8000 [MRG+1] Fix regression in silhouette_score for clusters of size 1 (#7… · scikit-learn/scikit-learn@6bc1257 · GitHub
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[MRG+1] Fix regression in silhouette_score for clusters of size 1 (#7438)
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‎doc/whats_new.rst

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@@ -467,6 +467,14 @@ Bug fixes
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(`#7350 <https://github.com/scikit-learn/scikit-learn/pull/7350/>`_)
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By `Russell Smith`_.
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- Fix bug in :func:`metrics.silhouette_score` in which clusters of
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size 1 were incorrectly scored. They should get a score of 0.
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By `Joel Nothman`_.
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- Fix bug in :func:`metrics.silhouette_samples` so that it now works with
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arbitrary labels, not just those ranging from 0 to n_clusters - 1.
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By `Joel Nothman`_.
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API changes summary
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-------------------

‎sklearn/metrics/cluster/tests/test_unsupervised.py

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@@ -5,13 +5,15 @@
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from sklearn import datasets
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from sklearn.utils.testing import assert_false
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from sklearn.utils.testing import assert_almost_equal
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from sklearn.utils.testing import assert_array_equal
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from sklearn.utils.testing import assert_equal
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from sklearn.utils.testing import assert_raises_regexp
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from sklearn.utils.testing import assert_raise_message
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from sklearn.utils.testing import assert_greater
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from sklearn.metrics.cluster import silhouette_score
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from sklearn.metrics.cluster import calinski_harabaz_score
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from sklearn.metrics.cluster import silhouette_samples
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from sklearn.metrics import pairwise_distances
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from sklearn.metrics.cluster import calinski_harabaz_score
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def test_silhouette():
@@ -56,16 +58,27 @@ def test_silhouette():
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assert_almost_equal(score_euclidean, score_dense_with_sampling)
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def test_no_nan():
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# Assert Silhouette Coefficient != nan when there is 1 sample in a class.
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# This tests for the condition that caused issue 960.
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# Note that there is only one sample in cluster 0. This used to cause the
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# silhouette_score to return nan (see bug #960).
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labels = np.array([1, 0, 1, 1, 1])
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# The distance matrix doesn't actually matter.
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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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def test_cluster_size_1():
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# Assert Silhouette Coefficient == 0 when there is 1 sample in a cluster
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# (cluster 0). We also test the case where there are identical samples
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# as the only members of a cluster (cluster 2). To our knowledge, this case
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# is not discussed in reference material, and we choose for it a sample
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# score of 1.
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X = [[0.], [1.], [1.], [2.], [3.], [3.]]
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labels = np.array([0, 1, 1, 1, 2, 2])
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# Cluster 0: 1 sample -> score of 0 by Rousseeuw's convention
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# Cluster 1: intra-cluster = [.5, .5, 1]
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# inter-cluster = [1, 1, 1]
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# silhouette = [.5, .5, 0]
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# Cluster 2: intra-cluster = [0, 0]
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# inter-cluster = [arbitrary, arbitrary]
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# silhouette = [1., 1.]
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silhouette = silhouette_score(X, labels)
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assert_false(np.isnan(silhouette))
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ss = silhouette_samples(X, labels)
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assert_array_equal(ss, [0, .5, .5, 0, 1, 1])
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def test_correct_labelsize():
@@ -93,7 +106,9 @@ def test_non_encoded_labels():
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X = dataset.data
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labels = dataset.target
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assert_equal(
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silhouette_score(X, labels + 10), silhouette_score(X, labels))
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silhouette_score(X, labels * 2 + 10), silhouette_score(X, labels))
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assert_array_equal(
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silhouette_samples(X, labels * 2 + 10), silhouette_samples(X, labels))
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def test_non_numpy_labels():

‎sklearn/metrics/cluster/unsupervised.py

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le = LabelEncoder()
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@@ -9,6 +9,7 @@
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from ...utils import check_random_state
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from ...utils import check_X_y
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from ...utils.fixes import bincount
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from ..pairwise import pairwise_distances
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from ...preprocessing import LabelEncoder
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@@ -88,15 +89,8 @@ def silhouette_score(X, labels, metric='euclidean', sample_size=None,
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<https://en.wikipedia.org/wiki/Silhouette_(clustering)>`_
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"""
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X, labels = check_X_y(X, labels, accept_sparse=['csc', 'csr'])
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labels = le.fit_transform(labels)
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n_labels = len(le.classes_)
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n_samples = X.shape[0]
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check_number_of_labels(n_labels, n_samples)
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if sample_size is not None:
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X, labels = check_X_y(X, labels, accept_sparse=['csc', 'csr'])
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random_state = check_random_state(random_state)
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indices = random_state.permutation(X.shape[0])[:sample_size]
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if metric == "precomputed":
@@ -166,36 +160,39 @@ def silhouette_samples(X, labels, metric='euclidean', **kwds):
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<https://en.wikipedia.org/wiki/Silhouette_(clustering)>`_
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"""
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X, labels = check_X_y(X, labels, accept_sparse=['csc', 'csr'])
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le = LabelEncoder()
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labels = le.fit_transform(labels)
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check_number_of_labels(len(le.classes_), X.shape[0])
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distances = pairwise_distances(X, metric=metric, **kwds)
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unique_labels = le.classes_
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n_samples_per_label = bincount(labels, minlength=len(unique_labels))
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# For sample i, store the mean distance of the cluster to which
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# it belongs in intra_clust_dists[i]
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intra_clust_dists = np.ones(distances.shape[0], dtype=distances.dtype)
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intra_clust_dists = np.zeros(distances.shape[0], dtype=distances.dtype)
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# For sample i, store the mean distance of the second closest
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# cluster in inter_clust_dists[i]
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inter_clust_dists = np.inf * intra_clust_dists
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inter_clust_dists = np.inf + intra_clust_dists
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for curr_label in unique_labels:
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for curr_label in range(len(unique_labels)):
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# Find inter_clust_dist for all samples belonging to the same
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# label.
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mask = labels == curr_label
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current_distances = distances[mask]
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# Leave out current sample.
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n_samples_curr_lab = np.sum(mask) - 1
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n_samples_curr_lab = n_samples_per_label[curr_label] - 1
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if n_samples_curr_lab != 0:
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intra_clust_dists[mask] = np.sum(
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current_distances[:, mask], axis=1) / n_samples_curr_lab
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# Now iterate over all other labels, finding the mean
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# cluster distance that is closest to every sample.
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for other_label in unique_labels:
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for other_label in range(len(unique_labels)):
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if other_label != curr_label:
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other_mask = labels == other_label
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other_distances = np.mean(
@@ -205,6 +202,8 @@ def silhouette_samples(X, labels, metric='euclidean', **kwds):
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sil_samples = inter_clust_dists - intra_clust_dists
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sil_samples /= np.maximum(intra_clust_dists, inter_clust_dists)
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# score 0 for clusters of size 1, according to the paper
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sil_samples[n_samples_per_label.take(labels) == 1] = 0
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return sil_samples
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