Add class to handle value filling #46
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added 30 commits
June 6, 2024 15:34
Modify Standardizer to ensure compatibility with the new parent class and method
…tested with the original standardizer class
…caler (issues with computing the median)
added 16 commits
June 20, 2024 13:33
…on was moved to the data_transformer class
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@dnerini Should we already aim for a ValueFiller that can differentiate between different variables ? |
dnerini
reviewed
Jun 27, 2024
mlpp_lib/value_filler.py
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| from typing_extensions import Self | ||
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| class ValueFiller: |
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I'd use "Imputer" since imputation is a widely used concept in ML, see e.g. https://scikit-learn.org/stable/modules/impute.html
louisPoulain
commented
Oct 23, 2024
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| def fit(self, | ||
| dataset: xr.Dataset, | ||
| dims: Optional[list] = None, | ||
| ): | ||
| self.fillvalue = self.fillvalue |
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Here we currently don't ask for all variables to be filled. This is not an issue as we remove NaNs afterwards anyway, but this behaviour may not be expected by users.
Should we force for all vars to be present ? Or have a "default" value assigned to the rest of variables ?
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