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Ravin Kohli: [ADD] feature preprocessors from autosklearn (#378)
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‎development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip‎

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‎development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip‎

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‎development/_images/sphx_glr_example_plot_over_time_001.png‎

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‎development/_images/sphx_glr_example_plot_over_time_thumb.png‎

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‎development/_images/sphx_glr_example_visualization_001.png‎

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‎development/_images/sphx_glr_example_visualization_thumb.png‎

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

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@@ -85,14 +85,16 @@ Image Classification
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Pipeline Random Config:
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________________________________________
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Configuration(values={
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'image_augmenter:GaussianBlur:use_augmenter': False,
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'image_augmenter:GaussianNoise:sigma_offset': 2.016605350658349,
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'image_augmenter:GaussianBlur:sigma_min': 0.10327976032627761,
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'image_augmenter:GaussianBlur:sigma_offset': 0.45340318994125184,
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'image_augmenter:GaussianBlur:use_augmenter': True,
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'image_augmenter:GaussianNoise:sigma_offset': 0.643177287015827,
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'image_augmenter:GaussianNoise:use_augmenter': True,
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'image_augmenter:RandomAffine:use_augmenter': False,
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'image_augmenter:RandomCutout:p': 0.7728298341875102,
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'image_augmenter:RandomCutout:p': 0.58899963145322,
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'image_augmenter:RandomCutout:use_augmenter': True,
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'image_augmenter:Resize:use_augmenter': False,
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'image_augmenter:ZeroPadAndCrop:percent': 0.4990785598329858,
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'image_augmenter:Resize:use_augmenter': True,
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'image_augmenter:ZeroPadAndCrop:percent': 0.062191847988016136,
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'normalizer:__choice__': 'ImageNormalizer',
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})
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@@ -173,7 +175,7 @@ Image Classification
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 8.510 seconds)
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**Total running time of the script:** ( 0 minutes 8.858 seconds)
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.. _sphx_glr_download_examples_20_basics_example_image_classification.py:

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

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@@ -134,7 +134,7 @@ Search for an ensemble of machine learning algorithms
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.. code-block:: none
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f444a4d0d90>
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7fdd8a34d640>
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@@ -168,22 +168,25 @@ Print the final ensemble performance
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{'accuracy': 0.861271676300578}
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| | Preprocessing | Estimator | Weight |
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|---:|:-------------------------------------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
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| 0 | None | CBLearner | 0.66 |
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| 1 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,TruncSVD | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.16 |
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| 2 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,StandardScaler,KernelPCA | embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 3 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,Normalizer,Nystroem | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 4 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,Normalizer,TruncSVD | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 5 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,StandardScaler,KernelPCA | embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 6 | None | RFLearner | 0.02 |
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| 7 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 0 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,QuantileTransformer,KitchenSink | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.24 |
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| 1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.16 |
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| 2 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,NoScaler,KitchenSink | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 3 | None | CBLearner | 0.1 |
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| 4 | None | SVMLearner | 0.08 |
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| 5 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,NoScaler,KitchenSink | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 6 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,NoScaler,KitchenSink | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 7 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,SRC | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 8 | None | LGBMLearner | 0.04 |
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| 9 | None | RFLearner | 0.04 |
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| 10 | None | KNNLearner | 0.04 |
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autoPyTorch results:
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Dataset name: Australian
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Optimisation Metric: accuracy
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Best validation score: 0.8713450292397661
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Number of target algorithm runs: 23
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Number of successful target algorithm runs: 20
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Number of crashed target algorithm runs: 2
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Number of target algorithms that exceeded the time limit: 1
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Number of target algorithm runs: 20
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Number of successful target algorithm runs: 18
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Number of crashed target algorithm runs: 0
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Number of target algorithms that exceeded the time limit: 2
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Number of target algorithms that exceeded the memory limit: 0
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 5 minutes 31.215 seconds)
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**Total running time of the script:** ( 5 minutes 27.922 seconds)
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.. _sphx_glr_download_examples_20_basics_example_tabular_classification.py:

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

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@@ -125,7 +125,7 @@ Search for an ensemble of machine learning algorithms
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.. code-block:: none
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<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f44e625f430>
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<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7fde27812430>
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.. code-block:: none
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{'r2': 0.9407884171054208}
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{'r2': 0.9412847640085195}
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| | Preprocessing | Estimator | Weight |
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|---:|:-------------------------------------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
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| 0 | None | CBLearner | 0.44 |
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| 1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.42 |
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| 0 | None | CBLearner | 0.46 |
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| 1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.4 |
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| 2 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
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| 3 | None | LGBMLearner | 0.04 |
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| 3 | None | LGBMLearner | 0.02 |
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| 4 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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autoPyTorch results:
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Dataset name: 6c9e9506-9a70-11ec-87d5-474e93987d34
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Dataset name: 0da2f587-9ae4-11ec-87e6-b1d9b8f13636
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Optimisation Metric: r2
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Best validation score: 0.8670098636440993
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Number of target algorithm runs: 24
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Number of successful target algorithm runs: 23
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Number of crashed target algorithm runs: 0
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Number of target algorithms that exceeded the time limit: 1
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Best validation score: 0.8669094525651709
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Number of target algorithm runs: 22
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Number of successful target algorithm runs: 17
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Number of crashed target algorithm runs: 3
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Number of target algorithms that exceeded the time limit: 2
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Number of target algorithms that exceeded the memory limit: 0
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 5 minutes 33.512 seconds)
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**Total running time of the script:** ( 5 minutes 49.239 seconds)
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.. _sphx_glr_download_examples_20_basics_example_tabular_regression.py:

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

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Computation times
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=================
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**11:13.237** total execution time for **examples_20_basics** files:
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**11:26.018** total execution time for **examples_20_basics** files:
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+--------------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:33.512 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:49.239 | 0.0 MB |
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+--------------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:31.215 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:27.922 | 0.0 MB |
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+--------------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:08.510 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:08.858 | 0.0 MB |
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+--------------------------------------------------------------------------------------------------------------+-----------+--------+

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