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Francisco Rivera Valverde: [Fix] Refactor development reproducibility (#172)
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‎refactor_development/_downloads/2f0f54a9831653ac5c12ee0e06324a62/basics_tabular_jupyter.zip

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‎refactor_development/_downloads/83c92a475e127571aad1d77baa11248d/example_tabular_classification.py

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X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
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X,
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y,
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random_state=1,
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random_state=42,
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)
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############################################################################
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output_directory='./tmp/autoPyTorch_example_out_01',
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# To maintain logs of the run, set the next two as False
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delete_tmp_folder_after_terminate=True,
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delete_output_folder_after_terminate=True
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delete_output_folder_after_terminate=True,
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seed=42,
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)
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############################################################################

‎refactor_development/_downloads/8ef6602cf8ed40221edd89244fb031af/basics_tabular_python.zip

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‎refactor_development/_downloads/b38295cf56f02b1c4547385a8f389f90/advanced_tabular_python.zip

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‎refactor_development/_downloads/df038041811db9bd567de83692a8f994/advanced_tabular_jupyter.zip

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‎refactor_development/_downloads/ff1caf117ce143ee4d6abd46896ef46a/example_tabular_classification.ipynb

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},
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"outputs": [],
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"source": [
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"import os\nimport tempfile as tmp\nimport warnings\n\nos.environ['JOBLIB_TEMP_FOLDER'] = tmp.gettempdir()\nos.environ['OMP_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_NUM_THREADS'] = '1'\n\nwarnings.simplefilter(action='ignore', category=UserWarning)\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nimport sklearn.datasets\nimport sklearn.model_selection\n\nfrom autoPyTorch.api.tabular_classification import TabularClassificationTask\n\n\nif __name__ == '__main__':\n\n ############################################################################\n # Data Loading\n # ============\n X, y = sklearn.datasets.fetch_openml(data_id=40981, return_X_y=True, as_frame=True)\n X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n X,\n y,\n random_state=1,\n )\n\n ############################################################################\n # Build and fit a classifier\n # ==========================\n api = TabularClassificationTask(\n temporary_directory='./tmp/autoPyTorch_example_tmp_01',\n output_directory='./tmp/autoPyTorch_example_out_01',\n # To maintain logs of the run, set the next two as False\n delete_tmp_folder_after_terminate=True,\n delete_output_folder_after_terminate=True\n )\n\n ############################################################################\n # Search for an ensemble of machine learning algorithms\n # =====================================================\n api.search(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test.copy(),\n y_test=y_test.copy(),\n optimize_metric='accuracy',\n total_walltime_limit=300,\n func_eval_time_limit_secs=50\n )\n\n ############################################################################\n # Print the final ensemble performance\n # ====================================\n print(api.run_history, api.trajectory)\n y_pred = api.predict(X_test)\n score = api.score(y_pred, y_test)\n print(score)\n # Print the final ensemble built by AutoPyTorch\n print(api.show_models())"
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"import os\nimport tempfile as tmp\nimport warnings\n\nos.environ['JOBLIB_TEMP_FOLDER'] = tmp.gettempdir()\nos.environ['OMP_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_NUM_THREADS'] = '1'\n\nwarnings.simplefilter(action='ignore', category=UserWarning)\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nimport sklearn.datasets\nimport sklearn.model_selection\n\nfrom autoPyTorch.api.tabular_classification import TabularClassificationTask\n\n\nif __name__ == '__main__':\n\n ############################################################################\n # Data Loading\n # ============\n X, y = sklearn.datasets.fetch_openml(data_id=40981, return_X_y=True, as_frame=True)\n X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n X,\n y,\n random_state=42,\n )\n\n ############################################################################\n # Build and fit a classifier\n # ==========================\n api = TabularClassificationTask(\n temporary_directory='./tmp/autoPyTorch_example_tmp_01',\n output_directory='./tmp/autoPyTorch_example_out_01',\n # To maintain logs of the run, set the next two as False\n delete_tmp_folder_after_terminate=True,\n delete_output_folder_after_terminate=True,\n seed=42,\n )\n\n ############################################################################\n # Search for an ensemble of machine learning algorithms\n # =====================================================\n api.search(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test.copy(),\n y_test=y_test.copy(),\n optimize_metric='accuracy',\n total_walltime_limit=300,\n func_eval_time_limit_secs=50\n )\n\n ############################################################################\n # Print the final ensemble performance\n # ====================================\n print(api.run_history, api.trajectory)\n y_pred = api.predict(X_test)\n score = api.score(y_pred, y_test)\n print(score)\n # Print the final ensemble built by AutoPyTorch\n print(api.show_models())"
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]
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}
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],

