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Ravin Kohli: handle nans in categorical columns (#118)
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‎refactor_development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip‎

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‎refactor_development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip‎

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‎refactor_development/_sources/examples/example_image_classification.rst.txt‎

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@@ -77,15 +77,19 @@ 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: 0.8480354370981031
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image_augmenter:GaussianBlur:sigma_offset, Value: 1.8662941407999276
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image_augmenter:GaussianBlur:sigma_min, Value: 1.0710079708867957
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image_augmenter:GaussianBlur:sigma_offset, Value: 0.5426415121176082
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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:p, Value: 0.3656551312711466
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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: 0.4929042351991033
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image_augmenter:GaussianNoise:sigma_offset, Value: 1.40212397459401
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image_augmenter:GaussianNoise:use_augmenter, Value: True
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image_augmenter:RandomAffine:rotate, Value: 281
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image_augmenter:RandomAffine:scale_offset, Value: 0.042566838354111396
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image_augmenter:RandomAffine:shear, Value: 6
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image_augmenter:RandomAffine:translate_percent_offset, Value: 0.06401515304310679
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image_augmenter:RandomAffine:use_augmenter, Value: True
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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: 0.45960159661876177
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normalizer:__choice__, Value: 'NoNormalizer'
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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 8.334 seconds)
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**Total running time of the script:** ( 0 minutes 6.047 seconds)
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.. _sphx_glr_download_examples_example_image_classification.py:

‎refactor_development/_sources/examples/example_tabular_classification.rst.txt‎

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@@ -36,7 +36,7 @@ with AutoPyTorch
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.. code-block:: none
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<smac.runhistory.runhistory.RunHistory object at 0x7f66907c4640> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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<smac.runhistory.runhistory.RunHistory object at 0x7fec89f16190> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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data_loader:batch_size, Value: 32
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encoder:__choice__, Value: 'OneHotEncoder'
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feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -67,7 +67,7 @@ with AutoPyTorch
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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=0.002040863037109375, budget=0), TrajEntry(train_perf=0.14619883040935677, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0020265579223632812, budget=0), TrajEntry(train_perf=0.1871345029239766, incumbent_id=1, incumbent=Configuration:
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data_loader:batch_size, Value: 32
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encoder:__choice__, Value: 'OneHotEncoder'
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feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -98,21 +98,19 @@ with AutoPyTorch
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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=4.407814264297485, wallclock_time=5.844383239746094, budget=5.555555555555555)]
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{'accuracy': 0.861271676300578}
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, ta_runs=1, ta_time_used=5.460636854171753, wallclock_time=6.8977086544036865, budget=5.555555555555555)]
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{'accuracy': 0.8728323699421965}
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| | Preprocessing | Estimator | Weight |
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|---:|:------------------------------------------------------------------|:------------------------------------------------------|---------:|
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| 0 | None | RFClassifier | 0.26 |
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| 1 | None | ExtraTreesClassifier | 0.22 |
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| 2 | SimpleImputer,OrdinalEncoder,NoScaler,Nystroem | ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 3 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 4 | SimpleImputer,OrdinalEncoder,Normalizer,PowerTransformer | MLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 5 | None | SVC | 0.06 |
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| 6 | None | KNNClassifier | 0.04 |
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| 7 | SimpleImputer,OrdinalEncoder,Normalizer,PowerTransformer | MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 8 | SimpleImputer,OrdinalEncoder,MinMaxScaler,KitchenSink | ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 9 | SimpleImputer,OrdinalEncoder,MinMaxScaler,KitchenSink | ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 10 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 0 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.22 |
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| 1 | SimpleImputer,OrdinalEncoder,Normalizer,PowerTransformer | MLPBackbone,FullyConnectedHead,nn.Sequential | 0.2 |
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| 2 | SimpleImputer,OrdinalEncoder,NoScaler,Nystroem | ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.2 |
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| 3 | None | RFClassifier | 0.14 |
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| 4 | None | ExtraTreesClassifier | 0.1 |
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| 5 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 6 | SimpleImputer,OneHotEncoder,MinMaxScaler,PowerTransformer | ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 7 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 8 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 9 minutes 9.069 seconds)
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**Total running time of the script:** ( 9 minutes 26.880 seconds)
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.. _sphx_glr_download_examples_example_tabular_classification.py:

‎refactor_development/_sources/examples/example_tabular_regression.rst.txt‎

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@@ -36,7 +36,7 @@ with AutoPyTorch
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.. code-block:: none
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<smac.runhistory.runhistory.RunHistory object at 0x7f66821e6b50> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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<smac.runhistory.runhistory.RunHistory object at 0x7fec729332b0> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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data_loader:batch_size, Value: 32
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encoder:__choice__, Value: 'NoEncoder'
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imputer:numerical_strategy, Value: 'mean'
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optimizer:__choice__, Value: 'AdamOptimizer'
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scaler:__choice__, Value: 'StandardScaler'
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0017652511596679688, budget=0), TrajEntry(train_perf=0.005070456150329972, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0020189285278320312, budget=0), TrajEntry(train_perf=0.001744201612121854, incumbent_id=1, incumbent=Configuration:
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data_loader:batch_size, Value: 32
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encoder:__choice__, Value: 'NoEncoder'
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imputer:numerical_strategy, Value: 'mean'
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optimizer:__choice__, Value: 'AdamOptimizer'
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scaler:__choice__, Value: 'StandardScaler'
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=1, ta_time_used=7.408640384674072, wallclock_time=10.797286748886108, budget=5.555555555555555)]
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{'r2': 0.9996077661516851}
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, ta_runs=1, ta_time_used=9.655506134033203, wallclock_time=13.354029893875122, budget=5.555555555555555)]
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{'r2': 0.999559711239828}
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 8 minutes 28.738 seconds)
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**Total running time of the script:** ( 8 minutes 38.433 seconds)
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.. _sphx_glr_download_examples_example_tabular_regression.py:

‎refactor_development/_sources/examples/sg_execution_times.rst.txt‎

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@@ -5,12 +5,12 @@
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Computation times
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=================
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**17:46.141** total execution time for **examples** files:
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**18:11.360** total execution time for **examples** files:
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+----------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_example_tabular_classification.py` (``example_tabular_classification.py``) | 09:09.069 | 0.0 MB |
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| :ref:`sphx_glr_examples_example_tabular_classification.py` (``example_tabular_classification.py``) | 09:26.880 | 0.0 MB |
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+----------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_example_tabular_regression.py` (``example_tabular_regression.py``) | 08:28.738 | 0.0 MB |
13+
| :ref:`sphx_glr_examples_example_tabular_regression.py` (``example_tabular_regression.py``) | 08:38.433 | 0.0 MB |
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+----------------------------------------------------------------------------------------------------+-----------+--------+
15-
| :ref:`sphx_glr_examples_example_image_classification.py` (``example_image_classification.py``) | 00:08.334 | 0.0 MB |
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| :ref:`sphx_glr_examples_example_image_classification.py` (``example_image_classification.py``) | 00:06.047 | 0.0 MB |
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+----------------------------------------------------------------------------------------------------+-----------+--------+

‎refactor_development/examples/example_image_classification.html‎

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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: 0.8480354370981031
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image_augmenter:GaussianBlur:sigma_offset, Value: 1.8662941407999276
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image_augmenter:GaussianBlur:sigma_min, Value: 1.0710079708867957
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image_augmenter:GaussianBlur:sigma_offset, Value: 0.5426415121176082
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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:p, Value: 0.3656551312711466
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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: 0.4929042351991033
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image_augmenter:GaussianNoise:sigma_offset, Value: 1.40212397459401
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image_augmenter:GaussianNoise:use_augmenter, Value: True
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image_augmenter:RandomAffine:rotate, Value: 281
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image_augmenter:RandomAffine:scale_offset, Value: 0.042566838354111396
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image_augmenter:RandomAffine:shear, Value: 6
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image_augmenter:RandomAffine:translate_percent_offset, Value: 0.06401515304310679
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image_augmenter:RandomAffine:use_augmenter, Value: True
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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: 0.45960159661876177
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normalizer:__choice__, Value: &#39;NoNormalizer&#39;
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Fitting the pipeline...
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<span class="nb">print</span><span class="p">(</span><span class="n">pipeline</span><span class="p">)</span>
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</pre></div>
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</div>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 8.334 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 6.047 seconds)</p>
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<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-example-image-classification-py">
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<div class="binder-badge docutils container">
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<a class="reference external image-reference" href="https://mybinder.org/v2/gh/automl/Auto-PyTorch/refactor_development?urlpath=lab/tree/notebooks/examples/example_image_classification.ipynb"><img alt="Launch binder" src="../_images/binder_badge_logo.svg" width="150px" /></a>

‎refactor_development/examples/example_tabular_classification.html‎

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<p>The following example shows how to fit a sample classification model
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with AutoPyTorch</p>
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<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;smac.runhistory.runhistory.RunHistory object at 0x7f66907c4640&gt; [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;smac.runhistory.runhistory.RunHistory object at 0x7fec89f16190&gt; [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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data_loader:batch_size, Value: 32
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encoder:__choice__, Value: &#39;OneHotEncoder&#39;
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feature_preprocessor:__choice__, Value: &#39;NoFeaturePreprocessor&#39;
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scaler:__choice__, Value: &#39;StandardScaler&#39;
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trainer:StandardTrainer:weighted_loss, Value: True
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trainer:__choice__, Value: &#39;StandardTrainer&#39;
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.002040863037109375, budget=0), TrajEntry(train_perf=0.14619883040935677, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0020265579223632812, budget=0), TrajEntry(train_perf=0.1871345029239766, incumbent_id=1, incumbent=Configuration:
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data_loader:batch_size, Value: 32
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encoder:__choice__, Value: &#39;OneHotEncoder&#39;
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feature_preprocessor:__choice__, Value: &#39;NoFeaturePreprocessor&#39;
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scaler:__choice__, Value: &#39;StandardScaler&#39;
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trainer:StandardTrainer:weighted_loss, Value: True
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trainer:__choice__, Value: &#39;StandardTrainer&#39;
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, ta_runs=1, ta_time_used=4.407814264297485, wallclock_time=5.844383239746094, budget=5.555555555555555)]
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{&#39;accuracy&#39;: 0.861271676300578}
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, ta_runs=1, ta_time_used=5.460636854171753, wallclock_time=6.8977086544036865, budget=5.555555555555555)]
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{&#39;accuracy&#39;: 0.8728323699421965}
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| | Preprocessing | Estimator | Weight |
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|---:|:------------------------------------------------------------------|:------------------------------------------------------|---------:|
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| 0 | None | RFClassifier | 0.26 |
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| 1 | None | ExtraTreesClassifier | 0.22 |
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| 2 | SimpleImputer,OrdinalEncoder,NoScaler,Nystroem | ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 3 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 4 | SimpleImputer,OrdinalEncoder,Normalizer,PowerTransformer | MLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 5 | None | SVC | 0.06 |
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| 6 | None | KNNClassifier | 0.04 |
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| 7 | SimpleImputer,OrdinalEncoder,Normalizer,PowerTransformer | MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 8 | SimpleImputer,OrdinalEncoder,MinMaxScaler,KitchenSink | ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 9 | SimpleImputer,OrdinalEncoder,MinMaxScaler,KitchenSink | ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 10 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 0 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.22 |
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| 1 | SimpleImputer,OrdinalEncoder,Normalizer,PowerTransformer | MLPBackbone,FullyConnectedHead,nn.Sequential | 0.2 |
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| 2 | SimpleImputer,OrdinalEncoder,NoScaler,Nystroem | ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.2 |
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| 3 | None | RFClassifier | 0.14 |
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| 4 | None | ExtraTreesClassifier | 0.1 |
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| 5 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 6 | SimpleImputer,OneHotEncoder,MinMaxScaler,PowerTransformer | ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 7 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 8 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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</pre></div>
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@@ -283,7 +281,7 @@
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<span class="nb">print</span><span class="p">(</span><span class="n">api</span><span class="o">.</span><span class="n">show_models</span><span class="p">())</span>
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</pre></div>
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</div>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 9 minutes 9.069 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 9 minutes 26.880 seconds)</p>
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<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-example-tabular-classification-py">
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<div class="binder-badge docutils container">
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<a class="reference external image-reference" href="https://mybinder.org/v2/gh/automl/Auto-PyTorch/refactor_development?urlpath=lab/tree/notebooks/examples/example_tabular_classification.ipynb"><img alt="Launch binder" src="../_images/binder_badge_logo.svg" width="150px" /></a>

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