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nabenabe0928: [fix] Update the SMAC version (#388)
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13 changes: 7 additions & 6 deletions development/_sources/examples/20_basics/example_image_classification.rst.txt
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Expand Up @@ -85,14 +85,15 @@ Image Classification
Pipeline Random Config:
________________________________________
Configuration(values={
'image_augmenter:GaussianBlur:sigma_min': 1.5935371448528923,
'image_augmenter:GaussianBlur:sigma_offset': 0.7844116418329459,
'image_augmenter:GaussianBlur:sigma_min': 2.068090231601102,
'image_augmenter:GaussianBlur:sigma_offset': 1.9329308760314452,
'image_augmenter:GaussianBlur:use_augmenter': True,
'image_augmenter:GaussianNoise:use_augmenter': False,
'image_augmenter:RandomAffine:use_augmenter': False,
'image_augmenter:RandomCutout:use_augmenter': False,
'image_augmenter:Resize:use_augmenter': True,
'image_augmenter:ZeroPadAndCrop:percent': 0.44360967675348173,
'image_augmenter:RandomCutout:p': 0.9739889046885781,
'image_augmenter:RandomCutout:use_augmenter': True,
'image_augmenter:Resize:use_augmenter': False,
'image_augmenter:ZeroPadAndCrop:percent': 0.27792641387037204,
'normalizer:__choice__': 'ImageNormalizer',
})

Expand Down Expand Up @@ -173,7 +174,7 @@ Image Classification
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 0 minutes 6.133 seconds)
**Total running time of the script:** ( 0 minutes 8.519 seconds)


.. _sphx_glr_download_examples_20_basics_example_image_classification.py:
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23 changes: 9 additions & 14 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 0x7fe3b78f6550>
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f5b9b54f7f0>
Expand Down Expand Up @@ -166,24 +166,19 @@ Print the final ensemble performance
.. code-block:: none
{'accuracy': 0.861271676300578}
| | Preprocessing | Estimator | Weight |
|---:|:-------------------------------------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
| 0 | None | CBLearner | 0.66 |
| 1 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,TruncSVD | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.16 |
| 2 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,StandardScaler,KernelPCA | embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
| 3 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,Normalizer,Nystroem | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
| 4 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,Normalizer,TruncSVD | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
| 5 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,StandardScaler,KernelPCA | embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
| 6 | None | RFLearner | 0.02 |
| 7 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
| | Preprocessing | Estimator | Weight |
|---:|:----------------------------------------------------------------------------------------|:-------------------------------------------------------------|---------:|
| 0 | None | CBLearner | 0.86 |
| 1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,QuantileTransformer,TruncSVD | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
| 2 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,Normalizer,Nystroem | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
autoPyTorch results:
Dataset name: Australian
Optimisation Metric: accuracy
Best validation score: 0.8713450292397661
Number of target algorithm runs: 22
Number of successful target algorithm runs: 20
Number of successful target algorithm runs: 19
Number of crashed target algorithm runs: 2
Number of target algorithms that exceeded the time limit: 0
Number of target algorithms that exceeded the time limit: 1
Number of target algorithms that exceeded the memory limit: 0
Expand All @@ -193,7 +188,7 @@ Print the final ensemble performance
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 5 minutes 31.550 seconds)
**Total running time of the script:** ( 5 minutes 19.116 seconds)


.. _sphx_glr_download_examples_20_basics_example_tabular_classification.py:
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24 changes: 13 additions & 11 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 0x7fe45362b700>
<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f5c373f5700>
Expand Down Expand Up @@ -159,19 +159,21 @@ Print the final ensemble performance

.. code-block:: none
{'r2': 0.9407884171054208}
{'r2': 0.9410530752009164}
| | Preprocessing | Estimator | Weight |
|---:|:-------------------------------------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
| 0 | None | CBLearner | 0.44 |
| 1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.42 |
| 2 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
| 3 | None | LGBMLearner | 0.04 |
| 0 | None | CBLearner | 0.46 |
| 1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.4 |
| 2 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
| 3 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,PolynomialFeatures | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
| 4 | None | LGBMLearner | 0.02 |
| 5 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
autoPyTorch results:
Dataset name: 074d83be-94f4-11ec-87fb-d5821471ed5e
Dataset name: be93974e-9646-11ec-87d9-dfb614d36051
Optimisation Metric: r2
Best validation score: 0.8670098636440993
Number of target algorithm runs: 24
Number of successful target algorithm runs: 23
Best validation score: 0.8669094525651709
Number of target algorithm runs: 23
Number of successful target algorithm runs: 22
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 All @@ -183,7 +185,7 @@ Print the final ensemble performance
.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 5 minutes 44.634 seconds)
**Total running time of the script:** ( 5 minutes 31.341 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
=================
**11:22.317** total execution time for **examples_20_basics** files:
**10:58.976** total execution time for **examples_20_basics** files:

+--------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:44.634 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:31.341 | 0.0 MB |
+--------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:31.550 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:19.116 | 0.0 MB |
+--------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:06.133 | 0.0 MB |
| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:08.519 | 0.0 MB |
+--------------------------------------------------------------------------------------------------------------+-----------+--------+

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