Eddie Bergman: Prevent workflow double trigger, Add PEP 561 compliance (#1348)

Author: Github Actions

development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

@@ -154,7 +154,7 @@ View the models found by auto-sklearn
 
               rank  ensemble_weight           type      cost  duration
     model_id                                                          
-    2            1              1.0  random_forest  0.447294  3.256835
+    2            1              1.0  random_forest  0.447294  4.008007
 
 
 
@@ -262,7 +262,7 @@ Get the Score of the final ensemble
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  12.465 seconds)
+   **Total running time of the script:** ( 0 minutes  16.563 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_multilabel_classification.py:

development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip

@@ -122,9 +122,9 @@ View the models found by auto-sklearn
 
  .. code-block:: none
 
-              rank  ensemble_weight              type          cost   duration
-    model_id                                                                  
-    18           1              1.0  gaussian_process  5.149491e-09  10.221251
+              rank  ensemble_weight              type          cost  duration
+    model_id                                                                 
+    24           1              1.0  gaussian_process  1.568908e-09  6.906833
 
 
 
@@ -151,7 +151,7 @@ Print the final ensemble constructed by auto-sklearn
 
  .. code-block:: none
 
-    [(1.000000, SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'extra_trees_preproc_for_regression', 'regressor:__choice__': 'gaussian_process', 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'no_coalescense', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'none', 'feature_preprocessor:extra_trees_preproc_for_regression:bootstrap': 'True', 'feature_preprocessor:extra_trees_preproc_for_regression:criterion': 'mae', 'feature_preprocessor:extra_trees_preproc_for_regression:max_depth': 'None', 'feature_preprocessor:extra_trees_preproc_for_regression:max_features': 0.10236588960344592, 'feature_preprocessor:extra_trees_preproc_for_regression:max_leaf_nodes': 'None', 'feature_preprocessor:extra_trees_preproc_for_regression:min_samples_leaf': 2, 'feature_preprocessor:extra_trees_preproc_for_regression:min_samples_split': 9, 'feature_preprocessor:extra_trees_preproc_for_regression:min_weight_fraction_leaf': 0.0, 'feature_preprocessor:extra_trees_preproc_for_regression:n_estimators': 100, 'regressor:gaussian_process:alpha': 1.893109218935316e-09, 'regressor:gaussian_process:thetaL': 2.303696108477476e-05, 'regressor:gaussian_process:thetaU': 51148.328195924485},
+    [(1.000000, SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'regressor:__choice__': 'gaussian_process', 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'no_coalescense', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'robust_scaler', 'regressor:gaussian_process:alpha': 6.424716434862854e-12, 'regressor:gaussian_process:thetaL': 2.006922082872977e-10, 'regressor:gaussian_process:thetaU': 1641.3005222558932, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max': 0.9411732931233598, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min': 0.25026710571104005},
     dataset_properties={
       'task': 5,
       'sparse': False,
@@ -186,7 +186,7 @@ Get the Score of the final ensemble
 
  .. code-block:: none
 
-    R2 score: 0.9999999915708525
+    R2 score: 0.9999999989653773
 
 
 
@@ -409,7 +409,7 @@ Get the configuration space
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  55.266 seconds)
+   **Total running time of the script:** ( 1 minutes  55.971 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_multioutput_regression.py:

development/_images/sphx_glr_example_inspect_predictions_001.png

@@ -121,10 +121,11 @@ View the models found by auto-sklearn
 
               rank  ensemble_weight               type      cost  duration
     model_id                                                              
-    25           1             0.44                sgd  0.436679  0.599595
-    6            2             0.34     ard_regression  0.455042  0.612967
-    39           3             0.18     ard_regression  0.474807  0.596258
-    7            4             0.04  gradient_boosting  0.518673  1.088064
+    25           1             0.46                sgd  0.436679  0.755302
+    6            2             0.32     ard_regression  0.455042  0.782081
+    27           3             0.14     ard_regression  0.462249  0.746363
+    11           4             0.02      random_forest  0.507400  9.937547
+    7            5             0.06  gradient_boosting  0.518673  1.478772
 
 
 