‎refactor_development/_sources/advanced_tabular/example_custom_configuration_space.rst.txt

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@@ -46,7 +46,7 @@ the search. Currently, there are two changes that can be made to the space:-
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.. code-block:: none
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<smac.runhistory.runhistory.RunHistory object at 0x7fd58f4d3d00> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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<smac.runhistory.runhistory.RunHistory object at 0x7f92f432d4f0> [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=0.001336812973022461, budget=0), TrajEntry(train_perf=0.14619883040935677, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0020940303802490234, budget=0), TrajEntry(train_perf=0.16374269005847952, 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=4.047108173370361, wallclock_time=5.4710657596588135, budget=5.555555555555555)]
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{'accuracy': 0.8901734104046243}
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, ta_runs=1, ta_time_used=5.8945159912109375, wallclock_time=7.446282625198364, budget=5.555555555555555), TrajEntry(train_perf=0.1578947368421053, incumbent_id=2, incumbent=Configuration:
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data_loader:batch_size, Value: 475
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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: 'most_frequent'
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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:MLPBackbone:activation, Value: 'tanh'
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network_backbone:MLPBackbone:dropout_1, Value: 0.579891279191762
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network_backbone:MLPBackbone:dropout_2, Value: 0.43202885747368863
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network_backbone:MLPBackbone:dropout_3, Value: 0.2053050533304992
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network_backbone:MLPBackbone:dropout_4, Value: 0.3628626567848122
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network_backbone:MLPBackbone:dropout_5, Value: 0.000687232634536894
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network_backbone:MLPBackbone:dropout_6, Value: 0.30779918180581656
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network_backbone:MLPBackbone:dropout_7, Value: 0.4566654226669556
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network_backbone:MLPBackbone:num_groups, Value: 7
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network_backbone:MLPBackbone:num_units_1, Value: 749
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network_backbone:MLPBackbone:num_units_2, Value: 751
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network_backbone:MLPBackbone:num_units_3, Value: 759
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network_backbone:MLPBackbone:num_units_4, Value: 664
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network_backbone:MLPBackbone:num_units_5, Value: 219
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network_backbone:MLPBackbone:num_units_6, Value: 757
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network_backbone:MLPBackbone:num_units_7, Value: 1005
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network_backbone:MLPBackbone:use_dropout, Value: True
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network_backbone:__choice__, Value: 'MLPBackbone'
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network_embedding:LearnedEntityEmbedding:dimension_reduction_0, Value: 0.9640640623783606
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network_embedding:LearnedEntityEmbedding:dimension_reduction_1, Value: 0.017233504391813814
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network_embedding:LearnedEntityEmbedding:dimension_reduction_2, Value: 0.24122690885917664
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network_embedding:LearnedEntityEmbedding:dimension_reduction_3, Value: 0.31247176333246596
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network_embedding:LearnedEntityEmbedding:dimension_reduction_4, Value: 0.41504826813841933
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network_embedding:LearnedEntityEmbedding:dimension_reduction_5, Value: 0.8395119637200936
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network_embedding:LearnedEntityEmbedding:dimension_reduction_6, Value: 0.8208414027523236
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network_embedding:LearnedEntityEmbedding:dimension_reduction_7, Value: 0.4284420622613293
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network_embedding:LearnedEntityEmbedding:min_unique_values_for_embedding, Value: 7
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network_embedding:__choice__, Value: 'LearnedEntityEmbedding'
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network_head:__choice__, Value: 'fully_connected'
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network_head:fully_connected:num_layers, Value: 1
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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:AdamOptimizer:beta1, Value: 0.9770847327434384
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optimizer:AdamOptimizer:beta2, Value: 0.9710627513919582
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optimizer:AdamOptimizer:lr, Value: 0.00010844892447274338
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optimizer:AdamOptimizer:weight_decay, Value: 0.05048412416506887
