From f1083747d31528d62fd7b3f7006f676a80f3c88e Mon Sep 17 00:00:00 2001 From: Github Actions Date: Fri, 7 May 2021 18:44:27 +0000 Subject: [PATCH] Francisco Rivera Valverde: [Fix] docs links (#201) --- development/.buildinfo | 2 +- .../basics_tabular_jupyter.zip | Bin 7622 -> 0 bytes .../example_resampling_strategy.py | 15 - .../example_resampling_strategy.ipynb | 54 - .../example_tabular_regression.ipynb | 2 +- .../example_visualization.ipynb | 0 .../example_custom_configuration_space.ipynb | 0 .../example_resampling_strategy.ipynb | 54 + .../example_custom_configuration_space.py | 0 .../example_tabular_classification.ipynb | 2 +- .../basics_tabular_python.zip | Bin 5593 -> 0 bytes .../example_visualization.py | 0 .../example_image_classification.ipynb | 54 + .../example_image_classification.py | 54 + .../example_tabular_regression.py | 8 +- .../examples_python.zip} | Bin 17805 -> 24451 bytes .../example_tabular_classification.py | 11 +- .../examples_jupyter.zip} | Bin 21084 -> 30743 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rename development/notebooks/{basics_tabular => examples/20_basics}/example_tabular_classification.ipynb (62%) rename development/notebooks/{basics_tabular => examples/20_basics}/example_tabular_regression.ipynb (65%) rename development/notebooks/{advanced_tabular => examples/40_advanced}/example_custom_configuration_space.ipynb (100%) create mode 100644 development/notebooks/examples/40_advanced/example_resampling_strategy.ipynb rename development/notebooks/{advanced_tabular => examples/40_advanced}/example_visualization.ipynb (100%) diff --git a/development/.buildinfo b/development/.buildinfo index 1ca470b75..a74c628b4 100644 --- a/development/.buildinfo +++ b/development/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. 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However, # one can also use CrossValTypes.stratified_k_fold_cross_validation. diff --git a/development/_downloads/342871cbb8ddcf6157ab171f9b9eab25/example_resampling_strategy.ipynb b/development/_downloads/342871cbb8ddcf6157ab171f9b9eab25/example_resampling_strategy.ipynb deleted file mode 100644 index 10f88db97..000000000 --- a/development/_downloads/342871cbb8ddcf6157ab171f9b9eab25/example_resampling_strategy.ipynb +++ /dev/null @@ -1,54 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Tabular Classification with different resampling strategy\n\nThe following example shows how to fit a sample classification model\nwith different resampling strategies in AutoPyTorch\nBy default, AutoPyTorch uses Holdout Validation with\na 67% train size split.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import os\nimport tempfile as tmp\nimport warnings\n\nos.environ['JOBLIB_TEMP_FOLDER'] = tmp.gettempdir()\nos.environ['OMP_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_NUM_THREADS'] = '1'\n\nwarnings.simplefilter(action='ignore', category=UserWarning)\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nimport sklearn.datasets\nimport sklearn.model_selection\n\nfrom autoPyTorch.api.tabular_classification import TabularClassificationTask\nfrom autoPyTorch.datasets.resampling_strategy import CrossValTypes, HoldoutValTypes\n\n\nif __name__ == '__main__':\n\n ############################################################################\n # Data Loading\n # ============\n X, y = sklearn.datasets.fetch_openml(data_id=40981, return_X_y=True, as_frame=True)\n X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n X,\n y,\n random_state=1,\n )\n\n ############################################################################\n # Build and fit a classifier with default resampling strategy\n # ===========================================================\n api = TabularClassificationTask(\n temporary_directory='./tmp/autoPyTorch_example_tmp_03',\n output_directory='./tmp/autoPyTorch_example_out_03',\n # To maintain logs of the run, set the next two as False\n delete_tmp_folder_after_terminate=True,\n delete_output_folder_after_terminate=True,\n # 'HoldoutValTypes.holdout_validation' with 'val_share': 0.33\n # is the default argument setting for TabularClassificationTask.\n # It is explicitly specified in this example for demonstrational\n # purpose.\n resampling_strategy=HoldoutValTypes.holdout_validation,\n resampling_strategy_args={'val_share': 0.33}\n )\n\n ############################################################################\n # Search for an ensemble of machine learning algorithms\n # =====================================================\n api.search(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test.copy(),\n y_test=y_test.copy(),\n optimize_metric='accuracy',\n total_walltime_limit=150,\n func_eval_time_limit_secs=30\n )\n\n ############################################################################\n # Print the final ensemble performance\n # ====================================\n print(api.run_history, api.trajectory)\n y_pred = api.predict(X_test)\n score = api.score(y_pred, y_test)\n print(score)\n # Print the final ensemble built by AutoPyTorch\n print(api.show_models())\n\n ############################################################################\n\n ############################################################################\n # Build and fit a classifier with Cross validation resampling strategy\n # ====================================================================\n api = TabularClassificationTask(\n temporary_directory='./tmp/autoPyTorch_example_tmp_04',\n output_directory='./tmp/autoPyTorch_example_out_04',\n # To maintain logs of the run, set the next two as False\n delete_tmp_folder_after_terminate=True,\n delete_output_folder_after_terminate=True,\n resampling_strategy=CrossValTypes.k_fold_cross_validation,\n resampling_strategy_args={'num_splits': 3}\n )\n\n ############################################################################\n # Search for an ensemble of machine learning algorithms\n # =====================================================\n api.search(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test.copy(),\n y_test=y_test.copy(),\n optimize_metric='accuracy',\n total_walltime_limit=150,\n func_eval_time_limit_secs=30\n )\n\n ############################################################################\n # Print the final ensemble performance\n # ====================================\n print(api.run_history, api.trajectory)\n y_pred = api.predict(X_test)\n score = api.score(y_pred, y_test)\n print(score)\n # Print the final ensemble built by AutoPyTorch\n print(api.show_models())\n\n ############################################################################\n\n ############################################################################\n # Build and fit a classifier with Stratified resampling strategy\n # ==============================================================\n api = TabularClassificationTask(\n temporary_directory='./tmp/autoPyTorch_example_tmp_05',\n output_directory='./tmp/autoPyTorch_example_out_05',\n # To maintain logs of the run, set the next two as False\n delete_tmp_folder_after_terminate=True,\n delete_output_folder_after_terminate=True,\n # For demonstration purposes, we use\n # Stratified hold out validation. However,\n # one can also use CrossValTypes.stratified_k_fold_cross_validation.\n resampling_strategy=HoldoutValTypes.stratified_holdout_validation,\n resampling_strategy_args={'val_share': 0.33}\n )\n\n ############################################################################\n # Search for an ensemble of machine learning algorithms\n # =====================================================\n api.search(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test.copy(),\n y_test=y_test.copy(),\n optimize_metric='accuracy',\n total_walltime_limit=150,\n func_eval_time_limit_secs=30\n )\n\n ############################################################################\n # Print the final ensemble performance\n # ====================================\n print(api.run_history, api.trajectory)\n y_pred = api.predict(X_test)\n score = api.score(y_pred, y_test)\n print(score)\n # Print the final ensemble built by AutoPyTorch\n print(api.show_models())" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/development/_downloads/e57ec61235a65031300d4a88d742ee2f/example_tabular_regression.ipynb b/development/_downloads/38ebc52de63d1626596d1647c695c721/example_tabular_regression.ipynb similarity index 65% rename from development/_downloads/e57ec61235a65031300d4a88d742ee2f/example_tabular_regression.ipynb rename to development/_downloads/38ebc52de63d1626596d1647c695c721/example_tabular_regression.ipynb index 5d2508408..e33d21abf 100644 --- a/development/_downloads/e57ec61235a65031300d4a88d742ee2f/example_tabular_regression.ipynb +++ b/development/_downloads/38ebc52de63d1626596d1647c695c721/example_tabular_regression.ipynb @@ -26,7 +26,7 @@ }, "outputs": [], "source": [ - "import os\nimport tempfile as tmp\nimport warnings\n\nimport sklearn.datasets\nimport sklearn.model_selection\n\nos.environ['JOBLIB_TEMP_FOLDER'] = tmp.gettempdir()\nos.environ['OMP_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_NUM_THREADS'] = '1'\n\nwarnings.simplefilter(action='ignore', category=UserWarning)\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nfrom autoPyTorch.api.tabular_regression import TabularRegressionTask\n\n\nif __name__ == '__main__':\n\n ############################################################################\n # Data Loading\n # ============\n X, y = sklearn.datasets.fetch_openml(name='boston', return_X_y=True, as_frame=True)\n X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n X,\n y,\n random_state=1,\n )\n\n # Scale the regression targets to have zero mean and unit variance.\n # This is important for Neural Networks since predicting large target values would require very large weights.\n # One can later rescale the network predictions like this: y_pred = y_pred_scaled * y_train_std + y_train_mean\n y_train_mean = y_train.mean()\n y_train_std = y_train.std()\n\n y_train_scaled = (y_train - y_train_mean) / y_train_std\n y_test_scaled = (y_test - y_train_mean) / y_train_std\n\n ############################################################################\n # Build and fit a regressor\n # ==========================\n api = TabularRegressionTask(\n temporary_directory='./tmp/autoPyTorch_example_tmp_02',\n output_directory='./tmp/autoPyTorch_example_out_02',\n # To maintain logs of the run, set the next two as False\n delete_tmp_folder_after_terminate=True,\n delete_output_folder_after_terminate=True\n )\n\n ############################################################################\n # Search for an ensemble of machine learning algorithms\n # =====================================================\n api.search(\n X_train=X_train,\n y_train=y_train_scaled,\n X_test=X_test.copy(),\n y_test=y_test_scaled.copy(),\n optimize_metric='r2',\n total_walltime_limit=300,\n func_eval_time_limit_secs=50,\n enable_traditional_pipeline=False,\n )\n\n ############################################################################\n # Print the final ensemble performance\n # ====================================\n print(api.run_history, api.trajectory)\n y_pred_scaled = api.predict(X_test)\n\n # Rescale the Neural Network predictions into the original target range\n y_pred = y_pred_scaled * y_train_std + y_train_mean\n score = api.score(y_pred, y_test)\n\n print(score)\n # Print the final ensemble built by AutoPyTorch\n print(api.show_models())" + "import os\nimport tempfile as tmp\nimport warnings\n\nimport sklearn.datasets\nimport sklearn.model_selection\n\nos.environ['JOBLIB_TEMP_FOLDER'] = tmp.gettempdir()\nos.environ['OMP_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_NUM_THREADS'] = '1'\n\nwarnings.simplefilter(action='ignore', category=UserWarning)\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nfrom autoPyTorch.api.tabular_regression import TabularRegressionTask\n\n\nif __name__ == '__main__':\n\n ############################################################################\n # Data Loading\n # ============\n X, y = sklearn.datasets.fetch_openml(name='boston', return_X_y=True, as_frame=True)\n X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n X,\n y,\n random_state=1,\n )\n\n # Scale the regression targets to have zero mean and unit variance.\n # This is important for Neural Networks since predicting large target values would require very large weights.\n # One can later rescale the network predictions like this: y_pred = y_pred_scaled * y_train_std + y_train_mean\n y_train_mean = y_train.mean()\n y_train_std = y_train.std()\n\n y_train_scaled = (y_train - y_train_mean) / y_train_std\n y_test_scaled = (y_test - y_train_mean) / y_train_std\n\n ############################################################################\n # Build and fit a regressor\n # ==========================\n api = TabularRegressionTask()\n\n ############################################################################\n # Search for an ensemble of machine learning algorithms\n # =====================================================\n api.search(\n X_train=X_train,\n y_train=y_train_scaled,\n X_test=X_test.copy(),\n y_test=y_test_scaled.copy(),\n optimize_metric='r2',\n total_walltime_limit=300,\n func_eval_time_limit_secs=50,\n enable_traditional_pipeline=False,\n )\n\n ############################################################################\n # Print the final ensemble performance\n # ====================================\n print(api.run_history, api.trajectory)\n y_pred_scaled = api.predict(X_test)\n\n # Rescale the Neural Network predictions into the original target range\n y_pred = y_pred_scaled * y_train_std + y_train_mean\n score = api.score(y_pred, y_test)\n\n print(score)\n # Print the final ensemble built by AutoPyTorch\n print(api.show_models())" ] } ], diff --git a/development/_downloads/ed47d0f9eacbdcd05b74852b15d3e8e1/example_visualization.ipynb b/development/_downloads/3b0b756ccfcac69e6a1673e56f2f543f/example_visualization.ipynb similarity index 100% rename from development/_downloads/ed47d0f9eacbdcd05b74852b15d3e8e1/example_visualization.ipynb rename to development/_downloads/3b0b756ccfcac69e6a1673e56f2f543f/example_visualization.ipynb diff --git a/development/_downloads/cae1c6e84ce2874c73fc5e45da55b1b6/example_custom_configuration_space.ipynb b/development/_downloads/3f9c66ebcc4532fdade3cdaa4d769bde/example_custom_configuration_space.ipynb similarity index 100% rename from development/_downloads/cae1c6e84ce2874c73fc5e45da55b1b6/example_custom_configuration_space.ipynb rename to development/_downloads/3f9c66ebcc4532fdade3cdaa4d769bde/example_custom_configuration_space.ipynb diff --git a/development/_downloads/4cbefcc88d68bf84110d315dc5fdb8e1/example_resampling_strategy.ipynb b/development/_downloads/4cbefcc88d68bf84110d315dc5fdb8e1/example_resampling_strategy.ipynb new file mode 100644 index 000000000..bf58d792f --- /dev/null +++ b/development/_downloads/4cbefcc88d68bf84110d315dc5fdb8e1/example_resampling_strategy.ipynb @@ -0,0 +1,54 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Tabular Classification with different resampling strategy\n\nThe following example shows how to fit a sample classification model\nwith different resampling strategies in AutoPyTorch\nBy default, AutoPyTorch uses Holdout Validation with\na 67% train size split.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\nimport tempfile as tmp\nimport warnings\n\nos.environ['JOBLIB_TEMP_FOLDER'] = tmp.gettempdir()\nos.environ['OMP_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_NUM_THREADS'] = '1'\n\nwarnings.simplefilter(action='ignore', category=UserWarning)\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nimport sklearn.datasets\nimport sklearn.model_selection\n\nfrom autoPyTorch.api.tabular_classification import TabularClassificationTask\nfrom autoPyTorch.datasets.resampling_strategy import CrossValTypes, HoldoutValTypes\n\n\nif __name__ == '__main__':\n\n ############################################################################\n # Data Loading\n # ============\n X, y = sklearn.datasets.fetch_openml(data_id=40981, return_X_y=True, as_frame=True)\n X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n X,\n y,\n random_state=1,\n )\n\n ############################################################################\n # Build and fit a classifier with default resampling strategy\n # ===========================================================\n api = TabularClassificationTask(\n # 'HoldoutValTypes.holdout_validation' with 'val_share': 0.33\n # is the default argument setting for TabularClassificationTask.\n # It is explicitly specified in this example for demonstrational\n # purpose.\n resampling_strategy=HoldoutValTypes.holdout_validation,\n resampling_strategy_args={'val_share': 0.33}\n )\n\n ############################################################################\n # Search for an ensemble of machine learning algorithms\n # =====================================================\n api.search(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test.copy(),\n y_test=y_test.copy(),\n optimize_metric='accuracy',\n total_walltime_limit=150,\n func_eval_time_limit_secs=30\n )\n\n ############################################################################\n # Print the final ensemble performance\n # ====================================\n print(api.run_history, api.trajectory)\n y_pred = api.predict(X_test)\n score = api.score(y_pred, y_test)\n print(score)\n # Print the final ensemble built by AutoPyTorch\n print(api.show_models())\n\n ############################################################################\n\n ############################################################################\n # Build and fit a classifier with Cross validation resampling strategy\n # ====================================================================\n api = TabularClassificationTask(\n resampling_strategy=CrossValTypes.k_fold_cross_validation,\n resampling_strategy_args={'num_splits': 3}\n )\n\n ############################################################################\n # Search for an ensemble of machine learning algorithms\n # =====================================================\n api.search(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test.copy(),\n y_test=y_test.copy(),\n optimize_metric='accuracy',\n total_walltime_limit=150,\n func_eval_time_limit_secs=30\n )\n\n ############################################################################\n # Print the final ensemble performance\n # ====================================\n print(api.run_history, api.trajectory)\n y_pred = api.predict(X_test)\n score = api.score(y_pred, y_test)\n print(score)\n # Print the final ensemble built by AutoPyTorch\n print(api.show_models())\n\n ############################################################################\n\n ############################################################################\n # Build and fit a classifier with Stratified resampling strategy\n # ==============================================================\n api = TabularClassificationTask(\n # For demonstration purposes, we use\n # Stratified hold out validation. However,\n # one can also use CrossValTypes.stratified_k_fold_cross_validation.\n resampling_strategy=HoldoutValTypes.stratified_holdout_validation,\n resampling_strategy_args={'val_share': 0.33}\n )\n\n ############################################################################\n # Search for an ensemble of machine learning algorithms\n # =====================================================\n api.search(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test.copy(),\n y_test=y_test.copy(),\n optimize_metric='accuracy',\n total_walltime_limit=150,\n func_eval_time_limit_secs=30\n )\n\n ############################################################################\n # Print the final ensemble performance\n # ====================================\n print(api.run_history, api.trajectory)\n y_pred = api.predict(X_test)\n score = api.score(y_pred, y_test)\n print(score)\n # Print the final ensemble built by AutoPyTorch\n print(api.show_models())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/development/_downloads/960ad64c5b5f2c5335df9e3864fd3c2d/example_custom_configuration_space.py b/development/_downloads/5517f58caf37183d75ea0dc7cedf8b58/example_custom_configuration_space.py similarity index 100% rename from development/_downloads/960ad64c5b5f2c5335df9e3864fd3c2d/example_custom_configuration_space.py rename to development/_downloads/5517f58caf37183d75ea0dc7cedf8b58/example_custom_configuration_space.py diff --git a/development/_downloads/ff1caf117ce143ee4d6abd46896ef46a/example_tabular_classification.ipynb b/development/_downloads/6ee656697d20c490e1d49bdbfb69d108/example_tabular_classification.ipynb similarity index 62% rename from development/_downloads/ff1caf117ce143ee4d6abd46896ef46a/example_tabular_classification.ipynb rename to development/_downloads/6ee656697d20c490e1d49bdbfb69d108/example_tabular_classification.ipynb index e3a4c37a4..c6d62a0a5 100644 --- a/development/_downloads/ff1caf117ce143ee4d6abd46896ef46a/example_tabular_classification.ipynb +++ b/development/_downloads/6ee656697d20c490e1d49bdbfb69d108/example_tabular_classification.ipynb @@ -26,7 +26,7 @@ }, "outputs": [], "source": [ - "import os\nimport tempfile as tmp\nimport warnings\n\nos.environ['JOBLIB_TEMP_FOLDER'] = tmp.gettempdir()\nos.environ['OMP_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_NUM_THREADS'] = '1'\n\nwarnings.simplefilter(action='ignore', category=UserWarning)\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nimport sklearn.datasets\nimport sklearn.model_selection\n\nfrom autoPyTorch.api.tabular_classification import TabularClassificationTask\n\n\nif __name__ == '__main__':\n\n ############################################################################\n # Data Loading\n # ============\n X, y = sklearn.datasets.fetch_openml(data_id=40981, return_X_y=True, as_frame=True)\n X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n X,\n y,\n random_state=42,\n )\n\n ############################################################################\n # Build and fit a classifier\n # ==========================\n api = TabularClassificationTask(\n temporary_directory='./tmp/autoPyTorch_example_tmp_01',\n output_directory='./tmp/autoPyTorch_example_out_01',\n # To maintain logs of the run, set the next two as False\n delete_tmp_folder_after_terminate=True,\n delete_output_folder_after_terminate=True,\n seed=42,\n )\n\n ############################################################################\n # Search for an ensemble of machine learning algorithms\n # =====================================================\n api.search(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test.copy(),\n y_test=y_test.copy(),\n optimize_metric='accuracy',\n total_walltime_limit=300,\n func_eval_time_limit_secs=50\n )\n\n ############################################################################\n # Print the final ensemble performance\n # ====================================\n print(api.run_history, api.trajectory)\n y_pred = api.predict(X_test)\n score = api.score(y_pred, y_test)\n print(score)\n # Print the final ensemble built by AutoPyTorch\n print(api.show_models())" + "import os\nimport tempfile as tmp\nimport warnings\n\nos.environ['JOBLIB_TEMP_FOLDER'] = tmp.gettempdir()\nos.environ['OMP_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_NUM_THREADS'] = '1'\n\nwarnings.simplefilter(action='ignore', category=UserWarning)\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nimport sklearn.datasets\nimport sklearn.model_selection\n\nfrom autoPyTorch.api.tabular_classification import TabularClassificationTask\n\n\nif __name__ == '__main__':\n\n ############################################################################\n # Data Loading\n # ============\n X, y = sklearn.datasets.fetch_openml(data_id=40981, return_X_y=True, as_frame=True)\n X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n X,\n y,\n random_state=42,\n )\n\n ############################################################################\n # Build and fit a classifier\n # ==========================\n api = TabularClassificationTask(\n # To maintain logs of the run, you can uncomment the\n # Following lines\n # temporary_directory='./tmp/autoPyTorch_example_tmp_01',\n # output_directory='./tmp/autoPyTorch_example_out_01',\n # delete_tmp_folder_after_terminate=False,\n # delete_output_folder_after_terminate=False,\n seed=42,\n )\n\n ############################################################################\n # Search for an ensemble of machine learning algorithms\n # =====================================================\n api.search(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test.copy(),\n y_test=y_test.copy(),\n optimize_metric='accuracy',\n total_walltime_limit=300,\n func_eval_time_limit_secs=50\n )\n\n ############################################################################\n # Print the final ensemble performance\n # ====================================\n print(api.run_history, api.trajectory)\n y_pred = api.predict(X_test)\n score = api.score(y_pred, y_test)\n print(score)\n # Print the final ensemble built by AutoPyTorch\n print(api.show_models())" ] } ], diff --git a/development/_downloads/8ef6602cf8ed40221edd89244fb031af/basics_tabular_python.zip b/development/_downloads/8ef6602cf8ed40221edd89244fb031af/basics_tabular_python.zip deleted file mode 100644 index a64e724fc751702b437ee12806c19b1191cb48e3..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 5593 zcmeHL&u<(x6b>yQFZD^wb0v#VFH2GPtCr3x>0mJ7 zWTd22Q<08IIWRF3H8liZ#40ipmM~(xNu+HGNz{8=z^g-vlgYErqx9MQ#PM_``?BCuw?EFX~ zrx%X7bzKdG?)F+b2avsc?+zV&bGUQs_K{cUY<1c){hhnp`?rqH6uh&yzf>lu>u~Ih8CxCCdzd>dQl4=#I)d@%3#uKVCo+h&ETB#UD zH#WcgVrzqFjulSneL9NxsTy#DhPg2pRzCEK zUWl4Z3fuJ^x;47LQQyv*Tg}p;N~p02pXkP?#f#pBt?G6z#E)xv-=)@+BGF-0C#zqH>$%_QjMT+g4sD z14UefT6~C9V?)%4SU{i7;Z)}_O zh}5~t$IhaBY4i)J7|s!s7w+&Q;6^+F}YhGwU=?5)bI zNW>#f6K=JLqYjJXT(fvK7h_ggCh3$(30Y1h6m0a_=4La-C{JU`Pa)Mv0A^zoeZJ|< zytqv5YlN>4(-9(0Hm=LKhBp(I#znk7FJ~J+d22J*XV;YivsU_uWFpgC#stNEIXc_eH}xSvj#|V3Q@svlg>( z$k~|fcnbB9Yr4H&(63ol^7Yfh-CzFv^|yD|*4*dAC9F#GvF6}aFUqJ6Yx#3yQ;iAd z%cRN@zE}ts>BB;?`k#R

