Augmentation benchmark #150
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…nto augmentation-benchmark
bcebere
reviewed
Mar 13, 2023
| @@ -65,7 +65,7 @@ def test_plugin_hyperparams(test_plugin: Plugin) -> None: | |||
| ], | |||
| ) | |||
| def test_plugin_fit(tte_strategy: str) -> None: | |||
| test_plugin = plugin(tte_strategy=tte_strategy, device="cpu", **plugins_args) | |||
| test_plugin = plugin(tte_strategy=tte_strategy, device="zz", **plugins_args) | |||
src/synthcity/metrics/eval.py
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| use_cache=use_cache, | ||
| ), | ||
| X_gt.sample(eval_cnt), | ||
| X_augmented.sample(eval_cnt), |
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Is sample(eval_cnt) relevant for the augmented dataset? The augmented dataset will be larger than X_gt everytime, right?
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Yes, good point. I'll remove the sample call in the next push
bcebere
suggested changes
Mar 14, 2023
src/synthcity/benchmark/utils.py
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| if not set(ad_hoc_augment_vals.keys()).issubset( | ||
| set(X_train[fairness_column].values) | ||
| ): | ||
| print(set(X_train[fairness_column].values)) |
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Don't leave prints in the code. use log if the logs are needed.
| @@ -290,8 +295,6 @@ class GenericDataLoader(DataLoader): | |||
| >>> from synthcity.plugins.core.dataloader import GenericDataLoader | |||
| >>> X, y = load_diabetes(return_X_y=True, as_frame=True) | |||
| >>> X["target"] = y | |||
| >>> # Important note: preprocessing data with OneHotEncoder or StandardScaler is not needed or recommended. | |||
| >>> # Synthcity handles feature encoding and standardization internally. | |||
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Why did you remove these lines?
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Accident, re-instated.
tests/metrics/test_api.py
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| @pytest.mark.parametrize( | ||
| "fairness_column, rule, strict, add_hoc_vals", |
bcebere
approved these changes
Mar 14, 2023
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Description
Added an augmentation benchmark pipeline.
closes #136
Affected Dependencies
None
How has this been tested?
Checklist