github-actions[bot]: chore: update pre-commit hooks (#1580)

Author: Github Actions

development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

@@ -150,7 +150,7 @@ View the models found by auto-sklearn
 
               rank  ensemble_weight           type      cost  duration
     model_id                                                          
-    2            1              1.0  random_forest  0.447294  3.529664
+    2            1              1.0  random_forest  0.447294  3.113838
 
 
 
@@ -176,11 +176,11 @@ 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 0x7f84bcd4ce20>,
+               'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f869d90c730>,
                'cost': 0.4472941828699525,
-               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f84bd0140d0>,
+               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f869dbf0430>,
                'ensemble_weight': 1.0,
-               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f84bcd4c3d0>,
+               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f869d90cf40>,
                'model_id': 2,
                'rank': 1,
                'sklearn_classifier': RandomForestClassifier(max_features=15, n_estimators=512, n_jobs=1,
@@ -253,7 +253,7 @@ Get the Score of the final ensemble
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  19.901 seconds)
+   **Total running time of the script:** ( 0 minutes  13.835 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_multilabel_classification.py:

development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip

@@ -120,10 +120,11 @@ View the models found by auto-sklearn
 
  .. code-block:: none
 
-              rank  ensemble_weight              type          cost   duration
-    model_id                                                                  
-    4            1             0.64  gaussian_process  7.018884e-08   9.991048
-    11           2             0.36  gaussian_process  8.962683e-08  18.655118
+              rank  ensemble_weight              type          cost  duration
+    model_id                                                                 
+    15           1              0.5  gaussian_process  3.140841e-08  9.127445
+    12           2              0.3  gaussian_process  3.834848e-08  9.812679
+    4            3              0.2  gaussian_process  4.714587e-08  3.800201
 
 
 
@@ -148,25 +149,36 @@ Print the final ensemble constructed by auto-sklearn
 
  .. code-block:: none
 
-    {   4: {   'cost': 7.018883696474632e-08,
-               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f84bb2460d0>,
-               'ensemble_weight': 0.64,
-               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f84b8459f40>,
+    {   4: {   'cost': 4.714586709919644e-08,
+               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f869de934f0>,
+               'ensemble_weight': 0.2,
+               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f869df58d90>,
                'model_id': 4,
                'rank': 1,
-               'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f84b8459af0>,
+               'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f869df58ee0>,
                'sklearn_regressor': GaussianProcessRegressor(alpha=2.6231667524556984e-13,
                              kernel=RBF(length_scale=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),
                              n_restarts_optimizer=10, normalize_y=True,
                              random_state=1)},
-        11: {   'cost': 8.962683140101291e-08,
-                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f84bb246fd0>,
-                'ensemble_weight': 0.36,
-                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f84b8611760>,
-                'model_id': 11,
+        12: {   'cost': 3.83484826116387e-08,
+                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f869dc44700>,
+                'ensemble_weight': 0.3,
+                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f869daeadc0>,
+                'model_id': 12,
                 'rank': 2,
-                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f84b8611790>,
-                'sklearn_regressor': GaussianProcessRegressor(alpha=8.548590540679561e-13,
+                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f869daea250>,
+                'sklearn_regressor': GaussianProcessRegressor(alpha=2.7695662487241213e-12,
+                             kernel=RBF(length_scale=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),
+                             n_restarts_optimizer=10, normalize_y=True,
+                             random_state=1)},
+        15: {   'cost': 3.140841220439228e-08,
+                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f869dc44250>,
+                'ensemble_weight': 0.5,
+                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f869ac2ee50>,
+                'model_id': 15,
+                'rank': 3,
+                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f869ac2ec10>,
+                'sklearn_regressor': GaussianProcessRegressor(alpha=1.0204391750758994e-13,
                              kernel=RBF(length_scale=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),
                              n_restarts_optimizer=10, normalize_y=True,
                              random_state=1)}}
@@ -195,7 +207,7 @@ Get the Score of the final ensemble
 
  .. code-block:: none
 
-    R2 score: 0.9999999634540817
+    R2 score: 0.9999999634454806
 
 
 
