Fixes for Boolean Input Tensors #666
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| else: | ||
| binary_mask = tuple( | ||
| curr_sample[0][feature_mask[j]] for j in range(len(feature_mask)) | ||
| curr_sample[0][feature_mask[j]].bool() for j in range(len(feature_mask)) |
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Is this change related to inputs being boolean or is it so that we can use ~ operator ?
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Yes, this is necessary since if this mask is an int / float, multiplying by a boolean still casts product to int / float causing the masked input to not be a boolean.
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| def test_simple_shapley_sampling_boolean_with_baseline(self) -> None: | ||
| net = BasicModelBoolInput() |
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Another way of testing would be using BasicModel_MultiLayer() with the transformed input and seeing if we get the same result but if we manually verified expected value, then it looks good to me.
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Yes, I double checked the values manually, they seem correct.
This adds support for boolean input tensors to perturbation-based methods (Shapley Value Sampling and Lime) with corresponding unit tests.