dependabot[bot]: Bump actions/checkout from 2 to 3.1.0 (#1592)

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  3.112699
+    2            1              1.0  random_forest  0.447294  3.745824
 
 
 
@@ -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 0x7fe2c4e90b50>,
+               'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f409f4ee8e0>,
                'cost': 0.4472941828699525,
-               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fe2c54b9fd0>,
+               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f40a301c2b0>,
                'ensemble_weight': 1.0,
-               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fe2c4e90ca0>,
+               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f409f4ee730>,
                '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  17.923 seconds)
+   **Total running time of the script:** ( 0 minutes  15.455 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_multilabel_classification.py:

development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip

@@ -122,7 +122,9 @@ View the models found by auto-sklearn
 
               rank  ensemble_weight              type          cost   duration
     model_id                                                                  
-    11           1              1.0  gaussian_process  8.757508e-08  29.665847
+    4            1             0.66  gaussian_process  9.653633e-08   4.770680
+    11           2             0.26  gaussian_process  1.096357e-07  18.604435
+    17           3             0.08  gaussian_process  1.391928e-07   3.342580
 
 
 
@@ -147,14 +149,36 @@ Print the final ensemble constructed by auto-sklearn
 
  .. code-block:: none
 
-    {   11: {   'cost': 8.757507707901624e-08,
-                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fe2db765f70>,
-                'ensemble_weight': 1.0,
-                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fe2db774160>,
+    {   4: {   'cost': 9.653632770945109e-08,
+               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f40b6b43730>,
+               'ensemble_weight': 0.66,
+               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f40a2fe6e50>,
+               'model_id': 4,
+               'rank': 1,
+               'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f409f34cbb0>,
+               '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': 1.0963572538713606e-07,
+                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f40a3868d90>,
+                'ensemble_weight': 0.26,
+                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f40a16f6340>,
                 'model_id': 11,
-                'rank': 1,
-                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fe2db774820>,
+                'rank': 2,
+                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f40a16f6610>,
                 'sklearn_regressor': GaussianProcessRegressor(alpha=4.658781733712728e-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)},
+        17: {   'cost': 1.3919280295038305e-07,
+                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f409e5ae370>,
+                'ensemble_weight': 0.08,
+                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f40a1334cd0>,
+                'model_id': 17,
+                'rank': 3,
+                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f40a13344f0>,
+                'sklearn_regressor': GaussianProcessRegressor(alpha=7.0489976142181925e-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)}}
@@ -183,7 +207,7 @@ Get the Score of the final ensemble
 
  .. code-block:: none
 
-    R2 score: 0.9999998849508759
+    R2 score: 0.9999999400709513
 
 
 
@@ -398,7 +422,7 @@ Get the configuration space
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  53.872 seconds)
+   **Total running time of the script:** ( 1 minutes  56.191 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_multioutput_regression.py:

development/_images/sphx_glr_example_inspect_predictions_001.png

@@ -119,13 +119,13 @@ View the models found by auto-sklearn
 
  .. code-block:: none
 
-              rank  ensemble_weight               type      cost  duration
-    model_id                                                              
-    25           1             0.24                sgd  0.436679  0.572841
-    6            2             0.26     ard_regression  0.455042  0.587081
-    31           3             0.28     ard_regression  0.461909  0.566196
-    35           4             0.18     ard_regression  0.468308  0.995362
-    7            5             0.04  gradient_boosting  0.518673  1.014187
+              rank  ensemble_weight               type      cost   duration
+    model_id                                                               
+    25           1             0.30                sgd  0.436679   0.721507
+    6            2             0.38     ard_regression  0.455042   0.741423
+    31           3             0.26     ard_regression  0.461909   0.695052
+    11           4             0.02      random_forest  0.507400  10.839403
+    7            5             0.04  gradient_boosting  0.518673   1.254648
 
 
 
@@ -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 0x7fe2c8048b80>,
-               'ensemble_weight': 0.26,
-               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fe2c4c06d60>,
+               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f40a32ea8e0>,
+               'ensemble_weight': 0.38,
+               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f40a31dd940>,
                'model_id': 6,
                'rank': 1,
-               'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fe2c4c06dc0>,
+               'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f40a31dddc0>,
                '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 0x7fe2c7f38070>,
+               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f40a332ed30>,
                'ensemble_weight': 0.04,
-               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fe2c7c66dc0>,
+               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f40a15caca0>,
                'model_id': 7,
                'rank': 2,
-               'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fe2c7c66bb0>,
+               'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f40a31679a0>,
                '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 0x7f40a32e5160>,
+                'ensemble_weight': 0.02,
+                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f40a31d9970>,
+                'model_id': 11,
+                'rank': 3,
+                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f40a31d9040>,
+                '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 0x7fe2c7f7c5b0>,
-                'ensemble_weight': 0.24,
-                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fe2c4c3a5b0>,
+                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f40a138d880>,
+                'ensemble_weight': 0.3,
+                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f40a31b0940>,
                 'model_id': 25,
-                'rank': 3,
-                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fe2c4c3a9d0>,
+                'rank': 4,
+                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f40a31b0ca0>,
                 '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 0x7fe2c7cd3ac0>,
-                'ensemble_weight': 0.28,
-                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fe2c503e850>,
+                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f40a143deb0>,
+                'ensemble_weight': 0.26,
+                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f409e8da040>,
                 'model_id': 31,
-                'rank': 4,
-                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fe2c503e700>,
+                'rank': 5,
+                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f409e8dab20>,
                 '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)},
-        35: {   'cost': 0.46830847406823906,
-                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fe2c8156be0>,
-                'ensemble_weight': 0.18,
-                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fe2c7ef6070>,
-                'model_id': 35,
-                'rank': 5,
-                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fe2c7ef61c0>,
-                '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)}}
+                  threshold_lambda=4787.837289272208, tol=0.0022718359328007297)}}
 
 
 
@@ -238,8 +238,8 @@ predicting the data mean has an R2 score of 0.
 
  .. code-block:: none
 
-    Train R2 score: 0.5910262435863187
-    Test R2 score: 0.4074697347767652
+    Train R2 score: 0.587810789938566
+    Test R2 score: 0.39825550070176285
 
 
 
@@ -284,7 +284,7 @@ the true value).
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  54.683 seconds)
+   **Total running time of the script:** ( 1 minutes  59.218 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
 =================
-**06:05.454** total execution time for **examples_20_basic** files:
+**06:10.281** total execution time for **examples_20_basic** files:
 
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``)                       | 01:58.975 | 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``)                               | 01:54.683 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``)                               | 01:59.218 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``)       | 01:53.872 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``)       | 01:56.191 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:17.923 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:15.455 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+