Eddie Bergman: Remove flaky dep (#1361)

Author: Github Actions

development/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 1281a8de7e72cc26a3b0fce1416780a7
+config: 8a26f7fbaa1576935d6b4916c5b79de9
 tags: 645f666f9bcd5a90fca523b33c5a78b7

development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

@@ -1344,7 +1344,7 @@ <h1>Source code for autosklearn.estimators</h1><div class="highlight"><pre>
 
     </p>
     <p>
-        &copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
+        &copy; Copyright 2014-2022, Machine Learning Professorship Freiburg.<br/>
       Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.2.0.<br/>
     </p>
   </div>

development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip

@@ -593,7 +593,7 @@ <h1>Source code for autosklearn.experimental.askl2</h1><div class="highlight"><p
 
     </p>
     <p>
-        &copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
+        &copy; Copyright 2014-2022, Machine Learning Professorship Freiburg.<br/>
       Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.2.0.<br/>
     </p>
   </div>

development/_images/sphx_glr_example_inspect_predictions_001.png

@@ -700,7 +700,7 @@ <h1>Source code for autosklearn.metrics</h1><div class="highlight"><pre>
 
     </p>
     <p>
-        &copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
+        &copy; Copyright 2014-2022, Machine Learning Professorship Freiburg.<br/>
       Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.2.0.<br/>
     </p>
   </div>

development/_images/sphx_glr_example_inspect_predictions_002.png

@@ -561,7 +561,7 @@ <h1>Source code for autosklearn.pipeline.components.base</h1><div class="highlig
 
     </p>
     <p>
-        &copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
+        &copy; Copyright 2014-2022, Machine Learning Professorship Freiburg.<br/>
       Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.2.0.<br/>
     </p>
   </div>

development/_images/sphx_glr_example_inspect_predictions_003.png

@@ -278,7 +278,7 @@ <h1>Source code for autosklearn.pipeline.components.classification</h1><div clas
 
     </p>
     <p>
-        &copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
+        &copy; Copyright 2014-2022, Machine Learning Professorship Freiburg.<br/>
       Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.2.0.<br/>
     </p>
   </div>

development/_images/sphx_glr_example_inspect_predictions_thumb.png

@@ -256,7 +256,7 @@ <h1>Source code for autosklearn.pipeline.components.feature_preprocessing</h1><d
 
     </p>
     <p>
-        &copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
+        &copy; Copyright 2014-2022, Machine Learning Professorship Freiburg.<br/>
       Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.2.0.<br/>
     </p>
   </div>

development/_images/sphx_glr_example_pandas_train_test_001.png

@@ -264,7 +264,7 @@ <h1>Source code for autosklearn.pipeline.components.regression</h1><div class="h
 
     </p>
     <p>
-        &copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
+        &copy; Copyright 2014-2022, Machine Learning Professorship Freiburg.<br/>
       Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.2.0.<br/>
     </p>
   </div>

development/_images/sphx_glr_example_pandas_train_test_thumb.png

@@ -133,7 +133,7 @@ <h1>All modules for which code is available</h1>
 
     </p>
     <p>
-        &copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
+        &copy; Copyright 2014-2022, Machine Learning Professorship Freiburg.<br/>
       Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.2.0.<br/>
     </p>
   </div>

development/_images/sphx_glr_example_regression_001.png

@@ -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   3.74924
+    2            1              1.0  random_forest  0.447294  3.220523
 
 
 
@@ -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 0x7f1e52b9a730>,
+               'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7fb34b449280>,
                'cost': 0.4472941828699525,
-               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f1e54895d30>,
+               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb347b6cf70>,
                'ensemble_weight': 1.0,
-               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f1e52b7d280>,
+               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb34b4497f0>,
                '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  14.270 seconds)
+   **Total running time of the script:** ( 0 minutes  12.221 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_multilabel_classification.py:

development/_images/sphx_glr_example_regression_thumb.png

@@ -123,12 +123,10 @@ View the models found by auto-sklearn
 
  .. code-block:: none
 
-              rank  ensemble_weight                 type      cost  duration
-    model_id                                                                
-    2            1             0.78        random_forest  0.127023  2.588092
-    5            2             0.16  k_nearest_neighbors  0.204675  0.498047
-    18           3             0.04  k_nearest_neighbors  0.459024  0.509598
-    14           4             0.02  k_nearest_neighbors  0.509078  1.071782
+              rank  ensemble_weight              type      cost  duration
+    model_id                                                             
+    18           1              0.8  gaussian_process  0.000007  2.596252
+    16           2              0.2  gaussian_process  0.000015  7.973967
 
 
 
