Comments (1)
Hi Deepak, it looks like the issue is that the package isn't correctly handling held-out features and making predictions with your model. This is one of the core operations when calculating SAGE values, and I wrote the package to work mainly with tabular data where the model input is size (batch, num_features)
. So it's just not currently set up for your use-case, but we should be able to make it work here.
The main thing we need to figure out is the feature imputer. Since you're working with embeddings, it may be simplest to impute held-out feature values with zeros (and this seems reasonable given that you're already training with 1d dropout in the second layer). The package's way of doing this is implemented in the DefaultImputer
class (here), but a couple possible issues jump out to me. First, can I ask which dimensions you want to consider as features? I'm guessing you want the dimension 32 to be features because the 64 dimension looks like the embedding size - is that right? Let me know and I can help write a corrected imputer class.
Also, can I ask what kind of data you're using with a GRU where you want to understand global rather than local feature importance?
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Related Issues (16)
- License HOT 1
- Parallelized computation HOT 15
- Possibility to use presegmented images HOT 3
- TreeSAGE ? HOT 6
- Zero-One Loss in Classification HOT 4
- Unstable SAGE values HOT 4
- All negative SAGE values in classification HOT 5
- Mismatch between feature importancies from `GroupedMarginalImputer` and `MarginalImputer` HOT 5
- pip install HOT 2
- Explanation about new changes in the SAGE package and addition of Model sensitivity module. HOT 6
- PermutationEstimator runs infinitely when gap = 0 HOT 3
- SAGE values on cross-validation HOT 2
- Shape mismatch on XGB.Classifier HOT 3
- adaptive estimator for online data HOT 2
- SAGE with NLP/Huggingface HOT 5
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