K-}?72*8&Yx`-)x^$!fI&Ip7j=#m;35UneVrN9R)O6W zER&;{q1yS%UuQG1Ok+=hO^G5CcFM^ku0a<#>Z2n@d5ZndDbvCsZ0KGip7F#4#~Cp z$xYnPvaG+Tm>4yx-iPiH8!rdMm4xe{5x^7Pw*{Q7P6kE6SAFtHt?c^ik?Zv{)GJ+- zjmp|W6%A)-dew8>eXJK9l0~9iX1aP6p4zW-IkNXamBHue z+S0TCK1N*TL_ob__3Z3j=#r@8$)@@u-kADFcj{*1VlO$8JF01M1IHB zhU@cp_DZt(PqOF+)0UCxGAs}00KQ#US~w2^uY5hjR&DGz24!`@LBhD$5WFh>pUvpK zsTWP@-?((;^6H~fcZBM$Cy(8CeSEO$;IzNjbhm7e;5u>FlgHoVEOxQGhTh=}w!in* Q+fMWn{(i@A?>+bQ7wqtTg#Z8m diff --git a/development/_downloads/891dce186e83b2762165f10ee2ec99ee/example_visualization.py b/development/_downloads/a4083e360cf01f594602cbaf737091b0/example_visualization.py similarity index 100% rename from development/_downloads/891dce186e83b2762165f10ee2ec99ee/example_visualization.py rename to development/_downloads/a4083e360cf01f594602cbaf737091b0/example_visualization.py diff --git a/development/_downloads/aabde7b67f18388826b238683edce405/example_image_classification.ipynb b/development/_downloads/aabde7b67f18388826b238683edce405/example_image_classification.ipynb new file mode 100644 index 000000000..4ea0b44ec --- /dev/null +++ b/development/_downloads/aabde7b67f18388826b238683edce405/example_image_classification.ipynb @@ -0,0 +1,54 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Image Classification\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n\nimport sklearn.model_selection\n\nimport torchvision.datasets\n\nfrom autoPyTorch.pipeline.image_classification import ImageClassificationPipeline\n\n# Get the training data for tabular classification\ntrainset = torchvision.datasets.FashionMNIST(root='../datasets/', train=True, download=True)\ndata = trainset.data.numpy()\ndata = np.expand_dims(data, axis=3)\n# Create a proof of concept pipeline!\ndataset_properties = dict()\npipeline = ImageClassificationPipeline(dataset_properties=dataset_properties)\n\n# Train and test split\ntrain_indices, val_indices = sklearn.model_selection.train_test_split(\n list(range(data.shape[0])),\n random_state=1,\n test_size=0.25,\n)\n\n# Configuration space\npipeline_cs = pipeline.get_hyperparameter_search_space()\nprint(\"Pipeline CS:\\n\", '_' * 40, f\"\\n{pipeline_cs}\")\nconfig = pipeline_cs.sample_configuration()\nprint(\"Pipeline Random Config:\\n\", '_' * 40, f\"\\n{config}\")\npipeline.set_hyperparameters(config)\n\n# Fit the pipeline\nprint(\"Fitting the pipeline...\")\n\npipeline.fit(X=dict(X_train=data,\n is_small_preprocess=True,\n dataset_properties=dict(mean=np.array([np.mean(data[:, :, :, i]) for i in range(1)]),\n std=np.array([np.std(data[:, :, :, i]) for i in range(1)]),\n num_classes=10,\n num_features=data.shape[1] * data.shape[2],\n image_height=data.shape[1],\n image_width=data.shape[2],\n is_small_preprocess=True),\n train_indices=train_indices,\n val_indices=val_indices,\n )\n )\n\n# Showcase some components of the pipeline\nprint(pipeline)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/development/_downloads/aadb4b239ce5d2908c9c7c2ba1b19df1/example_image_classification.py b/development/_downloads/aadb4b239ce5d2908c9c7c2ba1b19df1/example_image_classification.py new file mode 100644 index 000000000..881d18f06 --- /dev/null +++ b/development/_downloads/aadb4b239ce5d2908c9c7c2ba1b19df1/example_image_classification.py @@ -0,0 +1,54 @@ +""" +====================== +Image Classification +====================== +""" +import numpy as np + +import sklearn.model_selection + +import torchvision.datasets + +from autoPyTorch.pipeline.image_classification import ImageClassificationPipeline + +# Get the training data for tabular classification +trainset = torchvision.datasets.FashionMNIST(root='../datasets/', train=True, download=True) +data = trainset.data.numpy() +data = np.expand_dims(data, axis=3) +# Create a proof of concept pipeline! +dataset_properties = dict() +pipeline = ImageClassificationPipeline(dataset_properties=dataset_properties) + +# Train and test split +train_indices, val_indices = sklearn.model_selection.train_test_split( + list(range(data.shape[0])), + random_state=1, + test_size=0.25, +) + +# Configuration space +pipeline_cs = pipeline.get_hyperparameter_search_space() +print("Pipeline CS:\n", '_' * 40, f"\n{pipeline_cs}") +config = pipeline_cs.sample_configuration() +print("Pipeline Random Config:\n", '_' * 40, f"\n{config}") +pipeline.set_hyperparameters(config) + +# Fit the pipeline +print("Fitting the pipeline...") + +pipeline.fit(X=dict(X_train=data, + is_small_preprocess=True, + dataset_properties=dict(mean=np.array([np.mean(data[:, :, :, i]) for i in range(1)]), + std=np.array([np.std(data[:, :, :, i]) for i in range(1)]), + num_classes=10, + num_features=data.shape[1] * data.shape[2], + image_height=data.shape[1], + image_width=data.shape[2], + is_small_preprocess=True), + train_indices=train_indices, + val_indices=val_indices, + ) + ) + +# Showcase some components of the pipeline +print(pipeline) diff --git a/development/_downloads/000ffe6d9d5014b4165debb6cbf446f8/example_tabular_regression.py b/development/_downloads/bbd6ad27117588060207b551c10df82e/example_tabular_regression.py similarity index 88% rename from development/_downloads/000ffe6d9d5014b4165debb6cbf446f8/example_tabular_regression.py rename to development/_downloads/bbd6ad27117588060207b551c10df82e/example_tabular_regression.py index d2afedd52..ef8cacb37 100644 --- a/development/_downloads/000ffe6d9d5014b4165debb6cbf446f8/example_tabular_regression.py +++ b/development/_downloads/bbd6ad27117588060207b551c10df82e/example_tabular_regression.py @@ -48,13 +48,7 @@ ############################################################################ # Build and fit a regressor # ========================== - api = TabularRegressionTask( - temporary_directory='./tmp/autoPyTorch_example_tmp_02', - output_directory='./tmp/autoPyTorch_example_out_02', - # To maintain logs of the run, set the next two as False - delete_tmp_folder_after_terminate=True, - delete_output_folder_after_terminate=True - ) + api = TabularRegressionTask() ############################################################################ # Search for an ensemble of machine learning 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development/_downloads/83c92a475e127571aad1d77baa11248d/example_tabular_classification.py rename to development/_downloads/eaf3a203570ee37a60e53304131a24a3/example_tabular_classification.py index 1fbec6718..c0251fa90 100644 --- a/development/_downloads/83c92a475e127571aad1d77baa11248d/example_tabular_classification.py +++ b/development/_downloads/eaf3a203570ee37a60e53304131a24a3/example_tabular_classification.py @@ -40,11 +40,12 @@ # Build and fit a classifier # ========================== api = TabularClassificationTask( - temporary_directory='./tmp/autoPyTorch_example_tmp_01', - output_directory='./tmp/autoPyTorch_example_out_01', - # To maintain logs of the run, set the next two as False - delete_tmp_folder_after_terminate=True, - delete_output_folder_after_terminate=True, + # To maintain logs of the run, you can uncomment the + # Following lines + # temporary_directory='./tmp/autoPyTorch_example_tmp_01', + # output_directory='./tmp/autoPyTorch_example_out_01', + # 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z*E>XtjF1)mfYcgnSQ8jhe6Ik9Mcn0EadKCm=$g7QwO_wUaxkcwofFUx7)Oc{h%)bn zME3fsoxUq9;Odmgli5C}!=M9`BWiCEA@c0LiF|$o6E37gK^_2b-JO6)69|EheFie> zgsMaqgvBaS1g6u{3s8dkJUS;9IWj-QI|6dAXikVrNdDU!8BvK(6e=DShe{9zO!@oXn)Mz)%b<6h|Uo|!O-(=hA<5O(r{`P;X0ynP! diff --git a/development/_modules/autoPyTorch/api/tabular_classification.html b/development/_modules/autoPyTorch/api/tabular_classification.html index 838d14488..dfb7da617 100644 --- a/development/_modules/autoPyTorch/api/tabular_classification.html +++ b/development/_modules/autoPyTorch/api/tabular_classification.html @@ -407,7 +407,7 @@

Source code for autoPyTorch.api.tabular_classification

Sphinx 3.5.4.

diff --git a/development/_modules/index.html b/development/_modules/index.html index a8820ffd4..94ae8d410 100644 --- a/development/_modules/index.html +++ b/development/_modules/index.html @@ -128,7 +128,7 @@

All modules for which code is available

- © Copyright 2014-2019, Machine Learning Professorship Freiburg.
+ © Copyright 2014-2021, Machine Learning Professorship Freiburg.
Created using Sphinx 3.5.4.

diff --git a/development/_sources/advanced_tabular/index.rst.txt b/development/_sources/advanced_tabular/index.rst.txt deleted file mode 100644 index cc73fe7c8..000000000 --- a/development/_sources/advanced_tabular/index.rst.txt +++ /dev/null @@ -1,110 +0,0 @@ -:orphan: - - - -.. _sphx_glr_advanced_tabular: - -.. _examples_tabular_basics: - - -============================== -Advanced Tabular Dataset Examples -============================== - -Advanced examples for using *Auto-PyTorch* on tabular datasets. -We explain - 1. How to customise the search space - 2. How to split the data according to different resampling strategies - - - -.. raw:: html - -
- -.. only:: html - - .. figure:: /advanced_tabular/images/thumb/sphx_glr_example_custom_configuration_space_thumb.png - :alt: Tabular Classification with Custom Configuration Space - - :ref:`sphx_glr_advanced_tabular_example_custom_configuration_space.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /advanced_tabular/example_custom_configuration_space - -.. raw:: html - -
- -.. only:: html - - .. figure:: /advanced_tabular/images/thumb/sphx_glr_example_visualization_thumb.png - :alt: Visualizing the Results - - :ref:`sphx_glr_advanced_tabular_example_visualization.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /advanced_tabular/example_visualization - -.. raw:: html - -
- -.. only:: html - - .. figure:: /advanced_tabular/images/thumb/sphx_glr_example_resampling_strategy_thumb.png - :alt: Tabular Classification with different resampling strategy - - :ref:`sphx_glr_advanced_tabular_example_resampling_strategy.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /advanced_tabular/example_resampling_strategy -.. raw:: html - -
- - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-gallery - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples in Python source code: advanced_tabular_python.zip ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: advanced_tabular_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/development/_sources/advanced_tabular/sg_execution_times.rst.txt b/development/_sources/advanced_tabular/sg_execution_times.rst.txt deleted file mode 100644 index c5089885a..000000000 --- a/development/_sources/advanced_tabular/sg_execution_times.rst.txt +++ /dev/null @@ -1,16 +0,0 @@ - -:orphan: - -.. _sphx_glr_advanced_tabular_sg_execution_times: - -Computation times -================= -**26:10.373** total execution time for **advanced_tabular** files: - -+--------------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_advanced_tabular_example_custom_configuration_space.py` (``example_custom_configuration_space.py``) | 11:35.664 | 0.0 MB | -+--------------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_advanced_tabular_example_resampling_strategy.py` (``example_resampling_strategy.py``) | 10:06.674 | 0.0 MB | -+--------------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_advanced_tabular_example_visualization.py` (``example_visualization.py``) | 04:28.036 | 0.0 MB | -+--------------------------------------------------------------------------------------------------------------------+-----------+--------+ diff --git a/development/_sources/basics_tabular/index.rst.txt b/development/_sources/basics_tabular/index.rst.txt deleted file mode 100644 index 9f2e0c5b4..000000000 --- a/development/_sources/basics_tabular/index.rst.txt +++ /dev/null @@ -1,86 +0,0 @@ -:orphan: - - - -.. _sphx_glr_basics_tabular: - -.. _examples_tabular_basics: - - -============================== -Basic Tabular Dataset Examples -============================== - -Basic examples for using *Auto-PyTorch* on tabular datasets - - - -.. raw:: html - -
- -.. only:: html - - .. figure:: /basics_tabular/images/thumb/sphx_glr_example_tabular_classification_thumb.png - :alt: Tabular Classification - - :ref:`sphx_glr_basics_tabular_example_tabular_classification.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /basics_tabular/example_tabular_classification - -.. raw:: html - -
- -.. only:: html - - .. figure:: /basics_tabular/images/thumb/sphx_glr_example_tabular_regression_thumb.png - :alt: Tabular Regression - - :ref:`sphx_glr_basics_tabular_example_tabular_regression.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /basics_tabular/example_tabular_regression -.. raw:: html - -
- - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-gallery - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples in Python source code: basics_tabular_python.zip ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: basics_tabular_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/development/_sources/basics_tabular/sg_execution_times.rst.txt b/development/_sources/basics_tabular/sg_execution_times.rst.txt deleted file mode 100644 index 97a49c4a0..000000000 --- a/development/_sources/basics_tabular/sg_execution_times.rst.txt +++ /dev/null @@ -1,14 +0,0 @@ - -:orphan: - -.. _sphx_glr_basics_tabular_sg_execution_times: - -Computation times -================= -**11:10.597** total execution time for **basics_tabular** files: - -+----------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_basics_tabular_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:58.374 | 0.0 MB | -+----------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_basics_tabular_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:12.223 | 0.0 MB | -+----------------------------------------------------------------------------------------------------------+-----------+--------+ diff --git a/development/_sources/examples/20_basics/example_image_classification.rst.txt b/development/_sources/examples/20_basics/example_image_classification.rst.txt new file mode 100644 index 000000000..59bd0e39e --- /dev/null +++ b/development/_sources/examples/20_basics/example_image_classification.rst.txt @@ -0,0 +1,215 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/20_basics/example_image_classification.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + Click :ref:`here ` + to download the full example code or to run this example in your browser via Binder + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_20_basics_example_image_classification.py: + + +====================== +Image Classification +====================== + +.. GENERATED FROM PYTHON SOURCE LINES 6-55 + + + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz + Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ../datasets/FashionMNIST/raw/train-images-idx3-ubyte.gz + 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 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98.6% 98.6% 98.6% 98.6% 98.6% 98.6% 98.6% 98.6% 98.6% 98.6% 98.6% 98.6% 98.6% 98.6% 98.7% 98.7% 98.7% 98.7% 98.7% 98.7% 98.7% 98.7% 98.7% 98.7% 98.7% 98.7% 98.7% 98.7% 98.7% 98.7% 98.7% 98.7% 98.7% 98.7% 98.7% 98.7% 98.7% 98.7% 98.7% 98.7% 98.8% 98.8% 98.8% 98.8% 98.8% 98.8% 98.8% 98.8% 98.8% 98.8% 98.8% 98.8% 98.8% 98.8% 98.8% 98.8% 98.8% 98.8% 98.8% 98.8% 98.8% 98.8% 98.8% 98.8% 98.8% 98.9% 98.9% 98.9% 98.9% 98.9% 98.9% 98.9% 98.9% 98.9% 98.9% 98.9% 98.9% 98.9% 98.9% 98.9% 98.9% 98.9% 98.9% 98.9% 98.9% 98.9% 98.9% 98.9% 98.9% 98.9% 98.9% 99.0% 99.0% 99.0% 99.0% 99.0% 99.0% 99.0% 99.0% 99.0% 99.0% 99.0% 99.0% 99.0% 99.0% 99.0% 99.0% 99.0% 99.0% 99.0% 99.0% 99.0% 99.0% 99.0% 99.0% 99.0% 99.0% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% 99.2% 99.2% 99.2% 99.2% 99.2% 99.2% 99.2% 99.2% 99.2% 99.2% 99.2% 99.2% 99.2% 99.2% 99.2% 99.2% 99.2% 99.2% 99.2% 99.2% 99.2% 99.2% 99.2% 99.2% 99.2% 99.2% 99.3% 99.3% 99.3% 99.3% 99.3% 99.3% 99.3% 99.3% 99.3% 99.3% 99.3% 99.3% 99.3% 99.3% 99.3% 99.3% 99.3% 99.3% 99.3% 99.3% 99.3% 99.3% 99.3% 99.3% 99.3% 99.4% 99.4% 99.4% 99.4% 99.4% 99.4% 99.4% 99.4% 99.4% 99.4% 99.4% 99.4% 99.4% 99.4% 99.4% 99.4% 99.4% 99.4% 99.4% 99.4% 99.4% 99.4% 99.4% 99.4% 99.4% 99.4% 99.5% 99.5% 99.5% 99.5% 99.5% 99.5% 99.5% 99.5% 99.5% 99.5% 99.5% 99.5% 99.5% 99.5% 99.5% 99.5% 99.5% 99.5% 99.5% 99.5% 99.5% 99.5% 99.5% 99.5% 99.5% 99.5% 99.6% 99.6% 99.6% 99.6% 99.6% 99.6% 99.6% 99.6% 99.6% 99.6% 99.6% 99.6% 99.6% 99.6% 99.6% 99.6% 99.6% 99.6% 99.6% 99.6% 99.6% 99.6% 99.6% 99.6% 99.6% 99.6% 99.7% 99.7% 99.7% 99.7% 99.7% 99.7% 99.7% 99.7% 99.7% 99.7% 99.7% 99.7% 99.7% 99.7% 99.7% 99.7% 99.7% 99.7% 99.7% 99.7% 99.7% 99.7% 99.7% 99.7% 99.7% 99.7% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9% 99.9% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% + Extracting ../datasets/FashionMNIST/raw/train-images-idx3-ubyte.gz to ../datasets/FashionMNIST/raw + + Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz + Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ../datasets/FashionMNIST/raw/train-labels-idx1-ubyte.gz + 3.5% 6.9% 10.4% 13.9% 17.3% 20.8% 24.3% 27.8% 31.2% 34.7% 38.2% 41.6% 45.1% 48.6% 52.0% 55.5% 59.0% 62.4% 65.9% 69.4% 72.9% 76.3% 79.8% 83.3% 86.7% 90.2% 93.7% 97.1% 100.6% + Extracting ../datasets/FashionMNIST/raw/train-labels-idx1-ubyte.gz to ../datasets/FashionMNIST/raw + + Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz + Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ../datasets/FashionMNIST/raw/t10k-images-idx3-ubyte.gz + 0.0% 0.0% 0.1% 0.1% 0.1% 0.1% 0.2% 0.2% 0.2% 0.2% 0.3% 0.3% 0.3% 0.3% 0.3% 0.4% 0.4% 0.4% 0.4% 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.6% 0.7% 0.7% 0.7% 0.7% 0.8% 0.8% 0.8% 0.8% 0.9% 0.9% 0.9% 0.9% 0.9% 1.0% 1.0% 1.0% 1.0% 1.1% 1.1% 1.1% 1.1% 1.2% 1.2% 1.2% 1.2% 1.3% 1.3% 1.3% 1.3% 1.3% 1.4% 1.4% 1.4% 1.4% 1.5% 1.5% 1.5% 1.5% 1.6% 1.6% 1.6% 1.6% 1.6% 1.7% 1.7% 1.7% 1.7% 1.8% 1.8% 1.8% 1.8% 1.9% 1.9% 1.9% 1.9% 1.9% 2.0% 2.0% 2.0% 2.0% 2.1% 2.1% 2.1% 2.1% 2.2% 2.2% 2.2% 2.2% 2.2% 2.3% 2.3% 2.3% 2.3% 2.4% 2.4% 2.4% 2.4% 2.5% 2.5% 2.5% 2.5% 2.5% 2.6% 2.6% 2.6% 2.6% 2.7% 2.7% 2.7% 2.7% 2.8% 2.8% 2.8% 2.8% 2.8% 2.9% 2.9% 2.9% 2.9% 3.0% 3.0% 3.0% 3.0% 3.1% 3.1% 3.1% 3.1% 3.1% 3.2% 3.2% 3.2% 3.2% 3.3% 3.3% 3.3% 3.3% 3.4% 3.4% 3.4% 3.4% 3.5% 3.5% 3.5% 3.5% 3.5% 3.6% 3.6% 3.6% 3.6% 3.7% 3.7% 3.7% 3.7% 3.8% 3.8% 3.8% 3.8% 3.8% 3.9% 3.9% 3.9% 3.9% 4.0% 4.0% 4.0% 4.0% 4.1% 4.1% 4.1% 4.1% 4.1% 4.2% 4.2% 4.2% 4.2% 4.3% 4.3% 4.3% 4.3% 4.4% 4.4% 4.4% 4.4% 4.4% 4.5% 4.5% 4.5% 4.5% 4.6% 4.6% 4.6% 4.6% 4.7% 4.7% 4.7% 4.7% 4.7% 4.8% 4.8% 4.8% 4.8% 4.9% 4.9% 4.9% 4.9% 5.0% 5.0% 5.0% 5.0% 5.0% 5.1% 5.1% 5.1% 5.1% 5.2% 5.2% 5.2% 5.2% 5.3% 5.3% 5.3% 5.3% 5.3% 5.4% 5.4% 5.4% 5.4% 5.5% 5.5% 5.5% 5.5% 5.6% 5.6% 5.6% 5.6% 5.7% 5.7% 5.7% 5.7% 5.7% 5.8% 5.8% 5.8% 5.8% 5.9% 5.9% 5.9% 5.9% 6.0% 6.0% 6.0% 6.0% 6.0% 6.1% 6.1% 6.1% 6.1% 6.2% 6.2% 6.2% 6.2% 6.3% 6.3% 6.3% 6.3% 6.3% 6.4% 6.4% 6.4% 6.4% 6.5% 6.5% 6.5% 6.5% 6.6% 6.6% 6.6% 6.6% 6.6% 6.7% 6.7% 6.7% 6.7% 6.8% 6.8% 6.8% 6.8% 6.9% 6.9% 6.9% 6.9% 6.9% 7.0% 7.0% 7.0% 7.0% 7.1% 7.1% 7.1% 7.1% 7.2% 7.2% 7.2% 7.2% 7.2% 7.3% 7.3% 7.3% 7.3% 7.4% 7.4% 7.4% 7.4% 7.5% 7.5% 7.5% 7.5% 7.5% 7.6% 7.6% 7.6% 7.6% 7.7% 7.7% 7.7% 7.7% 7.8% 7.8% 7.8% 7.8% 7.9% 7.9% 7.9% 7.9% 7.9% 8.0% 8.0% 8.0% 8.0% 8.1% 8.1% 8.1% 8.1% 8.2% 8.2% 8.2% 8.2% 8.2% 8.3% 8.3% 8.3% 8.3% 8.4% 8.4% 8.4% 8.4% 8.5% 8.5% 8.5% 8.5% 8.5% 8.6% 8.6% 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93.4% 93.4% 93.5% 93.5% 93.5% 93.5% 93.6% 93.6% 93.6% 93.6% 93.6% 93.7% 93.7% 93.7% 93.7% 93.8% 93.8% 93.8% 93.8% 93.9% 93.9% 93.9% 93.9% 93.9% 94.0% 94.0% 94.0% 94.0% 94.1% 94.1% 94.1% 94.1% 94.2% 94.2% 94.2% 94.2% 94.2% 94.3% 94.3% 94.3% 94.3% 94.4% 94.4% 94.4% 94.4% 94.5% 94.5% 94.5% 94.5% 94.5% 94.6% 94.6% 94.6% 94.6% 94.7% 94.7% 94.7% 94.7% 94.8% 94.8% 94.8% 94.8% 94.8% 94.9% 94.9% 94.9% 94.9% 95.0% 95.0% 95.0% 95.0% 95.1% 95.1% 95.1% 95.1% 95.1% 95.2% 95.2% 95.2% 95.2% 95.3% 95.3% 95.3% 95.3% 95.4% 95.4% 95.4% 95.4% 95.5% 95.5% 95.5% 95.5% 95.5% 95.6% 95.6% 95.6% 95.6% 95.7% 95.7% 95.7% 95.7% 95.8% 95.8% 95.8% 95.8% 95.8% 95.9% 95.9% 95.9% 95.9% 96.0% 96.0% 96.0% 96.0% 96.1% 96.1% 96.1% 96.1% 96.1% 96.2% 96.2% 96.2% 96.2% 96.3% 96.3% 96.3% 96.3% 96.4% 96.4% 96.4% 96.4% 96.4% 96.5% 96.5% 96.5% 96.5% 96.6% 96.6% 96.6% 96.6% 96.7% 96.7% 96.7% 96.7% 96.7% 96.8% 96.8% 96.8% 96.8% 96.9% 96.9% 96.9% 96.9% 97.0% 97.0% 97.0% 97.0% 97.0% 97.1% 97.1% 97.1% 97.1% 97.2% 97.2% 97.2% 97.2% 97.3% 97.3% 97.3% 97.3% 97.3% 97.4% 97.4% 97.4% 97.4% 97.5% 97.5% 97.5% 97.5% 97.6% 97.6% 97.6% 97.6% 97.7% 97.7% 97.7% 97.7% 97.7% 97.8% 97.8% 97.8% 97.8% 97.9% 97.9% 97.9% 97.9% 98.0% 98.0% 98.0% 98.0% 98.0% 98.1% 98.1% 98.1% 98.1% 98.2% 98.2% 98.2% 98.2% 98.3% 98.3% 98.3% 98.3% 98.3% 98.4% 98.4% 98.4% 98.4% 98.5% 98.5% 98.5% 98.5% 98.6% 98.6% 98.6% 98.6% 98.6% 98.7% 98.7% 98.7% 98.7% 98.8% 98.8% 98.8% 98.8% 98.9% 98.9% 98.9% 98.9% 98.9% 99.0% 99.0% 99.0% 99.0% 99.1% 99.1% 99.1% 99.1% 99.2% 99.2% 99.2% 99.2% 99.2% 99.3% 99.3% 99.3% 99.3% 99.4% 99.4% 99.4% 99.4% 99.5% 99.5% 99.5% 99.5% 99.5% 99.6% 99.6% 99.6% 99.6% 99.7% 99.7% 99.7% 99.7% 99.8% 99.8% 99.8% 99.8% 99.9% 99.9% 99.9% 99.9% 99.9% 100.0% 100.0% 100.0% + Extracting ../datasets/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ../datasets/FashionMNIST/raw + + Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz + Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ../datasets/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz + 19.9% 39.8% 59.7% 79.6% 99.5% 119.3% + Extracting ../datasets/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ../datasets/FashionMNIST/raw + + Processing... + /opt/hostedtoolcache/Python/3.8.9/x64/lib/python3.8/site-packages/torchvision/datasets/mnist.py:502: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:143.) + return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s) + Done! + Pipeline CS: + ________________________________________ + Configuration space object: + Hyperparameters: + image_augmenter:GaussianBlur:sigma_min, Type: UniformFloat, Range: [0.0, 3.0], Default: 0.0 + image_augmenter:GaussianBlur:sigma_offset, Type: UniformFloat, Range: [0.0, 3.0], Default: 0.5 + image_augmenter:GaussianBlur:use_augmenter, Type: Categorical, Choices: {True, False}, Default: True + image_augmenter:GaussianNoise:sigma_offset, Type: UniformFloat, Range: [0.0, 3.0], Default: 0.3 + image_augmenter:GaussianNoise:use_augmenter, Type: Categorical, Choices: {True, False}, Default: True + image_augmenter:RandomAffine:rotate, Type: UniformInteger, Range: [0, 360], Default: 45 + image_augmenter:RandomAffine:scale_offset, Type: UniformFloat, Range: [0.0, 0.4], Default: 0.2 + image_augmenter:RandomAffine:shear, Type: UniformInteger, Range: [0, 45], Default: 30 + image_augmenter:RandomAffine:translate_percent_offset, Type: UniformFloat, Range: [0.0, 0.4], Default: 0.2 + image_augmenter:RandomAffine:use_augmenter, Type: Categorical, Choices: {True, False}, Default: True + image_augmenter:RandomCutout:p, Type: UniformFloat, Range: [0.2, 1.0], Default: 0.5 + image_augmenter:RandomCutout:use_augmenter, Type: Categorical, Choices: {True, False}, Default: True + image_augmenter:Resize:use_augmenter, Type: Categorical, Choices: {True, False}, Default: True + image_augmenter:ZeroPadAndCrop:percent, Type: UniformFloat, Range: [0.0, 0.5], Default: 0.1 + normalizer:__choice__, Type: Categorical, Choices: {ImageNormalizer, NoNormalizer}, Default: ImageNormalizer + Conditions: + image_augmenter:GaussianBlur:sigma_min | image_augmenter:GaussianBlur:use_augmenter == True + image_augmenter:GaussianBlur:sigma_offset | image_augmenter:GaussianBlur:use_augmenter == True + image_augmenter:GaussianNoise:sigma_offset | image_augmenter:GaussianNoise:use_augmenter == True + image_augmenter:RandomAffine:rotate | image_augmenter:RandomAffine:use_augmenter == True + image_augmenter:RandomAffine:scale_offset | image_augmenter:RandomAffine:use_augmenter == True + image_augmenter:RandomAffine:shear | image_augmenter:RandomAffine:use_augmenter == True + image_augmenter:RandomAffine:translate_percent_offset | image_augmenter:RandomAffine:use_augmenter == True + image_augmenter:RandomCutout:p | image_augmenter:RandomCutout:use_augmenter == True + + Pipeline Random Config: + ________________________________________ + Configuration: + image_augmenter:GaussianBlur:sigma_min, Value: 1.7739059525100553 + image_augmenter:GaussianBlur:sigma_offset, Value: 1.596831438367099 + image_augmenter:GaussianBlur:use_augmenter, Value: True + image_augmenter:GaussianNoise:use_augmenter, Value: False + image_augmenter:RandomAffine:use_augmenter, Value: False + image_augmenter:RandomCutout:p, Value: 0.8069029436295518 + image_augmenter:RandomCutout:use_augmenter, Value: True + image_augmenter:Resize:use_augmenter, Value: True + image_augmenter:ZeroPadAndCrop:percent, Value: 0.13165420478079426 + normalizer:__choice__, Value: 'ImageNormalizer' + + Fitting the pipeline... + ________________________________________ + ImageClassificationPipeline + ________________________________________ + 0-) normalizer: + ImageNormalizer + + 1-) preprocessing: + EarlyPreprocessing + + 2-) image_augmenter: + ImageAugmenter + + ________________________________________ + + + + + + +| + +.. code-block:: default + + import numpy as np + + import sklearn.model_selection + + import torchvision.datasets + + from autoPyTorch.pipeline.image_classification import ImageClassificationPipeline + + # Get the training data for tabular classification + trainset = torchvision.datasets.FashionMNIST(root='../datasets/', train=True, download=True) + data = trainset.data.numpy() + data = np.expand_dims(data, axis=3) + # Create a proof of concept pipeline! + dataset_properties = dict() + pipeline = ImageClassificationPipeline(dataset_properties=dataset_properties) + + # Train and test split + train_indices, val_indices = sklearn.model_selection.train_test_split( + list(range(data.shape[0])), + random_state=1, + test_size=0.25, + ) + + # Configuration space + pipeline_cs = pipeline.get_hyperparameter_search_space() + print("Pipeline CS:\n", '_' * 40, f"\n{pipeline_cs}") + config = pipeline_cs.sample_configuration() + print("Pipeline Random Config:\n", '_' * 40, f"\n{config}") + pipeline.set_hyperparameters(config) + + # Fit the pipeline + print("Fitting the pipeline...") + + pipeline.fit(X=dict(X_train=data, + is_small_preprocess=True, + dataset_properties=dict(mean=np.array([np.mean(data[:, :, :, i]) for i in range(1)]), + std=np.array([np.std(data[:, :, :, i]) for i in range(1)]), + num_classes=10, + num_features=data.shape[1] * data.shape[2], + image_height=data.shape[1], + image_width=data.shape[2], + is_small_preprocess=True), + train_indices=train_indices, + val_indices=val_indices, + ) + ) + + # Showcase some components of the pipeline + print(pipeline) + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 10.175 seconds) + + +.. _sphx_glr_download_examples_20_basics_example_image_classification.py: + + +.. only :: html + + .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + + + .. container:: binder-badge + + .. image:: images/binder_badge_logo.svg + :target: https://mybinder.org/v2/gh/automl/Auto-PyTorch/refactor_development?urlpath=lab/tree/notebooks/examples/20_basics/example_image_classification.ipynb + :alt: Launch binder + :width: 150 px + + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: example_image_classification.py ` + + + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: example_image_classification.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/development/_sources/basics_tabular/example_tabular_classification.rst.txt b/development/_sources/examples/20_basics/example_tabular_classification.rst.txt similarity index 73% rename from development/_sources/basics_tabular/example_tabular_classification.rst.txt rename to development/_sources/examples/20_basics/example_tabular_classification.rst.txt index d3c29a7d3..d61b87f57 100644 --- a/development/_sources/basics_tabular/example_tabular_classification.rst.txt +++ b/development/_sources/examples/20_basics/example_tabular_classification.rst.txt @@ -2,7 +2,7 @@ .. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "basics_tabular/example_tabular_classification.py" +.. "examples/20_basics/example_tabular_classification.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html @@ -10,12 +10,12 @@ .. note:: :class: sphx-glr-download-link-note - Click :ref:`here ` + Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title -.. _sphx_glr_basics_tabular_example_tabular_classification.py: +.. _sphx_glr_examples_20_basics_example_tabular_classification.py: ====================== @@ -25,7 +25,7 @@ Tabular Classification The following example shows how to fit a sample classification model with AutoPyTorch -.. GENERATED FROM PYTHON SOURCE LINES 9-73 +.. GENERATED FROM PYTHON SOURCE LINES 9-74 @@ -36,7 +36,7 @@ with AutoPyTorch .. code-block:: none - [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration: + [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration: data_loader:batch_size, Value: 64 encoder:__choice__, Value: 'OneHotEncoder' feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor' @@ -68,7 +68,7 @@ with AutoPyTorch scaler:__choice__, Value: 'StandardScaler' trainer:StandardTrainer:weighted_loss, Value: True trainer:__choice__, Value: 'StandardTrainer' - , ta_runs=0, ta_time_used=0.0, wallclock_time=0.001850128173828125, budget=0), TrajEntry(train_perf=0.16374269005847952, incumbent_id=1, incumbent=Configuration: + , ta_runs=0, ta_time_used=0.0, wallclock_time=0.001987457275390625, budget=0), TrajEntry(train_perf=0.16374269005847952, incumbent_id=1, incumbent=Configuration: data_loader:batch_size, Value: 64 encoder:__choice__, Value: 'OneHotEncoder' feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor' @@ -100,7 +100,7 @@ with AutoPyTorch scaler:__choice__, Value: 'StandardScaler' trainer:StandardTrainer:weighted_loss, Value: True trainer:__choice__, Value: 'StandardTrainer' - , ta_runs=1, ta_time_used=4.503080606460571, wallclock_time=5.962664842605591, budget=5.555555555555555), TrajEntry(train_perf=0.1578947368421053, incumbent_id=2, incumbent=Configuration: + , ta_runs=1, ta_time_used=5.665798902511597, wallclock_time=7.321491241455078, budget=5.555555555555555), TrajEntry(train_perf=0.1578947368421053, incumbent_id=2, incumbent=Configuration: data_loader:batch_size, Value: 224 encoder:__choice__, Value: 'OneHotEncoder' feature_preprocessor:Nystroem:kernel, Value: 'cosine' @@ -158,19 +158,53 @@ with AutoPyTorch scaler:__choice__, Value: 'NoScaler' trainer:StandardTrainer:weighted_loss, Value: True trainer:__choice__, Value: 'StandardTrainer' - , ta_runs=8, ta_time_used=61.97297525405884, wallclock_time=78.95482158660889, budget=5.555555555555555)] - {'accuracy': 0.8786127167630058} + , ta_runs=8, ta_time_used=82.00660920143127, wallclock_time=100.8822808265686, budget=5.555555555555555), TrajEntry(train_perf=0.1578947368421053, incumbent_id=3, incumbent=Configuration: + data_loader:batch_size, Value: 64 + encoder:__choice__, Value: 'OneHotEncoder' + feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor' + imputer:categorical_strategy, Value: 'most_frequent' + imputer:numerical_strategy, Value: 'mean' + lr_scheduler:ReduceLROnPlateau:factor, Value: 0.1 + lr_scheduler:ReduceLROnPlateau:mode, Value: 'min' + lr_scheduler:ReduceLROnPlateau:patience, Value: 10 + lr_scheduler:__choice__, Value: 'ReduceLROnPlateau' + network_backbone:ShapedMLPBackbone:activation, Value: 'relu' + network_backbone:ShapedMLPBackbone:max_units, Value: 200 + network_backbone:ShapedMLPBackbone:mlp_shape, Value: 'funnel' + network_backbone:ShapedMLPBackbone:num_groups, Value: 5 + network_backbone:ShapedMLPBackbone:output_dim, Value: 200 + network_backbone:ShapedMLPBackbone:use_dropout, Value: False + network_backbone:__choice__, Value: 'ShapedMLPBackbone' + network_embedding:__choice__, Value: 'NoEmbedding' + network_head:__choice__, Value: 'fully_connected' + network_head:fully_connected:activation, Value: 'relu' + network_head:fully_connected:num_layers, Value: 2 + network_head:fully_connected:units_layer_1, Value: 128 + network_init:XavierInit:bias_strategy, Value: 'Normal' + network_init:__choice__, Value: 'XavierInit' + optimizer:AdamOptimizer:beta1, Value: 0.9 + optimizer:AdamOptimizer:beta2, Value: 0.9 + optimizer:AdamOptimizer:lr, Value: 0.01 + optimizer:AdamOptimizer:weight_decay, Value: 0.0 + optimizer:__choice__, Value: 'AdamOptimizer' + scaler:__choice__, Value: 'StandardScaler' + trainer:StandardTrainer:weighted_loss, Value: True + trainer:__choice__, Value: 'StandardTrainer' + , ta_runs=11, ta_time_used=109.19088363647461, wallclock_time=136.7932755947113, budget=16.666666666666664)] + {'accuracy': 0.8670520231213873} | | Preprocessing | Estimator | Weight | |---:|:------------------------------------------------------------------|:-------------------------------------------------------------------|---------:| | 0 | None | RFClassifier | 0.24 | - | 1 | None | ExtraTreesClassifier | 0.24 | - | 2 | None | CatBoostClassifier | 0.18 | - | 3 | SimpleImputer,OneHotEncoder,MinMaxScaler,KernelPCA | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.16 | - | 4 | SimpleImputer,OneHotEncoder,MinMaxScaler,KernelPCA | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.06 | - | 5 | None | SVC | 0.06 | - | 6 | SimpleImputer,OneHotEncoder,Normalizer,Nystroem | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 | - | 7 | None | KNNClassifier | 0.02 | - | 8 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 | + | 1 | None | CatBoostClassifier | 0.22 | + | 2 | SimpleImputer,OneHotEncoder,MinMaxScaler,KernelPCA | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.16 | + | 3 | None | ExtraTreesClassifier | 0.08 | + | 4 | None | SVC | 0.08 | + | 5 | SimpleImputer,OneHotEncoder,NoScaler,Nystroem | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.06 | + | 6 | SimpleImputer,OneHotEncoder,MinMaxScaler,KernelPCA | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 | + | 7 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 | + | 8 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 | + | 9 | None | LGBMClassifier | 0.02 | + | 10 | None | KNNClassifier | 0.02 | @@ -215,11 +249,12 @@ with AutoPyTorch # Build and fit a classifier # ========================== api = TabularClassificationTask( - temporary_directory='./tmp/autoPyTorch_example_tmp_01', - output_directory='./tmp/autoPyTorch_example_out_01', - # To maintain logs of the run, set the next two as False - delete_tmp_folder_after_terminate=True, - delete_output_folder_after_terminate=True, + # To maintain logs of the run, you can uncomment the + # Following lines + # temporary_directory='./tmp/autoPyTorch_example_tmp_01', + # output_directory='./tmp/autoPyTorch_example_out_01', + # delete_tmp_folder_after_terminate=False, + # delete_output_folder_after_terminate=False, seed=42, ) @@ -249,10 +284,10 @@ with AutoPyTorch .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 5 minutes 58.374 seconds) + **Total running time of the script:** ( 6 minutes 0.058 seconds) -.. _sphx_glr_download_basics_tabular_example_tabular_classification.py: +.. _sphx_glr_download_examples_20_basics_example_tabular_classification.py: .. only :: html @@ -264,7 +299,7 @@ with AutoPyTorch .. container:: binder-badge .. image:: images/binder_badge_logo.svg - :target: https://mybinder.org/v2/gh/automl/Auto-PyTorch/refactor_development?urlpath=lab/tree/notebooks/basics_tabular/example_tabular_classification.ipynb + :target: https://mybinder.org/v2/gh/automl/Auto-PyTorch/refactor_development?urlpath=lab/tree/notebooks/examples/20_basics/example_tabular_classification.ipynb :alt: Launch binder :width: 150 px diff --git a/development/_sources/basics_tabular/example_tabular_regression.rst.txt b/development/_sources/examples/20_basics/example_tabular_regression.rst.txt similarity index 85% rename from development/_sources/basics_tabular/example_tabular_regression.rst.txt rename to development/_sources/examples/20_basics/example_tabular_regression.rst.txt index a82abe820..ca35f7a9e 100644 --- a/development/_sources/basics_tabular/example_tabular_regression.rst.txt +++ b/development/_sources/examples/20_basics/example_tabular_regression.rst.txt @@ -2,7 +2,7 @@ .. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "basics_tabular/example_tabular_regression.py" +.. "examples/20_basics/example_tabular_regression.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html @@ -10,12 +10,12 @@ .. note:: :class: sphx-glr-download-link-note - Click :ref:`here ` + Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title -.. _sphx_glr_basics_tabular_example_tabular_regression.py: +.. _sphx_glr_examples_20_basics_example_tabular_regression.py: ====================== @@ -25,7 +25,7 @@ Tabular Regression The following example shows how to fit a sample regression model with AutoPyTorch -.. GENERATED FROM PYTHON SOURCE LINES 9-86 +.. GENERATED FROM PYTHON SOURCE LINES 9-80 @@ -36,7 +36,7 @@ with AutoPyTorch .. code-block:: none - [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration: + [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration: data_loader:batch_size, Value: 64 encoder:__choice__, Value: 'OneHotEncoder' feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor' @@ -67,7 +67,7 @@ with AutoPyTorch optimizer:__choice__, Value: 'AdamOptimizer' scaler:__choice__, Value: 'StandardScaler' trainer:__choice__, Value: 'StandardTrainer' - , ta_runs=0, ta_time_used=0.0, wallclock_time=0.00196075439453125, budget=0), TrajEntry(train_perf=0.5922365951048683, incumbent_id=1, incumbent=Configuration: + , ta_runs=0, ta_time_used=0.0, wallclock_time=0.0020246505737304688, budget=0), TrajEntry(train_perf=0.5480914591658931, incumbent_id=1, incumbent=Configuration: data_loader:batch_size, Value: 64 encoder:__choice__, Value: 'OneHotEncoder' feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor' @@ -98,13 +98,13 @@ with AutoPyTorch optimizer:__choice__, Value: 'AdamOptimizer' scaler:__choice__, Value: 'StandardScaler' trainer:__choice__, Value: 'StandardTrainer' - , ta_runs=1, ta_time_used=3.864063024520874, wallclock_time=7.359654188156128, budget=5.555555555555555)] - {'r2': 0.8929146098903469} + , ta_runs=1, ta_time_used=4.880286455154419, wallclock_time=8.926568984985352, budget=5.555555555555555)] + {'r2': 0.9253036125186885} | | Preprocessing | Estimator | Weight | |---:|:------------------------------------------------------------------|:----------------------------------------------------------------|---------:| - | 0 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.52 | - | 1 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.46 | - | 2 | SimpleImputer,OneHotEncoder,Normalizer,Nystroem | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 | + | 0 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.82 | + | 1 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.14 | + | 2 | SimpleImputer,OneHotEncoder,Normalizer,Nystroem | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 | @@ -157,13 +157,7 @@ with AutoPyTorch ############################################################################ # Build and fit a regressor # ========================== - api = TabularRegressionTask( - temporary_directory='./tmp/autoPyTorch_example_tmp_02', - output_directory='./tmp/autoPyTorch_example_out_02', - # To maintain logs of the run, set the next two as False - delete_tmp_folder_after_terminate=True, - delete_output_folder_after_terminate=True - ) + api = TabularRegressionTask() ############################################################################ # Search for an ensemble of machine learning algorithms @@ -196,10 +190,10 @@ with AutoPyTorch .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 5 minutes 12.223 seconds) + **Total running time of the script:** ( 5 minutes 16.780 seconds) -.. _sphx_glr_download_basics_tabular_example_tabular_regression.py: +.. _sphx_glr_download_examples_20_basics_example_tabular_regression.py: .. only :: html @@ -211,7 +205,7 @@ with AutoPyTorch .. container:: binder-badge .. image:: images/binder_badge_logo.svg - :target: https://mybinder.org/v2/gh/automl/Auto-PyTorch/refactor_development?urlpath=lab/tree/notebooks/basics_tabular/example_tabular_regression.ipynb + :target: https://mybinder.org/v2/gh/automl/Auto-PyTorch/refactor_development?urlpath=lab/tree/notebooks/examples/20_basics/example_tabular_regression.ipynb :alt: Launch binder :width: 150 px diff --git a/development/_sources/examples/20_basics/sg_execution_times.rst.txt b/development/_sources/examples/20_basics/sg_execution_times.rst.txt new file mode 100644 index 000000000..2828fdd7c --- /dev/null +++ b/development/_sources/examples/20_basics/sg_execution_times.rst.txt @@ -0,0 +1,16 @@ + +:orphan: + +.. _sphx_glr_examples_20_basics_sg_execution_times: + +Computation times +================= +**11:27.012** total execution time for **examples_20_basics** files: + ++--------------------------------------------------------------------------------------------------------------+-----------+--------+ +| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 06:00.058 | 0.0 MB | ++--------------------------------------------------------------------------------------------------------------+-----------+--------+ +| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:16.780 | 0.0 MB | ++--------------------------------------------------------------------------------------------------------------+-----------+--------+ +| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:10.175 | 0.0 MB | ++--------------------------------------------------------------------------------------------------------------+-----------+--------+ diff --git a/development/_sources/advanced_tabular/example_custom_configuration_space.rst.txt b/development/_sources/examples/40_advanced/example_custom_configuration_space.rst.txt similarity index 89% rename from development/_sources/advanced_tabular/example_custom_configuration_space.rst.txt rename to development/_sources/examples/40_advanced/example_custom_configuration_space.rst.txt index 952b57dd4..a7051906c 100644 --- a/development/_sources/advanced_tabular/example_custom_configuration_space.rst.txt +++ b/development/_sources/examples/40_advanced/example_custom_configuration_space.rst.txt @@ -2,7 +2,7 @@ .. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "advanced_tabular/example_custom_configuration_space.py" +.. "examples/40_advanced/example_custom_configuration_space.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html @@ -10,12 +10,12 @@ .. note:: :class: sphx-glr-download-link-note - Click :ref:`here ` + Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title -.. _sphx_glr_advanced_tabular_example_custom_configuration_space.py: +.. _sphx_glr_examples_40_advanced_example_custom_configuration_space.py: ====================== @@ -46,7 +46,7 @@ the search. Currently, there are two changes that can be made to the space:- .. code-block:: none - [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration: + [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration: data_loader:batch_size, Value: 32 encoder:__choice__, Value: 'OneHotEncoder' feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor' @@ -80,7 +80,7 @@ the search. Currently, there are two changes that can be made to the space:- scaler:__choice__, Value: 'StandardScaler' trainer:StandardTrainer:weighted_loss, Value: True trainer:__choice__, Value: 'StandardTrainer' - , ta_runs=0, ta_time_used=0.0, wallclock_time=0.0018227100372314453, budget=0), TrajEntry(train_perf=0.16374269005847952, incumbent_id=1, incumbent=Configuration: + , ta_runs=0, ta_time_used=0.0, wallclock_time=0.0019881725311279297, budget=0), TrajEntry(train_perf=0.16374269005847952, incumbent_id=1, incumbent=Configuration: data_loader:batch_size, Value: 32 encoder:__choice__, Value: 'OneHotEncoder' feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor' @@ -114,7 +114,7 @@ the search. Currently, there are two changes that can be made to the space:- scaler:__choice__, Value: 'StandardScaler' trainer:StandardTrainer:weighted_loss, Value: True trainer:__choice__, Value: 'StandardTrainer' - , ta_runs=1, ta_time_used=4.638380527496338, wallclock_time=6.107091426849365, budget=5.555555555555555), TrajEntry(train_perf=0.1578947368421053, incumbent_id=2, incumbent=Configuration: + , ta_runs=1, ta_time_used=5.924399137496948, wallclock_time=7.483955383300781, budget=5.555555555555555), TrajEntry(train_perf=0.1578947368421053, incumbent_id=2, incumbent=Configuration: data_loader:batch_size, Value: 475 encoder:__choice__, Value: 'OneHotEncoder' feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor' @@ -162,22 +162,23 @@ the search. Currently, there are two changes that can be made to the space:- scaler:__choice__, Value: 'Normalizer' trainer:StandardTrainer:weighted_loss, Value: False trainer:__choice__, Value: 'StandardTrainer' - , ta_runs=12, ta_time_used=123.52750444412231, wallclock_time=150.68852996826172, budget=16.666666666666664)] + , ta_runs=12, ta_time_used=166.8644254207611, wallclock_time=197.1043405532837, budget=16.666666666666664)] {'accuracy': 0.8554913294797688} | | Preprocessing | Estimator | Weight | |---:|:------------------------------------------------------------------|:----------------------------------------------------------|---------:| - | 0 | SimpleImputer,OneHotEncoder,Normalizer,KernelPCA | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.18 | - | 1 | None | SVC | 0.16 | + | 0 | None | SVC | 0.18 | + | 1 | None | RFClassifier | 0.16 | | 2 | None | CatBoostClassifier | 0.14 | - | 3 | None | ExtraTreesClassifier | 0.14 | + | 3 | SimpleImputer,OneHotEncoder,Normalizer,KernelPCA | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.12 | | 4 | SimpleImputer,OneHotEncoder,Normalizer,KernelPCA | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.1 | - | 5 | None | RFClassifier | 0.08 | - | 6 | SimpleImputer,OneHotEncoder,Normalizer,NoFeaturePreprocessing | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 | - | 7 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 | - | 8 | None | KNNClassifier | 0.04 | + | 5 | SimpleImputer,OneHotEncoder,Normalizer,NoFeaturePreprocessing | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 | + | 6 | None | ExtraTreesClassifier | 0.08 | + | 7 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 | + | 8 | SimpleImputer,OneHotEncoder,Normalizer,NoFeaturePreprocessing | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 | | 9 | SimpleImputer,OneHotEncoder,Normalizer,NoFeaturePreprocessing | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 | - | 10 | SimpleImputer,OneHotEncoder,Normalizer,NoFeaturePreprocessing | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 | - [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration: + | 10 | None | KNNClassifier | 0.02 | + | 11 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 | + [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration: data_loader:batch_size, Value: 32 encoder:__choice__, Value: 'NoEncoder' feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor' @@ -209,7 +210,7 @@ the search. Currently, there are two changes that can be made to the space:- scaler:__choice__, Value: 'StandardScaler' trainer:StandardTrainer:weighted_loss, Value: True trainer:__choice__, Value: 'StandardTrainer' - , ta_runs=0, ta_time_used=0.0, wallclock_time=0.0014028549194335938, budget=0), TrajEntry(train_perf=0.16374269005847952, incumbent_id=1, incumbent=Configuration: + , ta_runs=0, ta_time_used=0.0, wallclock_time=0.0016286373138427734, budget=0), TrajEntry(train_perf=0.16374269005847952, incumbent_id=1, incumbent=Configuration: data_loader:batch_size, Value: 32 encoder:__choice__, Value: 'NoEncoder' feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor' @@ -241,18 +242,17 @@ the search. Currently, there are two changes that can be made to the space:- scaler:__choice__, Value: 'StandardScaler' trainer:StandardTrainer:weighted_loss, Value: True trainer:__choice__, Value: 'StandardTrainer' - , ta_runs=1, ta_time_used=4.025576591491699, wallclock_time=5.487119197845459, budget=5.555555555555555)] + , ta_runs=1, ta_time_used=5.018240690231323, wallclock_time=6.567354679107666, budget=5.555555555555555)] {'accuracy': 0.8554913294797688} | | Preprocessing | Estimator | Weight | |---:|:--------------------------------------------------------------|:-------------------------------------------------------------------|---------:| - | 0 | None | CatBoostClassifier | 0.28 | - | 1 | SimpleImputer,NoEncoder,MinMaxScaler,Nystroem | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.2 | - | 2 | SimpleImputer,NoEncoder,MinMaxScaler,Nystroem | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.16 | - | 3 | None | SVC | 0.14 | - | 4 | None | KNNClassifier | 0.12 | - | 5 | None | RFClassifier | 0.04 | - | 6 | None | ExtraTreesClassifier | 0.04 | - | 7 | SimpleImputer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 | + | 0 | None | CatBoostClassifier | 0.74 | + | 1 | None | SVC | 0.08 | + | 2 | None | KNNClassifier | 0.06 | + | 3 | None | RFClassifier | 0.04 | + | 4 | None | ExtraTreesClassifier | 0.04 | + | 5 | SimpleImputer,NoEncoder,MinMaxScaler,NoFeaturePreprocessing | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 | + | 6 | SimpleImputer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 | @@ -381,10 +381,10 @@ the search. Currently, there are two changes that can be made to the space:- .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 11 minutes 35.664 seconds) + **Total running time of the script:** ( 11 minutes 43.644 seconds) -.. _sphx_glr_download_advanced_tabular_example_custom_configuration_space.py: +.. _sphx_glr_download_examples_40_advanced_example_custom_configuration_space.py: .. only :: html @@ -396,7 +396,7 @@ the search. Currently, there are two changes that can be made to the space:- .. container:: binder-badge .. image:: images/binder_badge_logo.svg - :target: https://mybinder.org/v2/gh/automl/Auto-PyTorch/refactor_development?urlpath=lab/tree/notebooks/advanced_tabular/example_custom_configuration_space.ipynb + :target: https://mybinder.org/v2/gh/automl/Auto-PyTorch/refactor_development?urlpath=lab/tree/notebooks/examples/40_advanced/example_custom_configuration_space.ipynb :alt: Launch binder :width: 150 px diff --git a/development/_sources/advanced_tabular/example_resampling_strategy.rst.txt b/development/_sources/examples/40_advanced/example_resampling_strategy.rst.txt similarity index 83% rename from development/_sources/advanced_tabular/example_resampling_strategy.rst.txt rename to development/_sources/examples/40_advanced/example_resampling_strategy.rst.txt index 7e58ae242..f21ec2091 100644 --- a/development/_sources/advanced_tabular/example_resampling_strategy.rst.txt +++ b/development/_sources/examples/40_advanced/example_resampling_strategy.rst.txt @@ -2,7 +2,7 @@ .. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "advanced_tabular/example_resampling_strategy.py" +.. "examples/40_advanced/example_resampling_strategy.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html @@ -10,12 +10,12 @@ .. note:: :class: sphx-glr-download-link-note - Click :ref:`here ` + Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title -.. _sphx_glr_advanced_tabular_example_resampling_strategy.py: +.. _sphx_glr_examples_40_advanced_example_resampling_strategy.py: ====================== @@ -27,7 +27,7 @@ with different resampling strategies in AutoPyTorch By default, AutoPyTorch uses Holdout Validation with a 67% train size split. -.. GENERATED FROM PYTHON SOURCE LINES 11-160 +.. GENERATED FROM PYTHON SOURCE LINES 11-145 @@ -38,7 +38,7 @@ a 67% train size split. .. code-block:: none - [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration: + [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration: data_loader:batch_size, Value: 64 encoder:__choice__, Value: 'OneHotEncoder' feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor' @@ -70,7 +70,7 @@ a 67% train size split. scaler:__choice__, Value: 'StandardScaler' trainer:StandardTrainer:weighted_loss, Value: True trainer:__choice__, Value: 'StandardTrainer' - , ta_runs=0, ta_time_used=0.0, wallclock_time=0.0018551349639892578, budget=0), TrajEntry(train_perf=0.18128654970760238, incumbent_id=1, incumbent=Configuration: + , ta_runs=0, ta_time_used=0.0, wallclock_time=0.0025005340576171875, budget=0), TrajEntry(train_perf=0.18128654970760238, incumbent_id=1, incumbent=Configuration: data_loader:batch_size, Value: 64 encoder:__choice__, Value: 'OneHotEncoder' feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor' @@ -102,23 +102,22 @@ a 67% train size split. scaler:__choice__, Value: 'StandardScaler' trainer:StandardTrainer:weighted_loss, Value: True trainer:__choice__, Value: 'StandardTrainer' - , ta_runs=1, ta_time_used=4.567504167556763, wallclock_time=6.137902498245239, budget=5.555555555555555)] - {'accuracy': 0.861271676300578} + , ta_runs=1, ta_time_used=5.704225063323975, wallclock_time=7.272337436676025, budget=5.555555555555555)] + {'accuracy': 0.8554913294797688} | | Preprocessing | Estimator | Weight | |---:|:------------------------------------------------------------------|:----------------------------------------------------------------|---------:| - | 0 | None | RFClassifier | 0.22 | - | 1 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.18 | - | 2 | None | ExtraTreesClassifier | 0.12 | - | 3 | None | KNNClassifier | 0.12 | - | 4 | SimpleImputer,NoEncoder,NoScaler,Nystroem | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.08 | - | 5 | None | CatBoostClassifier | 0.08 | - | 6 | None | SVC | 0.06 | - | 7 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 | - | 8 | SimpleImputer,NoEncoder,NoScaler,Nystroem | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 | - | 9 | SimpleImputer,OneHotEncoder,NoScaler,NoFeaturePreprocessing | embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 | - | 10 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 | - | 11 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 | - [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration: + | 0 | None | KNNClassifier | 0.22 | + | 1 | None | CatBoostClassifier | 0.2 | + | 2 | SimpleImputer,NoEncoder,NoScaler,Nystroem | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.18 | + | 3 | SimpleImputer,OneHotEncoder,NoScaler,NoFeaturePreprocessing | embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.08 | + | 4 | None | SVC | 0.08 | + | 5 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 | + | 6 | None | RFClassifier | 0.04 | + | 7 | None | ExtraTreesClassifier | 0.04 | + | 8 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 | + | 9 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 | + | 10 | None | LGBMClassifier | 0.02 | + [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration: data_loader:batch_size, Value: 64 encoder:__choice__, Value: 'OneHotEncoder' feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor' @@ -150,7 +149,7 @@ a 67% train size split. scaler:__choice__, Value: 'StandardScaler' trainer:StandardTrainer:weighted_loss, Value: True trainer:__choice__, Value: 'StandardTrainer' - , ta_runs=0, ta_time_used=0.0, wallclock_time=0.0019214153289794922, budget=0), TrajEntry(train_perf=0.15480222378634426, incumbent_id=1, incumbent=Configuration: + , ta_runs=0, ta_time_used=0.0, wallclock_time=0.002166748046875, budget=0), TrajEntry(train_perf=0.15480222378634426, incumbent_id=1, incumbent=Configuration: data_loader:batch_size, Value: 64 encoder:__choice__, Value: 'OneHotEncoder' feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor' @@ -182,13 +181,13 @@ a 67% train size split. scaler:__choice__, Value: 'StandardScaler' trainer:StandardTrainer:weighted_loss, Value: True trainer:__choice__, Value: 'StandardTrainer' - , ta_runs=1, ta_time_used=8.385476112365723, wallclock_time=9.87209415435791, budget=5.555555555555555)] + , ta_runs=1, ta_time_used=10.995928049087524, wallclock_time=12.509535551071167, budget=5.555555555555555)] {'accuracy': 0.884393063583815} | | Preprocessing | Estimator | Weight | |---:|:-----------------------------------------------------|:----------------------------------------------------------------|---------:| | 0 | None | TabularClassifier | 0.86 | | 1 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.14 | - [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration: + [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration: data_loader:batch_size, Value: 64 encoder:__choice__, Value: 'OneHotEncoder' feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor' @@ -220,7 +219,7 @@ a 67% train size split. scaler:__choice__, Value: 'StandardScaler' trainer:StandardTrainer:weighted_loss, Value: True trainer:__choice__, Value: 'StandardTrainer' - , ta_runs=0, ta_time_used=0.0, wallclock_time=0.0019407272338867188, budget=0), TrajEntry(train_perf=0.17543859649122806, incumbent_id=1, incumbent=Configuration: + , ta_runs=0, ta_time_used=0.0, wallclock_time=0.0022859573364257812, budget=0), TrajEntry(train_perf=0.17543859649122806, incumbent_id=1, incumbent=Configuration: data_loader:batch_size, Value: 64 encoder:__choice__, Value: 'OneHotEncoder' feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor' @@ -252,20 +251,19 @@ a 67% train size split. scaler:__choice__, Value: 'StandardScaler' trainer:StandardTrainer:weighted_loss, Value: True trainer:__choice__, Value: 'StandardTrainer' - , ta_runs=1, ta_time_used=4.53068208694458, wallclock_time=6.004504919052124, budget=5.555555555555555)] - {'accuracy': 0.8554913294797688} + , ta_runs=1, ta_time_used=5.71893310546875, wallclock_time=7.28941011428833, budget=5.555555555555555)] + {'accuracy': 0.8728323699421965} | | Preprocessing | Estimator | Weight | |---:|:------------------------------------------------------------------|:----------------------------------------------------------------|---------:| - | 0 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.22 | - | 1 | SimpleImputer,NoEncoder,NoScaler,Nystroem | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.2 | - | 2 | None | CatBoostClassifier | 0.12 | + | 0 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.24 | + | 1 | SimpleImputer,NoEncoder,NoScaler,Nystroem | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.18 | + | 2 | None | LGBMClassifier | 0.12 | | 3 | None | RFClassifier | 0.12 | - | 4 | None | KNNClassifier | 0.12 | - | 5 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 | - | 6 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 | - | 7 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 | - | 8 | None | SVC | 0.02 | - | 9 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 | + | 4 | None | CatBoostClassifier | 0.1 | + | 5 | None | ExtraTreesClassifier | 0.1 | + | 6 | None | SVC | 0.06 | + | 7 | None | KNNClassifier | 0.04 | + | 8 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 | @@ -311,11 +309,6 @@ a 67% train size split. # Build and fit a classifier with default resampling strategy # =========================================================== api = TabularClassificationTask( - temporary_directory='./tmp/autoPyTorch_example_tmp_03', - output_directory='./tmp/autoPyTorch_example_out_03', - # To maintain logs of the run, set the next two as False - delete_tmp_folder_after_terminate=True, - delete_output_folder_after_terminate=True, # 'HoldoutValTypes.holdout_validation' with 'val_share': 0.33 # is the default argument setting for TabularClassificationTask. # It is explicitly specified in this example for demonstrational @@ -353,11 +346,6 @@ a 67% train size split. # Build and fit a classifier with Cross validation resampling strategy # ==================================================================== api = TabularClassificationTask( - temporary_directory='./tmp/autoPyTorch_example_tmp_04', - output_directory='./tmp/autoPyTorch_example_out_04', - # To maintain logs of the run, set the next two as False - delete_tmp_folder_after_terminate=True, - delete_output_folder_after_terminate=True, resampling_strategy=CrossValTypes.k_fold_cross_validation, resampling_strategy_args={'num_splits': 3} ) @@ -391,11 +379,6 @@ a 67% train size split. # Build and fit a classifier with Stratified resampling strategy # ============================================================== api = TabularClassificationTask( - temporary_directory='./tmp/autoPyTorch_example_tmp_05', - output_directory='./tmp/autoPyTorch_example_out_05', - # To maintain logs of the run, set the next two as False - delete_tmp_folder_after_terminate=True, - delete_output_folder_after_terminate=True, # For demonstration purposes, we use # Stratified hold out validation. However, # one can also use CrossValTypes.stratified_k_fold_cross_validation. @@ -429,10 +412,10 @@ a 67% train size split. .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 10 minutes 6.674 seconds) + **Total running time of the script:** ( 10 minutes 23.225 seconds) -.. _sphx_glr_download_advanced_tabular_example_resampling_strategy.py: +.. _sphx_glr_download_examples_40_advanced_example_resampling_strategy.py: .. only :: html @@ -444,7 +427,7 @@ a 67% train size split. .. container:: binder-badge .. image:: images/binder_badge_logo.svg - :target: https://mybinder.org/v2/gh/automl/Auto-PyTorch/refactor_development?urlpath=lab/tree/notebooks/advanced_tabular/example_resampling_strategy.ipynb + :target: https://mybinder.org/v2/gh/automl/Auto-PyTorch/refactor_development?urlpath=lab/tree/notebooks/examples/40_advanced/example_resampling_strategy.ipynb :alt: Launch binder :width: 150 px diff --git a/development/_sources/advanced_tabular/example_visualization.rst.txt b/development/_sources/examples/40_advanced/example_visualization.rst.txt similarity index 93% rename from development/_sources/advanced_tabular/example_visualization.rst.txt rename to development/_sources/examples/40_advanced/example_visualization.rst.txt index 40d4b1b42..30fd5093d 100644 --- a/development/_sources/advanced_tabular/example_visualization.rst.txt +++ b/development/_sources/examples/40_advanced/example_visualization.rst.txt @@ -2,7 +2,7 @@ .. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "advanced_tabular/example_visualization.py" +.. "examples/40_advanced/example_visualization.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html @@ -10,12 +10,12 @@ .. note:: :class: sphx-glr-download-link-note - Click :ref:`here ` + Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title -.. _sphx_glr_advanced_tabular_example_visualization.py: +.. _sphx_glr_examples_40_advanced_example_visualization.py: ======================= @@ -42,13 +42,13 @@ we show how to further interact with `Scikit-Learn Inspection * - .. image:: /advanced_tabular/images/sphx_glr_example_visualization_001.png + .. image:: /examples/40_advanced/images/sphx_glr_example_visualization_001.png :alt: Auto-PyTorch accuracy over time :class: sphx-glr-multi-img * - .. image:: /advanced_tabular/images/sphx_glr_example_visualization_002.png + .. image:: /examples/40_advanced/images/sphx_glr_example_visualization_002.png :alt: Permutation Importances (Train set) :class: sphx-glr-multi-img @@ -212,10 +212,10 @@ we show how to further interact with `Scikit-Learn Inspection .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 4 minutes 28.036 seconds) + **Total running time of the script:** ( 4 minutes 46.879 seconds) -.. _sphx_glr_download_advanced_tabular_example_visualization.py: +.. _sphx_glr_download_examples_40_advanced_example_visualization.py: .. only :: html @@ -227,7 +227,7 @@ we show how to further interact with `Scikit-Learn Inspection .. container:: binder-badge .. image:: images/binder_badge_logo.svg - :target: https://mybinder.org/v2/gh/automl/Auto-PyTorch/refactor_development?urlpath=lab/tree/notebooks/advanced_tabular/example_visualization.ipynb + :target: https://mybinder.org/v2/gh/automl/Auto-PyTorch/refactor_development?urlpath=lab/tree/notebooks/examples/40_advanced/example_visualization.ipynb :alt: Launch binder :width: 150 px diff --git a/development/_sources/examples/40_advanced/sg_execution_times.rst.txt b/development/_sources/examples/40_advanced/sg_execution_times.rst.txt new file mode 100644 index 000000000..adb03dc4c --- /dev/null +++ b/development/_sources/examples/40_advanced/sg_execution_times.rst.txt @@ -0,0 +1,16 @@ + +:orphan: + +.. _sphx_glr_examples_40_advanced_sg_execution_times: + +Computation times +================= +**26:53.748** total execution time for **examples_40_advanced** files: + ++------------------------------------------------------------------------------------------------------------------------+-----------+--------+ +| :ref:`sphx_glr_examples_40_advanced_example_custom_configuration_space.py` (``example_custom_configuration_space.py``) | 11:43.644 | 0.0 MB | ++------------------------------------------------------------------------------------------------------------------------+-----------+--------+ +| :ref:`sphx_glr_examples_40_advanced_example_resampling_strategy.py` (``example_resampling_strategy.py``) | 10:23.225 | 0.0 MB | ++------------------------------------------------------------------------------------------------------------------------+-----------+--------+ +| :ref:`sphx_glr_examples_40_advanced_example_visualization.py` (``example_visualization.py``) | 04:46.879 | 0.0 MB | ++------------------------------------------------------------------------------------------------------------------------+-----------+--------+ diff --git a/development/_sources/examples/index.rst.txt b/development/_sources/examples/index.rst.txt new file mode 100644 index 000000000..6f658b89a --- /dev/null +++ b/development/_sources/examples/index.rst.txt @@ -0,0 +1,208 @@ +:orphan: + + + +.. _sphx_glr_examples: + +.. _examples: + +======== +Examples +======== + +Practical examples for using *Auto-PyTorch*. + + +.. raw:: html + +
+ + + +.. _sphx_glr_examples_20_basics: + +.. _examples_tabular_basics: + + +============================== +Basic Tabular Dataset Examples +============================== + +Basic examples for using *Auto-PyTorch* on tabular datasets + + + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /examples/20_basics/images/thumb/sphx_glr_example_image_classification_thumb.png + :alt: Image Classification + + :ref:`sphx_glr_examples_20_basics_example_image_classification.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /examples/20_basics/example_image_classification + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /examples/20_basics/images/thumb/sphx_glr_example_tabular_classification_thumb.png + :alt: Tabular Classification + + :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /examples/20_basics/example_tabular_classification + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /examples/20_basics/images/thumb/sphx_glr_example_tabular_regression_thumb.png + :alt: Tabular Regression + + :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /examples/20_basics/example_tabular_regression +.. raw:: html + +
+ + + +.. _sphx_glr_examples_40_advanced: + +.. _examples_tabular_basics: + + +============================== +Advanced Tabular Dataset Examples +============================== + +Advanced examples for using *Auto-PyTorch* on tabular datasets. +We explain + 1. How to customise the search space + 2. How to split the data according to different resampling strategies + + + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /examples/40_advanced/images/thumb/sphx_glr_example_custom_configuration_space_thumb.png + :alt: Tabular Classification with Custom Configuration Space + + :ref:`sphx_glr_examples_40_advanced_example_custom_configuration_space.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /examples/40_advanced/example_custom_configuration_space + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /examples/40_advanced/images/thumb/sphx_glr_example_resampling_strategy_thumb.png + :alt: Tabular Classification with different resampling strategy + + :ref:`sphx_glr_examples_40_advanced_example_resampling_strategy.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /examples/40_advanced/example_resampling_strategy + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /examples/40_advanced/images/thumb/sphx_glr_example_visualization_thumb.png + :alt: Visualizing the Results + + :ref:`sphx_glr_examples_40_advanced_example_visualization.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /examples/40_advanced/example_visualization +.. raw:: html + +
+ + + +.. only :: html + + .. container:: sphx-glr-footer + :class: sphx-glr-footer-gallery + + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples in Python source code: examples_python.zip ` + + + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download all examples in Jupyter notebooks: examples_jupyter.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/development/_sources/manual.rst.txt b/development/_sources/manual.rst.txt index e6b64977d..60adc047c 100644 --- a/development/_sources/manual.rst.txt +++ b/development/_sources/manual.rst.txt @@ -16,11 +16,11 @@ We expand the support to image processing tasks in the future. Examples ======== -* `Classification `_ -* `Regression `_ -* `Customizing the search space `_ -* `Changing the resampling strategy `_ -* `Visualizing the results `_ +* `Classification `_ +* `Regression `_ +* `Customizing the search space `_ +* `Changing the resampling strategy `_ +* `Visualizing the results `_ Resource Allocation =================== diff --git a/development/api.html b/development/api.html index 15e3b306f..e1fefd64a 100644 --- a/development/api.html +++ b/development/api.html @@ -405,7 +405,7 @@

ClassificationSphinx 3.5.4.

diff --git a/development/basics_tabular/index.html b/development/basics_tabular/index.html deleted file mode 100644 index 6e094646e..000000000 --- a/development/basics_tabular/index.html +++ /dev/null @@ -1,169 +0,0 @@ - - - - - - - Basic Tabular Dataset Examples — AutoPyTorch 0.1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
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-
- - - - -
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- Back to top - -
- -

- -

-

- © Copyright 2014-2019, Machine Learning Professorship Freiburg.
- Created using Sphinx 3.5.4.
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- - \ No newline at end of file diff --git a/development/dev.html b/development/dev.html index d173a6ad1..75d785773 100644 --- a/development/dev.html +++ b/development/dev.html @@ -178,7 +178,7 @@

The AutoML PartSphinx 3.5.4.

diff --git a/development/examples/20_basics/example_image_classification.html b/development/examples/20_basics/example_image_classification.html new file mode 100644 index 000000000..fa4564bf6 --- /dev/null +++ b/development/examples/20_basics/example_image_classification.html @@ -0,0 +1,30458 @@ + + + + + + + Image Classification — AutoPyTorch 0.1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+ +
+
+ +
+

Note

+

Click here +to download the full example code or to run this example in your browser via Binder

+
+
+

Image Classification¶

+

Out:

+
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
+Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ../datasets/FashionMNIST/raw/train-images-idx3-ubyte.gz
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+Extracting ../datasets/FashionMNIST/raw/train-images-idx3-ubyte.gz to ../datasets/FashionMNIST/raw
+
+Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
+Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ../datasets/FashionMNIST/raw/train-labels-idx1-ubyte.gz
+
+3.5%
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+97.1%
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+Extracting ../datasets/FashionMNIST/raw/train-labels-idx1-ubyte.gz to ../datasets/FashionMNIST/raw
+
+Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
+Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ../datasets/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
+
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+Extracting ../datasets/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ../datasets/FashionMNIST/raw
+
+Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
+Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ../datasets/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
+
+19.9%
+39.8%
+59.7%
+79.6%
+99.5%
+119.3%
+Extracting ../datasets/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ../datasets/FashionMNIST/raw
+
+Processing...
+/opt/hostedtoolcache/Python/3.8.9/x64/lib/python3.8/site-packages/torchvision/datasets/mnist.py:502: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:143.)
+  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)
+Done!
+Pipeline CS:
+ ________________________________________
+Configuration space object:
+  Hyperparameters:
+    image_augmenter:GaussianBlur:sigma_min, Type: UniformFloat, Range: [0.0, 3.0], Default: 0.0
+    image_augmenter:GaussianBlur:sigma_offset, Type: UniformFloat, Range: [0.0, 3.0], Default: 0.5
+    image_augmenter:GaussianBlur:use_augmenter, Type: Categorical, Choices: {True, False}, Default: True
+    image_augmenter:GaussianNoise:sigma_offset, Type: UniformFloat, Range: [0.0, 3.0], Default: 0.3
+    image_augmenter:GaussianNoise:use_augmenter, Type: Categorical, Choices: {True, False}, Default: True
+    image_augmenter:RandomAffine:rotate, Type: UniformInteger, Range: [0, 360], Default: 45
+    image_augmenter:RandomAffine:scale_offset, Type: UniformFloat, Range: [0.0, 0.4], Default: 0.2
+    image_augmenter:RandomAffine:shear, Type: UniformInteger, Range: [0, 45], Default: 30
+    image_augmenter:RandomAffine:translate_percent_offset, Type: UniformFloat, Range: [0.0, 0.4], Default: 0.2
+    image_augmenter:RandomAffine:use_augmenter, Type: Categorical, Choices: {True, False}, Default: True
+    image_augmenter:RandomCutout:p, Type: UniformFloat, Range: [0.2, 1.0], Default: 0.5
+    image_augmenter:RandomCutout:use_augmenter, Type: Categorical, Choices: {True, False}, Default: True
+    image_augmenter:Resize:use_augmenter, Type: Categorical, Choices: {True, False}, Default: True
+    image_augmenter:ZeroPadAndCrop:percent, Type: UniformFloat, Range: [0.0, 0.5], Default: 0.1
+    normalizer:__choice__, Type: Categorical, Choices: {ImageNormalizer, NoNormalizer}, Default: ImageNormalizer
+  Conditions:
+    image_augmenter:GaussianBlur:sigma_min | image_augmenter:GaussianBlur:use_augmenter == True
+    image_augmenter:GaussianBlur:sigma_offset | image_augmenter:GaussianBlur:use_augmenter == True
+    image_augmenter:GaussianNoise:sigma_offset | image_augmenter:GaussianNoise:use_augmenter == True
+    image_augmenter:RandomAffine:rotate | image_augmenter:RandomAffine:use_augmenter == True
+    image_augmenter:RandomAffine:scale_offset | image_augmenter:RandomAffine:use_augmenter == True
+    image_augmenter:RandomAffine:shear | image_augmenter:RandomAffine:use_augmenter == True
+    image_augmenter:RandomAffine:translate_percent_offset | image_augmenter:RandomAffine:use_augmenter == True
+    image_augmenter:RandomCutout:p | image_augmenter:RandomCutout:use_augmenter == True
+
+Pipeline Random Config:
+ ________________________________________
+Configuration:
+  image_augmenter:GaussianBlur:sigma_min, Value: 1.7739059525100553
+  image_augmenter:GaussianBlur:sigma_offset, Value: 1.596831438367099
+  image_augmenter:GaussianBlur:use_augmenter, Value: True
+  image_augmenter:GaussianNoise:use_augmenter, Value: False
+  image_augmenter:RandomAffine:use_augmenter, Value: False
+  image_augmenter:RandomCutout:p, Value: 0.8069029436295518
+  image_augmenter:RandomCutout:use_augmenter, Value: True
+  image_augmenter:Resize:use_augmenter, Value: True
+  image_augmenter:ZeroPadAndCrop:percent, Value: 0.13165420478079426
+  normalizer:__choice__, Value: 'ImageNormalizer'
+
+Fitting the pipeline...
+________________________________________
+        ImageClassificationPipeline
+________________________________________
+0-) normalizer:
+        ImageNormalizer
+
+1-) preprocessing:
+        EarlyPreprocessing
+
+2-) image_augmenter:
+        ImageAugmenter
+
+________________________________________
+
+
+
+

+
+
import numpy as np
+
+import sklearn.model_selection
+
+import torchvision.datasets
+
+from autoPyTorch.pipeline.image_classification import ImageClassificationPipeline
+
+# Get the training data for tabular classification
+trainset = torchvision.datasets.FashionMNIST(root='../datasets/', train=True, download=True)
+data = trainset.data.numpy()
+data = np.expand_dims(data, axis=3)
+# Create a proof of concept pipeline!
+dataset_properties = dict()
+pipeline = ImageClassificationPipeline(dataset_properties=dataset_properties)
+
+# Train and test split
+train_indices, val_indices = sklearn.model_selection.train_test_split(
+    list(range(data.shape[0])),
+    random_state=1,
+    test_size=0.25,
+)
+
+# Configuration space
+pipeline_cs = pipeline.get_hyperparameter_search_space()
+print("Pipeline CS:\n", '_' * 40, f"\n{pipeline_cs}")
+config = pipeline_cs.sample_configuration()
+print("Pipeline Random Config:\n", '_' * 40, f"\n{config}")
+pipeline.set_hyperparameters(config)
+
+# Fit the pipeline
+print("Fitting the pipeline...")
+
+pipeline.fit(X=dict(X_train=data,
+                    is_small_preprocess=True,
+                    dataset_properties=dict(mean=np.array([np.mean(data[:, :, :, i]) for i in range(1)]),
+                                            std=np.array([np.std(data[:, :, :, i]) for i in range(1)]),
+                                            num_classes=10,
+                                            num_features=data.shape[1] * data.shape[2],
+                                            image_height=data.shape[1],
+                                            image_width=data.shape[2],
+                                            is_small_preprocess=True),
+                    train_indices=train_indices,
+                    val_indices=val_indices,
+                    )
+             )
+
+# Showcase some components of the pipeline
+print(pipeline)
+
+
+

Total running time of the script: ( 0 minutes 10.175 seconds)

+ +

Gallery generated by Sphinx-Gallery

+
+ + +
+ +
+
+
+
+

+ Back to top + +
+ +

+ +

+

+ © Copyright 2014-2021, Machine Learning Professorship Freiburg.
+ Created using Sphinx 3.5.4.
+

+
+
+ + \ No newline at end of file diff --git a/development/basics_tabular/example_tabular_classification.html b/development/examples/20_basics/example_tabular_classification.html similarity index 73% rename from development/basics_tabular/example_tabular_classification.html rename to development/examples/20_basics/example_tabular_classification.html index d3089dc23..6668b5054 100644 --- a/development/basics_tabular/example_tabular_classification.html +++ b/development/examples/20_basics/example_tabular_classification.html @@ -5,26 +5,26 @@ Tabular Classification — AutoPyTorch 0.1.0 documentation - - - - - - - - - - - - + + + + + + + + + + + + - - - - + + + + @@ -52,7 +52,7 @@ - + Auto-PyTorch 0.1.0 @@ -60,14 +60,14 @@
    -
  • Start
    • -
  • Releases
    • -
  • Installation
    • -
  • Manual
    • -
  • Examples
    • -
  • API
    • -
  • Dev
    • -
  • Extending
    • +
  • Start
    • +
  • Releases
    • +
  • Installation
    • +
  • Manual
    • +
  • Examples
    • +
  • API
    • +
  • Dev
    • +
  • Extending
  • @@ -75,7 +75,7 @@ id="dLabelGlobalToc" data-toggle="dropdown" data-target="#" - href="../index.html">Site + href="../../index.html">Site @@ -90,7 +90,7 @@ -
    +
    @@ -115,15 +115,15 @@

    Note

    -

    Click here +

    Click here to download the full example code or to run this example in your browser via Binder

    -

    Tabular Classification¶

    +

    Tabular Classification¶

    The following example shows how to fit a sample classification model with AutoPyTorch

    Out:

    -
    <smac.runhistory.runhistory.RunHistory object at 0x7f125b7a5790> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
+
    <smac.runhistory.runhistory.RunHistory object at 0x7f16fb6340d0> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
   data_loader:batch_size, Value: 64
   encoder:__choice__, Value: 'OneHotEncoder'
   feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -155,7 +155,7 @@
   scaler:__choice__, Value: 'StandardScaler'
   trainer:StandardTrainer:weighted_loss, Value: True
   trainer:__choice__, Value: 'StandardTrainer'
-, ta_runs=0, ta_time_used=0.0, wallclock_time=0.001850128173828125, budget=0), TrajEntry(train_perf=0.16374269005847952, incumbent_id=1, incumbent=Configuration:
+, ta_runs=0, ta_time_used=0.0, wallclock_time=0.001987457275390625, budget=0), TrajEntry(train_perf=0.16374269005847952, incumbent_id=1, incumbent=Configuration:
   data_loader:batch_size, Value: 64
   encoder:__choice__, Value: 'OneHotEncoder'
   feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -187,7 +187,7 @@
   scaler:__choice__, Value: 'StandardScaler'
   trainer:StandardTrainer:weighted_loss, Value: True
   trainer:__choice__, Value: 'StandardTrainer'
-, ta_runs=1, ta_time_used=4.503080606460571, wallclock_time=5.962664842605591, budget=5.555555555555555), TrajEntry(train_perf=0.1578947368421053, incumbent_id=2, incumbent=Configuration:
+, ta_runs=1, ta_time_used=5.665798902511597, wallclock_time=7.321491241455078, budget=5.555555555555555), TrajEntry(train_perf=0.1578947368421053, incumbent_id=2, incumbent=Configuration:
   data_loader:batch_size, Value: 224
   encoder:__choice__, Value: 'OneHotEncoder'
   feature_preprocessor:Nystroem:kernel, Value: 'cosine'
@@ -245,19 +245,53 @@
   scaler:__choice__, Value: 'NoScaler'
   trainer:StandardTrainer:weighted_loss, Value: True
   trainer:__choice__, Value: 'StandardTrainer'
-, ta_runs=8, ta_time_used=61.97297525405884, wallclock_time=78.95482158660889, budget=5.555555555555555)]
-{'accuracy': 0.8786127167630058}
+, ta_runs=8, ta_time_used=82.00660920143127, wallclock_time=100.8822808265686, budget=5.555555555555555), TrajEntry(train_perf=0.1578947368421053, incumbent_id=3, incumbent=Configuration:
+  data_loader:batch_size, Value: 64
+  encoder:__choice__, Value: 'OneHotEncoder'
+  feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
+  imputer:categorical_strategy, Value: 'most_frequent'
+  imputer:numerical_strategy, Value: 'mean'
+  lr_scheduler:ReduceLROnPlateau:factor, Value: 0.1
+  lr_scheduler:ReduceLROnPlateau:mode, Value: 'min'
+  lr_scheduler:ReduceLROnPlateau:patience, Value: 10
+  lr_scheduler:__choice__, Value: 'ReduceLROnPlateau'
+  network_backbone:ShapedMLPBackbone:activation, Value: 'relu'
+  network_backbone:ShapedMLPBackbone:max_units, Value: 200
+  network_backbone:ShapedMLPBackbone:mlp_shape, Value: 'funnel'
+  network_backbone:ShapedMLPBackbone:num_groups, Value: 5
+  network_backbone:ShapedMLPBackbone:output_dim, Value: 200
+  network_backbone:ShapedMLPBackbone:use_dropout, Value: False
+  network_backbone:__choice__, Value: 'ShapedMLPBackbone'
+  network_embedding:__choice__, Value: 'NoEmbedding'
+  network_head:__choice__, Value: 'fully_connected'
+  network_head:fully_connected:activation, Value: 'relu'
+  network_head:fully_connected:num_layers, Value: 2
+  network_head:fully_connected:units_layer_1, Value: 128
+  network_init:XavierInit:bias_strategy, Value: 'Normal'
+  network_init:__choice__, Value: 'XavierInit'
+  optimizer:AdamOptimizer:beta1, Value: 0.9
+  optimizer:AdamOptimizer:beta2, Value: 0.9
+  optimizer:AdamOptimizer:lr, Value: 0.01
+  optimizer:AdamOptimizer:weight_decay, Value: 0.0
+  optimizer:__choice__, Value: 'AdamOptimizer'
+  scaler:__choice__, Value: 'StandardScaler'
+  trainer:StandardTrainer:weighted_loss, Value: True
+  trainer:__choice__, Value: 'StandardTrainer'
+, ta_runs=11, ta_time_used=109.19088363647461, wallclock_time=136.7932755947113, budget=16.666666666666664)]
+{'accuracy': 0.8670520231213873}
 |    | Preprocessing                                                     | Estimator                                                          |   Weight |
 |---:|:------------------------------------------------------------------|:-------------------------------------------------------------------|---------:|
 |  0 | None                                                              | RFClassifier                                                       |     0.24 |
-|  1 | None                                                              | ExtraTreesClassifier                                               |     0.24 |
-|  2 | None                                                              | CatBoostClassifier                                                 |     0.18 |
-|  3 | SimpleImputer,OneHotEncoder,MinMaxScaler,KernelPCA                | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential |     0.16 |
-|  4 | SimpleImputer,OneHotEncoder,MinMaxScaler,KernelPCA                | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential |     0.06 |
-|  5 | None                                                              | SVC                                                                |     0.06 |
-|  6 | SimpleImputer,OneHotEncoder,Normalizer,Nystroem                   | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential       |     0.02 |
-|  7 | None                                                              | KNNClassifier                                                      |     0.02 |
-|  8 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential    |     0.02 |
+|  1 | None                                                              | CatBoostClassifier                                                 |     0.22 |
+|  2 | SimpleImputer,OneHotEncoder,MinMaxScaler,KernelPCA                | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential |     0.16 |
+|  3 | None                                                              | ExtraTreesClassifier                                               |     0.08 |
+|  4 | None                                                              | SVC                                                                |     0.08 |
+|  5 | SimpleImputer,OneHotEncoder,NoScaler,Nystroem                     | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential          |     0.06 |
+|  6 | SimpleImputer,OneHotEncoder,MinMaxScaler,KernelPCA                | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential |     0.04 |
+|  7 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential    |     0.04 |
+|  8 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential    |     0.04 |
+|  9 | None                                                              | LGBMClassifier                                                     |     0.02 |
+| 10 | None                                                              | KNNClassifier                                                      |     0.02 |
 
    
 
    
 
    
@@ -297,11 +331,12 @@
     # Build and fit a classifier
     # ==========================
     api = TabularClassificationTask(
-        temporary_directory='./tmp/autoPyTorch_example_tmp_01',
-        output_directory='./tmp/autoPyTorch_example_out_01',
-        # To maintain logs of the run, set the next two as False
-        delete_tmp_folder_after_terminate=True,
-        delete_output_folder_after_terminate=True,
+        # To maintain logs of the run, you can uncomment the
+        # Following lines
+        # temporary_directory='./tmp/autoPyTorch_example_tmp_01',
+        # output_directory='./tmp/autoPyTorch_example_out_01',
+        # delete_tmp_folder_after_terminate=False,
+        # delete_output_folder_after_terminate=False,
         seed=42,
     )
 
@@ -329,16 +364,16 @@
     print(api.show_models())
    -

    Total running time of the script: ( 5 minutes 58.374 seconds)

    - diff --git a/development/basics_tabular/example_tabular_regression.html b/development/examples/20_basics/example_tabular_regression.html similarity index 79% rename from development/basics_tabular/example_tabular_regression.html rename to development/examples/20_basics/example_tabular_regression.html index d2087dea2..4f45c02d8 100644 --- a/development/basics_tabular/example_tabular_regression.html +++ b/development/examples/20_basics/example_tabular_regression.html @@ -5,26 +5,26 @@ Tabular Regression — AutoPyTorch 0.1.0 documentation - - - - - - - - - - - - + + + + + + + + + + + + - - - - + + + + @@ -52,7 +52,7 @@ - + Auto-PyTorch 0.1.0
    @@ -60,14 +60,14 @@
      -
    • Start
      • -
    • Releases
      • -
    • Installation
      • -
    • Manual
      • -
    • Examples
      • -
    • API
      • -
    • Dev
      • -
    • Extending
      • +
    • Start
      • +
    • Releases
      • +
    • Installation
      • +
    • Manual
      • +
    • Examples
      • +
    • API
      • +
    • Dev
      • +
    • Extending
    • @@ -75,7 +75,7 @@ id="dLabelGlobalToc" data-toggle="dropdown" data-target="#" - href="../index.html">Site + href="../../index.html">Site @@ -90,7 +90,7 @@ - +
      @@ -115,15 +115,15 @@

      Note

      -

      Click here +

      Click here to download the full example code or to run this example in your browser via Binder

      -

      Tabular Regression¶

      +

      Tabular Regression¶

      The following example shows how to fit a sample regression model with AutoPyTorch

      Out:

      -
      <smac.runhistory.runhistory.RunHistory object at 0x7f1243539400> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
+
      <smac.runhistory.runhistory.RunHistory object at 0x7f16e3f9a8b0> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
   data_loader:batch_size, Value: 64
   encoder:__choice__, Value: 'OneHotEncoder'
   feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -154,7 +154,7 @@
   optimizer:__choice__, Value: 'AdamOptimizer'
   scaler:__choice__, Value: 'StandardScaler'
   trainer:__choice__, Value: 'StandardTrainer'
-, ta_runs=0, ta_time_used=0.0, wallclock_time=0.00196075439453125, budget=0), TrajEntry(train_perf=0.5922365951048683, incumbent_id=1, incumbent=Configuration:
+, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0020246505737304688, budget=0), TrajEntry(train_perf=0.5480914591658931, incumbent_id=1, incumbent=Configuration:
   data_loader:batch_size, Value: 64
   encoder:__choice__, Value: 'OneHotEncoder'
   feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -185,13 +185,13 @@
   optimizer:__choice__, Value: 'AdamOptimizer'
   scaler:__choice__, Value: 'StandardScaler'
   trainer:__choice__, Value: 'StandardTrainer'
-, ta_runs=1, ta_time_used=3.864063024520874, wallclock_time=7.359654188156128, budget=5.555555555555555)]
-{'r2': 0.8929146098903469}
+, ta_runs=1, ta_time_used=4.880286455154419, wallclock_time=8.926568984985352, budget=5.555555555555555)]
+{'r2': 0.9253036125186885}
 |    | Preprocessing                                                     | Estimator                                                       |   Weight |
 |---:|:------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
-|  0 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.52 |
-|  1 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.46 |
-|  2 | SimpleImputer,OneHotEncoder,Normalizer,Nystroem                   | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential    |     0.02 |
+|  0 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.82 |
+|  1 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.14 |
+|  2 | SimpleImputer,OneHotEncoder,Normalizer,Nystroem                   | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential    |     0.04 |
 
      
 
      
 
      
@@ -239,13 +239,7 @@
     ############################################################################
     # Build and fit a regressor
     # ==========================
-    api = TabularRegressionTask(
-        temporary_directory='./tmp/autoPyTorch_example_tmp_02',
-        output_directory='./tmp/autoPyTorch_example_out_02',
-        # To maintain logs of the run, set the next two as False
-        delete_tmp_folder_after_terminate=True,
-        delete_output_folder_after_terminate=True
-    )
+    api = TabularRegressionTask()
 
     ############################################################################
     # Search for an ensemble of machine learning algorithms
@@ -276,16 +270,16 @@
     print(api.show_models())
      -

      Total running time of the script: ( 5 minutes 12.223 seconds)

      - diff --git a/development/basics_tabular/sg_execution_times.html b/development/examples/20_basics/sg_execution_times.html similarity index 50% rename from development/basics_tabular/sg_execution_times.html rename to development/examples/20_basics/sg_execution_times.html index 4d9922956..4799a1485 100644 --- a/development/basics_tabular/sg_execution_times.html +++ b/development/examples/20_basics/sg_execution_times.html @@ -5,26 +5,26 @@ Computation times — AutoPyTorch 0.1.0 documentation - - - - - - - - - - - - + + + + + + + + + + + + - - - - + + + + @@ -52,7 +52,7 @@ - + Auto-PyTorch 0.1.0
      @@ -60,14 +60,14 @@
        -
      • Start
        • -
      • Releases
        • -
      • Installation
        • -
      • Manual
        • -
      • Examples
        • -
      • API
        • -
      • Dev
        • -
      • Extending
        • +
      • Start
        • +
      • Releases
        • +
      • Installation
        • +
      • Manual
        • +
      • Examples
        • +
      • API
        • +
      • Dev
        • +
      • Extending
      • @@ -75,7 +75,7 @@ id="dLabelGlobalToc" data-toggle="dropdown" data-target="#" - href="../index.html">Site + href="../../index.html">Site @@ -90,7 +90,7 @@ - +
        @@ -114,8 +114,8 @@
        -

        Computation times¶

        -

        11:10.597 total execution time for basics_tabular files:

        +

        Computation times¶

        +

        11:27.012 total execution time for examples_20_basics files:

        @@ -123,12 +123,16 @@ - - + + - - + + + + + + @@ -148,13 +152,13 @@

        - © Copyright 2014-2019, Machine Learning Professorship Freiburg.
        + © Copyright 2014-2021, Machine Learning Professorship Freiburg.
        Created using Sphinx 3.5.4.

        diff --git a/development/advanced_tabular/example_custom_configuration_space.html b/development/examples/40_advanced/example_custom_configuration_space.html similarity index 86% rename from development/advanced_tabular/example_custom_configuration_space.html rename to development/examples/40_advanced/example_custom_configuration_space.html index 5b408eb6c..6847ef2a1 100644 --- a/development/advanced_tabular/example_custom_configuration_space.html +++ b/development/examples/40_advanced/example_custom_configuration_space.html @@ -5,26 +5,26 @@ Tabular Classification with Custom Configuration Space — AutoPyTorch 0.1.0 documentation - - - - - - - - - - - - + + + + + + + + + + + + - - - - + + + + @@ -52,7 +52,7 @@ - + Auto-PyTorch0.1.0 @@ -60,14 +60,14 @@
          -
        • Start
          • -
        • Releases
          • -
        • Installation
          • -
        • Manual
          • -
        • Examples
          • -
        • API
          • -
        • Dev
          • -
        • Extending
          • +
        • Start
          • +
        • Releases
          • +
        • Installation
          • +
        • Manual
          • +
        • Examples
          • +
        • API
          • +
        • Dev
          • +
        • Extending
        • @@ -75,7 +75,7 @@ id="dLabelGlobalToc" data-toggle="dropdown" data-target="#" - href="../index.html">Site + href="../../index.html">Site @@ -90,7 +90,7 @@ - +
          @@ -115,11 +115,11 @@

          Note

          -

          Click here +

          Click here to download the full example code or to run this example in your browser via Binder

          -

          Tabular Classification with Custom Configuration Space¶

          +

          Tabular Classification with Custom Configuration Space¶

          The following example shows how adjust the configuration space of the search. Currently, there are two changes that can be made to the space:- 1. Adjust individual hyperparameters in the pipeline @@ -143,7 +143,7 @@

          Out:

          -
          <smac.runhistory.runhistory.RunHistory object at 0x7f12f48ef520> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
+
          <smac.runhistory.runhistory.RunHistory object at 0x7f16fb6925e0> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
   data_loader:batch_size, Value: 32
   encoder:__choice__, Value: 'OneHotEncoder'
   feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -177,7 +177,7 @@
   scaler:__choice__, Value: 'StandardScaler'
   trainer:StandardTrainer:weighted_loss, Value: True
   trainer:__choice__, Value: 'StandardTrainer'
-, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0018227100372314453, budget=0), TrajEntry(train_perf=0.16374269005847952, incumbent_id=1, incumbent=Configuration:
+, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0019881725311279297, budget=0), TrajEntry(train_perf=0.16374269005847952, incumbent_id=1, incumbent=Configuration:
   data_loader:batch_size, Value: 32
   encoder:__choice__, Value: 'OneHotEncoder'
   feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -211,7 +211,7 @@
   scaler:__choice__, Value: 'StandardScaler'
   trainer:StandardTrainer:weighted_loss, Value: True
   trainer:__choice__, Value: 'StandardTrainer'
-, ta_runs=1, ta_time_used=4.638380527496338, wallclock_time=6.107091426849365, budget=5.555555555555555), TrajEntry(train_perf=0.1578947368421053, incumbent_id=2, incumbent=Configuration:
+, ta_runs=1, ta_time_used=5.924399137496948, wallclock_time=7.483955383300781, budget=5.555555555555555), TrajEntry(train_perf=0.1578947368421053, incumbent_id=2, incumbent=Configuration:
   data_loader:batch_size, Value: 475
   encoder:__choice__, Value: 'OneHotEncoder'
   feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -259,22 +259,23 @@
   scaler:__choice__, Value: 'Normalizer'
   trainer:StandardTrainer:weighted_loss, Value: False
   trainer:__choice__, Value: 'StandardTrainer'
-, ta_runs=12, ta_time_used=123.52750444412231, wallclock_time=150.68852996826172, budget=16.666666666666664)]
+, ta_runs=12, ta_time_used=166.8644254207611, wallclock_time=197.1043405532837, budget=16.666666666666664)]
 {'accuracy': 0.8554913294797688}
 |    | Preprocessing                                                     | Estimator                                                 |   Weight |
 |---:|:------------------------------------------------------------------|:----------------------------------------------------------|---------:|
-|  0 | SimpleImputer,OneHotEncoder,Normalizer,KernelPCA                  | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential |     0.18 |
-|  1 | None                                                              | SVC                                                       |     0.16 |
+|  0 | None                                                              | SVC                                                       |     0.18 |
+|  1 | None                                                              | RFClassifier                                              |     0.16 |
 |  2 | None                                                              | CatBoostClassifier                                        |     0.14 |
-|  3 | None                                                              | ExtraTreesClassifier                                      |     0.14 |
+|  3 | SimpleImputer,OneHotEncoder,Normalizer,KernelPCA                  | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential |     0.12 |
 |  4 | SimpleImputer,OneHotEncoder,Normalizer,KernelPCA                  | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential |     0.1  |
-|  5 | None                                                              | RFClassifier                                              |     0.08 |
-|  6 | SimpleImputer,OneHotEncoder,Normalizer,NoFeaturePreprocessing     | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential    |     0.06 |
-|  7 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential |     0.06 |
-|  8 | None                                                              | KNNClassifier                                             |     0.04 |
+|  5 | SimpleImputer,OneHotEncoder,Normalizer,NoFeaturePreprocessing     | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential    |     0.1  |
+|  6 | None                                                              | ExtraTreesClassifier                                      |     0.08 |
+|  7 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential |     0.04 |
+|  8 | SimpleImputer,OneHotEncoder,Normalizer,NoFeaturePreprocessing     | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential    |     0.02 |
 |  9 | SimpleImputer,OneHotEncoder,Normalizer,NoFeaturePreprocessing     | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential    |     0.02 |
-| 10 | SimpleImputer,OneHotEncoder,Normalizer,NoFeaturePreprocessing     | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential    |     0.02 |
-<smac.runhistory.runhistory.RunHistory object at 0x7f12434c7400> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
+| 10 | None                                                              | KNNClassifier                                             |     0.02 |
+| 11 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential |     0.02 |
+<smac.runhistory.runhistory.RunHistory object at 0x7f16ca634490> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
   data_loader:batch_size, Value: 32
   encoder:__choice__, Value: 'NoEncoder'
   feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -306,7 +307,7 @@
   scaler:__choice__, Value: 'StandardScaler'
   trainer:StandardTrainer:weighted_loss, Value: True
   trainer:__choice__, Value: 'StandardTrainer'
-, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0014028549194335938, budget=0), TrajEntry(train_perf=0.16374269005847952, incumbent_id=1, incumbent=Configuration:
+, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0016286373138427734, budget=0), TrajEntry(train_perf=0.16374269005847952, incumbent_id=1, incumbent=Configuration:
   data_loader:batch_size, Value: 32
   encoder:__choice__, Value: 'NoEncoder'
   feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -338,18 +339,17 @@
   scaler:__choice__, Value: 'StandardScaler'
   trainer:StandardTrainer:weighted_loss, Value: True
   trainer:__choice__, Value: 'StandardTrainer'
-, ta_runs=1, ta_time_used=4.025576591491699, wallclock_time=5.487119197845459, budget=5.555555555555555)]
+, ta_runs=1, ta_time_used=5.018240690231323, wallclock_time=6.567354679107666, budget=5.555555555555555)]
 {'accuracy': 0.8554913294797688}
 |    | Preprocessing                                                 | Estimator                                                          |   Weight |
 |---:|:--------------------------------------------------------------|:-------------------------------------------------------------------|---------:|
-|  0 | None                                                          | CatBoostClassifier                                                 |     0.28 |
-|  1 | SimpleImputer,NoEncoder,MinMaxScaler,Nystroem                 | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential |     0.2  |
-|  2 | SimpleImputer,NoEncoder,MinMaxScaler,Nystroem                 | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential |     0.16 |
-|  3 | None                                                          | SVC                                                                |     0.14 |
-|  4 | None                                                          | KNNClassifier                                                      |     0.12 |
-|  5 | None                                                          | RFClassifier                                                       |     0.04 |
-|  6 | None                                                          | ExtraTreesClassifier                                               |     0.04 |
-|  7 | SimpleImputer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential    |     0.02 |
+|  0 | None                                                          | CatBoostClassifier                                                 |     0.74 |
+|  1 | None                                                          | SVC                                                                |     0.08 |
+|  2 | None                                                          | KNNClassifier                                                      |     0.06 |
+|  3 | None                                                          | RFClassifier                                                       |     0.04 |
+|  4 | None                                                          | ExtraTreesClassifier                                               |     0.04 |
+|  5 | SimpleImputer,NoEncoder,MinMaxScaler,NoFeaturePreprocessing   | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential |     0.02 |
+|  6 | SimpleImputer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential    |     0.02 |
 
          
 
          
 
          
@@ -471,16 +471,16 @@
     print(api.show_models())
          -

          Total running time of the script: ( 11 minutes 35.664 seconds)

          - diff --git a/development/advanced_tabular/example_resampling_strategy.html b/development/examples/40_advanced/example_resampling_strategy.html similarity index 80% rename from development/advanced_tabular/example_resampling_strategy.html rename to development/examples/40_advanced/example_resampling_strategy.html index 2fb79b3bf..8e3e431fb 100644 --- a/development/advanced_tabular/example_resampling_strategy.html +++ b/development/examples/40_advanced/example_resampling_strategy.html @@ -5,26 +5,26 @@ Tabular Classification with different resampling strategy — AutoPyTorch 0.1.0 documentation - - - - - - - - - - - - + + + + + + + + + + + + - - - - + + + + @@ -52,7 +52,7 @@ - + Auto-PyTorch 0.1.0
        @@ -60,14 +60,14 @@
          -
        • Start
          • -
        • Releases
          • -
        • Installation
          • -
        • Manual
          • -
        • Examples
          • -
        • API
          • -
        • Dev
          • -
        • Extending
          • +
        • Start
          • +
        • Releases
          • +
        • Installation
          • +
        • Manual
          • +
        • Examples
          • +
        • API
          • +
        • Dev
          • +
        • Extending
        • @@ -75,7 +75,7 @@ id="dLabelGlobalToc" data-toggle="dropdown" data-target="#" - href="../index.html">Site + href="../../index.html">Site @@ -90,7 +90,7 @@ - +
          @@ -115,17 +115,17 @@

          Note

          -

          Click here +

          Click here to download the full example code or to run this example in your browser via Binder

          -

          Tabular Classification with different resampling strategy¶

          +

          Tabular Classification with different resampling strategy¶

          The following example shows how to fit a sample classification model with different resampling strategies in AutoPyTorch By default, AutoPyTorch uses Holdout Validation with a 67% train size split.

