K-Means: Subtract X_means from initial centroids iff it's also subtracted from X #6741
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…cted from X
The bug happens when X is sparse and initial cluster centroids are
given. In this case the means of each of X's columns are computed and
subtracted from init for no reason.
To reproduce:
import numpy as np
import scipy
from sklearn.cluster import KMeans
from sklearn import datasets
iris = datasets.load_iris()
X = iris.data
'''Get a local optimum'''
centers = KMeans(n_clusters=3).fit(X).cluster_centers_
'''Fit starting from a local optimum shouldn't change the solution'''
np.testing.assert_allclose(
centers,
KMeans(n_clusters=3, init=centers, n_init=1).fit(X).cluster_centers_
)
'''The same should be true when X is sparse, but wasn't before the bug fix'''
X_sparse = scipy.sparse.csr_matrix(X)
np.testing.assert_allclose(
centers,
KMeans(n_clusters=3, init=centers, n_init=1).fit(X_sparse).cluster_centers_
)
|
Hi, thanks for the fix. It looks like the bug is now fixed. Can you please add the above example as a regression test? Thanks. |
You mean to say it will be fixed by this PR right? Besides +1 for the NRT like @maniteja123 suggested |
|
@tomtung Have you done any update of this PR ? |
|
are you still working on this? I'll put the "needs contributor" tag on this for now. |
|
Seems like bug is fixed and the code need some care to merge it? I think I can handle this. |
|
Thanks @kiote. It also needs a test. |
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Reference Issue
Fixes #6740
What does this implement/fix? Explain your changes.
Subtract X_means from initial centroids iff it's also subtracted from X
Any other comments?
The bug happens when X is sparse and initial cluster centroids are given. In this case the means of each of X's columns are computed and subtracted from init for no reason.
To reproduce: