VSLFi2Su7u7KisrtXr1ai1cuNDeYQMA2hDFMAAAcDoPPvigHn74YcXGxsrHx0cBAQH6+OOP5e7uroceekgLFy5UXFycfH19ZRiGbrvtNo0ePdreYQMA2pCbYRiGvYMAALiO9PR0e4dwQZzNCgByAQCA/fDOMAAAAADA5bBMGgAczA033KCcnBybtszMTH366acaNWqUVqxYoWeffVYeHh4ymUx69tlnFRUVZadoAQAA2ieKYQBwMO+//77NdX5+viZMmKC4uDilpaXp9ddfV3Jysry9vbV3715NnTpVmZmZ8vDwsFPEAAAA7Q/FMAA4uKeeekr33HOPPD09VVBQoPvuu896tmr//v0VFBSkwsJCde7c2c6R1uF9UgAA0B5QDAOAA8vOztaGDRu0ePFiSdKkSZNs7ufl5am2trbdFMIAAADtBRtoAYADe+yxx/S3v/1N7u4Nh/Pq6mrdeOONeuGFF+wQGQAAQPtGMQwADmr37t3avXu3pkyZ0uBebW2t5syZo5tuuklxcXF2iA4AAKB9Y5k0ADiohQsX6vHHH2/QbhiG5s6dqyuvvFKzZs2yQ2Rtp6mdtb/55hutWbPG5l52drb++Mc/auHChW0ZJgAAaIfcDMMw7B0EAKB1tmzZogcffFDr1q1rcO8vf/mLunfvrnvvvdcOkdnXyZ2109LS5OnZ8Pe98fHxev3119W3b187RAcAANoTZoYBwAE9+OCDeuKJJxq0v/vuu3r55Zd1+eWXKykpydq+aNEixcTEtGWIdnH6ztq/tnLlSg0YMIBCGAAASGJmGADgJLKzszVjxgylpaU12FDMYrEoLi5OH3/8sXr27GmnCAEAQHvCBloAAKfQ1M7ay5Yt0+jRoymEAQCAFcUwAMDhNbWzdnV1tRYvXqwHHnjADpG1raKiIv3hD3/Q+PHjNWHCBF1zzTXatGmTzTNVVVV6/PHH5ePjo9TUVDtFCgCA/VEMAwAcXmM7a0vSq6++qqlTpyo0NLSNo2p7//jHPzRs2DCtW7dOX3zxhV599VXdeOON1vtpaWmKi4uTyWRSdHS0HSMFAMD+2EALAODQtmzZouLiYo0bN67BvbKyMr300ksNZkedVa9evZSdna3a2lp5eHgoLy9P3bp1s94/evSoPvnkE/Xq1Utr1661Y6QAANgfxTAAwKE1trO2JD3//PP64x//qKCgoDaOyj5uu+02zZs3T127dlXnzp1lNpu1fv166/1p06bZMToAANoXimEAgEM701nLJy1cuLANI7G/p59+Wl5eXvr555/l5eWlNWvW6Prrr9eGDRvOeNwUAACujHeGAQBwEq+99pqee+45eXl5SZImTpyogQMHKiUlxb6BAQDQDlEMAwDgJMxmc4MZYB8fH1VUVNgpIgAA2i+KYQAAnMSECRO0ePFi63VmZqbWr1+vhIQEO0YFAED75GYYhmHvIAAAwLmrqKjQAw88oLS0NHl6eiogIEDPPPOMBg0aJKluGfWyZcskybp0Oj4+Xh4eHjYbbQEA4AoohgEAAAAALodl0gAAAAAAl0MxDAAAAABwORTDAAAAAACXQzEMAAAAAHA5FMMAAAAAAJdDMQwAAAAAcDkUwwAAAAAAl0MxDAAAAABwORTDAAAAAACXQzEMAAAAAHA5/x+4+YUJve59AQAAAABJRU5ErkJggg==\n", 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" ] @@ -1463,7 +1515,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1477,7 +1529,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.13" }, "toc": { "base_numbering": 1, @@ -1499,5 +1551,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "nbformat_minor": 4 +} diff --git a/sphinx/source/tutorials.rst b/sphinx/source/tutorials.rst index 1f25756b6..bada3beae 100644 --- a/sphinx/source/tutorials.rst +++ b/sphinx/source/tutorials.rst @@ -53,26 +53,6 @@ the code for yourself by downloading the notebook tutorial/Classification_with_Facet -.. _boot-sim-summary-tut: - -Simulation Tutorial: Feature Simulation on Drill Operations -************************************************************* - -Introduce yourself to the FACET's simulation module. This tutorial aims to provide -a step by step explanation about the simulation capabilities of FACET and -is based on the introductory Water Drilling Tutorial above. - -Start exploring the tutorial below by clicking on the section links, deepen your -understanding by reading the associated -`GAMMAscope article `__, and -download the notebook for yourself -:download:`here `. - - -.. toctree:: - :maxdepth: 1 - - tutorial/Model_simulation_deep_dive .. _scikit-learn-summary-tut: @@ -80,7 +60,7 @@ Generating Standard Scikit-learn Classifier Performance Summaries ******************************************************************* Find quick clear examples of how to generate standard *scikit-learn* performance -summaries for the best identified classifier from your FACET `LearnerRanker()`. +summaries for the best identified classifier from your FACET `LearnerSelector()`. Start exploring the tutorial by clicking on the section links below, and run the code for yourself by downloading the notebook diff --git a/src/facet/__init__.py b/src/facet/__init__.py index ad376e2ec..572a8a252 100644 --- a/src/facet/__init__.py +++ b/src/facet/__init__.py @@ -6,7 +6,7 @@ """ -__version__ = "2.0.dev1" +__version__ = "2.0rc0" __logo__ = ( r""" diff --git a/src/facet/data/_sample.py b/src/facet/data/_sample.py index dd90e43a3..30b00500a 100644 --- a/src/facet/data/_sample.py +++ b/src/facet/data/_sample.py @@ -2,10 +2,13 @@ Implementation of FACET's :class:`.Sample` class. """ +from __future__ import annotations + import logging from copy import copy from typing import Any, Collection, Iterable, List, Optional, Sequence, Set, Union +import numpy as np import pandas as pd from pytools.api import AllTracker, to_list, to_set @@ -37,7 +40,7 @@ class Sample: It provides basic methods for accessing features, targets and weights, and for selecting subsets of features and observations. - The underlying data structure is a :class:`pandas.DataFrame`. + The underlying data structure is a :class:`~pandas.DataFrame`. Supports :func:`.len`, returning the number of observations in this sample. """ @@ -71,7 +74,8 @@ def __init__( ) -> None: """ :param observations: a table of observational data; - each row represents one observation + each row represents one observation, names of all used columns must be + strings :param target_name: the name of the column representing the target variable; or an iterable of names representing multiple targets :param feature_names: optional iterable of strings naming the columns that @@ -81,28 +85,16 @@ def __init__( observation """ - def _ensure_columns_exist(column_type: str, columns: List[str]): - # check if all provided feature names actually exist in the observations df - available_columns: pd.Index = observations.columns - missing_columns = { - name for name in columns if name not in available_columns - } - if missing_columns: - raise KeyError( - f"observations table is missing {column_type} columns " - f"{missing_columns}" - ) - # check that the observations are valid + if not isinstance(observations, pd.DataFrame): + raise ValueError( + "arg observations must be a data frame, but is a " + f"{type(observations).__qualname__}" + ) - if observations is None or not isinstance(observations, pd.DataFrame): - raise ValueError("arg observations is not a DataFrame") - - observations_index = observations.index - - if observations_index.nlevels != 1: + if observations.index.nlevels != 1: raise ValueError( - f"index of arg observations has {observations_index.nlevels} levels, " + f"index of arg observations has {observations.index.nlevels} levels, " "but is required to have 1 level" ) @@ -111,18 +103,17 @@ def _ensure_columns_exist(column_type: str, columns: List[str]): targets_list: List[str] = to_list( target_name, element_type=str, arg_name="target_name" ) - _ensure_columns_exist(column_type="target", columns=targets_list) + _ensure_columns_exist(observations, column_type="target", columns=targets_list) self._target_names = targets_list # process the weight - if weight_name is not None: - if weight_name not in observations.columns: - raise KeyError( - f'arg weight_name="{weight_name}" ' - "is not a column in the observations table" - ) + if weight_name is not None and weight_name not in observations.columns: + raise KeyError( + f'arg weight_name="{weight_name}" ' + "is not a column in the observations table" + ) self._weight_name = weight_name @@ -142,7 +133,9 @@ def _ensure_columns_exist(column_type: str, columns: List[str]): features_list = to_list( feature_names, element_type=str, arg_name="feature_names" ) - _ensure_columns_exist(column_type="feature", columns=features_list) + _ensure_columns_exist( + observations, column_type="feature", columns=features_list + ) # ensure features and target(s) do not overlap shared = set(targets_list).intersection(features_list) @@ -151,18 +144,16 @@ def _ensure_columns_exist(column_type: str, columns: List[str]): self._feature_names = features_list - # make sure the index has a name - - if observations_index.name is None: - observations = observations.rename_axis(index=Sample.IDX_OBSERVATION) - # keep only the columns we need observation_columns = [*features_list, *targets_list] if weight_name is not None and weight_name not in observation_columns: observation_columns.append(weight_name) - self._observations = observations.loc[:, observation_columns] + # select just the columns we need to retain and tidy up the observations table + self._observations = _tidy_up_observations( + observations.loc[:, observation_columns] + ) @property def index(self) -> pd.Index: @@ -246,7 +237,7 @@ def subsample( *, loc: Optional[Union[slice, Sequence[Any]]] = None, iloc: Optional[Union[slice, Sequence[int]]] = None, - ) -> "Sample": + ) -> Sample: """ Return a new sample with a subset of this sample's observations. @@ -262,7 +253,7 @@ def subsample( subsample = copy(self) if iloc is None: if loc is None: - ValueError("either arg loc or arg iloc must be specified") + raise ValueError("either arg loc or arg iloc must be specified") else: subsample._observations = self._observations.loc[loc, :] elif loc is None: @@ -273,7 +264,7 @@ def subsample( ) return subsample - def keep(self, *, feature_names: Union[str, Collection[str]]) -> "Sample": + def keep(self, *, feature_names: Union[str, Iterable[str]]) -> Sample: """ Return a new sample which only includes the features with the given names. @@ -281,17 +272,17 @@ def keep(self, *, feature_names: Union[str, Collection[str]]) -> "Sample": :return: copy of this sample, containing only the features with the given names """ - feature_names: List[str] = to_list(feature_names, element_type=str) + feature_names_list: List[str] = to_list(feature_names, element_type=str) - if not set(feature_names).issubset(self._feature_names): + if not set(feature_names_list).issubset(self._feature_names): raise ValueError( "arg feature_names is not a subset of the features in this sample" ) subsample = copy(self) - subsample._feature_names = feature_names + subsample._feature_names = feature_names_list - columns = [*feature_names, *self._target_names] + columns = [*feature_names_list, *self._target_names] weight = self._weight_name if weight and weight not in columns: columns.append(weight) @@ -299,16 +290,16 @@ def keep(self, *, feature_names: Union[str, Collection[str]]) -> "Sample": return subsample - def drop(self, *, feature_names: Union[str, Collection[str]]) -> "Sample": + def drop(self, *, feature_names: Union[str, Collection[str]]) -> Sample: """ Return a copy of this sample, dropping the features with the given names. :param feature_names: name(s) of the features to be dropped :return: copy of this sample, excluding the features with the given names """ - feature_names: Set[str] = to_set(feature_names, element_type=str) + feature_names_set: Set[str] = to_set(feature_names, element_type=str) - unknown = feature_names.difference(self._feature_names) + unknown = feature_names_set.difference(self._feature_names) if unknown: raise ValueError(f"unknown features in arg feature_names: {unknown}") @@ -316,7 +307,7 @@ def drop(self, *, feature_names: Union[str, Collection[str]]) -> "Sample": feature_names=[ feature for feature in self._feature_names - if feature not in feature_names + if feature not in feature_names_set ] ) @@ -325,3 +316,46 @@ def __len__(self) -> int: __tracker.validate() + +# +# auxiliary functions +# + + +def _ensure_columns_exist( + observations: pd.DataFrame, column_type: str, columns: List[str] +) -> None: + # check if all provided feature names actually exist in the observations df + available_columns: pd.Index = observations.columns + missing_columns = {name for name in columns if name not in available_columns} + if missing_columns: + raise KeyError( + f"observations table is missing {column_type} columns {missing_columns}" + ) + + +def _tidy_up_observations(observations: pd.DataFrame) -> pd.DataFrame: + # ensure all column names are native Python strings + name_types = {type(name) for name in observations.columns} + name_types.discard(str) + invalid_name_types = [ + name_type + for name_type in name_types + if not np.issubdtype(name_type, np.character) + ] + if invalid_name_types: + # not all names are strings + raise TypeError( + "all column names in arg observations must be strings, but included: " + + ", ".join(t.__qualname__ for t in invalid_name_types) + ) + + # convert numpy string types to native Python strings + if name_types: + observations = observations.set_axis(observations.columns.astype(str), axis=1) + + # ensure the index has a name + if observations.index.name is None: + observations = observations.rename_axis(index=Sample.IDX_OBSERVATION) + + return observations diff --git a/src/facet/data/partition/_partition.py b/src/facet/data/partition/_partition.py index bfc01aec2..b4c666e8b 100644 --- a/src/facet/data/partition/_partition.py +++ b/src/facet/data/partition/_partition.py @@ -6,9 +6,10 @@ import math import operator as op from abc import ABCMeta, abstractmethod -from typing import Any, Generic, Optional, Sequence, Tuple, TypeVar +from typing import Any, Generic, Optional, Sequence, Tuple, TypeVar, Union, cast import numpy as np +import numpy.typing as npt from pytools.api import AllTracker, inheritdoc from pytools.fit import FittableMixin @@ -28,11 +29,19 @@ # Type variables # -T_Partitioner = TypeVar("T_Partitioner", bound="Partitioner") -T_RangePartitioner = TypeVar("T_RangePartitioner", bound="RangePartitioner") +T_Partitioner = TypeVar("T_Partitioner", bound="Partitioner[Any]") +T_RangePartitioner = TypeVar("T_RangePartitioner", bound="RangePartitioner[Any, Any]") T_CategoryPartitioner = TypeVar("T_CategoryPartitioner", bound="CategoryPartitioner") -T_Values = TypeVar("T_Values") -T_Values_Numeric = TypeVar("T_Values_Numeric", float, int) +T_Values = TypeVar("T_Values", bound=np.generic) +T_Values_Numeric = TypeVar("T_Values_Numeric", np.int_, np.float_) +T_Values_Scalar = TypeVar("T_Values_Scalar", int, float) + + +# +# Constants +# + +ASSERTION__PARTITIONER_IS_FITTED = "partitioner is fitted" # @@ -48,7 +57,7 @@ class Partitioner( - FittableMixin[Sequence[T_Values]], Generic[T_Values], metaclass=ABCMeta + FittableMixin[npt.NDArray[T_Values]], Generic[T_Values], metaclass=ABCMeta ): """ Abstract base class of all partitioners. @@ -60,7 +69,7 @@ class Partitioner( _partitions: Optional[Sequence[T_Values]] #: The count of values allocated to each partition. - _frequencies: Optional[np.ndarray] + _frequencies: Optional[npt.NDArray[np.int_]] def __init__(self, max_partitions: Optional[int] = None) -> None: """ @@ -96,17 +105,17 @@ def partitions_(self) -> Sequence[T_Values]: """ self.ensure_fitted() - assert self._partitions is not None, "Partitioner is fitted" + assert self._partitions is not None, ASSERTION__PARTITIONER_IS_FITTED return self._partitions @property - def frequencies_(self) -> np.ndarray: + def frequencies_(self) -> npt.NDArray[np.int_]: """ The count of values allocated to each partition. """ self.ensure_fitted() - assert self._frequencies is not None, "Partitioner is fitted" + assert self._frequencies is not None, ASSERTION__PARTITIONER_IS_FITTED return self._frequencies @property @@ -118,9 +127,8 @@ def is_categorical(self) -> bool: @abstractmethod def fit( # type: ignore[override] - # todo: remove 'type: ignore' once mypy correctly infers return type self: T_Partitioner, - values: Sequence[T_Values], + values: npt.NDArray[T_Values], **fit_params: Any, ) -> T_Partitioner: """ @@ -133,17 +141,19 @@ def fit( # type: ignore[override] """ @staticmethod - def _as_non_empty_array(values: Sequence[Any]) -> np.ndarray: + def _as_non_empty_array(values: npt.NDArray[T_Values]) -> npt.NDArray[T_Values]: # ensure arg values is a non-empty array - values = np.asarray(values) - if len(values) == 0: + values_arr = np.asarray(values) + if len(values_arr) == 0: raise ValueError("arg values is empty") - return values + return values_arr @inheritdoc(match="""[see superclass]""") class RangePartitioner( - Partitioner[T_Values_Numeric], Generic[T_Values_Numeric], metaclass=ABCMeta + Partitioner[T_Values_Numeric], + Generic[T_Values_Numeric, T_Values_Scalar], + metaclass=ABCMeta, ): """ Abstract base class of partitioners for numerical ranges. @@ -154,9 +164,9 @@ def __init__(self, max_partitions: Optional[int] = None) -> None: super().__init__(max_partitions) - self._step: Optional[T_Values_Numeric] = None + self._step: Optional[T_Values_Scalar] = None self._partition_bounds: Optional[ - Sequence[Tuple[T_Values_Numeric, T_Values_Numeric]] + Sequence[Tuple[T_Values_Scalar, T_Values_Scalar]] ] = None @property @@ -167,36 +177,35 @@ def is_categorical(self) -> bool: return False @property - def partition_bounds_(self) -> Sequence[Tuple[T_Values_Numeric, T_Values_Numeric]]: + def partition_bounds_(self) -> Sequence[Tuple[T_Values_Scalar, T_Values_Scalar]]: """ - Return the endpoints of the intervals that delineate each partitions. + Return the endpoints of the intervals that delineate each partition. :return: sequence of tuples (x, y) for every partition, where x is the - inclusive lower bound of a partition range, and y is the exclusive upper - bound of a partition range + inclusive lower bound of a partition range, and y is the exclusive upper + bound of a partition range """ self.ensure_fitted() - assert self._partition_bounds is not None, "Partitioner is fitted" + assert self._partition_bounds is not None, ASSERTION__PARTITIONER_IS_FITTED return self._partition_bounds @property - def partition_width_(self) -> T_Values_Numeric: + def partition_width_(self) -> T_Values_Scalar: """ The width of each partition. """ self.ensure_fitted() - assert self._step is not None, "Partitioner is fitted" + assert self._step is not None, ASSERTION__PARTITIONER_IS_FITTED return self._step def fit( # type: ignore[override] - # todo: remove 'type: ignore' once mypy correctly infers return type self: T_RangePartitioner, - values: Sequence[T_Values_Numeric], + values: npt.NDArray[T_Values_Numeric], *, - lower_bound: Optional[T_Values_Numeric] = None, - upper_bound: Optional[T_Values_Numeric] = None, + lower_bound: Union[T_Values_Numeric, float, int, None] = None, + upper_bound: Union[T_Values_Numeric, float, int, None] = None, **fit_params: Any, ) -> T_RangePartitioner: r""" @@ -220,14 +229,16 @@ def fit( # type: ignore[override] values = self._as_non_empty_array(values) if lower_bound is None or upper_bound is None: - q3q1 = np.nanquantile(values, q=[0.75, 0.25]) - inlier_range = op.sub(*q3q1) * 1.5 # iqr * 1.5 + # calculate the inner quartile range (IQR) + iqr = np.nanquantile(values, q=[0.75, 0.25]) + # calculate inlier range as IQR * 1.5 + inlier_range = op.sub(*iqr) * 1.5 if lower_bound is None: - lower_bound = values[values >= q3q1[1] - inlier_range].min() + lower_bound = values[values >= iqr[1] - inlier_range].min() if upper_bound is None: - upper_bound = values[values <= q3q1[0] + inlier_range].max() + upper_bound = values[values <= iqr[0] + inlier_range].max() if lower_bound == upper_bound: raise ValueError( @@ -273,7 +284,7 @@ def fit( # type: ignore[override] ) partition_indices = np.digitize(values, bins=partition_bins) - # frequency counts will include left and right outliers, hence n_partitions + 2 + # frequency counts will include left and right outliers, hence n_partitions + 2, # and we exclude the first and last element of the result frequencies = np.bincount(partition_indices, minlength=n_partitions + 2)[1:-1] @@ -286,35 +297,21 @@ def is_fitted(self) -> bool: """[see superclass]""" return self._frequencies is not None - @staticmethod - def _ceil_step(step: float): - """ - Round the step size (arbitrary float) to a human-readable number like 0.5, 1, 2. - - :param step: the step size to round by - :return: the nearest greater or equal step size in the series - (..., 0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50, ...) - """ - if step <= 0: - raise ValueError("arg step must be positive") - - return min(10 ** math.ceil(math.log10(step * m)) / m for m in [1, 2, 5]) - @abstractmethod def _step_size( - self, lower_bound: T_Values_Numeric, upper_bound: T_Values_Numeric - ) -> T_Values_Numeric: + self, lower_bound: T_Values_Scalar, upper_bound: T_Values_Scalar + ) -> T_Values_Scalar: # Compute the step size (interval length) used in the partitions pass @property @abstractmethod - def _partition_center_offset(self) -> T_Values_Numeric: + def _partition_center_offset(self) -> T_Values_Scalar: # Offset between center and endpoints of an interval pass -class ContinuousRangePartitioner(RangePartitioner[float]): +class ContinuousRangePartitioner(RangePartitioner[np.float_, float]): """ Partition numerical values in adjacent intervals of the same length. @@ -336,17 +333,15 @@ class ContinuousRangePartitioner(RangePartitioner[float]): """ def _step_size(self, lower_bound: float, upper_bound: float) -> float: - return RangePartitioner._ceil_step( - (upper_bound - lower_bound) / (self.max_partitions - 1) - ) + return _ceil_step((upper_bound - lower_bound) / (self.max_partitions - 1)) @property def _partition_center_offset(self) -> float: - assert self._step is not None, "Partitioner is fitted" + assert self._step is not None, ASSERTION__PARTITIONER_IS_FITTED return self._step / 2 -class IntegerRangePartitioner(RangePartitioner[int]): +class IntegerRangePartitioner(RangePartitioner[np.int_, int]): """ Partition integer values in adjacent intervals of the same length. @@ -370,21 +365,17 @@ class IntegerRangePartitioner(RangePartitioner[int]): def _step_size(self, lower_bound: int, upper_bound: int) -> int: return max( 1, - int( - RangePartitioner._ceil_step( - (upper_bound - lower_bound) / (self.max_partitions - 1) - ) - ), + int(_ceil_step((upper_bound - lower_bound) / (self.max_partitions - 1))), ) @property def _partition_center_offset(self) -> int: - assert self._step is not None, "Partitioner is fitted" + assert self._step is not None, ASSERTION__PARTITIONER_IS_FITTED return self._step // 2 @inheritdoc(match="[see superclass]") -class CategoryPartitioner(Partitioner[T_Values]): +class CategoryPartitioner(Partitioner[Any]): """ Partition categorical values. @@ -406,9 +397,8 @@ def is_categorical(self) -> bool: # noinspection PyMissingOrEmptyDocstring def fit( # type: ignore[override] - # todo: remove 'type: ignore' once mypy correctly infers return type self: T_CategoryPartitioner, - values: Sequence[T_Values], + values: npt.NDArray[Any], **fit_params: Any, ) -> T_CategoryPartitioner: """[see superclass]""" @@ -425,3 +415,24 @@ def fit( # type: ignore[override] __tracker.validate() + +# +# Auxiliary functions +# + + +def _ceil_step(step: T_Values_Scalar) -> T_Values_Scalar: + """ + Round the step size (arbitrary float) to a human-readable number like 0.5, 1, 2. + + :param step: the step size to round by + :return: the nearest greater or equal step size in the series + (..., 0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50, ...) + """ + if step <= 0: + raise ValueError("arg step must be positive") + + return cast( + T_Values_Scalar, + min(10 ** math.ceil(math.log10(step * m)) / m for m in [1, 2, 5]), + ) diff --git a/src/facet/inspection/__init__.py b/src/facet/inspection/__init__.py index d94610fc6..ba5888566 100644 --- a/src/facet/inspection/__init__.py +++ b/src/facet/inspection/__init__.py @@ -7,3 +7,4 @@ """ from ._explainer import * from ._inspection import * +from ._learner_inspector import * diff --git a/src/facet/inspection/_explainer.py b/src/facet/inspection/_explainer.py index d4fbca30d..d25731757 100644 --- a/src/facet/inspection/_explainer.py +++ b/src/facet/inspection/_explainer.py @@ -5,9 +5,22 @@ import functools import logging from abc import ABCMeta, abstractmethod -from typing import Any, Callable, Dict, Iterable, List, Mapping, Optional, Union, cast +from multiprocessing.synchronize import Lock as LockType +from typing import ( + Any, + Callable, + Dict, + Iterable, + List, + Mapping, + Optional, + Type, + Union, + cast, +) import numpy as np +import numpy.typing as npt import pandas as pd import shap from packaging import version @@ -55,6 +68,13 @@ catboost.Pool = type("Pool", (), {}) +# +# Type variables and aliases +# + +ArraysAny = Union[npt.NDArray[Any], List[npt.NDArray[Any]]] +ArraysFloat = Union[npt.NDArray[np.float_], List[npt.NDArray[np.float_]]] + # # Ensure all symbols introduced below are included in __all__ # @@ -74,14 +94,21 @@ class BaseExplainer(metaclass=ABCMeta): versions of the `shap` package. """ + def __init__(self, *args: Any, **kwargs: Any) -> None: + """ + :param args: positional args (should usually be empty) + :param kwargs: keyword args (should usually be empty) + """ + super().__init__(*args, **kwargs) + # noinspection PyPep8Naming,PyUnresolvedReferences @abstractmethod def shap_values( self, - X: Union[np.ndarray, pd.DataFrame, catboost.Pool], - y: Union[None, np.ndarray, pd.Series] = None, + X: Union[npt.NDArray[Any], pd.DataFrame, catboost.Pool], + y: Union[npt.NDArray[Any], pd.Series, None] = None, **kwargs: Any, - ) -> Union[np.ndarray, List[np.ndarray]]: + ) -> ArraysFloat: """ Estimate the SHAP values for a set of samples. @@ -99,10 +126,10 @@ def shap_values( @abstractmethod def shap_interaction_values( self, - X: Union[np.ndarray, pd.DataFrame, catboost.Pool], - y: Union[None, np.ndarray, pd.Series] = None, + X: Union[npt.NDArray[Any], pd.DataFrame, catboost.Pool], + y: Union[npt.NDArray[Any], pd.Series, None] = None, **kwargs: Any, - ) -> Union[np.ndarray, List[np.ndarray]]: + ) -> ArraysFloat: """ Estimate the SHAP interaction values for a set of samples. @@ -177,7 +204,7 @@ def _validate_background_dataset(self, data: Optional[pd.DataFrame]) -> None: @inheritdoc(match="""[see superclass]""") -class ExplainerJob(Job[Union[np.ndarray, List[np.ndarray]]]): +class ExplainerJob(Job[ArraysAny]): """ A call to an explainer function with given `X` and `y` values. """ @@ -189,10 +216,10 @@ class ExplainerJob(Job[Union[np.ndarray, List[np.ndarray]]]): interactions: bool #: the feature values of the observations to be explained - X: Union[np.ndarray, pd.DataFrame] + X: Union[npt.NDArray[Any], pd.DataFrame] #: the target values of the observations to be explained - y: Union[None, np.ndarray, pd.Series] + y: Union[None, npt.NDArray[Any], pd.Series] #: additional arguments specific to the explainer method kwargs: Dict[str, Any] @@ -201,8 +228,8 @@ class ExplainerJob(Job[Union[np.ndarray, List[np.ndarray]]]): def __init__( self, explainer: BaseExplainer, - X: Union[np.ndarray, pd.DataFrame], - y: Union[None, np.ndarray, pd.Series] = None, + X: Union[npt.NDArray[Any], pd.DataFrame], + y: Union[None, npt.NDArray[Any], pd.Series] = None, *, interactions: bool, **kwargs: Any, @@ -221,10 +248,10 @@ def __init__( self.interactions = interactions self.kwargs = kwargs - def run(self) -> Union[np.ndarray, List[np.ndarray]]: + def run(self) -> ArraysAny: """[see superclass]""" - shap_fn: Callable[..., Union[np.ndarray, List[np.ndarray]]] = ( + shap_fn: Callable[..., ArraysAny] = ( self.explainer.shap_interaction_values if self.interactions else self.explainer.shap_values @@ -237,13 +264,14 @@ def run(self) -> Union[np.ndarray, List[np.ndarray]]: @inheritdoc(match="""[see superclass]""") -class ExplainerQueue( - JobQueue[Union[np.ndarray, List[np.ndarray]], Union[np.ndarray, List[np.ndarray]]] -): +class ExplainerQueue(JobQueue[ArraysAny, ArraysAny]): """ A queue splitting a data set to be explained into multiple jobs. """ + # defined in superclass, repeated here for Sphinx + lock: LockType + #: the SHAP explainer to use explainer: BaseExplainer @@ -251,10 +279,10 @@ class ExplainerQueue( interactions: bool #: the feature values of the observations to be explained - X: np.ndarray + X: npt.NDArray[Any] #: the target values of the observations to be explained - y: Optional[np.ndarray] + y: Optional[npt.NDArray[Any]] #: the maximum number of observations to allocate to each job max_job_size: int @@ -266,8 +294,8 @@ class ExplainerQueue( def __init__( self, explainer: BaseExplainer, - X: Union[np.ndarray, pd.DataFrame], - y: Union[None, np.ndarray, pd.Series] = None, + X: Union[npt.NDArray[Any], pd.DataFrame], + y: Union[None, npt.NDArray[Any], pd.Series] = None, *, interactions: bool, max_job_size: int, @@ -291,7 +319,7 @@ def __init__( self.max_job_size = max_job_size self.kwargs = kwargs - def jobs(self) -> Iterable[Job[Union[np.ndarray, List[np.ndarray]]]]: + def jobs(self) -> Iterable[Job[ArraysAny]]: """[see superclass]""" x = self.X @@ -311,9 +339,7 @@ def jobs(self) -> Iterable[Job[Union[np.ndarray, List[np.ndarray]]]]: for start in range(0, n, job_size) ) - def aggregate( - self, job_results: List[Union[np.ndarray, List[np.ndarray]]] - ) -> Union[np.ndarray, List[np.ndarray]]: + def aggregate(self, job_results: List[ArraysAny]) -> ArraysAny: """[see superclass]""" if isinstance(job_results[0], np.ndarray): return np.vstack(job_results) @@ -331,6 +357,18 @@ class ParallelExplainer(BaseExplainer, ParallelizableMixin): chunks of observations in parallel. """ + # defined in superclass, repeated here for Sphinx + n_jobs: Optional[int] + + # defined in superclass, repeated here for Sphinx + shared_memory: Optional[bool] + + # defined in superclass, repeated here for Sphinx + pre_dispatch: Optional[Union[str, int]] + + # defined in superclass, repeated here for Sphinx + verbose: Optional[int] + #: The explainer being parallelized by this wrapper explainer: BaseExplainer @@ -374,20 +412,20 @@ def __init__( # noinspection PyPep8Naming def shap_values( self, - X: Union[np.ndarray, pd.DataFrame, catboost.Pool], - y: Union[None, np.ndarray, pd.Series] = None, + X: Union[npt.NDArray[Any], pd.DataFrame, catboost.Pool], + y: Union[npt.NDArray[Any], pd.Series, None] = None, **kwargs: Any, - ) -> Union[np.ndarray, List[np.ndarray]]: + ) -> ArraysFloat: """[see superclass]""" return self._run(self.explainer, X, y, interactions=False, **kwargs) # noinspection PyPep8Naming def shap_interaction_values( self, - X: Union[np.ndarray, pd.DataFrame, catboost.Pool], - y: Union[None, np.ndarray, pd.Series] = None, + X: Union[npt.NDArray[Any], pd.DataFrame, catboost.Pool], + y: Union[npt.NDArray[Any], pd.Series, None] = None, **kwargs: Any, - ) -> Union[np.ndarray, List[np.ndarray]]: + ) -> ArraysFloat: """[see superclass]""" return self._run(self.explainer, X, y, interactions=True, **kwargs) @@ -395,12 +433,12 @@ def shap_interaction_values( def _run( self, explainer: BaseExplainer, - X: Union[np.ndarray, pd.DataFrame, catboost.Pool], - y: Union[None, np.ndarray, pd.Series] = None, + X: Union[npt.NDArray[Any], pd.DataFrame, catboost.Pool], + y: Union[None, npt.NDArray[Any], pd.Series] = None, *, interactions: bool, **kwargs: Any, - ): + ) -> ArraysFloat: return JobRunner.from_parallelizable(self).run_queue( ExplainerQueue( explainer=explainer, @@ -417,7 +455,7 @@ def _run( # TreeExplainer factory # -_TreeExplainer: Optional[type] = None +_TreeExplainer: Optional[Type[BaseExplainer]] = None @inheritdoc(match="""[see superclass]""") @@ -434,8 +472,8 @@ def __init__( uses_background_dataset: bool = True, ) -> None: """ - :param model_output: (optional) override the default model output parameter - :param feature_perturbation: (optional) override the default + :param model_output: override the default model output parameter (optional) + :param feature_perturbation: override the default (optional) feature_perturbation parameter :param uses_background_dataset: if ``False``, don't pass the background dataset on to the tree explainer even if a background dataset is passed @@ -497,7 +535,9 @@ def make_explainer( ), ) - explainer.shap_values = functools.partial( + # we overwrite the shap_values method - need to tell mypy to allow this + # as an exception + explainer.shap_values = functools.partial( # type: ignore explainer.shap_values, check_additivity=False ) @@ -517,14 +557,17 @@ def to_expression(self) -> Expression: # -class _KernelExplainer(shap.KernelExplainer, BaseExplainer): +class _KernelExplainer( + shap.KernelExplainer, # type: ignore + BaseExplainer, +): # noinspection PyPep8Naming,PyUnresolvedReferences def shap_interaction_values( self, - X: Union[np.ndarray, pd.DataFrame, catboost.Pool], - y: Union[None, np.ndarray, pd.Series] = None, - **kwargs, - ) -> np.ndarray: + X: Union[npt.NDArray[Any], pd.DataFrame, catboost.Pool], + y: Union[npt.NDArray[Any], pd.Series, None] = None, + **kwargs: Any, + ) -> ArraysFloat: """ Not implemented. """ @@ -545,14 +588,13 @@ def __init__( data_size_limit: Optional[int] = 100, ) -> None: """ - :param link: (optional) override the default link parameter - :param l1_reg: (optional) override the default l1_reg parameter of method + :param link: override the default link parameter (optional) + :param l1_reg: override the default l1_reg parameter of method :meth:`~shap.KernelExplainer.shap_values`; pass ``None`` to use the - default value used by :meth:`~shap.KernelExplainer.shap_values` - :param data_size_limit: (optional) maximum number of observations to use as + default value used by :meth:`~shap.KernelExplainer.shap_values` (optional) + :param data_size_limit: maximum number of observations to use as the background data set; larger data sets will be down-sampled using - kmeans. - Pass ``None`` to prevent down-sampling the background data set + kmeans; don't downsample if omitted (optional) """ super().__init__() validate_type(link, expected_type=str, optional=True, name="arg link") diff --git a/src/facet/inspection/_inspection.py b/src/facet/inspection/_inspection.py index b7174dc72..38804dc18 100644 --- a/src/facet/inspection/_inspection.py +++ b/src/facet/inspection/_inspection.py @@ -2,63 +2,29 @@ Core implementation of :mod:`facet.inspection` """ import logging -from types import MethodType -from typing import ( - Any, - Callable, - Generic, - Iterable, - List, - Optional, - Sequence, - Tuple, - Type, - TypeVar, - Union, - cast, -) +from typing import List, Union import numpy as np +import numpy.typing as npt import pandas as pd -from scipy.cluster import hierarchy -from scipy.spatial import distance -from sklearn.base import is_classifier -from pytools.api import AllTracker, inheritdoc -from pytools.data import LinkageTree, Matrix -from pytools.fit import FittableMixin -from pytools.parallelization import ParallelizableMixin -from sklearndf import ClassifierDF, LearnerDF, RegressorDF -from sklearndf.pipeline import LearnerPipelineDF +from pytools.api import AllTracker from ..data import Sample -from ._explainer import ExplainerFactory, TreeExplainerFactory -from ._shap import ( - ClassifierShapInteractionValuesCalculator, - ClassifierShapValuesCalculator, - RegressorShapInteractionValuesCalculator, - RegressorShapValuesCalculator, - ShapCalculator, - ShapInteractionValuesCalculator, -) -from ._shap_global_explanation import ( - ShapGlobalExplainer, - ShapInteractionGlobalExplainer, -) -from ._shap_projection import ShapInteractionVectorProjector, ShapVectorProjector log = logging.getLogger(__name__) -__all__ = ["ShapPlotData", "LearnerInspector"] +__all__ = [ + "ShapPlotData", +] # -# Type variables +# Type aliases # -T_LearnerInspector = TypeVar("T_LearnerInspector", bound="LearnerInspector") -T_LearnerPipelineDF = TypeVar("T_LearnerPipelineDF", bound=LearnerPipelineDF) -T_SeriesOrDataFrame = TypeVar("T_SeriesOrDataFrame", pd.Series, pd.DataFrame) +FloatArray = npt.NDArray[np.float_] + # # Ensure all symbols introduced below are included in __all__ @@ -79,7 +45,7 @@ class ShapPlotData: """ def __init__( - self, shap_values: Union[np.ndarray, List[np.ndarray]], sample: Sample + self, shap_values: Union[FloatArray, List[FloatArray]], sample: Sample ) -> None: """ :param shap_values: the shap values for all observations and outputs @@ -90,7 +56,7 @@ def __init__( self._sample = sample @property - def shap_values(self) -> Union[np.ndarray, List[np.ndarray]]: + def shap_values(self) -> Union[FloatArray, List[FloatArray]]: """ Matrix of SHAP values (number of observations by number of features) or list of shap value matrices for multi-output models. @@ -114,984 +80,4 @@ def target(self) -> Union[pd.Series, pd.DataFrame]: return self._sample.target -@inheritdoc(match="[see superclass]") -class LearnerInspector( - FittableMixin[Sample], ParallelizableMixin, Generic[T_LearnerPipelineDF] -): - """ - Explain regressors and classifiers based on SHAP values. - - Focus is on explaining the overall model, but the inspector also delivers - SHAP explanations of the individual observations. - - Available inspection methods are: - - - SHAP values - - SHAP interaction values - - feature importance derived from SHAP values (either as mean absolute values - or as the root of mean squares) - - pairwise feature redundancy matrix (requires availability of SHAP interaction - values) - - pairwise feature synergy matrix (requires availability of SHAP interaction - values) - - pairwise feature association matrix (upper bound for redundancy but can be - inflated by synergy; available if SHAP interaction values are unknown) - - pairwise feature interaction matrix (direct feature interaction quantified by - SHAP interaction values) - - feature redundancy linkage (to visualize clusters of redundant features in a - dendrogram; requires availability of SHAP interaction values) - - feature synergy linkage (to visualize clusters of synergistic features in a - dendrogram; requires availability of SHAP interaction values) - - feature association linkage (to visualize clusters of associated features in a - dendrogram) - - All inspections that aggregate across observations will respect sample weights, if - specified in the underlying training sample. - """ - - #: Name for feature importance series or column. - COL_IMPORTANCE = "importance" - - #: The default explainer factory used by this inspector. - #: This is a tree explainer using the tree_path_dependent method for - #: feature perturbation, so we can calculate SHAP interaction values. - DEFAULT_EXPLAINER_FACTORY = TreeExplainerFactory( - feature_perturbation="tree_path_dependent", uses_background_dataset=False - ) - - def __init__( - self, - *, - pipeline: T_LearnerPipelineDF, - explainer_factory: Optional[ExplainerFactory] = None, - shap_interaction: bool = True, - n_jobs: Optional[int] = None, - shared_memory: Optional[bool] = None, - pre_dispatch: Optional[Union[str, int]] = None, - verbose: Optional[int] = None, - ) -> None: - """ - :param pipeline: the learner pipeline to inspect - :param explainer_factory: optional function that creates a shap Explainer - (default: ``TreeExplainerFactory``) - :param shap_interaction: if ``True``, calculate SHAP interaction values, else - only calculate SHAP contribution values. - SHAP interaction values are needed to determine feature synergy and - redundancy. - (default: ``True``) - """ - super().__init__( - n_jobs=n_jobs, - shared_memory=shared_memory, - pre_dispatch=pre_dispatch, - verbose=verbose, - ) - - if not pipeline.is_fitted: - raise ValueError("arg pipeline must be fitted") - - if not isinstance(pipeline.final_estimator, (ClassifierDF, RegressorDF)): - raise TypeError( - "learner in arg pipeline must be a classifier or a regressor," - f"but is a {type(pipeline.final_estimator).__name__}" - ) - - if explainer_factory: - if not explainer_factory.explains_raw_output: - raise ValueError( - "arg explainer_factory is not configured to explain raw output" - ) - else: - explainer_factory = self.DEFAULT_EXPLAINER_FACTORY - assert explainer_factory.explains_raw_output - - if shap_interaction: - if not explainer_factory.supports_shap_interaction_values: - log.warning( - "ignoring arg shap_interaction=True: " - f"explainers made by {explainer_factory!r} do not support " - "SHAP interaction values" - ) - shap_interaction = False - - self.pipeline = pipeline - self.explainer_factory = explainer_factory - self.shap_interaction = shap_interaction - - self._shap_calculator: Optional[ShapCalculator] = None - self._shap_global_decomposer: Optional[ShapGlobalExplainer] = None - self._shap_global_projector: Optional[ShapGlobalExplainer] = None - self._sample: Optional[Sample] = None - - __init__.__doc__ = cast(str, __init__.__doc__) + cast( - str, ParallelizableMixin.__init__.__doc__ - ) - - def fit( # type: ignore[override] - # todo: remove 'type: ignore' once mypy correctly infers return type - self: T_LearnerInspector, - sample: Sample, - **fit_params: Any, - ) -> T_LearnerInspector: - """ - Fit the inspector with the given sample. - - This will calculate SHAP values and, if enabled in the underlying SHAP - explainer, also SHAP interaction values. - - :param sample: the background sample to be used for the global explanation - of this model - :param fit_params: additional keyword arguments (ignored; accepted for - compatibility with :class:`.FittableMixin`) - :return: ``self`` - """ - - learner: LearnerDF = self.pipeline.final_estimator - - _is_classifier = is_classifier(learner) - if _is_classifier and isinstance(sample.target_name, list): - raise ValueError( - "only single-output classifiers (binary or multi-class) are supported, " - "but the given classifier has been fitted on multiple columns " - f"{sample.target_name}" - ) - - shap_global_projector: Union[ - ShapVectorProjector, ShapInteractionVectorProjector, None - ] - - shap_calculator_type: Type[ShapCalculator] - shap_calculator: ShapCalculator - - if self.shap_interaction: - if _is_classifier: - shap_calculator_type = ClassifierShapInteractionValuesCalculator - else: - shap_calculator_type = RegressorShapInteractionValuesCalculator - - shap_calculator = shap_calculator_type( - pipeline=self.pipeline, - explainer_factory=self.explainer_factory, - n_jobs=self.n_jobs, - shared_memory=self.shared_memory, - pre_dispatch=self.pre_dispatch, - verbose=self.verbose, - ) - - shap_global_projector = ShapInteractionVectorProjector() - - else: - if _is_classifier: - shap_calculator_type = ClassifierShapValuesCalculator - else: - shap_calculator_type = RegressorShapValuesCalculator - - shap_calculator = shap_calculator_type( - pipeline=self.pipeline, - explainer_factory=self.explainer_factory, - n_jobs=self.n_jobs, - shared_memory=self.shared_memory, - pre_dispatch=self.pre_dispatch, - verbose=self.verbose, - ) - - shap_global_projector = ShapVectorProjector() - - shap_calculator.fit(sample) - shap_global_projector.fit(shap_calculator=shap_calculator) - - self._sample = sample - self._shap_calculator = shap_calculator - self._shap_global_projector = shap_global_projector - - return self - - @property - def _shap_global_explainer(self) -> ShapGlobalExplainer: - self.ensure_fitted() - assert self._shap_global_projector is not None, "Inspector is fitted" - return self._shap_global_projector - - @property - def is_fitted(self) -> bool: - """[see superclass]""" - return self._sample is not None - - @property - def sample_(self) -> Sample: - """ - The background sample used to fit this inspector. - """ - - self.ensure_fitted() - assert self._sample is not None, "Inspector is fitted" - return self._sample - - @property - def output_names_(self) -> Sequence[str]: - """ - The names of the outputs explained by this inspector. - - For regressors, these are the names of the target columns. - - For binary classifiers, this is a list of length 1 with the name of a single - class, since the SHAP values of the second class can be trivially derived as - the negation of the SHAP values of the first class. - - For non-binary classifiers, this is the list of all classes. - """ - - self.ensure_fitted() - assert ( - self._shap_calculator is not None - and self._shap_calculator.output_names_ is not None - ), "Inspector is fitted" - return self._shap_calculator.output_names_ - - @property - def features_(self) -> List[str]: - """ - The names of the features used to fit the learner pipeline explained by this - inspector. - """ - return self.pipeline.feature_names_out_.to_list() - - def shap_values(self) -> Union[pd.DataFrame, List[pd.DataFrame]]: - """ - Calculate the SHAP values for all observations and features. - - Returns a data frame of SHAP values where each row corresponds to an - observation, and each column corresponds to a feature. - - :return: a data frame with SHAP values - """ - - self.ensure_fitted() - assert self._shap_calculator is not None, "Inspector is fitted" - return self.__split_multi_output_df(self._shap_calculator.get_shap_values()) - - def shap_interaction_values(self) -> Union[pd.DataFrame, List[pd.DataFrame]]: - """ - Calculate the SHAP interaction values for all observations and pairs of - features. - - Returns a data frame of SHAP interaction values where each row corresponds to an - observation and a feature (identified by a hierarchical index with two levels), - and each column corresponds to a feature. - - :return: a data frame with SHAP interaction values - """ - self.ensure_fitted() - return self.__split_multi_output_df( - self.__shap_interaction_values_calculator.get_shap_interaction_values() - ) - - def feature_importance( - self, *, method: str = "rms" - ) -> Union[pd.Series, pd.DataFrame]: - """ - Calculate the relative importance of each feature based on SHAP values. - - The importance values of all features always add up to `1.0`. - - The calculation applies sample weights if specified in the underlying - :class:`.Sample`. - - :param method: method for calculating feature importance. Supported methods - are ``rms`` (root of mean squares, default), ``mav`` (mean absolute - values) - :return: a series of length `n_features` for single-output models, or a - data frame of shape (n_features, n_outputs) for multi-output models - """ - - self.ensure_fitted() - - methods = {"rms", "mav"} - if method not in methods: - raise ValueError(f'arg method="{method}" must be one of {methods}') - - assert self._shap_calculator is not None - shap_matrix: pd.DataFrame = self._shap_calculator.get_shap_values() - weight: Optional[pd.Series] = self.sample_.weight - - abs_importance: pd.Series - if method == "rms": - if weight is None: - abs_importance = shap_matrix.pow(2).mean().pow(0.5) - else: - abs_importance = shap_matrix.pow(2).mul(weight, axis=0).mean().pow(0.5) - else: - assert method == "mav", f"method is in {methods}" - if weight is None: - abs_importance = shap_matrix.abs().mean() - else: - abs_importance = shap_matrix.abs().mul(weight, axis=0).mean() - - def _normalize_importance( - _importance: T_SeriesOrDataFrame, - ) -> T_SeriesOrDataFrame: - return _importance.divide(_importance.sum()) - - if len(self.output_names_) == 1: - return _normalize_importance(abs_importance).rename(self.output_names_[0]) - - else: - assert ( - abs_importance.index.nlevels == 2 - ), "2 index levels in place for multi-output models" - - return _normalize_importance(abs_importance.unstack(level=0)) - - def feature_synergy_matrix( - self, - *, - absolute: bool = False, - symmetrical: bool = False, - clustered: bool = True, - ) -> Union[Matrix, List[Matrix]]: - """ - Calculate the feature synergy matrix. - - This yields an asymmetric matrix where each row and column represents one - feature, and the values at the intersections are the pairwise feature synergies, - ranging from `0.0` (no synergy - both features contribute to predictions fully - autonomously of each other) to `1.0` (full synergy, both features rely on - combining all of their information to achieve any contribution to predictions). - - The synergy of a feature with itself is defined as `1.0`. - - Feature synergy calculations require SHAP interaction values; if only SHAP - values are available consider calculating feature associations instead - (see :meth:`.feature_association_matrix`). - - In the case of multi-target regression and non-binary classification, returns - a list of data frames with one matrix per output. - - :param absolute: if ``False``, return relative synergy as a percentage of - total feature importance; - if ``True``, return absolute synergy as a portion of feature importance - :param symmetrical: if ``True``, return a symmetrical matrix quantifying - mutual synergy; if ``False``, return an asymmetrical matrix quantifying - unilateral synergy of the features represented by rows with the - features represented by columns (default: ``False``) - :param clustered: if ``True``, reorder the rows and columns of the matrix - such that synergy between adjacent rows and columns is maximised; if - ``False``, keep rows and columns in the original features order - (default: ``True``) - :return: feature synergy matrix as a data frame of shape - `(n_features, n_features)`, or a list of data frames for multiple outputs - """ - - self.ensure_fitted() - - return self.__feature_affinity_matrix( - explainer_fn=self.__interaction_explainer.synergy, - absolute=absolute, - symmetrical=symmetrical, - clustered=clustered, - ) - - def feature_redundancy_matrix( - self, - *, - absolute: bool = False, - symmetrical: bool = False, - clustered: bool = True, - ) -> Union[Matrix, List[Matrix]]: - """ - Calculate the feature redundancy matrix. - - This yields an asymmetric matrix where each row and column represents one - feature, and the values at the intersections are the pairwise feature - redundancies, ranging from `0.0` (no redundancy - both features contribute to - predictions fully independently of each other) to `1.0` (full redundancy, either - feature can replace the other feature without loss of predictive power). - - The redundancy of a feature with itself is defined as `1.0`. - - Feature redundancy calculations require SHAP interaction values; if only SHAP - values are available consider calculating feature associations instead - (see :meth:`.feature_association_matrix`). - - In the case of multi-target regression and non-binary classification, returns - a list of data frames with one matrix per output. - - :param absolute: if ``False``, return relative redundancy as a percentage of - total feature importance; - if ``True``, return absolute redundancy as a portion of feature importance - :param symmetrical: if ``True``, return a symmetrical matrix quantifying - mutual redundancy; if ``False``, return an asymmetrical matrix quantifying - unilateral redundancy of the features represented by rows with the - features represented by columns (default: ``False``) - :param clustered: if ``True``, reorder the rows and columns of the matrix - such that redundancy between adjacent rows and columns is maximised; if - ``False``, keep rows and columns in the original features order - (default: ``True``) - :return: feature redundancy matrix as a data frame of shape - `(n_features, n_features)`, or a list of data frames for multiple outputs - """ - self.ensure_fitted() - - return self.__feature_affinity_matrix( - explainer_fn=self.__interaction_explainer.redundancy, - absolute=absolute, - symmetrical=symmetrical, - clustered=clustered, - ) - - def feature_association_matrix( - self, - *, - absolute: bool = False, - symmetrical: bool = False, - clustered: bool = True, - ) -> Union[Matrix, List[Matrix]]: - """ - Calculate the feature association matrix. - - This yields an asymmetric matrix where each row and column represents one - feature, and the values at the intersections are the pairwise feature - associations, ranging from `0.0` (no association) to `1.0` (full association). - - The association of a feature with itself is defined as `1.0`. - - Feature association provides an upper bound for feature redundancy but might be - inflated by feature synergy. - - While it is preferable to assess redundancy and synergy separately, association - can be calculated using only SHAP values, and thus can be used as a fallback - if no SHAP interaction values are available. - - In the case of multi-target regression and non-binary classification, returns - a list of data frames with one matrix per output. - - :param absolute: if ``False``, return relative association as a percentage of - total feature importance; - if ``True``, return absolute association as a portion of feature importance - :param symmetrical: if ``False``, return an asymmetrical matrix - quantifying unilateral association of the features represented by rows - with the features represented by columns; - if ``True``, return a symmetrical matrix quantifying mutual association - (default: ``False``) - :param clustered: if ``True``, reorder the rows and columns of the matrix - such that association between adjacent rows and columns is maximised; if - ``False``, keep rows and columns in the original features order - (default: ``True``) - :return: feature association matrix as a data frame of shape - `(n_features, n_features)`, or a list of data frames for multiple outputs - """ - - self.ensure_fitted() - - return self.__feature_affinity_matrix( - explainer_fn=self._shap_global_explainer.association, - absolute=absolute, - symmetrical=symmetrical, - clustered=clustered, - ) - - def feature_synergy_linkage(self) -> Union[LinkageTree, List[LinkageTree]]: - """ - Calculate a linkage tree based on the :meth:`.feature_synergy_matrix`. - - The linkage tree can be used to render a dendrogram indicating clusters of - synergistic features. - - In the case of multi-target regression and non-binary classification, returns - a list of linkage trees per target or class. - - :return: linkage tree of feature synergies; list of linkage trees - for multi-target regressors or non-binary classifiers - """ - - self.ensure_fitted() - feature_affinity_matrix = self.__interaction_explainer.synergy( - symmetrical=True, absolute=False - ) - assert ( - feature_affinity_matrix is not None - ), "Shap interaction values are supported" - - return self.__linkages_from_affinity_matrices( - feature_affinity_matrix=feature_affinity_matrix - ) - - def feature_redundancy_linkage(self) -> Union[LinkageTree, List[LinkageTree]]: - """ - Calculate a linkage tree based on the :meth:`.feature_redundancy_matrix`. - - The linkage tree can be used to render a dendrogram indicating clusters of - redundant features. - - In the case of multi-target regression and non-binary classification, returns - a list of linkage trees per target or class. - - :return: linkage tree of feature redundancies; list of linkage trees - for multi-target regressors or non-binary classifiers - """ - - self.ensure_fitted() - feature_affinity_matrix = self.__interaction_explainer.redundancy( - symmetrical=True, absolute=False - ) - assert ( - feature_affinity_matrix is not None - ), "Shap interaction values are supported" - - return self.__linkages_from_affinity_matrices( - feature_affinity_matrix=feature_affinity_matrix - ) - - def feature_association_linkage(self) -> Union[LinkageTree, List[LinkageTree]]: - """ - Calculate a linkage tree based on the :meth:`.feature_association_matrix`. - - The linkage tree can be used to render a dendrogram indicating clusters of - associated features. - - In the case of multi-target regression and non-binary classification, returns - a list of linkage trees per target or class. - - :return: linkage tree of feature associations; list of linkage trees - for multi-target regressors or non-binary classifiers - """ - - self.ensure_fitted() - feature_affinity_matrix = self._shap_global_explainer.association( - absolute=False, symmetrical=True - ) - assert ( - feature_affinity_matrix is not None - ), "Shap interaction values are supported" - - return self.__linkages_from_affinity_matrices( - feature_affinity_matrix=feature_affinity_matrix - ) - - def feature_interaction_matrix(self) -> Union[Matrix, List[Matrix]]: - """ - Calculate relative shap interaction values for all feature pairings. - - Shap interactions quantify direct interactions between pairs of features. - For a quantification of overall interaction (including indirect interactions - across more than two features), see :meth:`.feature_synergy_matrix`. - - The relative values are normalised to add up to `1.0`, and each value ranges - between `0.0` and `1.0`. - - For features :math:`f_i` and :math:`f_j`, relative feature interaction - :math:`I` is calculated as - - .. math:: - I_{ij} = \\frac - {\\sigma(\\vec{\\phi}_{ij})} - {\\sum_{a=1}^n \\sum_{b=1}^n \\sigma(\\vec{\\phi}_{ab})} - - where :math:`\\sigma(\\vec v)` is the standard deviation of all elements of - vector :math:`\\vec v`. - - The total average interaction of features - :math:`f_i` and :math:`f_j` is - :math:`I_{ij} \ - + I_{ji} \ - = 2 I_{ij}`. - - :math:`I_{ii}` is the residual, non-synergistic contribution - of feature :math:`f_i` - - The matrix returned by this method is a lower-triangular matrix - - .. math:: - - \\newcommand\\nan{\\mathit{nan}} - I_{} = \\begin{pmatrix} - I_{11} & \\nan & \\nan & \\dots & \\nan \\\\ - 2I_{21} & I_{22} & \\nan & \\dots & \\nan \\\\ - 2I_{31} & 2I_{32} & I_{33} & \\dots & \\nan \\\\ - \\vdots & \\vdots & \\vdots & \\ddots & \\vdots \\\\ - 2I_{n1} & 2I_{n2} & 2I_{n3} & \\dots & I_{nn} \\\\ - \\end{pmatrix} - - with :math:`\\sum_{a=1}^n \\sum_{b=a}^n I_{ab} = 1` - - In the case of multi-target regression and non-binary classification, returns - a list with one matrix per output. - - :return: relative shap interaction values as a data frame of shape - `(n_features, n_features)`; or a list of such data frames - """ - - n_features = len(self.features_) - n_outputs = len(self.output_names_) - - # get a feature interaction array with shape - # (n_observations, n_outputs, n_features, n_features) - # where the innermost feature x feature arrays are symmetrical - im_matrix_per_observation_and_output: np.ndarray = ( - # TODO missing proper handling for list of data frames - self.shap_interaction_values() # type: ignore - .values.reshape((-1, n_features, n_outputs, n_features)) - .swapaxes(1, 2) - ) - - # get the observation weights with shape - # (n_observations, n_outputs, n_features, n_features) - weight: Optional[np.ndarray] - _weight_sr = self.sample_.weight - if _weight_sr is not None: - # if sample weights are defined, convert them to an array - # and align the array with the dimensions of the feature interaction array - weight = _weight_sr.values.reshape((-1, 1, 1, 1)) - else: - weight = None - - # calculate the average interactions for each output and feature/feature - # interaction, based on the standard deviation assuming a mean of 0.0. - # The resulting matrix has shape (n_outputs, n_features, n_features) - _interaction_squared = im_matrix_per_observation_and_output**2 - if weight is not None: - _interaction_squared *= weight - interaction_matrix = np.sqrt(_interaction_squared.mean(axis=0)) - assert interaction_matrix.shape == (n_outputs, n_features, n_features) - - # we normalise the synergy matrix for each output to a total of 1.0 - interaction_matrix /= interaction_matrix.sum() - - # the total interaction effect for features i and j is the total of matrix - # cells (i,j) and (j,i); theoretically both should be the same but to minimize - # numerical errors we total both in the lower matrix triangle (but excluding the - # matrix diagonal, hence k=1) - interaction_matrix += np.triu(interaction_matrix, k=1).swapaxes(1, 2) - - # discard the upper matrix triangle by setting it to nan - interaction_matrix += np.triu( - np.full(shape=(n_features, n_features), fill_value=np.nan), k=1 - )[np.newaxis, :, :] - - # create a data frame from the feature matrix - return self.__arrays_to_matrix( - interaction_matrix, value_label="relative shap interaction" - ) - - def shap_plot_data(self) -> ShapPlotData: - """ - Consolidate SHAP values and corresponding feature values from this inspector - for use in SHAP plots offered by the - `shap `__ package. - - The `shap` package provides functions for creating various SHAP plots. - Most of these functions require - - - one or more SHAP value matrices as a single `numpy` array, - or a list of `numpy` arrays of shape `(n_observations, n_features)` - - a feature matrix of shape `(n_observations, n_features)`, which can be - provided as a data frame to preserve feature names - - This method provides this data inside a :class:`.ShapPlotData` object, plus - - - the names of all outputs (i.e., the target names in case of regression, - or the class names in case of classification) - - corresponding target values as a series, or as a data frame in the case of - multiple targets - - This method also ensures that the rows of all arrays, frames, and series are - aligned, even if only a subset of the observations in the original sample was - used to calculate SHAP values. - - Calculates mean shap values for each observation and feature, across all - splits for which SHAP values were calculated. - - :return: consolidated SHAP and feature values for use shap plots - """ - - shap_values: Union[pd.DataFrame, List[pd.DataFrame]] = self.shap_values() - - output_names: Sequence[str] = self.output_names_ - shap_values_numpy: Union[np.ndarray, List[np.ndarray]] - included_observations: pd.Index - - if len(output_names) > 1: - shap_values_numpy = [s.values for s in shap_values] - included_observations = shap_values[0].index - else: - shap_values = cast(pd.DataFrame, shap_values) - shap_values_numpy = shap_values.values - included_observations = shap_values.index - - sample: Sample = self.sample_.subsample(loc=included_observations) - - return ShapPlotData( - shap_values=shap_values_numpy, - sample=sample, - ) - - def __arrays_to_matrix( - self, matrix: np.ndarray, value_label: str - ) -> Union[Matrix, List[Matrix]]: - # transform a matrix of shape (n_outputs, n_features, n_features) - # to a data frame - - feature_index = self.pipeline.feature_names_out_.rename(Sample.IDX_FEATURE) - - n_features = len(feature_index) - assert matrix.shape == (len(self.output_names_), n_features, n_features) - - # convert array to data frame(s) with features as row and column indices - if len(matrix) == 1: - return self.