MNT Don't normalize sample weights in KMeans#17848
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jnothman
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jnothman
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This is also more consistent with how other clusterers deal with sample weight
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I wonder if it's worth updating the docstring of fit to indicate that sample_weight is equivalent to replication? |
@jnothman, well it's not in many situations. In KMeans with the k-means++ or random init. In MinibatchKMeans no matter the init. Because sampling X will be different with duplicated points. So I'm not sure it's worth to advertise that when it only works in rare situations |
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True, thanks.
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| - |Fix| Fixed a bug in :class:`cluster.KMeans` and |
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Since the inertia will change, we should add this change in the Changed Model section.
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Thanks @jeremiedbb |
`sample_weight` should not be normalized in KMeans. The weight magnitude should have an influence on the `inertia_`, larger the weights, larger should be the inertia.
`sample_weight` should not be normalized in KMeans. The weight magnitude should have an influence on the `inertia_`, larger the weights, larger should be the inertia.
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sample_weights are normalized such that their sum = n_samples. Doing this normalization has absolutely no impact on the clustering. Moreover it's currently bugged since we don't invert the normalization to report the inertia (Fixes #16594).
This is extracted from #17622 to facilitate the reviews.
@rth @ogrisel @glemaitre