Eddie Bergman: Fix: MLPRegressor tests (#1367)

Author: Github Actions

development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

@@ -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  4.579435
+    2            1              1.0  random_forest  0.447294  3.615119
 
 
 
@@ -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 0x7f8344032f10>,
+               'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7fdfac2599a0>,
                'cost': 0.4472941828699525,
-               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f83428e22b0>,
+               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fdfaccdf070>,
                'ensemble_weight': 1.0,
-               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f8344032c10>,
+               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fdfac259250>,
                'model_id': 2,
                'rank': 1,
                'sklearn_classifier': RandomForestClassifier(max_features=15, n_estimators=512, n_jobs=1,
@@ -264,7 +264,7 @@ Get the Score of the final ensemble
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  18.439 seconds)
+   **Total running time of the script:** ( 0 minutes  34.280 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_multilabel_classification.py:

development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip

@@ -123,10 +123,9 @@ View the models found by auto-sklearn
 
  .. code-block:: none
 
-              rank  ensemble_weight              type      cost   duration
-    model_id                                                              
-    16           1             0.94  gaussian_process  0.000009  18.825621
-    14           2             0.06  gaussian_process  0.000073   3.463700
+              rank  ensemble_weight              type      cost  duration
+    model_id                                                             
+    12           1              1.0  gaussian_process  0.000005   2.62024
 
 
 
@@ -153,25 +152,14 @@ Print the final ensemble constructed by auto-sklearn
 
  .. code-block:: none
 
-    {   14: {   'cost': 7.294978049132705e-05,
-                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f833fa19d00>,
-                'ensemble_weight': 0.06,
-                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f83441fd070>,
-                'model_id': 14,
-                'rank': 2,
-                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f83441fd0a0>,
-                'sklearn_regressor': GaussianProcessRegressor(alpha=0.0001892420474677165,
-                             kernel=RBF(length_scale=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),
-                             n_restarts_optimizer=10, normalize_y=True,
-                             random_state=1)},
-        16: {   'cost': 8.604209460028045e-06,
-                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f8344334670>,
-                'ensemble_weight': 0.94,
-                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f833fc612e0>,
-                'model_id': 16,
+    {   12: {   'cost': 5.194507913142132e-06,
+                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fdfab0429a0>,
+                'ensemble_weight': 1.0,
+                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fdfab042700>,
+                'model_id': 12,
                 'rank': 1,
-                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f833fc616a0>,
-                'sklearn_regressor': GaussianProcessRegressor(alpha=6.883531961818898e-12,
+                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fdfab042a00>,
+                'sklearn_regressor': GaussianProcessRegressor(alpha=6.212085562385133e-06,
                              kernel=RBF(length_scale=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),
                              n_restarts_optimizer=10, normalize_y=True,
                              random_state=1)}}
@@ -202,7 +190,7 @@ Get the Score of the final ensemble
 
  .. code-block:: none
 
-    R2 score: 0.9999851720273112
+    R2 score: 0.9999981665106872
 
 
 
@@ -425,7 +413,7 @@ Get the configuration space
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  58.336 seconds)
+   **Total running time of the script:** ( 1 minutes  59.863 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_multioutput_regression.py:

development/_images/sphx_glr_example_inspect_predictions_001.png

@@ -123,11 +123,11 @@ View the models found by auto-sklearn
 
               rank  ensemble_weight               type      cost   duration
     model_id                                                               
-    25           1             0.46                sgd  0.436679   0.782460
-    6            2             0.32     ard_regression  0.455042   0.800511
-    27           3             0.14     ard_regression  0.462249   0.788985
-    11           4             0.02      random_forest  0.507400  10.530246
-    7            5             0.06  gradient_boosting  0.518673   1.700823
+    25           1             0.46                sgd  0.436679   0.680917
+    6            2             0.32     ard_regression  0.455042   0.699064
+    27           3             0.14     ard_regression  0.462249   0.680735
+    11           4             0.02      random_forest  0.507400  10.409721
+    7            5             0.06  gradient_boosting  0.518673   1.241716
 
 
 
@@ -155,58 +155,58 @@ 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 0x7f8344014640>,
+               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fdfafb4fdf0>,
                'ensemble_weight': 0.32,
-               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f834408d1f0>,
+               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fdfb03f3850>,
                'model_id': 6,
                'rank': 2,
-               'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f834408dbe0>,
+               'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fdfb03f3250>,
                '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 0x7f833fc839d0>,
+               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fdfac2ae520>,
                'ensemble_weight': 0.06,
-               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f8344108970>,
+               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fdfafd356d0>,
                'model_id': 7,
                'rank': 5,
-               'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f835733d940>,
+               'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fdfafd35400>,
                '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 0x7f834416e8b0>,
+                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fdfafab06d0>,
                 'ensemble_weight': 0.02,
-                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f833fe9b7f0>,
+                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fdfacd2aa00>,
                 'model_id': 11,
                 'rank': 4,
-                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f833fe9b8e0>,
+                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fdfacd2adf0>,
                 '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 0x7f83440427f0>,
+                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fdfacd21b20>,
                 'ensemble_weight': 0.46,
-                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f834266e4c0>,
+                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fdfacd21c70>,
                 'model_id': 25,
                 'rank': 1,
-                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f834266e5e0>,
+                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fdfacd218e0>,
                 '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 0x7f83442101f0>,
+                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fdfb07beca0>,
                 'ensemble_weight': 0.14,
-                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f83426df640>,
+                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fdfab184910>,
                 'model_id': 27,
                 'rank': 3,
-                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f83426df0a0>,
+                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fdfab1849d0>,
                 '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,
@@ -290,7 +290,7 @@ the true value).
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  1.625 seconds)
+   **Total running time of the script:** ( 1 minutes  53.928 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_regression.py:

development/_images/sphx_glr_example_inspect_predictions_002.png

@@ -5,14 +5,14 @@
 
 Computation times
 =================
-**06:23.041** total execution time for **examples_20_basic** files:
+**06:30.491** total execution time for **examples_20_basic** files:
 
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``)                       | 02:04.642 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``)                       | 02:02.421 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``)                               | 02:01.625 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``)       | 01:59.863 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``)       | 01:58.336 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``)                               | 01:53.928 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:18.439 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:34.280 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+