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Explain CNN for time series classification - possible? #1920
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Also wondering about the use of SHAP for 3D CNN. I have been working with ParititonShap for satellite images: both RGB and 13-band multispectral. From my understanding, it is not really considering the channels in a 3D way:
The partition can be provided as an input instead of generated within the library, but I am not sure that even a partition into cubes will be sufficient since the blurring is 2D. I could be totally wrong, this is just my understanding from exploring the code. Hopefully someone has some insights on working with 3D. |
I have spent more time studying the code. I think there is potential for 3D CNN support. Hopefully a dev can weigh in, since I could be totally off.
Thus:
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Check this PR: #1944 I only added a channel-first partition scheme and a way to view the channel-wise SHAP values. But you can check what I did and see if it is at all useful to use or build off of. I would be very interested if you have success with 3D CNN SHAP values! |
Many thanks, Evan. It's a pretty great addition already! Will send you feedback asap. |
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Hi,
thank you for this great work!
Is it possible to apply SHAP for explaining CNN models applied for multivariate time series classification (i.e., i CNN using 3D input - x samples x timesteps x features)?
I found some discussion and examples for RNN/LSTM architectures, but so far could not find an example for CNN. Do you know if this is possible? Any examples (from here or elsewhere) would also be greatly appreciated.
Many thanks in advance.
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