Eddie Bergman: Fix logging server cleanup (#1503)

Author: Github Actions

development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

@@ -123,27 +123,27 @@ View the models found by auto-sklearn
 
               rank  ensemble_weight                type      cost  duration
     model_id                                                               
-    7            1             0.10         extra_trees  0.014184  1.484751
-    16           2             0.04   gradient_boosting  0.021277  1.040947
-    2            7             0.02       random_forest  0.028369  1.599890
-    3            9             0.08                 mlp  0.028369  0.921702
-    6            8             0.02                 mlp  0.028369  1.053133
-    11          10             0.04       random_forest  0.028369  1.972334
-    14           6             0.04                 mlp  0.028369  1.470708
-    19           4             0.12         extra_trees  0.028369  2.935899
-    22           3             0.06   gradient_boosting  0.028369  1.082520
-    31           5             0.04         extra_trees  0.028369  1.528929
-    8           12             0.06       random_forest  0.035461  1.823699
-    12          11             0.02   gradient_boosting  0.035461  1.210213
-    17          13             0.02   gradient_boosting  0.035461  1.521270
-    18          14             0.02       random_forest  0.035461  1.845248
-    9           15             0.06         extra_trees  0.042553  1.730569
-    30          16             0.12       liblinear_svc  0.042553  0.934347
-    15          17             0.02                 mlp  0.049645  3.349891
-    28          18             0.04                 mlp  0.056738  0.949584
-    29          19             0.04         extra_trees  0.056738  1.620865
-    20          20             0.02  passive_aggressive  0.078014  0.741188
-    32          21             0.02         extra_trees  0.106383  2.921923
+    7            1             0.10         extra_trees  0.014184  1.489359
+    16           2             0.04   gradient_boosting  0.021277  1.007459
+    2            7             0.02       random_forest  0.028369  1.607176
+    3            9             0.08                 mlp  0.028369  0.936909
+    6            8             0.02                 mlp  0.028369  1.053704
+    11          10             0.04       random_forest  0.028369  1.985902
+    14           6             0.04                 mlp  0.028369  1.467198
+    19           4             0.12         extra_trees  0.028369  2.925784
+    22           3             0.06   gradient_boosting  0.028369  1.084217
+    31           5             0.04         extra_trees  0.028369  1.575149
+    8           12             0.06       random_forest  0.035461  1.813525
+    12          11             0.02   gradient_boosting  0.035461  1.203338
+    17          13             0.02   gradient_boosting  0.035461  1.531853
+    18          14             0.02       random_forest  0.035461  1.871601
+    9           15             0.06         extra_trees  0.042553  1.724815
+    30          16             0.12       liblinear_svc  0.042553  0.936215
+    15          17             0.02                 mlp  0.049645  3.367088
+    28          18             0.04                 mlp  0.056738  0.998461
+    29          19             0.04         extra_trees  0.056738  1.628379
+    20          20             0.02  passive_aggressive  0.078014  0.741469
+    32          21             0.02         extra_trees  0.106383  2.998276
 
 
 
@@ -170,7 +170,7 @@ Print the final ensemble constructed by auto-sklearn
 
  .. code-block:: none
 
-    /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/automl.py:2136: UserWarning: No models in the ensemble. Kindly provide an ensemble class.
+    /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/automl.py:2152: UserWarning: No models in the ensemble. Kindly provide an ensemble class.
       warnings.warn(
     {}
 
@@ -207,7 +207,7 @@ Get the Score of the final ensemble
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  58.367 seconds)
+   **Total running time of the script:** ( 2 minutes  1.003 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_classification.py:

development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.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  3.234739
+    2            1              1.0  random_forest  0.447294  3.337104
 
 
 
@@ -182,7 +182,7 @@ Print the final ensemble constructed by auto-sklearn
 
  .. code-block:: none
 
-    /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/automl.py:2136: UserWarning: No models in the ensemble. Kindly provide an ensemble class.
+    /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/automl.py:2152: UserWarning: No models in the ensemble. Kindly provide an ensemble class.
       warnings.warn(
     {}
 
@@ -257,7 +257,7 @@ Get the Score of the final ensemble
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  13.786 seconds)
+   **Total running time of the script:** ( 0 minutes  15.060 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_multilabel_classification.py:

development/_images/sphx_glr_example_inspect_predictions_002.png

@@ -123,9 +123,9 @@ View the models found by auto-sklearn
 
  .. code-block:: none
 
-              rank  ensemble_weight              type      cost  duration
-    model_id                                                             
-    11           1              1.0  gaussian_process  0.006723  4.867781
+              rank  ensemble_weight           type      cost  duration
+    model_id                                                          
+    2            1              1.0  random_forest  0.166453  2.309653
 
 
 
@@ -152,7 +152,7 @@ Print the final ensemble constructed by auto-sklearn
 
  .. code-block:: none
 
-    /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/automl.py:2136: UserWarning: No models in the ensemble. Kindly provide an ensemble class.
+    /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/automl.py:2152: UserWarning: No models in the ensemble. Kindly provide an ensemble class.
       warnings.warn(
     {}
 
@@ -182,7 +182,7 @@ Get the Score of the final ensemble
 
  .. code-block:: none
 
-    R2 score: 0.9953185473305864
+    R2 score: 0.8159024348248028
 
 
 
@@ -435,7 +435,7 @@ Get the configuration space
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  57.717 seconds)
+   **Total running time of the script:** ( 1 minutes  57.959 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_multioutput_regression.py:

development/_images/sphx_glr_example_pandas_train_test_001.png

@@ -124,10 +124,10 @@ View the models found by auto-sklearn
 
               rank  ensemble_weight            type      cost  duration
     model_id                                                           
-    31           1             0.48  ard_regression  0.428169  0.762867
-    25           2             0.26             sgd  0.436679  0.676045
-    33           3             0.20   liblinear_svr  0.472547  0.774536
-    29           4             0.06  ard_regression  0.493390  0.660402
+    31           1             0.48  ard_regression  0.428169  0.728216
+    25           2             0.26             sgd  0.436679  0.674747
+    33           3             0.20   liblinear_svr  0.472547  0.783565
+    29           4             0.06  ard_regression  0.493390  0.658636
 
 
 
@@ -154,7 +154,7 @@ Print the final ensemble constructed by auto-sklearn
 
  .. code-block:: none
 
-    /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/automl.py:2136: UserWarning: No models in the ensemble. Kindly provide an ensemble class.
+    /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/automl.py:2152: UserWarning: No models in the ensemble. Kindly provide an ensemble class.
       warnings.warn(
     {}
 
@@ -236,7 +236,7 @@ the true value).
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  54.434 seconds)
+   **Total running time of the script:** ( 2 minutes  1.947 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_regression.py:

development/_images/sphx_glr_example_pandas_train_test_thumb.png

@@ -5,14 +5,14 @@
 
 Computation times
 =================
-**06:04.303** total execution time for **examples_20_basic** files:
+**06:15.969** total execution time for **examples_20_basic** files:
 
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``)                       | 01:58.367 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``)                               | 02:01.947 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``)       | 01:57.717 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``)                       | 02:01.003 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``)                               | 01:54.434 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``)       | 01:57.959 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:13.786 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:15.060 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+

development/_sources/examples/20_basic/example_classification.rst.txt

@@ -196,7 +196,7 @@ Get the Score of the final ensemble
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  56.219 seconds)
+   **Total running time of the script:** ( 2 minutes  1.545 seconds)
 
 
 .. _sphx_glr_download_examples_40_advanced_example_calc_multiple_metrics.py:

development/_sources/examples/20_basic/example_multilabel_classification.rst.txt

@@ -175,21 +175,21 @@ Build and fit a classifier
 
     /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/data/target_validator.py:187: UserWarning: Fitting transformer with a pandas series which has the dtype category. Inverse transform may not be able preserve dtype when converting to np.ndarray
       warnings.warn(
-    2022-06-14 20:17:04,601 - Client-EnsembleBuilder - INFO - DummyFuture: ([{'Timestamp': Timestamp('2022-06-14 20:17:04.586474'), 'ensemble_optimization_score': 0.9886363636363636, 'ensemble_test_score': 0.9899874843554443}], 50)/SingleThreadedClient() Started Ensemble builder job at 2022.06.14-20.17.04 for iteration 0.
-    2022-06-14 20:17:08,243 - Client-EnsembleBuilder - INFO - DummyFuture: ([{'Timestamp': Timestamp('2022-06-14 20:17:08.227768'), 'ensemble_optimization_score': 0.98989898989899, 'ensemble_test_score': 0.9924906132665833}], 50)/SingleThreadedClient() Started Ensemble builder job at 2022.06.14-20.17.08 for iteration 1.
-    tmp_folder/AutoML(1):f2fbaed3-ec1e-11ec-8812-4f4bfc9e3a70.log
+    2022-06-15 06:41:06,404 - Client-EnsembleBuilder - INFO - DummyFuture: ([{'Timestamp': Timestamp('2022-06-15 06:41:06.389160'), 'ensemble_optimization_score': 0.9886363636363636, 'ensemble_test_score': 0.9899874843554443}], 50)/SingleThreadedClient() Started Ensemble builder job at 2022.06.15-06.41.06 for iteration 0.
+    2022-06-15 06:41:10,057 - Client-EnsembleBuilder - INFO - DummyFuture: ([{'Timestamp': Timestamp('2022-06-15 06:41:10.040909'), 'ensemble_optimization_score': 0.98989898989899, 'ensemble_test_score': 0.9924906132665833}], 50)/SingleThreadedClient() Started Ensemble builder job at 2022.06.15-06.41.10 for iteration 1.
     tmp_folder/smac3-output
     tmp_folder/space.json
     tmp_folder/.auto-sklearn
     tmp_folder/distributed.log
+    tmp_folder/AutoML(1):20044fa6-ec76-11ec-8813-abcc8c91ab9a.log
 
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  12.488 seconds)
+   **Total running time of the script:** ( 0 minutes  12.546 seconds)
 
 
 .. _sphx_glr_download_examples_40_advanced_example_debug_logging.py:

development/_sources/examples/20_basic/example_multioutput_regression.rst.txt

@@ -136,9 +136,9 @@ Build and fit a classifier
       'feature_preprocessor:polynomial:interaction_only': 'False',
     })
     , instance='{"task_id": "breast_cancer"}', instance_specific='0', seed=0, cutoff=30.0, capped=False, budget=0.0, source_id=0)
-    RunValue(cost=0.014184397163120588, time=1.5290343761444092, status=<StatusType.SUCCESS: 1>, starttime=1655237047.351109, endtime=1655237048.8983047, additional_info={'duration': 1.450284719467163, 'num_run': 7, 'train_loss': 0.0, 'configuration_origin': 'Initial design'})
+    RunValue(cost=0.014184397163120588, time=1.560791015625, status=<StatusType.SUCCESS: 1>, starttime=1655274472.1032858, endtime=1655274473.682053, additional_info={'duration': 1.4833316802978516, 'num_run': 7, 'train_loss': 0.0, 'configuration_origin': 'Initial design'})
 
-    AutoSklearnClassifier(get_trials_callback=<function callback at 0x7fd1edcdfd30>,
+    AutoSklearnClassifier(get_trials_callback=<function callback at 0x7f0bd36c4040>,
                           per_run_time_limit=30, time_left_for_this_task=120)
 
 
@@ -167,9 +167,9 @@ View the models found by auto-sklearn
 
               rank  ensemble_weight           type      cost  duration
     model_id                                                          
-    7            1             0.68    extra_trees  0.014184  1.529034
-    2            2             0.10  random_forest  0.028369  1.632582
-    3            3             0.22            mlp  0.028369  0.936133
+    7            1             0.68    extra_trees  0.014184  1.560791
+    2            2             0.10  random_forest  0.028369  1.650856
+    3            3             0.22            mlp  0.028369  0.940310
 
 
 
@@ -196,7 +196,7 @@ Print the final ensemble constructed by auto-sklearn
 
  .. code-block:: none
 
-    /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/automl.py:2136: UserWarning: No models in the ensemble. Kindly provide an ensemble class.
+    /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/automl.py:2152: UserWarning: No models in the ensemble. Kindly provide an ensemble class.
       warnings.warn(
     {}
 
@@ -233,7 +233,7 @@ Get the Score of the final ensemble
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  15.394 seconds)
+   **Total running time of the script:** ( 0 minutes  19.546 seconds)
 
 
 .. _sphx_glr_download_examples_40_advanced_example_early_stopping_and_callbacks.py:

development/_sources/examples/20_basic/example_regression.rst.txt

@@ -162,7 +162,7 @@ Get the Score of the final ensemble
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  11.209 seconds)
+   **Total running time of the script:** ( 0 minutes  13.716 seconds)
 
 
 .. _sphx_glr_download_examples_40_advanced_example_feature_types.py: