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Ravin Kohli: Update README.md, remove cat requirements.txt
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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_visualization_001.png‎

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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:
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image_augmenter:GaussianBlur:use_augmenter, Value: False
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image_augmenter:GaussianNoise:sigma_offset, Value: 2.939416777169848
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image_augmenter:GaussianNoise:use_augmenter, Value: True
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image_augmenter:RandomAffine:rotate, Value: 182
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image_augmenter:RandomAffine:scale_offset, Value: 0.2653043460236015
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image_augmenter:RandomAffine:shear, Value: 7
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image_augmenter:RandomAffine:translate_percent_offset, Value: 0.2981323677443226
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image_augmenter:RandomAffine:use_augmenter, Value: True
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image_augmenter:RandomCutout:p, Value: 0.46400105311756495
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image_augmenter:RandomCutout:use_augmenter, Value: True
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image_augmenter:Resize:use_augmenter, Value: False
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.05116366366228997
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image_augmenter:GaussianBlur:sigma_min, Value: 1.6194687445458993
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image_augmenter:GaussianBlur:sigma_offset, Value: 0.15419645415994476
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image_augmenter:GaussianBlur:use_augmenter, Value: True
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image_augmenter:GaussianNoise:use_augmenter, Value: False
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image_augmenter:RandomAffine:use_augmenter, Value: False
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image_augmenter:RandomCutout:use_augmenter, Value: False
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image_augmenter:Resize:use_augmenter, Value: True
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.09356660599930805
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normalizer:__choice__, Value: 'ImageNormalizer'
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Fitting the pipeline...
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 7.489 seconds)
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**Total running time of the script:** ( 0 minutes 6.174 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 0x7f4c008a98e0>
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f4974e3e820>
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 5 minutes 35.087 seconds)
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**Total running time of the script:** ( 5 minutes 31.629 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 0x7f4ca3439dc0>
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<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f4a1775cca0>
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| 2 | None | LGBMLearner | 0.08 |
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| 3 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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autoPyTorch results:
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Dataset name: 37d86b62-4bdf-11ec-877d-1fe05d8e2eef
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Dataset name: 8f803876-4c46-11ec-88db-27319b5e5fd4
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Optimisation Metric: r2
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Best validation score: 0.8644967965917701
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Number of target algorithm runs: 24
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 5 minutes 36.563 seconds)
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**Total running time of the script:** ( 5 minutes 32.308 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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**11:19.139** total execution time for **examples_20_basics** files:
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**11:10.111** 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``) | 05:36.563 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:32.308 | 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``) | 05:35.087 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:31.629 | 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``) | 00:07.489 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:06.174 | 0.0 MB |
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+--------------------------------------------------------------------------------------------------------------+-----------+--------+

‎development/_sources/examples/40_advanced/example_custom_configuration_space.rst.txt‎

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.. code-block:: none
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f4bee1254c0>
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f4963740280>
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| 5 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 6 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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autoPyTorch results:
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Dataset name: 5482ec4e-4be2-11ec-877d-1fe05d8e2eef
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Dataset name: af5ef152-4c49-11ec-88db-27319b5e5fd4
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Optimisation Metric: accuracy
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Best validation score: 0.8596491228070176
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Number of target algorithm runs: 16
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f4bffcdb5b0>
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f4973f39bb0>
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{'accuracy': 0.8728323699421965}
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{'accuracy': 0.861271676300578}
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| | Preprocessing | Estimator | Weight |
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|---:|:--------------------------------------------------------------|:----------------------------------------------------------------|---------:|
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| 0 | None | RFLearner | 0.34 |
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| 1 | None | ETLearner | 0.26 |
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| 2 | None | LGBMLearner | 0.2 |
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| 3 | None | KNNLearner | 0.08 |
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| 4 | SimpleImputer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 5 | None | SVMLearner | 0.04 |
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| 6 | SimpleImputer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 0 | SimpleImputer,NoEncoder,Normalizer,PowerTransformer | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.26 |
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| 1 | None | KNNLearner | 0.14 |
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| 2 | SimpleImputer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.12 |
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| 3 | None | RFLearner | 0.1 |
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| 4 | SimpleImputer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
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| 5 | None | ETLearner | 0.08 |
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| 6 | None | SVMLearner | 0.08 |
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| 7 | SimpleImputer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
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| 8 | None | LGBMLearner | 0.04 |
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autoPyTorch results:
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Dataset name: c0472c05-4be2-11ec-877d-1fe05d8e2eef
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Dataset name: 18b4b156-4c4a-11ec-88db-27319b5e5fd4
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Optimisation Metric: accuracy
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Best validation score: 0.8596491228070176
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Number of target algorithm runs: 20
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Number of successful target algorithm runs: 13
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Number of crashed target algorithm runs: 6
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Number of successful target algorithm runs: 14
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Number of crashed target algorithm runs: 5
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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 57.148 seconds)
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**Total running time of the script:** ( 5 minutes 53.673 seconds)
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.. _sphx_glr_download_examples_40_advanced_example_custom_configuration_space.py:

‎development/_sources/examples/40_advanced/example_parallel_n_jobs.rst.txt‎

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{'accuracy': 0.8728323699421965}
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autoPyTorch results:
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Dataset name: 8cc9600d-4be1-11ec-877d-1fe05d8e2eef
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Dataset name: de71e0fc-4c48-11ec-88db-27319b5e5fd4
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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: 40
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Number of successful target algorithm runs: 31
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Number of crashed target algorithm runs: 6
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Number of target algorithm runs: 42
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Number of successful target algorithm runs: 32
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Number of crashed target algorithm runs: 7
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**Total running time of the script:** ( 5 minutes 34.761 seconds)
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**Total running time of the script:** ( 5 minutes 50.220 seconds)
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.. _sphx_glr_download_examples_40_advanced_example_parallel_n_jobs.py:

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