Is the example output real?

[NasonZ]

Hi there,

First I wanted to say fantastic work, I'm looking forward to hopefully implementing this on some projects.

I've just run your example code:
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 my results are much lower than your example output.

`AUC scores of downstream classifiers on test data :

LR: 0.3808226623159139

Random Forest: 0.501662624031914

Neural Network: 0.43066009020256046

GaussianNB: 0.5190902722941861

GradientBoostingClassifier: 0.5755160128038637

`

Results were obtained on epoch 243, here's the final console output before training stopped:

Epoch : 283 Loss D real : 0.011110783401113983 Loss D fake : 0.010858841290446964 Loss G : 0.010988074410009374 Epsilon spent : 8.001855949312862

Any ideas why my output results are much lower and how I can fix this?

I did have another issue where the parser failed to pass the target variable to the pandas data frame of the train and test data in the evaluate.py. I fixed this by replacing all instances of opt.target_variable with 'income'. Not sure if the two issues are linked so I thought I would mention it.

[akki47]

I am getting the same AUC results with the default command on the adult_preprocessed dataset.

Did you figure out what the issue was or can the developers of this library help out on this?

[simkass]

@NasonZ I seem to be getting similar results to yours. Also, the synthetic data that is outputted doesn't seem to make much sense. It's very different from the real input data.

Have you figured out how to run this code properly? What are the parameters you ended up using?

Thanks