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Eddie Bergman: Testing: ignore kernal pca config error with sparse data (#1368)
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‎development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

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‎development/_images/sphx_glr_example_inspect_predictions_001.png

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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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@@ -155,7 +155,7 @@ View the models found by auto-sklearn
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rank ensemble_weight type cost duration
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2 1 1.0 random_forest 0.447294 3.615119
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2 1 1.0 random_forest 0.447294 4.406952
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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 0x7fdfac2599a0>,
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'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7fa4ed5b9c70>,
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'cost': 0.4472941828699525,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fdfaccdf070>,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fa4f1f48eb0>,
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'ensemble_weight': 1.0,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fdfac259250>,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fa4ed5b9250>,
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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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.. _sphx_glr_download_examples_20_basic_example_multilabel_classification.py:

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

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.. code-block:: none
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rank ensemble_weight type cost duration
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model_id
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12 1 1.0 gaussian_process 0.000005 2.62024
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rank ensemble_weight type cost duration
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model_id
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24 1 0.72 extra_trees 0.112835 3.449075
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2 2 0.16 random_forest 0.166371 2.792659
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11 3 0.02 k_nearest_neighbors 0.242884 0.561164
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14 4 0.06 k_nearest_neighbors 0.501810 1.096981
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21 5 0.04 decision_tree 0.510766 0.625484
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{ 12: { 'cost': 5.194507913142132e-06,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fdfab0429a0>,
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'ensemble_weight': 1.0,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fdfab042700>,
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'model_id': 12,
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{ 2: { 'cost': 0.16637090899710183,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fa4f1e730a0>,
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'ensemble_weight': 0.16,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fa4f03f3610>,
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'model_id': 2,
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'rank': 2,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fa4f03f3700>,
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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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11: { 'cost': 0.24288428078315416,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fa4ed32a970>,
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'ensemble_weight': 0.02,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fa4f044f6a0>,
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'model_id': 11,
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'rank': 3,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fa4f044f6d0>,
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'sklearn_regressor': KNeighborsRegressor(n_neighbors=3)},
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14: { 'cost': 0.5018102612386459,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fa4f03fcdc0>,
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'ensemble_weight': 0.06,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fa4f0402be0>,
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'rank': 4,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fa4ed5b9370>,
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'sklearn_regressor': KNeighborsRegressor(n_neighbors=1, p=1, weights='distance')},
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21: { 'cost': 0.5107661594162471,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fa4f0445490>,
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'ensemble_weight': 0.04,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fa4f036afa0>,
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'model_id': 21,
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'rank': 5,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fa4f036a370>,
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'sklearn_regressor': DecisionTreeRegressor(max_depth=10, min_samples_leaf=5, min_samples_split=3,
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random_state=1)},
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24: { 'cost': 0.11283470888386893,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fa4ed7ebdf0>,
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'ensemble_weight': 0.72,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fa4ed62dca0>,
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'model_id': 24,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fdfab042a00>,
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'sklearn_regressor': GaussianProcessRegressor(alpha=6.212085562385133e-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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random_state=1)}}
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fa4ed62d4c0>,
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'sklearn_regressor': ExtraTreesRegressor(criterion='friedman_mse', max_features=0.8510680946985372,
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min_samples_leaf=16, min_samples_split=17, n_estimators=512,
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n_jobs=1, random_state=1, warm_start=True)}}
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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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6 2 0.32 ard_regression 0.455042 0.699064
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27 3 0.14 ard_regression 0.462249 0.680735
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11 4 0.02 random_forest 0.507400 10.409721
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7 5 0.06 gradient_boosting 0.518673 1.241716
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25 1 0.46 sgd 0.436679 0.810855
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6 2 0.32 ard_regression 0.455042 0.869901
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27 3 0.14 ard_regression 0.462249 0.836544
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11 4 0.02 random_forest 0.507400 10.837491
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fa4f0402370>,
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'ensemble_weight': 0.32,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fdfb03f3850>,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fa4f220f6a0>,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fdfb03f3250>,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fa4f1e9f460>,
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'sklearn_regressor': ARDRegression(alpha_1=0.0003701926442639788, alpha_2=2.2118001735899097e-07,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fdfac2ae520>,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fa4f1bdb520>,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fdfafd356d0>,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fa4f1c2dc40>,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fa4f1c2deb0>,
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'sklearn_regressor': HistGradientBoostingRegressor(l2_regularization=1.8428972335335263e-10,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fa4f1f21910>,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fdfacd2adf0>,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fa4f1f21d60>,
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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 0x7fdfacd21b20>,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fa4f0402b20>,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fdfacd21c70>,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fa4f025a9d0>,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fdfacd218e0>,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fa4f025abb0>,
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'sklearn_regressor': SGDRegressor(alpha=0.0006517033225329654, epsilon=0.012150149892783745,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fdfb07beca0>,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fa504eae730>,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fdfab184910>,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fa4f0218f10>,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fa4f0218ca0>,
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.. _sphx_glr_download_examples_20_basic_example_regression.py:

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

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+-------------------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``) | 02:02.421 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``) | 02:00.438 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``) | 01:59.863 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``) | 01:58.345 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``) | 01:53.928 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``) | 01:54.380 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:34.280 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:14.845 | 0.0 MB |
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+-------------------------------------------------------------------------------------------------------------------+-----------+--------+

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