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Francisco Rivera Valverde: FIX_123 (#133)
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‎refactor_development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

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‎refactor_development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip

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‎refactor_development/_sources/examples/example_image_classification.rst.txt

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@@ -81,20 +81,27 @@ Image Classification
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Pipeline Random Config:
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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:use_augmenter, Value: False
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image_augmenter:RandomAffine:use_augmenter, Value: False
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image_augmenter:GaussianBlur:sigma_min, Value: 0.7923318281094287
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image_augmenter:GaussianBlur:sigma_offset, Value: 0.5630460313493758
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image_augmenter:GaussianBlur:use_augmenter, Value: True
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image_augmenter:GaussianNoise:sigma_offset, Value: 0.5913000614764757
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image_augmenter:GaussianNoise:use_augmenter, Value: True
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image_augmenter:RandomAffine:rotate, Value: 100
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image_augmenter:RandomAffine:scale_offset, Value: 0.21422298475725798
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image_augmenter:RandomAffine:shear, Value: 3
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image_augmenter:RandomAffine:translate_percent_offset, Value: 0.15943617036310528
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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: True
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.208578408262465
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normalizer:__choice__, Value: 'NoNormalizer'
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.11241231994734702
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normalizer:__choice__, Value: 'ImageNormalizer'
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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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ImageNormalizer
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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 9.911 seconds)
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**Total running time of the script:** ( 0 minutes 9.403 seconds)
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.. _sphx_glr_download_examples_example_image_classification.py:

‎refactor_development/_sources/examples/example_tabular_classification.rst.txt

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@@ -36,7 +36,7 @@ with AutoPyTorch
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.. code-block:: none
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<smac.runhistory.runhistory.RunHistory object at 0x7f983433d160> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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<smac.runhistory.runhistory.RunHistory object at 0x7fe9245bcd60> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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data_loader:batch_size, Value: 32
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encoder:__choice__, Value: 'OneHotEncoder'
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feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
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scaler:__choice__, Value: 'StandardScaler'
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trainer:StandardTrainer:weighted_loss, Value: True
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.00208282470703125, budget=0), TrajEntry(train_perf=0.16959064327485385, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0018763542175292969, budget=0), TrajEntry(train_perf=0.15204678362573099, incumbent_id=1, incumbent=Configuration:
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data_loader:batch_size, Value: 32
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encoder:__choice__, Value: 'OneHotEncoder'
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feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
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scaler:__choice__, Value: 'StandardScaler'
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trainer:StandardTrainer:weighted_loss, Value: True
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=1, ta_time_used=4.665428161621094, wallclock_time=6.140820026397705, budget=5.555555555555555), TrajEntry(train_perf=0.14619883040935677, incumbent_id=2, incumbent=Configuration:
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, ta_runs=1, ta_time_used=4.428625822067261, wallclock_time=5.859556198120117, budget=5.555555555555555), TrajEntry(train_perf=0.11695906432748537, incumbent_id=2, incumbent=Configuration:
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data_loader:batch_size, Value: 224
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encoder:__choice__, Value: 'OneHotEncoder'
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feature_preprocessor:KernelPCA:gamma, Value: 0.6217858094449208
@@ -151,19 +151,19 @@ with AutoPyTorch
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trainer:MixUpTrainer:alpha, Value: 0.7490557199071863
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trainer:MixUpTrainer:weighted_loss, Value: False
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trainer:__choice__, Value: 'MixUpTrainer'
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, ta_runs=12, ta_time_used=105.31449437141418, wallclock_time=135.89934992790222, budget=16.666666666666664)]
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{'accuracy': 0.884393063583815}
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| | Preprocessing | Estimator | Weight |
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|---:|:------------------------------------------------------------------|:-------------------------------------------------------------------|---------:|
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| 0 | SimpleImputer,OneHotEncoder,Normalizer,KitchenSink | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.42 |
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| 1 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 2 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 3 | None | KNNClassifier | 0.1 |
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| 4 | None | RFClassifier | 0.08 |
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| 5 | SimpleImputer,OneHotEncoder,Normalizer,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 6 | None | ExtraTreesClassifier | 0.04 |
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| 7 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 8 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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, ta_runs=4, ta_time_used=35.06738233566284, wallclock_time=42.592618465423584, budget=5.555555555555555)]
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{'accuracy': 0.8786127167630058}
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| | Preprocessing | Estimator | Weight |
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|---:|:-----------------------------------------------------|:-------------------------------------------------------------------|---------:|
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| 0 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.2 |
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| 1 | SimpleImputer,OneHotEncoder,Normalizer,KitchenSink | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.2 |
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| 2 | SimpleImputer,NoEncoder,NoScaler,PolynomialFeatures | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.18 |
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| 3 | SimpleImputer,OneHotEncoder,Normalizer,KitchenSink | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.16 |
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| 4 | SimpleImputer,OneHotEncoder,Normalizer,KitchenSink | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.16 |
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| 5 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 6 | SimpleImputer,NoEncoder,NoScaler,Nystroem | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 7 | None | ExtraTreesClassifier | 0.02 |
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| 8 | None | KNNClassifier | 0.02 |
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 9 minutes 10.157 seconds)
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**Total running time of the script:** ( 9 minutes 5.493 seconds)
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.. _sphx_glr_download_examples_example_tabular_classification.py:

