253 lines
9.5 KiB
Python
253 lines
9.5 KiB
Python
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import numpy as np
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import scipy.sparse as sp
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import pytest
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from scipy.sparse import csr_matrix
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from sklearn import datasets
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from sklearn.utils._testing import assert_array_equal
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from sklearn.metrics.cluster import silhouette_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_harabasz_score
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from sklearn.metrics.cluster import davies_bouldin_score
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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_dense = dataset.data
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X_csr = csr_matrix(X_dense)
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X_dok = sp.dok_matrix(X_dense)
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X_lil = sp.lil_matrix(X_dense)
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y = dataset.target
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for X in [X_dense, X_csr, X_dok, X_lil]:
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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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score_precomputed = silhouette_score(D, y, metric='precomputed')
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assert score_precomputed > 0
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# Test without calculating D
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score_euclidean = silhouette_score(X, y, metric='euclidean')
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pytest.approx(score_precomputed, score_euclidean)
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if X is X_dense:
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score_dense_without_sampling = score_precomputed
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else:
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pytest.approx(score_euclidean,
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score_dense_without_sampling)
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# Test with sampling
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score_precomputed = silhouette_score(D, y, metric='precomputed',
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sample_size=int(X.shape[0] / 2),
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random_state=0)
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score_euclidean = silhouette_score(X, y, metric='euclidean',
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sample_size=int(X.shape[0] / 2),
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random_state=0)
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assert score_precomputed > 0
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assert score_euclidean > 0
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pytest.approx(score_euclidean, score_precomputed)
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if X is X_dense:
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score_dense_with_sampling = score_precomputed
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else:
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pytest.approx(score_euclidean, score_dense_with_sampling)
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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 not 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_silhouette_paper_example():
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# Explicitly check per-sample results against Rousseeuw (1987)
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# Data from Table 1
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lower = [5.58,
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7.00, 6.50,
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7.08, 7.00, 3.83,
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4.83, 5.08, 8.17, 5.83,
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2.17, 5.75, 6.67, 6.92, 4.92,
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6.42, 5.00, 5.58, 6.00, 4.67, 6.42,
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3.42, 5.50, 6.42, 6.42, 5.00, 3.92, 6.17,
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2.50, 4.92, 6.25, 7.33, 4.50, 2.25, 6.33, 2.75,
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6.08, 6.67, 4.25, 2.67, 6.00, 6.17, 6.17, 6.92, 6.17,
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5.25, 6.83, 4.50, 3.75, 5.75, 5.42, 6.08, 5.83, 6.67, 3.67,
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4.75, 3.00, 6.08, 6.67, 5.00, 5.58, 4.83, 6.17, 5.67, 6.50, 6.92]
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D = np.zeros((12, 12))
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D[np.tril_indices(12, -1)] = lower
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D += D.T
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names = ['BEL', 'BRA', 'CHI', 'CUB', 'EGY', 'FRA', 'IND', 'ISR', 'USA',
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'USS', 'YUG', 'ZAI']
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# Data from Figure 2
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labels1 = [1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 2, 1]
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expected1 = {'USA': .43, 'BEL': .39, 'FRA': .35, 'ISR': .30, 'BRA': .22,
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'EGY': .20, 'ZAI': .19, 'CUB': .40, 'USS': .34, 'CHI': .33,
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'YUG': .26, 'IND': -.04}
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score1 = .28
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# Data from Figure 3
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labels2 = [1, 2, 3, 3, 1, 1, 2, 1, 1, 3, 3, 2]
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expected2 = {'USA': .47, 'FRA': .44, 'BEL': .42, 'ISR': .37, 'EGY': .02,
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'ZAI': .28, 'BRA': .25, 'IND': .17, 'CUB': .48, 'USS': .44,
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'YUG': .31, 'CHI': .31}
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score2 = .33
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for labels, expected, score in [(labels1, expected1, score1),
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(labels2, expected2, score2)]:
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expected = [expected[name] for name in names]
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# we check to 2dp because that's what's in the paper
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pytest.approx(expected,
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silhouette_samples(D, np.array(labels),
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metric='precomputed'),
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abs=1e-2)
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pytest.approx(score,
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silhouette_score(D, np.array(labels),
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metric='precomputed'),
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abs=1e-2)
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def test_correct_labelsize():
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# Assert 1 < n_labels < n_samples
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dataset = datasets.load_iris()
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X = dataset.data
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# n_labels = n_samples
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y = np.arange(X.shape[0])
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err_msg = (r'Number of labels is %d\. Valid values are 2 '
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r'to n_samples - 1 \(inclusive\)' % len(np.unique(y)))
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with pytest.raises(ValueError, match=err_msg):
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silhouette_score(X, y)
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# n_labels = 1
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y = np.zeros(X.shape[0])
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err_msg = (r'Number of labels is %d\. Valid values are 2 '
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r'to n_samples - 1 \(inclusive\)' % len(np.unique(y)))
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with pytest.raises(ValueError, match=err_msg):
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silhouette_score(X, y)
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def test_non_encoded_labels():
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dataset = datasets.load_iris()
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X = dataset.data
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labels = dataset.target
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assert (
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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():
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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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assert (
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silhouette_score(list(X), list(y)) == silhouette_score(X, y))
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@pytest.mark.parametrize('dtype', (np.float32, np.float64))
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def test_silhouette_nonzero_diag(dtype):
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# Make sure silhouette_samples requires diagonal to be zero.
