371 lines
10 KiB
Python
371 lines
10 KiB
Python
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import warnings
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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, score_dense_without_sampling)
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# Test with sampling
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score_precomputed = silhouette_score(
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D, y, metric="precomputed", sample_size=int(X.shape[0] / 2), random_state=0
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)
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score_euclidean = silhouette_score(
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X, y, metric="euclidean", sample_size=int(X.shape[0] / 2), random_state=0
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)
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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 = [[0.0], [1.0], [1.0], [2.0], [3.0], [3.0]]
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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, 0.5, 0.5, 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 = [
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5.58,
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7.00,
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6.50,
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7.08,
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7.00,
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3.83,
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4.83,
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5.08,
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8.17,
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5.83,
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2.17,
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5.75,
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6.67,
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6.92,
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4.92,
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6.42,
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5.00,
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5.58,
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6.00,
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4.67,
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6.42,
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3.42,
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5.50,
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6.42,
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6.42,
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5.00,
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3.92,
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6.17,
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2.50,
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4.92,
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6.25,
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7.33,
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4.50,
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2.25,
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6.33,
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2.75,
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6.08,
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6.67,
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4.25,
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2.67,
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6.00,
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6.17,
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6.17,
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6.92,
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6.17,
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5.25,
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6.83,
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4.50,
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3.75,
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5.75,
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5.42,
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6.08,
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5.83,
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6.67,
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3.67,
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4.75,
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3.00,
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6.08,
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6.67,
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5.00,
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5.58,
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4.83,
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6.17,
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5.67,
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6.50,
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6.92,
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]
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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 = [
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"BEL",
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"BRA",
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"CHI",
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"CUB",
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"EGY",
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"FRA",
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"IND",
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"ISR",
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"USA",
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"USS",
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"YUG",
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"ZAI",
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]
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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 = {
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"USA": 0.43,
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"BEL": 0.39,
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"FRA": 0.35,
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"ISR": 0.30,
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"BRA": 0.22,
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"EGY": 0.20,
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"ZAI": 0.19,
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"CUB": 0.40,
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"USS": 0.34,
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"CHI": 0.33,
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"YUG": 0.26,
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"IND": -0.04,
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}
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score1 = 0.28
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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 = {
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"USA": 0.47,
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"FRA": 0.44,
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"BEL": 0.42,
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"ISR": 0.37,
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"EGY": 0.02,
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"ZAI": 0.28,
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"BRA": 0.25,
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"IND": 0.17,
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"CUB": 0.48,
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"USS": 0.44,
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"YUG": 0.31,
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"CHI": 0.31,
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}
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score2 = 0.33
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for labels, expected, score in [
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(labels1, expected1, score1),
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(labels2, expected2, score2),
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]:
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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(
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expected,
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silhouette_samples(D, np.array(labels), metric="precomputed"),
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abs=1e-2,
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)
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pytest.approx(
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score, silhouette_score(D, np.array(labels), metric="precomputed"), abs=1e-2
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)
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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 = (
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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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)
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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 = (
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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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)
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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 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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)
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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 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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)
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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 1.0 == calinski_harabasz_score(np.ones((10, 2)), [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 0.0 == calinski_harabasz_score([[-1, -1], [1, 1]] * 10, [0] * 10 + [1] * 10)
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# General case (with non numpy arrays)
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X = (
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[[0, 0], [1, 1]] * 5
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+ [[3, 3], [4, 4]] * 5
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+ [[0, 4], [1, 3]] * 5
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+ [[3, 1], [4, 0]] * 5
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)
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labels = [0] * 10 + [1] * 10 + [2] * 10 + [3] * 10
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pytest.approx(calinski_harabasz_score(X, labels), 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)), [0] * 5 + [1] * 5) == pytest.approx(
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0.0
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)
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# Assert the value is 0. when all the mean cluster are equal
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assert davies_bouldin_score(
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[[-1, -1], [1, 1]] * 10, [0] * 10 + [1] * 10
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) == pytest.approx(0.0)
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# General case (with non numpy arrays)
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X = (
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[[0, 0], [1, 1]] * 5
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+ [[3, 3], [4, 4]] * 5
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+ [[0, 4], [1, 3]] * 5
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+ [[3, 1], [4, 0]] * 5
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)
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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 warnings.catch_warnings():
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warnings.simplefilter("error", RuntimeWarning)
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davies_bouldin_score(X, labels)
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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), (5.0 / 4) / 3)
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def test_silhouette_score_integer_precomputed():
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"""Check that silhouette_score works for precomputed metrics that are integers.
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Non-regression test for #22107.
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"""
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result = silhouette_score(
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[[0, 1, 2], [1, 0, 1], [2, 1, 0]], [0, 0, 1], metric="precomputed"
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)
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assert result == pytest.approx(1 / 6)
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# non-zero on diagonal for ints raises an error
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with pytest.raises(ValueError, match="contains non-zero"):
|
||
|
|
silhouette_score(
|
||
|
|
[[1, 1, 2], [1, 0, 1], [2, 1, 0]], [0, 0, 1], metric="precomputed"
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||
|
|
)
|