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Ravin Kohli: change config_id to config_id+1 (#129)
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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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@@ -81,26 +81,26 @@ 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:sigma_min, Value: 2.171718885748658
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image_augmenter:GaussianBlur:sigma_offset, Value: 0.23112994670692544
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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: 8
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image_augmenter:RandomAffine:scale_offset, Value: 0.04636110307060335
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image_augmenter:RandomAffine:shear, Value: 15
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image_augmenter:RandomAffine:translate_percent_offset, Value: 0.2439160322493358
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image_augmenter:GaussianBlur:use_augmenter, Value: False
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image_augmenter:GaussianNoise:sigma_offset, Value: 1.98731511231425
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image_augmenter:GaussianNoise:use_augmenter, Value: True
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image_augmenter:RandomAffine:rotate, Value: 50
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image_augmenter:RandomAffine:scale_offset, Value: 0.014555060261484254
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image_augmenter:RandomAffine:shear, Value: 37
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image_augmenter:RandomAffine:translate_percent_offset, Value: 0.25465139302111295
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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: 0.43248776062268
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normalizer:__choice__, Value: 'NoNormalizer'
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image_augmenter:RandomCutout:p, Value: 0.4035801393808609
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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: 0.23991178688053533
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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 14.058 seconds)
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**Total running time of the script:** ( 0 minutes 11.818 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 0x7f1e0826a220> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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<smac.runhistory.runhistory.RunHistory object at 0x7f42044b7c10> [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'
@@ -77,7 +77,7 @@ 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=0, ta_time_used=0.0, wallclock_time=0.0022144317626953125, budget=0), TrajEntry(train_perf=0.15204678362573099, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0021822452545166016, 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: 'OneHotEncoder'
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feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -118,43 +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=5.521448612213135, wallclock_time=6.911151885986328, budget=5.555555555555555), TrajEntry(train_perf=0.17543859649122806, incumbent_id=2, incumbent=Configuration:
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data_loader:batch_size, Value: 294
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encoder:__choice__, Value: 'NoEncoder'
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feature_preprocessor:TruncatedSVD:target_dim, Value: 3
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feature_preprocessor:__choice__, Value: 'TruncatedSVD'
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imputer:categorical_strategy, Value: 'most_frequent'
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imputer:numerical_strategy, Value: 'median'
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lr_scheduler:CyclicLR:base_lr, Value: 0.002717430088118658
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lr_scheduler:CyclicLR:max_lr, Value: 0.07976595067755171
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lr_scheduler:CyclicLR:mode, Value: 'triangular'
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lr_scheduler:CyclicLR:step_size_up, Value: 1956
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lr_scheduler:__choice__, Value: 'CyclicLR'
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network_backbone:ShapedMLPBackbone:activation, Value: 'relu'
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network_backbone:ShapedMLPBackbone:max_units, Value: 73
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network_backbone:ShapedMLPBackbone:mlp_shape, Value: 'diamond'
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network_backbone:ShapedMLPBackbone:num_groups, Value: 10
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network_backbone:ShapedMLPBackbone:output_dim, Value: 43
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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: '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: 429
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network_head:fully_connected:units_layer_2, Value: 456
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network_head:fully_connected:units_layer_3, Value: 395
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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:AdamWOptimizer:beta1, Value: 0.8939249786995366
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optimizer:AdamWOptimizer:beta2, Value: 0.9683138279885987
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optimizer:AdamWOptimizer:lr, Value: 0.03487526144039154
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optimizer:AdamWOptimizer:weight_decay, Value: 0.03698959567275315
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optimizer:__choice__, Value: 'AdamWOptimizer'
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scaler:__choice__, Value: 'MinMaxScaler'
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trainer:StandardTrainer:weighted_loss, Value: False
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=19, ta_time_used=285.0864462852478, wallclock_time=344.9483382701874, budget=50.0), TrajEntry(train_perf=0.1578947368421053, incumbent_id=3, incumbent=Configuration:
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, ta_runs=1, ta_time_used=4.781935930252075, wallclock_time=6.252290487289429, budget=5.555555555555555), TrajEntry(train_perf=0.14619883040935677, incumbent_id=2, incumbent=Configuration:
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data_loader:batch_size, Value: 295
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encoder:__choice__, Value: 'OneHotEncoder'
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feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
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scaler:__choice__, Value: 'Normalizer'
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trainer:StandardTrainer:weighted_loss, Value: False
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=20, ta_time_used=292.15107345581055, wallclock_time=355.5941684246063, budget=50.0)]
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, ta_runs=20, ta_time_used=229.40343284606934, wallclock_time=290.8017210960388, budget=50.0)]
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{'accuracy': 0.8670520231213873}
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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 | 0.2 |
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| 1 | SimpleImputer,OneHotEncoder,Normalizer,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 2 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.12 |
