8000 Reverted the change, added regression test · scikit-learn/scikit-learn@c48626c · GitHub
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Reverted the change, added regression test
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‎sklearn/metrics/cluster/tests/test_unsupervised.py

Lines changed: 3 additions & 2 deletions
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@@ -2,7 +2,7 @@
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from scipy.sparse import csr_matrix
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from sklearn import datasets
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from sklearn.metrics.cluster.unsupervised import silhouette_score
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from sklearn.metrics.cluster.unsupervised import silhouette_score, silhouette_samples
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from sklearn.metrics import pairwise_distances
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from sklearn.utils.testing import assert_false, assert_almost_equal
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from sklearn.utils.testing import assert_raises_regexp
@@ -48,7 +48,8 @@ def test_no_nan():
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D = np.random.RandomState(0).rand(len(labels), len(labels))
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silhouette = silhouette_score(D, labels, metric='precomputed')
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assert_false(np.isnan(silhouette))
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silhouette_sample = silhouette_samples([[3],[3]], np.array([2,4]))
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assert_false(np.isnan(silhouette_sample).any())
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def test_correct_labelsize():
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# Assert 1 < n_labels < n_samples

‎sklearn/metrics/cluster/unsupervised.py

Lines changed: 1 addition & 1 deletion
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@@ -162,7 +162,7 @@ def silhouette_samples(X, labels, metric='euclidean', **kwds):
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B = np.array([_nearest_cluster_distance(distances[i], labels, i)
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for i in range(n)])
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sil_samples = (B - A) / np.maximum(A, B)
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return sil_samples
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return np.nan_to_num(sil_samples)
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def _intra_cluster_distance(distances_row, labels, i):

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