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Eddie Bergman: Remove flaky dep (#1361)
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‎development/.buildinfo

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# Sphinx build info version 1
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# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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config: 1281a8de7e72cc26a3b0fce1416780a7
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config: 8a26f7fbaa1576935d6b4916c5b79de9
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tags: 645f666f9bcd5a90fca523b33c5a78b7

‎development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

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‎development/_images/sphx_glr_example_inspect_predictions_001.png

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‎development/_images/sphx_glr_example_inspect_predictions_003.png

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‎development/_images/sphx_glr_example_inspect_predictions_thumb.png

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‎development/_images/sphx_glr_example_pandas_train_test_001.png

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‎development/_images/sphx_glr_example_regression_001.png

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‎development/_modules/autosklearn/estimators.html

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&copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
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&copy; Copyright 2014-2022, Machine Learning Professorship Freiburg.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.2.0.<br/>
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‎development/_modules/autosklearn/experimental/askl2.html

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&copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
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&copy; Copyright 2014-2022, Machine Learning Professorship Freiburg.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.2.0.<br/>
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‎development/_modules/autosklearn/metrics.html

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&copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
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&copy; Copyright 2014-2022, Machine Learning Professorship Freiburg.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.2.0.<br/>
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‎development/_modules/autosklearn/pipeline/components/base.html

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&copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
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&copy; Copyright 2014-2022, Machine Learning Professorship Freiburg.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.2.0.<br/>
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‎development/_modules/autosklearn/pipeline/components/classification.html

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&copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
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&copy; Copyright 2014-2022, Machine Learning Professorship Freiburg.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.2.0.<br/>
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‎development/_modules/autosklearn/pipeline/components/feature_preprocessing.html

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&copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
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&copy; Copyright 2014-2022, Machine Learning Professorship Freiburg.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.2.0.<br/>
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‎development/_modules/autosklearn/pipeline/components/regression.html

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&copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
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&copy; Copyright 2014-2022, Machine Learning Professorship Freiburg.<br/>
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‎development/_modules/index.html

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&copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
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&copy; Copyright 2014-2022, Machine Learning Professorship Freiburg.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.2.0.<br/>
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‎development/_sources/examples/20_basic/example_classification.rst.txt

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‎development/_sources/examples/20_basic/example_multilabel_classification.rst.txt

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rank ensemble_weight type cost duration
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2 1 1.0 random_forest 0.447294 3.74924
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2 1 1.0 random_forest 0.447294 3.220523
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.. code-block:: none
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{ 2: { 'balancing': Balancing(random_state=1),
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'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f1e52b9a730>,
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'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7fb34b449280>,
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'cost': 0.4472941828699525,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f1e54895d30>,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb347b6cf70>,
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'ensemble_weight': 1.0,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f1e52b7d280>,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb34b4497f0>,
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'model_id': 2,
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'rank': 1,
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'sklearn_classifier': RandomForestClassifier(max_features=15, n_estimators=512, n_jobs=1,
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 14.270 seconds)
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**Total running time of the script:** ( 0 minutes 12.221 seconds)
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.. _sphx_glr_download_examples_20_basic_example_multilabel_classification.py:

