246 lines
8.8 KiB
Python
246 lines
8.8 KiB
Python
"""
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Testing for Clustering methods
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"""
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import numpy as np
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import pytest
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from scipy.sparse import csr_matrix
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from sklearn.exceptions import ConvergenceWarning
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from sklearn.utils._testing import (
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assert_array_equal, assert_warns,
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assert_warns_message, assert_no_warnings)
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from sklearn.cluster import AffinityPropagation
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from sklearn.cluster._affinity_propagation import (
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_equal_similarities_and_preferences
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)
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from sklearn.cluster import affinity_propagation
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from sklearn.datasets import make_blobs
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from sklearn.metrics import euclidean_distances
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n_clusters = 3
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centers = np.array([[1, 1], [-1, -1], [1, -1]]) + 10
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X, _ = make_blobs(n_samples=60, n_features=2, centers=centers,
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cluster_std=0.4, shuffle=True, random_state=0)
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def test_affinity_propagation():
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# Affinity Propagation algorithm
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# Compute similarities
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S = -euclidean_distances(X, squared=True)
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preference = np.median(S) * 10
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# Compute Affinity Propagation
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cluster_centers_indices, labels = affinity_propagation(
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S, preference=preference, random_state=39)
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n_clusters_ = len(cluster_centers_indices)
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assert n_clusters == n_clusters_
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af = AffinityPropagation(preference=preference, affinity="precomputed",
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random_state=28)
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labels_precomputed = af.fit(S).labels_
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af = AffinityPropagation(preference=preference, verbose=True,
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random_state=37)
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labels = af.fit(X).labels_
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assert_array_equal(labels, labels_precomputed)
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cluster_centers_indices = af.cluster_centers_indices_
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n_clusters_ = len(cluster_centers_indices)
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assert np.unique(labels).size == n_clusters_
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assert n_clusters == n_clusters_
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# Test also with no copy
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_, labels_no_copy = affinity_propagation(S, preference=preference,
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copy=False, random_state=74)
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assert_array_equal(labels, labels_no_copy)
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# Test input validation
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with pytest.raises(ValueError):
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affinity_propagation(S[:, :-1])
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with pytest.raises(ValueError):
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affinity_propagation(S, damping=0)
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af = AffinityPropagation(affinity="unknown", random_state=78)
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with pytest.raises(ValueError):
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af.fit(X)
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af_2 = AffinityPropagation(affinity='precomputed', random_state=21)
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with pytest.raises(TypeError):
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af_2.fit(csr_matrix((3, 3)))
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def test_affinity_propagation_predict():
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# Test AffinityPropagation.predict
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af = AffinityPropagation(affinity="euclidean", random_state=63)
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labels = af.fit_predict(X)
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labels2 = af.predict(X)
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assert_array_equal(labels, labels2)
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def test_affinity_propagation_predict_error():
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# Test exception in AffinityPropagation.predict
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# Not fitted.
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af = AffinityPropagation(affinity="euclidean")
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with pytest.raises(ValueError):
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af.predict(X)
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# Predict not supported when affinity="precomputed".
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S = np.dot(X, X.T)
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af = AffinityPropagation(affinity="precomputed", random_state=57)
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af.fit(S)
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with pytest.raises(ValueError):
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af.predict(X)
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def test_affinity_propagation_fit_non_convergence():
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# In case of non-convergence of affinity_propagation(), the cluster
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# centers should be an empty array and training samples should be labelled
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# as noise (-1)
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X = np.array([[0, 0], [1, 1], [-2, -2]])
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# Force non-convergence by allowing only a single iteration
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af = AffinityPropagation(preference=-10, max_iter=1, random_state=82)
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assert_warns(ConvergenceWarning, af.fit, X)
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assert_array_equal(np.empty((0, 2)), af.cluster_centers_)
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assert_array_equal(np.array([-1, -1, -1]), af.labels_)
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def test_affinity_propagation_equal_mutual_similarities():
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X = np.array([[-1, 1], [1, -1]])
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S = -euclidean_distances(X, squared=True)
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# setting preference > similarity
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cluster_center_indices, labels = assert_warns_message(
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UserWarning, "mutually equal", affinity_propagation, S, preference=0)
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# expect every sample to become an exemplar
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assert_array_equal([0, 1], cluster_center_indices)
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assert_array_equal([0, 1], labels)
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# setting preference < similarity
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cluster_center_indices, labels = assert_warns_message(
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UserWarning, "mutually equal", affinity_propagation, S, preference=-10)
