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dengdifan: fix dist twine check for github (automl#439)
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16 changes: 7 additions & 9 deletions development/_sources/examples/20_basics/example_image_classification.rst.txt
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Expand Up @@ -85,17 +85,15 @@ Image Classification
Pipeline Random Config:
________________________________________
Configuration(values={
'image_augmenter:GaussianBlur:use_augmenter': False,
'image_augmenter:GaussianBlur:sigma_min': 1.321282449895124,
'image_augmenter:GaussianBlur:sigma_offset': 1.170627862969532,
'image_augmenter:GaussianBlur:use_augmenter': True,
'image_augmenter:GaussianNoise:use_augmenter': False,
'image_augmenter:RandomAffine:rotate': 25,
'image_augmenter:RandomAffine:scale_offset': 0.17911084673615535,
'image_augmenter:RandomAffine:shear': 29,
'image_augmenter:RandomAffine:translate_percent_offset': 0.2595162834252603,
'image_augmenter:RandomAffine:use_augmenter': True,
'image_augmenter:RandomCutout:p': 0.41657143992832846,
'image_augmenter:RandomAffine:use_augmenter': False,
'image_augmenter:RandomCutout:p': 0.924326689627855,
'image_augmenter:RandomCutout:use_augmenter': True,
'image_augmenter:Resize:use_augmenter': False,
'image_augmenter:ZeroPadAndCrop:percent': 0.2504071930090588,
'image_augmenter:ZeroPadAndCrop:percent': 0.2936727016383677,
'normalizer:__choice__': 'NoNormalizer',
})

Expand Down Expand Up @@ -176,7 +174,7 @@ Image Classification
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 0 minutes 5.591 seconds)
**Total running time of the script:** ( 0 minutes 5.420 seconds)


.. _sphx_glr_download_examples_20_basics_example_image_classification.py:
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24 changes: 13 additions & 11 deletions development/_sources/examples/20_basics/example_tabular_classification.rst.txt
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Expand Up @@ -134,7 +134,7 @@ Search for an ensemble of machine learning algorithms
.. code-block:: none
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7fdc1b398100>
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f41d91834f0>
Expand Down Expand Up @@ -165,23 +165,25 @@ Print the final ensemble performance

.. code-block:: none
{'accuracy': 0.8670520231213873}
{'accuracy': 0.861271676300578}
| | Preprocessing | Estimator | Weight |
|---:|:-------------------------------------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
| 0 | None | CBLearner | 0.32 |
| 1 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,QuantileTransformer,KitchenSink | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.2 |
| 2 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,SRC | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.2 |
| 3 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,NoScaler,KitchenSink | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.12 |
| 4 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,QuantileTransformer,KitchenSink | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
| 5 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,QuantileTransformer,KitchenSink | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
| 0 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,SRC | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.38 |
| 1 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,NoScaler,KitchenSink | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.18 |
| 2 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,QuantileTransformer,KitchenSink | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.16 |
| 3 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
| 4 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,NoScaler,KitchenSink | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
| 5 | None | SVMLearner | 0.04 |
| 6 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,NoScaler,KitchenSink | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
| 7 | None | CBLearner | 0.02 |
autoPyTorch results:
Dataset name: Australian
Optimisation Metric: accuracy
Best validation score: 0.8713450292397661
Number of target algorithm runs: 21
Number of target algorithm runs: 22
Number of successful target algorithm runs: 19
Number of crashed target algorithm runs: 0
Number of target algorithms that exceeded the time limit: 2
Number of target algorithms that exceeded the time limit: 3
Number of target algorithms that exceeded the memory limit: 0
Expand All @@ -191,7 +193,7 @@ Print the final ensemble performance
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 5 minutes 20.003 seconds)
**Total running time of the script:** ( 5 minutes 29.291 seconds)


.. _sphx_glr_download_examples_20_basics_example_tabular_classification.py:
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6 changes: 3 additions & 3 deletions development/_sources/examples/20_basics/example_tabular_regression.rst.txt
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Expand Up @@ -125,7 +125,7 @@ Search for an ensemble of machine learning algorithms
.. code-block:: none
<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7fdca57c8b50>
<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f426a157cd0>
Expand Down Expand Up @@ -167,7 +167,7 @@ Print the final ensemble performance
| 2 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
| 3 | None | LGBMLearner | 0.04 |
autoPyTorch results:
Dataset name: 7a5ffe66-f075-11ec-8806-a30cbc8a0bb8
Dataset name: 3828ee5f-f23e-11ec-87fd-dbbaad031c38
Optimisation Metric: r2
Best validation score: 0.8670098636440993
Number of target algorithm runs: 24
Expand All @@ -183,7 +183,7 @@ Print the final ensemble performance
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 5 minutes 35.570 seconds)
**Total running time of the script:** ( 5 minutes 32.769 seconds)


.. _sphx_glr_download_examples_20_basics_example_tabular_regression.py:
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8 changes: 4 additions & 4 deletions development/_sources/examples/20_basics/sg_execution_times.rst.txt
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Expand Up @@ -5,12 +5,12 @@

