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Ravin Kohli: [ADD] dataset compression (#387)
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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/_modules/autoPyTorch/api/tabular_classification.html

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‎development/_modules/autoPyTorch/api/tabular_regression.html

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

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Pipeline Random Config:
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________________________________________
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Configuration(values={
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'image_augmenter:GaussianBlur:sigma_min': 2.068090231601102,
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'image_augmenter:GaussianBlur:sigma_offset': 1.9329308760314452,
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'image_augmenter:GaussianBlur:sigma_min': 2.128854080819082,
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'image_augmenter:GaussianBlur:sigma_offset': 1.685286865327408,
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'image_augmenter:GaussianBlur:use_augmenter': True,
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'image_augmenter:GaussianNoise:use_augmenter': False,
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'image_augmenter:RandomAffine:use_augmenter': False,
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'image_augmenter:RandomCutout:p': 0.9739889046885781,
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'image_augmenter:RandomCutout:p': 0.8505129226214392,
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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': 0.27792641387037204,
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'normalizer:__choice__': 'ImageNormalizer',
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'image_augmenter:ZeroPadAndCrop:percent': 0.3503163793523587,
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'normalizer:__choice__': 'NoNormalizer',
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})
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Fitting the pipeline...
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________________________________________
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ImageClassificationPipeline
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________________________________________
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0-) normalizer:
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ImageNormalizer
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NoNormalizer
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1-) preprocessing:
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EarlyPreprocessing
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 8.519 seconds)
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**Total running time of the script:** ( 0 minutes 6.887 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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.. code-block:: none
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f5b9b54f7f0>
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f7e22bfd7f0>
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.. code-block:: none
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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 | 0.86 |
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| 1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,QuantileTransformer,TruncSVD | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
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| 2 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,Normalizer,Nystroem | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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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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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: 22
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Number of successful target algorithm runs: 19
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Number of target algorithm runs: 23
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Number of successful target algorithm runs: 21
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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 algorithms that exceeded the time limit: 0
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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 19.116 seconds)
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**Total running time of the script:** ( 5 minutes 28.953 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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.. code-block:: none
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<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f5c373f5700>
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<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f7ebdf86d90>
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.. code-block:: none
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{'r2': 0.9410530752009164}
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{'r2': 0.9407884171054208}
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| | Preprocessing | Estimator | Weight |
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|---:|:-------------------------------------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
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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 | 0.08 |
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| 3 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,PolynomialFeatures | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 4 | None | LGBMLearner | 0.02 |
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| 5 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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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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| 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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autoPyTorch results:
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Dataset name: be93974e-9646-11ec-87d9-dfb614d36051
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Dataset name: 4d60e332-964c-11ec-87e7-95e9f01c72a4
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Optimisation Metric: r2
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Best validation score: 0.8669094525651709
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Number of target algorithm runs: 23
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Number of successful target algorithm runs: 22
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Number of crashed target algorithm runs: 0
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Best validation score: 0.8670098636440993
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Number of target algorithm runs: 27
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Number of successful target algorithm runs: 25
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Number of crashed target algorithm runs: 1
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Number of target algorithms that exceeded the time limit: 1
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Number of target algorithms that exceeded the memory limit: 0
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**Total running time of the script:** ( 5 minutes 31.341 seconds)
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**Total running time of the script:** ( 5 minutes 34.491 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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**10:58.976** total execution time for **examples_20_basics** files:
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**11:10.330** 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``) | 05:31.341 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:34.491 | 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``) | 05:19.116 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:28.953 | 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``) | 00:08.519 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:06.887 | 0.0 MB |
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+--------------------------------------------------------------------------------------------------------------+-----------+--------+

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