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Ravin Kohli: Add dropout shape as a hyperparameter (#213)
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‎refactor_development_regularization_cocktails/.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: ad8f043e67f31d900fdc9a0563a66d13
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config: 6390f8eeb9cecf93d81b9bfef48d8855
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tags: 645f666f9bcd5a90fca523b33c5a78b7

‎refactor_development_regularization_cocktails/_downloads/0b35eb5fea161bb1ba2c30e6b7323b44/example_single_configuration.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.9"
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"version": "3.8.10"
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}
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},
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"nbformat": 4,

‎refactor_development_regularization_cocktails/_downloads/2f0f54a9831653ac5c12ee0e06324a62/basics_tabular_jupyter.zip

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‎refactor_development_regularization_cocktails/_downloads/342871cbb8ddcf6157ab171f9b9eab25/example_resampling_strategy.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.9"
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"version": "3.8.10"
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}
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},
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"nbformat": 4,

‎refactor_development_regularization_cocktails/_downloads/8ef6602cf8ed40221edd89244fb031af/basics_tabular_python.zip

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‎refactor_development_regularization_cocktails/_downloads/b38295cf56f02b1c4547385a8f389f90/advanced_tabular_python.zip

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‎refactor_development_regularization_cocktails/_downloads/cae1c6e84ce2874c73fc5e45da55b1b6/example_custom_configuration_space.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.9"
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"version": "3.8.10"
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}
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},
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"nbformat": 4,

‎refactor_development_regularization_cocktails/_downloads/df038041811db9bd567de83692a8f994/advanced_tabular_jupyter.zip

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‎refactor_development_regularization_cocktails/_downloads/e57ec61235a65031300d4a88d742ee2f/example_tabular_regression.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.9"
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"version": "3.8.10"
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}
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},
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"nbformat": 4,

‎refactor_development_regularization_cocktails/_downloads/ff1caf117ce143ee4d6abd46896ef46a/example_tabular_classification.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.9"
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"version": "3.8.10"
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}
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"nbformat": 4,

‎refactor_development_regularization_cocktails/_sources/advanced_tabular/example_custom_configuration_space.rst.txt

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.. code-block:: none
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<smac.runhistory.runhistory.RunHistory object at 0x7fd8ab4db220> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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<smac.runhistory.runhistory.RunHistory object at 0x7ff942707d30> [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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trainer:StandardTrainer:use_stochastic_weight_averaging, Value: True
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trainer:StandardTrainer:weighted_loss, Constant: 1
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.001977682113647461, budget=0), TrajEntry(train_perf=0.21052631578947367, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0023491382598876953, budget=0), TrajEntry(train_perf=0.21052631578947367, 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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trainer:StandardTrainer:use_stochastic_weight_averaging, Value: True
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trainer:StandardTrainer:weighted_loss, Constant: 1
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=1, ta_time_used=14.220442295074463, wallclock_time=15.671681880950928, budget=5.555555555555555)]
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{'accuracy': 0.8728323699421965}
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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 | ExtraTreesClassifier | 0.14 |
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| 2 | None | LGBMClassifier | 0.12 |
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| 3 | None | CatBoostClassifier | 0.12 |
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| 4 | None | SVC | 0.1 |
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| 5 | None | KNNClassifier | 0.1 |
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| 6 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
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| 7 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
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<smac.runhistory.runhistory.RunHistory object at 0x7fd8abeaa640> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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, ta_runs=1, ta_time_used=13.149612665176392, wallclock_time=14.657279014587402, budget=5.555555555555555)]
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{'accuracy': 0.884393063583815}
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| | Preprocessing | Estimator | Weight |
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|---:|:------------------------------------------------------------------|:-------------------------------------------------------------|---------:|
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| 0 | None | CatBoostClassifier | 0.18 |
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| 1 | SimpleImputer,OneHotEncoder,NoScaler,TruncSVD | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.16 |
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| 2 | None | LGBMClassifier | 0.14 |
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| 3 | None | ExtraTreesClassifier | 0.14 |
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| 4 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 5 | SimpleImputer,OneHotEncoder,NoScaler,TruncSVD | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.12 |
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| 6 | None | RFClassifier | 0.06 |
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| 7 | None | SVC | 0.04 |
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| 8 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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<smac.runhistory.runhistory.RunHistory object at 0x7ff93092de50> [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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feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
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trainer:StandardTrainer:use_stochastic_weight_averaging, Value: True
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trainer:StandardTrainer:weighted_loss, Constant: 1
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0015747547149658203, budget=0), TrajEntry(train_perf=0.1871345029239766, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0016913414001464844, budget=0), TrajEntry(train_perf=0.1871345029239766, 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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feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
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trainer:StandardTrainer:use_stochastic_weight_averaging, Value: True
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trainer:StandardTrainer:weighted_loss, Constant: 1
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=1, ta_time_used=9.06000542640686, wallclock_time=10.521122455596924, budget=5.555555555555555)]
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{'accuracy': 0.861271676300578}
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| | Preprocessing | Estimator | Weight |
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|---:|:--------------------------------------------------------------|:----------------------------------------------------------------|---------:|
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| 0 | SimpleImputer,NoEncoder,NoScaler,PolynomialFeatures | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.56 |
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| 1 | None | RFClassifier | 0.16 |
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| 2 | None | ExtraTreesClassifier | 0.08 |
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| 3 | SimpleImputer,NoEncoder,NoScaler,PolynomialFeatures | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 4 | None | CatBoostClassifier | 0.06 |
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| 5 | None | LGBMClassifier | 0.04 |
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| 6 | None | SVC | 0.02 |
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| 7 | SimpleImputer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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, ta_runs=1, ta_time_used=8.595646619796753, wallclock_time=10.101649761199951, budget=5.555555555555555)]
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{'accuracy': 0.8728323699421965}
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| | Preprocessing | Estimator | Weight |
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|---:|:----------------|:---------------------|---------:|
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| 0 | None | ExtraTreesClassifier | 0.38 |
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| 1 | None | LGBMClassifier | 0.2 |
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| 2 | None | RFClassifier | 0.2 |
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| 3 | None | KNNClassifier | 0.14 |
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| 4 | None | CatBoostClassifier | 0.08 |
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 11 minutes 29.448 seconds)
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**Total running time of the script:** ( 11 minutes 46.494 seconds)
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.. _sphx_glr_download_advanced_tabular_example_custom_configuration_space.py:

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