Ravin Kohli: [ADD] dataset compression (#387)

Author: Github Actions

development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

@@ -85,24 +85,24 @@ Image Classification
     Pipeline Random Config:
      ________________________________________ 
     Configuration(values={
-      'image_augmenter:GaussianBlur:sigma_min': 2.068090231601102,
-      'image_augmenter:GaussianBlur:sigma_offset': 1.9329308760314452,
+      'image_augmenter:GaussianBlur:sigma_min': 2.128854080819082,
+      'image_augmenter:GaussianBlur:sigma_offset': 1.685286865327408,
       'image_augmenter:GaussianBlur:use_augmenter': True,
       'image_augmenter:GaussianNoise:use_augmenter': False,
       'image_augmenter:RandomAffine:use_augmenter': False,
-      'image_augmenter:RandomCutout:p': 0.9739889046885781,
+      'image_augmenter:RandomCutout:p': 0.8505129226214392,
       'image_augmenter:RandomCutout:use_augmenter': True,
       'image_augmenter:Resize:use_augmenter': False,
-      'image_augmenter:ZeroPadAndCrop:percent': 0.27792641387037204,
-      'normalizer:__choice__': 'ImageNormalizer',
+      'image_augmenter:ZeroPadAndCrop:percent': 0.3503163793523587,
+      'normalizer:__choice__': 'NoNormalizer',
     })
 
     Fitting the pipeline...
     ________________________________________
             ImageClassificationPipeline
     ________________________________________
     0-) normalizer: 
-            ImageNormalizer
+            NoNormalizer
 
     1-) preprocessing: 
             EarlyPreprocessing
@@ -174,7 +174,7 @@ Image Classification
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  8.519 seconds)
+   **Total running time of the script:** ( 0 minutes  6.887 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 0x7f5b9b54f7f0>
+    <autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f7e22bfd7f0>
 
 
 
@@ -166,19 +166,24 @@ Print the final ensemble performance
  .. code-block:: none
 
     {'accuracy': 0.861271676300578}
-    |    | Preprocessing                                                                           | Estimator                                                    |   Weight |
-    |---:|:----------------------------------------------------------------------------------------|:-------------------------------------------------------------|---------:|
-    |  0 | None                                                                                    | CBLearner                                                    |     0.86 |
-    |  1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,QuantileTransformer,TruncSVD | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.08 |
-    |  2 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,Normalizer,Nystroem    | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential |     0.06 |
+    |    | Preprocessing                                                                                    | Estimator                                                       |   Weight |
+    |---:|:-------------------------------------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
+    |  0 | None                                                                                             | CBLearner                                                       |     0.66 |
+    |  1 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,TruncSVD                   | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.16 |
+    |  2 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,StandardScaler,KernelPCA        | embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential |     0.06 |
+    |  3 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,Normalizer,Nystroem             | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential    |     0.04 |
+    |  4 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,Normalizer,TruncSVD                   | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential    |     0.02 |
+    |  5 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,StandardScaler,KernelPCA        | embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential |     0.02 |
+    |  6 | None                                                                                             | RFLearner                                                       |     0.02 |
+    |  7 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.02 |
     autoPyTorch results:
             Dataset name: Australian
             Optimisation Metric: accuracy
             Best validation score: 0.8713450292397661
-            Number of target algorithm runs: 22
-            Number of successful target algorithm runs: 19
+            Number of target algorithm runs: 23
+            Number of successful target algorithm runs: 21
             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
 
 
@@ -188,7 +193,7 @@ Print the final ensemble performance
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  19.116 seconds)
+   **Total running time of the script:** ( 5 minutes  28.953 seconds)
 
 
 .. _sphx_glr_download_examples_20_basics_example_tabular_classification.py: