322 lines
12 KiB
Python
322 lines
12 KiB
Python
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"""
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Testing for Clustering methods
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"""
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import warnings
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import numpy as np
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import pytest
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from sklearn.cluster import AffinityPropagation, affinity_propagation
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from sklearn.cluster._affinity_propagation import _equal_similarities_and_preferences
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from sklearn.datasets import make_blobs
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from sklearn.exceptions import ConvergenceWarning, NotFittedError
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from sklearn.metrics import euclidean_distances
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from sklearn.utils._testing import assert_allclose, assert_array_equal
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from sklearn.utils.fixes import CSR_CONTAINERS
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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(
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n_samples=60,
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n_features=2,
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centers=centers,
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cluster_std=0.4,
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shuffle=True,
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random_state=0,
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)
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# TODO: AffinityPropagation must preserve dtype for its fitted attributes
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# and test must be created accordingly to this new behavior.
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# For more details, see: https://github.com/scikit-learn/scikit-learn/issues/11000
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def test_affinity_propagation(global_random_seed, global_dtype):
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"""Test consistency of the affinity propagations."""
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S = -euclidean_distances(X.astype(global_dtype, copy=False), squared=True)
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preference = np.median(S) * 10
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cluster_centers_indices, labels = affinity_propagation(
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S, preference=preference, random_state=global_random_seed
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)
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n_clusters_ = len(cluster_centers_indices)
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assert n_clusters == n_clusters_
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def test_affinity_propagation_precomputed():
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"""Check equality of precomputed affinity matrix to internally computed affinity
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matrix.
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"""
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S = -euclidean_distances(X, squared=True)
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preference = np.median(S) * 10
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af = AffinityPropagation(
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preference=preference, affinity="precomputed", random_state=28
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)
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labels_precomputed = af.fit(S).labels_
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af = AffinityPropagation(preference=preference, verbose=True, 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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def test_affinity_propagation_no_copy():
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"""Check behaviour of not copying the input data."""
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S = -euclidean_distances(X, squared=True)
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S_original = S.copy()
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preference = np.median(S) * 10
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assert not np.allclose(S.diagonal(), preference)
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# with copy=True S should not be modified
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affinity_propagation(S, preference=preference, copy=True, random_state=0)
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assert_allclose(S, S_original)
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assert not np.allclose(S.diagonal(), preference)
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assert_allclose(S.diagonal(), np.zeros(S.shape[0]))
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# with copy=False S will be modified inplace
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affinity_propagation(S, preference=preference, copy=False, random_state=0)
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assert_allclose(S.diagonal(), preference)
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# test that copy=True and copy=False lead to the same result
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S = S_original.copy()
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af = AffinityPropagation(preference=preference, verbose=True, random_state=0)
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labels = af.fit(X).labels_
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_, labels_no_copy = affinity_propagation(
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S, preference=preference, copy=False, random_state=74
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)
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assert_array_equal(labels, labels_no_copy)
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def test_affinity_propagation_affinity_shape():
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"""Check the shape of the affinity matrix when using `affinity_propagation."""
