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Ravin Kohli: [ADD] Get incumbent config (#175)
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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/_images/sphx_glr_example_visualization_002.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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@@ -87,20 +87,18 @@ Image Classification
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Pipeline Random Config:
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________________________________________
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Configuration:
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image_augmenter:GaussianBlur:sigma_min, Value: 2.9545690656889394
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image_augmenter:GaussianBlur:sigma_offset, Value: 1.3774783139375384
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image_augmenter:GaussianBlur:use_augmenter, Value: True
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image_augmenter:GaussianNoise:sigma_offset, Value: 0.9690842972383632
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image_augmenter:GaussianBlur:use_augmenter, Value: False
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image_augmenter:GaussianNoise:sigma_offset, Value: 2.6558905363999727
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image_augmenter:GaussianNoise:use_augmenter, Value: True
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image_augmenter:RandomAffine:rotate, Value: 21
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image_augmenter:RandomAffine:scale_offset, Value: 0.3197301426317749
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image_augmenter:RandomAffine:shear, Value: 4
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image_augmenter:RandomAffine:translate_percent_offset, Value: 0.15940178952269865
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image_augmenter:RandomAffine:rotate, Value: 297
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image_augmenter:RandomAffine:scale_offset, Value: 0.10167631197409942
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image_augmenter:RandomAffine:shear, Value: 26
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image_augmenter:RandomAffine:translate_percent_offset, Value: 0.018225027710844
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image_augmenter:RandomAffine:use_augmenter, Value: True
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image_augmenter:RandomCutout:p, Value: 0.8357008862667328
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image_augmenter:RandomCutout:p, Value: 0.9504486965250005
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image_augmenter:RandomCutout:use_augmenter, Value: True
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image_augmenter:Resize:use_augmenter, Value: True
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.07711092922862206
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image_augmenter:Resize:use_augmenter, Value: False
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.48642929026494963
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normalizer:__choice__, Value: 'NoNormalizer'
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Fitting the pipeline...
@@ -180,7 +178,7 @@ Image Classification
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 8.111 seconds)
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**Total running time of the script:** ( 0 minutes 5.588 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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@@ -133,7 +133,7 @@ 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 0x7efbed0116d0>
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f0eb3815eb0>
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@@ -162,7 +162,7 @@ Print the final ensemble performance
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.. code-block:: none
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<smac.runhistory.runhistory.RunHistory object at 0x7efbed0212b0> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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<smac.runhistory.runhistory.RunHistory object at 0x7f0eb35b7790> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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data_loader:batch_size, Value: 64
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encoder:__choice__, Value: 'OneHotEncoder'
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feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -194,7 +194,7 @@ Print the final ensemble performance
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scaler:__choice__, Value: 'StandardScaler'
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trainer:StandardTrainer:weighted_loss, Value: True
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0013036727905273438, budget=0), TrajEntry(train_perf=0.17543859649122806, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0013079643249511719, budget=0), TrajEntry(train_perf=0.17543859649122806, incumbent_id=1, incumbent=Configuration:
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data_loader:batch_size, Value: 64
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encoder:__choice__, Value: 'OneHotEncoder'
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feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
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scaler:__choice__, Value: 'StandardScaler'
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trainer:StandardTrainer:weighted_loss, Value: True
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=1, ta_time_used=2.208378314971924, wallclock_time=3.245152473449707, budget=5.555555555555555), TrajEntry(train_perf=0.14619883040935677, incumbent_id=2, incumbent=Configuration:
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, ta_runs=1, ta_time_used=2.003859281539917, wallclock_time=3.0306620597839355, budget=5.555555555555555), TrajEntry(train_perf=0.14619883040935677, incumbent_id=2, incumbent=Configuration:
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data_loader:batch_size, Value: 54
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encoder:__choice__, Value: 'OneHotEncoder'
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feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -259,31 +259,26 @@ Print the final ensemble performance
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trainer:MixUpTrainer:alpha, Value: 0.8559230573827334
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trainer:MixUpTrainer:weighted_loss, Value: True
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trainer:__choice__, Value: 'MixUpTrainer'
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, ta_runs=18, ta_time_used=113.46635627746582, wallclock_time=169.39868116378784, budget=50.0)]
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{'accuracy': 0.8728323699421965}
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, ta_runs=18, ta_time_used=191.332772731781, wallclock_time=243.9774169921875, budget=50.0)]
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{'accuracy': 0.861271676300578}
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| | Preprocessing | Estimator | Weight |
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|---:|:------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
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| 0 | SimpleImputer,NoEncoder,Normalizer,Nystroem | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.12 |
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| 1 | None | CatBoostClassifier | 0.12 |
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| 2 | None | RFClassifier | 0.12 |
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| 3 | SimpleImputer,NoEncoder,NoScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
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| 4 | None | ExtraTreesClassifier | 0.1 |
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| 5 | None | KNNClassifier | 0.1 |
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| 6 | SimpleImputer,OneHotEncoder,StandardScaler,Nystroem | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
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| 7 | None | SVC | 0.08 |
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| 8 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 9 | SimpleImputer,OneHotEncoder,StandardScaler,Nystroem | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 10 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 11 | SimpleImputer,OneHotEncoder,StandardScaler,Nystroem | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 12 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 0 | SimpleImputer,NoEncoder,Normalizer,Nystroem | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.2 |
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| 1 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.2 |
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| 2 | None | CatBoostClassifier | 0.18 |
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| 3 | None | SVC | 0.18 |
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| 4 | None | RFClassifier | 0.08 |
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| 5 | None | ExtraTreesClassifier | 0.06 |
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| 6 | None | KNNClassifier | 0.06 |
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| 7 | SimpleImputer,OneHotEncoder,MinMaxScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 5 minutes 21.948 seconds)
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**Total running time of the script:** ( 5 minutes 19.640 seconds)
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.. _sphx_glr_download_examples_20_basics_example_tabular_classification.py:

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