@@ -38,7 +38,7 @@ a 67% train size split.
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.. code-block :: none
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- <smac.runhistory.runhistory.RunHistory object at 0x7f35a79806a0 > [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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+ <smac.runhistory.runhistory.RunHistory object at 0x7f69eda5e670 > [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'
@@ -77,7 +77,7 @@ a 67% train size split.
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trainer:StandardTrainer:use_stochastic_weight_averaging, Value: True
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trainer:StandardTrainer:weighted_loss, Constant: 1
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trainer:__choice__, Value: 'StandardTrainer'
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- , ta_runs=0, ta_time_used=0.0, wallclock_time=0.0018994808197021484 , budget=0), TrajEntry(train_perf=0.16959064327485385, incumbent_id=1, incumbent=Configuration:
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+ , ta_runs=0, ta_time_used=0.0, wallclock_time=0.0019121170043945312 , budget=0), TrajEntry(train_perf=0.16959064327485385, 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'
@@ -116,19 +116,18 @@ a 67% train size split.
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trainer:StandardTrainer:use_stochastic_weight_averaging, Value: True
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trainer:StandardTrainer:weighted_loss, Constant: 1
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trainer:__choice__, Value: 'StandardTrainer'
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- , ta_runs=1, ta_time_used=12.164043426513672 , wallclock_time=13.653866529464722 , budget=5.555555555555555)]
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- {'accuracy': 0.8786127167630058 }
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+ , ta_runs=1, ta_time_used=14.97559404373169 , wallclock_time=16.456933736801147 , 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 | SimpleImputer,OneHotEncoder,Normalizer,PolynomialFeatures | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.18 |
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- | 1 | None | LGBMClassifier | 0.18 |
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- | 2 | None | RFClassifier | 0.18 |
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- | 3 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.16 |
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- | 4 | None | CatBoostClassifier | 0.14 |
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- | 5 | None | ExtraTreesClassifier | 0.1 |
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- | 6 | None | SVC | 0.04 |
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- | 7 | None | KNNClassifier | 0.02 |
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- <smac.runhistory.runhistory.RunHistory object at 0x7f35c2192be0> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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+ | 0 | None | LGBMClassifier | 0.22 |
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+ | 1 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.22 |
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+ | 2 | None | ExtraTreesClassifier | 0.2 |
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+ | 3 | None | RFClassifier | 0.16 |
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+ | 4 | None | SVC | 0.1 |
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+ | 5 | None | CatBoostClassifier | 0.08 |
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+ | 6 | None | KNNClassifier | 0.02 |
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+ <smac.runhistory.runhistory.RunHistory object at 0x7f69d7e87f10> [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'
@@ -167,7 +166,7 @@ a 67% train size split.
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trainer:StandardTrainer:use_stochastic_weight_averaging, Value: True
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trainer:StandardTrainer:weighted_loss, Constant: 1
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trainer:__choice__, Value: 'StandardTrainer'
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- , ta_runs=0, ta_time_used=0.0, wallclock_time=0.0021066665649414062 , budget=0), TrajEntry(train_perf=0.1623768221379145 , incumbent_id=1, incumbent=Configuration:
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+ , ta_runs=0, ta_time_used=0.0, wallclock_time=0.0020830631256103516 , budget=0), TrajEntry(train_perf=1.0 , 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'
@@ -206,18 +205,17 @@ a 67% train size split.
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trainer:StandardTrainer:use_stochastic_weight_averaging, Value: True
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trainer:StandardTrainer:weighted_loss, Constant: 1
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trainer:__choice__, Value: 'StandardTrainer'
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- , ta_runs=1, ta_time_used=30.45268940925598, wallclock_time=31.921412467956543, 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 | TabularClassifier | 0.22 |
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- | 1 | None | TabularClassifier | 0.22 |
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- | 2 | None | TabularClassifier | 0.22 |
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- | 3 | None | TabularClassifier | 0.16 |
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- | 4 | None | TabularClassifier | 0.1 |
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- | 5 | None | TabularClassifier | 0.06 |
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- | 6 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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- <smac.runhistory.runhistory.RunHistory object at 0x7f35a75718e0> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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+ , ta_runs=1, ta_time_used=32.47600293159485, wallclock_time=33.94159984588623, 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 | TabularClassifier | 0.24 |
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+ | 1 | None | TabularClassifier | 0.24 |
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+ | 2 | None | TabularClassifier | 0.18 |
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+ | 3 | None | TabularClassifier | 0.16 |
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+ | 4 | None | TabularClassifier | 0.12 |
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+ | 5 | None | TabularClassifier | 0.06 |
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+ <smac.runhistory.runhistory.RunHistory object at 0x7f69edc521f0> [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'
@@ -256,7 +254,7 @@ a 67% train size split.
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trainer:StandardTrainer:use_stochastic_weight_averaging, Value: True
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trainer:StandardTrainer:weighted_loss, Constant: 1
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trainer:__choice__, Value: 'StandardTrainer'
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- , ta_runs=0, ta_time_used=0.0, wallclock_time=0.0021975040435791016 , budget=0), TrajEntry(train_perf=0.16374269005847952, incumbent_id=1, incumbent=Configuration:
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+ , ta_runs=0, ta_time_used=0.0, wallclock_time=0.0020017623901367188 , budget=0), TrajEntry(train_perf=0.16374269005847952, 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'
@@ -295,7 +293,7 @@ a 67% train size split.
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trainer:StandardTrainer:use_stochastic_weight_averaging, Value: True
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trainer:StandardTrainer:weighted_loss, Constant: 1
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trainer:__choice__, Value: 'StandardTrainer'
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- , ta_runs=1, ta_time_used=12.529094219207764 , wallclock_time=14.05341625213623 , budget=5.555555555555555)]
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+ , ta_runs=1, ta_time_used=15.390185832977295 , wallclock_time=16.844798803329468 , budget=5.555555555555555)]
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{'accuracy': 0.884393063583815}
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| | Preprocessing | Estimator | Weight |
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|---:|:----------------|:---------------------|---------:|
@@ -466,7 +464,7 @@ a 67% train size split.
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.. rst-class :: sphx-glr-timing
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- **Total running time of the script: ** ( 9 minutes 58.270 seconds)
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+ **Total running time of the script: ** ( 10 minutes 7.307 seconds)
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.. _sphx_glr_download_advanced_tabular_example_resampling_strategy.py :
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