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# Non-regression test for #12178
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# Construct a zero-diagonal matrix
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dists = pairwise_distances(
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np.array([[0.2, 0.1, 0.12, 1.34, 1.11, 1.6]], dtype=dtype).T)
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labels = [0, 0, 0, 1, 1, 1]
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# small values on the diagonal are OK
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dists[2][2] = np.finfo(dists.dtype).eps * 10
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silhouette_samples(dists, labels, metric='precomputed')
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# values bigger than eps * 100 are not
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dists[2][2] = np.finfo(dists.dtype).eps * 1000
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with pytest.raises(ValueError, match='contains non-zero'):
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silhouette_samples(dists, labels, metric='precomputed')
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def assert_raises_on_only_one_label(func):
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"""Assert message when there is only one label"""
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rng = np.random.RandomState(seed=0)
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with pytest.raises(ValueError, match="Number of labels is"):
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func(rng.rand(10, 2), np.zeros(10))
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def assert_raises_on_all_points_same_cluster(func):
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"""Assert message when all point are in different clusters"""
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rng = np.random.RandomState(seed=0)
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with pytest.raises(ValueError, match="Number of labels is"):
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func(rng.rand(10, 2), np.arange(10))
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def test_calinski_harabasz_score():
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assert_raises_on_only_one_label(calinski_harabasz_score)
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assert_raises_on_all_points_same_cluster(calinski_harabasz_score)
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# Assert the value is 1. when all samples are equals
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assert 1. == calinski_harabasz_score(np.ones((10, 2)),
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[0] * 5 + [1] * 5)
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# Assert the value is 0. when all the mean cluster are equal
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assert 0. == calinski_harabasz_score([[-1, -1], [1, 1]] * 10,
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[0] * 10 + [1] * 10)
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# General case (with non numpy arrays)
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X = ([[0, 0], [1, 1]] * 5 + [[3, 3], [4, 4]] * 5 +
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[[0, 4], [1, 3]] * 5 + [[3, 1], [4, 0]] * 5)
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labels = [0] * 10 + [1] * 10 + [2] * 10 + [3] * 10
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pytest.approx(calinski_harabasz_score(X, labels),
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45 * (40 - 4) / (5 * (4 - 1)))
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def test_davies_bouldin_score():
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assert_raises_on_only_one_label(davies_bouldin_score)
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assert_raises_on_all_points_same_cluster(davies_bouldin_score)
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# Assert the value is 0. when all samples are equals
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assert davies_bouldin_score(np.ones((10, 2)),
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[0] * 5 + [1] * 5) == pytest.approx(0.0)
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# Assert the value is 0. when all the mean cluster are equal
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assert davies_bouldin_score([[-1, -1], [1, 1]] * 10,
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[0] * 10 + [1] * 10) == pytest.approx(0.0)
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# General case (with non numpy arrays)
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X = ([[0, 0], [1, 1]] * 5 + [[3, 3], [4, 4]] * 5 +
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[[0, 4], [1, 3]] * 5 + [[3, 1], [4, 0]] * 5)
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labels = [0] * 10 + [1] * 10 + [2] * 10 + [3] * 10
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pytest.approx(davies_bouldin_score(X, labels), 2 * np.sqrt(0.5) / 3)
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# Ensure divide by zero warning is not raised in general case
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with pytest.warns(None) as record:
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davies_bouldin_score(X, labels)
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div_zero_warnings = [
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warning for warning in record
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if "divide by zero encountered" in warning.message.args[0]
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]
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assert len(div_zero_warnings) == 0
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# General case - cluster have one sample
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X = ([[0, 0], [2, 2], [3, 3], [5, 5]])
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labels = [0, 0, 1, 2]
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pytest.approx(davies_bouldin_score(X, labels), (5. / 4) / 3)
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