Ravin Kohli: [FIX] Silence catboost (#338)

Author: Github Actions

development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

@@ -85,15 +85,17 @@ Image Classification
     Pipeline Random Config:
      ________________________________________ 
     Configuration:
-      image_augmenter:GaussianBlur:sigma_min, Value: 1.4046883169529134
-      image_augmenter:GaussianBlur:sigma_offset, Value: 0.2827315442973467
-      image_augmenter:GaussianBlur:use_augmenter, Value: True
-      image_augmenter:GaussianNoise:sigma_offset, Value: 2.560369532733963
-      image_augmenter:GaussianNoise:use_augmenter, Value: True
-      image_augmenter:RandomAffine:use_augmenter, Value: False
-      image_augmenter:RandomCutout:use_augmenter, Value: False
+      image_augmenter:GaussianBlur:use_augmenter, Value: False
+      image_augmenter:GaussianNoise:use_augmenter, Value: False
+      image_augmenter:RandomAffine:rotate, Value: 239
+      image_augmenter:RandomAffine:scale_offset, Value: 0.056925342345860934
+      image_augmenter:RandomAffine:shear, Value: 40
+      image_augmenter:RandomAffine:translate_percent_offset, Value: 0.32451436908855363
+      image_augmenter:RandomAffine:use_augmenter, Value: True
+      image_augmenter:RandomCutout:p, Value: 0.5746859534468014
+      image_augmenter:RandomCutout:use_augmenter, Value: True
       image_augmenter:Resize:use_augmenter, Value: True
-      image_augmenter:ZeroPadAndCrop:percent, Value: 0.06242203913307903
+      image_augmenter:ZeroPadAndCrop:percent, Value: 0.033334448480574985
       normalizer:__choice__, Value: 'NoNormalizer'
 
     Fitting the pipeline...
@@ -173,7 +175,7 @@ Image Classification
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  8.240 seconds)
+   **Total running time of the script:** ( 0 minutes  8.526 seconds)
 
 
 .. _sphx_glr_download_examples_20_basics_example_image_classification.py:

development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip

@@ -134,7 +134,7 @@ Search for an ensemble of machine learning algorithms
  .. code-block:: none
 
 
-    <autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f5b88889250>
+    <autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f90b38a3220>
 
 
 
@@ -199,7 +199,7 @@ Print the final ensemble performance
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  37.526 seconds)
+   **Total running time of the script:** ( 5 minutes  35.166 seconds)
 
 
 .. _sphx_glr_download_examples_20_basics_example_tabular_classification.py:

development/_images/sphx_glr_example_visualization_001.png

@@ -125,7 +125,7 @@ Search for an ensemble of machine learning algorithms
  .. code-block:: none
 
 
-    <autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f5c2b1dbca0>
+    <autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f9155e95ca0>
 
 
 
@@ -167,7 +167,7 @@ Print the final ensemble performance
     |  2 | None                                                              | LGBMLearner                                                     |     0.08 |
     |  3 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.06 |
     autoPyTorch results:
-            Dataset name: 1126c30d-4b9d-11ec-8734-1bc53ddd4b3d
+            Dataset name: 70b3044c-4b9e-11ec-870d-13e6b1aae6bc
             Optimisation Metric: r2
             Best validation score: 0.8644967965917701
             Number of target algorithm runs: 23
@@ -183,7 +183,7 @@ Print the final ensemble performance
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  36.092 seconds)
+   **Total running time of the script:** ( 5 minutes  36.742 seconds)
 
 
 .. _sphx_glr_download_examples_20_basics_example_tabular_regression.py:

development/_images/sphx_glr_example_visualization_thumb.png

@@ -5,12 +5,12 @@
 
 Computation times
 =================
-**11:21.858** total execution time for **examples_20_basics** files:
+**11:20.434** total execution time for **examples_20_basics** files:
 
 +--------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:37.526 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``)         | 05:36.742 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``)         | 05:36.092 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:35.166 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``)     | 00:08.240 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``)     | 00:08.526 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------------------+-----------+--------+

development/_sources/examples/20_basics/example_image_classification.rst.txt

@@ -57,7 +57,7 @@ the search. Currently, there are two changes that can be made to the space:-
     |  5 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential |     0.02 |
     |  6 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential |     0.02 |
     autoPyTorch results:
-            Dataset name: 82ccc0cf-4ba1-11ec-8734-1bc53ddd4b3d
+            Dataset name: c6d94f6c-4ba2-11ec-870d-13e6b1aae6bc
             Optimisation Metric: accuracy
             Best validation score: 0.8596491228070176
             Number of target algorithm runs: 17
@@ -77,14 +77,14 @@ the search. Currently, there are two changes that can be made to the space:-
     |  5 | None                                                          | SVMLearner                                                      |     0.04 |
     |  6 | SimpleImputer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.02 |
     autoPyTorch results:
-            Dataset name: ecc6b68d-4ba1-11ec-8734-1bc53ddd4b3d
+            Dataset name: 306ecee6-4ba3-11ec-870d-13e6b1aae6bc
             Optimisation Metric: accuracy
             Best validation score: 0.8596491228070176
             Number of target algorithm runs: 20
             Number of successful target algorithm runs: 13
-            Number of crashed target algorithm runs: 6
+            Number of crashed target algorithm runs: 5
             Number of target algorithms that exceeded the time limit: 1
-            Number of target algorithms that exceeded the memory limit: 0
+            Number of target algorithms that exceeded the memory limit: 1
 
