149 lines
5.7 KiB
Python
149 lines
5.7 KiB
Python
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import numpy as np
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import scipy.sparse as sp
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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_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 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():
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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_greater(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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assert_almost_equal(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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assert_almost_equal(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_greater(score_precomputed, 0)
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assert_greater(score_euclidean, 0)
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assert_almost_equal(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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assert_almost_equal(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_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():
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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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assert_raises_regexp(ValueError,
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'Number of labels is %d\. Valid values are 2 '
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'to n_samples - 1 \(inclusive\)' % len(np.unique(y)),
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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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assert_raises_regexp(ValueError,
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'Number of labels is %d\. Valid values are 2 '
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'to n_samples - 1 \(inclusive\)' % len(np.unique(y)),
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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_equal(
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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_equal(
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silhouette_score(list(X), list(y)), silhouette_score(X, y))
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def test_calinski_harabaz_score():
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rng = np.random.RandomState(seed=0)
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# Assert message when there is only one label
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assert_raise_message(ValueError, "Number of labels is",
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calinski_harabaz_score,
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rng.rand(10, 2), np.zeros(10))
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# Assert message when all point are in different clusters
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assert_raise_message(ValueError, "Number of labels is",
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calinski_harabaz_score,
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rng.rand(10, 2), np.arange(10))
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# Assert the value is 1. when all samples are equals
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assert_equal(1., calinski_harabaz_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_equal(0., calinski_harabaz_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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assert_almost_equal(calinski_harabaz_score(X, labels),
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45 * (40 - 4) / (5 * (4 - 1)))
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