__array_to_matrix( - matrix[0], - feature_importance=self.feature_importance(), - value_label=value_label, - ) - else: - return [ - self.__array_to_matrix( - m, - feature_importance=feature_importance, - value_label=f"{value_label} ({output_name})", - ) - for m, (_, feature_importance), output_name in zip( - matrix, self.feature_importance().items(), self.output_names_ - ) - ] - - def __feature_affinity_matrix( - self, - *, - explainer_fn: Callable[..., np.ndarray], - absolute: bool, - symmetrical: bool, - clustered: bool, - ): - affinity_matrices = explainer_fn(symmetrical=symmetrical, absolute=absolute) - - explainer: ShapGlobalExplainer = cast( - ShapGlobalExplainer, cast(MethodType, explainer_fn).__self__ - ) - affinity_matrices = explainer.to_frames(affinity_matrices) - - if clustered: - affinity_symmetrical = explainer_fn(symmetrical=True, absolute=False) - assert ( - affinity_symmetrical is not None - ), "Shap interaction values are supported" - - affinity_matrices = self.__sort_affinity_matrices( - affinity_matrices=affinity_matrices, - symmetrical_affinity_matrices=affinity_symmetrical, - ) - - return self.__isolate_single_frame( - affinity_matrices, affinity_metric=explainer_fn.__name__ - ) - - @staticmethod - def __sort_affinity_matrices( - affinity_matrices: List[pd.DataFrame], - symmetrical_affinity_matrices: np.ndarray, - ) -> List[pd.DataFrame]: - # abbreviate a very long function name to stay within the permitted line length - fn_linkage = LearnerInspector.__linkage_matrix_from_affinity_matrix_for_output - - return [ - (lambda feature_order: affinity_matrix.iloc[feature_order, feature_order])( - feature_order=hierarchy.leaves_list( - Z=fn_linkage(feature_affinity_matrix=symmetrical_affinity_matrix) - ) - ) - for affinity_matrix, symmetrical_affinity_matrix in zip( - affinity_matrices, symmetrical_affinity_matrices - ) - ] - - @staticmethod - def __split_multi_output_df( - multi_output_df: pd.DataFrame, - ) -> Union[pd.DataFrame, List[pd.DataFrame]]: - # Split a multi-output data frame into a list of single-output data frames. - # Return single-output data frames as is. - # Multi-output data frames are grouped by level 0 in the column index. - if multi_output_df.columns.nlevels == 1: - return multi_output_df - else: - return [ - multi_output_df.xs(key=output_name, axis=1, level=0, drop_level=True) - for output_name in ( - cast(pd.MultiIndex, multi_output_df.columns).levels[0] - ) - ] - - def __linkages_from_affinity_matrices( - self, feature_affinity_matrix: np.ndarray - ) -> Union[LinkageTree, List[LinkageTree]]: - # calculate the linkage trees for all outputs in a feature distance matrix; - # matrix has shape (n_outputs, n_features, n_features) with values ranging from - # (1 = closest, 0 = most distant) - # return a linkage tree if there is only one output, else return a list of - # linkage trees - - feature_importance = self.feature_importance(method="rms") - - if len(feature_affinity_matrix) == 1: - # we have only a single output - # feature importance is already a series - return self.__linkage_tree_from_affinity_matrix_for_output( - feature_affinity_matrix[0], feature_importance - ) - - else: - # noinspection PyCompatibility - feature_importance_iter: ( - Iterable[Tuple[Any, pd.Series]] - ) = feature_importance.iteritems() - - return [ - self.__linkage_tree_from_affinity_matrix_for_output( - feature_affinity_for_output, - feature_importance_for_output, - ) - for feature_affinity_for_output, ( - _, - feature_importance_for_output, - ) in zip(feature_affinity_matrix, feature_importance_iter) - ] - - @staticmethod - def __linkage_tree_from_affinity_matrix_for_output( - feature_affinity_matrix: np.ndarray, feature_importance: pd.Series - ) -> LinkageTree: - # calculate the linkage tree from the a given output in a feature distance - # matrix; - # matrix has shape (n_features, n_features) with values ranging from - # (1 = closest, 0 = most distant) - - linkage_matrix: np.ndarray = ( - LearnerInspector.__linkage_matrix_from_affinity_matrix_for_output( - feature_affinity_matrix - ) - ) - - # Feature labels and weights will be used as the leaves of the linkage tree. - # Select only the features that appear in the distance matrix, and in the - # correct order - - # build and return the linkage tree - return LinkageTree( - scipy_linkage_matrix=linkage_matrix, - leaf_names=feature_importance.index, - leaf_weights=feature_importance.values, - max_distance=1.0, - distance_label="feature distance", - leaf_label="feature", - weight_label="feature importance", - ) - - @staticmethod - def __linkage_matrix_from_affinity_matrix_for_output( - feature_affinity_matrix: np.ndarray, - ) -> np.ndarray: - # calculate the linkage matrix from the a given output in a feature distance - # matrix; - # matrix has shape (n_features, n_features) with values ranging from - # (1 = closest, 0 = most distant) - - # compress the distance matrix (required by SciPy) - distance_matrix = 1.0 - abs(feature_affinity_matrix) - np.fill_diagonal(distance_matrix, 0.0) - compressed_distance_matrix: np.ndarray = distance.squareform(distance_matrix) - - # calculate the linkage matrix - leaf_ordering: np.ndarray = hierarchy.optimal_leaf_ordering( - Z=hierarchy.linkage(y=compressed_distance_matrix, method="single"), - y=compressed_distance_matrix, - ) - - # reverse the leaf ordering, so that larger values tend to end up on top - leaf_ordering[:, [1, 0]] = leaf_ordering[:, [0, 1]] - - return leaf_ordering - - def _ensure_shap_interaction(self) -> None: - if not self.shap_interaction: - raise RuntimeError( - "SHAP interaction values have not been calculated. " - "Create an inspector with parameter 'shap_interaction=True' to " - "enable calculations involving SHAP interaction values." - ) - - def __isolate_single_frame( - self, - frames: List[pd.DataFrame], - affinity_metric: str, - ) -> Union[Matrix, List[Matrix]]: - feature_importance = self.feature_importance() - - if len(frames) == 1: - assert isinstance(feature_importance, pd.Series) - return self.__frame_to_matrix( - frames[0], - affinity_metric=affinity_metric, - feature_importance=feature_importance, - ) - else: - return [ - self.__frame_to_matrix( - frame, - affinity_metric=affinity_metric, - feature_importance=frame_importance, - feature_importance_category=str(frame_name), - ) - for frame, (frame_name, frame_importance) in zip( - frames, feature_importance.items() - ) - ] - - @staticmethod - def __array_to_matrix( - a: np.ndarray, - *, - feature_importance: pd.Series, - value_label: str, - ) -> Matrix: - return Matrix( - a, - names=(feature_importance.index, feature_importance.index), - weights=(feature_importance, feature_importance), - value_label=value_label, - name_labels=("feature", "feature"), - ) - - @staticmethod - def __frame_to_matrix( - frame: pd.DataFrame, - *, - affinity_metric: str, - feature_importance: pd.Series, - feature_importance_category: Optional[str] = None, - ) -> Matrix: - return Matrix.from_frame( - frame, - weights=( - feature_importance.reindex(frame.index), - feature_importance.reindex(frame.columns), - ), - value_label=( - f"{affinity_metric} ({feature_importance_category})" - if feature_importance_category - else affinity_metric - ), - name_labels=("primary feature", "associated feature"), - ) - - @property - def __shap_interaction_values_calculator(self) -> ShapInteractionValuesCalculator: - self._ensure_shap_interaction() - return cast(ShapInteractionValuesCalculator, self._shap_calculator) - - @property - def __interaction_explainer(self) -> ShapInteractionGlobalExplainer: - self._ensure_shap_interaction() - return cast(ShapInteractionGlobalExplainer, self._shap_global_explainer) - - __tracker.validate() diff --git a/src/facet/inspection/_learner_inspector.py b/src/facet/inspection/_learner_inspector.py new file mode 100644 index 000000000..fe6d1b457 --- /dev/null +++ b/src/facet/inspection/_learner_inspector.py @@ -0,0 +1,1107 @@ +""" +Core implementation of :mod:`facet.inspection` +""" +import logging +from abc import ABCMeta +from types import MethodType +from typing import ( + Any, + Callable, + Generic, + Iterable, + List, + Optional, + Sequence, + Tuple, + Type, + TypeVar, + Union, + cast, +) + +import numpy as np +import numpy.typing as npt +import pandas as pd +from scipy.cluster import hierarchy +from scipy.spatial import distance +from sklearn.base import is_classifier + +from pytools.api import AllTracker, inheritdoc +from pytools.data import LinkageTree, Matrix +from pytools.fit import FittableMixin +from pytools.parallelization import ParallelizableMixin +from sklearndf import ClassifierDF, LearnerDF, RegressorDF +from sklearndf.pipeline import LearnerPipelineDF + +from ..data import Sample +from ._explainer import ExplainerFactory, TreeExplainerFactory +from ._inspection import ShapPlotData +from ._shap import ( + ClassifierShapInteractionValuesCalculator, + ClassifierShapValuesCalculator, + RegressorShapInteractionValuesCalculator, + RegressorShapValuesCalculator, + ShapCalculator, + ShapInteractionValuesCalculator, +) +from ._shap_global_explanation import ( + ShapGlobalExplainer, + ShapInteractionGlobalExplainer, +) +from ._shap_projection import ShapInteractionVectorProjector, ShapVectorProjector + +log = logging.getLogger(__name__) + +__all__ = [ + "LearnerInspector", + "ModelInspector", +] + + +# +# Type aliases +# + +FloatArray = npt.NDArray[np.float_] +FloatMatrix = Matrix[np.float_] + + +# +# Type variables +# + +T_LearnerInspector = TypeVar("T_LearnerInspector", bound="LearnerInspector[Any]") +T_LearnerPipelineDF = TypeVar("T_LearnerPipelineDF", bound=LearnerPipelineDF[Any]) +T_SeriesOrDataFrame = TypeVar("T_SeriesOrDataFrame", pd.Series, pd.DataFrame) +T_Number = TypeVar("T_Number", bound="np.number[Any]") + + +# +# Constants +# + +ASSERTION__INSPECTOR_IS_FITTED = "inspector is fitted" +ASSERTION__SHAP_INTERACTION_SUPPORTED = "SHAP interaction values are supported" + + +# +# Ensure all symbols introduced below are included in __all__ +# + +__tracker = AllTracker(globals()) + + +# +# Class definitions +# + + +class ModelInspector(FittableMixin[Sample], metaclass=ABCMeta): + """ + Base class of `inspectors` to explain different kinds of `models` based on SHAP + values. + """ + + pass + + +@inheritdoc(match="[see superclass]") +class LearnerInspector( + ModelInspector, ParallelizableMixin, Generic[T_LearnerPipelineDF] +): + """ + Explain regressors and classifiers based on SHAP values. + + Focus is on explaining the overall model, but the inspector also delivers + SHAP explanations of the individual observations. + + Available inspection methods are: + + - SHAP values + - SHAP interaction values + - feature importance derived from SHAP values (either as mean absolute values + or as the root of mean squares) + - pairwise feature redundancy matrix (requires availability of SHAP interaction + values) + - pairwise feature synergy matrix (requires availability of SHAP interaction + values) + - pairwise feature association matrix (upper bound for redundancy but can be + inflated by synergy; available if SHAP interaction values are unknown) + - pairwise feature interaction matrix (direct feature interaction quantified by + SHAP interaction values) + - feature redundancy linkage (to visualize clusters of redundant features in a + dendrogram; requires availability of SHAP interaction values) + - feature synergy linkage (to visualize clusters of synergistic features in a + dendrogram; requires availability of SHAP interaction values) + - feature association linkage (to visualize clusters of associated features in a + dendrogram) + + All inspections that aggregate across observations will respect sample weights, if + specified in the underlying training sample. + """ + + # defined in superclass, repeated here for Sphinx + n_jobs: Optional[int] + + # defined in superclass, repeated here for Sphinx + shared_memory: Optional[bool] + + # defined in superclass, repeated here for Sphinx + pre_dispatch: Optional[Union[str, int]] + + # defined in superclass, repeated here for Sphinx + verbose: Optional[int] + + #: Name for feature importance series or column. + COL_IMPORTANCE = "importance" + + #: The default explainer factory used by this inspector. + #: This is a tree explainer using the tree_path_dependent method for + #: feature perturbation, so we can calculate SHAP interaction values. + DEFAULT_EXPLAINER_FACTORY = TreeExplainerFactory( + feature_perturbation="tree_path_dependent", uses_background_dataset=False + ) + + def __init__( + self, + *, + pipeline: T_LearnerPipelineDF, + explainer_factory: Optional[ExplainerFactory] = None, + shap_interaction: bool = True, + n_jobs: Optional[int] = None, + shared_memory: Optional[bool] = None, + pre_dispatch: Optional[Union[str, int]] = None, + verbose: Optional[int] = None, + ) -> None: + """ + :param pipeline: the learner pipeline to inspect + :param explainer_factory: optional function that creates a shap Explainer + (default: ``TreeExplainerFactory``) + :param shap_interaction: if ``True``, calculate SHAP interaction values, else + only calculate SHAP contribution values. + SHAP interaction values are needed to determine feature synergy and + redundancy. + (default: ``True``) + """ + super().__init__( + n_jobs=n_jobs, + shared_memory=shared_memory, + pre_dispatch=pre_dispatch, + verbose=verbose, + ) + + if not pipeline.is_fitted: + raise ValueError("arg pipeline must be fitted") + + if not isinstance(pipeline.final_estimator, (ClassifierDF, RegressorDF)): + raise TypeError( + "learner in arg pipeline must be a classifier or a regressor," + f"but is a {type(pipeline.final_estimator).__name__}" + ) + + if explainer_factory: + if not explainer_factory.explains_raw_output: + raise ValueError( + "arg explainer_factory is not configured to explain raw output" + ) + else: + explainer_factory = self.DEFAULT_EXPLAINER_FACTORY + assert explainer_factory.explains_raw_output + + if shap_interaction: + if not explainer_factory.supports_shap_interaction_values: + log.warning( + "ignoring arg shap_interaction=True: " + f"explainers made by {explainer_factory!r} do not support " + "SHAP interaction values" + ) + shap_interaction = False + + self.pipeline = pipeline + self.explainer_factory = explainer_factory + self.shap_interaction = shap_interaction + + self._shap_calculator: Optional[ShapCalculator[T_LearnerPipelineDF]] = None + self._shap_global_decomposer: Optional[ShapGlobalExplainer] = None + self._shap_global_projector: Optional[ShapGlobalExplainer] = None + self._sample: Optional[Sample] = None + + __init__.__doc__ = cast(str, __init__.__doc__) + cast( + str, ParallelizableMixin.__init__.__doc__ + ) + + def fit( # type: ignore[override] + # todo: remove 'type: ignore' once mypy correctly infers return type + self: T_LearnerInspector, + sample: Sample, + **fit_params: Any, + ) -> T_LearnerInspector: + """ + Fit the inspector with the given sample, creating global explanations including + feature redundancy and synergy. + + This will calculate SHAP values and, if enabled in the underlying SHAP + explainer, also SHAP interaction values. + + :param sample: the background sample to be used for the global explanation + of this model + :param fit_params: additional keyword arguments (ignored; accepted for + compatibility with :class:`.FittableMixin`) + :return: ``self`` + """ + + learner: LearnerDF = self.pipeline.final_estimator + + _is_classifier = is_classifier(learner) + if _is_classifier and isinstance(sample.target_name, list): + raise ValueError( + "only single-output classifiers (binary or multi-class) are supported, " + "but the given classifier has been fitted on multiple columns " + f"{sample.target_name}" + ) + + shap_global_projector: Union[ + ShapVectorProjector, ShapInteractionVectorProjector, None + ] + + shap_calculator_type: Type[ShapCalculator[T_LearnerPipelineDF]] + shap_calculator: ShapCalculator[T_LearnerPipelineDF] + + if self.shap_interaction: + if _is_classifier: + shap_calculator_type = ClassifierShapInteractionValuesCalculator + else: + shap_calculator_type = RegressorShapInteractionValuesCalculator + + shap_calculator = shap_calculator_type( + pipeline=self.pipeline, + explainer_factory=self.explainer_factory, + n_jobs=self.n_jobs, + shared_memory=self.shared_memory, + pre_dispatch=self.pre_dispatch, + verbose=self.verbose, + ) + + shap_global_projector = ShapInteractionVectorProjector() + + else: + if _is_classifier: + shap_calculator_type = ClassifierShapValuesCalculator + else: + shap_calculator_type = RegressorShapValuesCalculator + + shap_calculator = shap_calculator_type( + pipeline=self.pipeline, + explainer_factory=self.explainer_factory, + n_jobs=self.n_jobs, + shared_memory=self.shared_memory, + pre_dispatch=self.pre_dispatch, + verbose=self.verbose, + ) + + shap_global_projector = ShapVectorProjector() + + shap_calculator.fit(sample) + shap_global_projector.fit(shap_calculator=shap_calculator) + + self._sample = sample + self._shap_calculator = shap_calculator + self._shap_global_projector = shap_global_projector + + return self + + @property + def _shap_global_explainer(self) -> ShapGlobalExplainer: + self.ensure_fitted() + assert self._shap_global_projector is not None, ASSERTION__INSPECTOR_IS_FITTED + return self._shap_global_projector + + @property + def is_fitted(self) -> bool: + """[see superclass]""" + return self._sample is not None + + @property + def sample_(self) -> Sample: + """ + The background sample used to fit this inspector. + """ + + self.ensure_fitted() + assert self._sample is not None, ASSERTION__INSPECTOR_IS_FITTED + return self._sample + + @property + def output_names_(self) -> Sequence[str]: + """ + The names of the outputs explained by this inspector. + + For regressors, these are the names of the target columns. + + For binary classifiers, this is a list of length 1 with the name of a single + class, since the SHAP values of the second class can be trivially derived as + the negation of the SHAP values of the first class. + + For non-binary classifiers, this is the list of all classes. + """ + + self.ensure_fitted() + assert ( + self._shap_calculator is not None + and self._shap_calculator.output_names_ is not None + ), ASSERTION__INSPECTOR_IS_FITTED + return self._shap_calculator.output_names_ + + @property + def features_(self) -> List[str]: + """ + The names of the features used to fit the learner pipeline explained by this + inspector. + """ + return cast(List[str], self.pipeline.feature_names_out_.to_list()) + + def shap_values(self) -> Union[pd.DataFrame, List[pd.DataFrame]]: + """ + Calculate the SHAP values for all observations and features. + + Returns a data frame of SHAP values where each row corresponds to an + observation, and each column corresponds to a feature. + + :return: a data frame with SHAP values + """ + + self.ensure_fitted() + assert self._shap_calculator is not None, ASSERTION__INSPECTOR_IS_FITTED + return self.__split_multi_output_df(self._shap_calculator.get_shap_values()) + + def shap_interaction_values(self) -> Union[pd.DataFrame, List[pd.DataFrame]]: + """ + Calculate the SHAP interaction values for all observations and pairs of + features. + + Returns a data frame of SHAP interaction values where each row corresponds to an + observation and a feature (identified by a hierarchical index with two levels), + and each column corresponds to a feature. + + :return: a data frame with SHAP interaction values + """ + self.ensure_fitted() + return self.__split_multi_output_df( + self.__shap_interaction_values_calculator.get_shap_interaction_values() + ) + + def feature_importance( + self, *, method: str = "rms" + ) -> Union[pd.Series, pd.DataFrame]: + """ + Calculate the relative importance of each feature based on SHAP values. + + The importance values of all features always add up to `1.0`. + + The calculation applies sample weights if specified in the underlying + :class:`.Sample`. + + :param method: method for calculating feature importance. Supported methods + are ``rms`` (root of mean squares, default), ``mav`` (mean absolute + values) + :return: a series of length `n_features` for single-output models, or a + data frame of shape (n_features, n_outputs) for multi-output models + """ + + self.ensure_fitted() + + methods = {"rms", "mav"} + if method not in methods: + raise ValueError(f'arg method="{method}" must be one of {methods}') + + assert self._shap_calculator is not None + shap_matrix: pd.DataFrame = self._shap_calculator.get_shap_values() + weight: Optional[pd.Series] = self.sample_.weight + + abs_importance: pd.Series + if method == "rms": + if weight is None: + abs_importance = shap_matrix.pow(2).mean().pow(0.5) + else: + abs_importance = shap_matrix.pow(2).mul(weight, axis=0).mean().pow(0.5) + else: + assert method == "mav", f"method is in {methods}" + if weight is None: + abs_importance = shap_matrix.abs().mean() + else: + abs_importance = shap_matrix.abs().mul(weight, axis=0).mean() + + def _normalize_importance( + _importance: T_SeriesOrDataFrame, + ) -> T_SeriesOrDataFrame: + return _importance.divide(_importance.sum()) + + if len(self.output_names_) == 1: + return _normalize_importance(abs_importance).rename(self.output_names_[0]) + + else: + assert ( + abs_importance.index.nlevels == 2 + ), "2 index levels in place for multi-output models" + + return _normalize_importance(abs_importance.unstack(level=0)) + + def feature_synergy_matrix( + self, + *, + absolute: bool = False, + symmetrical: bool = False, + clustered: bool = True, + ) -> Union[FloatMatrix, List[FloatMatrix]]: + """ + Calculate the feature synergy matrix. + + This yields an asymmetric matrix where each row and column represents one + feature, and the values at the intersections are the pairwise feature synergies, + ranging from `0.0` (no synergy - both features contribute to predictions fully + autonomously of each other) to `1.0` (full synergy, both features rely on + combining all of their information to achieve any contribution to predictions). + + The synergy of a feature with itself is defined as `1.0`. + + Feature synergy calculations require SHAP interaction values; if only SHAP + values are available consider calculating feature associations instead + (see :meth:`.feature_association_matrix`). + + In the case of multi-target regression and non-binary classification, returns + a list of data frames with one matrix per output. + + :param absolute: if ``False``, return relative synergy as a percentage of + total feature importance; + if ``True``, return absolute synergy as a portion of feature importance + :param symmetrical: if ``True``, return a symmetrical matrix quantifying + mutual synergy; if ``False``, return an asymmetrical matrix quantifying + unilateral synergy of the features represented by rows with the + features represented by columns (default: ``False``) + :param clustered: if ``True``, reorder the rows and columns of the matrix + such that synergy between adjacent rows and columns is maximised; if + ``False``, keep rows and columns in the original features order + (default: ``True``) + :return: feature synergy matrix as a data frame of shape + `(n_features, n_features)`, or a list of data frames for multiple outputs + """ + + self.ensure_fitted() + + return self.__feature_affinity_matrix( + explainer_fn=self.__interaction_explainer.synergy, + absolute=absolute, + symmetrical=symmetrical, + clustered=clustered, + ) + + def feature_redundancy_matrix( + self, + *, + absolute: bool = False, + symmetrical: bool = False, + clustered: bool = True, + ) -> Union[FloatMatrix, List[FloatMatrix]]: + """ + Calculate the feature redundancy matrix. + + This yields an asymmetric matrix where each row and column represents one + feature, and the values at the intersections are the pairwise feature + redundancies, ranging from `0.0` (no redundancy - both features contribute to + predictions fully independently of each other) to `1.0` (full redundancy, either + feature can replace the other feature without loss of predictive power). + + The redundancy of a feature with itself is defined as `1.0`. + + Feature redundancy calculations require SHAP interaction values; if only SHAP + values are available consider calculating feature associations instead + (see :meth:`.feature_association_matrix`). + + In the case of multi-target regression and non-binary classification, returns + a list of data frames with one matrix per output. + + :param absolute: if ``False``, return relative redundancy as a percentage of + total feature importance; + if ``True``, return absolute redundancy as a portion of feature importance + :param symmetrical: if ``True``, return a symmetrical matrix quantifying + mutual redundancy; if ``False``, return an asymmetrical matrix quantifying + unilateral redundancy of the features represented by rows with the + features represented by columns (default: ``False``) + :param clustered: if ``True``, reorder the rows and columns of the matrix + such that redundancy between adjacent rows and columns is maximised; if + ``False``, keep rows and columns in the original features order + (default: ``True``) + :return: feature redundancy matrix as a data frame of shape + `(n_features, n_features)`, or a list of data frames for multiple outputs + """ + self.ensure_fitted() + + return self.__feature_affinity_matrix( + explainer_fn=self.__interaction_explainer.redundancy, + absolute=absolute, + symmetrical=symmetrical, + clustered=clustered, + ) + + def feature_association_matrix( + self, + *, + absolute: bool = False, + symmetrical: bool = False, + clustered: bool = True, + ) -> Union[FloatMatrix, List[FloatMatrix]]: + """ + Calculate the feature association matrix. + + This yields an asymmetric matrix where each row and column represents one + feature, and the values at the intersections are the pairwise feature + associations, ranging from `0.0` (no association) to `1.0` (full association). + + The association of a feature with itself is defined as `1.0`. + + Feature association provides an upper bound for feature redundancy but might be + inflated by feature synergy. + + While it is preferable to assess redundancy and synergy separately, association + can be calculated using only SHAP values, and thus can be used as a fallback + if no SHAP interaction values are available. + + In the case of multi-target regression and non-binary classification, returns + a list of data frames with one matrix per output. + + :param absolute: if ``False``, return relative association as a percentage of + total feature importance; + if ``True``, return absolute association as a portion of feature importance + :param symmetrical: if ``False``, return an asymmetrical matrix + quantifying unilateral association of the features represented by rows + with the features represented by columns; + if ``True``, return a symmetrical matrix quantifying mutual association + (default: ``False``) + :param clustered: if ``True``, reorder the rows and columns of the matrix + such that association between adjacent rows and columns is maximised; if + ``False``, keep rows and columns in the original features order + (default: ``True``) + :return: feature association matrix as a data frame of shape + `(n_features, n_features)`, or a list of data frames for multiple outputs + """ + + self.ensure_fitted() + + return self.__feature_affinity_matrix( + explainer_fn=self._shap_global_explainer.association, + absolute=absolute, + symmetrical=symmetrical, + clustered=clustered, + ) + + def feature_synergy_linkage(self) -> Union[LinkageTree, List[LinkageTree]]: + """ + Calculate a linkage tree based on the :meth:`.feature_synergy_matrix`. + + The linkage tree can be used to render a dendrogram indicating clusters of + synergistic features. + + In the case of multi-target regression and non-binary classification, returns + a list of linkage trees per target or class. + + :return: linkage tree of feature synergies; list of linkage trees + for multi-target regressors or non-binary classifiers + """ + + self.ensure_fitted() + feature_affinity_matrix = self.__interaction_explainer.synergy( + symmetrical=True, absolute=False + ) + assert ( + feature_affinity_matrix is not None + ), ASSERTION__SHAP_INTERACTION_SUPPORTED + + return self.__linkages_from_affinity_matrices( + feature_affinity_matrix=feature_affinity_matrix + ) + + def feature_redundancy_linkage(self) -> Union[LinkageTree, List[LinkageTree]]: + """ + Calculate a linkage tree based on the :meth:`.feature_redundancy_matrix`. + + The linkage tree can be used to render a dendrogram indicating clusters of + redundant features. + + In the case of multi-target regression and non-binary classification, returns + a list of linkage trees per target or class. + + :return: linkage tree of feature redundancies; list of linkage trees + for multi-target regressors or non-binary classifiers + """ + + self.ensure_fitted() + feature_affinity_matrix = self.__interaction_explainer.redundancy( + symmetrical=True, absolute=False + ) + assert ( + feature_affinity_matrix is not None + ), ASSERTION__SHAP_INTERACTION_SUPPORTED + + return self.