Stratified/equal sampling in FewShotDataLoader #15
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liltab/data/datasets.py
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| @@ -66,8 +67,10 @@ def __init__( | |||
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| self.y = self.df[self.target_columns] | |||
| if self.encode_categorical_target: | |||
| self.y = pd.get_dummies(self.y.astype("category")) | |||
| self.y = torch.from_numpy(self.y.to_numpy()).type(torch.float32) | |||
| self.one_hot_encoder = OneHotEncoder(sparse=False).set_output(transform="pandas") | |||
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RandomFeaturesPandasDataset needs to have the same logic applied (target_column and cases, self.raw_y etc.)
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| self.curr_episode = 0 | ||
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| self.n_rows = len(self.dataset) | ||
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| if sample_classes_equally: | ||
| self.y = dataset.raw_y |
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This step can be done in datasets module with one-hot from pytorch (https://pytorch.org/docs/stable/generated/torch.nn.functional.one_hot.html) and reduce memory allocation (both self.y and self.raw_y in datasets)
DawidPludowski
requested changes
Dec 4, 2023
DawidPludowski
requested changes
Dec 4, 2023
DawidPludowski
approved these changes
Dec 5, 2023
DawidPludowski
approved these changes
Dec 5, 2023
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