@@ -155,39 +153,28 @@ Print the final ensemble constructed by auto-sklearn
 
  .. code-block:: none
 
-    {   2: {   'cost': 0.12702266427368414,
-               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f1e544680d0>,
-               'ensemble_weight': 0.78,
-               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f1e54468970>,
-               'model_id': 2,
-               'rank': 1,
-               'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f1e54468400>,
-               'sklearn_regressor': RandomForestRegressor(max_features=1.0, n_estimators=512, n_jobs=1,
-                          random_state=1, warm_start=True)},
-        5: {   'cost': 0.20467501696131973,
-               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f1e543d8640>,
-               'ensemble_weight': 0.16,
-               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f1e52b49af0>,
-               'model_id': 5,
-               'rank': 2,
-               'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f1e52b49e50>,
-               'sklearn_regressor': KNeighborsRegressor(n_neighbors=14, p=1)},
-        14: {   'cost': 0.5090778359252635,
-                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f1e4ee6c700>,
-                'ensemble_weight': 0.02,
-                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f1e50a110a0>,
-                'model_id': 14,
-                'rank': 4,
-                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f1e50a11610>,
-                'sklearn_regressor': KNeighborsRegressor(n_neighbors=1, p=1, weights='distance')},
-        18: {   'cost': 0.4590244851288239,
-                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f1e52cd0d90>,
-                'ensemble_weight': 0.04,
-                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f1e50a419d0>,
+    {   16: {   'cost': 1.453992678268623e-05,
+                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb34aacdd90>,
+                'ensemble_weight': 0.2,
+                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb347b2b220>,
+                'model_id': 16,
+                'rank': 2,
+                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fb347d22910>,
+                'sklearn_regressor': GaussianProcessRegressor(alpha=6.883531961818898e-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)},
+        18: {   'cost': 6.532830369665454e-06,
+                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb3459dc730>,
+                'ensemble_weight': 0.8,
+                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb34aacdbe0>,
                 'model_id': 18,
-                'rank': 3,
-                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f1e50a41f10>,
-                'sklearn_regressor': KNeighborsRegressor(n_neighbors=1, p=1, weights='distance')}}
+                'rank': 1,
+                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fb34aacda00>,
+                'sklearn_regressor': GaussianProcessRegressor(alpha=9.054973819256506e-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)}}
 
 
 
@@ -215,7 +202,7 @@ Get the Score of the final ensemble
 
  .. code-block:: none
 
-    R2 score: 0.8669192933582243
+    R2 score: 0.999993617907473
 
 
 
@@ -438,7 +425,7 @@ Get the configuration space
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  54.549 seconds)
+   **Total running time of the script:** ( 1 minutes  57.174 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_multioutput_regression.py:

development/_modules/autosklearn/estimators.html

@@ -121,13 +121,12 @@ View the models found by auto-sklearn
 
  .. code-block:: none
 
-              rank  ensemble_weight               type      cost   duration
-    model_id                                                               
-    25           1             0.46                sgd  0.436679   0.717185
-    6            2             0.32     ard_regression  0.455042   0.729688
-    27           3             0.14     ard_regression  0.462249   0.721807
-    11           4             0.02      random_forest  0.507400  10.729273
-    7            5             0.06  gradient_boosting  0.518673   1.277604
+              rank  ensemble_weight               type      cost  duration
+    model_id                                                              
+    25           1             0.44                sgd  0.436679  0.605110
+    6            2             0.34     ard_regression  0.455042  0.629450
+    39           3             0.18     ard_regression  0.474807  0.603827
+    7            4             0.04  gradient_boosting  0.518673  1.111901
 
 
 
@@ -155,62 +154,51 @@ 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 0x7f1e54425c40>,
-               'ensemble_weight': 0.32,
-               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f1e54431580>,
+               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb34b258970>,
+               'ensemble_weight': 0.34,
+               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb34aba8310>,
                'model_id': 6,
                'rank': 2,
-               'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f1e54431fd0>,
+               'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fb34aba8a60>,
                '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 0x7f1e52b1dc40>,
-               'ensemble_weight': 0.06,
-               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f1e52b2e040>,
+               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb347cca280>,
+               'ensemble_weight': 0.04,
+               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb347b8ca30>,
                'model_id': 7,
-               'rank': 5,
-               'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f1e52b2e790>,
+               'rank': 4,
+               'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fb347b8c820>,
                '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 0x7f1e54207a30>,
-                'ensemble_weight': 0.02,
-                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f1e4ed21820>,
-                'model_id': 11,
-                'rank': 4,
-                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f1e4ed21dc0>,
-                '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 0x7f1e542bbe80>,
-                'ensemble_weight': 0.46,
-                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f1e541b3c70>,
+                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb34aac2880>,
+                'ensemble_weight': 0.44,
+                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb347ccad30>,
                 'model_id': 25,
                 'rank': 1,
-                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f1e541b3310>,
+                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fb345820c70>,
                 '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 0x7f1e4ed322e0>,
-                'ensemble_weight': 0.14,
-                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f1e52b41880>,
-                'model_id': 27,
+        39: {   'cost': 0.4748068089650166,
+                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb34b258eb0>,
+                'ensemble_weight': 0.18,
+                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb34674f6a0>,
+                'model_id': 39,
                 'rank': 3,
-                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f1e5443d3d0>,
-                '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)}}
+                'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7fb34674fa90>,
+                'sklearn_regressor': ARDRegression(alpha_1=0.0005012365297609799, alpha_2=3.025360750168211e-08,
+                  copy_X=False, lambda_1=4.9749646614525684e-05,
+                  lambda_2=3.2368037115065363e-10,
+                  threshold_lambda=18669.665899307194, tol=0.0012624032013298571)}}
 
 
 
@@ -244,8 +232,8 @@ predicting the data mean has an R2 score of 0.
 
  .. code-block:: none
 
-    Train R2 score: 0.5944780427522034
-    Test R2 score: 0.3959585042866587
+    Train R2 score: 0.5855373845454157
+    Test R2 score: 0.39879073225079487
 
 
 
@@ -290,7 +278,7 @@ the true value).
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  55.698 seconds)
+   **Total running time of the script:** ( 1 minutes  55.959 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_regression.py: