Fix bugs in cutout training#233
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ArlindKadra merged 2 commits intorefactor_development_regularization_cocktailsfrom May 21, 2021
Merged
Fix bugs in cutout training#233ArlindKadra merged 2 commits intorefactor_development_regularization_cocktailsfrom
ArlindKadra merged 2 commits intorefactor_development_regularization_cocktailsfrom
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ArlindKadra
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May 21, 2021
| @@ -39,7 +39,8 @@ def data_preparation(self, X: np.ndarray, y: np.ndarray, | |||
| # It is unlikely that the batch size is lower than the number of features, but | |||
| # be safe | |||
| size = min(X.shape[0], X.shape[1]) | |||
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This should also be changed to size=X.shape[1] right ?
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True, I missed that
ArlindKadra
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May 21, 2021
| replace=False) | ||
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| # We use an ordinal encoder on the tabular data | ||
| if not isinstance(self.numerical_columns, typing.Iterable): |
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What if the numerical columns are None, we should still continue with only categorical imputing in this case or not.
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Also if there are only numerical columns, there should not be a conversion for categorical ones.
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Actually when there are no numerical columns, it is not none but it is an empty list. And indexing with an empty list does not affect the tensor so this should work
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Is numerical_columns always in dataset_properties ?
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when its tabular data then yeah
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* Fix bugs in cutout training * Address comments from arlind
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This PR fixes the following bugs: