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Ravin Kohli: [ADD] variance thresholding (#373)
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@@ -140,6 +140,8 @@ <h1>Source code for autoPyTorch.pipeline.tabular_classification</h1><div class="
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<span class="kn">from</span> <span class="nn">autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.imputation.SimpleImputer</span> <span class="kn">import</span> <span class="n">SimpleImputer</span>
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<span class="kn">from</span> <span class="nn">autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.scaling</span> <span class="kn">import</span> <span class="n">ScalerChoice</span>
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<span class="kn">from</span> <span class="nn">autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.variance_thresholding.</span> \
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<span class="n">VarianceThreshold</span> <span class="kn">import</span> <span class="nn">VarianceThreshold</span>
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<span class="kn">from</span> <span class="nn">autoPyTorch.pipeline.components.setup.early_preprocessor.EarlyPreprocessing</span> <span class="kn">import</span> <span class="n">EarlyPreprocessing</span>
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<span class="kn">from</span> <span class="nn">autoPyTorch.pipeline.components.setup.lr_scheduler</span> <span class="kn">import</span> <span class="n">SchedulerChoice</span>
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<span class="kn">from</span> <span class="nn">autoPyTorch.pipeline.components.setup.network.base_network</span> <span class="kn">import</span> <span class="n">NetworkComponent</span>
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<span class="n">steps</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span>
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<span class="p">(</span><span class="s2">&quot;imputer&quot;</span><span class="p">,</span> <span class="n">SimpleImputer</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">)),</span>
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<span class="p">(</span><span class="s2">&quot;variance_threshold&quot;</span><span class="p">,</span> <span class="n">VarianceThreshold</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">)),</span>
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<span class="p">(</span><span class="s2">&quot;encoder&quot;</span><span class="p">,</span> <span class="n">EncoderChoice</span><span class="p">(</span><span class="n">default_dataset_properties</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">)),</span>
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<span class="p">(</span><span class="s2">&quot;scaler&quot;</span><span class="p">,</span> <span class="n">ScalerChoice</span><span class="p">(</span><span class="n">default_dataset_properties</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">)),</span>
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<span class="p">(</span><span class="s2">&quot;feature_preprocessor&quot;</span><span class="p">,</span> <span class="n">FeatureProprocessorChoice</span><span class="p">(</span><span class="n">default_dataset_properties</span><span class="p">,</span>

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<span class="kn">from</span> <span class="nn">autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.imputation.SimpleImputer</span> <span class="kn">import</span> <span class="n">SimpleImputer</span>
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<span class="kn">from</span> <span class="nn">autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.scaling</span> <span class="kn">import</span> <span class="n">ScalerChoice</span>
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<span class="kn">from</span> <span class="nn">autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.variance_thresholding.</span> \
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<span class="n">VarianceThreshold</span> <span class="kn">import</span> <span class="nn">VarianceThreshold</span>
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<span class="kn">from</span> <span class="nn">autoPyTorch.pipeline.components.setup.early_preprocessor.EarlyPreprocessing</span> <span class="kn">import</span> <span class="n">EarlyPreprocessing</span>
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<span class="kn">from</span> <span class="nn">autoPyTorch.pipeline.components.setup.lr_scheduler</span> <span class="kn">import</span> <span class="n">SchedulerChoice</span>
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<span class="kn">from</span> <span class="nn">autoPyTorch.pipeline.components.setup.network.base_network</span> <span class="kn">import</span> <span class="n">NetworkComponent</span>
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<span class="p">(</span><span class="s2">&quot;imputer&quot;</span><span class="p">,</span> <span class="n">SimpleImputer</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">)),</span>
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<span class="p">(</span><span class="s2">&quot;variance_threshold&quot;</span><span class="p">,</span> <span class="n">VarianceThreshold</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">)),</span>
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<span class="p">(</span><span class="s2">&quot;encoder&quot;</span><span class="p">,</span> <span class="n">EncoderChoice</span><span class="p">(</span><span class="n">default_dataset_properties</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">)),</span>
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<span class="p">(</span><span class="s2">&quot;scaler&quot;</span><span class="p">,</span> <span class="n">ScalerChoice</span><span class="p">(</span><span class="n">default_dataset_properties</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">)),</span>
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<span class="p">(</span><span class="s2">&quot;feature_preprocessor&quot;</span><span class="p">,</span> <span class="n">FeatureProprocessorChoice</span><span class="p">(</span><span class="n">default_dataset_properties</span><span class="p">,</span>

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‎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: 2.4981384751028837
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image_augmenter:GaussianBlur:sigma_offset, Value: 1.3265778731850062
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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:rotate, Value: 332
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image_augmenter:RandomAffine:scale_offset, Value: 0.20046574757452593
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image_augmenter:RandomAffine:shear, Value: 31
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image_augmenter:RandomAffine:translate_percent_offset, Value: 0.14608328354295247
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image_augmenter:RandomAffine:use_augmenter, Value: True
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image_augmenter:RandomCutout:p, Value: 0.2648052996462756
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image_augmenter:RandomCutout:use_augmenter, Value: True
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image_augmenter:Resize:use_augmenter, Value: False
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.17842798149849115
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normalizer:__choice__, Value: 'NoNormalizer'
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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: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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})
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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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NoNormalizer
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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 8.128 seconds)
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**Total running time of the script:** ( 0 minutes 6.799 seconds)
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.. _sphx_glr_download_examples_20_basics_example_image_classification.py:

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