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optimizer:__choice__, Value: 'AdamOptimizer'
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scaler:Normalizer:norm, Value: 'max'
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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=12, ta_time_used=167.8572690486908, wallclock_time=197.65661692619324, budget=16.666666666666664)]
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{'accuracy': 0.8554913294797688}
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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,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.2 |
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| 2 | None | ExtraTreesClassifier | 0.18 |
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| 3 | SimpleImputer,OneHotEncoder,Normalizer,KernelPCA | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 4 | None | KNNClassifier | 0.06 |
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| 5 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 6 | SimpleImputer,OneHotEncoder,Normalizer,KernelPCA | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 7 | None | RFClassifier | 0.04 |
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<smac.runhistory.runhistory.RunHistory object at 0x7fd57e5f4d00> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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| 0 | None | SVC | 0.18 |
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| 1 | None | RFClassifier | 0.16 |
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| 2 | None | CatBoostClassifier | 0.14 |
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| 3 | SimpleImputer,OneHotEncoder,Normalizer,KernelPCA | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.12 |
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| 4 | SimpleImputer,OneHotEncoder,Normalizer,KernelPCA | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
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| 5 | SimpleImputer,OneHotEncoder,Normalizer,NoFeaturePreprocessing | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
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| 6 | None | ExtraTreesClassifier | 0.08 |
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| 7 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 8 | SimpleImputer,OneHotEncoder,Normalizer,NoFeaturePreprocessing | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 9 | SimpleImputer,OneHotEncoder,Normalizer,NoFeaturePreprocessing | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 10 | None | KNNClassifier | 0.02 |
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| 11 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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<smac.runhistory.runhistory.RunHistory object at 0x7f92c7b660d0> [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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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=0.0012526512145996094, budget=0), TrajEntry(train_perf=0.19298245614035092, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0016224384307861328, budget=0), TrajEntry(train_perf=0.16374269005847952, 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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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=3.548128843307495, wallclock_time=4.968382835388184, 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 | CatBoostClassifier | 0.24 |
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| 1 | None | RFClassifier | 0.22 |
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| 2 | None | ExtraTreesClassifier | 0.18 |
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| 3 | None | KNNClassifier | 0.14 |
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| 4 | None | LGBMClassifier | 0.1 |
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| 5 | SimpleImputer,NoEncoder,MinMaxScaler,KitchenSink | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 6 | None | SVC | 0.06 |
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, ta_runs=1, ta_time_used=4.966748952865601, wallclock_time=6.570724964141846, budget=5.555555555555555)]
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{'accuracy': 0.8554913294797688}
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| | Preprocessing | Estimator | Weight |
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|---:|:--------------------------------------------------------------|:-------------------------------------------------------------------|---------:|
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| 0 | None | CatBoostClassifier | 0.74 |
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| 1 | None | SVC | 0.08 |
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| 2 | None | KNNClassifier | 0.06 |
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| 3 | None | RFClassifier | 0.04 |
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| 4 | None | ExtraTreesClassifier | 0.04 |
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| 5 | SimpleImputer,NoEncoder,MinMaxScaler,NoFeaturePreprocessing | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 6 | SimpleImputer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no 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:** ( 11 minutes 24.080 seconds)
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**Total running time of the script:** ( 11 minutes 43.212 seconds)
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.. _sphx_glr_download_advanced_tabular_example_custom_configuration_space.py:

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