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S = -euclidean_distances(X, squared=True)
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err_msg = "The matrix of similarities must be a square array"
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with pytest.raises(ValueError, match=err_msg):
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affinity_propagation(S[:, :-1])
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_affinity_propagation_precomputed_with_sparse_input(csr_container):
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err_msg = "Sparse data was passed for X, but dense data is required"
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with pytest.raises(TypeError, match=err_msg):
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AffinityPropagation(affinity="precomputed").fit(csr_container((3, 3)))
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def test_affinity_propagation_predict(global_random_seed, global_dtype):
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# Test AffinityPropagation.predict
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af = AffinityPropagation(affinity="euclidean", random_state=global_random_seed)
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X_ = X.astype(global_dtype, copy=False)
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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(NotFittedError):
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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, match="expecting 60 features as input"):
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af.predict(X)
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def test_affinity_propagation_fit_non_convergence(global_dtype):
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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]], dtype=global_dtype)
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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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with pytest.warns(ConvergenceWarning):
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af.fit(X)
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assert_allclose(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(global_dtype):
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X = np.array([[-1, 1], [1, -1]], dtype=global_dtype)
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S = -euclidean_distances(X, squared=True)
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# setting preference > similarity
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with pytest.warns(UserWarning, match="mutually equal"):
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cluster_center_indices, labels = 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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with pytest.warns(UserWarning, match="mutually equal"):
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cluster_center_indices, labels = 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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with warnings.catch_warnings():
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warnings.simplefilter("error", UserWarning)
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cluster_center_indices, labels = affinity_propagation(
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S, preference=[-20, -10], random_state=37
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)
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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(global_dtype):
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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]], dtype=global_dtype)
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# Force non-convergence by allowing only a single iteration
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with pytest.warns(ConvergenceWarning):
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af = AffinityPropagation(preference=-10, 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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with pytest.warns(ConvergenceWarning):
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y = 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(global_dtype):
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X = np.array(
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[[1, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0], [0, 0, 1, 0, 0, 1]], dtype=global_dtype
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)
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af = AffinityPropagation(affinity="euclidean", max_iter=2, random_state=34)
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msg = (
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"Affinity propagation did not converge, this model may return degenerate"
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" cluster centers and labels."
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)
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with pytest.warns(ConvergenceWarning, match=msg):
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af.fit(X)
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assert_array_equal(np.array([0, 0, 0]), af.labels_)
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def test_equal_similarities_and_preferences(global_dtype):
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# Unequal distances
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X = np.array([[0, 0], [1, 1], [-2, -2]], dtype=global_dtype)
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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]], dtype=global_dtype)
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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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"""Check that different random states lead to different initialisations
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by looking at the center locations after two iterations.
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"""
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centers = [[1, 1], [-1, -1], [1, -1]]
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X, labels_true = make_blobs(
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n_samples=300, centers=centers, cluster_std=0.5, random_state=0
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)
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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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# check that the centers have not yet converged to the same solution
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assert np.mean((centers0 - centers76) ** 2) > 1
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@pytest.mark.parametrize("container", CSR_CONTAINERS + [np.array])
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def test_affinity_propagation_convergence_warning_dense_sparse(container, global_dtype):
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"""
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Check that having sparse or dense `centers` format should not
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influence the convergence.
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Non-regression test for gh-13334.
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"""
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centers = container(np.zeros((1, 10)))
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rng = np.random.RandomState(42)
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X = rng.rand(40, 10).astype(global_dtype, copy=False)
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y = (4 * rng.rand(40)).astype(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 warnings.catch_warnings():
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warnings.simplefilter("error", ConvergenceWarning)
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assert_array_equal(ap.predict(X), np.zeros(X.shape[0], dtype=int))
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# FIXME; this test is broken with different random states, needs to be revisited
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def test_correct_clusters(global_dtype):
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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(
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[[1, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 1]], dtype=global_dtype
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)
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afp = AffinityPropagation(preference=1, affinity="precomputed", random_state=0).fit(
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X
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)
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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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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_sparse_input_for_predict(csr_container):
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# Test to make sure sparse inputs are accepted for predict
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# (non-regression test for issue #20049)
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af = AffinityPropagation(affinity="euclidean", random_state=42)
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af.fit(X)
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labels = af.predict(csr_container((2, 2)))
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assert_array_equal(labels, (2, 2))
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_sparse_input_for_fit_predict(csr_container):
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# Test to make sure sparse inputs are accepted for fit_predict
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# (non-regression test for issue #20049)
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af = AffinityPropagation(affinity="euclidean", random_state=42)
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rng = np.random.RandomState(42)
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X = csr_container(rng.randint(0, 2, size=(5, 5)))
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labels = af.fit_predict(X)
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assert_array_equal(labels, (0, 1, 1, 2, 3))
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def test_affinity_propagation_equal_points():
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"""Make sure we do not assign multiple clusters to equal points.
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Non-regression test for:
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https://github.com/scikit-learn/scikit-learn/pull/20043
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"""
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X = np.zeros((8, 1))
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af = AffinityPropagation(affinity="euclidean", damping=0.5, random_state=42).fit(X)
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assert np.all(af.labels_ == 0)
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