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nabenabe0928: [feat] Add __str__ to autoPyTorchEnum (#405)
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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/_sources/examples/20_basics/example_image_classification.rst.txt

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@@ -85,16 +85,17 @@ 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: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': 0.58899963145322,
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'image_augmenter:GaussianBlur:use_augmenter': False,
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'image_augmenter:GaussianNoise:use_augmenter': False,
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'image_augmenter:RandomAffine:rotate': 237,
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'image_augmenter:RandomAffine:scale_offset': 0.32734086551151986,
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'image_augmenter:RandomAffine:shear': 43,
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'image_augmenter:RandomAffine:translate_percent_offset': 0.2150833219469362,
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'image_augmenter:RandomAffine:use_augmenter': True,
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'image_augmenter:RandomCutout:p': 0.6425251463645631,
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'image_augmenter:RandomCutout:use_augmenter': True,
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'image_augmenter:Resize:use_augmenter': True,
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'image_augmenter:ZeroPadAndCrop:percent': 0.062191847988016136,
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'image_augmenter:Resize:use_augmenter': False,
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'image_augmenter:ZeroPadAndCrop:percent': 0.2638607299100123,
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'normalizer:__choice__': 'ImageNormalizer',
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})
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.. rst-class:: sphx-glr-timing
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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 0x7fdd8a34d640>
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7fcb49398130>
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**Total running time of the script:** ( 5 minutes 32.091 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 0x7fde27812430>
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<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7fcbe670c430>
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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: 0da2f587-9ae4-11ec-87e6-b1d9b8f13636
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Dataset name: 3f905480-9ba1-11ec-87e5-c7420ab3e9b2
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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: 22
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Number of target algorithm runs: 23
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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 time limit: 3
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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 49.239 seconds)
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**Total running time of the script:** ( 5 minutes 55.597 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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**11:26.018** total execution time for **examples_20_basics** files:
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**11:36.489** 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``) | 05:49.239 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:55.597 | 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``) | 05:27.922 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:32.091 | 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``) | 00:08.858 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:08.801 | 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 0x7fdd898c7e80>
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7fcb484bb1c0>
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| 3 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 4 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,RobustScaler,KitchenSink | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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autoPyTorch results:
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Dataset name: b63068fd-9ae7-11ec-87e6-b1d9b8f13636
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Dataset name: ee97fa70-9ba4-11ec-87e5-c7420ab3e9b2
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Optimisation Metric: accuracy
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Best validation score: 0.8654970760233918
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Number of target algorithm runs: 14
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Number of successful target algorithm runs: 13
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Number of successful target algorithm runs: 14
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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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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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.. code-block:: none
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7fdd88e7b580>
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7fcb48ec1df0>
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.. code-block:: none
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{'accuracy': 0.861271676300578}
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{'accuracy': 0.8728323699421965}
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| | Preprocessing | Estimator | Weight |
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|---:|:--------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------|---------:|
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| 0 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,QuantileTransformer,LinearSVC Preprocessor | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.6 |
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| 1 | None | CBLearner | 0.12 |
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| 2 | SimpleImputer,Variance Threshold,MinorityCoalescer,NoEncoder,StandardScaler,FeatureAgglomeration | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
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| 3 | None | RFLearner | 0.06 |
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| 4 | SimpleImputer,Variance Threshold,MinorityCoalescer,NoEncoder,QuantileTransformer,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 5 | None | KNNLearner | 0.04 |
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| 6 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 7 | None | SVMLearner | 0.02 |
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| 0 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,QuantileTransformer,LinearSVC Preprocessor | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.38 |
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| 1 | SimpleImputer,Variance Threshold,MinorityCoalescer,NoEncoder,StandardScaler,FeatureAgglomeration | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 2 | None | KNNLearner | 0.12 |
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| 3 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
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| 4 | None | ETLearner | 0.06 |
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| 5 | None | SVMLearner | 0.06 |
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| 6 | SimpleImputer,Variance Threshold,MinorityCoalescer,NoEncoder,QuantileTransformer,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 7 | None | LGBMLearner | 0.04 |
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| 8 | None | CBLearner | 0.04 |
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| 9 | None | RFLearner | 0.04 |
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Dataset name: 5633f366-9ba5-11ec-87e5-c7420ab3e9b2
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Optimisation Metric: accuracy
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Number of target algorithm runs: 15
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Number of target algorithm runs: 16
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Number of successful target algorithm runs: 14
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**Total running time of the script:** ( 5 minutes 44.992 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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Dataset name: c2e42c9d-9ae6-11ec-87e6-b1d9b8f13636
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Dataset name: fd089d7e-9ba3-11ec-87e5-c7420ab3e9b2
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Number of target algorithm runs: 36
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Number of successful target algorithm runs: 29
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Number of crashed target algorithm runs: 6
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Number of successful target algorithm runs: 31
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Number of crashed target algorithm runs: 7
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.. _sphx_glr_download_examples_40_advanced_example_parallel_n_jobs.py:

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

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.. _sphx_glr_download_examples_40_advanced_example_plot_over_time.py:

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

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Dataset name: bc693a22-9ba5-11ec-87e5-c7420ab3e9b2
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Optimisation Metric: accuracy
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Best validation score: 0.8654970760233918
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Number of target algorithm runs: 13
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Dataset name: f1a266fa-9ae8-11ec-87e6-b1d9b8f13636
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Dataset name: 2a965f67-9ba6-11ec-87e5-c7420ab3e9b2
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Dataset name: 6168cfba-9ae9-11ec-87e6-b1d9b8f13636
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Dataset name: 9e90662e-9ba6-11ec-87e5-c7420ab3e9b2
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Number of target algorithm runs: 13
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**Total running time of the script:** ( 9 minutes 8.712 seconds)
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.. _sphx_glr_download_examples_40_advanced_example_resampling_strategy.py:

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

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| 9 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 10 | 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: a21b5453-9ae5-11ec-87e6-b1d9b8f13636
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Dataset name: dad64c81-9ba2-11ec-87e5-c7420ab3e9b2
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Optimisation Metric: accuracy
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Best validation score: 0.8771929824561403
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.. _sphx_glr_download_examples_40_advanced_example_run_with_portfolio.py:

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