Gaurav Gupta

@prunprun, could you turn off the `posthoc_sparsity_param` by setting this to None in your `generate_counterfactuals()` function call? That way the function `do_posthoc_sparsity_enhancement()` wouldn't get called. Let's see if the counterfactuals...

@finnschwall, the model that you are wrapping is still a pytorch model. So the genetic method may not apply to it. As Amit mentioned could you paste the line of...

Hi, The training data is just required for learning different things about the data. The generate_counterfactuals() is the actual call that generates the counterfactuals. So size of the training data...

No it is not normal. It seems like a bug. Are you able to reproduce this consistently? Regards,

I don't think DiCE library supports counterfactual generation for time series data as yet. CF for continuous variables is supported but not sure how do you capture the notion of...

@ramaswami1gomathy, can you provide the sample notebook, dice-ml version and dataset that you running? Regards,

Thanks @ramaswami1gomathy. Is dice-ml able to generate CFs for one point or 10 points? It seems like you are using 330 points. dice-ml takes roughly 0.5 second to generate counterfactuals...

@vdesai24, the output for the counterfactual is currently only shows the desired_class which is either 0 or 1. We could add another column which can contain the probabilities of the...

@msank00, sorry that you are facing this issue. There seems to be a bug in dice_random.py and it may not work with private dataset. I will have a PR out...

@amit-sharma, does the dice-ml private data interface work with dice_random.py, dice_genetic.py and dice_kd_tree.py? Or is this scenario not supported as of now?