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| 3 | SimpleImputer,OneHotEncoder,Normalizer,KitchenSink | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
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| 4 | None | RFClassifier | 0.08 |
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| 5 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 6 | None | KNNClassifier | 0.06 |
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| 7 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 8 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 9 | SimpleImputer,NoEncoder,NoScaler,Nystroem | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 10 | None | ExtraTreesClassifier | 0.04 |
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| 11 | None | LGBMClassifier | 0.02 |
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| 12 | None | SVC | 0.02 |
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| 13 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| | Preprocessing | Estimator | Weight |
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|---:|:------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
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| 0 | SimpleImputer,OneHotEncoder,Normalizer,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 1 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
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| 2 | None | SVC | 0.1 |
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| 3 | None | ExtraTreesClassifier | 0.08 |
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| 4 | None | KNNClassifier | 0.08 |
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| 5 | None | RFClassifier | 0.08 |
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| 6 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 7 | SimpleImputer,OneHotEncoder,Normalizer,NoFeaturePreprocessing | 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,OneHotEncoder,StandardScaler,KernelPCA | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 11 | SimpleImputer,OneHotEncoder,StandardScaler,KernelPCA | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 12 | None | LGBMClassifier | 0.04 |
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| 13 | SimpleImputer,OneHotEncoder,NoScaler,NoFeaturePreprocessing | embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 14 | SimpleImputer,NoEncoder,MinMaxScaler,TruncSVD | no 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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| 16 | 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 15.541 seconds)
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**Total running time of the script:** ( 9 minutes 17.694 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 0x7f1e00a06e50> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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<smac.runhistory.runhistory.RunHistory object at 0x7f41fc4762e0> [TrajEntry(train_perf=2147483648, 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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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=0.0017309188842773438, budget=0), TrajEntry(train_perf=0.0003191593212231725, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0018343925476074219, budget=0), TrajEntry(train_perf=0.0006981695502918939, incumbent_id=1, incumbent=Configuration:
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, ta_runs=1, ta_time_used=8.489260911941528, wallclock_time=12.06636643409729, budget=5.555555555555555)]
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{'r2': 0.9996971295334267}
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, ta_runs=1, ta_time_used=7.188728332519531, wallclock_time=10.688534498214722, budget=5.555555555555555)]
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{'r2': 0.9998706896933107}
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 8 minutes 33.372 seconds)
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**Total running time of the script:** ( 8 minutes 33.280 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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**18:02.970** total execution time for **examples** files:
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**18:02.792** 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``) | 09:15.541 | 0.0 MB |
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| :ref:`sphx_glr_examples_example_tabular_classification.py` (``example_tabular_classification.py``) | 09:17.694 | 0.0 MB |
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+----------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_example_tabular_regression.py` (``example_tabular_regression.py``) | 08:33.372 | 0.0 MB |
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| :ref:`sphx_glr_examples_example_tabular_regression.py` (``example_tabular_regression.py``) | 08:33.280 | 0.0 MB |
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+----------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_example_image_classification.py` (``example_image_classification.py``) | 00:14.058 | 0.0 MB |
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| :ref:`sphx_glr_examples_example_image_classification.py` (``example_image_classification.py``) | 00:11.818 | 0.0 MB |
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+----------------------------------------------------------------------------------------------------+-----------+--------+

‎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:sigma_min, Value: 2.171718885748658
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image_augmenter:GaussianBlur:sigma_offset, Value: 0.23112994670692544
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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: 8
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image_augmenter:RandomAffine:scale_offset, Value: 0.04636110307060335
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image_augmenter:RandomAffine:shear, Value: 15
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image_augmenter:RandomAffine:translate_percent_offset, Value: 0.2439160322493358
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image_augmenter:GaussianBlur:use_augmenter, Value: False
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image_augmenter:GaussianNoise:sigma_offset, Value: 1.98731511231425
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image_augmenter:GaussianNoise:use_augmenter, Value: True
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image_augmenter:RandomAffine:rotate, Value: 50
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image_augmenter:RandomAffine:scale_offset, Value: 0.014555060261484254
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image_augmenter:RandomAffine:shear, Value: 37
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image_augmenter:RandomAffine:translate_percent_offset, Value: 0.25465139302111295
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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: 0.43248776062268
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normalizer:__choice__, Value: &#39;NoNormalizer&#39;
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image_augmenter:RandomCutout:p, Value: 0.4035801393808609
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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: 0.23991178688053533
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normalizer:__choice__, Value: &#39;ImageNormalizer&#39;
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Fitting the pipeline...
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ImageClassificationPipeline
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________________________________________
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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 14.058 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 11.818 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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