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Ravin Kohli: [ADD] Pytest schedule (#234)
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‎development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

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‎development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip

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‎development/_images/sphx_glr_example_visualization_001.png

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‎development/_images/sphx_glr_example_visualization_002.png

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‎development/_images/sphx_glr_example_visualization_thumb.png

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‎development/_sources/examples/20_basics/example_image_classification.rst.txt

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@@ -87,26 +87,25 @@ 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: 2.7815796172433904
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image_augmenter:GaussianBlur:sigma_offset, Value: 2.2162192719004334
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image_augmenter:GaussianBlur:use_augmenter, Value: True
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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:rotate, Value: 211
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image_augmenter:RandomAffine:scale_offset, Value: 0.1303552201986558
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image_augmenter:RandomAffine:shear, Value: 24
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image_augmenter:RandomAffine:translate_percent_offset, Value: 0.1470709970650069
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image_augmenter:RandomAffine:rotate, Value: 139
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image_augmenter:RandomAffine:scale_offset, Value: 0.23068913599566782
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image_augmenter:RandomAffine:shear, Value: 15
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image_augmenter:RandomAffine:translate_percent_offset, Value: 0.3649730895991128
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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:RandomCutout:p, Value: 0.8026417457168646
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image_augmenter:RandomCutout:use_augmenter, Value: True
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image_augmenter:Resize:use_augmenter, Value: True
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.29994917149110223
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normalizer:__choice__, Value: 'ImageNormalizer'
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.26059070078518315
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normalizer:__choice__, Value: 'NoNormalizer'
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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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ImageNormalizer
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NoNormalizer
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1-) preprocessing:
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EarlyPreprocessing
@@ -178,7 +177,7 @@ Image Classification
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.. rst-class:: sphx-glr-timing
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.. _sphx_glr_download_examples_20_basics_example_image_classification.py:

