Open Saladino93 opened 3 years ago
All these are great ideas @Saladino93 Thanks for sharing them. The first two can be implemented. The fourth one is on our roadmap but needs more thought for timeseries. Causality is harder and it may take some time for the research to mature to think of putting it in DiCE.
For the last two suggestions, would you like to contribute to DiCE? It will be really nice to have the last one especially on english reason generator.
@amit-sharma Sure, I can try to help! Will write up something and share with you!
@amit-sharma would you mind sharing some thoughts on the time series counterfactuals please?
I am starting to get more familiar with DICE. It is a fantastic library thanks a lot guys.
I wanted to ask what it is your roadmap for improvements (apart from the things mentioned in the intro docs)?
I am thinking:
feature_importance
function in http://interpret.ml/DiCE/_modules/dice_ml/explainer_interfaces/explainer_base.html#ExplainerBase.local_feature_importance for example there is thenp.close
: I think this can be improved by using an absolute or relative tolerance, as sometimes changing a value from x to x(1+1e-5) is not really changing it. Then you exclude all the examples that are within the tolerance, given a feature. (Also, seems sometimes that the feature attribution scores are greater than one! I am not sure if this is a bug or not)cf_examples_list
. I implemented something, but basically, once you generate your counterfactuals, you can set up some tabu_changes, and any allowed change in your CFs is excluded from the final count.I am sure you have already thought about some of these (e.g. English), and other things are going on. Curious to know your thoughts!