 
 
@@ -218,7 +218,7 @@ the search. Currently, there are two changes that can be made to the space:-
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  55.959 seconds)
+   **Total running time of the script:** ( 5 minutes  55.214 seconds)
 
 
 .. _sphx_glr_download_examples_40_advanced_example_custom_configuration_space.py:

development/_sources/examples/20_basics/example_tabular_classification.rst.txt

@@ -36,7 +36,7 @@ with AutoPyTorch
 
  .. code-block:: none
 
-    <smac.runhistory.runhistory.RunHistory object at 0x7f5b8789c490> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
+    <smac.runhistory.runhistory.RunHistory object at 0x7f90b2b4c4c0> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
       data_loader:batch_size, Value: 64
       encoder:__choice__, Value: 'OneHotEncoder'
       feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -68,7 +68,7 @@ with AutoPyTorch
       scaler:__choice__, Value: 'StandardScaler'
       trainer:StandardTrainer:weighted_loss, Value: True
       trainer:__choice__, Value: 'StandardTrainer'
-    , ta_runs=0, ta_time_used=0.0, wallclock_time=0.001440286636352539, budget=0), TrajEntry(train_perf=0.4444444444444444, incumbent_id=1, incumbent=Configuration:
+    , ta_runs=0, ta_time_used=0.0, wallclock_time=0.0012407302856445312, budget=0), TrajEntry(train_perf=0.4444444444444444, incumbent_id=1, incumbent=Configuration:
       data_loader:batch_size, Value: 142
       encoder:__choice__, Value: 'NoEncoder'
       feature_preprocessor:PowerTransformer:standardize, Value: True
@@ -104,7 +104,7 @@ with AutoPyTorch
       trainer:MixUpTrainer:alpha, Value: 0.13179357367568267
       trainer:MixUpTrainer:weighted_loss, Value: True
       trainer:__choice__, Value: 'MixUpTrainer'
-    , ta_runs=1, ta_time_used=5.004559516906738, wallclock_time=6.179516315460205, budget=5.555555555555555), TrajEntry(train_perf=0.1578947368421053, incumbent_id=2, incumbent=Configuration:
+    , ta_runs=1, ta_time_used=4.805485248565674, wallclock_time=5.9671196937561035, budget=5.555555555555555), TrajEntry(train_perf=0.1578947368421053, incumbent_id=2, incumbent=Configuration:
       data_loader:batch_size, Value: 64
       encoder:__choice__, Value: 'OneHotEncoder'
       feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -136,7 +136,7 @@ with AutoPyTorch
       scaler:__choice__, Value: 'StandardScaler'
       trainer:StandardTrainer:weighted_loss, Value: True
       trainer:__choice__, Value: 'StandardTrainer'
-    , ta_runs=2, ta_time_used=10.349637746810913, wallclock_time=6.549610614776611, budget=5.555555555555555), TrajEntry(train_perf=0.14035087719298245, incumbent_id=3, incumbent=Configuration:
+    , ta_runs=2, ta_time_used=10.325108528137207, wallclock_time=6.79562520980835, budget=5.555555555555555), TrajEntry(train_perf=0.14035087719298245, incumbent_id=3, incumbent=Configuration:
       data_loader:batch_size, Value: 32
       encoder:__choice__, Value: 'OneHotEncoder'
       feature_preprocessor:PowerTransformer:standardize, Value: False
@@ -168,7 +168,7 @@ with AutoPyTorch
       trainer:MixUpTrainer:alpha, Value: 0.9660122415558304
       trainer:MixUpTrainer:weighted_loss, Value: True
       trainer:__choice__, Value: 'MixUpTrainer'
-    , ta_runs=6, ta_time_used=30.700660943984985, wallclock_time=28.790034294128418, budget=5.555555555555555), TrajEntry(train_perf=0.13450292397660824, incumbent_id=4, incumbent=Configuration:
+    , ta_runs=6, ta_time_used=31.190066814422607, wallclock_time=29.54458737373352, budget=5.555555555555555), TrajEntry(train_perf=0.13450292397660824, incumbent_id=4, incumbent=Configuration:
       data_loader:batch_size, Value: 44
       encoder:__choice__, Value: 'OneHotEncoder'
       feature_preprocessor:TruncatedSVD:target_dim, Value: 5
@@ -202,7 +202,7 @@ with AutoPyTorch
       scaler:__choice__, Value: 'MinMaxScaler'
       trainer:StandardTrainer:weighted_loss, Value: False
       trainer:__choice__, Value: 'StandardTrainer'
-    , ta_runs=25, ta_time_used=266.3682427406311, wallclock_time=169.28211450576782, budget=50.0)]
+    , ta_runs=25, ta_time_used=268.8976037502289, wallclock_time=171.64299654960632, budget=50.0)]
     {'accuracy': 0.8728323699421965}
     |    | Preprocessing                                                     | Estimator                                                          |   Weight |
     |---:|:------------------------------------------------------------------|:-------------------------------------------------------------------|---------:|
@@ -302,7 +302,7 @@ with AutoPyTorch
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  52.256 seconds)
+   **Total running time of the script:** ( 5 minutes  34.117 seconds)
 
 
 .. _sphx_glr_download_examples_40_advanced_example_parallel_n_jobs.py: