Closed AvantiShri closed 4 years ago
Hi Avanti, thank you very much for looking into the codebase. It is a mistake in the documentation. For DeepLift it uses all input baselines provided in the argument. In fact we can also support function as a baseline in the future.
I copied the documentation from GradientShap, where it does draw from the distribution and forgot to fix it. Thank you for pointing out to it :)
Ah a function as a baseline would be an awesome feature because in our lab we generate the baselines dynamically by shuffling each input sequence. Thank you!
Hi all,
Thank you so much for the invitation to captum. Very grateful to all of you for putting this together! I had a quick question regarding the documentation. Currently, in the arguments description for DeepLiftShap, it says "The first dimension in baseline tensors defines the distribution from which we randomly draw samples" (https://github.com/pytorch/captum/blob/6447777f8f63be7994feb031a3872d87dab5d972/captum/attr/_core/deep_lift.py#L313). However, when I look at the code, it seems as though all the baselines are used for all the inputs (i.e. I'm not seeing any code that I would associate with sampling). Is my understanding correct? I actually prefer the deterministic behavior because in my lab we typically supply multiple baselines per input and we want all the baselines to be used.
Thanks, Avanti