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Eddie Bergman: fix-1527-Fix-mlp-regressor-test-fixture-values (#1528)
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210 changes: 105 additions & 105 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 3.239878
2 1 1.0 random_forest 0.447294 3.20855
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 0x7fbe500c3fa0>,
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f7e0c3c7a90>,
'cost': 0.4472941828699525,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fbe4aaaad00>,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f7df43bf250>,
'ensemble_weight': 1.0,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fbe500c3280>,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f7e0c3c7730>,
'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 32.104 seconds)
**Total running time of the script:** ( 0 minutes 16.190 seconds)


.. _sphx_glr_download_examples_20_basic_example_multilabel_classification.py:
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99 changes: 85 additions & 14 deletions development/_sources/examples/20_basic/example_multioutput_regression.rst.txt
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Expand Up @@ -123,9 +123,16 @@ View the models found by auto-sklearn

.. code-block:: none
rank ensemble_weight type cost duration
rank ensemble_weight type cost duration
model_id
28 1 1.0 gaussian_process 1.718292e-08 3.467824
18 1 0.74 gaussian_process 0.074587 2.560028
2 2 0.06 random_forest 0.143340 2.270703
19 3 0.02 random_forest 0.193081 1.668876
4 4 0.02 extra_trees 0.272649 1.086047
10 5 0.08 decision_tree 0.287538 0.488245
23 6 0.04 extra_trees 0.386024 14.196754
13 7 0.02 k_nearest_neighbors 0.402076 0.477249
11 8 0.02 k_nearest_neighbors 0.445704 0.578057
Expand All @@ -152,17 +159,81 @@ Print the final ensemble constructed by auto-sklearn

.. code-block:: none
{ 28: { 'cost': 1.71829196426998e-08,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fbe4fe3f970>,
'ensemble_weight': 1.0,
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'model_id': 28,
'rank': 1,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fbe50675820>,
'sklearn_regressor': GaussianProcessRegressor(alpha=1.0000000000000018e-08,
kernel=RBF(length_scale=[1, 1, 1, 1, 1]),
{ 2: { 'cost': 0.1433397183147025,
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'sklearn_regressor': RandomForestRegressor(max_features=1.0, n_estimators=512, n_jobs=1,
random_state=1, warm_start=True)},
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'rank': 2,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f7df6dc7b80>,
'sklearn_regressor': ExtraTreesRegressor(criterion='friedman_mse', max_features=0.5144630032500372,
min_samples_leaf=2, min_samples_split=12, n_estimators=512,
n_jobs=1, random_state=1, warm_start=True)},
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f7df46963d0>,
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min_samples_leaf=13, min_samples_split=5, random_state=1)},
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f7df38da190>,
'sklearn_regressor': KNeighborsRegressor(n_neighbors=1, weights='distance')},
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kernel=RBF(length_scale=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),
n_restarts_optimizer=10, normalize_y=True,
random_state=1)}}
random_state=1)},
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f7df6e0c490>,
'ensemble_weight': 0.02,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f7df8ddb3a0>,
'model_id': 19,
'rank': 7,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f7df4f34610>,
'sklearn_regressor': RandomForestRegressor(bootstrap=False, max_features=0.9700603657014197,
min_samples_leaf=4, n_estimators=512, n_jobs=1,
random_state=1, warm_start=True)},
23: { 'cost': 0.38602414835028154,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f7df50b81f0>,
'ensemble_weight': 0.04,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f7df90c91f0>,
'model_id': 23,
'rank': 8,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f7df90c9460>,
'sklearn_regressor': ExtraTreesRegressor(criterion='mae', max_features=0.9574780663890835,
min_samples_leaf=2, min_samples_split=17, n_estimators=512,
n_jobs=1, random_state=1, warm_start=True)}}
Expand Down Expand Up @@ -190,7 +261,7 @@ Get the Score of the final ensemble

.. code-block:: none
R2 score: 0.9999998896841648
R2 score: 0.9288059150193577
Expand Down Expand Up @@ -443,7 +514,7 @@ Get the configuration space
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 1 minutes 55.016 seconds)
**Total running time of the script:** ( 1 minutes 55.450 seconds)


.. _sphx_glr_download_examples_20_basic_example_multioutput_regression.py:
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50 changes: 25 additions & 25 deletions development/_sources/examples/20_basic/example_regression.rst.txt
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Expand Up @@ -124,12 +124,12 @@ View the models found by auto-sklearn
rank ensemble_weight type cost duration
model_id
25 1 0.46 sgd 0.436679 0.682268
29 2 0.04 gaussian_process 0.445373 13.078738
6 3 0.30 ard_regression 0.455042 0.696445
27 4 0.12 ard_regression 0.462249 0.678429
11 5 0.02 random_forest 0.507400 9.128839
7 6 0.06 gradient_boosting 0.518673 1.157540
25 1 0.46 sgd 0.436679 0.651642
29 2 0.04 gaussian_process 0.445373 11.476568
6 3 0.30 ard_regression 0.455042 0.670588
27 4 0.12 ard_regression 0.462249 0.657819
11 5 0.02 random_forest 0.507400 9.832774
7 6 0.06 gradient_boosting 0.518673 1.109341
Expand Down Expand Up @@ -157,69 +157,69 @@ 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 0x7fbe4b698310>,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f7df8d78e80>,
'ensemble_weight': 0.3,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fbe4a87edc0>,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f7df8c789a0>,
'model_id': 6,
'rank': 1,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fbe4a87e2b0>,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f7df8c78100>,
'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 0x7fbe4a10c130>,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f7df8d597c0>,
'ensemble_weight': 0.06,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fbe4de46130>,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f7df46ac2b0>,
'model_id': 7,
'rank': 2,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fbe4de46bb0>,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f7df46ac8b0>,
'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)},
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'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fbe50068550>,
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'ensemble_weight': 0.02,
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f7df8f6c850>,
'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)},
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f7df8e443a0>,
'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,
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'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,
threshold_lambda=78251.58542976103, tol=0.0007301343236220855)},
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'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f7df6dc7ee0>,
'sklearn_regressor': GaussianProcessRegressor(alpha=0.323250809620855,
kernel=RBF(length_scale=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),
n_restarts_optimizer=10, normalize_y=True,
Expand Down Expand Up @@ -303,7 +303,7 @@ the true value).

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

**Total running time of the script:** ( 1 minutes 59.300 seconds)
**Total running time of the script:** ( 2 minutes 2.092 seconds)


.. _sphx_glr_download_examples_20_basic_example_regression.py:
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