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Francisco Rivera Valverde: [Refactor] Use the backend implementation from automl common (#185)
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‎development/.buildinfo‎

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# Sphinx build info version 1
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# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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config: fe944f1b1d3cb4e7269e41b4f8b98b0d
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config: 920b50cbcaae19cbf75b2f09049dd94a
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tags: 645f666f9bcd5a90fca523b33c5a78b7

‎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_visualization_001.png‎

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‎development/_images/sphx_glr_example_visualization_thumb.png‎

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‎development/_modules/autoPyTorch/api/tabular_classification.html‎

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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>autoPyTorch.api.tabular_classification &#8212; AutoPyTorch 0.1.0 documentation</title>
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<link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
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<link rel="stylesheet" href="../../../_static/bootstrap-sphinx.css" type="text/css" />
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<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
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<link rel="stylesheet" type="text/css" href="../../../_static/bootstrap-sphinx.css" />
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<link rel="stylesheet" type="text/css" href="../../../_static/gallery.css" />
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<link rel="stylesheet" type="text/css" href="../../../_static/gallery-binder.css" />
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<link rel="stylesheet" type="text/css" href="../../../_static/gallery-dataframe.css" />
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<link rel="stylesheet" type="text/css" href="../../../_static/gallery-rendered-html.css" />
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<script id="documentation_options" data-url_root="../../../" src="../../../_static/documentation_options.js"></script>
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<script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
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<script src="../../../_static/jquery.js"></script>
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<script src="../../../_static/underscore.js"></script>
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<script src="../../../_static/doctools.js"></script>
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<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
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<span class="kn">from</span> <span class="nn">autoPyTorch.api.base_task</span> <span class="kn">import</span> <span class="n">BaseTask</span>
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<span class="kn">from</span> <span class="nn">autoPyTorch.automl_common.common.utils.backend</span> <span class="kn">import</span> <span class="n">Backend</span>
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<span class="kn">from</span> <span class="nn">autoPyTorch.constants</span> <span class="kn">import</span> <span class="p">(</span>
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<span class="n">TABULAR_CLASSIFICATION</span><span class="p">,</span>
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<span class="n">TASK_TYPES_TO_STRING</span><span class="p">,</span>
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<span class="p">)</span>
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<span class="kn">from</span> <span class="nn">autoPyTorch.datasets.tabular_dataset</span> <span class="kn">import</span> <span class="n">TabularDataset</span>
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<span class="kn">from</span> <span class="nn">autoPyTorch.pipeline.tabular_classification</span> <span class="kn">import</span> <span class="n">TabularClassificationPipeline</span>
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<span class="kn">from</span> <span class="nn">autoPyTorch.utils.backend</span> <span class="kn">import</span> <span class="n">Backend</span>
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<span class="kn">from</span> <span class="nn">autoPyTorch.utils.hyperparameter_search_space_update</span> <span class="kn">import</span> <span class="n">HyperparameterSearchSpaceUpdates</span>
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</p>
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<p>
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&copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.5.4.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.0.1.<br/>
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</p>
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</div>
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</footer>

‎development/_modules/index.html‎

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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>Overview: module code &#8212; AutoPyTorch 0.1.0 documentation</title>
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<link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
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<link rel="stylesheet" href="../_static/bootstrap-sphinx.css" type="text/css" />
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<link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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<link rel="stylesheet" type="text/css" href="../_static/bootstrap-sphinx.css" />
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<link rel="stylesheet" type="text/css" href="../_static/gallery.css" />
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<link rel="stylesheet" type="text/css" href="../_static/gallery-binder.css" />
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<link rel="stylesheet" type="text/css" href="../_static/gallery-dataframe.css" />
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<link rel="stylesheet" type="text/css" href="../_static/gallery-rendered-html.css" />
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<script id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
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<script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
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<script src="../_static/jquery.js"></script>
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<script src="../_static/underscore.js"></script>
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<script src="../_static/doctools.js"></script>
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</p>
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<p>
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&copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.5.4.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.0.1.<br/>
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</footer>

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

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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: 1.7739059525100553
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image_augmenter:GaussianBlur:sigma_offset, Value: 1.596831438367099
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image_augmenter:GaussianBlur:sigma_min, Value: 1.9367108344066843
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image_augmenter:GaussianBlur:sigma_offset, Value: 0.33014268022985527
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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: 0.8069029436295518
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image_augmenter:RandomCutout: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: 0.13165420478079426
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.25002123480092114
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normalizer:__choice__, Value: 'ImageNormalizer'
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**Total running time of the script:** ( 0 minutes 10.175 seconds)
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**Total running time of the script:** ( 0 minutes 7.646 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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.. code-block:: none
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<smac.runhistory.runhistory.RunHistory object at 0x7f16fb6340d0> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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<smac.runhistory.runhistory.RunHistory object at 0x7f236f959190> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.001987457275390625, budget=0), TrajEntry(train_perf=0.16374269005847952, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0018346309661865234, budget=0), TrajEntry(train_perf=0.16374269005847952, incumbent_id=1, incumbent=Configuration:
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, ta_runs=1, ta_time_used=5.665798902511597, wallclock_time=7.321491241455078, budget=5.555555555555555), TrajEntry(train_perf=0.1578947368421053, incumbent_id=2, incumbent=Configuration:
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, ta_runs=1, ta_time_used=5.441782712936401, wallclock_time=7.060664176940918, budget=5.555555555555555), TrajEntry(train_perf=0.1578947368421053, incumbent_id=2, incumbent=Configuration:
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, ta_runs=8, ta_time_used=74.08682036399841, wallclock_time=91.81088304519653, budget=5.555555555555555), TrajEntry(train_perf=0.1578947368421053, incumbent_id=3, incumbent=Configuration:
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, ta_runs=11, ta_time_used=109.19088363647461, wallclock_time=136.7932755947113, budget=16.666666666666664)]
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, ta_runs=11, ta_time_used=96.91014266014099, wallclock_time=122.55177569389343, budget=16.666666666666664)]
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{'accuracy': 0.8670520231213873}
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| | Preprocessing | Estimator | Weight |
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|---:|:------------------------------------------------------------------|:-------------------------------------------------------------------|---------:|
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| 0 | None | RFClassifier | 0.24 |
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| 1 | None | CatBoostClassifier | 0.22 |
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| 2 | SimpleImputer,OneHotEncoder,MinMaxScaler,KernelPCA | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.16 |
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| 3 | None | ExtraTreesClassifier | 0.08 |
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| 4 | None | SVC | 0.08 |
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| 5 | SimpleImputer,OneHotEncoder,NoScaler,Nystroem | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 6 | SimpleImputer,OneHotEncoder,MinMaxScaler,KernelPCA | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 7 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 8 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 9 | None | LGBMClassifier | 0.02 |
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| 10 | None | KNNClassifier | 0.02 |
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| 0 | None | RFClassifier | 0.3 |
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| 1 | None | CatBoostClassifier | 0.24 |
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| 2 | SimpleImputer,OneHotEncoder,MinMaxScaler,KernelPCA | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 3 | None | SVC | 0.1 |
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| 4 | None | ExtraTreesClassifier | 0.08 |
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| 5 | SimpleImputer,OneHotEncoder,Normalizer,Nystroem | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 6 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 7 | SimpleImputer,OneHotEncoder,NoScaler,Nystroem | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 8 | None | LGBMClassifier | 0.02 |
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| 9 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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.. _sphx_glr_download_examples_20_basics_example_tabular_classification.py:

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