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Eddie Bergman: Port over to AutoML common (#1318)
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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_002.png

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‎development/_images/sphx_glr_example_pandas_train_test_001.png

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‎development/_images/sphx_glr_example_pandas_train_test_thumb.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 4.364232
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2 1 1.0 random_forest 0.447294 4.122788
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‎development/_sources/examples/20_basic/example_multioutput_regression.rst.txt

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rank ensemble_weight type cost duration
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18 1 1.0 gaussian_process 1.021347e-08 2.858771
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12 1 1.0 gaussian_process 1.125731e-10 6.505112
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.. code-block:: none
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[(1.000000, SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'regressor:__choice__': 'gaussian_process', 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'minority_coalescer', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'median', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'none', 'regressor:gaussian_process:alpha': 6.862614540380134e-10, 'regressor:gaussian_process:thetaL': 1.944608422766674e-09, 'regressor:gaussian_process:thetaU': 917.8440681079683, 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.00018156864684755125},
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[(1.000000, SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'regressor:__choice__': 'gaussian_process', 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'one_hot_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'no_coalescense', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'minmax', 'regressor:gaussian_process:alpha': 5.779680060624107e-12, 'regressor:gaussian_process:thetaL': 1.1426676475875125e-10, 'regressor:gaussian_process:thetaU': 67194.35311216833},
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dataset_properties={
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'task': 5,
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R2 score: 0.9999999870724605
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R2 score: 0.9999999999006857
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‎development/_sources/examples/20_basic/example_regression.rst.txt

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25 1 0.46 sgd 0.436679 0.831828
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6 2 0.32 ard_regression 0.455042 0.847448
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27 3 0.14 ard_regression 0.462249 0.826625
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11 4 0.02 random_forest 0.507400 10.825404
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7 5 0.06 gradient_boosting 0.518673 1.553152
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25 1 0.46 sgd 0.436679 0.752868
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6 2 0.32 ard_regression 0.455042 0.802489
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27 3 0.14 ard_regression 0.462249 0.785675
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11 4 0.02 random_forest 0.507400 10.257058
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7 5 0.06 gradient_boosting 0.518673 1.482558
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‎development/_sources/examples/20_basic/sg_execution_times.rst.txt

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=================
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**06:15.313** total execution time for **examples_20_basic** files:
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**06:07.703** total execution time for **examples_20_basic** files:
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| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``) | 02:03.425 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``) | 02:01.477 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``) | 01:59.808 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``) | 01:55.217 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``) | 01:55.142 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``) | 01:55.166 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:16.939 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:15.843 | 0.0 MB |
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‎development/_sources/examples/40_advanced/example_calc_multiple_metrics.rst.txt