          Out:

          -
          <smac.runhistory.runhistory.RunHistory object at 0x7f121ad9efd0> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
+
          <smac.runhistory.runhistory.RunHistory object at 0x7f16e0167700> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
   data_loader:batch_size, Value: 64
   encoder:__choice__, Value: 'OneHotEncoder'
   feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -157,7 +157,7 @@
   scaler:__choice__, Value: 'StandardScaler'
   trainer:StandardTrainer:weighted_loss, Value: True
   trainer:__choice__, Value: 'StandardTrainer'
-, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0018551349639892578, budget=0), TrajEntry(train_perf=0.18128654970760238, incumbent_id=1, incumbent=Configuration:
+, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0025005340576171875, budget=0), TrajEntry(train_perf=0.18128654970760238, incumbent_id=1, incumbent=Configuration:
   data_loader:batch_size, Value: 64
   encoder:__choice__, Value: 'OneHotEncoder'
   feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -189,23 +189,22 @@
   scaler:__choice__, Value: 'StandardScaler'
   trainer:StandardTrainer:weighted_loss, Value: True
   trainer:__choice__, Value: 'StandardTrainer'
-, ta_runs=1, ta_time_used=4.567504167556763, wallclock_time=6.137902498245239, budget=5.555555555555555)]
-{'accuracy': 0.861271676300578}
+, ta_runs=1, ta_time_used=5.704225063323975, wallclock_time=7.272337436676025, budget=5.555555555555555)]
+{'accuracy': 0.8554913294797688}
 |    | Preprocessing                                                     | Estimator                                                       |   Weight |
 |---:|:------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
-|  0 | None                                                              | RFClassifier                                                    |     0.22 |
-|  1 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA              | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.18 |
-|  2 | None                                                              | ExtraTreesClassifier                                            |     0.12 |
-|  3 | None                                                              | KNNClassifier                                                   |     0.12 |
-|  4 | SimpleImputer,NoEncoder,NoScaler,Nystroem                         | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.08 |
-|  5 | None                                                              | CatBoostClassifier                                              |     0.08 |
-|  6 | None                                                              | SVC                                                             |     0.06 |
-|  7 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.06 |
-|  8 | SimpleImputer,NoEncoder,NoScaler,Nystroem                         | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.02 |
-|  9 | SimpleImputer,OneHotEncoder,NoScaler,NoFeaturePreprocessing       | embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential |     0.02 |
-| 10 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.02 |
-| 11 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.02 |
-<smac.runhistory.runhistory.RunHistory object at 0x7f125b0db550> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
+|  0 | None                                                              | KNNClassifier                                                   |     0.22 |
+|  1 | None                                                              | CatBoostClassifier                                              |     0.2  |
+|  2 | SimpleImputer,NoEncoder,NoScaler,Nystroem                         | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.18 |
+|  3 | SimpleImputer,OneHotEncoder,NoScaler,NoFeaturePreprocessing       | embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential |     0.08 |
+|  4 | None                                                              | SVC                                                             |     0.08 |
+|  5 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA              | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.06 |
+|  6 | None                                                              | RFClassifier                                                    |     0.04 |
+|  7 | None                                                              | ExtraTreesClassifier                                            |     0.04 |
+|  8 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.04 |
+|  9 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.04 |
+| 10 | None                                                              | LGBMClassifier                                                  |     0.02 |
+<smac.runhistory.runhistory.RunHistory object at 0x7f16e0137520> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
   data_loader:batch_size, Value: 64
   encoder:__choice__, Value: 'OneHotEncoder'
   feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -237,7 +236,7 @@
   scaler:__choice__, Value: 'StandardScaler'
   trainer:StandardTrainer:weighted_loss, Value: True
   trainer:__choice__, Value: 'StandardTrainer'
-, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0019214153289794922, budget=0), TrajEntry(train_perf=0.15480222378634426, incumbent_id=1, incumbent=Configuration:
+, ta_runs=0, ta_time_used=0.0, wallclock_time=0.002166748046875, budget=0), TrajEntry(train_perf=0.15480222378634426, incumbent_id=1, incumbent=Configuration:
   data_loader:batch_size, Value: 64
   encoder:__choice__, Value: 'OneHotEncoder'
   feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -269,13 +268,13 @@
   scaler:__choice__, Value: 'StandardScaler'
   trainer:StandardTrainer:weighted_loss, Value: True
   trainer:__choice__, Value: 'StandardTrainer'
-, ta_runs=1, ta_time_used=8.385476112365723, wallclock_time=9.87209415435791, budget=5.555555555555555)]
+, ta_runs=1, ta_time_used=10.995928049087524, wallclock_time=12.509535551071167, budget=5.555555555555555)]
 {'accuracy': 0.884393063583815}
 |    | Preprocessing                                        | Estimator                                                       |   Weight |
 |---:|:-----------------------------------------------------|:----------------------------------------------------------------|---------:|
 |  0 | None                                                 | TabularClassifier                                               |     0.86 |
 |  1 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.14 |
-<smac.runhistory.runhistory.RunHistory object at 0x7f121b0f26d0> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
+<smac.runhistory.runhistory.RunHistory object at 0x7f16cad6ab80> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
   data_loader:batch_size, Value: 64
   encoder:__choice__, Value: 'OneHotEncoder'
   feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -307,7 +306,7 @@
   scaler:__choice__, Value: 'StandardScaler'
   trainer:StandardTrainer:weighted_loss, Value: True
   trainer:__choice__, Value: 'StandardTrainer'
-, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0019407272338867188, budget=0), TrajEntry(train_perf=0.17543859649122806, incumbent_id=1, incumbent=Configuration:
+, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0022859573364257812, budget=0), TrajEntry(train_perf=0.17543859649122806, incumbent_id=1, incumbent=Configuration:
   data_loader:batch_size, Value: 64
   encoder:__choice__, Value: 'OneHotEncoder'
   feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -339,20 +338,19 @@
   scaler:__choice__, Value: 'StandardScaler'
   trainer:StandardTrainer:weighted_loss, Value: True
   trainer:__choice__, Value: 'StandardTrainer'
-, ta_runs=1, ta_time_used=4.53068208694458, wallclock_time=6.004504919052124, budget=5.555555555555555)]
-{'accuracy': 0.8554913294797688}
+, ta_runs=1, ta_time_used=5.71893310546875, wallclock_time=7.28941011428833, budget=5.555555555555555)]
+{'accuracy': 0.8728323699421965}
 |    | Preprocessing                                                     | Estimator                                                       |   Weight |
 |---:|:------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
-|  0 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA              | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.22 |
-|  1 | SimpleImputer,NoEncoder,NoScaler,Nystroem                         | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.2  |
-|  2 | None                                                              | CatBoostClassifier                                              |     0.12 |
+|  0 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA              | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.24 |
+|  1 | SimpleImputer,NoEncoder,NoScaler,Nystroem                         | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.18 |
+|  2 | None                                                              | LGBMClassifier                                                  |     0.12 |
 |  3 | None                                                              | RFClassifier                                                    |     0.12 |
-|  4 | None                                                              | KNNClassifier                                                   |     0.12 |
-|  5 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA              | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.06 |
-|  6 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.06 |
-|  7 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.06 |
-|  8 | None                                                              | SVC                                                             |     0.02 |
-|  9 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.02 |
+|  4 | None                                                              | CatBoostClassifier                                              |     0.1  |
+|  5 | None                                                              | ExtraTreesClassifier                                            |     0.1  |
+|  6 | None                                                              | SVC                                                             |     0.06 |
+|  7 | None                                                              | KNNClassifier                                                   |     0.04 |
+|  8 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.04 |
 
          
 
          
 
          
@@ -393,11 +391,6 @@
     # Build and fit a classifier with default resampling strategy
     # ===========================================================
     api = TabularClassificationTask(
-        temporary_directory='./tmp/autoPyTorch_example_tmp_03',
-        output_directory='./tmp/autoPyTorch_example_out_03',
-        # To maintain logs of the run, set the next two as False
-        delete_tmp_folder_after_terminate=True,
-        delete_output_folder_after_terminate=True,
         # 'HoldoutValTypes.holdout_validation' with 'val_share': 0.33
         # is the default argument setting for TabularClassificationTask.
         # It is explicitly specified in this example for demonstrational
@@ -435,11 +428,6 @@
     # Build and fit a classifier with Cross validation resampling strategy
     # ====================================================================
     api = TabularClassificationTask(
-        temporary_directory='./tmp/autoPyTorch_example_tmp_04',
-        output_directory='./tmp/autoPyTorch_example_out_04',
-        # To maintain logs of the run, set the next two as False
-        delete_tmp_folder_after_terminate=True,
-        delete_output_folder_after_terminate=True,
         resampling_strategy=CrossValTypes.k_fold_cross_validation,
         resampling_strategy_args={'num_splits': 3}
     )
@@ -473,11 +461,6 @@
     # Build and fit a classifier with Stratified resampling strategy
     # ==============================================================
     api = TabularClassificationTask(
-        temporary_directory='./tmp/autoPyTorch_example_tmp_05',
-        output_directory='./tmp/autoPyTorch_example_out_05',
-        # To maintain logs of the run, set the next two as False
-        delete_tmp_folder_after_terminate=True,
-        delete_output_folder_after_terminate=True,
         # For demonstration purposes, we use
         # Stratified hold out validation. However,
         # one can also use CrossValTypes.stratified_k_fold_cross_validation.
@@ -509,16 +492,16 @@
     print(api.show_models())
          -

          Total running time of the script: ( 10 minutes 6.674 seconds)

          - diff --git a/development/advanced_tabular/example_visualization.html b/development/examples/40_advanced/example_visualization.html similarity index 86% rename from development/advanced_tabular/example_visualization.html rename to development/examples/40_advanced/example_visualization.html index 1c9fff62b..2e9071e14 100644 --- a/development/advanced_tabular/example_visualization.html +++ b/development/examples/40_advanced/example_visualization.html @@ -5,26 +5,26 @@ Visualizing the Results — AutoPyTorch 0.1.0 documentation - - - - - - - - - - - - + + + + + + + + + + + + - - - - + + + + @@ -52,7 +52,7 @@ - + Auto-PyTorch 0.1.0
          @@ -60,14 +60,14 @@

        Tabular Classification (example_tabular_classification.py)

        05:58.374

        Tabular Classification (example_tabular_classification.py)

        06:00.058

        0.0 MB

        Tabular Regression (example_tabular_regression.py)

        05:12.223

        Tabular Regression (example_tabular_regression.py)

        05:16.780

        0.0 MB

        Image Classification (example_image_classification.py)

        00:10.175

        0.0 MB
        @@ -123,16 +123,16 @@ - - + + - - + + - - + + @@ -152,13 +152,13 @@

        - © Copyright 2014-2019, Machine Learning Professorship Freiburg.
        + © Copyright 2014-2021, Machine Learning Professorship Freiburg.
        Created using Sphinx 3.5.4.

        diff --git a/development/advanced_tabular/index.html b/development/examples/index.html similarity index 57% rename from development/advanced_tabular/index.html rename to development/examples/index.html index ca6380290..9fe4a17b7 100644 --- a/development/advanced_tabular/index.html +++ b/development/examples/index.html @@ -4,7 +4,7 @@ - Advanced Tabular Dataset Examples — AutoPyTorch 0.1.0 documentation + Examples — AutoPyTorch 0.1.0 documentation @@ -64,7 +64,7 @@
      • Releases
      • Installation
      • Manual
        • -
      • Examples
        • +
      • Examples
      • API
      • Dev
      • Extending
        • @@ -106,7 +106,11 @@
        -
        -

        Advanced Tabular Dataset Examples¶

        +
        +

        Examples¶

        +

        Practical examples for using Auto-PyTorch.

        +
        +
        +

        Basic Tabular Dataset Examples¶

        +

        Basic examples for using Auto-PyTorch on tabular datasets

        +
        +
        +
        +
        +
        +
        +
        +
        +
        +
        +
        +

        Advanced Tabular Dataset Examples¶

        Advanced examples for using Auto-PyTorch on tabular datasets. We explain

        @@ -125,30 +155,30 @@

      • How to split the data according to different resampling strategies

        • -
        +
        -
        -Visualizing the Results -

        Visualizing the Results¶

        +
        -
        diff --git a/development/extending.html b/development/extending.html index 069a996ee..5419bd141 100644 --- a/development/extending.html +++ b/development/extending.html @@ -136,7 +136,7 @@

        - © Copyright 2014-2019, Machine Learning Professorship Freiburg.
        + © Copyright 2014-2021, Machine Learning Professorship Freiburg.
        Created using Sphinx 3.5.4.

        diff --git a/development/genindex.html b/development/genindex.html index e5df64d4f..e70397627 100644 --- a/development/genindex.html +++ b/development/genindex.html @@ -205,7 +205,7 @@

        T

        - © Copyright 2014-2019, Machine Learning Professorship Freiburg.
        + © Copyright 2014-2021, Machine Learning Professorship Freiburg.
        Created using Sphinx 3.5.4.

        diff --git a/development/index.html b/development/index.html index 3d48a0236..955b4209e 100644 --- a/development/index.html +++ b/development/index.html @@ -207,7 +207,7 @@

        ContributingSphinx 3.5.4.

        diff --git a/development/installation.html b/development/installation.html index 721e84cee..51eee2bb6 100644 --- a/development/installation.html +++ b/development/installation.html @@ -167,7 +167,7 @@

        Docker ImageSphinx 3.5.4.

        diff --git a/development/manual.html b/development/manual.html index 830be4f74..0332ec9c7 100644 --- a/development/manual.html +++ b/development/manual.html @@ -130,11 +130,11 @@ diff --git a/development/notebooks/advanced_tabular/example_resampling_strategy.ipynb b/development/notebooks/advanced_tabular/example_resampling_strategy.ipynb deleted file mode 100644 index 10f88db97..000000000 --- a/development/notebooks/advanced_tabular/example_resampling_strategy.ipynb +++ /dev/null @@ -1,54 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Tabular Classification with different resampling strategy\n\nThe following example shows how to fit a sample classification model\nwith different resampling strategies in AutoPyTorch\nBy default, AutoPyTorch uses Holdout Validation with\na 67% train size split.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import os\nimport tempfile as tmp\nimport warnings\n\nos.environ['JOBLIB_TEMP_FOLDER'] = tmp.gettempdir()\nos.environ['OMP_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_NUM_THREADS'] = '1'\n\nwarnings.simplefilter(action='ignore', category=UserWarning)\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nimport sklearn.datasets\nimport sklearn.model_selection\n\nfrom autoPyTorch.api.tabular_classification import TabularClassificationTask\nfrom autoPyTorch.datasets.resampling_strategy import CrossValTypes, HoldoutValTypes\n\n\nif __name__ == '__main__':\n\n ############################################################################\n # Data Loading\n # ============\n X, y = sklearn.datasets.fetch_openml(data_id=40981, return_X_y=True, as_frame=True)\n X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n X,\n y,\n random_state=1,\n )\n\n ############################################################################\n # Build and fit a classifier with default resampling strategy\n # ===========================================================\n api = TabularClassificationTask(\n temporary_directory='./tmp/autoPyTorch_example_tmp_03',\n output_directory='./tmp/autoPyTorch_example_out_03',\n # To maintain logs of the run, set the next two as False\n delete_tmp_folder_after_terminate=True,\n delete_output_folder_after_terminate=True,\n # 'HoldoutValTypes.holdout_validation' with 'val_share': 0.33\n # is the default argument setting for TabularClassificationTask.\n # It is explicitly specified in this example for demonstrational\n # purpose.\n resampling_strategy=HoldoutValTypes.holdout_validation,\n resampling_strategy_args={'val_share': 0.33}\n )\n\n ############################################################################\n # Search for an ensemble of machine learning algorithms\n # =====================================================\n api.search(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test.copy(),\n y_test=y_test.copy(),\n optimize_metric='accuracy',\n total_walltime_limit=150,\n func_eval_time_limit_secs=30\n )\n\n ############################################################################\n # Print the final ensemble performance\n # ====================================\n print(api.run_history, api.trajectory)\n y_pred = api.predict(X_test)\n score = api.score(y_pred, y_test)\n print(score)\n # Print the final ensemble built by AutoPyTorch\n print(api.show_models())\n\n ############################################################################\n\n ############################################################################\n # Build and fit a classifier with Cross validation resampling strategy\n # ====================================================================\n api = TabularClassificationTask(\n temporary_directory='./tmp/autoPyTorch_example_tmp_04',\n output_directory='./tmp/autoPyTorch_example_out_04',\n # To maintain logs of the run, set the next two as False\n delete_tmp_folder_after_terminate=True,\n delete_output_folder_after_terminate=True,\n resampling_strategy=CrossValTypes.k_fold_cross_validation,\n resampling_strategy_args={'num_splits': 3}\n )\n\n ############################################################################\n # Search for an ensemble of machine learning algorithms\n # =====================================================\n api.search(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test.copy(),\n y_test=y_test.copy(),\n optimize_metric='accuracy',\n total_walltime_limit=150,\n func_eval_time_limit_secs=30\n )\n\n ############################################################################\n # Print the final ensemble performance\n # ====================================\n print(api.run_history, api.trajectory)\n y_pred = api.predict(X_test)\n score = api.score(y_pred, y_test)\n print(score)\n # Print the final ensemble built by AutoPyTorch\n print(api.show_models())\n\n ############################################################################\n\n ############################################################################\n # Build and fit a classifier with Stratified resampling strategy\n # ==============================================================\n api = TabularClassificationTask(\n temporary_directory='./tmp/autoPyTorch_example_tmp_05',\n output_directory='./tmp/autoPyTorch_example_out_05',\n # To maintain logs of the run, set the next two as False\n delete_tmp_folder_after_terminate=True,\n delete_output_folder_after_terminate=True,\n # For demonstration purposes, we use\n # Stratified hold out validation. However,\n # one can also use CrossValTypes.stratified_k_fold_cross_validation.\n resampling_strategy=HoldoutValTypes.stratified_holdout_validation,\n resampling_strategy_args={'val_share': 0.33}\n )\n\n ############################################################################\n # Search for an ensemble of machine learning algorithms\n # =====================================================\n api.search(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test.copy(),\n y_test=y_test.copy(),\n optimize_metric='accuracy',\n total_walltime_limit=150,\n func_eval_time_limit_secs=30\n )\n\n ############################################################################\n # Print the final ensemble performance\n # ====================================\n print(api.run_history, api.trajectory)\n y_pred = api.predict(X_test)\n score = api.score(y_pred, y_test)\n print(score)\n # Print the final ensemble built by AutoPyTorch\n print(api.show_models())" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/development/notebooks/examples/20_basics/example_image_classification.ipynb b/development/notebooks/examples/20_basics/example_image_classification.ipynb new file mode 100644 index 000000000..4ea0b44ec --- /dev/null +++ b/development/notebooks/examples/20_basics/example_image_classification.ipynb @@ -0,0 +1,54 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Image Classification\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n\nimport sklearn.model_selection\n\nimport torchvision.datasets\n\nfrom autoPyTorch.pipeline.image_classification import ImageClassificationPipeline\n\n# Get the training data for tabular classification\ntrainset = torchvision.datasets.FashionMNIST(root='../datasets/', train=True, download=True)\ndata = trainset.data.numpy()\ndata = np.expand_dims(data, axis=3)\n# Create a proof of concept pipeline!\ndataset_properties = dict()\npipeline = ImageClassificationPipeline(dataset_properties=dataset_properties)\n\n# Train and test split\ntrain_indices, val_indices = sklearn.model_selection.train_test_split(\n list(range(data.shape[0])),\n random_state=1,\n test_size=0.25,\n)\n\n# Configuration space\npipeline_cs = pipeline.get_hyperparameter_search_space()\nprint(\"Pipeline CS:\\n\", '_' * 40, f\"\\n{pipeline_cs}\")\nconfig = pipeline_cs.sample_configuration()\nprint(\"Pipeline Random Config:\\n\", '_' * 40, f\"\\n{config}\")\npipeline.set_hyperparameters(config)\n\n# Fit the pipeline\nprint(\"Fitting the pipeline...\")\n\npipeline.fit(X=dict(X_train=data,\n is_small_preprocess=True,\n dataset_properties=dict(mean=np.array([np.mean(data[:, :, :, i]) for i in range(1)]),\n std=np.array([np.std(data[:, :, :, i]) for i in range(1)]),\n num_classes=10,\n num_features=data.shape[1] * data.shape[2],\n image_height=data.shape[1],\n image_width=data.shape[2],\n is_small_preprocess=True),\n train_indices=train_indices,\n val_indices=val_indices,\n )\n )\n\n# Showcase some components of the pipeline\nprint(pipeline)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/development/notebooks/basics_tabular/example_tabular_classification.ipynb b/development/notebooks/examples/20_basics/example_tabular_classification.ipynb similarity index 62% rename from development/notebooks/basics_tabular/example_tabular_classification.ipynb rename to development/notebooks/examples/20_basics/example_tabular_classification.ipynb index e3a4c37a4..c6d62a0a5 100644 --- a/development/notebooks/basics_tabular/example_tabular_classification.ipynb +++ b/development/notebooks/examples/20_basics/example_tabular_classification.ipynb @@ -26,7 +26,7 @@ }, "outputs": [], "source": [ - "import os\nimport tempfile as tmp\nimport warnings\n\nos.environ['JOBLIB_TEMP_FOLDER'] = tmp.gettempdir()\nos.environ['OMP_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_NUM_THREADS'] = '1'\n\nwarnings.simplefilter(action='ignore', category=UserWarning)\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nimport sklearn.datasets\nimport sklearn.model_selection\n\nfrom autoPyTorch.api.tabular_classification import TabularClassificationTask\n\n\nif __name__ == '__main__':\n\n ############################################################################\n # Data Loading\n # ============\n X, y = sklearn.datasets.fetch_openml(data_id=40981, return_X_y=True, as_frame=True)\n X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n X,\n y,\n random_state=42,\n )\n\n ############################################################################\n # Build and fit a classifier\n # ==========================\n api = TabularClassificationTask(\n temporary_directory='./tmp/autoPyTorch_example_tmp_01',\n output_directory='./tmp/autoPyTorch_example_out_01',\n # To maintain logs of the run, set the next two as False\n delete_tmp_folder_after_terminate=True,\n delete_output_folder_after_terminate=True,\n seed=42,\n )\n\n ############################################################################\n # Search for an ensemble of machine learning algorithms\n # =====================================================\n api.search(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test.copy(),\n y_test=y_test.copy(),\n optimize_metric='accuracy',\n total_walltime_limit=300,\n func_eval_time_limit_secs=50\n )\n\n ############################################################################\n # Print the final ensemble performance\n # ====================================\n print(api.run_history, api.trajectory)\n y_pred = api.predict(X_test)\n score = api.score(y_pred, y_test)\n print(score)\n # Print the final ensemble built by AutoPyTorch\n print(api.show_models())" + "import os\nimport tempfile as tmp\nimport warnings\n\nos.environ['JOBLIB_TEMP_FOLDER'] = tmp.gettempdir()\nos.environ['OMP_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_NUM_THREADS'] = '1'\n\nwarnings.simplefilter(action='ignore', category=UserWarning)\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nimport sklearn.datasets\nimport sklearn.model_selection\n\nfrom autoPyTorch.api.tabular_classification import TabularClassificationTask\n\n\nif __name__ == '__main__':\n\n ############################################################################\n # Data Loading\n # ============\n X, y = sklearn.datasets.fetch_openml(data_id=40981, return_X_y=True, as_frame=True)\n X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n X,\n y,\n random_state=42,\n )\n\n ############################################################################\n # Build and fit a classifier\n # ==========================\n api = TabularClassificationTask(\n # To maintain logs of the run, you can uncomment the\n # Following lines\n # temporary_directory='./tmp/autoPyTorch_example_tmp_01',\n # output_directory='./tmp/autoPyTorch_example_out_01',\n # delete_tmp_folder_after_terminate=False,\n # delete_output_folder_after_terminate=False,\n seed=42,\n )\n\n ############################################################################\n # Search for an ensemble of machine learning algorithms\n # =====================================================\n api.search(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test.copy(),\n y_test=y_test.copy(),\n optimize_metric='accuracy',\n total_walltime_limit=300,\n func_eval_time_limit_secs=50\n )\n\n ############################################################################\n # Print the final ensemble performance\n # ====================================\n print(api.run_history, api.trajectory)\n y_pred = api.predict(X_test)\n score = api.score(y_pred, y_test)\n print(score)\n # Print the final ensemble built by AutoPyTorch\n print(api.show_models())" ] } ], diff --git a/development/notebooks/basics_tabular/example_tabular_regression.ipynb b/development/notebooks/examples/20_basics/example_tabular_regression.ipynb similarity index 65% rename from development/notebooks/basics_tabular/example_tabular_regression.ipynb rename to development/notebooks/examples/20_basics/example_tabular_regression.ipynb index 5d2508408..e33d21abf 100644 --- a/development/notebooks/basics_tabular/example_tabular_regression.ipynb +++ b/development/notebooks/examples/20_basics/example_tabular_regression.ipynb @@ -26,7 +26,7 @@ }, "outputs": [], "source": [ - "import os\nimport tempfile as tmp\nimport warnings\n\nimport sklearn.datasets\nimport sklearn.model_selection\n\nos.environ['JOBLIB_TEMP_FOLDER'] = tmp.gettempdir()\nos.environ['OMP_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_NUM_THREADS'] = '1'\n\nwarnings.simplefilter(action='ignore', category=UserWarning)\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nfrom autoPyTorch.api.tabular_regression import TabularRegressionTask\n\n\nif __name__ == '__main__':\n\n ############################################################################\n # Data Loading\n # ============\n X, y = sklearn.datasets.fetch_openml(name='boston', return_X_y=True, as_frame=True)\n X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n X,\n y,\n random_state=1,\n )\n\n # Scale the regression targets to have zero mean and unit variance.\n # This is important for Neural Networks since predicting large target values would require very large weights.\n # One can later rescale the network predictions like this: y_pred = y_pred_scaled * y_train_std + y_train_mean\n y_train_mean = y_train.mean()\n y_train_std = y_train.std()\n\n y_train_scaled = (y_train - y_train_mean) / y_train_std\n y_test_scaled = (y_test - y_train_mean) / y_train_std\n\n ############################################################################\n # Build and fit a regressor\n # ==========================\n api = TabularRegressionTask(\n temporary_directory='./tmp/autoPyTorch_example_tmp_02',\n output_directory='./tmp/autoPyTorch_example_out_02',\n # To maintain logs of the run, set the next two as False\n delete_tmp_folder_after_terminate=True,\n delete_output_folder_after_terminate=True\n )\n\n ############################################################################\n # Search for an ensemble of machine learning algorithms\n # =====================================================\n api.search(\n X_train=X_train,\n y_train=y_train_scaled,\n X_test=X_test.copy(),\n y_test=y_test_scaled.copy(),\n optimize_metric='r2',\n total_walltime_limit=300,\n func_eval_time_limit_secs=50,\n enable_traditional_pipeline=False,\n )\n\n ############################################################################\n # Print the final ensemble performance\n # ====================================\n print(api.run_history, api.trajectory)\n y_pred_scaled = api.predict(X_test)\n\n # Rescale the Neural Network predictions into the original target range\n y_pred = y_pred_scaled * y_train_std + y_train_mean\n score = api.score(y_pred, y_test)\n\n print(score)\n # Print the final ensemble built by AutoPyTorch\n print(api.show_models())" + "import os\nimport tempfile as tmp\nimport warnings\n\nimport sklearn.datasets\nimport sklearn.model_selection\n\nos.environ['JOBLIB_TEMP_FOLDER'] = tmp.gettempdir()\nos.environ['OMP_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_NUM_THREADS'] = '1'\n\nwarnings.simplefilter(action='ignore', category=UserWarning)\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nfrom autoPyTorch.api.tabular_regression import TabularRegressionTask\n\n\nif __name__ == '__main__':\n\n ############################################################################\n # Data Loading\n # ============\n X, y = sklearn.datasets.fetch_openml(name='boston', return_X_y=True, as_frame=True)\n X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n X,\n y,\n random_state=1,\n )\n\n # Scale the regression targets to have zero mean and unit variance.\n # This is important for Neural Networks since predicting large target values would require very large weights.\n # One can later rescale the network predictions like this: y_pred = y_pred_scaled * y_train_std + y_train_mean\n y_train_mean = y_train.mean()\n y_train_std = y_train.std()\n\n y_train_scaled = (y_train - y_train_mean) / y_train_std\n y_test_scaled = (y_test - y_train_mean) / y_train_std\n\n ############################################################################\n # Build and fit a regressor\n # ==========================\n api = TabularRegressionTask()\n\n ############################################################################\n # Search for an ensemble of machine learning algorithms\n # =====================================================\n api.search(\n X_train=X_train,\n y_train=y_train_scaled,\n X_test=X_test.copy(),\n y_test=y_test_scaled.copy(),\n optimize_metric='r2',\n total_walltime_limit=300,\n func_eval_time_limit_secs=50,\n enable_traditional_pipeline=False,\n )\n\n ############################################################################\n # Print the final ensemble performance\n # ====================================\n print(api.run_history, api.trajectory)\n y_pred_scaled = api.predict(X_test)\n\n # Rescale the Neural Network predictions into the original target range\n y_pred = y_pred_scaled * y_train_std + y_train_mean\n score = api.score(y_pred, y_test)\n\n print(score)\n # Print the final ensemble built by AutoPyTorch\n print(api.show_models())" ] } ], diff --git a/development/notebooks/advanced_tabular/example_custom_configuration_space.ipynb b/development/notebooks/examples/40_advanced/example_custom_configuration_space.ipynb similarity index 100% rename from development/notebooks/advanced_tabular/example_custom_configuration_space.ipynb rename to development/notebooks/examples/40_advanced/example_custom_configuration_space.ipynb diff --git a/development/notebooks/examples/40_advanced/example_resampling_strategy.ipynb b/development/notebooks/examples/40_advanced/example_resampling_strategy.ipynb new file mode 100644 index 000000000..bf58d792f --- /dev/null +++ b/development/notebooks/examples/40_advanced/example_resampling_strategy.ipynb @@ -0,0 +1,54 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Tabular Classification with different resampling strategy\n\nThe following example shows how to fit a sample classification model\nwith different resampling strategies in AutoPyTorch\nBy default, AutoPyTorch uses Holdout Validation with\na 67% train size split.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\nimport tempfile as tmp\nimport warnings\n\nos.environ['JOBLIB_TEMP_FOLDER'] = tmp.gettempdir()\nos.environ['OMP_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_NUM_THREADS'] = '1'\n\nwarnings.simplefilter(action='ignore', category=UserWarning)\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nimport sklearn.datasets\nimport sklearn.model_selection\n\nfrom autoPyTorch.api.tabular_classification import TabularClassificationTask\nfrom autoPyTorch.datasets.resampling_strategy import CrossValTypes, HoldoutValTypes\n\n\nif __name__ == '__main__':\n\n ############################################################################\n # Data Loading\n # ============\n X, y = sklearn.datasets.fetch_openml(data_id=40981, return_X_y=True, as_frame=True)\n X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n X,\n y,\n random_state=1,\n )\n\n ############################################################################\n # Build and fit a classifier with default resampling strategy\n # ===========================================================\n api = TabularClassificationTask(\n # 'HoldoutValTypes.holdout_validation' with 'val_share': 0.33\n # is the default argument setting for TabularClassificationTask.\n # It is explicitly specified in this example for demonstrational\n # purpose.\n resampling_strategy=HoldoutValTypes.holdout_validation,\n resampling_strategy_args={'val_share': 0.33}\n )\n\n ############################################################################\n # Search for an ensemble of machine learning algorithms\n # =====================================================\n api.search(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test.copy(),\n y_test=y_test.copy(),\n optimize_metric='accuracy',\n total_walltime_limit=150,\n func_eval_time_limit_secs=30\n )\n\n ############################################################################\n # Print the final ensemble performance\n # ====================================\n print(api.run_history, api.trajectory)\n y_pred = api.predict(X_test)\n score = api.score(y_pred, y_test)\n print(score)\n # Print the final ensemble built by AutoPyTorch\n print(api.show_models())\n\n ############################################################################\n\n ############################################################################\n # Build and fit a classifier with Cross validation resampling strategy\n # ====================================================================\n api = TabularClassificationTask(\n resampling_strategy=CrossValTypes.k_fold_cross_validation,\n resampling_strategy_args={'num_splits': 3}\n )\n\n ############################################################################\n # Search for an ensemble of machine learning algorithms\n # =====================================================\n api.search(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test.copy(),\n y_test=y_test.copy(),\n optimize_metric='accuracy',\n total_walltime_limit=150,\n func_eval_time_limit_secs=30\n )\n\n ############################################################################\n # Print the final ensemble performance\n # ====================================\n print(api.run_history, api.trajectory)\n y_pred = api.predict(X_test)\n score = api.score(y_pred, y_test)\n print(score)\n # Print the final ensemble built by AutoPyTorch\n print(api.show_models())\n\n ############################################################################\n\n ############################################################################\n # Build and fit a classifier with Stratified resampling strategy\n # ==============================================================\n api = TabularClassificationTask(\n # For demonstration purposes, we use\n # Stratified hold out validation. However,\n # one can also use CrossValTypes.stratified_k_fold_cross_validation.\n resampling_strategy=HoldoutValTypes.stratified_holdout_validation,\n resampling_strategy_args={'val_share': 0.33}\n )\n\n ############################################################################\n # Search for an ensemble of machine learning algorithms\n # =====================================================\n api.search(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test.copy(),\n y_test=y_test.copy(),\n optimize_metric='accuracy',\n total_walltime_limit=150,\n func_eval_time_limit_secs=30\n )\n\n ############################################################################\n # Print the final ensemble performance\n # ====================================\n print(api.run_history, api.trajectory)\n y_pred = api.predict(X_test)\n score = api.score(y_pred, y_test)\n print(score)\n # Print the final ensemble built by AutoPyTorch\n print(api.show_models())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/development/notebooks/advanced_tabular/example_visualization.ipynb b/development/notebooks/examples/40_advanced/example_visualization.ipynb similarity index 100% rename from development/notebooks/advanced_tabular/example_visualization.ipynb rename to development/notebooks/examples/40_advanced/example_visualization.ipynb diff --git a/development/objects.inv b/development/objects.inv index 2216721db98e1b5ba33746df8f77f57929dc1161..080a42f2298b661faec9ae0eb297849d5fe617a6 100644 GIT binary patch delta 1518 zcmV*r-p{W<%H1-ZR9$(OCS62oq$r8HwHTYsp!#(Uo@VZ^ zznGW7Hu#ZD)J2sV&i9=MhXb5J4J#kdFJ+w^x2PgphW1T{>Nv>|rR0?)h>^0`zR2NQ zXY>Wp-=I22LKa+u7Jp=UcJ&W{8}v@%?Y<#d8ds#k87Xk9Cx6H6Sf(GY=#{VwvL7%r zPQ+!^j?)XPqZn~~Oe>UJGHz9kQ<7XVb~V22+R&t|FBu`Z5EoToC09}wujCNWRuMp1 z8l`0dU^fSD9`C7{%+MZZ_B4lTfr$U39w{+p1YHL_ke-n1o}kGQDL`JPO@@7XTM?*5 z9xVAyichw?{C`urdxuXrD=S=syE18VTrjCn%NrdU!6OjBM50cS;s8jIk`qZAl(jbC zjC$xa0CoVV0N*v&eaH16rd(C*hygAr&i5IX%BFO21k?zrty4B+6WUVabZR^8(PX5d zD;)}5tGL|*d{rt`GY@lXx|D)K>V>0j`1iw4@W{V6_mWh80e-CmTQ|0(|}RE~{I=?C$LLnuR8HaL0galN+P$Pi{}>H&H3dZDeh z-~8z>AFdNTQWi?tI|&r8P?vIvX^yPlO7$_7U>^2CuHAQZD&LsOH%liyQ-*VX{D1!2 zkQ%pr5jv$$LZAbA9@1>A1W~IkN8~!I#2CvWON_6oBN&+mk!eCzb(!$DnxDe-n@kr< zLw;y7(}Q}HGe+BJwRXq{Ra!xL+$x-~;QhuX>g1iP%l#;gRx%YmcM~cQ=O~++zZm$k zmSf!qn?VahOZZ6>+?cUVUZx{uyMH=EJ)u@zbVXGGw`M0`Javy;v4gI0hWSzJ+|Klv zL_Mi5SJY^6ghgvwk3@3-B)uMLh)%c`orK-?db>acmla4*h z`Ny^>(dzgXAF?`5%Rfa{qBPbi8Jp*S9BjT-=MQM!wPrwW2GnMl3ljXggMX4n55cic zA1_;c1r~YZX@xEDC)7+^5bJkSSXc$b$A*~#XqDx?kVFCe7r<0 z;{-0eei?ax#|p7^COHn~Xb%mAmxW^wjZM(w5NTCe!g=8!) ze@d$#@fqh;jd_r&v@Q7E zL+%tUZ*0*BUNL7dN%sO5XER?bawZu+t3}PiIrY$K0PF!!0lsOjUC;H@O}VPXH2{|s z7l#~6Wg%VSLZJ~-Td!=))@jR()0yqGN0X6;uJl~!gktf!8Gz0zC2)C8GCvHMEH&DK)zz=pyf440X_u9_q>gEG~=8yR6QRFBvJ)R^dk zdDeyCNlzr=1M3SD0<)~<6FLx&sv|D@+Gr^CsC05C{#}hqsD0J6Db*#?Yp;Dhw1p-} zQo`nax45LN>-1LQrdTslWG0f650P^JZLWQv zRa!xL+-jV%*{8Zq)XO_B@!ceiRuYPyy9pJDb5w1U@^diHtEm@)&7g&$CH$fZ9?aN^ zpWzc_yS_j@p;ov4d`Kj`{iM+#>auM7^jlSJY^6gu6zp9!X*V zB)cDLNY1$FUI2HS`_0`PTnWNEcn|NuPaC#U?P&)s$E0Jga{gHqC0d_8_Q$;04GSe>d2OpV?EF-3v;K5rG=J>wL{l{6 z=X_r@?5f*djCOLOD;ajr0SU)^4NJe-p6BTDzM0=zdV~hyeKQ&h-RDB=Q^IJMB4+0X zo)R9d`#?8nxT;t5k~{{RrS&xZy~!Kxiq*DR=*F<&6YD~cv(2}9LO#<&xf$8A@Y%U6 zFSF`*nO7+LpHz5l&b6f;E-&%y#^pQgi>Hn+a^ePsIc)f-LQk3e=ZcSUU~v_?!jBtN Qo>=?c5(HQO0e-Q(C)_aHrT_o{ diff --git a/development/releases.html b/development/releases.html index 05611f775..a81eb6ee7 100644 --- a/development/releases.html +++ b/development/releases.html @@ -143,7 +143,7 @@

        Version 0.1.0Sphinx 3.5.4.

        diff --git a/development/search.html b/development/search.html index b8d6a769e..c0ab0cca1 100644 --- a/development/search.html +++ b/development/search.html @@ -153,7 +153,7 @@

        Search

        - © Copyright 2014-2019, Machine Learning Professorship Freiburg.
        + © Copyright 2014-2021, Machine Learning Professorship Freiburg.
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        Tabular Classification with Custom Configuration Space (example_custom_configuration_space.py)

        11:35.664

        Tabular Classification with Custom Configuration Space (example_custom_configuration_space.py)

        11:43.644

        0.0 MB

        Tabular Classification with different resampling strategy (example_resampling_strategy.py)

        10:06.674

        Tabular Classification with different resampling strategy (example_resampling_strategy.py)

        10:23.225

        0.0 MB

        Visualizing the Results (example_visualization.py)

        04:28.036

        Visualizing the Results (example_visualization.py)

        04:46.879

        0.0 MB