MNT Refactor fit method of NearestCentroid. #28072
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glemaitre
reviewed
Jan 11, 2024
| @@ -162,73 +169,86 @@ def fit(self, X, y): | ||
| X, y = self._validate_data(X, y, accept_sparse=["csc"]) | ||
| else: | ||
| X, y = self._validate_data(X, y, accept_sparse=["csr", "csc"]) | ||
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Please remove the break line to minimize the diff. They don't break more clarity to the codebase.
| multiple cancer types by shrunken centroids of gene expression. Proceedings | ||
| of the National Academy of Sciences of the United States of America, | ||
| 99(10), 6567-6572. The National Academy of Sciences. | ||
| <https://www.pnas.org/doi/full/10.1073/pnas.082099299>`_ |
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Nowadays we use :doi: sphinx marker. You can check other occurrences.
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| # Choose the transformation for boolean class mask vector. | ||
| if is_X_sparse: | ||
| mask_trf = lambda mask: np.where(mask)[0] # noqa: E731 |
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We don't use lambda and do not add exception to ruff.
| n_samples, n_features = X.shape | ||
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| # Compute the centroids. | ||
| self.centroids_ = np.empty((n_classes, n_features), dtype=np.float64) |
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Actually, I am wondering if this refactoring is necessary since we are modifying this code in this PR: #26689
I have to check if we can just combine both approaches.
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Reference Issues/PRs
None.
What does this implement/fix? Explain your changes.
This PR refactors the
fitmethod of the NearestCentroid classifier.Changes include:
Any other comments?
None.