Error when using continuous features 'capital_gain' and 'capital_loss' on Adult Income about dice

[raam93]

Can you post the error you are getting? It's hard to say anything without looking at the error.

Yes, you are right regarding cappital_gain and capital_loss - they are highly skewed and most of the observations had 0 value. So we did not use those variables in our experiments. As we mention in the paper, the data preparation is mostly based on the analysis here: https://rpubs.com/H_Zhu/235617.

[aivodji]

Thank you for your feedback on cappital_gain and capital_loss.
The error is "RuntimeWarning: divide by zero encountered in double_scalars feature_weights[feature] = round(1/normalized_mads[feature], 2)", and the relataded file is DiCE/dice_ml/dice_interfaces/dice_tensorflow1.py at line 279

[raam93]

Yeah, that happens when the MAD is 0. The code is now updated and it will replace all such occurrences with 1. So, currently, features with 0 MAD are not given any weights in optimization. Though it might make sense in many cases, we welcome other ways of handling this situation.

[aivodji]

Thanks for your feedback. By the way, I think the PrivateData class in private_data_interface.py also need a get_valid_mads method.

[raam93]

Ah, good catch! Thanks for pointing that out. I will update the private data notebook in a while.