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Eddie Bergman: Fix: add error to be ignored during test (#1382)
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‎development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

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‎development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.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/_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 3.954243
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2 1 1.0 random_forest 0.447294 3.783007
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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 0x7f8e64679d60>,
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'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7facbcd12430>,
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'cost': 0.4472941828699525,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f8e5fb023d0>,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7facbee85550>,
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'ensemble_weight': 1.0,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f8e646790a0>,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7facbcd12580>,
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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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rank ensemble_weight type cost duration
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19 1 1.0 gaussian_process 2.891363e-08 5.137209
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rank ensemble_weight type cost duration
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2 1 0.94 random_forest 0.144406 2.573841
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25 2 0.06 random_forest 0.341540 3.723741
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{ 19: { 'cost': 2.891363148904702e-08,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f8e645b3b80>,
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'ensemble_weight': 1.0,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f8e5fa9f0d0>,
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'model_id': 19,
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'rank': 1,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f8e5fa9fbb0>,
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'sklearn_regressor': GaussianProcessRegressor(alpha=8.710120446620487e-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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{ 2: { 'cost': 0.14440599167790935,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7facc1fcca30>,
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'ensemble_weight': 0.94,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7facc1880eb0>,
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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 0x7facc1880f40>,
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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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25: { 'cost': 0.34154027518036534,
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7facbe5d7610>,
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'ensemble_weight': 0.06,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7facbc985f40>,
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'model_id': 25,
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'rank': 2,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7facbc86ae80>,
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'sklearn_regressor': RandomForestRegressor(criterion='friedman_mse', max_features=0.929991924163003,
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min_samples_leaf=4, min_samples_split=18,
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n_estimators=512, n_jobs=1, random_state=1,
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warm_start=True)}}
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R2 score: 0.8681408622884761
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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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/home/runner/work/auto-sklearn/auto-sklearn/autosklearn/metalearning/metalearning/meta_base.py:68: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
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self.metafeatures = self.metafeatures.append(metafeatures)
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/home/runner/work/auto-sklearn/auto-sklearn/autosklearn/metalearning/metalearning/meta_base.py:72: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
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self.algorithm_runs[metric].append(runs)
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27 3 0.14 ard_regression 0.462249 0.662217
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11 4 0.02 random_forest 0.507400 8.655567
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6 2 0.32 ard_regression 0.455042 0.727717
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7facbc801a60>,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7facbc801fa0>,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7facbd655df0>,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7facbd655d90>,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7facc1a38c40>,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7facc1a38a90>,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f8e602fadc0>,
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'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7facbee76a30>,
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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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| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``) | 02:04.606 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``) | 02:01.540 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``) | 01:57.283 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``) | 01:56.083 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``) | 01:55.982 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``) | 01:55.260 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:14.831 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:12.302 | 0.0 MB |
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