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nabenabe0928: [fix] Hotfix debug no training in simple intensifier (#370)
1 parent cc16148 commit dc473bb

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‎development/_downloads/83b132442a977189d27fd09560b2bd34/example_single_configuration.ipynb‎

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},
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"outputs": [],
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"source": [
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"pipeline, run_info, run_value, dataset = estimator.fit_pipeline(dataset=dataset,\n configuration=configuration,\n budget_type='epochs',\n budget=10,\n run_time_limit_secs=100\n )\n\n# The fit_pipeline command also returns a named tuple with the pipeline constraints\nprint(run_info)\n\n# The fit_pipeline command also returns a named tuple with train/test performance\nprint(run_value)\n\n# This object complies with Scikit-Learn Pipeline API.\n# https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html\nprint(pipeline.named_steps)"
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"pipeline, run_info, run_value, dataset = estimator.fit_pipeline(dataset=dataset,\n configuration=configuration,\n budget_type='epochs',\n budget=5,\n run_time_limit_secs=75\n )\n\n# The fit_pipeline command also returns a named tuple with the pipeline constraints\nprint(run_info)\n\n# The fit_pipeline command also returns a named tuple with train/test performance\nprint(run_value)\n\n# This object complies with Scikit-Learn Pipeline API.\n# https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html\nprint(pipeline.named_steps)"
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]
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}
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],

‎development/_downloads/b71f91b170d4a46b842cd9568511ced2/example_single_configuration.py‎

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pipeline, run_info, run_value, dataset = estimator.fit_pipeline(dataset=dataset,
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configuration=configuration,
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budget_type='epochs',
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budget=10,
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run_time_limit_secs=100
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budget=5,
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run_time_limit_secs=75
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)
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# The fit_pipeline command also returns a named tuple with the pipeline constraints

‎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_plot_over_time_thumb.png‎

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‎development/_images/sphx_glr_example_visualization_001.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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@@ -86,12 +86,16 @@ Image Classification
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________________________________________
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Configuration:
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image_augmenter:GaussianBlur:use_augmenter, Value: False
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image_augmenter:GaussianNoise:sigma_offset, Value: 1.1139913028820034
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image_augmenter:GaussianNoise:sigma_offset, Value: 2.6813471803623146
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image_augmenter:GaussianNoise:use_augmenter, Value: True
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image_augmenter:RandomAffine:use_augmenter, Value: False
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image_augmenter:RandomAffine:rotate, Value: 121
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image_augmenter:RandomAffine:scale_offset, Value: 0.001572340665842953
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image_augmenter:RandomAffine:shear, Value: 11
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image_augmenter:RandomAffine:translate_percent_offset, Value: 0.3794747658517839
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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: False
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.261983365182016
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.1087846184479066
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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 5.513 seconds)
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**Total running time of the script:** ( 0 minutes 5.177 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 0x7f01b0687f70>
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f4512b33a60>
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@@ -165,25 +165,27 @@ Print the final ensemble performance
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.. code-block:: none
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{'accuracy': 0.8670520231213873}
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| | Preprocessing | Estimator | Weight |
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|---:|:------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
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| 0 | SimpleImputer,OneHotEncoder,MinMaxScaler,PowerTransformer | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.16 |
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| 1 | SimpleImputer,OneHotEncoder,Normalizer,KernelPCA | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.16 |
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| 2 | SimpleImputer,NoEncoder,Normalizer,Nystroem | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 3 | None | CBLearner | 0.14 |
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| 4 | SimpleImputer,OneHotEncoder,Normalizer,PowerTransformer | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.12 |
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| 5 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.12 |
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| 6 | None | RFLearner | 0.06 |
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| 7 | SimpleImputer,OneHotEncoder,MinMaxScaler,TruncSVD | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 8 | None | SVMLearner | 0.04 |
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| 9 | None | KNNLearner | 0.02 |
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{'accuracy': 0.8554913294797688}
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| | Preprocessing | Estimator | Weight |
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|---:|:----------------------------------------------------------|:-------------------------------------------------------------------|---------:|
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| 0 | SimpleImputer,OneHotEncoder,Normalizer,KernelPCA | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.18 |
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| 1 | SimpleImputer,NoEncoder,Normalizer,KitchenSink | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 2 | None | RFLearner | 0.14 |
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| 3 | SimpleImputer,OneHotEncoder,Normalizer,KitchenSink | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
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| 4 | None | SVMLearner | 0.1 |
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| 5 | None | CBLearner | 0.08 |
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| 6 | None | KNNLearner | 0.08 |
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| 7 | None | ETLearner | 0.06 |
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| 8 | SimpleImputer,NoEncoder,Normalizer,Nystroem | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 9 | SimpleImputer,NoEncoder,Normalizer,KernelPCA | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 10 | SimpleImputer,OneHotEncoder,MinMaxScaler,PowerTransformer | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 11 | SimpleImputer,OneHotEncoder,MinMaxScaler,TruncSVD | 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: 26
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Number of successful target algorithm runs: 23
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Number of target algorithm runs: 30
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Number of successful target algorithm runs: 27
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Number of crashed target algorithm runs: 2
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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 22.339 seconds)
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**Total running time of the script:** ( 5 minutes 22.397 seconds)
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.. _sphx_glr_download_examples_20_basics_example_tabular_classification.py:

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