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Eddie Bergman: Update: Contributing guide with respect to #1358 (#1389)
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386 changes: 194 additions & 192 deletions development/_sources/examples/20_basic/example_classification.rst.txt
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10 changes: 5 additions & 5 deletions development/_sources/examples/20_basic/example_multilabel_classification.rst.txt
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Expand Up @@ -155,7 +155,7 @@ View the models found by auto-sklearn
rank ensemble_weight type cost duration
model_id
2 1 1.0 random_forest 0.447294 4.386928
2 1 1.0 random_forest 0.447294 3.369015
Expand Down Expand Up @@ -183,11 +183,11 @@ Print the final ensemble constructed by auto-sklearn
.. code-block:: none
{ 2: { 'balancing': Balancing(random_state=1),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7fb4742b6a60>,
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f4078720670>,
'cost': 0.4472941828699525,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb474a41310>,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f40786505b0>,
'ensemble_weight': 1.0,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb4742b64f0>,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f4078720be0>,
'model_id': 2,
'rank': 1,
'sklearn_classifier': RandomForestClassifier(max_features=15, n_estimators=512, n_jobs=1,
Expand Down Expand Up @@ -264,7 +264,7 @@ Get the Score of the final ensemble
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 0 minutes 16.724 seconds)
**Total running time of the script:** ( 0 minutes 12.235 seconds)


.. _sphx_glr_download_examples_20_basic_example_multilabel_classification.py:
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14 changes: 7 additions & 7 deletions development/_sources/examples/20_basic/example_multioutput_regression.rst.txt
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Expand Up @@ -125,7 +125,7 @@ View the models found by auto-sklearn
rank ensemble_weight type cost duration
model_id
4 1 1.0 gaussian_process 2.823473e-07 3.041426
4 1 1.0 gaussian_process 3.606933e-08 2.58529
Expand All @@ -152,13 +152,13 @@ Print the final ensemble constructed by auto-sklearn

.. code-block:: none
{ 4: { 'cost': 2.823472750046463e-07,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb478429fd0>,
{ 4: { 'cost': 3.606932585942246e-08,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f4078da8b20>,
'ensemble_weight': 1.0,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb476869dc0>,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f4073932ca0>,
'model_id': 4,
'rank': 1,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fb476869700>,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f4073932dc0>,
'sklearn_regressor': GaussianProcessRegressor(alpha=3.388513090075372e-08,
kernel=RBF(length_scale=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),
n_restarts_optimizer=10, normalize_y=True,
Expand Down Expand Up @@ -190,7 +190,7 @@ Get the Score of the final ensemble

.. code-block:: none
R2 score: 0.9999997715465639
R2 score: 0.9999999524877129
Expand Down Expand Up @@ -443,7 +443,7 @@ Get the configuration space
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 2 minutes 1.451 seconds)
**Total running time of the script:** ( 1 minutes 54.664 seconds)