‎refactor_development/_sources/examples/example_tabular_regression.rst.txt

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.. code-block:: none
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<smac.runhistory.runhistory.RunHistory object at 0x7f9827376ee0> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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<smac.runhistory.runhistory.RunHistory object at 0x7fe9569b5430> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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data_loader:batch_size, Value: 32
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encoder:__choice__, Value: 'NoEncoder'
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imputer:numerical_strategy, Value: 'mean'
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optimizer:__choice__, Value: 'AdamOptimizer'
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scaler:__choice__, Value: 'StandardScaler'
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0018258094787597656, budget=0), TrajEntry(train_perf=0.022066190658357243, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0018329620361328125, budget=0), TrajEntry(train_perf=0.001492405628839566, incumbent_id=1, incumbent=Configuration:
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encoder:__choice__, Value: 'NoEncoder'
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imputer:numerical_strategy, Value: 'mean'
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optimizer:__choice__, Value: 'AdamOptimizer'
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=1, ta_time_used=7.395039081573486, wallclock_time=10.92704463005066, budget=5.555555555555555)]
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{'r2': 0.9995185033602313}
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, ta_runs=1, ta_time_used=7.251036643981934, wallclock_time=10.61331295967102, budget=5.555555555555555)]
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{'r2': 0.999784077535095}
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 8 minutes 34.410 seconds)
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**Total running time of the script:** ( 8 minutes 31.487 seconds)
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.. _sphx_glr_download_examples_example_tabular_regression.py:

‎refactor_development/_sources/examples/sg_execution_times.rst.txt

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Computation times
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=================
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**17:54.478** total execution time for **examples** files:
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**17:46.383** total execution time for **examples** files:
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+----------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_example_tabular_classification.py` (``example_tabular_classification.py``) | 09:10.157 | 0.0 MB |
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| :ref:`sphx_glr_examples_example_tabular_classification.py` (``example_tabular_classification.py``) | 09:05.493 | 0.0 MB |
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+----------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_example_tabular_regression.py` (``example_tabular_regression.py``) | 08:34.410 | 0.0 MB |
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| :ref:`sphx_glr_examples_example_tabular_regression.py` (``example_tabular_regression.py``) | 08:31.487 | 0.0 MB |
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+----------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_example_image_classification.py` (``example_image_classification.py``) | 00:09.911 | 0.0 MB |
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| :ref:`sphx_glr_examples_example_image_classification.py` (``example_image_classification.py``) | 00:09.403 | 0.0 MB |
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+----------------------------------------------------------------------------------------------------+-----------+--------+

‎refactor_development/api.html

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<blockquote>
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<div><p><a href="#id1"><span class="problematic" id="id2">**</span></a>pipeline_config_kwargs: Valid config options include “num_run”,
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“device”, “budget_type”, “epochs”, “runtime”, “torch_num_threads”,
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“early_stopping”, “use_tensorboard_logger”, “use_pynisher”,
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“early_stopping”, “use_tensorboard_logger”,
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<dl class="simple">

‎refactor_development/examples/example_image_classification.html

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Pipeline Random Config:
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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:use_augmenter, Value: False
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image_augmenter:RandomAffine:use_augmenter, Value: False
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image_augmenter:GaussianBlur:sigma_min, Value: 0.7923318281094287
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image_augmenter:GaussianBlur:sigma_offset, Value: 0.5630460313493758
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image_augmenter:GaussianBlur:use_augmenter, Value: True
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image_augmenter:GaussianNoise:sigma_offset, Value: 0.5913000614764757
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image_augmenter:GaussianNoise:use_augmenter, Value: True
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image_augmenter:RandomAffine:rotate, Value: 100
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image_augmenter:RandomAffine:scale_offset, Value: 0.21422298475725798
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image_augmenter:RandomAffine:shear, Value: 3
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image_augmenter:RandomAffine:translate_percent_offset, Value: 0.15943617036310528
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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:ZeroPadAndCrop:percent, Value: 0.208578408262465
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normalizer:__choice__, Value: &#39;NoNormalizer&#39;
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.11241231994734702
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normalizer:__choice__, Value: &#39;ImageNormalizer&#39;
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Fitting the pipeline...
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<span class="nb">print</span><span class="p">(</span><span class="n">pipeline</span><span class="p">)</span>
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</pre></div>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 9.911 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 9.403 seconds)</p>
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<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-example-image-classification-py">
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<div class="binder-badge docutils container">
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<a class="reference external image-reference" href="https://mybinder.org/v2/gh/automl/Auto-PyTorch/refactor_development?urlpath=lab/tree/notebooks/examples/example_image_classification.ipynb"><img alt="Launch binder" src="../_images/binder_badge_logo.svg" width="150px" /></a>

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