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

automl · Feb 25, 2022 · b9cc98b · b9cc98b
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diff --git a/development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip b/development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip
diff --git a/development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip b/development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip
diff --git a/development/_images/sphx_glr_example_plot_over_time_001.png b/development/_images/sphx_glr_example_plot_over_time_001.png
diff --git a/development/_images/sphx_glr_example_plot_over_time_thumb.png b/development/_images/sphx_glr_example_plot_over_time_thumb.png
diff --git a/development/_images/sphx_glr_example_visualization_001.png b/development/_images/sphx_glr_example_visualization_001.png
diff --git a/development/_images/sphx_glr_example_visualization_thumb.png b/development/_images/sphx_glr_example_visualization_thumb.png
diff --git a/development/_modules/autoPyTorch/api/tabular_classification.html b/development/_modules/autoPyTorch/api/tabular_classification.html
diff --git a/development/_modules/autoPyTorch/api/tabular_regression.html b/development/_modules/autoPyTorch/api/tabular_regression.html
diff --git a/development/_sources/examples/20_basics/example_image_classification.rst.txt b/development/_sources/examples/20_basics/example_image_classification.rst.txt
@@ -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:

diff --git a/development/_sources/examples/20_basics/example_tabular_classification.rst.txt b/development/_sources/examples/20_basics/example_tabular_classification.rst.txt
@@ -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:

diff --git a/development/_sources/examples/20_basics/example_tabular_regression.rst.txt b/development/_sources/examples/20_basics/example_tabular_regression.rst.txt
@@ -125,7 +125,7 @@ Search for an ensemble of machine learning algorithms
  .. code-block:: none
 
 
-    <autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f5c373f5700>
+    <autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f7ebdf86d90>
 
 
 
@@ -159,22 +159,20 @@ Print the final ensemble performance
 
  .. code-block:: none
 
-    {'r2': 0.9410530752009164}
+    {'r2': 0.9407884171054208}
     |    | Preprocessing                                                                                    | Estimator                                                       |   Weight |
     |---:|:-------------------------------------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
-    |  0 | None                                                                                             | CBLearner                                                       |     0.46 |
-    |  1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.4  |
-    |  2 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.08 |
-    |  3 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,PolynomialFeatures         | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.02 |
-    |  4 | None                                                                                             | LGBMLearner                                                     |     0.02 |
-    |  5 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.02 |
+    |  0 | None                                                                                             | CBLearner                                                       |     0.44 |
+    |  1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.42 |
+    |  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: be93974e-9646-11ec-87d9-dfb614d36051
+            Dataset name: 4d60e332-964c-11ec-87e7-95e9f01c72a4
             Optimisation Metric: r2
-            Best validation score: 0.8669094525651709
-            Number of target algorithm runs: 23
-            Number of successful target algorithm runs: 22
-            Number of crashed target algorithm runs: 0
+            Best validation score: 0.8670098636440993
+            Number of target algorithm runs: 27
+            Number of successful target algorithm runs: 25
+            Number of crashed target algorithm runs: 1
             Number of target algorithms that exceeded the time limit: 1
             Number of target algorithms that exceeded the memory limit: 0
 
@@ -185,7 +183,7 @@ Print the final ensemble performance
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  31.341 seconds)
+   **Total running time of the script:** ( 5 minutes  34.491 seconds)
 
 
 .. _sphx_glr_download_examples_20_basics_example_tabular_regression.py:

diff --git a/development/_sources/examples/20_basics/sg_execution_times.rst.txt b/development/_sources/examples/20_basics/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
 
 Computation times
 =================
-**10:58.976** total execution time for **examples_20_basics** files:
+**11:10.330** total execution time for **examples_20_basics** files:
 
 +--------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``)         | 05:31.341 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``)         | 05:34.491 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:19.116 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:28.953 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``)     | 00:08.519 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``)     | 00:06.887 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------------------+-----------+--------+