‎development/_sources/examples/20_basic/example_multioutput_regression.rst.txt

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rank ensemble_weight type cost duration
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2 1 0.78 random_forest 0.127023 2.588092
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5 2 0.16 k_nearest_neighbors 0.204675 0.498047
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18 3 0.04 k_nearest_neighbors 0.459024 0.509598
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14 4 0.02 k_nearest_neighbors 0.509078 1.071782
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18 1 0.8 gaussian_process 0.000007 2.596252
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16 2 0.2 gaussian_process 0.000015 7.973967
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{ 2: { 'cost': 0.12702266427368414,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f1e544680d0>,
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'ensemble_weight': 0.78,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f1e54468970>,
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'model_id': 2,
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'rank': 1,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f1e54468400>,
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'sklearn_regressor': RandomForestRegressor(max_features=1.0, n_estimators=512, n_jobs=1,
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random_state=1, warm_start=True)},
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5: { 'cost': 0.20467501696131973,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f1e543d8640>,
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'ensemble_weight': 0.16,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f1e52b49af0>,
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'model_id': 5,
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'rank': 2,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f1e52b49e50>,
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'sklearn_regressor': KNeighborsRegressor(n_neighbors=14, p=1)},
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14: { 'cost': 0.5090778359252635,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f1e4ee6c700>,
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'ensemble_weight': 0.02,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f1e50a110a0>,
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'model_id': 14,
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'rank': 4,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f1e50a11610>,
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'sklearn_regressor': KNeighborsRegressor(n_neighbors=1, p=1, weights='distance')},
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18: { 'cost': 0.4590244851288239,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f1e52cd0d90>,
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'ensemble_weight': 0.04,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f1e50a419d0>,
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{ 16: { 'cost': 1.453992678268623e-05,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb34aacdd90>,
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'ensemble_weight': 0.2,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb347b2b220>,
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'model_id': 16,
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'rank': 2,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fb347d22910>,
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'sklearn_regressor': GaussianProcessRegressor(alpha=6.883531961818898e-12,
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kernel=RBF(length_scale=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),
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n_restarts_optimizer=10, normalize_y=True,
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random_state=1)},
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18: { 'cost': 6.532830369665454e-06,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb3459dc730>,
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'ensemble_weight': 0.8,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb34aacdbe0>,
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'model_id': 18,
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'rank': 3,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f1e50a41f10>,
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'sklearn_regressor': KNeighborsRegressor(n_neighbors=1, p=1, weights='distance')}}
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'rank': 1,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fb34aacda00>,
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'sklearn_regressor': GaussianProcessRegressor(alpha=9.054973819256506e-06,
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kernel=RBF(length_scale=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),
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n_restarts_optimizer=10, normalize_y=True,
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R2 score: 0.999993617907473
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**Total running time of the script:** ( 1 minutes 54.549 seconds)
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**Total running time of the script:** ( 1 minutes 57.174 seconds)
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.. _sphx_glr_download_examples_20_basic_example_multioutput_regression.py:

‎development/_sources/examples/20_basic/example_regression.rst.txt

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27 3 0.14 ard_regression 0.462249 0.721807
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11 4 0.02 random_forest 0.507400 10.729273
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7 5 0.06 gradient_boosting 0.518673 1.277604
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25 1 0.44 sgd 0.436679 0.605110
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6 2 0.34 ard_regression 0.455042 0.629450
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39 3 0.18 ard_regression 0.474807 0.603827
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7 4 0.04 gradient_boosting 0.518673 1.111901
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'ensemble_weight': 0.32,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f1e54431580>,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb34b258970>,
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'ensemble_weight': 0.34,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb34aba8310>,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f1e54431fd0>,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fb34aba8a60>,
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'sklearn_regressor': ARDRegression(alpha_1=0.0003701926442639788, alpha_2=2.2118001735899097e-07,
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'ensemble_weight': 0.06,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f1e52b2e040>,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb347cca280>,
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'ensemble_weight': 0.04,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb347b8ca30>,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f1e52b2e790>,
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'rank': 4,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fb347b8c820>,
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'sklearn_regressor': HistGradientBoostingRegressor(l2_regularization=1.8428972335335263e-10,
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'ensemble_weight': 0.02,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f1e4ed21820>,
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'rank': 4,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f1e4ed21dc0>,
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'sklearn_regressor': RandomForestRegressor(bootstrap=False, criterion='mae',
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f1e542bbe80>,
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'ensemble_weight': 0.46,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f1e541b3c70>,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb34aac2880>,
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'ensemble_weight': 0.44,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb347ccad30>,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f1e541b3310>,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fb345820c70>,
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'sklearn_regressor': SGDRegressor(alpha=0.0006517033225329654, epsilon=0.012150149892783745,
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27: { 'cost': 0.4622486119001967,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f1e4ed322e0>,
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'ensemble_weight': 0.14,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f1e52b41880>,
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'model_id': 27,
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39: { 'cost': 0.4748068089650166,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb34b258eb0>,
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'ensemble_weight': 0.18,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb34674f6a0>,
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'model_id': 39,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f1e5443d3d0>,
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'sklearn_regressor': ARDRegression(alpha_1=2.7664515192592053e-05, alpha_2=9.504988116581138e-07,
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copy_X=False, lambda_1=6.50650698230178e-09,
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threshold_lambda=78251.58542976103, tol=0.0007301343236220855)}}
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fb34674fa90>,
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'sklearn_regressor': ARDRegression(alpha_1=0.0005012365297609799, alpha_2=3.025360750168211e-08,
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copy_X=False, lambda_1=4.9749646614525684e-05,
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lambda_2=3.2368037115065363e-10,
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threshold_lambda=18669.665899307194, tol=0.0012624032013298571)}}
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Train R2 score: 0.5855373845454157
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Test R2 score: 0.39879073225079487
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**Total running time of the script:** ( 1 minutes 55.959 seconds)
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.. _sphx_glr_download_examples_20_basic_example_regression.py:

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