@@ -410,7 +422,7 @@ Get the configuration space
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  54.463 seconds)
+   **Total running time of the script:** ( 1 minutes  56.004 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_multioutput_regression.py:

development/_images/sphx_glr_example_inspect_predictions_001.png

@@ -121,11 +121,11 @@ View the models found by auto-sklearn
 
               rank  ensemble_weight               type      cost  duration
     model_id                                                              
-    25           1             0.30                sgd  0.436679  0.684569
-    6            2             0.38     ard_regression  0.455042  0.664772
-    31           3             0.26     ard_regression  0.461909  0.646299
-    11           4             0.02      random_forest  0.507400  9.829460
-    7            5             0.04  gradient_boosting  0.518673  1.178347
+    25           1             0.24                sgd  0.436679  0.578382
+    6            2             0.26     ard_regression  0.455042  0.588125
+    31           3             0.28     ard_regression  0.461909  0.569287
+    35           4             0.18     ard_regression  0.468308  0.995609
+    7            5             0.04  gradient_boosting  0.518673  1.024478
 
 
 
@@ -151,62 +151,62 @@ 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 0x7f84d07146a0>,
-               'ensemble_weight': 0.38,
-               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f84b89d0490>,
+               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f869dbfa160>,
+               'ensemble_weight': 0.26,
+               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f869dd6a730>,
                'model_id': 6,
                'rank': 1,
-               'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f84d0a1a280>,
+               'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f869dd6acd0>,
                '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 0x7f84bb2fc490>,
+               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f869dd0ef10>,
                'ensemble_weight': 0.04,
-               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f84bb0f40d0>,
+               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f869aed6ca0>,
                'model_id': 7,
                'rank': 2,
-               'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f84bb0f4dc0>,
+               'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f869aed6bb0>,
                '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 0x7f84b89d09a0>,
-                'ensemble_weight': 0.02,
-                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f84bb0bc940>,
-                'model_id': 11,
-                'rank': 3,
-                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f84bb0bc3a0>,
-                '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 0x7f84bcd43cd0>,
-                'ensemble_weight': 0.3,
-                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f84bb27d670>,
+                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f86b15a0f70>,
+                'ensemble_weight': 0.24,
+                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f869e2df280>,
                 'model_id': 25,
-                'rank': 4,
-                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f84bb27daf0>,
+                'rank': 3,
+                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f869e2dfe20>,
                 '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)},
         31: {   'cost': 0.4619090472287596,
-                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f84baf978b0>,
-                'ensemble_weight': 0.26,
-                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f84bb2b81c0>,
+                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f869dc963a0>,
+                'ensemble_weight': 0.28,
+                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f869de5e1c0>,
                 'model_id': 31,
-                'rank': 5,
-                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f84bb2b88e0>,
+                'rank': 4,
+                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f869de5e7c0>,
                 'sklearn_regressor': ARDRegression(alpha_1=0.0003231549748466066, alpha_2=0.0002550914122041718,
                   copy_X=False, lambda_1=2.0381967509909317e-05,
                   lambda_2=6.669372163715464e-06,
-                  threshold_lambda=4787.837289272208, tol=0.0022718359328007297)}}
+                  threshold_lambda=4787.837289272208, tol=0.0022718359328007297)},
+        35: {   'cost': 0.46830847406823906,
+                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f869ae06ac0>,
+                'ensemble_weight': 0.18,
+                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f869ae83dc0>,
+                'model_id': 35,
+                'rank': 5,
+                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f869ae83400>,
+                'sklearn_regressor': ARDRegression(alpha_1=0.00028378747975261987, alpha_2=2.480473124043016e-08,
+                  copy_X=False, lambda_1=2.443383072629711e-07,
+                  lambda_2=8.793566274927655e-05,
+                  threshold_lambda=4237.033213139775, tol=0.003058396088909069)}}
 
 
 
@@ -238,8 +238,8 @@ predicting the data mean has an R2 score of 0.
 
  .. code-block:: none
 
-    Train R2 score: 0.587810789938566
-    Test R2 score: 0.39825550070176285
+    Train R2 score: 0.5910262435863187
+    Test R2 score: 0.4074697347767652
 
 
 
@@ -284,7 +284,7 @@ the true value).
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  59.395 seconds)
+   **Total running time of the script:** ( 1 minutes  54.263 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
 =================
-**06:14.841** total execution time for **examples_20_basic** files:
+**06:03.519** total execution time for **examples_20_basic** files:
 
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``)                       | 02:01.081 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``)                       | 01:59.417 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``)                               | 01:59.395 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``)       | 01:56.004 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``)       | 01:54.463 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``)                               | 01:54.263 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:19.901 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:13.835 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+