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# expect one cluster, with arbitrary (first) sample as exemplar
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assert_array_equal([0], cluster_center_indices)
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assert_array_equal([0, 0], labels)
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# setting different preferences
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cluster_center_indices, labels = assert_no_warnings(
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affinity_propagation, S, preference=[-20, -10], random_state=37)
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# expect one cluster, with highest-preference sample as exemplar
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assert_array_equal([1], cluster_center_indices)
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assert_array_equal([0, 0], labels)
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def test_affinity_propagation_predict_non_convergence():
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# In case of non-convergence of affinity_propagation(), the cluster
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# centers should be an empty array
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X = np.array([[0, 0], [1, 1], [-2, -2]])
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# Force non-convergence by allowing only a single iteration
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af = assert_warns(ConvergenceWarning,
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AffinityPropagation(preference=-10,
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max_iter=1, random_state=75).fit, X)
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# At prediction time, consider new samples as noise since there are no
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# clusters
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to_predict = np.array([[2, 2], [3, 3], [4, 4]])
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y = assert_warns(ConvergenceWarning, af.predict, to_predict)
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assert_array_equal(np.array([-1, -1, -1]), y)
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def test_affinity_propagation_non_convergence_regressiontest():
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X = np.array([[1, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 0],
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[0, 0, 1, 0, 0, 1]])
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af = AffinityPropagation(affinity='euclidean',
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max_iter=2, random_state=34).fit(X)
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assert_array_equal(np.array([-1, -1, -1]), af.labels_)
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def test_equal_similarities_and_preferences():
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# Unequal distances
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X = np.array([[0, 0], [1, 1], [-2, -2]])
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S = -euclidean_distances(X, squared=True)
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assert not _equal_similarities_and_preferences(S, np.array(0))
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assert not _equal_similarities_and_preferences(S, np.array([0, 0]))
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assert not _equal_similarities_and_preferences(S, np.array([0, 1]))
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# Equal distances
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X = np.array([[0, 0], [1, 1]])
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S = -euclidean_distances(X, squared=True)
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# Different preferences
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assert not _equal_similarities_and_preferences(S, np.array([0, 1]))
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# Same preferences
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assert _equal_similarities_and_preferences(S, np.array([0, 0]))
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assert _equal_similarities_and_preferences(S, np.array(0))
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def test_affinity_propagation_random_state():
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# Significance of random_state parameter
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# Generate sample data
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centers = [[1, 1], [-1, -1], [1, -1]]
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X, labels_true = make_blobs(n_samples=300, centers=centers,
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cluster_std=0.5, random_state=0)
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# random_state = 0
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ap = AffinityPropagation(convergence_iter=1, max_iter=2, random_state=0)
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ap.fit(X)
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centers0 = ap.cluster_centers_
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# random_state = 76
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ap = AffinityPropagation(convergence_iter=1, max_iter=2, random_state=76)
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ap.fit(X)
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centers76 = ap.cluster_centers_
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assert np.mean((centers0 - centers76) ** 2) > 1
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# FIXME: to be removed in 0.25
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def test_affinity_propagation_random_state_warning():
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# test that a warning is raised when random_state is not defined.
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X = np.array([[0, 0], [1, 1], [-2, -2]])
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match = ("'random_state' has been introduced in 0.23. "
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"It will be set to None starting from 0.25 which "
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"means that results will differ at every function "
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"call. Set 'random_state' to None to silence this "
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"warning, or to 0 to keep the behavior of versions "
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"<0.23.")
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with pytest.warns(FutureWarning, match=match):
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AffinityPropagation().fit(X)
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@pytest.mark.parametrize('centers', [csr_matrix(np.zeros((1, 10))),
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np.zeros((1, 10))])
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def test_affinity_propagation_convergence_warning_dense_sparse(centers):
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"""Non-regression, see #13334"""
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rng = np.random.RandomState(42)
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X = rng.rand(40, 10)
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y = (4 * rng.rand(40)).astype(np.int)
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ap = AffinityPropagation(random_state=46)
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ap.fit(X, y)
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ap.cluster_centers_ = centers
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with pytest.warns(None) as record:
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assert_array_equal(ap.predict(X),
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np.zeros(X.shape[0], dtype=int))
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assert len(record) == 0
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def test_affinity_propagation_float32():
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# Test to fix incorrect clusters due to dtype change
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# (non-regression test for issue #10832)
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X = np.array([[1, 0, 0, 0],
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[0, 1, 1, 0],
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[0, 1, 1, 0],
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[0, 0, 0, 1]], dtype='float32')
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afp = AffinityPropagation(preference=1, affinity='precomputed',
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random_state=0).fit(X)
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expected = np.array([0, 1, 1, 2])
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assert_array_equal(afp.labels_, expected)
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