MNT Refactor center initialization in KMeans #17928
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glemaitre
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Jul 16, 2020
sklearn/cluster/_kmeans.py
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| Squared euclidean norm of each data point. Pass it if you have it | ||
| at hands already to avoid it being recomputed here. | ||
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| init : {'k-means++', 'random', ndarray, callable} |
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Suggested change
| init : {'k-means++', 'random', ndarray, callable} | |
| init : {'k-means++', 'random'}, callable or ndarray of shape (n_clusters, n_features) |
glemaitre
approved these changes
Jul 16, 2020
sklearn/cluster/_kmeans.py
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| Returns | ||
| ------- | ||
| centers : ndarray of shape(n_clusters, n_features) |
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| centers : ndarray of shape(n_clusters, n_features) | |
| centers : ndarray of shape (n_clusters, n_features) |
| @@ -624,7 +554,6 @@ def _labels_inertia(X, sample_weight, x_squared_norms, centers, | ||
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| n_threads = _openmp_effective_n_threads(n_threads) | ||
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| sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype) |
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This has been done in the main method (fit, predict, etc.)
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Right. The goal is to do all validations in the fit, predict, etc methods and have all private helpers assume validation is already done, to avoid duplicating validation
jayzed82
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Oct 22, 2020
* refactor center init in KMeans * address comments
This was referenced Apr 27, 2021
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Make the center initialization in a single place, i.e in
fit.Avoid repeated validations.
Make
_init_centroidsa method KMeans to make cleaner use of KMeans attributes.Extracted from #17622 to facilitate the reviews.
ping @glemaitre