‎development/_sources/examples/20_basics/example_tabular_classification.rst.txt

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@@ -133,7 +133,7 @@ Search for an ensemble of machine learning algorithms
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.. code-block:: none
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f7f96017d90>
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7fe257cbbe50>
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@@ -162,7 +162,7 @@ Print the final ensemble performance
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.. code-block:: none
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<smac.runhistory.runhistory.RunHistory object at 0x7f7f96017130> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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<smac.runhistory.runhistory.RunHistory object at 0x7fe257b3a0d0> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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data_loader:batch_size, Value: 64
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encoder:__choice__, Value: 'OneHotEncoder'
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feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -194,7 +194,7 @@ Print the final ensemble performance
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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=0.0012085437774658203, budget=0), TrajEntry(train_perf=0.17543859649122806, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0010366439819335938, budget=0), TrajEntry(train_perf=0.17543859649122806, incumbent_id=1, incumbent=Configuration:
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data_loader:batch_size, Value: 64
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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=2.6043033599853516, wallclock_time=3.6323750019073486, budget=5.555555555555555), TrajEntry(train_perf=0.1578947368421053, incumbent_id=2, incumbent=Configuration:
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data_loader:batch_size, Value: 131
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encoder:__choice__, Value: 'NoEncoder'
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feature_preprocessor:KernelPCA:coef0, Value: -0.2027355777455664
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feature_preprocessor:KernelPCA:degree, Value: 2
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feature_preprocessor:KernelPCA:gamma, Value: 0.0029756156161293078
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feature_preprocessor:KernelPCA:kernel, Value: 'poly'
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feature_preprocessor:KernelPCA:n_components, Value: 4
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feature_preprocessor:__choice__, Value: 'KernelPCA'
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, ta_runs=1, ta_time_used=1.9255990982055664, wallclock_time=2.953387975692749, budget=5.555555555555555), TrajEntry(train_perf=0.14619883040935677, incumbent_id=2, incumbent=Configuration:
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data_loader:batch_size, Value: 54
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encoder:__choice__, Value: 'OneHotEncoder'
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feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
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imputer:categorical_strategy, Value: 'constant_!missing!'
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imputer:numerical_strategy, Value: 'mean'
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lr_scheduler:CosineAnnealingWarmRestarts:T_0, Value: 20
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lr_scheduler:CosineAnnealingWarmRestarts:T_mult, Value: 1.2502829975237466
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lr_scheduler:__choice__, Value: 'CosineAnnealingWarmRestarts'
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network_backbone:ShapedResNetBackbone:activation, Value: 'sigmoid'
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network_backbone:ShapedResNetBackbone:blocks_per_group, Value: 2
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network_backbone:ShapedResNetBackbone:max_units, Value: 21
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network_backbone:ShapedResNetBackbone:num_groups, Value: 11
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network_backbone:ShapedResNetBackbone:output_dim, Value: 128
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network_backbone:ShapedResNetBackbone:resnet_shape, Value: 'stairs'
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network_backbone:ShapedResNetBackbone:use_dropout, Value: False
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network_backbone:ShapedResNetBackbone:use_shake_drop, Value: False
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network_backbone:ShapedResNetBackbone:use_shake_shake, Value: False
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network_backbone:__choice__, Value: 'ShapedResNetBackbone'
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lr_scheduler:CosineAnnealingLR:T_max, Value: 307
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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: 0.543030049110043
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network_backbone:ShapedMLPBackbone:max_units, Value: 35
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network_backbone:ShapedMLPBackbone:mlp_shape, Value: 'hexagon'
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network_backbone:ShapedMLPBackbone:num_groups, Value: 3
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network_backbone:ShapedMLPBackbone:output_dim, Value: 18
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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: 'tanh'
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network_head:fully_connected:num_layers, Value: 4
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network_head:fully_connected:units_layer_1, Value: 415
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network_head:fully_connected:units_layer_2, Value: 290
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network_head:fully_connected:units_layer_3, Value: 313
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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:AdamOptimizer:beta1, Value: 0.9981587455677909
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optimizer:AdamOptimizer:beta2, Value: 0.9934737249657393
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optimizer:AdamOptimizer:lr, Value: 0.0015351906927605823
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optimizer:AdamOptimizer:weight_decay, Value: 0.06126849297256112
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optimizer:__choice__, Value: 'AdamOptimizer'
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network_head:fully_connected:activation, Value: 'relu'
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network_head:fully_connected:num_layers, Value: 3
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network_head:fully_connected:units_layer_1, Value: 316
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network_head:fully_connected:units_layer_2, Value: 503
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network_init:SparseInit:bias_strategy, Value: 'Normal'
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network_init:__choice__, Value: 'SparseInit'
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optimizer:AdamWOptimizer:beta1, Value: 0.9489565046389004
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optimizer:AdamWOptimizer:beta2, Value: 0.9647522172509646
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optimizer:AdamWOptimizer:lr, Value: 0.0030477242366055836
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optimizer:AdamWOptimizer:weight_decay, Value: 0.061913730296919815
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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=20, ta_time_used=161.57836866378784, wallclock_time=232.07247638702393, budget=50.0)]
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{'accuracy': 0.8728323699421965}
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| | Preprocessing | Estimator | Weight |
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|---:|:------------------------------------------------------------------|:-------------------------------------------------------------------|---------:|
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| 0 | None | CatBoostClassifier | 0.92 |
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| 1 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 2 | SimpleImputer,NoEncoder,MinMaxScaler,KernelPCA | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 3 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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trainer:MixUpTrainer:alpha, Value: 0.8559230573827334
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trainer:MixUpTrainer:weighted_loss, Value: True
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trainer:__choice__, Value: 'MixUpTrainer'
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, ta_runs=18, ta_time_used=170.50614953041077, wallclock_time=220.6971218585968, budget=50.0)]
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{'accuracy': 0.861271676300578}
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| | Preprocessing | Estimator | Weight |
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|---:|:------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
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| 0 | None | CatBoostClassifier | 0.28 |
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| 1 | SimpleImputer,NoEncoder,Normalizer,Nystroem | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.2 |
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| 2 | None | KNNClassifier | 0.1 |
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| 3 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
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| 4 | SimpleImputer,OneHotEncoder,StandardScaler,Nystroem | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 5 | None | LGBMClassifier | 0.06 |
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| 6 | None | RFClassifier | 0.06 |
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| 7 | SimpleImputer,OneHotEncoder,MinMaxScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 8 | SimpleImputer,NoEncoder,NoScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 9 | None | SVC | 0.04 |
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| 10 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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**Total running time of the script:** ( 5 minutes 24.537 seconds)
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.. _sphx_glr_download_examples_20_basics_example_tabular_classification.py:

‎development/_sources/examples/20_basics/example_tabular_regression.rst.txt

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@@ -126,7 +126,7 @@ Search for an ensemble of machine learning algorithms
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.. code-block:: none
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<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f803aa3a6a0>
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<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7fe2fda056a0>
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<smac.runhistory.runhistory.RunHistory object at 0x7f802dfa0040> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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<smac.runhistory.runhistory.RunHistory object at 0x7fe2f0fa5040> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0011713504791259766, budget=0), TrajEntry(train_perf=5.132313191053143, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0010111331939697266, budget=0), TrajEntry(train_perf=4.001648104799329, incumbent_id=1, incumbent=Configuration:
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, ta_runs=1, ta_time_used=1.2097136974334717, wallclock_time=2.233612060546875, budget=5.555555555555555), TrajEntry(train_perf=3.1930668561547075, incumbent_id=2, incumbent=Configuration:
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, ta_runs=3, ta_time_used=5.242362022399902, wallclock_time=14.604888677597046, budget=5.555555555555555), TrajEntry(train_perf=1.63612106672499, incumbent_id=3, incumbent=Configuration:
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, ta_runs=4, ta_time_used=7.889399766921997, wallclock_time=18.329752445220947, budget=5.555555555555555), TrajEntry(train_perf=1.303718113076385, incumbent_id=4, incumbent=Configuration:
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, ta_runs=6, ta_time_used=11.728835821151733, wallclock_time=27.961014986038208, budget=5.555555555555555), TrajEntry(train_perf=1.0, incumbent_id=5, incumbent=Configuration:
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trainer:__choice__, Value: 'MixUpTrainer'
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, ta_runs=9, ta_time_used=51.96426844596863, wallclock_time=80.42096281051636, budget=5.555555555555555), TrajEntry(train_perf=1.0008126311940073, incumbent_id=6, incumbent=Configuration:
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, ta_runs=9, ta_time_used=38.322489976882935, wallclock_time=61.81372833251953, budget=5.555555555555555), TrajEntry(train_perf=1.0090091679942723, incumbent_id=6, incumbent=Configuration:
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, ta_runs=10, ta_time_used=39.92764472961426, wallclock_time=64.51126146316528, budget=16.666666666666664), TrajEntry(train_perf=1.0, incumbent_id=7, incumbent=Configuration:
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, ta_runs=17, ta_time_used=165.81303334236145, wallclock_time=210.14278411865234, budget=50.0)]
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{'r2': -0.002203283150869373}
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| | Preprocessing | Estimator | Weight |
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|---:|:------------------------------------------------|:-------------------------------------------------------------|---------:|
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| 0 | SimpleImputer,OneHotEncoder,Normalizer,TruncSVD | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 1 |
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, ta_runs=17, ta_time_used=133.23015427589417, wallclock_time=170.4870789051056, budget=50.0)]
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{'r2': 0.13995994112861065}
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| | Preprocessing | Estimator | Weight |
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|---:|:--------------------------------------------------------------|:-------------------------------------------------------------------|---------:|
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| 0 | SimpleImputer,OneHotEncoder,StandardScaler,PolynomialFeatures | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 1 |
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.. rst-class:: sphx-glr-timing
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.. _sphx_glr_download_examples_20_basics_example_tabular_regression.py:

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