450 lines
17 KiB
Python
450 lines
17 KiB
Python
import numpy as np
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import pytest
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from sklearn.metrics.cluster import adjusted_mutual_info_score
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from sklearn.metrics.cluster import adjusted_rand_score
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from sklearn.metrics.cluster import rand_score
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from sklearn.metrics.cluster import completeness_score
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from sklearn.metrics.cluster import contingency_matrix
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from sklearn.metrics.cluster import pair_confusion_matrix
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from sklearn.metrics.cluster import entropy
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from sklearn.metrics.cluster import expected_mutual_information
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from sklearn.metrics.cluster import fowlkes_mallows_score
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from sklearn.metrics.cluster import homogeneity_completeness_v_measure
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from sklearn.metrics.cluster import homogeneity_score
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from sklearn.metrics.cluster import mutual_info_score
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from sklearn.metrics.cluster import normalized_mutual_info_score
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from sklearn.metrics.cluster import v_measure_score
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from sklearn.metrics.cluster._supervised import _generalized_average
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from sklearn.metrics.cluster._supervised import check_clusterings
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from sklearn.utils import assert_all_finite
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from sklearn.utils._testing import (
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assert_almost_equal, ignore_warnings)
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from numpy.testing import (
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assert_array_equal, assert_array_almost_equal, assert_allclose)
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score_funcs = [
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adjusted_rand_score,
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rand_score,
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homogeneity_score,
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completeness_score,
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v_measure_score,
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adjusted_mutual_info_score,
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normalized_mutual_info_score,
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]
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@ignore_warnings(category=FutureWarning)
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def test_error_messages_on_wrong_input():
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for score_func in score_funcs:
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expected = (r'Found input variables with inconsistent numbers '
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r'of samples: \[2, 3\]')
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with pytest.raises(ValueError, match=expected):
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score_func([0, 1], [1, 1, 1])
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expected = r"labels_true must be 1D: shape is \(2"
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with pytest.raises(ValueError, match=expected):
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score_func([[0, 1], [1, 0]], [1, 1, 1])
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expected = r"labels_pred must be 1D: shape is \(2"
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with pytest.raises(ValueError, match=expected):
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score_func([0, 1, 0], [[1, 1], [0, 0]])
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def test_generalized_average():
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a, b = 1, 2
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methods = ["min", "geometric", "arithmetic", "max"]
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means = [_generalized_average(a, b, method) for method in methods]
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assert means[0] <= means[1] <= means[2] <= means[3]
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c, d = 12, 12
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means = [_generalized_average(c, d, method) for method in methods]
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assert means[0] == means[1] == means[2] == means[3]
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@ignore_warnings(category=FutureWarning)
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def test_perfect_matches():
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for score_func in score_funcs:
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assert score_func([], []) == pytest.approx(1.0)
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assert score_func([0], [1]) == pytest.approx(1.0)
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assert score_func([0, 0, 0], [0, 0, 0]) == pytest.approx(1.0)
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assert score_func([0, 1, 0], [42, 7, 42]) == pytest.approx(1.0)
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assert score_func([0., 1., 0.], [42., 7., 42.]) == pytest.approx(1.0)
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assert score_func([0., 1., 2.], [42., 7., 2.]) == pytest.approx(1.0)
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assert score_func([0, 1, 2], [42, 7, 2]) == pytest.approx(1.0)
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score_funcs_with_changing_means = [
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normalized_mutual_info_score,
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adjusted_mutual_info_score,
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]
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means = {"min", "geometric", "arithmetic", "max"}
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for score_func in score_funcs_with_changing_means:
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for mean in means:
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assert score_func([], [], mean) == pytest.approx(1.0)
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assert score_func([0], [1], mean) == pytest.approx(1.0)
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assert score_func([0, 0, 0], [0, 0, 0], mean) == pytest.approx(1.0)
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assert score_func(
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[0, 1, 0], [42, 7, 42], mean) == pytest.approx(1.0)
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assert score_func(
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[0., 1., 0.], [42., 7., 42.], mean) == pytest.approx(1.0)
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assert score_func(
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[0., 1., 2.], [42., 7., 2.], mean) == pytest.approx(1.0)
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assert score_func(
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[0, 1, 2], [42, 7, 2], mean) == pytest.approx(1.0)
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def test_homogeneous_but_not_complete_labeling():
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# homogeneous but not complete clustering
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h, c, v = homogeneity_completeness_v_measure(
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[0, 0, 0, 1, 1, 1],
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[0, 0, 0, 1, 2, 2])
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assert_almost_equal(h, 1.00, 2)
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assert_almost_equal(c, 0.69, 2)
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assert_almost_equal(v, 0.81, 2)
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def test_complete_but_not_homogeneous_labeling():
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# complete but not homogeneous clustering
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h, c, v = homogeneity_completeness_v_measure(
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[0, 0, 1, 1, 2, 2],
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[0, 0, 1, 1, 1, 1])
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assert_almost_equal(h, 0.58, 2)
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assert_almost_equal(c, 1.00, 2)
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assert_almost_equal(v, 0.73, 2)
