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BorealisAI / private-data-generation

A toolbox for differentially private data generation
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Different auc with the result #6

Open Hiramdu opened 2 years ago

Hiramdu commented 2 years ago

Hi I run demo: python evaluate.py --target-variable='income' --train-data-path=./data/adult_processed_train.csv --test-data-path=./data/adult_processed_test.csv --normalize-data dp-wgan --enable-privacy --sigma=0.8 --target-epsilon=8

But I get very low auc: AUC scores of downstream classifiers on test data : LR: 0.3407119900995038 Random Forest: 0.2610576777879843 Neural Network: 0.3578788366713348 GaussianNB: 0.49315881768882325 GradientBoostingClassifier: 0.2606982987000339

May I know how to run the code to get your listed result?

Hiramdu commented 2 years ago

Another demo on diabete data: AUC scores of downstream classifiers on test data :

LR: 0.5467898193760263

Random Forest: 0.39399288451012593

Neural Network: 0.5228927203065135

GaussianNB: 0.4653694581280788

GradientBoostingClassifier: 0.5630377668308703