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[DEBUG] TST check sparse dense equivalence in KMeans #19403

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[DEBUG] TST check sparse dense equivalence in KMeans #19403

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713f6c7
TST check sparse dense equivalence
glemaitre Feb 8, 2021
37ce75a
fix n_init
glemaitre Feb 8, 2021
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28 changes: 27 additions & 1 deletion sklearn/cluster/tests/test_k_means.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,8 @@

import pytest

from sklearn.base import clone
from sklearn.utils import _safe_indexing
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.utils._testing import assert_allclose
Expand All @@ -30,7 +32,7 @@
from sklearn.cluster._k_means_fast import _euclidean_sparse_dense_wrapper
from sklearn.cluster._k_means_fast import _inertia_dense
from sklearn.cluster._k_means_fast import _inertia_sparse
from sklearn.datasets import make_blobs
from sklearn.datasets import make_blobs, make_classification
from io import StringIO


Expand Down Expand Up @@ -1091,3 +1093,27 @@ def test_kmeans_plusplus_dataorder():
centers_fortran, _ = kmeans_plusplus(X_fortran, n_clusters, random_state=0)

assert_allclose(centers_c, centers_fortran)


def test_xxx():
X, y = make_classification(
n_samples=1000,
n_classes=3,
n_informative=4,
weights=[0.2, 0.3, 0.5],
random_state=0,
)

target_class_indices = np.flatnonzero(y == 2)
X_class = _safe_indexing(X, target_class_indices)
X_class_sparse = sp.csr_matrix(X_class)

kmeans = KMeans(n_clusters=201, n_init=1, algorithm="full", random_state=0)

kmeans_dense = clone(kmeans).fit(X_class)
kmeans_sparse = clone(kmeans).fit(X_class_sparse)

np.testing.assert_allclose(
kmeans_dense.cluster_centers_,
kmeans_sparse.cluster_centers_
)