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Francisco Rivera Valverde: move to a minimization problem (#113)
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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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________________________________________
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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: 0.5614286279360542
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image_augmenter:GaussianNoise: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:use_augmenter, Value: False
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image_augmenter:Resize:use_augmenter, Value: True
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.2076158247310454
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.208578408262465
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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 9.520 seconds)
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**Total running time of the script:** ( 0 minutes 9.911 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 0x7ff7fbf2b190> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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<smac.runhistory.runhistory.RunHistory object at 0x7f983433d160> [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=0.0019752979278564453, budget=0), TrajEntry(train_perf=0.216374269005848, incumbent_id=1, incumbent=Configuration:
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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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data_loader:batch_size, Value: 32
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encoder:__choice__, Value: 'OneHotEncoder'
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feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -118,40 +118,7 @@ with AutoPyTorch
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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=5.269052505493164, wallclock_time=6.652451276779175, budget=5.555555555555555), TrajEntry(train_perf=0.21052631578947367, incumbent_id=2, incumbent=Configuration:
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data_loader:batch_size, Value: 472
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encoder:__choice__, Value: 'NoEncoder'
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feature_preprocessor:Nystroem:gamma, Value: 0.07411722362589628
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feature_preprocessor:Nystroem:kernel, Value: 'rbf'
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feature_preprocessor:Nystroem:n_components, Value: 5
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feature_preprocessor:__choice__, Value: 'Nystroem'
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imputer:categorical_strategy, Value: 'constant_!missing!'
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imputer:numerical_strategy, Value: 'constant_zero'
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lr_scheduler:__choice__, Value: 'NoScheduler'
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network_backbone:ShapedMLPBackbone:activation, Value: 'tanh'
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network_backbone:ShapedMLPBackbone:max_units, Value: 766
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network_backbone:ShapedMLPBackbone:mlp_shape, Value: 'diamond'
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network_backbone:ShapedMLPBackbone:num_groups, Value: 9
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network_backbone:ShapedMLPBackbone:output_dim, Value: 243
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network_backbone:ShapedMLPBackbone:use_dropout, Value: False
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network_backbone:__choice__, Value: 'ShapedMLPBackbone'
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network_embedding:__choice__, Value: 'NoEmbedding'
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network_head:__choice__, Value: 'fully_connected'
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network_head:fully_connected:activation, Value: 'tanh'
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network_head:fully_connected:num_layers, Value: 3
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network_head:fully_connected:units_layer_1, Value: 323
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network_head:fully_connected:units_layer_2, Value: 332
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network_init:KaimingInit:bias_strategy, Value: 'Normal'
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network_init:__choice__, Value: 'KaimingInit'
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optimizer:SGDOptimizer:lr, Value: 0.0024087824203718427
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optimizer:SGDOptimizer:momentum, Value: 0.418322885741585
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optimizer:SGDOptimizer:weight_decay, Value: 0.015250671878609713
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optimizer:__choice__, Value: 'SGDOptimizer'
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scaler:__choice__, Value: 'NoScaler'
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trainer:MixUpTrainer:alpha, Value: 0.4043218849947128
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trainer:MixUpTrainer:weighted_loss, Value: True
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trainer:__choice__, Value: 'MixUpTrainer'
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, ta_runs=3, ta_time_used=40.172093629837036, wallclock_time=46.57782244682312, budget=5.555555555555555), TrajEntry(train_perf=0.17543859649122806, incumbent_id=3, incumbent=Configuration:
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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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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
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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=4, ta_time_used=48.74088931083679, wallclock_time=56.56371068954468, budget=5.555555555555555)]
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{'accuracy': 0.861271676300578}
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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,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 1 | None | ExtraTreesClassifier | 0.12 |
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| 2 | None | KNNClassifier | 0.12 |
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| 3 | SimpleImputer,OneHotEncoder,Normalizer,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
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| 4 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
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| 5 | SimpleImputer,OneHotEncoder,Normalizer,KitchenSink | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
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| 6 | SimpleImputer,OneHotEncoder,NoScaler,TruncSVD | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
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| 7 | SimpleImputer,NoEncoder,NoScaler,Nystroem | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 8 | SimpleImputer,OneHotEncoder,MinMaxScaler,PolynomialFeatures | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 9 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 10 | SimpleImputer,NoEncoder,NoScaler,Nystroem | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 11 | None | LGBMClassifier | 0.02 |
