Comments (12)
Working on this now. I am undoing the numba dependency I added today, which will allow travis to check this again.
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Travis is happy now with 13.1, and it works on my checkout. Let me know if that fixed it. (seems like numba and pip don't play nice)
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Hi, I just installed - Successfully installed iml-0.5.1 shap-0.13.1
One hour ago - fresh package, I too get this error:
ModuleNotFoundError: No module named 'shap.explainers'
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@firmai what OS are you on?
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I just pushed a fix to the setup.py script that should fix it (0.13.2). I'll also test more here though
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Everything seems to be working again now. This was all started by pushing a new core for the Tree SHAP method to support sklearn models. So now it looks like that core alg, from what I can tell, is working.
If anyone still has issues let me know and I'll reopen this.
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Thanks @slundberg, I had this issue yesterday however it is fixed now after the update.
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Hi! I tried to install shap using pip install shap
; it appears to install properly inside my environment. I' using Python 3.6.4 on a Mac OS. Would you please have any suggestions? Thank you!
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@e7dud7e is anything wrong?
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Hi! Thanks for checking back! Pip install works fine on a virtual machine that I’m running, so I think I’ll be okay. It just didn’t work on my local machine, but will just stick with the virtual machine. Thanks!
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Related Issues (20)
- ENH: Faster import performance HOT 1
- ENH: Label dots in scatterplot according to classes and add a legend
- BUG: SHAP DeepExplainer cannot get SHAP values from TorchScript model HOT 1
- CIBuildWheel failing on windows runners
- ENH: Limiting number of CPU cores used by shap HOT 4
- [Meta issue] Release 0.45.0 HOT 5
- BUG: Waterfall feature names IndexError HOT 1
- BUG: DeepExplainer throws error when using `__call__`
- CI failing on tensorflow 2.16+ due to incompatibility between transformers & keras V3
- BUG: Output 0 of BackwardHookFunctionBackward is a view and is being modified inplace. This view was created inside a custom Function (or because an input was returned as-is) and the autograd logic to handle view+inplace would override the custom backward associated with the custom Function, leading to incorrect gradients. This behavior is forbidden. You can fix this by cloning the output of the custom Functio HOT 3
- ENH: Display worst features with barplot
- Allow shap to take list or dict as input HOT 3
- BUG: LightGBM with multiclass interaction TreeShap produces explainer error HOT 12
- ENH: Integrate Fasttreeshap speedup into SHAP HOT 2
- BUG: `base_score` attribute of the `XGBTreeModelLoader` is broken for all exponential losses (e.g. tweedie, poisson) HOT 4
- BUG: ERROR USING LLAMA-2 HOT 15
- BUG: 0.45.0 update breaks pytorch example on docs HOT 1
- x
- BUG: Error using Falcon for text-generation HOT 4
- BUG: Error when using DeepExplainer on LSTM Model HOT 1
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