46 lines
1.6 KiB
Python
46 lines
1.6 KiB
Python
import numpy as np
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from scipy.sparse import csr_matrix
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from .... import datasets
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from ..unsupervised import silhouette_score
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from ... import pairwise_distances
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from nose.tools import assert_false
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def test_silhouette():
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"""Tests the Silhouette Coefficient. """
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dataset = datasets.load_iris()
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X = dataset.data
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y = dataset.target
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D = pairwise_distances(X, metric='euclidean')
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# Given that the actual labels are used, we can assume that S would be
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# positive.
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silhouette = silhouette_score(D, y, metric='precomputed')
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assert(silhouette > 0)
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# Test without calculating D
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silhouette_metric = silhouette_score(X, y, metric='euclidean')
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assert(silhouette == silhouette_metric)
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# Test with sampling
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silhouette = silhouette_score(D, y, metric='precomputed',
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sample_size=int(X.shape[0] / 2))
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assert(silhouette > 0)
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# Test with sparse X
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X_sparse = csr_matrix(X)
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D = pairwise_distances(X_sparse, metric='euclidean')
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silhouette = silhouette_score(D, y, metric='precomputed')
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assert(silhouette > 0)
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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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"""
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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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assert_false(np.isnan(silhouette))
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