Ravin Kohli: Feature preprocessors, Loss strategies (#86)

Author: Github Actions

refactor_development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

@@ -36,9 +36,10 @@ with AutoPyTorch
 
  .. code-block:: none
 
-    <smac.runhistory.runhistory.RunHistory object at 0x7f6bb408e9d0> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
+    <smac.runhistory.runhistory.RunHistory object at 0x7f1bb4637490> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
       data_loader:batch_size, Value: 32
       encoder:__choice__, Value: 'OneHotEncoder'
+      feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
       imputer:categorical_strategy, Value: 'most_frequent'
       imputer:numerical_strategy, Value: 'mean'
       lr_scheduler:ReduceLROnPlateau:factor, Value: 0.1
@@ -65,9 +66,10 @@ with AutoPyTorch
       optimizer:__choice__, Value: 'AdamOptimizer'
       scaler:__choice__, Value: 'StandardScaler'
       trainer:__choice__, Value: 'StandardTrainer'
-    , ta_runs=0, ta_time_used=0.0, wallclock_time=0.0018229484558105469, budget=0), TrajEntry(train_perf=0.18128654970760238, incumbent_id=1, incumbent=Configuration:
+    , ta_runs=0, ta_time_used=0.0, wallclock_time=0.0025849342346191406, budget=0), TrajEntry(train_perf=0.16374269005847952, incumbent_id=1, incumbent=Configuration:
       data_loader:batch_size, Value: 32
       encoder:__choice__, Value: 'OneHotEncoder'
+      feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
       imputer:categorical_strategy, Value: 'most_frequent'
       imputer:numerical_strategy, Value: 'mean'
       lr_scheduler:ReduceLROnPlateau:factor, Value: 0.1
@@ -94,8 +96,8 @@ with AutoPyTorch
       optimizer:__choice__, Value: 'AdamOptimizer'
       scaler:__choice__, Value: 'StandardScaler'
       trainer:__choice__, Value: 'StandardTrainer'
-    , ta_runs=1, ta_time_used=7.783486366271973, wallclock_time=9.27663254737854, budget=5.555555555555555)]
-    {'accuracy': 0.8670520231213873}
+    , ta_runs=1, ta_time_used=8.814017295837402, wallclock_time=10.166006088256836, budget=5.555555555555555)]
+    {'accuracy': 0.8959537572254336}
 
 
 
@@ -202,7 +204,7 @@ with AutoPyTorch
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 9 minutes  26.113 seconds)
+   **Total running time of the script:** ( 9 minutes  33.319 seconds)
 
 
 .. _sphx_glr_download_examples_example_tabular_classification.py:

refactor_development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip

@@ -5,10 +5,10 @@
 
 Computation times
 =================
-**09:36.925** total execution time for **examples** files:
+**09:38.856** total execution time for **examples** files:
 
 +----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_example_tabular_classification.py` (``example_tabular_classification.py``) | 09:26.113 | 0.0 MB |
+| :ref:`sphx_glr_examples_example_tabular_classification.py` (``example_tabular_classification.py``) | 09:33.319 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_example_image_classification.py` (``example_image_classification.py``)     | 00:10.812 | 0.0 MB |
+| :ref:`sphx_glr_examples_example_image_classification.py` (``example_image_classification.py``)     | 00:05.536 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------+-----------+--------+