.. _sphx_glr_download_examples_20_basic_example_multioutput_regression.py:
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42 changes: 21 additions & 21 deletions development/_sources/examples/20_basic/example_regression.rst.txt
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Expand Up @@ -128,11 +128,11 @@ View the models found by auto-sklearn
rank ensemble_weight type cost duration
model_id
25 1 0.46 sgd 0.436679 0.885694
6 2 0.32 ard_regression 0.455042 0.929514
27 3 0.14 ard_regression 0.462249 0.908910
11 4 0.02 random_forest 0.507400 9.798115
7 5 0.06 gradient_boosting 0.518673 1.651652
25 1 0.46 sgd 0.436679 0.713142
6 2 0.32 ard_regression 0.455042 0.735295
27 3 0.14 ard_regression 0.462249 0.723961
11 4 0.02 random_forest 0.507400 9.237415
7 5 0.06 gradient_boosting 0.518673 1.220923
Expand Down Expand Up @@ -160,58 +160,58 @@ Print the final ensemble constructed by auto-sklearn
.. code-block:: none
{ 6: { 'cost': 0.4550418898836528,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb477f27430>,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f4076e61b50>,
'ensemble_weight': 0.32,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb4780e2a60>,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f4073b1a400>,
'model_id': 6,
'rank': 2,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fb4780e2c40>,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f4073b1aa00>,
'sklearn_regressor': ARDRegression(alpha_1=0.0003701926442639788, alpha_2=2.2118001735899097e-07,
copy_X=False, lambda_1=1.2037591637980971e-06,
lambda_2=4.358378124977852e-09,
threshold_lambda=1136.5286041327277, tol=0.021944240404849075)},
7: { 'cost': 0.5186726734789994,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb4780eb160>,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f407c28d820>,
'ensemble_weight': 0.06,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb47689b910>,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f4073d7beb0>,
'model_id': 7,
'rank': 5,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fb47689b850>,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f4073d7b700>,
'sklearn_regressor': HistGradientBoostingRegressor(l2_regularization=1.8428972335335263e-10,
learning_rate=0.012607824914758717, max_iter=512,
max_leaf_nodes=10, min_samples_leaf=8,
n_iter_no_change=0, random_state=1,
validation_fraction=None, warm_start=True)},
11: { 'cost': 0.5073997164657239,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb476a2cee0>,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f407878f970>,
'ensemble_weight': 0.02,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb473559940>,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f40789722b0>,
'model_id': 11,
'rank': 4,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fb4735591f0>,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f4078972cd0>,
'sklearn_regressor': RandomForestRegressor(bootstrap=False, criterion='mae',
max_features=0.6277363920171745, min_samples_leaf=6,
min_samples_split=15, n_estimators=512, n_jobs=1,
random_state=1, warm_start=True)},
25: { 'cost': 0.43667876507897496,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb4735b6910>,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f4078988a60>,
'ensemble_weight': 0.46,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb4735b6bb0>,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f4078956370>,
'model_id': 25,
'rank': 1,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fb4735b6100>,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f40789568b0>,
'sklearn_regressor': SGDRegressor(alpha=0.0006517033225329654, epsilon=0.012150149892783745,
eta0=0.016444224834275295, l1_ratio=1.7462342366289323e-09,
loss='epsilon_insensitive', max_iter=16, penalty='elasticnet',
power_t=0.21521743568582094, random_state=1,
tol=0.002431731981071206, warm_start=True)},
27: { 'cost': 0.4622486119001967,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb477f3e0a0>,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f40786a9a90>,
'ensemble_weight': 0.14,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb4768a2760>,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f407384e1f0>,
'model_id': 27,
'rank': 3,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fb4768a2370>,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f4076d36a00>,
'sklearn_regressor': ARDRegression(alpha_1=2.7664515192592053e-05, alpha_2=9.504988116581138e-07,
copy_X=False, lambda_1=6.50650698230178e-09,
lambda_2=4.238533890074848e-07,
Expand Down Expand Up @@ -295,7 +295,7 @@ the true value).

.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 1 minutes 55.832 seconds)
**Total running time of the script:** ( 1 minutes 55.044 seconds)


.. _sphx_glr_download_examples_20_basic_example_regression.py:
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10 changes: 5 additions & 5 deletions development/_sources/examples/20_basic/sg_execution_times.rst.txt
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Expand Up @@ -5,14 +5,14 @@

Computation times
=================
**06:19.287** total execution time for **examples_20_basic** files:
**06:05.160** total execution time for **examples_20_basic** files:

+-------------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``) | 02:05.280 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``) | 02:03.217 | 0.0 MB |
+-------------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``) | 02:01.451 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``) | 01:55.044 | 0.0 MB |
+-------------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``) | 01:55.832 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``) | 01:54.664 | 0.0 MB |
+-------------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:16.724 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:12.235 | 0.0 MB |
+-------------------------------------------------------------------------------------------------------------------+-----------+--------+
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