@@ -151,28 +152,35 @@ Print the final ensemble constructed by auto-sklearn
 
  .. code-block:: none
 
-    [(0.440000, SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'polynomial', 'regressor:__choice__': 'sgd', 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'no_coalescense', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'minmax', 'feature_preprocessor:polynomial:degree': 3, 'feature_preprocessor:polynomial:include_bias': 'False', 'feature_preprocessor:polynomial:interaction_only': 'False', 'regressor:sgd:alpha': 0.0006517033225329654, 'regressor:sgd:average': 'False', 'regressor:sgd:fit_intercept': 'True', 'regressor:sgd:learning_rate': 'invscaling', 'regressor:sgd:loss': 'epsilon_insensitive', 'regressor:sgd:penalty': 'elasticnet', 'regressor:sgd:tol': 0.002431731981071206, 'regressor:sgd:epsilon': 0.012150149892783745, 'regressor:sgd:eta0': 0.016444224834275295, 'regressor:sgd:l1_ratio': 1.7462342366289323e-09, 'regressor:sgd:power_t': 0.21521743568582094},
+    [(0.460000, SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'polynomial', 'regressor:__choice__': 'sgd', 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'no_coalescense', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'minmax', 'feature_preprocessor:polynomial:degree': 3, 'feature_preprocessor:polynomial:include_bias': 'False', 'feature_preprocessor:polynomial:interaction_only': 'False', 'regressor:sgd:alpha': 0.0006517033225329654, 'regressor:sgd:average': 'False', 'regressor:sgd:fit_intercept': 'True', 'regressor:sgd:learning_rate': 'invscaling', 'regressor:sgd:loss': 'epsilon_insensitive', 'regressor:sgd:penalty': 'elasticnet', 'regressor:sgd:tol': 0.002431731981071206, 'regressor:sgd:epsilon': 0.012150149892783745, 'regressor:sgd:eta0': 0.016444224834275295, 'regressor:sgd:l1_ratio': 1.7462342366289323e-09, 'regressor:sgd:power_t': 0.21521743568582094},
     dataset_properties={
       'task': 4,
       'sparse': False,
       'multioutput': False,
       'target_type': 'regression',
       'signed': False})),
-    (0.340000, SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'polynomial', 'regressor:__choice__': 'ard_regression', 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'one_hot_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'no_coalescense', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'standardize', 'feature_preprocessor:polynomial:degree': 2, 'feature_preprocessor:polynomial:include_bias': 'True', 'feature_preprocessor:polynomial:interaction_only': 'False', 'regressor:ard_regression:alpha_1': 0.0003701926442639788, 'regressor:ard_regression:alpha_2': 2.2118001735899097e-07, 'regressor:ard_regression:fit_intercept': 'True', 'regressor:ard_regression:lambda_1': 1.2037591637980971e-06, 'regressor:ard_regression:lambda_2': 4.358378124977852e-09, 'regressor:ard_regression:n_iter': 300, 'regressor:ard_regression:threshold_lambda': 1136.5286041327277, 'regressor:ard_regression:tol': 0.021944240404849075},
+    (0.320000, SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'polynomial', 'regressor:__choice__': 'ard_regression', 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'one_hot_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'no_coalescense', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'standardize', 'feature_preprocessor:polynomial:degree': 2, 'feature_preprocessor:polynomial:include_bias': 'True', 'feature_preprocessor:polynomial:interaction_only': 'False', 'regressor:ard_regression:alpha_1': 0.0003701926442639788, 'regressor:ard_regression:alpha_2': 2.2118001735899097e-07, 'regressor:ard_regression:fit_intercept': 'True', 'regressor:ard_regression:lambda_1': 1.2037591637980971e-06, 'regressor:ard_regression:lambda_2': 4.358378124977852e-09, 'regressor:ard_regression:n_iter': 300, 'regressor:ard_regression:threshold_lambda': 1136.5286041327277, 'regressor:ard_regression:tol': 0.021944240404849075},
     dataset_properties={
       'task': 4,
       'sparse': False,
       'multioutput': False,
       'target_type': 'regression',
       'signed': False})),
-    (0.180000, SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'fast_ica', 'regressor:__choice__': 'ard_regression', 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'no_coalescense', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'normalize', 'feature_preprocessor:fast_ica:algorithm': 'parallel', 'feature_preprocessor:fast_ica:fun': 'logcosh', 'feature_preprocessor:fast_ica:whiten': 'False', 'regressor:ard_regression:alpha_1': 0.0005012365297609799, 'regressor:ard_regression:alpha_2': 3.025360750168211e-08, 'regressor:ard_regression:fit_intercept': 'True', 'regressor:ard_regression:lambda_1': 4.9749646614525684e-05, 'regressor:ard_regression:lambda_2': 3.2368037115065363e-10, 'regressor:ard_regression:n_iter': 300, 'regressor:ard_regression:threshold_lambda': 18669.665899307194, 'regressor:ard_regression:tol': 0.0012624032013298571},
+    (0.140000, SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'select_rates_regression', 'regressor:__choice__': 'ard_regression', 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'one_hot_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'minority_coalescer', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'robust_scaler', 'feature_preprocessor:select_rates_regression:alpha': 0.3621762718897781, 'feature_preprocessor:select_rates_regression:mode': 'fwe', 'feature_preprocessor:select_rates_regression:score_func': 'f_regression', 'regressor:ard_regression:alpha_1': 2.7664515192592053e-05, 'regressor:ard_regression:alpha_2': 9.504988116581138e-07, 'regressor:ard_regression:fit_intercept': 'True', 'regressor:ard_regression:lambda_1': 6.50650698230178e-09, 'regressor:ard_regression:lambda_2': 4.238533890074848e-07, 'regressor:ard_regression:n_iter': 300, 'regressor:ard_regression:threshold_lambda': 78251.58542976103, 'regressor:ard_regression:tol': 0.0007301343236220855, 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.0001745391328519669, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max': 0.8057830372269097, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min': 0.24982831110057324},
     dataset_properties={
       'task': 4,
       'sparse': False,
       'multioutput': False,
       'target_type': 'regression',
       'signed': False})),
-    (0.040000, SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'regressor:__choice__': 'gradient_boosting', 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'one_hot_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'no_coalescense', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'quantile_transformer', 'regressor:gradient_boosting:early_stop': 'off', 'regressor:gradient_boosting:l2_regularization': 1.8428972335335263e-10, 'regressor:gradient_boosting:learning_rate': 0.012607824914758717, 'regressor:gradient_boosting:loss': 'least_squares', 'regressor:gradient_boosting:max_bins': 255, 'regressor:gradient_boosting:max_depth': 'None', 'regressor:gradient_boosting:max_leaf_nodes': 10, 'regressor:gradient_boosting:min_samples_leaf': 8, 'regressor:gradient_boosting:scoring': 'loss', 'regressor:gradient_boosting:tol': 1e-07, 'data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles': 952, 'data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution': 'uniform'},
+    (0.060000, SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'regressor:__choice__': 'gradient_boosting', 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'one_hot_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'no_coalescense', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'quantile_transformer', 'regressor:gradient_boosting:early_stop': 'off', 'regressor:gradient_boosting:l2_regularization': 1.8428972335335263e-10, 'regressor:gradient_boosting:learning_rate': 0.012607824914758717, 'regressor:gradient_boosting:loss': 'least_squares', 'regressor:gradient_boosting:max_bins': 255, 'regressor:gradient_boosting:max_depth': 'None', 'regressor:gradient_boosting:max_leaf_nodes': 10, 'regressor:gradient_boosting:min_samples_leaf': 8, 'regressor:gradient_boosting:scoring': 'loss', 'regressor:gradient_boosting:tol': 1e-07, 'data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles': 952, 'data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution': 'uniform'},
+    dataset_properties={
+      'task': 4,
+      'sparse': False,
+      'multioutput': False,
+      'target_type': 'regression',
+      'signed': False})),
+    (0.020000, SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'polynomial', 'regressor:__choice__': 'random_forest', 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'one_hot_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'minority_coalescer', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'minmax', 'feature_preprocessor:polynomial:degree': 2, 'feature_preprocessor:polynomial:include_bias': 'True', 'feature_preprocessor:polynomial:interaction_only': 'True', 'regressor:random_forest:bootstrap': 'False', 'regressor:random_forest:criterion': 'mae', 'regressor:random_forest:max_depth': 'None', 'regressor:random_forest:max_features': 0.6277363920171745, 'regressor:random_forest:max_leaf_nodes': 'None', 'regressor:random_forest:min_impurity_decrease': 0.0, 'regressor:random_forest:min_samples_leaf': 6, 'regressor:random_forest:min_samples_split': 15, 'regressor:random_forest:min_weight_fraction_leaf': 0.0, 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.0010413452644415357},
     dataset_properties={
       'task': 4,
       'sparse': False,
@@ -213,8 +221,8 @@ predicting the data mean has an R2 score of 0.
 
  .. code-block:: none
 
-    Train R2 score: 0.5855373845454157
-    Test R2 score: 0.39879073225079487
+    Train R2 score: 0.5944780427522034
+    Test R2 score: 0.3959585042866587
 
 
 
@@ -259,7 +267,7 @@ the true value).
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  12.638 seconds)
+   **Total running time of the script:** ( 1 minutes  56.026 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
 =================
-**06:22.508** total execution time for **examples_20_basic** files:
+**06:17.763** total execution time for **examples_20_basic** files:
 
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``)                               | 02:12.638 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``)                       | 02:09.203 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``)                       | 02:02.138 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``)                               | 01:56.026 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``)       | 01:55.266 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``)       | 01:55.971 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:12.465 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:16.563 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+