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Accuracy score 0.958041958041958
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Accuracy score 0.9440559440559441
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################################################################################
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Metric results
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rank_test_scores param_classifier:__choice__ mean_test_score metric_balanced_accuracy metric_precision metric_recall metric_f1 metric_custom_error
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5 random_forest 0.971631 0.969533 0.977528 0.977528 0.977528 0.028369
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5 mlp 0.971631 0.961538 0.956989 1.000000 0.978022 0.028369
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27 mlp 0.943262 0.935069 0.945055 0.966292 0.955556 0.056738
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15 random_forest 0.964539 0.959918 0.966667 0.977528 0.972067 0.035461
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5 mlp 0.971631 0.961538 0.956989 1.000000 0.978022 0.028369
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6 random_forest 0.971631 0.969533 0.977528 0.977528 0.977528 0.028369
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6 mlp 0.971631 0.961538 0.956989 1.000000 0.978022 0.028369
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28 mlp 0.943262 0.935069 0.945055 0.966292 0.955556 0.056738
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16 random_forest 0.964539 0.959918 0.966667 0.977528 0.972067 0.035461
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6 mlp 0.971631 0.961538 0.956989 1.000000 0.978022 0.028369
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1 extra_trees 0.985816 0.984767 0.988764 0.988764 0.988764 0.014184
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16 random_forest 0.964539 0.963915 0.977273 0.966292 0.971751 0.035461
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21 extra_trees 0.957447 0.954300 0.966292 0.966292 0.966292 0.042553
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6 random_forest 0.971631 0.969533 0.977528 0.977528 0.977528 0.028369
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6 random_forest 0.971631 0.969533 0.977528 0.977528 0.977528 0.028369
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16 gradient_boosting 0.964539 0.963915 0.977273 0.966292 0.971751 0.035461
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6 gradient_boosting 0.971631 0.965536 0.967033 0.988764 0.977778 0.028369
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6 mlp 0.971631 0.965536 0.967033 0.988764 0.977778 0.028369
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25 mlp 0.950355 0.948682 0.965909 0.955056 0.960452 0.049645
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3 gradient_boosting 0.978723 0.975151 0.977778 0.988764 0.983240 0.021277
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16 gradient_boosting 0.964539 0.959918 0.966667 0.977528 0.972067 0.035461
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16 random_forest 0.964539 0.959918 0.966667 0.977528 0.972067 0.035461
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6 extra_trees 0.971631 0.969533 0.977528 0.977528 0.977528 0.028369
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33 passive_aggressive 0.921986 0.894231 0.890000 1.000000 0.941799 0.078014
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3 extra_trees 0.978723 0.975151 0.977778 0.988764 0.983240 0.021277
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6 gradient_boosting 0.971631 0.965536 0.967033 0.988764 0.977778 0.028369
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25 mlp 0.950355 0.940687 0.945652 0.977528 0.961326 0.049645
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31 random_forest 0.929078 0.923833 0.943820 0.943820 0.943820 0.070922
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21 adaboost 0.957447 0.950303 0.956044 0.977528 0.966667 0.042553
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6 extra_trees 0.971631 0.965536 0.967033 0.988764 0.977778 0.028369
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21 extra_trees 0.957447 0.954300 0.966292 0.966292 0.966292 0.042553
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31 bernoulli_nb 0.929078 0.923833 0.943820 0.943820 0.943820 0.070922
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3 extra_trees 0.978723 0.979149 0.988636 0.977528 0.983051 0.021277
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21 liblinear_svc 0.957447 0.954300 0.966292 0.966292 0.966292 0.042553
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28 gaussian_nb 0.943262 0.935069 0.945055 0.966292 0.955556 0.056738
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25 extra_trees 0.950355 0.936690 0.936170 0.988764 0.961749 0.049645
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28 random_forest 0.943262 0.943064 0.965517 0.943820 0.954545 0.056738
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1 extra_trees 0.985816 0.984767 0.988764 0.988764 0.988764 0.014184
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15 random_forest 0.964539 0.963915 0.977273 0.966292 0.971751 0.035461
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20 extra_trees 0.957447 0.954300 0.966292 0.966292 0.966292 0.042553
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5 random_forest 0.971631 0.969533 0.977528 0.977528 0.977528 0.028369
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5 random_forest 0.971631 0.969533 0.977528 0.977528 0.977528 0.028369
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15 gradient_boosting 0.964539 0.963915 0.977273 0.966292 0.971751 0.035461
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5 gradient_boosting 0.971631 0.965536 0.967033 0.988764 0.977778 0.028369
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5 mlp 0.971631 0.965536 0.967033 0.988764 0.977778 0.028369
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24 mlp 0.950355 0.948682 0.965909 0.955056 0.960452 0.049645
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2 gradient_boosting 0.978723 0.975151 0.977778 0.988764 0.983240 0.021277
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15 gradient_boosting 0.964539 0.959918 0.966667 0.977528 0.972067 0.035461
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15 random_forest 0.964539 0.959918 0.966667 0.977528 0.972067 0.035461
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5 extra_trees 0.971631 0.969533 0.977528 0.977528 0.977528 0.028369
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32 passive_aggressive 0.921986 0.894231 0.890000 1.000000 0.941799 0.078014
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2 extra_trees 0.978723 0.975151 0.977778 0.988764 0.983240 0.021277
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5 gradient_boosting 0.971631 0.965536 0.967033 0.988764 0.977778 0.028369
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24 mlp 0.950355 0.940687 0.945652 0.977528 0.961326 0.049645
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30 random_forest 0.929078 0.923833 0.943820 0.943820 0.943820 0.070922
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20 adaboost 0.957447 0.950303 0.956044 0.977528 0.966667 0.042553
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5 extra_trees 0.971631 0.965536 0.967033 0.988764 0.977778 0.028369
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20 extra_trees 0.957447 0.954300 0.966292 0.966292 0.966292 0.042553
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30 bernoulli_nb 0.929078 0.923833 0.943820 0.943820 0.943820 0.070922
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2 extra_trees 0.978723 0.979149 0.988636 0.977528 0.983051 0.021277
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20 liblinear_svc 0.957447 0.954300 0.966292 0.966292 0.966292 0.042553
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27 gaussian_nb 0.943262 0.935069 0.945055 0.966292 0.955556 0.056738
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24 extra_trees 0.950355 0.936690 0.936170 0.988764 0.961749 0.049645
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27 random_forest 0.943262 0.943064 0.965517 0.943820 0.954545 0.056738
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.. _sphx_glr_download_examples_40_advanced_example_calc_multiple_metrics.py:

‎development/_sources/examples/40_advanced/example_debug_logging.rst.txt

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2021-12-02 12:18:34,208 - Client-EnsembleBuilder - INFO - DummyFuture: ([{'Timestamp': Timestamp('2021-12-02 12:18:34.186127'), 'ensemble_optimization_score': 0.47853535353535354, 'ensemble_test_score': 0.47434292866082606}], 50, None, None, None)/SingleThreadedClient() Started Ensemble builder job at 2021.12.02-12.18.34 for iteration 0.
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2021-12-02 12:18:40,928 - Client-EnsembleBuilder - INFO - DummyFuture: ([], 50, None, None, None)/SingleThreadedClient() Started Ensemble builder job at 2021.12.02-12.18.40 for iteration 1.
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2021-12-02 14:30:28,383 - Client-EnsembleBuilder - INFO - DummyFuture: ([{'Timestamp': Timestamp('2021-12-02 14:30:28.363787'), 'ensemble_optimization_score': 0.47853535353535354, 'ensemble_test_score': 0.47434292866082606}], 50, None, None, None)/SingleThreadedClient() Started Ensemble builder job at 2021.12.02-14.30.28 for iteration 0.
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2021-12-02 14:30:34,938 - Client-EnsembleBuilder - INFO - DummyFuture: ([], 50, None, None, None)/SingleThreadedClient() Started Ensemble builder job at 2021.12.02-14.30.34 for iteration 1.
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‎development/_sources/examples/40_advanced/example_feature_types.rst.txt

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