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| 12 | None | RFClassifier | 0.02 |
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| 13 | None | SVC | 0.02 |
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| 14 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 15 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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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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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 9 minutes 22.257 seconds)
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**Total running time of the script:** ( 9 minutes 10.157 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 0x7ff7fa161700> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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<smac.runhistory.runhistory.RunHistory object at 0x7f9827376ee0> [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=0.001894235610961914, budget=0), TrajEntry(train_perf=0.0033956446141917285, incumbent_id=1, incumbent=Configuration:
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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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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=8.826370239257812, wallclock_time=12.347334861755371, budget=5.555555555555555), TrajEntry(train_perf=0.16943683190326564, incumbent_id=2, incumbent=Configuration:
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data_loader:batch_size, Value: 324
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encoder:__choice__, Value: 'NoEncoder'
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imputer:numerical_strategy, Value: 'median'
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lr_scheduler:CosineAnnealingLR:T_max, Value: 58
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lr_scheduler:__choice__, Value: 'CosineAnnealingLR'
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network_backbone:ShapedMLPBackbone:activation, Value: 'relu'
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network_backbone:ShapedMLPBackbone:max_dropout, Value: 0.08110946585949352
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network_backbone:ShapedMLPBackbone:max_units, Value: 77
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network_backbone:ShapedMLPBackbone:mlp_shape, Value: 'diamond'
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network_backbone:ShapedMLPBackbone:num_groups, Value: 12
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network_backbone:ShapedMLPBackbone:output_dim, Value: 383
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network_backbone:ShapedMLPBackbone:use_dropout, Value: True
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network_backbone:__choice__, Value: 'ShapedMLPBackbone'
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network_embedding:__choice__, Value: 'NoEmbedding'
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network_head:__choice__, Value: 'fully_connected'
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network_head:fully_connected:activation, Value: 'relu'
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network_head:fully_connected:num_layers, Value: 4
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network_head:fully_connected:units_layer_1, Value: 376
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network_head:fully_connected:units_layer_2, Value: 67
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network_head:fully_connected:units_layer_3, Value: 333
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network_init:KaimingInit:bias_strategy, Value: 'Zero'
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network_init:__choice__, Value: 'KaimingInit'
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optimizer:RMSpropOptimizer:alpha, Value: 0.3647734535713807
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optimizer:RMSpropOptimizer:lr, Value: 0.00037624565303773076
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optimizer:RMSpropOptimizer:momentum, Value: 0.9118730100154911
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optimizer:RMSpropOptimizer:weight_decay, Value: 0.039774901898577665
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optimizer:__choice__, Value: 'RMSpropOptimizer'
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scaler:Normalizer:norm, Value: 'mean_abs'
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scaler:__choice__, Value: 'Normalizer'
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trainer:MixUpTrainer:alpha, Value: 0.6330663830411708
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trainer:__choice__, Value: 'MixUpTrainer'
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, ta_runs=17, ta_time_used=394.7455403804779, wallclock_time=445.0417649745941, budget=50.0)]
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{'r2': 0.9992392159989721}
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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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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 8 minutes 37.201 seconds)
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**Total running time of the script:** ( 8 minutes 34.410 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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**18:08.978** total execution time for **examples** files:
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**17:54.478** 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``) | 09:22.257 | 0.0 MB |
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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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+----------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_example_tabular_regression.py` (``example_tabular_regression.py``) | 08:37.201 | 0.0 MB |
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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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+----------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_example_image_classification.py` (``example_image_classification.py``) | 00:09.520 | 0.0 MB |
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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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+----------------------------------------------------------------------------------------------------+-----------+--------+

‎refactor_development/examples/example_image_classification.html‎

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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: 0.5614286279360542
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image_augmenter:GaussianNoise: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:use_augmenter, Value: False
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image_augmenter:Resize:use_augmenter, Value: True
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.2076158247310454
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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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<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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</div>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 9.520 seconds)</p>
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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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<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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