Computation times
=================
**11:01.164** total execution time for **examples_20_basics** files:
**11:07.480** total execution time for **examples_20_basics** files:

+--------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:35.570 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:32.769 | 0.0 MB |
+--------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:20.003 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:29.291 | 0.0 MB |
+--------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:05.591 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:05.420 | 0.0 MB |
+--------------------------------------------------------------------------------------------------------------+-----------+--------+
70 changes: 37 additions & 33 deletions ...opment/_sources/examples/40_advanced/example_custom_configuration_space.rst.txt
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Expand Up @@ -163,7 +163,7 @@ Search for an ensemble of machine learning algorithms
.. code-block:: none
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7fdc1a971220>
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f41d811c160>
Expand Down Expand Up @@ -194,25 +194,28 @@ Print the final ensemble performance

.. code-block:: none
{'accuracy': 0.8728323699421965}
{'accuracy': 0.861271676300578}
| | Preprocessing | Estimator | Weight |
|---:|:-----------------------------------------------------------------------------------------------------|:-------------------------------------------------------------|---------:|
| 0 | None | LGBMLearner | 0.26 |
| 1 | None | RFLearner | 0.22 |
| 2 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,RobustScaler,LinearSVC Preprocessor | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.18 |
| 3 | None | ETLearner | 0.14 |
| 4 | None | SVMLearner | 0.08 |
| 5 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
| 6 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,FastICA | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
| 7 | None | KNNLearner | 0.02 |
| 8 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
| 9 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,RobustScaler,KitchenSink | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
| 0 | None | CBLearner | 0.32 |
| 1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.12 |
| 2 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,Normalizer,PCA | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
| 3 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
| 4 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,RobustScaler,LinearSVC Preprocessor | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
| 5 | None | ETLearner | 0.06 |
| 6 | None | SVMLearner | 0.06 |
| 7 | None | KNNLearner | 0.06 |
| 8 | None | LGBMLearner | 0.04 |
| 9 | None | RFLearner | 0.04 |
| 10 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,MinMaxScaler,FastICA | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
| 11 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
| 12 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,RobustScaler,KitchenSink | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
autoPyTorch results:
Dataset name: ecd73cc9-f078-11ec-8806-a30cbc8a0bb8
Dataset name: b0494da8-f241-11ec-87fd-dbbaad031c38
Optimisation Metric: accuracy
Best validation score: 0.8596491228070176
Number of target algorithm runs: 17
Number of successful target algorithm runs: 16
Best validation score: 0.8654970760233918
Number of target algorithm runs: 18
Number of successful target algorithm runs: 17
Number of crashed target algorithm runs: 0
Number of target algorithms that exceeded the time limit: 1
Number of target algorithms that exceeded the memory limit: 0
Expand Down Expand Up @@ -273,7 +276,7 @@ Search for an ensemble of machine learning algorithms
.. code-block:: none
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7fdc194c6ac0>
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f41d757dd60>
Expand Down Expand Up @@ -303,23 +306,24 @@ Print the final ensemble performance

.. code-block:: none
{'accuracy': 0.8786127167630058}
| | Preprocessing | Estimator | Weight |
|---:|:--------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
| 0 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,PowerTransformer,Nystroem | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.4 |
| 1 | None | ETLearner | 0.26 |
| 2 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,QuantileTransformer,LinearSVC Preprocessor | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.16 |
| 3 | None | RFLearner | 0.1 |
| 4 | SimpleImputer,Variance Threshold,MinorityCoalescer,NoEncoder,QuantileTransformer,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
| 5 | None | LGBMLearner | 0.02 |
| 6 | None | SVMLearner | 0.02 |
| 7 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
{'accuracy': 0.8554913294797688}
| | Preprocessing | Estimator | Weight |
|---:|:--------------------------------------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
| 0 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,PowerTransformer,Nystroem | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.42 |
| 1 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,QuantileTransformer,LinearSVC Preprocessor | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.36 |
| 2 | None | CBLearner | 0.06 |
| 3 | None | KNNLearner | 0.04 |
| 4 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
| 5 | None | RFLearner | 0.02 |
| 6 | None | ETLearner | 0.02 |
| 7 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
| 8 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
autoPyTorch results:
Dataset name: 54da1919-f079-11ec-8806-a30cbc8a0bb8
Dataset name: 1b2f2066-f242-11ec-87fd-dbbaad031c38
Optimisation Metric: accuracy
Best validation score: 0.8596491228070176
Number of target algorithm runs: 17
Number of successful target algorithm runs: 17
Best validation score: 0.8654970760233918
Number of target algorithm runs: 18
Number of successful target algorithm runs: 18
Number of crashed target algorithm runs: 0
Number of target algorithms that exceeded the time limit: 0
Number of target algorithms that exceeded the memory limit: 0
Expand All @@ -331,7 +335,7 @@ Print the final ensemble performance
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 5 minutes 43.078 seconds)
**Total running time of the script:** ( 5 minutes 45.518 seconds)


.. _sphx_glr_download_examples_40_advanced_example_custom_configuration_space.py:
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