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Ravin Kohli: [ADD] scalers from autosklearn (#372)
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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_visualization_001.png

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‎development/_sources/examples/20_basics/example_image_classification.rst.txt

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@@ -85,24 +85,25 @@ 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': 2.5825628129058305,
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'image_augmenter:GaussianBlur:sigma_offset': 0.4548687619380444,
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'image_augmenter:GaussianBlur:use_augmenter': True,
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'image_augmenter:GaussianNoise:sigma_offset': 2.065404323877688,
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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:GaussianBlur:use_augmenter': False,
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'image_augmenter:GaussianNoise:use_augmenter': False,
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'image_augmenter:RandomAffine:rotate': 171,
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'image_augmenter:RandomAffine:scale_offset': 0.16968859827986923,
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'image_augmenter:RandomAffine:shear': 42,
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'image_augmenter:RandomAffine:translate_percent_offset': 0.0006590163048096454,
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'image_augmenter:RandomAffine:use_augmenter': True,
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'image_augmenter:RandomCutout:use_augmenter': False,
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'image_augmenter:Resize:use_augmenter': False,
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'image_augmenter:ZeroPadAndCrop:percent': 0.0806044230263594,
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'normalizer:__choice__': 'ImageNormalizer',
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'image_augmenter:Resize:use_augmenter': True,
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'image_augmenter:ZeroPadAndCrop:percent': 0.058124084899998096,
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'normalizer:__choice__': 'NoNormalizer',
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})
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Fitting the pipeline...
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________________________________________
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ImageClassificationPipeline
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________________________________________
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0-) normalizer:
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ImageNormalizer
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NoNormalizer
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1-) preprocessing:
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EarlyPreprocessing
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 6.799 seconds)
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**Total running time of the script:** ( 0 minutes 5.506 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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@@ -134,7 +134,7 @@ 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 0x7f94a2ebb6d0>
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f5f46714070>
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@@ -165,26 +165,33 @@ Print the final ensemble performance
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.. code-block:: none
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{'accuracy': 0.861271676300578}
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| | Preprocessing | Estimator | Weight |
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|---:|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------|---------:|
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| 0 | None | CBLearner | 0.24 |
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| 1 | None | KNNLearner | 0.16 |
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| 2 | SimpleImputer,Variance Threshold,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 3 | SimpleImputer,Variance Threshold,OneHotEncoder,MinMaxScaler,PowerTransformer | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.12 |
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| 4 | SimpleImputer,Variance Threshold,OneHotEncoder,Normalizer,PowerTransformer | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
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| 5 | None | SVMLearner | 0.08 |
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| 6 | SimpleImputer,Variance Threshold,NoEncoder,NoScaler,TruncSVD | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 7 | None | RFLearner | 0.06 |
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| 8 | None | ETLearner | 0.02 |
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| 9 | SimpleImputer,Variance Threshold,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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{'accuracy': 0.8497109826589595}
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| | Preprocessing | Estimator | Weight |
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|---:|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------|---------:|
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| 0 | SimpleImputer,Variance Threshold,NoEncoder,PowerTransformer,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 1 | SimpleImputer,Variance Threshold,NoEncoder,MinMaxScaler,KitchenSink | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.12 |
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| 2 | None | CBLearner | 0.12 |
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| 3 | None | SVMLearner | 0.12 |
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| 4 | None | RFLearner | 0.08 |
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| 5 | SimpleImputer,Variance Threshold,NoEncoder,MinMaxScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 6 | None | KNNLearner | 0.06 |
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| 7 | SimpleImputer,Variance Threshold,OneHotEncoder,QuantileTransformer,PolynomialFeatures | embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 8 | SimpleImputer,Variance Threshold,OneHotEncoder,MinMaxScaler,PolynomialFeatures | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 9 | SimpleImputer,Variance Threshold,OneHotEncoder,NoScaler,PolynomialFeatures | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 10 | None | LGBMLearner | 0.04 |
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| 11 | None | ETLearner | 0.04 |
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| 12 | SimpleImputer,Variance Threshold,OneHotEncoder,NoScaler,PolynomialFeatures | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 13 | SimpleImputer,Variance Threshold,OneHotEncoder,QuantileTransformer,PolynomialFeatures | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 14 | SimpleImputer,Variance Threshold,NoEncoder,PowerTransformer,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 15 | SimpleImputer,Variance Threshold,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 16 | SimpleImputer,Variance Threshold,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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autoPyTorch results:
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Dataset name: Australian
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Optimisation Metric: accuracy
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Best validation score: 0.8713450292397661
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Number of target algorithm runs: 29
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Number of successful target algorithm runs: 26
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Number of crashed target algorithm runs: 2
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Number of target algorithm runs: 26
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Number of successful target algorithm runs: 24
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Number of crashed target algorithm runs: 1
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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 memory limit: 0
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 5 minutes 23.687 seconds)
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**Total running time of the script:** ( 5 minutes 22.134 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 0x7f953db27760>
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<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f5fe1484d90>
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.. code-block:: none
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{'r2': 0.9407884171054208}
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| | Preprocessing | Estimator | Weight |
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|---:|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
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| 0 | None | CBLearner | 0.44 |
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| 1 | SimpleImputer,Variance Threshold,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.42 |
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| 2 | SimpleImputer,Variance Threshold,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
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| 3 | None | LGBMLearner | 0.04 |
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{'r2': 0.9408102126984811}
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| | Preprocessing | Estimator | Weight |
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|---:|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------|---------:|
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| 0 | None | CBLearner | 0.44 |
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| 1 | SimpleImputer,Variance Threshold,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.44 |
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| 2 | SimpleImputer,Variance Threshold,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 3 | SimpleImputer,Variance Threshold,NoEncoder,RobustScaler,NoFeaturePreprocessing | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 4 | SimpleImputer,Variance Threshold,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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autoPyTorch results:
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Dataset name: 1b326a77-898e-11ec-87f5-056c45306849
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Dataset name: 41e1340b-8997-11ec-881e-d58abe4e22c6
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Optimisation Metric: r2
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Best validation score: 0.8670098636440993
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Number of target algorithm runs: 28
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Number of successful target algorithm runs: 24
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Number of crashed target algorithm runs: 3
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Number of target algorithm runs: 24
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Number of successful target algorithm runs: 23
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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 memory limit: 0
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**Total running time of the script:** ( 5 minutes 30.214 seconds)
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**Total running time of the script:** ( 5 minutes 30.224 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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**11:00.701** total execution time for **examples_20_basics** files:
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**10:57.864** 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``) | 05:30.214 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:30.224 | 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``) | 05:23.687 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:22.134 | 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``) | 00:06.799 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:05.506 | 0.0 MB |
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+--------------------------------------------------------------------------------------------------------------+-----------+--------+

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