nabenabe0928: [feat] Add new task inference for APT (#386)

Author: Github Actions

development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

@@ -85,26 +85,23 @@ Image Classification
     Pipeline Random Config:
      ________________________________________ 
     Configuration(values={
-      'image_augmenter:GaussianBlur:use_augmenter': False,
-      'image_augmenter:GaussianNoise:sigma_offset': 2.925784534700399,
-      'image_augmenter:GaussianNoise:use_augmenter': True,
-      'image_augmenter:RandomAffine:rotate': 79,
-      'image_augmenter:RandomAffine:scale_offset': 0.3303982552097816,
-      'image_augmenter:RandomAffine:shear': 22,
-      'image_augmenter:RandomAffine:translate_percent_offset': 0.068262359976932,
-      'image_augmenter:RandomAffine:use_augmenter': True,
+      'image_augmenter:GaussianBlur:sigma_min': 1.5935371448528923,
+      'image_augmenter:GaussianBlur:sigma_offset': 0.7844116418329459,
+      'image_augmenter:GaussianBlur:use_augmenter': True,
+      'image_augmenter:GaussianNoise:use_augmenter': False,
+      'image_augmenter:RandomAffine:use_augmenter': False,
       'image_augmenter:RandomCutout:use_augmenter': False,
-      'image_augmenter:Resize:use_augmenter': False,
-      'image_augmenter:ZeroPadAndCrop:percent': 0.019686612530780023,
-      'normalizer:__choice__': 'NoNormalizer',
+      'image_augmenter:Resize:use_augmenter': True,
+      'image_augmenter:ZeroPadAndCrop:percent': 0.44360967675348173,
+      'normalizer:__choice__': 'ImageNormalizer',
     })
 
     Fitting the pipeline...
     ________________________________________
             ImageClassificationPipeline
     ________________________________________
     0-) normalizer: 
-            NoNormalizer
+            ImageNormalizer
 
     1-) preprocessing: 
             EarlyPreprocessing
@@ -176,7 +173,7 @@ Image Classification
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  6.576 seconds)
+   **Total running time of the script:** ( 0 minutes  6.133 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 0x7fc57dba9700>
+    <autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7fe3b78f6550>
 
 
 
@@ -180,10 +180,10 @@ Print the final ensemble performance
             Dataset name: Australian
             Optimisation Metric: accuracy
             Best validation score: 0.8713450292397661
-            Number of target algorithm runs: 24
-            Number of successful target algorithm runs: 21
+            Number of target algorithm runs: 22
+            Number of successful target algorithm runs: 20
             Number of crashed target algorithm runs: 2
-            Number of target algorithms that exceeded the time limit: 1
+            Number of target algorithms that exceeded the time limit: 0
             Number of target algorithms that exceeded the memory limit: 0
 
 
@@ -193,7 +193,7 @@ Print the final ensemble performance
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  16.919 seconds)
+   **Total running time of the script:** ( 5 minutes  31.550 seconds)
 
 
 .. _sphx_glr_download_examples_20_basics_example_tabular_classification.py:

development/_images/sphx_glr_example_plot_over_time_001.png

@@ -125,7 +125,7 @@ Search for an ensemble of machine learning algorithms
  .. code-block:: none
 
 
-    <autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7fc61981a700>
+    <autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7fe45362b700>
 
 
 
@@ -167,13 +167,13 @@ Print the final ensemble performance
     |  2 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.1  |
     |  3 | None                                                                                             | LGBMLearner                                                     |     0.04 |
     autoPyTorch results:
-            Dataset name: e30391f0-94ca-11ec-8816-6597b230fd31
+            Dataset name: 074d83be-94f4-11ec-87fb-d5821471ed5e
             Optimisation Metric: r2
             Best validation score: 0.8670098636440993
-            Number of target algorithm runs: 28
-            Number of successful target algorithm runs: 27
-            Number of crashed target algorithm runs: 1
-            Number of target algorithms that exceeded the time limit: 0
+            Number of target algorithm runs: 24
+            Number of successful target algorithm runs: 23
+            Number of crashed target algorithm runs: 0
+            Number of target algorithms that exceeded the time limit: 1
             Number of target algorithms that exceeded the memory limit: 0
 
 
@@ -183,7 +183,7 @@ Print the final ensemble performance
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  27.461 seconds)
+   **Total running time of the script:** ( 5 minutes  44.634 seconds)
 
 
 .. _sphx_glr_download_examples_20_basics_example_tabular_regression.py:

development/_images/sphx_glr_example_plot_over_time_thumb.png

@@ -5,12 +5,12 @@
 
 Computation times
 =================
-**10:50.955** total execution time for **examples_20_basics** files:
+**11:22.317** total execution time for **examples_20_basics** files:
 
 +--------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``)         | 05:27.461 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``)         | 05:44.634 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:16.919 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:31.550 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``)     | 00:06.576 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``)     | 00:06.133 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------------------+-----------+--------+