__linkages_from_affinity_matrices( + feature_affinity_matrix=feature_affinity_matrix + ) + + def feature_association_linkage(self) -> Union[LinkageTree, List[LinkageTree]]: + """ + Calculate a linkage tree based on the :meth:`.feature_association_matrix`. + + The linkage tree can be used to render a dendrogram indicating clusters of + associated features. + + In the case of multi-target regression and non-binary classification, returns + a list of linkage trees per target or class. + + :return: linkage tree of feature associations; list of linkage trees + for multi-target regressors or non-binary classifiers + """ + + self.ensure_fitted() + feature_affinity_matrix = self._shap_global_explainer.association( + absolute=False, symmetrical=True + ) + assert ( + feature_affinity_matrix is not None + ), ASSERTION__SHAP_INTERACTION_SUPPORTED + + return self.__linkages_from_affinity_matrices( + feature_affinity_matrix=feature_affinity_matrix + ) + + def feature_interaction_matrix(self) -> Union[FloatMatrix, List[FloatMatrix]]: + """ + Calculate relative shap interaction values for all feature pairings. + + Shap interactions quantify direct interactions between pairs of features. + For a quantification of overall interaction (including indirect interactions + across more than two features), see :meth:`.feature_synergy_matrix`. + + The relative values are normalised to add up to `1.0`, and each value ranges + between `0.0` and `1.0`. + + For features :math:`f_i` and :math:`f_j`, relative feature interaction + :math:`I` is calculated as + + .. math:: + I_{ij} = \\frac + {\\sigma(\\vec{\\phi}_{ij})} + {\\sum_{a=1}^n \\sum_{b=1}^n \\sigma(\\vec{\\phi}_{ab})} + + where :math:`\\sigma(\\vec v)` is the standard deviation of all elements of + vector :math:`\\vec v`. + + The total average interaction of features + :math:`f_i` and :math:`f_j` is + :math:`I_{ij} \ + + I_{ji} \ + = 2 I_{ij}`. + + :math:`I_{ii}` is the residual, non-synergistic contribution + of feature :math:`f_i` + + The matrix returned by this method is a lower-triangular matrix + + .. math:: + + \\newcommand\\nan{\\mathit{nan}} + I_{} = \\begin{pmatrix} + I_{11} & \\nan & \\nan & \\dots & \\nan \\\\ + 2I_{21} & I_{22} & \\nan & \\dots & \\nan \\\\ + 2I_{31} & 2I_{32} & I_{33} & \\dots & \\nan \\\\ + \\vdots & \\vdots & \\vdots & \\ddots & \\vdots \\\\ + 2I_{n1} & 2I_{n2} & 2I_{n3} & \\dots & I_{nn} \\\\ + \\end{pmatrix} + + with :math:`\\sum_{a=1}^n \\sum_{b=a}^n I_{ab} = 1` + + In the case of multi-target regression and non-binary classification, returns + a list with one matrix per output. + + :return: relative shap interaction values as a data frame of shape + `(n_features, n_features)`; or a list of such data frames + """ + + n_features = len(self.features_) + n_outputs = len(self.output_names_) + + # get a feature interaction array with shape + # (n_observations, n_outputs, n_features, n_features) + # where the innermost feature x feature arrays are symmetrical + im_matrix_per_observation_and_output: FloatArray = ( + # TODO missing proper handling for list of data frames + self.shap_interaction_values() # type: ignore + .values.reshape((-1, n_features, n_outputs, n_features)) + .swapaxes(1, 2) + ) + + # get the observation weights with shape + # (n_observations, n_outputs, n_features, n_features) + weight: Optional[FloatArray] + _weight_sr = self.sample_.weight + if _weight_sr is not None: + # if sample weights are defined, convert them to an array + # and align the array with the dimensions of the feature interaction array + weight = _weight_sr.values.reshape((-1, 1, 1, 1)) + else: + weight = None + + # calculate the average interactions for each output and feature/feature + # interaction, based on the standard deviation assuming a mean of 0.0. + # The resulting matrix has shape (n_outputs, n_features, n_features) + _interaction_squared = im_matrix_per_observation_and_output**2 + if weight is not None: + _interaction_squared *= weight + interaction_matrix = np.sqrt(_interaction_squared.mean(axis=0)) + assert interaction_matrix.shape == (n_outputs, n_features, n_features) + + # we normalise the synergy matrix for each output to a total of 1.0 + interaction_matrix /= interaction_matrix.sum() + + # the total interaction effect for features i and j is the total of matrix + # cells (i,j) and (j,i); theoretically both should be the same but to minimize + # numerical errors we total both in the lower matrix triangle (but excluding the + # matrix diagonal, hence k=1) + interaction_matrix += np.triu(interaction_matrix, k=1).swapaxes(1, 2) + + # discard the upper matrix triangle by setting it to nan + interaction_matrix += np.triu( + np.full(shape=(n_features, n_features), fill_value=np.nan), k=1 + )[np.newaxis, :, :] + + # create a data frame from the feature matrix + return self.__arrays_to_matrix( + interaction_matrix, value_label="relative shap interaction" + ) + + def shap_plot_data(self) -> ShapPlotData: + """ + Consolidate SHAP values and corresponding feature values from this inspector + for use in SHAP plots offered by the + `shap `__ package. + + The `shap` package provides functions for creating various SHAP plots. + Most of these functions require + + - one or more SHAP value matrices as a single `numpy` array, + or a list of `numpy` arrays of shape `(n_observations, n_features)` + - a feature matrix of shape `(n_observations, n_features)`, which can be + provided as a data frame to preserve feature names + + This method provides this data inside a :class:`.ShapPlotData` object, plus + + - the names of all outputs (i.e., the target names in case of regression, + or the class names in case of classification) + - corresponding target values as a series, or as a data frame in the case of + multiple targets + + This method also ensures that the rows of all arrays, frames, and series are + aligned, even if only a subset of the observations in the original sample was + used to calculate SHAP values. + + Calculates mean shap values for each observation and feature, across all + splits for which SHAP values were calculated. + + :return: consolidated SHAP and feature values for use shap plots + """ + + shap_values: Union[pd.DataFrame, List[pd.DataFrame]] = self.shap_values() + + output_names: Sequence[str] = self.output_names_ + shap_values_numpy: Union[FloatArray, List[FloatArray]] + included_observations: pd.Index + + if len(output_names) > 1: + shap_values_numpy = [s.values for s in shap_values] + included_observations = shap_values[0].index + else: + shap_values = cast(pd.DataFrame, shap_values) + shap_values_numpy = shap_values.values + included_observations = shap_values.index + + sample: Sample = self.sample_.subsample(loc=included_observations) + + return ShapPlotData( + shap_values=shap_values_numpy, + sample=sample, + ) + + def __arrays_to_matrix( + self, matrix: FloatArray, value_label: str + ) -> Union[FloatMatrix, List[FloatMatrix]]: + # transform a matrix of shape (n_outputs, n_features, n_features) + # to a data frame + + feature_index = self.pipeline.feature_names_out_.rename(Sample.IDX_FEATURE) + + n_features = len(feature_index) + assert matrix.shape == (len(self.output_names_), n_features, n_features) + + # convert array to data frame(s) with features as row and column indices + if len(matrix) == 1: + return self.__array_to_matrix( + matrix[0], + feature_importance=self.feature_importance(), + value_label=value_label, + ) + else: + return [ + self.__array_to_matrix( + m, + feature_importance=feature_importance, + value_label=f"{value_label} ({output_name})", + ) + for m, (_, feature_importance), output_name in zip( + matrix, self.feature_importance().items(), self.output_names_ + ) + ] + + def __feature_affinity_matrix( + self, + *, + explainer_fn: Callable[..., FloatArray], + absolute: bool, + symmetrical: bool, + clustered: bool, + ) -> Union[FloatMatrix, List[FloatMatrix]]: + affinity_matrices = explainer_fn(symmetrical=symmetrical, absolute=absolute) + + explainer: ShapGlobalExplainer = cast( + ShapGlobalExplainer, cast(MethodType, explainer_fn).__self__ + ) + affinity_matrices_df: List[pd.DataFrame] = explainer.to_frames( + affinity_matrices + ) + + if clustered: + affinity_symmetrical = explainer_fn(symmetrical=True, absolute=False) + assert ( + affinity_symmetrical is not None + ), ASSERTION__SHAP_INTERACTION_SUPPORTED + + affinity_matrices_df = self.__sort_affinity_matrices( + affinity_matrices=affinity_matrices_df, + symmetrical_affinity_matrices=affinity_symmetrical, + ) + + return self.__isolate_single_frame( + affinity_matrices_df, affinity_metric=explainer_fn.__name__ + ) + + @staticmethod + def __sort_affinity_matrices( + affinity_matrices: List[pd.DataFrame], + symmetrical_affinity_matrices: FloatArray, + ) -> List[pd.DataFrame]: + # abbreviate a very long function name to stay within the permitted line length + fn_linkage = LearnerInspector.__linkage_matrix_from_affinity_matrix_for_output + + return [ + (lambda feature_order: affinity_matrix.iloc[feature_order, feature_order])( + feature_order=hierarchy.leaves_list( + Z=fn_linkage(feature_affinity_matrix=symmetrical_affinity_matrix) + ) + ) + for affinity_matrix, symmetrical_affinity_matrix in zip( + affinity_matrices, symmetrical_affinity_matrices + ) + ] + + @staticmethod + def __split_multi_output_df( + multi_output_df: pd.DataFrame, + ) -> Union[pd.DataFrame, List[pd.DataFrame]]: + # Split a multi-output data frame into a list of single-output data frames. + # Return single-output data frames as is. + # Multi-output data frames are grouped by level 0 in the column index. + if multi_output_df.columns.nlevels == 1: + return multi_output_df + else: + return [ + multi_output_df.xs(key=output_name, axis=1, level=0, drop_level=True) + for output_name in ( + cast(pd.MultiIndex, multi_output_df.columns).levels[0] + ) + ] + + def __linkages_from_affinity_matrices( + self, feature_affinity_matrix: FloatArray + ) -> Union[LinkageTree, List[LinkageTree]]: + # calculate the linkage trees for all outputs in a feature distance matrix; + # matrix has shape (n_outputs, n_features, n_features) with values ranging from + # (1 = closest, 0 = most distant) + # return a linkage tree if there is only one output, else return a list of + # linkage trees + + feature_importance = self.feature_importance(method="rms") + + if len(feature_affinity_matrix) == 1: + # we have only a single output + # feature importance is already a series + return self.__linkage_tree_from_affinity_matrix_for_output( + feature_affinity_matrix[0], feature_importance + ) + + else: + # noinspection PyCompatibility + feature_importance_iter: ( + Iterable[Tuple[Any, pd.Series]] + ) = feature_importance.iteritems() + + return [ + self.__linkage_tree_from_affinity_matrix_for_output( + feature_affinity_for_output, + feature_importance_for_output, + ) + for feature_affinity_for_output, ( + _, + feature_importance_for_output, + ) in zip(feature_affinity_matrix, feature_importance_iter) + ] + + @staticmethod + def __linkage_tree_from_affinity_matrix_for_output( + feature_affinity_matrix: FloatArray, feature_importance: pd.Series + ) -> LinkageTree: + # calculate the linkage tree from the a given output in a feature distance + # matrix; + # matrix has shape (n_features, n_features) with values ranging from + # (1 = closest, 0 = most distant) + + linkage_matrix: FloatArray = ( + LearnerInspector.__linkage_matrix_from_affinity_matrix_for_output( + feature_affinity_matrix + ) + ) + + # Feature labels and weights will be used as the leaves of the linkage tree. + # Select only the features that appear in the distance matrix, and in the + # correct order + + # build and return the linkage tree + return LinkageTree( + scipy_linkage_matrix=linkage_matrix, + leaf_names=feature_importance.index, + leaf_weights=feature_importance.values, + max_distance=1.0, + distance_label="feature distance", + leaf_label="feature", + weight_label="feature importance", + ) + + @staticmethod + def __linkage_matrix_from_affinity_matrix_for_output( + feature_affinity_matrix: FloatArray, + ) -> FloatArray: + # calculate the linkage matrix from the a given output in a feature distance + # matrix; + # matrix has shape (n_features, n_features) with values ranging from + # (1 = closest, 0 = most distant) + + # compress the distance matrix (required by SciPy) + distance_matrix = 1.0 - abs(feature_affinity_matrix) + np.fill_diagonal(distance_matrix, 0.0) + compressed_distance_matrix: FloatArray = distance.squareform(distance_matrix) + + # calculate the linkage matrix + leaf_ordering: FloatArray = hierarchy.optimal_leaf_ordering( + Z=hierarchy.linkage(y=compressed_distance_matrix, method="single"), + y=compressed_distance_matrix, + ) + + # reverse the leaf ordering, so that larger values tend to end up on top + leaf_ordering[:, [1, 0]] = leaf_ordering[:, [0, 1]] + + return leaf_ordering + + def _ensure_shap_interaction(self) -> None: + if not self.shap_interaction: + raise RuntimeError( + "SHAP interaction values have not been calculated. " + "Create an inspector with parameter 'shap_interaction=True' to " + "enable calculations involving SHAP interaction values." + ) + + def __isolate_single_frame( + self, + frames: List[pd.DataFrame], + affinity_metric: str, + ) -> Union[FloatMatrix, List[FloatMatrix]]: + feature_importance = self.feature_importance() + + if len(frames) == 1: + assert isinstance(feature_importance, pd.Series) + return self.__frame_to_matrix( + frames[0], + affinity_metric=affinity_metric, + feature_importance=feature_importance, + ) + else: + return [ + self.__frame_to_matrix( + frame, + affinity_metric=affinity_metric, + feature_importance=frame_importance, + feature_importance_category=str(frame_name), + ) + for frame, (frame_name, frame_importance) in zip( + frames, feature_importance.items() + ) + ] + + @staticmethod + def __array_to_matrix( + a: npt.NDArray[T_Number], + *, + feature_importance: pd.Series, + value_label: str, + ) -> Matrix[T_Number]: + return Matrix( + a, + names=(feature_importance.index, feature_importance.index), + weights=(feature_importance, feature_importance), + value_label=value_label, + name_labels=("feature", "feature"), + ) + + @staticmethod + def __frame_to_matrix( + frame: pd.DataFrame, + *, + affinity_metric: str, + feature_importance: pd.Series, + feature_importance_category: Optional[str] = None, + ) -> FloatMatrix: + return Matrix.from_frame( + frame, + weights=( + feature_importance.reindex(frame.index), + feature_importance.reindex(frame.columns), + ), + value_label=( + f"{affinity_metric} ({feature_importance_category})" + if feature_importance_category + else affinity_metric + ), + name_labels=("primary feature", "associated feature"), + ) + + @property + def __shap_interaction_values_calculator( + self, + ) -> ShapInteractionValuesCalculator[T_LearnerPipelineDF]: + self._ensure_shap_interaction() + return cast( + ShapInteractionValuesCalculator[T_LearnerPipelineDF], self._shap_calculator + ) + + @property + def __interaction_explainer(self) -> ShapInteractionGlobalExplainer: + self._ensure_shap_interaction() + return cast(ShapInteractionGlobalExplainer, self._shap_global_explainer) + + +__tracker.validate() diff --git a/src/facet/inspection/_shap.py b/src/facet/inspection/_shap.py index 4470c9e67..760ebd6ad 100644 --- a/src/facet/inspection/_shap.py +++ b/src/facet/inspection/_shap.py @@ -4,9 +4,10 @@ import logging from abc import ABCMeta, abstractmethod -from typing import Callable, Generic, List, Optional, Sequence, TypeVar, Union, cast +from typing import Any, Generic, List, Optional, Sequence, TypeVar, Union, cast import numpy as np +import numpy.typing as npt import pandas as pd from pytools.api import AllTracker, inheritdoc @@ -39,16 +40,15 @@ # Type variables # -T_ShapCalculator = TypeVar("T_ShapCalculator", bound="ShapCalculator") -T_LearnerPipelineDF = TypeVar("T_LearnerPipelineDF", bound=LearnerPipelineDF) +T_ShapCalculator = TypeVar("T_ShapCalculator", bound="ShapCalculator[Any]") +T_LearnerPipelineDF = TypeVar("T_LearnerPipelineDF", bound=LearnerPipelineDF[Any]) + # -# Type definitions +# Constants # -ShapToDataFrameFunction = Callable[ - [List[np.ndarray], pd.Index, pd.Index], List[pd.DataFrame] -] +ASSERTION__CALCULATOR_IS_FITTED = "calculator is fitted" # @@ -73,8 +73,8 @@ class ShapCalculator( """ Base class for all SHAP calculators. - A SHAP calculator uses the ``shap`` package to calculate SHAP tensors for OOB - samples across splits of a crossfit, then consolidates and aggregates results + A SHAP calculator uses the ``shap`` package to calculate SHAP tensors for all + observations in a given sample, then consolidates and aggregates results in a data frame. """ @@ -118,10 +118,9 @@ def is_fitted(self) -> bool: return self.shap_ is not None def fit( # type: ignore[override] - # todo: remove 'type: ignore' once mypy correctly infers return type self: T_ShapCalculator, sample: Sample, - **fit_params, + **fit_params: Any, ) -> T_ShapCalculator: """ Calculate the SHAP values. @@ -262,9 +261,12 @@ def _calculate_shap( pass def _convert_shap_tensors_to_list( - self, *, shap_tensors: Union[np.ndarray, List[np.ndarray]], n_outputs: int - ): - def _validate_shap_tensor(_t: np.ndarray) -> None: + self, + *, + shap_tensors: Union[npt.NDArray[np.float_], List[npt.NDArray[np.float_]]], + n_outputs: int, + ) -> List[npt.NDArray[np.float_]]: + def _validate_shap_tensor(_t: npt.NDArray[np.float_]) -> None: if np.isnan(np.sum(_t)): raise AssertionError( "Output of SHAP explainer includes NaN values. " @@ -289,8 +291,6 @@ def _validate_shap_tensor(_t: np.ndarray) -> None: return shap_tensors def _preprocess_features(self, sample: Sample) -> pd.DataFrame: - # get the out-of-bag subsample of the training sample, with feature columns - # in the sequence that was used to fit the learner # get the model pipeline = self.pipeline @@ -308,7 +308,7 @@ def _preprocess_features(self, sample: Sample) -> pd.DataFrame: @staticmethod @abstractmethod def _convert_raw_shap_to_df( - raw_shap_tensors: List[np.ndarray], + raw_shap_tensors: List[npt.NDArray[np.float_]], observations: pd.Index, features_in_split: pd.Index, ) -> List[pd.DataFrame]: @@ -324,7 +324,7 @@ def _convert_raw_shap_to_df( pass @abstractmethod - def _get_output_names(self, sample: Sample) -> List[str]: + def _get_output_names(self, sample: Sample) -> Sequence[str]: pass @@ -367,11 +367,11 @@ def _calculate_shap( multi_output_type = self.get_multi_output_type() multi_output_names = self.get_multi_output_names(sample=sample) - assert self.feature_index_ is not None, "Calculator is fitted" + assert self.feature_index_ is not None, ASSERTION__CALCULATOR_IS_FITTED features_out = self.feature_index_ # calculate the shap values, and ensure the result is a list of arrays - shap_values: List[np.ndarray] = self._convert_shap_tensors_to_list( + shap_values: List[npt.NDArray[np.float_]] = self._convert_shap_tensors_to_list( shap_tensors=explainer.shap_values(x), n_outputs=len(multi_output_names) ) @@ -409,14 +409,14 @@ def get_shap_values(self) -> pd.DataFrame: """[see superclass]""" self.ensure_fitted() - assert self.shap_ is not None, "Calculator is fitted" + assert self.shap_ is not None, ASSERTION__CALCULATOR_IS_FITTED return self.shap_.groupby(level=0).sum() def get_shap_interaction_values(self) -> pd.DataFrame: """[see superclass]""" self.ensure_fitted() - assert self.shap_ is not None, "Calculator is fitted" + assert self.shap_ is not None, ASSERTION__CALCULATOR_IS_FITTED return self.shap_ def get_diagonals(self) -> pd.DataFrame: @@ -435,7 +435,7 @@ def get_diagonals(self) -> pd.DataFrame: self.shap_ is not None and self.sample_ is not None and self.feature_index_ is not None - ), "Calculator is fitted" + ), ASSERTION__CALCULATOR_IS_FITTED n_observations = len(self.sample_) n_features = len(self.feature_index_) @@ -462,11 +462,13 @@ def _calculate_shap( multi_output_type = self.get_multi_output_type() multi_output_names = self.get_multi_output_names(sample) - assert self.feature_index_ is not None, "Calculator is fitted" + assert self.feature_index_ is not None, ASSERTION__CALCULATOR_IS_FITTED features_out = self.feature_index_ # calculate the shap interaction values; ensure the result is a list of arrays - shap_interaction_tensors: List[np.ndarray] = self._convert_shap_tensors_to_list( + shap_interaction_tensors: List[ + npt.NDArray[np.float_] + ] = self._convert_shap_tensors_to_list( shap_tensors=explainer.shap_interaction_values(x), n_outputs=len(multi_output_names), ) @@ -501,7 +503,9 @@ def _calculate_shap( @inheritdoc(match="[see superclass]") -class RegressorShapCalculator(ShapCalculator[RegressorPipelineDF], metaclass=ABCMeta): +class RegressorShapCalculator( + ShapCalculator[RegressorPipelineDF[Any]], metaclass=ABCMeta +): """ Calculates SHAP (interaction) values for regression models. """ @@ -522,7 +526,7 @@ def get_multi_output_names(self, sample: Sample) -> List[str]: class RegressorShapValuesCalculator( - RegressorShapCalculator, ShapValuesCalculator[RegressorPipelineDF] + RegressorShapCalculator, ShapValuesCalculator[RegressorPipelineDF[Any]] ): """ Calculates SHAP values for regression models. @@ -530,7 +534,7 @@ class RegressorShapValuesCalculator( @staticmethod def _convert_raw_shap_to_df( - raw_shap_tensors: List[np.ndarray], + raw_shap_tensors: List[npt.NDArray[np.float_]], observations: pd.Index, features_in_split: pd.Index, ) -> List[pd.DataFrame]: @@ -543,7 +547,7 @@ def _convert_raw_shap_to_df( class RegressorShapInteractionValuesCalculator( - RegressorShapCalculator, ShapInteractionValuesCalculator[RegressorPipelineDF] + RegressorShapCalculator, ShapInteractionValuesCalculator[RegressorPipelineDF[Any]] ): """ Calculates SHAP interaction matrices for regression models. @@ -551,7 +555,7 @@ class RegressorShapInteractionValuesCalculator( @staticmethod def _convert_raw_shap_to_df( - raw_shap_tensors: List[np.ndarray], + raw_shap_tensors: List[npt.NDArray[np.float_]], observations: pd.Index, features_in_split: pd.Index, ) -> List[pd.DataFrame]: @@ -573,7 +577,9 @@ def _convert_raw_shap_to_df( @inheritdoc(match="[see superclass]") -class ClassifierShapCalculator(ShapCalculator[ClassifierPipelineDF], metaclass=ABCMeta): +class ClassifierShapCalculator( + ShapCalculator[ClassifierPipelineDF[Any]], metaclass=ABCMeta +): """ Calculates SHAP (interaction) values for classification models. """ @@ -581,8 +587,11 @@ class ClassifierShapCalculator(ShapCalculator[ClassifierPipelineDF], metaclass=A COL_CLASS = "class" def _convert_shap_tensors_to_list( - self, *, shap_tensors: Union[np.ndarray, List[np.ndarray]], n_outputs: int - ): + self, + *, + shap_tensors: Union[npt.NDArray[np.float_], List[npt.NDArray[np.float_]]], + n_outputs: int, + ) -> List[npt.NDArray[np.float_]]: if n_outputs == 2 and isinstance(shap_tensors, np.ndarray): # if we have a single output *and* binary classification, the explainer @@ -591,7 +600,6 @@ def _convert_shap_tensors_to_list( (shap_tensors,) = super()._convert_shap_tensors_to_list( shap_tensors=shap_tensors, n_outputs=1 ) - shap_tensors = cast(np.ndarray, shap_tensors) return [-shap_tensors, shap_tensors] else: return super()._convert_shap_tensors_to_list( @@ -601,7 +609,7 @@ def _convert_shap_tensors_to_list( def _get_output_names( self, sample: Sample, - ) -> List[str]: + ) -> Sequence[str]: assert not isinstance( sample.target_name, list ), "classification model is single-output" @@ -609,8 +617,7 @@ def _get_output_names( assert classifier_df.is_fitted, "classifier must be fitted" try: - # noinspection PyUnresolvedReferences - output_names = classifier_df.classes_ + output_names: List[str] = classifier_df.classes_.tolist() except Exception as cause: raise AssertionError("classifier must define classes_ attribute") from cause @@ -648,7 +655,7 @@ def get_multi_output_names(self, sample: Sample) -> List[str]: class ClassifierShapValuesCalculator( - ClassifierShapCalculator, ShapValuesCalculator[ClassifierPipelineDF] + ClassifierShapCalculator, ShapValuesCalculator[ClassifierPipelineDF[Any]] ): """ Calculates SHAP matrices for classification models. @@ -657,7 +664,7 @@ class ClassifierShapValuesCalculator( # noinspection DuplicatedCode @staticmethod def _convert_raw_shap_to_df( - raw_shap_tensors: List[np.ndarray], + raw_shap_tensors: List[npt.NDArray[np.float_]], observations: pd.Index, features_in_split: pd.Index, ) -> List[pd.DataFrame]: @@ -694,7 +701,7 @@ def _convert_raw_shap_to_df( class ClassifierShapInteractionValuesCalculator( - ClassifierShapCalculator, ShapInteractionValuesCalculator[ClassifierPipelineDF] + ClassifierShapCalculator, ShapInteractionValuesCalculator[ClassifierPipelineDF[Any]] ): """ Calculates SHAP interaction matrices for classification models. @@ -703,7 +710,7 @@ class ClassifierShapInteractionValuesCalculator( # noinspection DuplicatedCode @staticmethod def _convert_raw_shap_to_df( - raw_shap_tensors: List[np.ndarray], + raw_shap_tensors: List[npt.NDArray[np.float_]], observations: pd.Index, features_in_split: pd.Index, ) -> List[pd.DataFrame]: diff --git a/src/facet/inspection/_shap_global_explanation.py b/src/facet/inspection/_shap_global_explanation.py index ac94098cf..fd14ad209 100644 --- a/src/facet/inspection/_shap_global_explanation.py +++ b/src/facet/inspection/_shap_global_explanation.py @@ -3,11 +3,14 @@ pairings of features onto the SHAP importance vector in partitions of for synergy, redundancy, and independence. """ +from __future__ import annotations + import logging from abc import ABCMeta, abstractmethod -from typing import Any, List, Optional, TypeVar, Union +from typing import Any, List, Optional, TypeVar, Union, cast import numpy as np +import numpy.typing as npt import pandas as pd from pytools.api import AllTracker, inheritdoc @@ -45,7 +48,13 @@ # T_ShapGlobalExplainer = TypeVar("T_ShapGlobalExplainer", bound="ShapGlobalExplainer") -T_ShapCalculator = TypeVar("T_ShapCalculator", bound=ShapCalculator) +T_ShapCalculator = TypeVar("T_ShapCalculator", bound=ShapCalculator[Any]) + + +# +# Constants +# +ASSERTION__CALCULATOR_IS_FITTED = "calculator is fitted" # @@ -66,9 +75,9 @@ class AffinityMatrix: """ # shape: (2, 2, n_outputs, n_features, n_features) - _matrices: np.ndarray + _matrices: npt.NDArray[np.float_] - def __init__(self, matrices: np.ndarray) -> None: + def __init__(self, matrices: npt.NDArray[np.float_]) -> None: shape = matrices.shape assert len(shape) == 5 assert shape[:2] == (2, 2) @@ -78,8 +87,8 @@ def __init__(self, matrices: np.ndarray) -> None: @staticmethod def from_relative_affinity( - affinity_rel_ij: np.ndarray, std_p_i: np.ndarray - ) -> "AffinityMatrix": + affinity_rel_ij: npt.NDArray[np.float_], std_p_i: npt.NDArray[np.float_] + ) -> AffinityMatrix: """ :param affinity_rel_ij: the affinity matrix from which to create all variations, shaped `(n_outputs, n_features, n_features)` @@ -127,18 +136,20 @@ def from_relative_affinity( ).reshape((2, 2, *affinity_rel_ij.shape)) ) - def get_values(self, symmetrical: bool, absolute: bool) -> np.ndarray: + def get_values(self, symmetrical: bool, absolute: bool) -> npt.NDArray[np.float_]: """ Get the matrix matching the given criteria. :param symmetrical: if ``True``, get the symmetrical version of the matrix :param absolute: if ``True``, get the absolute version of the matrix :return: the affinity matrix """ - return self._matrices[int(symmetrical), int(absolute)] + return cast( + npt.NDArray[np.float_], self._matrices[int(symmetrical), int(absolute)] + ) @inheritdoc(match="""[see superclass]""") -class ShapGlobalExplainer(FittableMixin[ShapCalculator], metaclass=ABCMeta): +class ShapGlobalExplainer(FittableMixin[ShapCalculator[Any]], metaclass=ABCMeta): """ Derives feature association as a global metric of SHAP values for multiple observations. @@ -154,9 +165,8 @@ def is_fitted(self) -> bool: return self.feature_index_ is not None def fit( # type: ignore[override] - # todo: remove 'type: ignore' once mypy correctly infers return type self: T_ShapGlobalExplainer, - shap_calculator: ShapCalculator, + shap_calculator: ShapCalculator[Any], **fit_params: Any, ) -> T_ShapGlobalExplainer: """ @@ -184,7 +194,7 @@ def fit( # type: ignore[override] return self @abstractmethod - def association(self, absolute: bool, symmetrical: bool) -> np.ndarray: + def association(self, absolute: bool, symmetrical: bool) -> npt.NDArray[np.float_]: """ The association matrix for all feature pairs. @@ -200,7 +210,7 @@ def association(self, absolute: bool, symmetrical: bool) -> np.ndarray: :returns: the matrix as an array of shape (n_outputs, n_features, n_features) """ - def to_frames(self, matrix: np.ndarray) -> List[pd.DataFrame]: + def to_frames(self, matrix: npt.NDArray[np.float_]) -> List[pd.DataFrame]: """ Transforms one or more affinity matrices into a list of data frames. @@ -225,7 +235,7 @@ def to_frames(self, matrix: np.ndarray) -> List[pd.DataFrame]: ] @abstractmethod - def _fit(self, shap_calculator: ShapCalculator) -> None: + def _fit(self, shap_calculator: ShapCalculator[Any]) -> None: pass def _reset_fit(self) -> None: @@ -239,7 +249,7 @@ class ShapInteractionGlobalExplainer(ShapGlobalExplainer, metaclass=ABCMeta): """ @abstractmethod - def synergy(self, symmetrical: bool, absolute: bool) -> np.ndarray: + def synergy(self, symmetrical: bool, absolute: bool) -> npt.NDArray[np.float_]: """ The synergy matrix for all feature pairs. @@ -256,7 +266,7 @@ def synergy(self, symmetrical: bool, absolute: bool) -> np.ndarray: """ @abstractmethod - def redundancy(self, symmetrical: bool, absolute: bool) -> np.ndarray: + def redundancy(self, symmetrical: bool, absolute: bool) -> npt.NDArray[np.float_]: """ The redundancy matrix for all feature pairs. @@ -278,7 +288,7 @@ def redundancy(self, symmetrical: bool, absolute: bool) -> np.ndarray: # -def ensure_last_axis_is_fast(array: np.ndarray) -> np.ndarray: +def ensure_last_axis_is_fast(array: npt.NDArray[np.float_]) -> npt.NDArray[np.float_]: """ For future implementations, ensure that the last axis of the given array is `fast` to allow for `partial summation`. @@ -295,7 +305,7 @@ def ensure_last_axis_is_fast(array: np.ndarray) -> np.ndarray: return array -def sqrt(array: np.ndarray) -> np.ndarray: +def sqrt(array: npt.NDArray[np.float_]) -> npt.NDArray[np.float_]: """ Get the square root of each element in the given array. @@ -310,7 +320,7 @@ def sqrt(array: np.ndarray) -> np.ndarray: return np.sqrt(np.clip(array, 0, None)) -def make_symmetric(m: np.ndarray) -> np.ndarray: +def make_symmetric(m: npt.NDArray[np.float_]) -> npt.NDArray[np.float_]: """ Enforce matrix symmetry by transposing the `feature x feature` matrix for each output and averaging it with the original matrix. @@ -322,7 +332,7 @@ def make_symmetric(m: np.ndarray) -> np.ndarray: return (m + transpose(m)) / 2 -def transpose(m: np.ndarray, ndim: int = 3) -> np.ndarray: +def transpose(m: npt.NDArray[np.float_], ndim: int = 3) -> npt.NDArray[np.float_]: """ Transpose the `feature x feature` matrix for each output. @@ -342,7 +352,7 @@ def transpose(m: np.ndarray, ndim: int = 3) -> np.ndarray: return m.swapaxes(1, 2) -def diagonal(m: np.ndarray) -> np.ndarray: +def diagonal(m: npt.NDArray[np.float_]) -> npt.NDArray[np.float_]: """ Get the diagonal of the `feature x feature` matrix for each output. @@ -354,9 +364,11 @@ def diagonal(m: np.ndarray) -> np.ndarray: return m.diagonal(axis1=1, axis2=2) -def fill_diagonal(m: np.ndarray, value: Union[float, np.ndarray]) -> None: +def fill_diagonal( + m: npt.NDArray[np.float_], value: Union[float, npt.NDArray[np.float_]] +) -> None: """ - Fill the diagonal of the `feature x feature` matrix for each output with the given + In each `feature x feature` matrix for each output, fill the diagonal with the given value. :param m: array of shape `(n_outputs, n_features, n_features)` @@ -373,7 +385,9 @@ def fill_diagonal(m: np.ndarray, value: Union[float, np.ndarray]) -> None: np.fill_diagonal(m_i, value, wrap=True) -def cov(vectors: np.ndarray, weight: Optional[np.ndarray]) -> np.ndarray: +def cov( + vectors: npt.NDArray[np.float_], weight: Optional[npt.NDArray[np.float_]] +) -> npt.NDArray[np.float_]: """ Calculate the covariance matrix of pairs of vectors along the observations axis and for each output, assuming all vectors are centered (µ=0). @@ -398,12 +412,17 @@ def cov(vectors: np.ndarray, weight: Optional[np.ndarray]) -> np.ndarray: vectors_weighted = vectors * weight.reshape((1, 1, -1)) weight_total = weight.sum() - return np.matmul(vectors_weighted, vectors.swapaxes(1, 2)) / weight_total + return cast( + npt.NDArray[np.float_], + np.matmul(vectors_weighted, vectors.swapaxes(1, 2)) / weight_total, + ) def cov_broadcast( - vector_sequence: np.ndarray, vector_grid: np.ndarray, weight: Optional[np.ndarray] -) -> np.ndarray: + vector_sequence: npt.NDArray[np.float_], + vector_grid: npt.NDArray[np.float_], + weight: Optional[npt.NDArray[np.float_]], +) -> npt.NDArray[np.float_]: """ Calculate the covariance matrix between a sequence of vectors and a grid of vectors along the observations axis and for each output, assuming all vectors are centered @@ -437,8 +456,9 @@ def cov_broadcast( vectors_weighted = vector_sequence * weight.reshape((1, 1, -1)) weight_total = weight.sum() - return ( - np.einsum("...io,...ijo->...ij", vectors_weighted, vector_grid) / weight_total + return cast( + npt.NDArray[np.float_], + np.einsum("...io,...ijo->...ij", vectors_weighted, vector_grid) / weight_total, ) @@ -447,31 +467,31 @@ class ShapContext(metaclass=ABCMeta): Contextual data for global SHAP calculations. """ - #: SHAP vectors, + #: SHAP vectors #: with shape `(n_outputs, n_features, n_observations)` - p_i: np.ndarray + p_i: npt.NDArray[np.float_] #: observation weights (optional), #: with shape `(n_observations)` - weight: Optional[np.ndarray] + weight: Optional[npt.NDArray[np.float_]] #: Covariance matrix for p[i], #: with shape `(n_outputs, n_features, n_features)` - cov_p_i_p_j: np.ndarray + cov_p_i_p_j: npt.NDArray[np.float_] #: Variances for p[i], #: with shape `(n_outputs, n_features, 1)` - var_p_i: np.ndarray + var_p_i: npt.NDArray[np.float_] - #: SHAP interaction vectors, + #: SHAP interaction vectors #: with shape `(n_outputs, n_features, n_features, n_observations)` - p_ij: Optional[np.ndarray] + p_ij: Optional[npt.NDArray[np.float_]] def __init__( self, - p_i: np.ndarray, - p_ij: Optional[np.ndarray], - weight: Optional[np.ndarray], + p_i: npt.NDArray[np.float_], + p_ij: Optional[npt.NDArray[np.float_]], + weight: Optional[npt.NDArray[np.float_]], ) -> None: assert p_i.ndim == 3 if weight is not None: @@ -498,19 +518,19 @@ class ShapValueContext(ShapContext): Contextual data for global SHAP calculations based on SHAP values. """ - def __init__(self, shap_calculator: ShapCalculator) -> None: + def __init__(self, shap_calculator: ShapCalculator[Any]) -> None: shap_values: pd.DataFrame = shap_calculator.get_shap_values() - def _p_i() -> np.ndarray: + def _p_i() -> npt.NDArray[np.float_]: assert ( shap_calculator.output_names_ is not None and shap_calculator.feature_index_ is not None - ), "calculator is fitted" + ), ASSERTION__CALCULATOR_IS_FITTED n_outputs: int = len(shap_calculator.output_names_) n_features: int = len(shap_calculator.feature_index_) n_observations: int = len(shap_values) - # p[i] = p_i + # p[i] # shape: (n_outputs, n_features, n_observations) # the vector of shap values for every output and feature return ensure_last_axis_is_fast( @@ -520,15 +540,20 @@ def _p_i() -> np.ndarray: ) ) - def _weight() -> Optional[np.ndarray]: + def _weight() -> Optional[npt.NDArray[np.float_]]: # weights # shape: (n_observations) # return a 1d array of weights that aligns with the observations axis of the # SHAP values tensor (axis 1) - assert shap_calculator.sample_ is not None and "calculator is fitted" + assert ( + shap_calculator.sample_ is not None and ASSERTION__CALCULATOR_IS_FITTED + ) _weight_sr = shap_calculator.sample_.weight if _weight_sr is not None: - return _weight_sr.loc[shap_values.index.get_level_values(-1)].values + return cast( + npt.NDArray[np.float_], + _weight_sr.loc[shap_values.index.get_level_values(-1)].values, + ) else: return None @@ -540,13 +565,13 @@ class ShapInteractionValueContext(ShapContext): Contextual data for global SHAP calculations based on SHAP interaction values. """ - def __init__(self, shap_calculator: ShapCalculator) -> None: + def __init__(self, shap_calculator: ShapCalculator[Any]) -> None: shap_values: pd.DataFrame = shap_calculator.get_shap_interaction_values() assert ( shap_calculator.output_names_ is not None and shap_calculator.feature_index_ is not None - ), "calculator is fitted" + ), ASSERTION__CALCULATOR_IS_FITTED n_features: int = len(shap_calculator.feature_index_) n_outputs: int = len(shap_calculator.output_names_) n_observations: int = len(shap_values) // n_features @@ -562,8 +587,8 @@ def __init__(self, shap_calculator: ShapCalculator) -> None: # shape: (n_observations) # return a 1d array of weights that aligns with the observations axis of the # SHAP values tensor (axis 1) - weight: Optional[np.ndarray] - assert shap_calculator.sample_ is not None and "calculator is fitted" + weight: Optional[npt.NDArray[np.float_]] + assert shap_calculator.sample_ is not None and ASSERTION__CALCULATOR_IS_FITTED _weight_sr = shap_calculator.sample_.weight if _weight_sr is not None: _observation_indices = shap_values.index.get_level_values( @@ -578,8 +603,8 @@ def __init__(self, shap_calculator: ShapCalculator) -> None: # p[i, j] # shape: (n_outputs, n_features, n_features, n_observations) # the vector of interaction values for every output and feature pairing - # for improved numerical precision, we ensure the last axis is the fast axis - # i.e. stride size equals item size (see documentation for numpy.sum) + # for improved numerical precision, we ensure the last axis is the fast axis, + # i.e., stride size equals item size (see documentation for numpy.sum) p_ij = ensure_last_axis_is_fast( np.transpose( shap_values.values.reshape( @@ -591,6 +616,7 @@ def __init__(self, shap_calculator: ShapCalculator) -> None: # p[i] # shape: (n_outputs, n_features, n_observations) + # the vector of shap values for every output and feature super().__init__( p_i=ensure_last_axis_is_fast(p_ij.sum(axis=2)), p_ij=ensure_last_axis_is_fast( @@ -601,8 +627,8 @@ def __init__(self, shap_calculator: ShapCalculator) -> None: @staticmethod def __get_orthogonalized_interaction_vectors( - p_ij: np.ndarray, weight: Optional[np.ndarray] - ) -> np.ndarray: + p_ij: npt.NDArray[np.float_], weight: Optional[npt.NDArray[np.float_]] + ) -> npt.NDArray[np.float_]: # p_ij: shape: (n_outputs, n_features, n_features, n_observations) assert p_ij.ndim == 4 @@ -647,9 +673,9 @@ def __get_orthogonalized_interaction_vectors( _denominator = cov_p_ii_p_jj**2 - var_p_ii * var_p_jj - # The denominator is <= 0 due to the Cauchy-Schwarz inequality. + # The denominator is ≤ 0 due to the Cauchy-Schwarz inequality. # It is 0 only if the variance of p_ii or p_jj are zero (i.e., no main effect). - # In that fringe case, the nominator will also be zero and we set the adjustment + # In that edge case, the nominator will also be zero, and we set the adjustment # factor to 0 (intuitively, there is nothing to adjust in a zero-length vector) adjustment_factors_ij = np.zeros(_nominator.shape) # todo: prevent catastrophic cancellation where nominator/denominator are ~0.0 diff --git a/src/facet/inspection/_shap_projection.py b/src/facet/inspection/_shap_projection.py index b29dd51c9..9a1a8941d 100644 --- a/src/facet/inspection/_shap_projection.py +++ b/src/facet/inspection/_shap_projection.py @@ -5,9 +5,10 @@ """ import logging from abc import ABCMeta, abstractmethod -from typing import Optional, Tuple, TypeVar +from typing import Any, Optional, Tuple, TypeVar import numpy as np +import numpy.typing as npt from pytools.api import AllTracker, inheritdoc @@ -65,14 +66,14 @@ def __init__(self) -> None: super().__init__() self.association_: Optional[AffinityMatrix] = None - def association(self, absolute: bool, symmetrical: bool) -> np.ndarray: + def association(self, absolute: bool, symmetrical: bool) -> npt.NDArray[np.float_]: """[see superclass]""" self.ensure_fitted() assert self.association_ is not None return self.association_.get_values(symmetrical=symmetrical, absolute=absolute) - def _fit(self, shap_calculator: ShapCalculator) -> None: + def _fit(self, shap_calculator: ShapCalculator[Any]) -> None: self._reset_fit() self._calculate(self._get_context(shap_calculator=shap_calculator)) @@ -82,7 +83,7 @@ def _reset_fit(self) -> None: self.association_ = None @abstractmethod - def _get_context(self, shap_calculator: ShapCalculator) -> ShapContext: + def _get_context(self, shap_calculator: ShapCalculator[Any]) -> ShapContext: pass @abstractmethod @@ -127,7 +128,7 @@ class ShapVectorProjector(ShapProjector): onto a feature's main SHAP vector. """ - def _get_context(self, shap_calculator: ShapCalculator) -> ShapContext: + def _get_context(self, shap_calculator: ShapCalculator[Any]) -> ShapContext: return ShapValueContext(shap_calculator=shap_calculator) def _calculate(self, context: ShapContext) -> None: @@ -154,21 +155,21 @@ def __init__(self) -> None: self.synergy_: Optional[AffinityMatrix] = None self.redundancy_: Optional[AffinityMatrix] = None - def synergy(self, symmetrical: bool, absolute: bool) -> np.ndarray: + def synergy(self, symmetrical: bool, absolute: bool) -> npt.NDArray[np.float_]: """[see superclass]""" self.ensure_fitted() assert self.synergy_ is not None, "Projector is fitted" return self.synergy_.get_values(symmetrical=symmetrical, absolute=absolute) - def redundancy(self, symmetrical: bool, absolute: bool) -> np.ndarray: + def redundancy(self, symmetrical: bool, absolute: bool) -> npt.NDArray[np.float_]: """[see superclass]""" self.ensure_fitted() assert self.redundancy_ is not None, "Projector is fitted" return self.redundancy_.get_values(symmetrical=symmetrical, absolute=absolute) - def _get_context(self, shap_calculator: ShapCalculator) -> ShapContext: + def _get_context(self, shap_calculator: ShapCalculator[Any]) -> ShapContext: return ShapInteractionValueContext(shap_calculator=shap_calculator) def _calculate(self, context: ShapContext) -> None: diff --git a/src/facet/selection/_parameters.py b/src/facet/selection/_parameters.py index f86fa6382..98c3daea7 100644 --- a/src/facet/selection/_parameters.py +++ b/src/facet/selection/_parameters.py @@ -1,6 +1,7 @@ """ Core implementation of :mod:`facet.selection` """ +from __future__ import annotations import logging import warnings @@ -46,15 +47,19 @@ ParameterSet = Union[List[Any], stats.rv_continuous, stats.rv_discrete] ParameterDict = Dict[str, ParameterSet] -rv_frozen = type(stats.uniform()) -assert rv_frozen.__name__ == "rv_frozen", "type of stats.uniform() is rv_frozen" +try: + rv_frozen = next( + t for t in type(stats.uniform()).mro() if t.__name__ == "rv_frozen" + ) +except StopIteration: + raise AssertionError("stats.uniform() is based on class rv_frozen") # # Type variables # -T_Candidate_co = TypeVar("T_Candidate_co", covariant=True, bound=EstimatorDF) +T_Estimator_co = TypeVar("T_Estimator_co", covariant=True, bound=EstimatorDF) # # Ensure all symbols introduced below are included in __all__ @@ -69,13 +74,13 @@ @inheritdoc(match="""[see superclass]""") -class ParameterSpace(BaseParameterSpace[T_Candidate_co], Generic[T_Candidate_co]): +class ParameterSpace(BaseParameterSpace[T_Estimator_co], Generic[T_Estimator_co]): """ A set of parameter choices or distributions spanning a parameter space for - optimizing the hyper-parameters of a single estimator. + optimizing the hyperparameters of a single estimator. Parameter spaces provide an easy approach to define and validate search spaces - for hyper-parameter tuning of ML pipelines using `scikit-learn`'s + for hyperparameter tuning of ML pipelines using `scikit-learn`'s :class:`~sklearn.model_selection.GridSearchCV` and :class:`~sklearn.model_selection.RandomizedSearchCV`. @@ -111,12 +116,12 @@ class ParameterSpace(BaseParameterSpace[T_Candidate_co], Generic[T_Candidate_co] """ - def __init__(self, estimator: T_Candidate_co, name: Optional[str] = None) -> None: + def __init__(self, estimator: T_Estimator_co, name: Optional[str] = None) -> None: """ - :param estimator: the estimator candidate to which to apply the parameters to - :param name: a name for the estimator candidate to be used in summary reports; - defaults to the type of the estimator, or the type of the final estimator - if arg estimator is a pipeline + :param estimator: the estimator to which to apply the parameters + :param name: a name for the estimator to be used in summary reports; + defaults to the name of the estimator's class, or the name of the final + estimator's class if arg ``estimator`` is a pipeline """ super().__init__(estimator=estimator) @@ -127,7 +132,7 @@ def __init__(self, estimator: T_Candidate_co, name: Optional[str] = None) -> Non if "__" not in name } - self._children: Dict[str, ParameterSpace] = { + self._children: Dict[str, ParameterSpace[BaseEstimator]] = { name: ParameterSpace(estimator=value) for name, value in params.items() if isinstance(value, BaseEstimator) @@ -139,16 +144,16 @@ def __init__(self, estimator: T_Candidate_co, name: Optional[str] = None) -> Non def get_name(self) -> str: """ - Get the name for this parameter space. + Get the name for this parameter space's estimator. - If no name was passed to the constructor, determine the `default name` + If no name was passed to the constructor, determine the default name recursively as follows: - - for meta-estimators, this is the `default name` of the delegate estimator - - for pipelines, this is the `default name` of the final estimator - - for all other estimators, this is the name of the estimator's type + - for meta-estimators, this is the default name of the delegate estimator + - for pipelines, this is the default name of the final estimator + - for all other estimators, this is the name of the estimator's class - :return: the name for this parameter space + :return: the name for this parameter space's estimator """ if self._name is None: @@ -159,6 +164,10 @@ def get_name(self) -> str: @subsdoc( pattern="or a list of such dictionaries, ", replacement="", + ) + @subsdoc( + pattern="one or more dictionaries, each mapping", + replacement="a dictionary mapping", using=BaseParameterSpace.get_parameters, ) def get_parameters(self, prefix: Optional[str] = None) -> ParameterDict: @@ -210,6 +219,8 @@ def __dir__(self) -> Iterable[str]: def __getattr__(self, key: str) -> Any: if not key.startswith("_"): + result: Union[ParameterSpace[Any], ParameterSet, None] + result = self._children.get(key, None) if result is not None: return result @@ -243,7 +254,9 @@ def _to_expression(self, path_prefix: Union[str, List[str]]) -> Expression: def _values_to_expression(values: ParameterSet) -> Expression: if isinstance(values, rv_frozen): - return Id(values.dist.name)(*values.args, **values.kwds) + # disabling type-checks: mypy cannot access the class signature + # of private class rv_frozen, which is obtained dynamically at runtime + return Id(values.dist.name)(*values.args, **values.kwds) # type: ignore elif isinstance(values, (stats.rv_continuous, stats.rv_discrete)): try: return Id(values.name)(values.a, values.b) @@ -271,23 +284,23 @@ def _values_to_expression(values: ParameterSet) -> Expression: @inheritdoc(match="""[see superclass]""") class MultiEstimatorParameterSpace( - BaseParameterSpace[T_Candidate_co], Generic[T_Candidate_co] + BaseParameterSpace[T_Estimator_co], Generic[T_Estimator_co] ): """ A collection of parameter spaces, each representing a competing estimator from which - to select the best-performing candidate with optimal hyper-parameters. + to select the best-performing candidate with optimal hyperparameters. See :class:`.ParameterSpace` for details on setting up and using parameter spaces. """ #: The parameter spaces constituting this multi-estimator parameter space. - spaces: Tuple[ParameterSpace[T_Candidate_co], ...] + spaces: Tuple[ParameterSpace[T_Estimator_co], ...] - def __init__(self, *spaces: ParameterSpace[T_Candidate_co]) -> None: + def __init__(self, *spaces: ParameterSpace[T_Estimator_co]) -> None: """ :param spaces: the parameter spaces from which to select the best estimator """ - validate_element_types(spaces, expected_type=ParameterSpace) + validate_element_types(spaces, expected_type=ParameterSpace, name="arg spaces") validate_spaces(spaces) if len(spaces) == 0: @@ -303,6 +316,10 @@ def __init__(self, *spaces: ParameterSpace[T_Candidate_co]) -> None: r"or a list of such dictionaries" ), replacement="a list of dictionaries of parameter distributions", + ) + @subsdoc( + pattern="one or more dictionaries,", + replacement="a list of dictionaries,", using=BaseParameterSpace.get_parameters, ) def get_parameters(self, prefix: Optional[str] = None) -> List[ParameterDict]: @@ -323,7 +340,7 @@ def get_parameters(self, prefix: Optional[str] = None) -> List[ParameterDict]: for space in self.spaces ] - def to_expression(self) -> "Expression": + def to_expression(self) -> Expression: """[see superclass]""" # noinspection PyProtectedMember return Id(type(self))(*self.spaces) @@ -338,7 +355,7 @@ def to_expression(self) -> "Expression": def ensure_subclass( - estimator_type: Type[T_Candidate_co], expected_type: Type[T_Candidate_co] + estimator_type: Type[T_Estimator_co], expected_type: Type[T_Estimator_co] ) -> None: """ Ensure that the given estimator type is a subclass of the expected estimator type. @@ -353,12 +370,12 @@ def ensure_subclass( ) -def validate_spaces(spaces: Collection[ParameterSpace[T_Candidate_co]]) -> None: +def validate_spaces(spaces: Collection[ParameterSpace[T_Estimator_co]]) -> None: """ - Ensure that all candidates implement the same estimator type (typically regressors + Ensure that all parameter spaces use the same estimator type (typically regressors or classifiers) - :param spaces: the candidates to check + :param spaces: the parameter spaces to check """ estimator_types: Set[str] = { @@ -367,7 +384,7 @@ def validate_spaces(spaces: Collection[ParameterSpace[T_Candidate_co]]) -> None: if len(estimator_types) > 1: raise TypeError( - "all candidate estimators must have the same estimator type, " + "all parameter spaces must use the same estimator type, " "but got multiple types: " + ", ".join(sorted(estimator_types)) ) @@ -376,11 +393,10 @@ def get_default_estimator_name(estimator: EstimatorDF) -> str: """ Get a default name of the estimator. - For meta-estimators, this is the default name of the delegate estimator. - - For pipelines, this is the default name of the final estimator. + - for meta-estimators, this is the default name of the delegate estimator + - for pipelines, this is the default name of the final estimator + - for all other estimators, this is the name of the estimator's class - For all other estimators, this is the name of the estimator's type. :param estimator: the estimator to get the default name for :return: the default name diff --git a/src/facet/selection/_selection.py b/src/facet/selection/_selection.py index 71eb6ba07..b20aa5e49 100644 --- a/src/facet/selection/_selection.py +++ b/src/facet/selection/_selection.py @@ -5,14 +5,15 @@ import itertools import logging import re -from re import Pattern from typing import ( Any, Callable, Dict, Generic, + Iterable, List, Optional, + Pattern, Sequence, Tuple, TypeVar, @@ -21,22 +22,25 @@ ) import numpy as np +import numpy.typing as npt import pandas as pd +from sklearn.base import BaseEstimator from sklearn.metrics import get_scorer from sklearn.model_selection import BaseCrossValidator, GridSearchCV -from pytools.api import AllTracker, inheritdoc +from pytools.api import AllTracker, inheritdoc, to_list from pytools.fit import FittableMixin from pytools.parallelization import ParallelizableMixin from sklearndf import EstimatorDF from sklearndf.pipeline import LearnerPipelineDF from facet.data import Sample +from facet.selection import MultiEstimatorParameterSpace, ParameterSpace from facet.selection.base import BaseParameterSpace, CandidateEstimatorDF log = logging.getLogger(__name__) -__all__ = ["ModelSelector"] +__all__ = ["LearnerSelector"] # # Type constants @@ -52,7 +56,7 @@ # Type variables # -T_ModelSelector = TypeVar("T_ModelSelector", bound="ModelSelector") +T_LearnerSelector = TypeVar("T_LearnerSelector", bound="LearnerSelector[Any, Any]") T_EstimatorDF = TypeVar("T_EstimatorDF", bound=EstimatorDF) # mypy - disabling due to lack of support for dynamic types T_SearchCV = TypeVar("T_SearchCV", bound=BaseSearchCV) # type: ignore @@ -76,23 +80,33 @@ @inheritdoc(match="[see superclass]") -class ModelSelector( +class LearnerSelector( FittableMixin[Sample], ParallelizableMixin, Generic[T_EstimatorDF, T_SearchCV] ): """ Select the best model obtained by fitting an estimator using different - choices of hyper-parameters from a :class:`.ParameterSpace`, or even - simultaneously evaluating multiple competing estimators from a - :class:`.MultiEstimatorParameterSpace`. + choices of hyperparameters from one or more :class:`.ParameterSpace` objects. """ + # defined in superclass, repeated here for Sphinx + n_jobs: Optional[int] + + # defined in superclass, repeated here for Sphinx + shared_memory: Optional[bool] + + # defined in superclass, repeated here for Sphinx + pre_dispatch: Optional[Union[str, int]] + + # defined in superclass, repeated here for Sphinx + verbose: Optional[int] + #: A cross-validation searcher class, or any other callable #: that instantiates a cross-validation searcher, wrapped in #: a tuple to avoid confusion with methods searcher_type: Tuple[Callable[..., T_SearchCV]] #: The parameter space to search. - parameter_space: BaseParameterSpace + parameter_space: BaseParameterSpace[T_EstimatorDF] #: The cross-validator to be used by the searcher. cv: Optional[BaseCrossValidator] @@ -101,17 +115,14 @@ class ModelSelector( #: (optional; use learner's default scorer if not specified here) scoring: Union[ str, - Callable[ - [EstimatorDF, pd.Series, pd.Series], - float, - ], + Callable[[EstimatorDF, pd.Series, pd.Series], float], None, ] #: Additional parameters to be passed on to the searcher. searcher_params: Dict[str, Any] - #: The searcher used to fit this ModelSelector; ``None`` if not fitted. + #: The searcher used to fit this LearnerSelector; ``None`` if not fitted. searcher_: Optional[T_SearchCV] # regular expressions and replacement patterns for selecting and renaming @@ -119,17 +130,26 @@ class ModelSelector( _CV_RESULT_COLUMNS = [ (r"rank_test_(\w+)", r"\1__test__rank"), (r"(mean|std)_test_(\w+)", r"\2__test__\1"), + (r"candidate_name", r"candidate"), (r"param_(\w+)", r"param__\1"), (r"(rank|mean|std)_(\w+)_time", r"time__\2__\1"), (r"(rank|mean|std)_(\w+)_(\w+)", r"\3__\2__\1"), ] - # noinspection PyTypeChecker - _CV_RESULT_PATTERNS: List[Tuple[Pattern, str]] = [ + + _CV_RESULT_PATTERNS: List[Tuple[Pattern[str], str]] = [ (re.compile(pattern), repl) for pattern, repl in _CV_RESULT_COLUMNS ] _CV_RESULT_CANDIDATE_PATTERN, _CV_RESULT_CANDIDATE_REPL = ( - re.compile(r"^(?:(param__)candidate__|param__(candidate(?:_name)?)$)"), + re.compile( + r"^(?:" + # remove the candidate prefix from proper params + r"(param_)candidate__" + r"|" + # remove the param prefix from candidate properties + r"param_(candidate(?:_name)?)$" + r")" + ), r"\1\2", ) @@ -140,8 +160,11 @@ class ModelSelector( def __init__( self, searcher_type: Callable[..., T_SearchCV], - parameter_space: BaseParameterSpace, - *, + parameter_space: Union[ + ParameterSpace[T_EstimatorDF], + MultiEstimatorParameterSpace[T_EstimatorDF], + Iterable[ParameterSpace[T_EstimatorDF]], + ], cv: Optional[BaseCrossValidator] = None, scoring: Union[ str, @@ -160,7 +183,9 @@ def __init__( """ :param searcher_type: a cross-validation searcher class, or any other callable that instantiates a cross-validation searcher - :param parameter_space: the parameter space to search + :param parameter_space: one or more parameter spaces to search; when passing + multiple parameter spaces as an iterable, they are combined into a + :class:`.MultiEstimatorParameterSpace` :param cv: the cross-validator to be used by the searcher (e.g., :class:`~sklearn.model_selection.RepeatedKFold`) :param scoring: a scoring function (by name, or as a callable) to be used by the @@ -181,6 +206,24 @@ def __init__( ) self.searcher_type = (searcher_type,) + if not isinstance(parameter_space, BaseParameterSpace): + parameter_spaces: List[ + Union[ + ParameterSpace[T_EstimatorDF], + MultiEstimatorParameterSpace[T_EstimatorDF], + ] + ] = to_list( + parameter_space, + element_type=(ParameterSpace, MultiEstimatorParameterSpace), + arg_name="parameter_space", + ) + if len(parameter_spaces) == 1: + parameter_space = parameter_spaces[0] + else: + parameter_space = MultiEstimatorParameterSpace( + *cast(List[ParameterSpace[T_EstimatorDF]], parameter_spaces) + ) + self.parameter_space = parameter_space self.cv = cv self.scoring = scoring @@ -258,14 +301,14 @@ def best_estimator_(self) -> T_EstimatorDF: def fit( # type: ignore[override] # todo: remove 'type: ignore' once mypy correctly infers return type - self: T_ModelSelector, + self: T_LearnerSelector, sample: Sample, - groups: Union[pd.Series, np.ndarray, Sequence, None] = None, + groups: Union[pd.Series, npt.NDArray[Any], Sequence[Any], None] = None, **fit_params: Any, - ) -> T_ModelSelector: + ) -> T_LearnerSelector: """ - Search this model selector's parameter space to identify the model with the - best-performing hyper-parameter combination, using the given sample to fit and + Search this learner selector's parameter space to identify the model with the + best-performing hyperparameter combination, using the given sample to fit and score the candidate estimators. :param sample: the sample used to fit and score the estimators @@ -279,7 +322,8 @@ def fit( # type: ignore[override] if ARG_SAMPLE_WEIGHT in fit_params: raise ValueError( - "arg sample_weight is not supported, use arg sample.weight instead" + "arg sample_weight is not supported, use 'weight' property of arg " + "sample instead" ) if isinstance(groups, pd.Series): @@ -287,11 +331,10 @@ def fit( # type: ignore[override] raise ValueError( "index of arg groups is not equal to index of arg sample" ) - elif groups is not None: - if len(groups) != len(sample): - raise ValueError( - "length of arg groups is not equal to length of arg sample" - ) + elif groups is not None and len(groups) != len(sample): + raise ValueError( + "length of arg groups is not equal to length of arg sample" + ) parameter_space = self.parameter_space (searcher_type,) = self.searcher_type @@ -324,15 +367,25 @@ def summary_report(self, *, sort_by: Optional[str] = None) -> pd.DataFrame: sort_by = self._DEFAULT_REPORT_SORT_COLUMN assert self.searcher_ is not None, "Ranker is fitted" + + # get the raw CV results cv_results: Dict[str, Any] = self.searcher_.cv_results_ + if isinstance(self.parameter_space.estimator, CandidateEstimatorDF): + # our estimator is a candidate estimator, so we need to unpack the + # candidate's parameter names + cv_results = { + self._CV_RESULT_CANDIDATE_PATTERN.sub( + self._CV_RESULT_CANDIDATE_REPL, name + ): values + for name, values in cv_results.items() + } + # we create a table using a subset of the cv results, to keep the report # relevant and readable - cv_results_processed: Dict[str, np.ndarray] = {} - unpack_candidate: bool = isinstance( - self.parameter_space.estimator, CandidateEstimatorDF - ) + pattern: Pattern[str] + repl: str def _process(name: str) -> Optional[str]: # process the name of the original cv_results_ record @@ -341,20 +394,15 @@ def _process(name: str) -> Optional[str]: match = pattern.fullmatch(name) if match is None: # we could not match the name: - # return None so we don't include it in the summary report + # return None, so we don't include it in the summary report return None - - name = match.expand(repl) - if unpack_candidate: - # remove the "candidate" layer in the parameter output if we're dealing - # with a multi parameter space - return ModelSelector._CV_RESULT_CANDIDATE_PATTERN.sub( - ModelSelector._CV_RESULT_CANDIDATE_REPL, name - ) else: - return name + return match.expand(repl) # add all columns that match any of the pre-defined patterns + + cv_results_processed: Dict[str, Tuple[str, npt.NDArray[np.float_]]] = {} + for pattern, repl in self._CV_RESULT_PATTERNS: cv_results_processed.update( { @@ -370,6 +418,7 @@ def _process(name: str) -> Optional[str]: ) # add the sorting column as the leftmost column of the report + sort_column_processed: Optional[str] sort_column_processed, _ = cv_results_processed.get(sort_by, None) @@ -382,6 +431,7 @@ def _process(name: str) -> Optional[str]: cv_results_processed[sort_by] = cv_results[sort_by] # convert the results into a data frame and sort + report = pd.DataFrame( { name_processed: values @@ -390,6 +440,7 @@ def _process(name: str) -> Optional[str]: ) # sort the report, if applicable + if sort_column_processed is not None: report = report.sort_values(by=sort_column_processed) @@ -433,7 +484,9 @@ def _get_scorer( return None elif isinstance(scoring, str): - scorer = get_scorer(scoring) + scorer: Callable[ + [BaseEstimator, pd.DataFrame, pd.Series], float + ] = get_scorer(scoring) # noinspection PyPep8Naming def _scorer_fn(estimator: EstimatorDF, X: pd.DataFrame, y: pd.Series) -> float: diff --git a/src/facet/selection/base/_parameters.py b/src/facet/selection/base/_parameters.py index 846e0408b..6bd0bdc5e 100644 --- a/src/facet/selection/base/_parameters.py +++ b/src/facet/selection/base/_parameters.py @@ -4,8 +4,9 @@ import logging from abc import ABCMeta, abstractmethod -from typing import Any, Dict, Generic, List, Optional, Sequence, TypeVar, Union +from typing import Any, Dict, Generic, List, Optional, TypeVar, Union +import numpy.typing as npt import pandas as pd from scipy import stats @@ -50,7 +51,7 @@ class BaseParameterSpace(HasExpressionRepr, Generic[T_Estimator], metaclass=ABCMeta): """ - A collection of parameters spanning a parameter space for hyper-parameter + A collection of parameters spanning a parameter space for hyperparameter optimization. """ @@ -93,7 +94,7 @@ def get_parameters( :param prefix: an optional prefix to prepend to all parameter names in the resulting dictionary, separated by two underscore characters (``__``) as per `scikit-learn`'s convention for hierarchical parameter names - :return: a dictionary mapping parameter names to parameter + :return: one or more dictionaries, each mapping parameter names to parameter choices (as lists) or distributions (from :mod:`scipy.stats`) """ pass @@ -150,7 +151,7 @@ def _get_candidate(self) -> Union[ClassifierDF, RegressorDF, TransformerDF]: return self.candidate @property - def classes_(self) -> Sequence[Any]: + def classes_(self) -> Union[npt.NDArray[Any], List[npt.NDArray[Any]]]: """[see superclass]""" return self._get_candidate().classes_ diff --git a/src/facet/simulation/__init__.py b/src/facet/simulation/__init__.py index bb5e2f902..d5cdd459a 100644 --- a/src/facet/simulation/__init__.py +++ b/src/facet/simulation/__init__.py @@ -1,9 +1,9 @@ """ Historical univariate simulation of changes to predicted outcome(s) based on selected fixed values of a selected input feature. - -For further details on the simulation approach see the -:ref:`Bootstrap simulation with FACET` tutorial. - """ +# For further details on the simulation approach see the +# :ref:`Model Simulation with FACET` tutorial. + +from ._result import * from ._simulation import * diff --git a/src/facet/simulation/_result.py b/src/facet/simulation/_result.py new file mode 100644 index 000000000..7c1b68f01 --- /dev/null +++ b/src/facet/simulation/_result.py @@ -0,0 +1,162 @@ +""" +Core implementation of :mod:`facet.simulation` +""" + + +import logging +from typing import Generic, Sequence, TypeVar + +import numpy as np +import pandas as pd +from scipy import stats + +from pytools.api import AllTracker + +from facet.data.partition import Partitioner + +log = logging.getLogger(__name__) + +__all__ = [ + "UnivariateSimulationResult", +] + + +# +# Type variables +# + +T_Values = TypeVar("T_Values", bound=np.generic) + + +# +# Ensure all symbols introduced below are included in __all__ +# + +__tracker = AllTracker(globals()) + + +class UnivariateSimulationResult(Generic[T_Values]): + """ + Summary result of a univariate simulation. + """ + + #: The simulation result as a data frame, indexed by the central values of the + #: partitions for which the simulation was run, with the following columns: + #: + #: - :attr:`.COL_MEAN`: the mean predictions for the simulated values + #: - :attr:`.COL_SEM`: the standard errors of the mean predictions + #: - :attr:`.COL_LOWER_BOUND`: the lower bounds of the confidence intervals for the + #: simulation outcomes, based on mean, standard error of the mean, and + #: :attr:`confidence_level` + #: - :attr:`.COL_UPPER_BOUND`: the upper bounds of the confidence intervals for the + #: simulation outcomes, based on mean, standard error of the mean, and + #: :attr:`confidence_level` + data: pd.DataFrame + + #: The partitioner used to generate feature values to be simulated. + partitioner: Partitioner[T_Values] + + #: Name of the simulated feature. + feature_name: str + + #: Name of the target for which outputs are simulated. + output_name: str + + #: The unit of the simulated outputs (e.g., uplift or class probability). + output_unit: str + + #: The average observed actual output, acting as the baseline of the simulation. + baseline: float + + #: The width :math:`\alpha` of the confidence interval + #: determined by bootstrapping, with :math:`0 < \alpha < 1`. + confidence_level: float + + #: The name of the column index of attribute :attr:`.output`, denoting partitions + #: represented by their central values or by a category. + IDX_PARTITION = "partition" + + #: The name of a series of mean simulated values per partition. + COL_MEAN = "mean" + + #: The name of a series of standard errors of mean simulated values per partition. + COL_SEM = "sem" + + #: The name of a series of lower CI bounds of simulated values per partition. + COL_LOWER_BOUND = "lower_bound" + + #: The name of a series of upper CI bounds of simulated values per partition. + COL_UPPER_BOUND = "upper_bound" + + def __init__( + self, + *, + partitioner: Partitioner[T_Values], + mean: Sequence[float], + sem: Sequence[float], + feature_name: str, + output_name: str, + output_unit: str, + baseline: float, + confidence_level: float, + ) -> None: + """ + :param partitioner: the partitioner used to generate feature values to be + simulated + :param mean: mean predictions for the values representing each partition + :param sem: standard errors of the mean predictions for the values representing + each partition + :param feature_name: name of the simulated feature + :param output_name: name of the target for which outputs are simulated + :param output_unit: the unit of the simulated outputs + (e.g., uplift or class probability) + :param baseline: the average observed actual output, acting as the baseline + of the simulation + :param confidence_level: the width of the confidence interval determined by + bootstrapping, ranging between 0.0 and 1.0 (exclusive) + """ + super().__init__() + + if not partitioner.is_fitted: + raise ValueError("arg partitioner must be fitted") + + n_partitions = len(partitioner.partitions_) + + for seq, seq_name in [(mean, "mean"), (sem, "sem")]: + if len(seq) != n_partitions: + raise ValueError( + f"length of arg {seq_name} must correspond to " + f"the number of partitions (n={n_partitions})" + ) + + if not (0.0 < confidence_level < 1.0): + raise ValueError( + f"arg confidence_level={confidence_level} is not " + "in the range between 0.0 and 1.0 (exclusive)" + ) + + self.partitioner = partitioner + self.feature_name = feature_name + self.output_name = output_name + self.output_unit = output_unit + self.baseline = baseline + self.confidence_level = confidence_level + + # convert mean and sem to numpy arrays + mean_arr = np.array(mean) + sem_arr = np.array(sem) + + # get the width of the confidence interval (this is a negative number) + ci_width = stats.norm.ppf((1.0 - self.confidence_level) / 2.0) * sem_arr + + self.data = pd.DataFrame( + data={ + UnivariateSimulationResult.COL_MEAN: mean_arr, + UnivariateSimulationResult.COL_SEM: sem_arr, + UnivariateSimulationResult.COL_LOWER_BOUND: mean_arr + ci_width, + UnivariateSimulationResult.COL_UPPER_BOUND: mean_arr - ci_width, + }, + index=pd.Index( + partitioner.partitions_, name=UnivariateSimulationResult.IDX_PARTITION + ), + ) diff --git a/src/facet/simulation/_simulation.py b/src/facet/simulation/_simulation.py index 5bb8a1eca..6c5a930dd 100644 --- a/src/facet/simulation/_simulation.py +++ b/src/facet/simulation/_simulation.py @@ -3,36 +3,22 @@ """ import logging -from abc import ABCMeta, abstractmethod -from typing import ( - Any, - Generic, - Iterable, - Optional, - Sequence, - Tuple, - Type, - TypeVar, - Union, - cast, -) +from typing import Any, Optional, Tuple, Type, TypeVar, Union, cast import numpy as np import pandas as pd -from scipy import stats from pytools.api import AllTracker, inheritdoc -from pytools.parallelization import Job, JobRunner, ParallelizableMixin -from sklearndf import ClassifierDF, LearnerDF, RegressorDF +from sklearndf import ClassifierDF, RegressorDF from ..data import Sample from ..data.partition import Partitioner +from ._result import UnivariateSimulationResult +from .base import BaseUnivariateSimulator, UnivariateRegressionSimulator log = logging.getLogger(__name__) __all__ = [ - "UnivariateSimulationResult", - "BaseUnivariateSimulator", "UnivariateProbabilitySimulator", "UnivariateTargetSimulator", "UnivariateUpliftSimulator", @@ -43,8 +29,7 @@ # Type variables # -T_LearnerDF = TypeVar("T_LearnerDF", bound=LearnerDF) -T_Partition = TypeVar("T_Partition") +T_Values = TypeVar("T_Values", bound=np.generic) # @@ -59,328 +44,6 @@ # -class UnivariateSimulationResult(Generic[T_Partition]): - """ - Summary result of a univariate simulation. - """ - - #: The simulation result as a data frame, indexed by the central values of the - #: partitions for which the simulation was run, with the following columns: - #: - #: - :attr:`.COL_MEAN`: the mean predictions for the simulated values - #: - :attr:`.COL_SEM`: the standard errors of the mean predictions - #: - :attr:`.COL_LOWER_BOUND`: the lower bounds of the confidence intervals for the - #: simulation outcomes, based on mean, standard error of the mean, and - #: :attr:`confidence_level` - #: - :attr:`.COL_UPPER_BOUND`: the upper bounds of the confidence intervals for the - #: simulation outcomes, based on mean, standard error of the mean, and - #: :attr:`confidence_level` - data: pd.DataFrame - - #: The partitioner used to generate feature values to be simulated. - partitioner: Partitioner - - #: Name of the simulated feature. - feature_name: str - - #: Name of the target for which outputs are simulated. - output_name: str - - #: The unit of the simulated outputs (e.g., uplift or class probability). - output_unit: str - - #: The average observed actual output, acting as the baseline of the simulation. - baseline: float - - #: The width :math:`\alpha` of the confidence interval - #: determined by bootstrapping, with :math:`0 < \alpha < 1`. - confidence_level: float - - #: The name of the column index of attribute :attr:`.output`, denoting partitions - #: represented by their central values or by a category. - IDX_PARTITION = "partition" - - #: The name of a series of mean simulated values per partition. - COL_MEAN = "mean" - - #: The name of a series of standard errors of mean simulated values per partition. - COL_SEM = "sem" - - #: The name of a series of lower CI bounds of simulated values per partition. - COL_LOWER_BOUND = "lower_bound" - - #: The name of a series of upper CI bounds of simulated values per partition. - COL_UPPER_BOUND = "upper_bound" - - def __init__( - self, - *, - partitioner: Partitioner, - mean: Sequence[float], - sem: Sequence[float], - feature_name: str, - output_name: str, - output_unit: str, - baseline: float, - confidence_level: float, - ) -> None: - """ - :param partitioner: the partitioner used to generate feature values to be - simulated - :param mean: mean predictions for the values representing each partition - :param sem: standard errors of the mean predictions for the values representing - each partition - :param feature_name: name of the simulated feature - :param output_name: name of the target for which outputs are simulated - :param output_unit: the unit of the simulated outputs - (e.g., uplift or class probability) - :param baseline: the average observed actual output, acting as the baseline - of the simulation - :param confidence_level: the width of the confidence interval determined by - bootstrapping, ranging between 0.0 and 1.0 (exclusive) - """ - super().__init__() - - if not partitioner.is_fitted: - raise ValueError("arg partitioner must be fitted") - - n_partitions = len(partitioner.partitions_) - - for seq, seq_name in [(mean, "mean"), (sem, "sem")]: - if len(seq) != n_partitions: - raise ValueError( - f"length of arg {seq_name} must correspond to " - f"the number of partitions (n={n_partitions})" - ) - - if not (0.0 < confidence_level < 1.0): - raise ValueError( - f"arg confidence_level={confidence_level} is not " - "in the range between 0.0 and 1.0 (exclusive)" - ) - - self.partitioner = partitioner - self.feature_name = feature_name - self.output_name = output_name - self.output_unit = output_unit - self.baseline = baseline - self.confidence_level = confidence_level - - # convert mean and sem to numpy arrays - mean_arr = np.array(mean) - sem_arr = np.array(sem) - - # get the width of the confidence interval (this is a negative number) - ci_width = stats.norm.ppf((1.0 - self.confidence_level) / 2.0) * sem_arr - - self.data = pd.DataFrame( - data={ - UnivariateSimulationResult.COL_MEAN: mean_arr, - UnivariateSimulationResult.COL_SEM: sem_arr, - UnivariateSimulationResult.COL_LOWER_BOUND: mean_arr + ci_width, - UnivariateSimulationResult.COL_UPPER_BOUND: mean_arr - ci_width, - }, - index=pd.Index( - partitioner.partitions_, name=UnivariateSimulationResult.IDX_PARTITION - ), - ) - - -class BaseUnivariateSimulator( - ParallelizableMixin, Generic[T_LearnerDF], metaclass=ABCMeta -): - """ - Base class for univariate simulations. - """ - - #: The learner pipeline used to conduct simulations - model: T_LearnerDF - - #: The sample to be used in baseline calculations and simulations - sample: Sample - - #: The width of the confidence interval used to calculate the lower/upper bound - #: of the simulation - confidence_level: float - - def __init__( - self, - model: T_LearnerDF, - sample: Sample, - *, - confidence_level: float = 0.95, - n_jobs: Optional[int] = None, - shared_memory: Optional[bool] = None, - pre_dispatch: Optional[Union[str, int]] = None, - verbose: Optional[int] = None, - ) -> None: - """ - :param model: a fitted learner to use for calculating simulated outputs - :param sample: the sample to be used for baseline calculations and simulations - :param confidence_level: the width :math:`\\alpha` of the confidence interval - to be estimated for simulation results - """ - super().__init__( - n_jobs=n_jobs, - shared_memory=shared_memory, - pre_dispatch=pre_dispatch, - verbose=verbose, - ) - - if not isinstance(model, self._expected_learner_type()): - raise TypeError( - "arg model must be a learner of type " - f"{self._expected_learner_type().__name__}." - ) - - if not model.is_fitted: - raise ValueError("arg model must be fitted") - - if isinstance(sample.target_name, list): - raise NotImplementedError("multi-output simulations are not supported") - - if not 0.0 < confidence_level < 1.0: - raise ValueError( - f"arg confidence_level={confidence_level} " - "must range between 0.0 and 1.0 (exclusive)" - ) - - self.model = model - self.sample = sample - self.output_name = cast(str, sample.target_name) - self.confidence_level = confidence_level - - # add parallelization parameters to __init__ docstring - __init__.__doc__ = cast(str, __init__.__doc__) + cast( - str, ParallelizableMixin.__init__.__doc__ - ) - - def simulate_feature( - self, - feature_name: str, - *, - partitioner: Partitioner[T_Partition], - ) -> UnivariateSimulationResult: - """ - Simulate the average target uplift when fixing the value of the given feature - across all observations. - - :param feature_name: the feature to run the simulation for - :param partitioner: the partitioner of feature values to run simulations for - :return: a mapping of output names to simulation results - """ - - sample = self.sample - - mean, sem = self._simulate_feature_with_values( - feature_name=feature_name, - simulation_values=partitioner.fit( - sample.features.loc[:, feature_name] - ).partitions_, - ) - return UnivariateSimulationResult( - partitioner=partitioner, - mean=mean, - sem=sem, - feature_name=feature_name, - output_name=self.output_name, - output_unit=self.output_unit, - baseline=self.baseline(), - confidence_level=self.confidence_level, - ) - - @property - @abstractmethod - def output_unit(self) -> str: - """ - Unit of the output values calculated by the simulation. - """ - - def baseline(self) -> float: - """ - Calculate the expectation value of the simulation result, based on historically - observed actuals. - - :return: the expectation value of the simulation results - """ - return self.expected_output() - - @abstractmethod - def expected_output(self) -> float: - """ - Calculate the expectation value of the actual model output, based on - historically observed actuals. - - :return: the expectation value of the actual model output - """ - pass - - @staticmethod - @abstractmethod - def _expected_learner_type() -> Type[T_LearnerDF]: - pass - - @staticmethod - @abstractmethod - def _simulate( - model: T_LearnerDF, x: pd.DataFrame, name: str, value: Any - ) -> Tuple[float, float]: - pass - - @staticmethod - def _set_constant_feature_value( - x: pd.DataFrame, feature_name: str, value: Any - ) -> pd.DataFrame: - return x.assign( - **{ - feature_name: np.full( - shape=len(x), - fill_value=value, - dtype=x.loc[:, feature_name].dtype, - ) - } - ) - - @staticmethod - def _aggregate_simulation_results(predictions: pd.Series) -> Tuple[float, float]: - # generate summary stats for a series of predictions - return predictions.mean(), predictions.sem() - - def _simulate_feature_with_values( - self, - feature_name: str, - simulation_values: Sequence[T_Partition], - ) -> Tuple[Sequence[float], Sequence[float]]: - """ - Run a simulation on a feature. - - For each simulation value, compute the mean and sem of predictions when - substituting the value for the feature being simulated. - - :param feature_name: name of the feature to use in the simulation - :param simulation_values: values to use in the simulation - :return: a tuple with mean predictions and standard errors of mean predictions - for each partition - """ - - if feature_name not in self.sample.features.columns: - raise ValueError(f"feature not in sample: {feature_name}") - - # for a list of values to be simulated, calculate a sequence of mean predictions - # and a sequence of standard errors of those means - features = self.sample.features - - outputs_mean_sem: Iterable[Tuple[float, float]] = JobRunner.from_parallelizable( - self - ).run_jobs( - Job.delayed(self._simulate)(self.model, features, feature_name, value) - for value in simulation_values - ) - - outputs_mean, outputs_sem = zip(*outputs_mean_sem) - return outputs_mean, outputs_sem - - @inheritdoc(match="[see superclass]") class UnivariateProbabilitySimulator(BaseUnivariateSimulator[ClassifierDF]): """ @@ -408,6 +71,27 @@ class UnivariateProbabilitySimulator(BaseUnivariateSimulator[ClassifierDF]): as the weighted samples will impact predicted class probabilities. """ + # defined in superclass, repeated here for Sphinx + n_jobs: Optional[int] + + # defined in superclass, repeated here for Sphinx + shared_memory: Optional[bool] + + # defined in superclass, repeated here for Sphinx + pre_dispatch: Optional[Union[str, int]] + + # defined in superclass, repeated here for Sphinx + verbose: Optional[int] + + # defined in superclass, repeated here for Sphinx + model: ClassifierDF + + # defined in superclass, repeated here for Sphinx + sample: Sample + + # defined in superclass, repeated here for Sphinx + confidence_level: float + @property def output_unit(self) -> str: """[see superclass]""" @@ -420,9 +104,11 @@ def expected_output(self) -> float: :return: observed frequency of the positive class """ - actual_outputs = self.sample.target + actual_outputs: pd.Series = self.sample.target - return (actual_outputs == self._positive_class()).sum() / len(actual_outputs) + return cast(int, (actual_outputs == self._positive_class()).sum()) / len( + actual_outputs + ) def _positive_class(self) -> Any: """ @@ -457,34 +143,8 @@ def _simulate( ) -class _UnivariateRegressionSimulator( - BaseUnivariateSimulator[RegressorDF], metaclass=ABCMeta -): - def expected_output(self) -> float: - """ - Calculate the mean of actually observed values for the target. - - :return: mean observed value of the target - """ - return self.sample.target.mean() - - @staticmethod - def _expected_learner_type() -> Type[RegressorDF]: - return RegressorDF - - @staticmethod - def _simulate( - model: RegressorDF, x: pd.DataFrame, name: str, value: Any - ) -> Tuple[float, float]: - predictions = model.predict( - X=BaseUnivariateSimulator._set_constant_feature_value(x, name, value) - ) - assert predictions.ndim == 1, "single-target regressor required" - return BaseUnivariateSimulator._aggregate_simulation_results(predictions) - - @inheritdoc(match="[see superclass]") -class UnivariateTargetSimulator(_UnivariateRegressionSimulator): +class UnivariateTargetSimulator(UnivariateRegressionSimulator): """ Univariate simulation of the absolute output of a regression model. @@ -507,6 +167,27 @@ class UnivariateTargetSimulator(_UnivariateRegressionSimulator): have been specified for the sample. """ + # defined in superclass, repeated here for Sphinx + n_jobs: Optional[int] + + # defined in superclass, repeated here for Sphinx + shared_memory: Optional[bool] + + # defined in superclass, repeated here for Sphinx + pre_dispatch: Optional[Union[str, int]] + + # defined in superclass, repeated here for Sphinx + verbose: Optional[int] + + # defined in superclass, repeated here for Sphinx + model: RegressorDF + + # defined in superclass, repeated here for Sphinx + sample: Sample + + # defined in superclass, repeated here for Sphinx + confidence_level: float + @property def output_unit(self) -> str: """[see superclass]""" @@ -514,7 +195,7 @@ def output_unit(self) -> str: @inheritdoc(match="[see superclass]") -class UnivariateUpliftSimulator(_UnivariateRegressionSimulator): +class UnivariateUpliftSimulator(UnivariateRegressionSimulator): """ Univariate simulation of the relative uplift of the output of a regression model. @@ -540,6 +221,27 @@ class UnivariateUpliftSimulator(_UnivariateRegressionSimulator): have been specified for the sample. """ + # defined in superclass, repeated here for Sphinx + n_jobs: Optional[int] + + # defined in superclass, repeated here for Sphinx + shared_memory: Optional[bool] + + # defined in superclass, repeated here for Sphinx + pre_dispatch: Optional[Union[str, int]] + + # defined in superclass, repeated here for Sphinx + verbose: Optional[int] + + # defined in superclass, repeated here for Sphinx + model: RegressorDF + + # defined in superclass, repeated here for Sphinx + sample: Sample + + # defined in superclass, repeated here for Sphinx + confidence_level: float + @property def output_unit(self) -> str: """[see superclass]""" @@ -554,8 +256,8 @@ def baseline(self) -> float: return 0.0 def simulate_feature( - self, feature_name: str, *, partitioner: Partitioner[T_Partition] - ) -> UnivariateSimulationResult: + self, feature_name: str, *, partitioner: Partitioner[T_Values] + ) -> UnivariateSimulationResult[T_Values]: """[see superclass]""" result = super().simulate_feature( diff --git a/src/facet/simulation/base/__init__.py b/src/facet/simulation/base/__init__.py new file mode 100644 index 000000000..d0e6278c1 --- /dev/null +++ b/src/facet/simulation/base/__init__.py @@ -0,0 +1,4 @@ +""" +Base classes for simulation. +""" +from ._base import * diff --git a/src/facet/simulation/base/_base.py b/src/facet/simulation/base/_base.py new file mode 100644 index 000000000..001a515b6 --- /dev/null +++ b/src/facet/simulation/base/_base.py @@ -0,0 +1,306 @@ +""" +Core implementation of :mod:`facet.simulation.base` +""" + +import logging +from abc import ABCMeta, abstractmethod +from typing import ( + Any, + Generic, + Iterable, + Optional, + Sequence, + Tuple, + Type, + TypeVar, + Union, + cast, +) + +import numpy as np +import pandas as pd + +from pytools.api import AllTracker +from pytools.parallelization import Job, JobRunner, ParallelizableMixin +from sklearndf import LearnerDF, RegressorDF + +from facet.data import Sample +from facet.data.partition import Partitioner +from facet.simulation._result import UnivariateSimulationResult + +log = logging.getLogger(__name__) + +__all__ = [ + "BaseUnivariateSimulator", + "UnivariateRegressionSimulator", +] + + +# +# Type variables +# + +T_Value = TypeVar("T_Value", bound=np.generic) +T_LearnerDF = TypeVar("T_LearnerDF", bound=LearnerDF) + + +# +# Ensure all symbols introduced below are included in __all__ +# + +__tracker = AllTracker(globals()) + + +class BaseUnivariateSimulator( + ParallelizableMixin, Generic[T_LearnerDF], metaclass=ABCMeta +): + """ + Base class for univariate simulations. + """ + + # defined in superclass, repeated here for Sphinx + n_jobs: Optional[int] + + # defined in superclass, repeated here for Sphinx + shared_memory: Optional[bool] + + # defined in superclass, repeated here for Sphinx + pre_dispatch: Optional[Union[str, int]] + + # defined in superclass, repeated here for Sphinx + verbose: Optional[int] + + #: The learner pipeline used to conduct simulations + model: T_LearnerDF + + #: The sample to be used in baseline calculations and simulations + sample: Sample + + #: The width of the confidence interval used to calculate the lower/upper bound + #: of the simulation + confidence_level: float + + def __init__( + self, + model: T_LearnerDF, + sample: Sample, + *, + confidence_level: float = 0.95, + n_jobs: Optional[int] = None, + shared_memory: Optional[bool] = None, + pre_dispatch: Optional[Union[str, int]] = None, + verbose: Optional[int] = None, + ) -> None: + """ + :param model: a fitted learner to use for calculating simulated outputs + :param sample: the sample to be used for baseline calculations and simulations + :param confidence_level: the width :math:`\\alpha` of the confidence interval + to be estimated for simulation results + """ + super().__init__( + n_jobs=n_jobs, + shared_memory=shared_memory, + pre_dispatch=pre_dispatch, + verbose=verbose, + ) + + if not isinstance(model, self._expected_learner_type()): + raise TypeError( + "arg model must be a learner of type " + f"{self._expected_learner_type().__name__}." + ) + + if not model.is_fitted: + raise ValueError("arg model must be fitted") + + if isinstance(sample.target_name, list): + raise NotImplementedError("multi-output simulations are not supported") + + if not 0.0 < confidence_level < 1.0: + raise ValueError( + f"arg confidence_level={confidence_level} " + "must range between 0.0 and 1.0 (exclusive)" + ) + + self.model = model + self.sample = sample + self.output_name = sample.target_name + self.confidence_level = confidence_level + + # add parallelization parameters to __init__ docstring + __init__.__doc__ = cast(str, __init__.__doc__) + cast( + str, ParallelizableMixin.__init__.__doc__ + ) + + def simulate_feature( + self, feature_name: str, *, partitioner: Partitioner[T_Value] + ) -> UnivariateSimulationResult[T_Value]: + """ + Simulate the average target uplift when fixing the value of the given feature + across all observations. + + :param feature_name: the feature to run the simulation for + :param partitioner: the partitioner of feature values to run simulations for + :return: a mapping of output names to simulation results + """ + + sample = self.sample + + mean, sem = self._simulate_feature_with_values( + feature_name=feature_name, + simulation_values=partitioner.fit( + sample.features.loc[:, feature_name] + ).partitions_, + ) + return UnivariateSimulationResult( + partitioner=partitioner, + mean=mean, + sem=sem, + feature_name=feature_name, + output_name=self.output_name, + output_unit=self.output_unit, + baseline=self.baseline(), + confidence_level=self.confidence_level, + ) + + @property + @abstractmethod + def output_unit(self) -> str: + """ + Unit of the output values calculated by the simulation. + """ + + def baseline(self) -> float: + """ + Calculate the expectation value of the simulation result, based on historically + observed actuals. + + :return: the expectation value of the simulation results + """ + return self.expected_output() + + @abstractmethod + def expected_output(self) -> float: + """ + Calculate the expectation value of the actual model output, based on + historically observed actuals. + + :return: the expectation value of the actual model output + """ + pass + + @staticmethod + @abstractmethod + def _expected_learner_type() -> Type[T_LearnerDF]: + pass + + @staticmethod + @abstractmethod + def _simulate( + model: T_LearnerDF, x: pd.DataFrame, name: str, value: Any + ) -> Tuple[float, float]: + pass + + @staticmethod + def _set_constant_feature_value( + x: pd.DataFrame, feature_name: str, value: Any + ) -> pd.DataFrame: + return x.assign( + **{ + feature_name: np.full( + shape=len(x), + fill_value=value, + dtype=x.loc[:, feature_name].dtype, + ) + } + ) + + @staticmethod + def _aggregate_simulation_results(predictions: pd.Series) -> Tuple[float, float]: + # generate summary stats for a series of predictions + return predictions.mean(), predictions.sem() + + def _simulate_feature_with_values( + self, + feature_name: str, + simulation_values: Sequence[T_Value], + ) -> Tuple[Sequence[float], Sequence[float]]: + """ + Run a simulation on a feature. + + For each simulation value, compute the mean and sem of predictions when + substituting the value for the feature being simulated. + + :param feature_name: name of the feature to use in the simulation + :param simulation_values: values to use in the simulation + :return: a tuple with mean predictions and standard errors of mean predictions + for each partition + """ + + if feature_name not in self.sample.features.columns: + raise ValueError(f"feature not in sample: {feature_name}") + + # for a list of values to be simulated, calculate a sequence of mean predictions + # and a sequence of standard errors of those means + features = self.sample.features + + outputs_mean_sem: Iterable[Tuple[float, float]] = JobRunner.from_parallelizable( + self + ).run_jobs( + Job.delayed(self._simulate)(self.model, features, feature_name, value) + for value in simulation_values + ) + + outputs_mean, outputs_sem = zip(*outputs_mean_sem) + return outputs_mean, outputs_sem + + +class UnivariateRegressionSimulator( + BaseUnivariateSimulator[RegressorDF], metaclass=ABCMeta +): + """ + Base class for univariate simulations using regression models. + """ + + # defined in superclass, repeated here for Sphinx + n_jobs: Optional[int] + + # defined in superclass, repeated here for Sphinx + shared_memory: Optional[bool] + + # defined in superclass, repeated here for Sphinx + pre_dispatch: Optional[Union[str, int]] + + # defined in superclass, repeated here for Sphinx + verbose: Optional[int] + + # defined in superclass, repeated here for Sphinx + model: RegressorDF + + # defined in superclass, repeated here for Sphinx + sample: Sample + + # defined in superclass, repeated here for Sphinx + confidence_level: float + + def expected_output(self) -> float: + """ + Calculate the mean of actually observed values for the target. + + :return: mean observed value of the target + """ + return cast(float, self.sample.target.mean()) + + @staticmethod + def _expected_learner_type() -> Type[RegressorDF]: + return RegressorDF + + @staticmethod + def _simulate( + model: RegressorDF, x: pd.DataFrame, name: str, value: Any + ) -> Tuple[float, float]: + predictions = model.predict( + X=BaseUnivariateSimulator._set_constant_feature_value(x, name, value) + ) + assert predictions.ndim == 1, "single-target regressor required" + return BaseUnivariateSimulator._aggregate_simulation_results(predictions) diff --git a/src/facet/simulation/viz/__init__.py b/src/facet/simulation/viz/__init__.py index 3413bd981..b48b70558 100644 --- a/src/facet/simulation/viz/__init__.py +++ b/src/facet/simulation/viz/__init__.py @@ -2,5 +2,5 @@ Drawers and styles for simulation results. """ -from ._draw import SimulationDrawer -from ._style import SimulationMatplotStyle, SimulationReportStyle, SimulationStyle +from ._draw import * +from ._style import * diff --git a/src/facet/simulation/viz/_draw.py b/src/facet/simulation/viz/_draw.py index 41681cabb..0b9858ed3 100644 --- a/src/facet/simulation/viz/_draw.py +++ b/src/facet/simulation/viz/_draw.py @@ -2,7 +2,7 @@ Visualizations of simulation results. """ -from typing import Iterable, Optional, Type, TypeVar, Union, cast +from typing import Any, Iterable, Optional, Type, TypeVar, Union, cast import pandas as pd @@ -34,12 +34,15 @@ @inheritdoc(match="[see superclass]") -class SimulationDrawer(Drawer[UnivariateSimulationResult, SimulationStyle]): +class SimulationDrawer(Drawer[UnivariateSimulationResult[Any], SimulationStyle]): """ Draws the result of a univariate simulation, represented by a :class:`.UnivariateSimulationResult` object. """ + # defined in superclass, repeated here for Sphinx + style: SimulationStyle + #: if ``True``, plot the histogram of observed values for the feature being #: simulated; if ``False``, do not plot the histogram histogram: bool @@ -60,7 +63,7 @@ def __init__( __init__.__doc__ = cast(str, Drawer.__init__.__doc__) + cast(str, __init__.__doc__) def draw( - self, data: UnivariateSimulationResult, *, title: Optional[str] = None + self, data: UnivariateSimulationResult[Any], *, title: Optional[str] = None ) -> None: """ Draw the simulation chart. @@ -82,7 +85,7 @@ def get_style_classes(cls) -> Iterable[Type[SimulationStyle]]: SimulationReportStyle, ] - def _draw(self, result: UnivariateSimulationResult) -> None: + def _draw(self, result: UnivariateSimulationResult[Any]) -> None: # If the partitioning of the simulation is categorical, sort partitions in # ascending order of the mean output simulation_result: pd.DataFrame = result.data.assign( diff --git a/src/facet/simulation/viz/_style.py b/src/facet/simulation/viz/_style.py index f31d17b99..92c10feb0 100644 --- a/src/facet/simulation/viz/_style.py +++ b/src/facet/simulation/viz/_style.py @@ -4,7 +4,7 @@ import logging from abc import ABCMeta, abstractmethod -from typing import Any, Sequence, Tuple, TypeVar, Union +from typing import Any, Sequence, TextIO, Tuple, TypeVar, Union from matplotlib.axes import Axes from mpl_toolkits.axes_grid1 import make_axes_locatable @@ -174,9 +174,8 @@ def draw_uplift( bottom=True, labelrotation=45 if is_categorical_feature else 0, ) - if is_categorical_feature or True: - ax.set_xticks(x) - ax.set_xticklabels(labels=partitions) + ax.set_xticks(x) + ax.set_xticklabels(labels=partitions) # remove the top and right spines for pos in ["top", "right"]: @@ -205,11 +204,15 @@ def _make_sub_axes() -> Axes: uplift_height = abs(y_max - y_min) def _x_axis_height() -> float: + axis_below_size_pixels: float _, axis_below_size_pixels = main_ax.get_xaxis().get_text_heights( self.get_renderer() ) + + y0: float + y1: float ((_, y0), (_, y1)) = main_ax.transData.inverted().transform( - ((0, 0), (0, axis_below_size_pixels)) + ((0.0, 0.0), (0.0, axis_below_size_pixels)) ) return abs(y1 - y0) @@ -293,6 +296,12 @@ class SimulationReportStyle(SimulationStyle, TextStyle): Renders simulation results as a text report. """ + # defined in superclass, repeated here for Sphinx + out: TextIO + + # defined in superclass, repeated here for Sphinx + width: int + # general format wih sufficient space for potential sign and "e" notation __NUM_PRECISION = 3 __NUM_WIDTH = __NUM_PRECISION + 6 @@ -311,7 +320,7 @@ class SimulationReportStyle(SimulationStyle, TextStyle): __FREQUENCY_FORMAT = f"{__FREQUENCY_WIDTH}g" @staticmethod - def _num_format(heading: str): + def _num_format(heading: str) -> str: return f"> {len(heading)}.{SimulationReportStyle.__NUM_PRECISION}g" def draw_uplift( diff --git a/src/facet/validation/__init__.py b/src/facet/validation/__init__.py index 50a58443c..9c01d8796 100644 --- a/src/facet/validation/__init__.py +++ b/src/facet/validation/__init__.py @@ -1,5 +1,5 @@ """ -Bootstrap cross-validation including a stationary version for use with time series -data; used as the basis to generate confidence intervals for simulations. +Bootstrap cross-validation, including a stationary version for use with time series +data. """ from ._validation import * diff --git a/src/facet/validation/_validation.py b/src/facet/validation/_validation.py index 641d81585..001bfc5a8 100644 --- a/src/facet/validation/_validation.py +++ b/src/facet/validation/_validation.py @@ -3,9 +3,10 @@ """ import warnings from abc import ABCMeta, abstractmethod -from typing import Generator, Iterator, Optional, Sequence, Tuple, Union +from typing import Any, Generator, Iterator, Optional, Tuple, Union, cast import numpy as np +import numpy.typing as npt import pandas as pd from sklearn.model_selection import BaseCrossValidator from sklearn.utils import check_random_state @@ -19,6 +20,7 @@ "StationaryBootstrapCV", ] + # # Ensure all symbols introduced below are included in __all__ # @@ -31,7 +33,10 @@ # -class BaseBootstrapCV(BaseCrossValidator, metaclass=ABCMeta): +class BaseBootstrapCV( + BaseCrossValidator, # type: ignore + metaclass=ABCMeta, +): """ Base class for bootstrap cross-validators. """ @@ -54,9 +59,9 @@ def __init__( # noinspection PyPep8Naming def get_n_splits( self, - X: Optional[Union[np.ndarray, pd.DataFrame]] = None, - y: Optional[Union[np.ndarray, pd.Series, pd.DataFrame]] = None, - groups: Sequence = None, + X: Optional[Union[npt.NDArray[Any], pd.DataFrame]] = None, + y: Optional[Union[npt.NDArray[Any], pd.Series, pd.DataFrame]] = None, + groups: Optional[npt.ArrayLike] = None, ) -> int: """ Return the number of splits generated by this cross-validator. @@ -67,22 +72,15 @@ def get_n_splits( :return: the number of splits """ - for arg_name, arg in ("X", X), ("y", y), ("groups", groups): - if arg is not None: - warnings.warn( - f"arg {arg_name} is not used but got {arg_name}={arg!r}", - stacklevel=2, - ) - return self.n_splits # noinspection PyPep8Naming def split( self, - X: Union[np.ndarray, pd.DataFrame], - y: Union[np.ndarray, pd.Series, pd.DataFrame, None] = None, - groups: Union[np.ndarray, pd.Series, pd.DataFrame, None] = None, - ) -> Generator[Tuple[np.ndarray, np.ndarray], None, None]: + X: Union[npt.NDArray[Any], pd.DataFrame], + y: Union[npt.NDArray[Any], pd.Series, pd.DataFrame, None] = None, + groups: Union[npt.NDArray[Any], pd.Series, pd.DataFrame, None] = None, + ) -> Generator[Tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]], None, None,]: """ Generate indices to split data into training and test set. @@ -94,7 +92,7 @@ def split( train and test are numpy arrays with train and test indices, respectively """ - n = len(X) + n: int = len(X) if n < 2: raise ValueError("arg X must have at least 2 rows") @@ -106,15 +104,18 @@ def split( warnings.warn(f"ignoring arg groups={groups!r}", stacklevel=2) rs = check_random_state(self.random_state) - indices = np.arange(n) - for i in range(self.n_splits): + indices: npt.NDArray[np.int_] = np.arange(n) + for _ in range(self.n_splits): while True: - train = self._select_train_indices(n_samples=n, random_state=rs, y=y) - test_mask = np.ones(n, dtype=bool) + train: npt.NDArray[np.int_] = self._select_train_indices( + n_samples=n, random_state=rs, y=y + ) + test_mask: npt.NDArray[np.bool_] = np.ones(n, dtype=bool) test_mask[train] = False - test = indices[test_mask] + test: npt.NDArray[np.int_] = indices[test_mask] # make sure test is not empty, else sample another train set if len(test) > 0: + yield train, test break @@ -123,8 +124,8 @@ def _select_train_indices( self, n_samples: int, random_state: np.random.RandomState, - y: Union[np.ndarray, pd.Series, pd.DataFrame, None], - ) -> np.ndarray: + y: Union[npt.NDArray[Any], pd.Series, pd.DataFrame, None], + ) -> npt.NDArray[np.int_]: """ :param n_samples: number of indices to sample :param random_state: random state object to be used for random sampling @@ -134,7 +135,9 @@ def _select_train_indices( pass # noinspection PyPep8Naming - def _iter_test_indices(self, X=None, y=None, groups=None) -> Iterator: + def _iter_test_indices( + self, X: Any = None, y: Any = None, groups: Any = None + ) -> Iterator[Any]: # adding this stub just so all abstract methods are implemented pass @@ -155,8 +158,8 @@ def _select_train_indices( self, n_samples: int, random_state: np.random.RandomState, - y: Union[np.ndarray, pd.Series, pd.DataFrame, None], - ) -> np.ndarray: + y: Union[npt.NDArray[Any], pd.Series, pd.DataFrame, None], + ) -> npt.NDArray[np.int_]: return random_state.randint(n_samples, size=n_samples) @@ -177,8 +180,8 @@ def _select_train_indices( self, n_samples: int, random_state: np.random.RandomState, - y: Union[np.ndarray, pd.Series, pd.DataFrame, None], - ) -> np.ndarray: + y: Union[npt.NDArray[Any], pd.Series, pd.DataFrame, None], + ) -> npt.NDArray[np.int_]: if y is None: raise ValueError( "no target variable specified in arg y as labels for stratification" @@ -190,7 +193,8 @@ def _select_train_indices( "target labels must be provided as a Series or a 1d numpy array" ) - return ( + return cast( + npt.NDArray[np.int_], pd.Series(np.arange(len(y))) .groupby(by=y) .apply( @@ -198,7 +202,7 @@ def _select_train_indices( n=len(group), replace=True, random_state=random_state ) ) - .values + .values, ) @@ -248,8 +252,8 @@ def _select_train_indices( self, n_samples: int, random_state: np.random.RandomState, - y: Union[np.ndarray, pd.Series, pd.DataFrame, None], - ) -> np.ndarray: + y: Union[npt.NDArray[Any], pd.Series, pd.DataFrame, None], + ) -> npt.NDArray[np.int_]: mean_block_size = self.mean_block_size if mean_block_size < 1: diff --git a/test/test/conftest.py b/test/test/conftest.py index a811663c7..fbdb22574 100644 --- a/test/test/conftest.py +++ b/test/test/conftest.py @@ -1,7 +1,8 @@ import logging -from typing import Any, Dict, List, Mapping, Optional, Sequence, Set, Tuple +from typing import Any, Dict, List, Mapping, Optional, Sequence, Set, Tuple, cast import numpy as np +import numpy.typing as npt import pandas as pd import pytest from numpy.testing import assert_allclose, assert_array_equal @@ -30,7 +31,7 @@ import facet from facet.data import Sample from facet.inspection import LearnerInspector, TreeExplainerFactory -from facet.selection import ModelSelector, MultiEstimatorParameterSpace, ParameterSpace +from facet.selection import LearnerSelector, ParameterSpace from facet.validation import BootstrapCV, StratifiedBootstrapCV logging.basicConfig(level=logging.DEBUG) @@ -43,8 +44,11 @@ logging.getLogger("shap").setLevel(logging.WARNING) # configure pandas text output -pd.set_option("display.width", None) # get display width from terminal -pd.set_option("display.precision", 3) # 3 digits precision for easier readability + +# get display width from terminal +pd.set_option("display.width", None) +# 3 digits precision for easier readability +pd.set_option("display.precision", 3) K_FOLDS = 5 N_BOOTSTRAPS = 30 @@ -53,43 +57,43 @@ STEP_ONE_HOT_ENCODE = "one-hot-encode" -@pytest.fixture +@pytest.fixture # type: ignore def boston_target() -> str: return "price" -@pytest.fixture +@pytest.fixture # type: ignore def iris_target_name() -> str: return "species" -@pytest.fixture +@pytest.fixture # type: ignore def n_jobs() -> int: return -1 -@pytest.fixture +@pytest.fixture # type: ignore def cv_kfold() -> KFold: # define a CV return KFold(n_splits=K_FOLDS, shuffle=True, random_state=42) -@pytest.fixture +@pytest.fixture # type: ignore def cv_bootstrap() -> BaseCrossValidator: # define a CV return BootstrapCV(n_splits=N_BOOTSTRAPS, random_state=42) -@pytest.fixture +@pytest.fixture # type: ignore def cv_stratified_bootstrap() -> BaseCrossValidator: # define a CV return StratifiedBootstrapCV(n_splits=N_BOOTSTRAPS, random_state=42) -@pytest.fixture +@pytest.fixture # type: ignore def regressor_parameters( simple_preprocessor: TransformerDF, -) -> MultiEstimatorParameterSpace[RegressorPipelineDF]: +) -> List[ParameterSpace[RegressorPipelineDF[LGBMRegressorDF]]]: random_state = {"random_state": 42} space_1 = ParameterSpace( @@ -123,7 +127,7 @@ def regressor_parameters( regressor=DecisionTreeRegressorDF(**random_state), ) ) - space_4.regressor.max_depth = [0.5, 1.0] + space_4.regressor.max_depth = [3, 5] space_4.regressor.max_features = [0.5, 1.0] space_5 = ParameterSpace( @@ -145,27 +149,19 @@ def regressor_parameters( preprocessing=simple_preprocessor, regressor=LinearRegressionDF() ) ) - space_7.regressor.normalize = [False, True] - - return MultiEstimatorParameterSpace( - space_1, - space_2, - space_3, - space_4, - space_5, - space_6, - space_7, - ) + space_7.regressor.fit_intercept = [False, True] + + return [space_1, space_2, space_3, space_4, space_5, space_6, space_7] -@pytest.fixture +@pytest.fixture # type: ignore def regressor_selector( cv_kfold: KFold, - regressor_parameters: MultiEstimatorParameterSpace[RegressorPipelineDF], + regressor_parameters: List[ParameterSpace[RegressorPipelineDF[LGBMRegressorDF]]], sample: Sample, n_jobs: int, -) -> ModelSelector[RegressorPipelineDF, GridSearchCV]: - return ModelSelector( +) -> LearnerSelector[RegressorPipelineDF[LGBMRegressorDF], GridSearchCV]: + return LearnerSelector( searcher_type=GridSearchCV, parameter_space=regressor_parameters, cv=cv_kfold, @@ -177,14 +173,16 @@ def regressor_selector( PARAM_CANDIDATE__ = "param_candidate__" -@pytest.fixture +@pytest.fixture # type: ignore def best_lgbm_model( - regressor_selector, + regressor_selector: LearnerSelector[ + RegressorPipelineDF[LGBMRegressorDF], GridSearchCV + ], sample: Sample, -) -> RegressorPipelineDF: +) -> RegressorPipelineDF[LGBMRegressorDF]: # we get the best model_evaluation which is a LGBM - for the sake of test # performance - # noinspection PyTypeChecker + assert regressor_selector.searcher_ is not None best_lgbm_params: Dict[str, Any] = ( pd.DataFrame(regressor_selector.searcher_.cv_results_) .pipe( @@ -195,7 +193,7 @@ def best_lgbm_model( len_param_candidate = len(PARAM_CANDIDATE__) return ( - best_lgbm_params["candidate"] + cast(RegressorPipelineDF[LGBMRegressorDF], best_lgbm_params["candidate"]) .clone() .set_params( **{ @@ -208,18 +206,20 @@ def best_lgbm_model( ) -@pytest.fixture -def preprocessed_feature_names(best_lgbm_model: RegressorPipelineDF) -> Set[str]: +@pytest.fixture # type: ignore +def preprocessed_feature_names( + best_lgbm_model: RegressorPipelineDF[LGBMRegressorDF], +) -> Set[str]: """ Names of all features after preprocessing """ return set(best_lgbm_model.feature_names_out_) -@pytest.fixture +@pytest.fixture # type: ignore def regressor_inspector( - best_lgbm_model: RegressorPipelineDF, sample: Sample, n_jobs: int -) -> LearnerInspector: + best_lgbm_model: RegressorPipelineDF[LGBMRegressorDF], sample: Sample, n_jobs: int +) -> LearnerInspector[RegressorPipelineDF[LGBMRegressorDF]]: inspector = LearnerInspector( pipeline=best_lgbm_model, explainer_factory=TreeExplainerFactory( @@ -231,7 +231,7 @@ def regressor_inspector( return inspector -@pytest.fixture +@pytest.fixture # type: ignore def simple_preprocessor(sample: Sample) -> TransformerDF: features = sample.features @@ -260,7 +260,7 @@ def simple_preprocessor(sample: Sample) -> TransformerDF: return ColumnTransformerDF(transformers=column_transforms) -@pytest.fixture +@pytest.fixture # type: ignore def boston_df(boston_target: str) -> pd.DataFrame: # load sklearn test-data and convert to pd boston: Bunch = datasets.load_boston() @@ -271,12 +271,12 @@ def boston_df(boston_target: str) -> pd.DataFrame: ) -@pytest.fixture +@pytest.fixture # type: ignore def sample(boston_df: pd.DataFrame, boston_target: str) -> Sample: return Sample(observations=boston_df.iloc[:100, :], target_name=boston_target) -@pytest.fixture +@pytest.fixture # type: ignore def iris_df(iris_target_name: str) -> pd.DataFrame: # load sklearn test-data and convert to pd iris: Bunch = datasets.load_iris() @@ -296,7 +296,7 @@ def iris_df(iris_target_name: str) -> pd.DataFrame: return iris_df -@pytest.fixture +@pytest.fixture # type: ignore def iris_sample_multi_class(iris_df: pd.DataFrame, iris_target_name: str) -> Sample: # the iris dataset return Sample( @@ -306,25 +306,28 @@ def iris_sample_multi_class(iris_df: pd.DataFrame, iris_target_name: str) -> Sam ) -@pytest.fixture -def iris_sample_binary(iris_sample_multi_class) -> Sample: - # the iris dataset, retaining only two categories so we can do binary classification +@pytest.fixture # type: ignore +def iris_sample_binary(iris_sample_multi_class: Sample) -> Sample: + # the iris dataset, retaining only two categories, + # so we can do binary classification return iris_sample_multi_class.subsample( loc=iris_sample_multi_class.target.isin(["virginica", "versicolor"]) ) -@pytest.fixture +@pytest.fixture # type: ignore def iris_sample_binary_dual_target( - iris_sample_binary: Sample, iris_target_name + iris_sample_binary: Sample, iris_target_name: str ) -> Sample: - # the iris dataset, retaining only two categories so we can do binary classification + # the iris dataset, retaining only two categories, + # so we can do binary classification target = pd.Series( index=iris_sample_binary.index, data=pd.Categorical(iris_sample_binary.target).codes, name=iris_target_name, ) iris_target_2 = f"{iris_target_name}2" + assert isinstance(iris_sample_binary.target_name, str) return Sample( iris_sample_binary.features.join(target).join(target.rename(iris_target_2)), target_name=[iris_sample_binary.target_name, iris_target_2], @@ -333,7 +336,6 @@ def iris_sample_binary_dual_target( COL_PARAM = "param" COL_CANDIDATE = "candidate" -COL_CANDIDATE_NAME = "candidate_name" COL_CLASSIFIER = "classifier" COL_REGRESSOR = "regressor" COL_SCORE = ("score", "test", "mean") @@ -382,8 +384,8 @@ def check_ranking( ) if candidate_names_expected: - candidates_actual: np.ndarray = ranking.loc[ - :, (COL_CANDIDATE_NAME, "-", "-") + candidates_actual: npt.NDArray[np.object_] = ranking.loc[ + :, (COL_CANDIDATE, "-", "-") ].values[: len(candidate_names_expected)] assert_array_equal( candidates_actual, @@ -395,54 +397,56 @@ def check_ranking( ) -@pytest.fixture +@pytest.fixture # type: ignore def iris_classifier_selector_binary( iris_sample_binary: Sample, cv_stratified_bootstrap: StratifiedBootstrapCV, n_jobs: int, -) -> ModelSelector[ClassifierPipelineDF[RandomForestClassifierDF], GridSearchCV]: +) -> LearnerSelector[ClassifierPipelineDF[RandomForestClassifierDF], GridSearchCV]: return fit_classifier_selector( sample=iris_sample_binary, cv=cv_stratified_bootstrap, n_jobs=n_jobs ) -@pytest.fixture +@pytest.fixture # type: ignore def iris_classifier_selector_multi_class( iris_sample_multi_class: Sample, cv_stratified_bootstrap: StratifiedBootstrapCV, n_jobs: int, -) -> ModelSelector[ClassifierPipelineDF[RandomForestClassifierDF], GridSearchCV]: +) -> LearnerSelector[ClassifierPipelineDF[RandomForestClassifierDF], GridSearchCV]: return fit_classifier_selector( sample=iris_sample_multi_class, cv=cv_stratified_bootstrap, n_jobs=n_jobs ) -@pytest.fixture +@pytest.fixture # type: ignore def iris_classifier_selector_dual_target( iris_sample_binary_dual_target: Sample, cv_bootstrap: BootstrapCV, n_jobs: int -) -> ModelSelector[ClassifierPipelineDF[RandomForestClassifierDF], GridSearchCV]: +) -> LearnerSelector[ClassifierPipelineDF[RandomForestClassifierDF], GridSearchCV]: return fit_classifier_selector( sample=iris_sample_binary_dual_target, cv=cv_bootstrap, n_jobs=n_jobs ) -@pytest.fixture +@pytest.fixture # type: ignore def iris_classifier_binary( - iris_classifier_selector_binary: ModelSelector[ClassifierPipelineDF, GridSearchCV], + iris_classifier_selector_binary: LearnerSelector[ + ClassifierPipelineDF[RandomForestClassifierDF], GridSearchCV + ], ) -> ClassifierPipelineDF[RandomForestClassifierDF]: return iris_classifier_selector_binary.best_estimator_ -@pytest.fixture +@pytest.fixture # type: ignore def iris_classifier_multi_class( - iris_classifier_selector_multi_class: ModelSelector[ - ClassifierPipelineDF, GridSearchCV + iris_classifier_selector_multi_class: LearnerSelector[ + ClassifierPipelineDF[RandomForestClassifierDF], GridSearchCV ], ) -> ClassifierPipelineDF[RandomForestClassifierDF]: return iris_classifier_selector_multi_class.best_estimator_ -@pytest.fixture +@pytest.fixture # type: ignore def iris_inspector_multi_class( iris_classifier_multi_class: ClassifierPipelineDF[RandomForestClassifierDF], iris_sample_multi_class: Sample, @@ -460,7 +464,7 @@ def iris_inspector_multi_class( def fit_classifier_selector( sample: Sample, cv: BaseCrossValidator, n_jobs: int -) -> ModelSelector[ClassifierPipelineDF[RandomForestClassifierDF], GridSearchCV]: +) -> LearnerSelector[ClassifierPipelineDF[RandomForestClassifierDF], GridSearchCV]: # define the parameter space parameter_space = ParameterSpace( ClassifierPipelineDF( @@ -473,7 +477,7 @@ def fit_classifier_selector( # pipeline inspector only supports binary classification, # therefore filter the sample down to only 2 target classes - return ModelSelector( + return LearnerSelector( searcher_type=GridSearchCV, parameter_space=parameter_space, cv=cv, diff --git a/test/test/facet/test_inspection.py b/test/test/facet/test_inspection.py index 69fe8fd5f..2cc75441e 100644 --- a/test/test/facet/test_inspection.py +++ b/test/test/facet/test_inspection.py @@ -3,7 +3,7 @@ """ import logging import warnings -from typing import List, Optional, Set, TypeVar, Union +from typing import List, Optional, Set, TypeVar, cast import numpy as np import pandas as pd @@ -13,12 +13,14 @@ from sklearn.datasets import make_classification from sklearn.model_selection import GridSearchCV +from pytools.data import LinkageTree, Matrix from pytools.viz.dendrogram import DendrogramDrawer, DendrogramReportStyle from sklearndf.classification import ( GradientBoostingClassifierDF, RandomForestClassifierDF, ) from sklearndf.pipeline import ClassifierPipelineDF, RegressorPipelineDF +from sklearndf.regression.extra import LGBMRegressorDF from ..conftest import check_ranking from facet.data import Sample @@ -27,7 +29,7 @@ LearnerInspector, TreeExplainerFactory, ) -from facet.selection import ModelSelector +from facet.selection import LearnerSelector # noinspection PyMissingOrEmptyDocstring @@ -37,22 +39,24 @@ def test_regressor_selector( - regressor_selector: ModelSelector[RegressorPipelineDF, GridSearchCV] -): + regressor_selector: LearnerSelector[ + RegressorPipelineDF[LGBMRegressorDF], GridSearchCV + ] +) -> None: check_ranking( ranking=regressor_selector.summary_report(), is_classifier=False, scores_expected=( - [0.820, 0.818, 0.808, 0.806, 0.797, 0.797, 0.652, 0.651, 0.651, 0.651] + [0.820, 0.818, 0.808, 0.806, 0.797, 0.768, 0.652, 0.651, 0.651, 0.651] ), params_expected=None, ) def test_model_inspection( - best_lgbm_model: RegressorPipelineDF, + best_lgbm_model: RegressorPipelineDF[LGBMRegressorDF], preprocessed_feature_names: Set[str], - regressor_inspector: LearnerInspector, + regressor_inspector: LearnerInspector[RegressorPipelineDF[LGBMRegressorDF]], sample: Sample, n_jobs: int, ) -> None: @@ -74,7 +78,7 @@ def test_model_inspection( # calculate the difference between total SHAP values and prediction # for every observation. This is always the same constant value, - # therefore the mean absolute deviation is zero + # therefore the mean absolute deviation is zero. shap_minus_pred = shap_totals - best_lgbm_model.predict(X=sample.features) assert round(shap_minus_pred.mad(), 12) == 0.0, "predictions matching total SHAP" @@ -88,13 +92,17 @@ def test_model_inspection( ).fit(sample=sample) inspector_2.shap_values() - linkage_tree = inspector_2.feature_association_linkage() + linkage_tree = cast(LinkageTree, inspector_2.feature_association_linkage()) print() DendrogramDrawer(style="text").draw(data=linkage_tree, title="Test") -def test_binary_classifier_ranking(iris_classifier_selector_binary) -> None: +def test_binary_classifier_ranking( + iris_classifier_selector_binary: LearnerSelector[ + ClassifierPipelineDF[RandomForestClassifierDF], GridSearchCV + ] +) -> None: expected_learner_scores = [0.938, 0.936, 0.936, 0.929] @@ -115,7 +123,7 @@ def test_binary_classifier_ranking(iris_classifier_selector_binary) -> None: # noinspection DuplicatedCode def test_model_inspection_classifier_binary( - iris_classifier_binary: ClassifierPipelineDF, + iris_classifier_binary: ClassifierPipelineDF[RandomForestClassifierDF], iris_sample_binary: Sample, n_jobs: int, ) -> None: @@ -141,8 +149,11 @@ def test_model_inspection_classifier_binary( # Shap decomposition matrices (feature dependencies) try: - association_matrix = model_inspector.feature_association_matrix( - clustered=True, symmetrical=True + association_matrix = cast( + Matrix[np.float_], + model_inspector.feature_association_matrix( + clustered=True, symmetrical=True + ), ) assert_allclose( association_matrix.values, @@ -160,7 +171,7 @@ def test_model_inspection_classifier_binary( print_expected_matrix(error=error) raise - linkage_tree = model_inspector.feature_association_linkage() + linkage_tree = cast(LinkageTree, model_inspector.feature_association_linkage()) print() DendrogramDrawer(style=DendrogramReportStyle()).draw( @@ -192,8 +203,9 @@ def test_model_inspection_classifier_binary_single_shap_output() -> None: # noinspection DuplicatedCode def test_model_inspection_classifier_multi_class( - iris_inspector_multi_class: LearnerInspector[ClassifierPipelineDF], - n_jobs: int, + iris_inspector_multi_class: LearnerInspector[ + ClassifierPipelineDF[RandomForestClassifierDF] + ], ) -> None: iris_classifier = iris_inspector_multi_class.pipeline iris_sample = iris_inspector_multi_class.sample_ @@ -231,8 +243,9 @@ def test_model_inspection_classifier_multi_class( # Shap decomposition matrices (feature dependencies) try: - synergy_matrix = iris_inspector_multi_class.feature_synergy_matrix( - clustered=False + synergy_matrix = cast( + List[Matrix[np.float_]], + iris_inspector_multi_class.feature_synergy_matrix(clustered=False), ) assert_allclose( @@ -252,8 +265,9 @@ def test_model_inspection_classifier_multi_class( atol=0.02, ) - redundancy_matrix = iris_inspector_multi_class.feature_redundancy_matrix( - clustered=False + redundancy_matrix = cast( + List[Matrix[np.float_]], + iris_inspector_multi_class.feature_redundancy_matrix(clustered=False), ) assert_allclose( np.hstack([m.values for m in redundancy_matrix]), @@ -272,8 +286,9 @@ def test_model_inspection_classifier_multi_class( atol=0.02, ) - association_matrix = iris_inspector_multi_class.feature_association_matrix( - clustered=False + association_matrix = cast( + List[Matrix[np.float_]], + iris_inspector_multi_class.feature_association_matrix(clustered=False), ) assert_allclose( np.hstack([m.values for m in association_matrix]), @@ -295,7 +310,9 @@ def test_model_inspection_classifier_multi_class( print_expected_matrix(error=error, split=True) raise - linkage_trees = iris_inspector_multi_class.feature_association_linkage() + linkage_trees = cast( + List[LinkageTree], iris_inspector_multi_class.feature_association_linkage() + ) for output, linkage_tree in zip( iris_inspector_multi_class.output_names_, linkage_trees @@ -307,8 +324,10 @@ def test_model_inspection_classifier_multi_class( def _validate_shap_values_against_predictions( - shap_values: pd.DataFrame, model: ClassifierPipelineDF, sample: Sample -): + shap_values: pd.DataFrame, + model: ClassifierPipelineDF[RandomForestClassifierDF], + sample: Sample, +) -> None: # calculate the matching predictions, so we can check if the SHAP values add up # correctly @@ -391,7 +410,7 @@ def test_model_inspection_classifier_interaction( ).fit(sample=iris_sample_binary) # calculate shap interaction values - shap_interaction_values = model_inspector.shap_interaction_values() + shap_interaction_values: pd.DataFrame = model_inspector.shap_interaction_values() # calculate shap values from interaction values shap_values = shap_interaction_values.groupby(by="observation").sum() @@ -399,7 +418,6 @@ def test_model_inspection_classifier_interaction( # shap interaction values add up to shap values # we have to live with differences of up to 0.020, given the different results # returned for SHAP values and SHAP interaction values - # todo: review accuracy after implementing use of a background dataset assert ( model_inspector_no_interaction.shap_values() - shap_values ).abs().max().max() < 0.015 @@ -420,7 +438,7 @@ def test_model_inspection_classifier_interaction( # do the shap values add up to predictions minus a constant value? _validate_shap_values_against_predictions( - shap_values=model_inspector.shap_interaction_values().groupby(level=0).sum(), + shap_values=shap_interaction_values.groupby(level=0).sum(), model=iris_classifier_binary, sample=iris_sample_binary, ) @@ -430,11 +448,13 @@ def test_model_inspection_classifier_interaction( ) try: - synergy_matrix = model_inspector.feature_synergy_matrix( - clustered=False, symmetrical=True - ) assert_allclose( - synergy_matrix.values, + cast( + Matrix[np.float_], + model_inspector.feature_synergy_matrix( + clustered=False, symmetrical=True + ), + ).values, np.array( [ [np.nan, 0.011, 0.006, 0.007], @@ -446,8 +466,9 @@ def test_model_inspection_classifier_interaction( atol=0.02, ) assert_allclose( - model_inspector.feature_synergy_matrix( - absolute=True, symmetrical=True + cast( + Matrix[np.float_], + model_inspector.feature_synergy_matrix(absolute=True, symmetrical=True), ).values, np.array( [ @@ -461,7 +482,10 @@ def test_model_inspection_classifier_interaction( ) assert_allclose( - model_inspector.feature_synergy_matrix(clustered=True).values, + cast( + Matrix[np.float_], + model_inspector.feature_synergy_matrix(clustered=True), + ).values, np.array( [ [np.nan, 0.000, 0.000, 0.001], @@ -474,7 +498,9 @@ def test_model_inspection_classifier_interaction( ) assert_allclose( - model_inspector.feature_synergy_matrix(absolute=True).values, + cast( + Matrix[np.float_], model_inspector.feature_synergy_matrix(absolute=True) + ).values, np.array( [ [np.nan, 0.000, 0.000, 0.001], @@ -487,8 +513,11 @@ def test_model_inspection_classifier_interaction( ) assert_allclose( - model_inspector.feature_redundancy_matrix( - clustered=False, symmetrical=True + cast( + Matrix[np.float_], + model_inspector.feature_redundancy_matrix( + clustered=False, symmetrical=True + ), ).values, np.array( [ @@ -501,8 +530,11 @@ def test_model_inspection_classifier_interaction( atol=0.02, ) assert_allclose( - model_inspector.feature_redundancy_matrix( - absolute=True, symmetrical=True + cast( + Matrix[np.float_], + model_inspector.feature_redundancy_matrix( + absolute=True, symmetrical=True + ), ).values, np.array( [ @@ -516,7 +548,10 @@ def test_model_inspection_classifier_interaction( ) assert_allclose( - model_inspector.feature_redundancy_matrix(clustered=True).values, + cast( + Matrix[np.float_], + model_inspector.feature_redundancy_matrix(clustered=True), + ).values, np.array( [ [np.nan, 0.677, 0.384, 0.003], @@ -529,7 +564,10 @@ def test_model_inspection_classifier_interaction( ) assert_allclose( - model_inspector.feature_redundancy_matrix(absolute=True).values, + cast( + Matrix[np.float_], + model_inspector.feature_redundancy_matrix(absolute=True), + ).values, np.array( [ [np.nan, 0.323, 0.183, 0.002], @@ -541,11 +579,13 @@ def test_model_inspection_classifier_interaction( atol=0.02, ) - association_matrix = model_inspector.feature_association_matrix( - clustered=False, symmetrical=True - ) assert_allclose( - association_matrix.values, + cast( + Matrix[np.float_], + model_inspector.feature_association_matrix( + clustered=False, symmetrical=True + ), + ).values, np.array( [ [np.nan, 0.009, 0.447, 0.383], @@ -558,8 +598,11 @@ def test_model_inspection_classifier_interaction( ) assert_allclose( - model_inspector.feature_association_matrix( - absolute=True, symmetrical=True + cast( + Matrix[np.float_], + model_inspector.feature_association_matrix( + absolute=True, symmetrical=True + ), ).values, np.array( [ @@ -573,7 +616,10 @@ def test_model_inspection_classifier_interaction( ) assert_allclose( - model_inspector.feature_association_matrix(clustered=True).values, + cast( + Matrix[np.float_], + model_inspector.feature_association_matrix(clustered=True), + ).values, np.array( [ [np.nan, 0.678, 0.383, 0.001], @@ -586,7 +632,10 @@ def test_model_inspection_classifier_interaction( ) assert_allclose( - model_inspector.feature_association_matrix(absolute=True).values, + cast( + Matrix[np.float_], + model_inspector.feature_association_matrix(absolute=True), + ).values, np.array( [ [np.nan, 0.323, 0.182, 0.001], @@ -602,7 +651,7 @@ def test_model_inspection_classifier_interaction( print_expected_matrix(error=error) raise - linkage_tree = model_inspector.feature_redundancy_linkage() + linkage_tree = cast(LinkageTree, model_inspector.feature_redundancy_linkage()) print() DendrogramDrawer(style=DendrogramReportStyle()).draw( @@ -612,10 +661,10 @@ def test_model_inspection_classifier_interaction( def test_model_inspection_classifier_interaction_dual_target( iris_sample_binary_dual_target: Sample, - iris_classifier_selector_dual_target: ModelSelector[ + iris_classifier_selector_dual_target: LearnerSelector[ ClassifierPipelineDF[RandomForestClassifierDF], GridSearchCV ], - iris_target_name, + iris_target_name: str, n_jobs: int, ) -> None: iris_classifier_dual_target = iris_classifier_selector_dual_target.best_estimator_ @@ -633,8 +682,10 @@ def test_model_inspection_classifier_interaction_dual_target( def test_shap_plot_data( - iris_sample_multi_class, - iris_inspector_multi_class: LearnerInspector[ClassifierPipelineDF], + iris_sample_multi_class: Sample, + iris_inspector_multi_class: LearnerInspector[ + ClassifierPipelineDF[RandomForestClassifierDF] + ], ) -> None: shap_plot_data = iris_inspector_multi_class.shap_plot_data() # noinspection SpellCheckingInspection @@ -665,12 +716,12 @@ def test_shap_plot_data( # -def print_expected_matrix(error: AssertionError, *, split: bool = False): +def print_expected_matrix(error: AssertionError, *, split: bool = False) -> None: # print expected output for copy/paste into assertion statement import re - array: Optional[re.Match] = re.search(r"array\(([^)]+)\)", error.args[0]) + array: Optional[re.Match[str]] = re.search(r"array\(([^)]+)\)", error.args[0]) if array is not None: matrix: List[List[float]] = eval( array[1].replace(r"\n", "\n").replace("nan", "np.nan") @@ -679,7 +730,7 @@ def print_expected_matrix(error: AssertionError, *, split: bool = False): print_matrix(matrix, split=split) -def print_matrix(matrix: Union[List[List[float]], np.ndarray], *, split: bool): +def print_matrix(matrix: List[List[float]], *, split: bool) -> None: print("==== matrix assertion failed ====\nExpected Matrix:") print("[") for row in matrix: diff --git a/test/test/facet/test_partition.py b/test/test/facet/test_partition.py index 7f89cca54..cd2cf0113 100644 --- a/test/test/facet/test_partition.py +++ b/test/test/facet/test_partition.py @@ -7,11 +7,17 @@ IntegerRangePartitioner, ) +# constants for error messages +MSG_ARG_VALUES_EMPTY = "arg values is empty" +MSG_CANNOT_INFER_BOUNDS = ( + "insufficient variance in values; cannot infer partitioning bounds" +) + def test_discrete_partitioning() -> None: np.random.seed(42) - for i in range(10): + for _ in range(10): values = np.random.randint( low=0, high=10000, size=np.random.randint(low=100, high=200) @@ -44,7 +50,7 @@ def test_discrete_partitioning() -> None: def test_continuous_partitioning() -> None: np.random.seed(42) - for i in range(10): + for _ in range(10): values = np.random.normal( loc=3.0, scale=8.0, size=np.random.randint(low=2000, high=4000) @@ -79,9 +85,9 @@ def test_continuous_partitioning() -> None: def test_category_partitioning() -> None: np.random.seed(42) - for i in range(10): + for _ in range(10): values = np.random.randint( - low=0, high=10, size=np.random.randint(low=100, high=200) + low=0, high=10, size=np.random.randint(low=100, high=200), dtype=np.int_ ) cp = CategoryPartitioner(max_partitions=4).fit(values=values) # test correct number of partitions @@ -95,48 +101,52 @@ def test_category_partitioning() -> None: def test_partition_with_invalid_values() -> None: + arr_empty = np.array([]) + arr_single = np.array([1]) + arr_multi = np.array([1, 1, 1, 10, 1]) + with pytest.raises( ValueError, - match="arg values is empty", + match=MSG_ARG_VALUES_EMPTY, ): - ContinuousRangePartitioner().fit([]) + ContinuousRangePartitioner().fit(arr_empty) with pytest.raises( ValueError, - match="insufficient variance in values; cannot infer partitioning bounds", + match=MSG_CANNOT_INFER_BOUNDS, ): - ContinuousRangePartitioner().fit([1]) + ContinuousRangePartitioner().fit(arr_single) with pytest.raises( ValueError, - match="insufficient variance in values; cannot infer partitioning bounds", + match=MSG_CANNOT_INFER_BOUNDS, ): - ContinuousRangePartitioner().fit([1, 1, 1, 10, 1]) + ContinuousRangePartitioner().fit(arr_multi) with pytest.raises( ValueError, - match="arg values is empty", + match=MSG_ARG_VALUES_EMPTY, ): - IntegerRangePartitioner().fit([]) + IntegerRangePartitioner().fit(arr_empty) with pytest.raises( ValueError, - match="insufficient variance in values; cannot infer partitioning bounds", + match=MSG_CANNOT_INFER_BOUNDS, ): - IntegerRangePartitioner().fit([1]) + IntegerRangePartitioner().fit(arr_single) with pytest.raises( ValueError, - match="insufficient variance in values; cannot infer partitioning bounds", + match=MSG_CANNOT_INFER_BOUNDS, ): - IntegerRangePartitioner().fit([1, 1, 1, 10, 1]) + IntegerRangePartitioner().fit(arr_multi) with pytest.raises( ValueError, - match="arg values is empty", + match=MSG_ARG_VALUES_EMPTY, ): - CategoryPartitioner().fit([]) + CategoryPartitioner().fit(arr_empty) - CategoryPartitioner().fit([1]) + CategoryPartitioner().fit(arr_single) - CategoryPartitioner().fit([1, 1, 1, 10, 1]) + CategoryPartitioner().fit(arr_multi) diff --git a/test/test/facet/test_sample.py b/test/test/facet/test_sample.py index 3f2e9ecea..2d457ab15 100644 --- a/test/test/facet/test_sample.py +++ b/test/test/facet/test_sample.py @@ -1,3 +1,5 @@ +from typing import Any, List, cast + import numpy as np import pandas as pd import pytest @@ -21,10 +23,10 @@ def test_sample_init(boston_df: pd.DataFrame, boston_target: str) -> None: # noinspection PyTypeChecker Sample(observations=[], target_name=boston_target) - # 2. no features and no target specified + # 2. no valid target specified with pytest.raises(TypeError): # noinspection PyTypeChecker - Sample(observations=boston_df, target_name=None) + Sample(observations=boston_df, target_name=None) # type: ignore # store list of feature columns: f_columns = list(boston_df.columns) @@ -69,8 +71,9 @@ def test_sample_init(boston_df: pd.DataFrame, boston_target: str) -> None: def test_sample(boston_df: pd.DataFrame, boston_target: str) -> None: # define various assertions we want to test: - def run_assertions(sample: Sample): + def run_assertions(sample: Sample) -> None: assert sample.target.name == boston_target + assert sample.weight is not None assert sample.weight.name == boston_target assert boston_target not in sample.feature_names assert len(sample.feature_names) == len(boston_df.columns) - 1 @@ -138,8 +141,21 @@ def run_assertions(sample: Sample): # global python environment variable PYTHONHASHSEED parallel = Parallel(n_jobs=-3) - def get_column(sample: Sample): - return list(sample.features.columns) + def get_column(sample: Sample) -> List[Any]: + return cast(List[Any], sample.features.columns.to_list()) columns1, columns2 = parallel(delayed(get_column)(sample) for sample in [s, s]) assert columns1 == columns2 + + # creating a sample with non-string column names raises an exception + with pytest.raises( + TypeError, + match=( + "^all column names in arg observations must be strings, " + "but included: int$" + ), + ): + Sample( + boston_df.set_axis([*boston_df.columns[1:], 1], axis=1), + target_name=boston_target, + ) diff --git a/test/test/facet/test_selection.py b/test/test/facet/test_selection.py index e056eb382..86740a6ec 100644 --- a/test/test/facet/test_selection.py +++ b/test/test/facet/test_selection.py @@ -11,7 +11,7 @@ from sklearn import datasets from sklearn.model_selection import GridSearchCV -from pytools.expression import freeze +from pytools.expression import Expression, freeze from pytools.expression.atomic import Id from sklearndf import TransformerDF from sklearndf.classification import SVCDF, RandomForestClassifierDF @@ -25,24 +25,29 @@ from ..conftest import check_ranking from facet.data import Sample -from facet.selection import ModelSelector, MultiEstimatorParameterSpace, ParameterSpace +from facet.selection import ( + LearnerSelector, + MultiEstimatorParameterSpace, + ParameterSpace, +) from facet.validation import BootstrapCV, StratifiedBootstrapCV log = logging.getLogger(__name__) -def test_model_selector( - regressor_parameters: MultiEstimatorParameterSpace[RegressorPipelineDF], +def test_learner_selector( + regressor_parameters: List[ParameterSpace[RegressorPipelineDF[LGBMRegressorDF]]], sample: Sample, n_jobs: int, ) -> None: + expected_scores = [ 0.840, 0.837, 0.812, - 0.812, 0.793, 0.790, + 0.777, 0.758, 0.758, 0.758, @@ -54,9 +59,9 @@ def test_model_selector( RandomForestRegressorDF, RandomForestRegressorDF, LinearRegressionDF, - LinearRegressionDF, AdaBoostRegressorDF, AdaBoostRegressorDF, + LinearRegressionDF, LGBMRegressorDF, LGBMRegressorDF, LGBMRegressorDF, @@ -66,20 +71,66 @@ def test_model_selector( expected_parameters = { 0: dict(n_estimators=80), 1: dict(n_estimators=50), - 4: dict(n_estimators=50), - 5: dict(n_estimators=80), + 3: dict(n_estimators=50), + 4: dict(n_estimators=80), } # define the circular cross validator with just 5 splits (to speed up testing) cv = BootstrapCV(n_splits=5, random_state=42) - ranker: ModelSelector[RegressorPipelineDF, GridSearchCV] = ModelSelector( + with pytest.raises( + TypeError, + match=( + r"^arg parameter_space requires instances of one of " + r"{ParameterSpace, MultiEstimatorParameterSpace} but got: int$" + ), + ): + LearnerSelector( + searcher_type=GridSearchCV, + parameter_space=1, # type: ignore + cv=cv, + ) + + with pytest.raises( + TypeError, + match=( + r"^arg parameter_space requires instances of one of " + r"{ParameterSpace, MultiEstimatorParameterSpace} but got: int$" + ), + ): + LearnerSelector( + searcher_type=GridSearchCV, + parameter_space=[1], # type: ignore + cv=cv, + ) + + with pytest.raises( + TypeError, + match=( + r"^arg spaces requires instances of ParameterSpace but got: " + r"MultiEstimatorParameterSpace$" + ), + ): + multi_ps = MultiEstimatorParameterSpace(*regressor_parameters) + LearnerSelector( + searcher_type=GridSearchCV, + parameter_space=[multi_ps, multi_ps], # type: ignore + cv=cv, + ) + + # define the learner selector + ranker: LearnerSelector[ + RegressorPipelineDF[LGBMRegressorDF], GridSearchCV + ] = LearnerSelector( searcher_type=GridSearchCV, parameter_space=regressor_parameters, cv=cv, scoring="r2", n_jobs=n_jobs, - ).fit(sample=sample) + error_score="raise", + ).fit( + sample=sample + ) log.debug(f"\n{ranker.summary_report()}") @@ -103,7 +154,7 @@ def test_model_selector( ) -def test_model_selector_no_preprocessing(n_jobs) -> None: +def test_model_selector_no_preprocessing(n_jobs: int) -> None: expected_learner_scores = [0.961, 0.957, 0.957, 0.936] # define a yield-engine circular CV: @@ -124,9 +175,9 @@ def test_model_selector_no_preprocessing(n_jobs) -> None: ) test_sample: Sample = Sample(observations=test_data, target_name="target") - model_selector: ModelSelector[ + model_selector: LearnerSelector[ ClassifierPipelineDF[SVCDF], GridSearchCV - ] = ModelSelector( + ] = LearnerSelector( searcher_type=GridSearchCV, parameter_space=parameter_space, cv=cv, @@ -148,14 +199,13 @@ def test_model_selector_no_preprocessing(n_jobs) -> None: }, ) + min_best_performance = 0.8 assert ( - summary_report[("score", "test", "mean")].iloc[0] >= 0.8 - ), "expected a best performance of at least 0.8" + summary_report[("score", "test", "mean")].iloc[0] >= min_best_performance + ), f"expected the best performance to be at least {min_best_performance}" -def test_parameter_space( - sample: Sample, simple_preprocessor: TransformerDF, n_jobs: int -) -> None: +def test_parameter_space(simple_preprocessor: TransformerDF) -> None: # distributions randint_3_10 = randint(3, 10) @@ -170,7 +220,9 @@ def test_parameter_space( preprocessing=simple_preprocessor, ) ps_1_name = "rf_regressor" - ps_1 = ParameterSpace(pipeline_1, name=ps_1_name) + ps_1: ParameterSpace[RegressorPipelineDF[RandomForestRegressorDF]] = ParameterSpace( + pipeline_1, name=ps_1_name + ) ps_1.regressor.min_weight_fraction_leaf = reciprocal_0_01_0_10 ps_1.regressor.max_depth = randint_3_10 ps_1.regressor.min_samples_leaf = reciprocal_0_05_0_10 @@ -181,11 +233,11 @@ def test_parameter_space( ): ps_1.regressor.unknown = 1 + # noinspection GrazieInspection with pytest.raises( TypeError, match=( - "^expected list or distribution for parameter min_samples_leaf " - "but got: 1$" + r"^expected list or distribution for parameter min_samples_leaf but got: 1$" ), ): ps_1.regressor.min_samples_leaf = 1 @@ -197,7 +249,9 @@ def test_parameter_space( preprocessing=simple_preprocessor, ) ps_2_name = "lgbm" - ps_2 = ParameterSpace(pipeline_2, name=ps_2_name) + ps_2: ParameterSpace[RegressorPipelineDF[LGBMRegressorDF]] = ParameterSpace( + pipeline_2, name=ps_2_name + ) ps_2.regressor.max_depth = randint_3_10 ps_2.regressor.min_child_samples = randint_1_32 @@ -206,12 +260,11 @@ def test_parameter_space( with pytest.raises( TypeError, match=( - r"^all candidate estimators must have the same estimator type, " + r"^all parameter spaces must use the same estimator type, " r"but got multiple types: classifier, regressor$" ), ): - # noinspection PyTypeChecker - MultiEstimatorParameterSpace( + MultiEstimatorParameterSpace( # type: ignore ps_1, ps_2, ParameterSpace(ClassifierPipelineDF(classifier=SVCDF())) ) @@ -219,7 +272,7 @@ def test_parameter_space( # test - def regressor_repr(model: Id): + def regressor_repr(model: Id) -> Expression: return Id.RegressorPipelineDF( preprocessing=Id.ColumnTransformerDF( transformers=[ @@ -292,7 +345,7 @@ def regressor_repr(model: Id): def test_model_selector_regression( - regressor_parameters: MultiEstimatorParameterSpace[RegressorPipelineDF], + regressor_parameters: List[ParameterSpace[RegressorPipelineDF[LGBMRegressorDF]]], sample: Sample, n_jobs: int, ) -> None: @@ -306,15 +359,19 @@ def test_model_selector_regression( "of arg searcher_type, but included: param_grid" ), ): - ModelSelector(GridSearchCV, regressor_parameters, param_grid=None) + LearnerSelector(GridSearchCV, regressor_parameters, param_grid=None) - ranker: ModelSelector[RegressorPipelineDF, GridSearchCV] = ModelSelector( + ranker: LearnerSelector[ + RegressorPipelineDF[LGBMRegressorDF], GridSearchCV + ] = LearnerSelector( GridSearchCV, regressor_parameters, scoring="r2", cv=cv, n_jobs=n_jobs, - ).fit(sample=sample) + ).fit( + sample=sample + ) assert isinstance(ranker.best_estimator_, RegressorPipelineDF) @@ -332,11 +389,13 @@ def test_model_selector_regression( def test_model_selector_classification( - iris_sample_multi_class, cv_stratified_bootstrap: StratifiedBootstrapCV, n_jobs: int + iris_sample_multi_class: Sample, + cv_stratified_bootstrap: StratifiedBootstrapCV, + n_jobs: int, ) -> None: expected_learner_scores = [0.965, 0.964, 0.957, 0.956] - # define parameters and crossfit + # define parameters ps1 = ParameterSpace( ClassifierPipelineDF(classifier=RandomForestClassifierDF(random_state=42)) ) @@ -352,16 +411,16 @@ def test_model_selector_classification( with pytest.raises( TypeError, match=( - "^all candidate estimators must have the same estimator type, " + r"^all parameter spaces must use the same estimator type, " "but got multiple types: classifier, regressor$" ), ): # define an illegal grid list, mixing classification with regression - MultiEstimatorParameterSpace(ps1, ps2) + MultiEstimatorParameterSpace(ps1, ps2) # type: ignore - model_selector: ModelSelector[ + model_selector: LearnerSelector[ ClassifierPipelineDF[RandomForestClassifierDF], GridSearchCV - ] = ModelSelector( + ] = LearnerSelector( searcher_type=GridSearchCV, parameter_space=ps1, cv=cv_stratified_bootstrap, @@ -371,7 +430,10 @@ def test_model_selector_classification( with pytest.raises( ValueError, - match="arg sample_weight is not supported, use arg sample.weight instead", + match=( + "arg sample_weight is not supported, use 'weight' property " + "of arg sample instead" + ), ): model_selector.fit( sample=iris_sample_multi_class, sample_weight=iris_sample_multi_class.weight diff --git a/test/test/facet/test_shap_decomposition.py b/test/test/facet/test_shap_decomposition.py index b07e8d8de..3fd3210d9 100644 --- a/test/test/facet/test_shap_decomposition.py +++ b/test/test/facet/test_shap_decomposition.py @@ -6,13 +6,18 @@ import numpy as np +from pytools.data import Matrix +from sklearndf.pipeline import RegressorPipelineDF +from sklearndf.regression.extra import LGBMRegressorDF + from facet.inspection import LearnerInspector log = logging.getLogger(__name__) def test_feature_affinity_matrices( - preprocessed_feature_names: Set[str], regressor_inspector: LearnerInspector + preprocessed_feature_names: Set[str], + regressor_inspector: LearnerInspector[RegressorPipelineDF[LGBMRegressorDF]], ) -> None: # feature affinity matrices (feature dependencies) # check that dimensions of pairwise feature matrices are equal to # of features, @@ -27,11 +32,14 @@ def test_feature_affinity_matrices( ): matrix_full_name = f"feature {matrix_name} matrix" n_features = len(preprocessed_feature_names) + assert isinstance(matrix, Matrix) assert matrix.values.shape[0] == n_features, f"rows in {matrix_full_name}" assert matrix.values.shape[1] == n_features, f"columns in {matrix_full_name}" + assert matrix.names[0] is not None assert ( set(matrix.names[0]) == preprocessed_feature_names ), f"row names in {matrix_full_name}" + assert matrix.names[1] is not None assert ( set(matrix.names[1]) == preprocessed_feature_names ), f"column names in {matrix_full_name}" diff --git a/test/test/facet/test_simulation.py b/test/test/facet/test_simulation.py index b9674fba8..a87bc9f6e 100644 --- a/test/test/facet/test_simulation.py +++ b/test/test/facet/test_simulation.py @@ -1,5 +1,6 @@ import logging +import numpy as np import pandas as pd import pytest from numpy.testing import assert_array_equal @@ -26,8 +27,10 @@ N_SPLITS = 10 -@pytest.fixture -def model(sample: Sample, simple_preprocessor: TransformerDF) -> RegressorPipelineDF: +@pytest.fixture # type: ignore +def model( + sample: Sample, simple_preprocessor: TransformerDF +) -> RegressorPipelineDF[LGBMRegressorDF]: # use a pre-optimised model return RegressorPipelineDF( preprocessing=simple_preprocessor, @@ -37,7 +40,7 @@ def model(sample: Sample, simple_preprocessor: TransformerDF) -> RegressorPipeli ).fit(X=sample.features, y=sample.target) -@pytest.fixture +@pytest.fixture # type: ignore def subsample(sample: Sample) -> Sample: return sample.subsample( iloc=( @@ -48,18 +51,18 @@ def subsample(sample: Sample) -> Sample: ) -@pytest.fixture +@pytest.fixture # type: ignore def target_simulator( - model: RegressorPipelineDF, sample: Sample, n_jobs: int + model: RegressorPipelineDF[LGBMRegressorDF], sample: Sample, n_jobs: int ) -> UnivariateTargetSimulator: return UnivariateTargetSimulator( model=model, sample=sample, confidence_level=0.8, n_jobs=n_jobs, verbose=50 ) -@pytest.fixture +@pytest.fixture # type: ignore def uplift_simulator( - model: RegressorPipelineDF, sample: Sample, n_jobs: int + model: RegressorPipelineDF[LGBMRegressorDF], sample: Sample, n_jobs: int ) -> UnivariateUpliftSimulator: return UnivariateUpliftSimulator( model=model, sample=sample, confidence_level=0.8, n_jobs=n_jobs, verbose=50 @@ -73,7 +76,9 @@ def test_univariate_target_simulation( parameterized_feature = "LSTAT" partitioner = ContinuousRangePartitioner(max_partitions=10) - simulation_result: UnivariateSimulationResult = target_simulator.simulate_feature( + simulation_result: UnivariateSimulationResult[ + np.float_ + ] = target_simulator.simulate_feature( feature_name=parameterized_feature, partitioner=partitioner, ) @@ -124,7 +129,7 @@ def test_univariate_target_simulation( def test_univariate_target_subsample_simulation_80( - model: RegressorPipelineDF, subsample: Sample, n_jobs: int + model: RegressorPipelineDF[LGBMRegressorDF], subsample: Sample, n_jobs: int ) -> None: parameterized_feature = "LSTAT" @@ -134,7 +139,9 @@ def test_univariate_target_subsample_simulation_80( model=model, sample=subsample, confidence_level=0.8, n_jobs=n_jobs, verbose=50 ) - simulation_result: UnivariateSimulationResult = target_simulator.simulate_feature( + simulation_result: UnivariateSimulationResult[ + np.float_ + ] = target_simulator.simulate_feature( feature_name=parameterized_feature, partitioner=partitioner, ) @@ -188,7 +195,7 @@ def test_univariate_target_subsample_simulation_80( def test_univariate_uplift_subsample_simulation_95( - model: RegressorPipelineDF, subsample: Sample, n_jobs: int + model: RegressorPipelineDF[LGBMRegressorDF], subsample: Sample, n_jobs: int ) -> None: parameterized_feature = "LSTAT" @@ -198,7 +205,9 @@ def test_univariate_uplift_subsample_simulation_95( model=model, sample=subsample, confidence_level=0.95, n_jobs=n_jobs, verbose=50 ) - simulation_result: UnivariateSimulationResult = target_simulator.simulate_feature( + simulation_result: UnivariateSimulationResult[ + np.float_ + ] = target_simulator.simulate_feature( feature_name=parameterized_feature, partitioner=partitioner, ) @@ -262,7 +271,9 @@ def test_univariate_uplift_simulation( parameterized_feature = "LSTAT" partitioner = ContinuousRangePartitioner(max_partitions=10) - simulation_result: UnivariateSimulationResult = uplift_simulator.simulate_feature( + simulation_result: UnivariateSimulationResult[ + np.float_ + ] = uplift_simulator.simulate_feature( feature_name=parameterized_feature, partitioner=partitioner, ) @@ -313,7 +324,7 @@ def test_univariate_uplift_simulation( def test_univariate_uplift_subsample_simulation( - model: RegressorPipelineDF, subsample: Sample, n_jobs: int + model: RegressorPipelineDF[LGBMRegressorDF], subsample: Sample, n_jobs: int ) -> None: parameterized_feature = "LSTAT" @@ -323,7 +334,9 @@ def test_univariate_uplift_subsample_simulation( model=model, sample=subsample, confidence_level=0.8, n_jobs=n_jobs, verbose=50 ) - simulation_result: UnivariateSimulationResult = uplift_simulator.simulate_feature( + simulation_result: UnivariateSimulationResult[ + np.float_ + ] = uplift_simulator.simulate_feature( feature_name=parameterized_feature, partitioner=partitioner ) @@ -389,7 +402,9 @@ def test_univariate_probability_simulation( verbose=50, ) - simulation_result: UnivariateSimulationResult = proba_simulator.simulate_feature( + simulation_result: UnivariateSimulationResult[ + np.float_ + ] = proba_simulator.simulate_feature( feature_name=parameterized_feature, partitioner=partitioner ) diff --git a/test/test/facet/test_validation.py b/test/test/facet/test_validation.py index ccdc8095a..11b3977db 100644 --- a/test/test/facet/test_validation.py +++ b/test/test/facet/test_validation.py @@ -5,6 +5,7 @@ from typing import List import numpy as np +import numpy.typing as npt import pytest from sklearn import datasets, svm, tree from sklearn.model_selection import GridSearchCV @@ -37,10 +38,10 @@ def test_get_train_test_splits_as_indices() -> None: my_cv = BootstrapCV(n_splits=n_test_splits, random_state=42) - def _generate_splits() -> List[np.ndarray]: + def _generate_splits() -> List[npt.NDArray[np.int_]]: return [test_split for _, test_split in my_cv.split(X=test_x)] - test_splits: List[np.ndarray] = _generate_splits() + test_splits: List[npt.NDArray[np.int_]] = _generate_splits() # assert we get right amount of splits @@ -56,7 +57,7 @@ def _generate_splits() -> List[np.ndarray]: # check that re-generating the split yields the same result - test_splits_2: List[np.ndarray] = _generate_splits() + test_splits_2: List[npt.NDArray[np.int_]] = _generate_splits() assert len(test_splits) == len( test_splits_2 @@ -88,6 +89,7 @@ def test_bootstrap_cv_with_sk_learn() -> None: # use the defined my_cv bootstrap CV within GridSearchCV if __sklearn_version__ < __sklearn_0_22__: + # noinspection PyArgumentList clf = GridSearchCV(svc, parameters, cv=my_cv, iid=False) else: clf = GridSearchCV(svc, parameters, cv=my_cv) @@ -137,9 +139,10 @@ def test_stratified_bootstrap_cv() -> None: ): next(my_cv.split(X=test_x, groups=test_groups)) - with pytest.warns(None) as checker: + with warnings.catch_warnings(): + warnings.simplefilter("error") + warnings.simplefilter("default", category=DeprecationWarning) train1, test1 = next(my_cv.split(X=test_x, y=test_groups)) - assert len(checker) == 0 with pytest.warns(UserWarning, match=r"^ignoring arg groups=array\(\["): train2, test2 = next(my_cv.split(X=test_x, y=test_groups, groups=test_groups)) @@ -147,10 +150,10 @@ def test_stratified_bootstrap_cv() -> None: assert np.array_equal(train1, train2) assert np.array_equal(test1, test2) - def _generate_splits() -> List[np.ndarray]: + def _generate_splits() -> List[npt.NDArray[np.int_]]: return [test_split for _, test_split in my_cv.split(X=test_x, y=test_groups)] - test_splits: List[np.ndarray] = _generate_splits() + test_splits: List[npt.NDArray[np.int_]] = _generate_splits() # assert we get right amount of splits @@ -177,7 +180,7 @@ def _generate_splits() -> List[np.ndarray]: # check that re-generating the split yields the same result - test_splits_2: List[np.ndarray] = _generate_splits() + test_splits_2: List[npt.NDArray[np.int_]] = _generate_splits() assert len(test_splits) == len( test_splits_2 diff --git a/tox.ini b/tox.ini index 4594f65c1..ad2e9f896 100644 --- a/tox.ini +++ b/tox.ini @@ -21,7 +21,7 @@ setenv = # This is necessary to prevent the notorious "RuntimeError: module compiled against API # version 0x… but this version of numpy is 0x…" error. install_command = - python -m pip install {opts} {packages} --no-binary matplotlib,shap + python -m pip install {opts} {packages} --no-binary '{env:FACET_NO_BINARY}' extras = testing