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def test_not_complete_and_not_homogeneous_labeling():
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# neither complete nor homogeneous but not so bad either
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h, c, v = homogeneity_completeness_v_measure(
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[0, 0, 0, 1, 1, 1],
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[0, 1, 0, 1, 2, 2])
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assert_almost_equal(h, 0.67, 2)
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assert_almost_equal(c, 0.42, 2)
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assert_almost_equal(v, 0.52, 2)
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def test_beta_parameter():
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# test for when beta passed to
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# homogeneity_completeness_v_measure
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# and v_measure_score
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beta_test = 0.2
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h_test = 0.67
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c_test = 0.42
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v_test = ((1 + beta_test) * h_test * c_test
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/ (beta_test * h_test + c_test))
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h, c, v = homogeneity_completeness_v_measure(
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[0, 0, 0, 1, 1, 1],
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[0, 1, 0, 1, 2, 2],
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beta=beta_test)
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assert_almost_equal(h, h_test, 2)
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assert_almost_equal(c, c_test, 2)
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assert_almost_equal(v, v_test, 2)
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v = v_measure_score(
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[0, 0, 0, 1, 1, 1],
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[0, 1, 0, 1, 2, 2],
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beta=beta_test)
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assert_almost_equal(v, v_test, 2)
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def test_non_consecutive_labels():
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# regression tests for labels with gaps
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h, c, v = homogeneity_completeness_v_measure(
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[0, 0, 0, 2, 2, 2],
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[0, 1, 0, 1, 2, 2])
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assert_almost_equal(h, 0.67, 2)
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assert_almost_equal(c, 0.42, 2)
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assert_almost_equal(v, 0.52, 2)
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h, c, v = homogeneity_completeness_v_measure(
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[0, 0, 0, 1, 1, 1],
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[0, 4, 0, 4, 2, 2])
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assert_almost_equal(h, 0.67, 2)
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assert_almost_equal(c, 0.42, 2)
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assert_almost_equal(v, 0.52, 2)
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ari_1 = adjusted_rand_score([0, 0, 0, 1, 1, 1], [0, 1, 0, 1, 2, 2])
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ari_2 = adjusted_rand_score([0, 0, 0, 1, 1, 1], [0, 4, 0, 4, 2, 2])
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assert_almost_equal(ari_1, 0.24, 2)
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assert_almost_equal(ari_2, 0.24, 2)
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ri_1 = rand_score([0, 0, 0, 1, 1, 1], [0, 1, 0, 1, 2, 2])
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ri_2 = rand_score([0, 0, 0, 1, 1, 1], [0, 4, 0, 4, 2, 2])
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assert_almost_equal(ri_1, 0.66, 2)
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assert_almost_equal(ri_2, 0.66, 2)
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@ignore_warnings(category=FutureWarning)
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def uniform_labelings_scores(score_func, n_samples, k_range, n_runs=10,
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seed=42):
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# Compute score for random uniform cluster labelings
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random_labels = np.random.RandomState(seed).randint
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scores = np.zeros((len(k_range), n_runs))
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for i, k in enumerate(k_range):
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for j in range(n_runs):
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labels_a = random_labels(low=0, high=k, size=n_samples)
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labels_b = random_labels(low=0, high=k, size=n_samples)
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scores[i, j] = score_func(labels_a, labels_b)
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return scores
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@ignore_warnings(category=FutureWarning)
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def test_adjustment_for_chance():
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# Check that adjusted scores are almost zero on random labels
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n_clusters_range = [2, 10, 50, 90]
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n_samples = 100
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n_runs = 10
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scores = uniform_labelings_scores(
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adjusted_rand_score, n_samples, n_clusters_range, n_runs)
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max_abs_scores = np.abs(scores).max(axis=1)
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assert_array_almost_equal(max_abs_scores, [0.02, 0.03, 0.03, 0.02], 2)
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def test_adjusted_mutual_info_score():
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# Compute the Adjusted Mutual Information and test against known values
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labels_a = np.array([1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3])
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labels_b = np.array([1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 3, 1, 3, 3, 3, 2, 2])
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# Mutual information
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mi = mutual_info_score(labels_a, labels_b)
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assert_almost_equal(mi, 0.41022, 5)
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# with provided sparse contingency
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C = contingency_matrix(labels_a, labels_b, sparse=True)
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mi = mutual_info_score(labels_a, labels_b, contingency=C)
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assert_almost_equal(mi, 0.41022, 5)
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# with provided dense contingency
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C = contingency_matrix(labels_a, labels_b)
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mi = mutual_info_score(labels_a, labels_b, contingency=C)
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assert_almost_equal(mi, 0.41022, 5)
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# Expected mutual information
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n_samples = C.sum()
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emi = expected_mutual_information(C, n_samples)
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assert_almost_equal(emi, 0.15042, 5)
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# Adjusted mutual information
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ami = adjusted_mutual_info_score(labels_a, labels_b)
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assert_almost_equal(ami, 0.27821, 5)
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ami = adjusted_mutual_info_score([1, 1, 2, 2], [2, 2, 3, 3])
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assert ami == pytest.approx(1.0)
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# Test with a very large array
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a110 = np.array([list(labels_a) * 110]).flatten()
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b110 = np.array([list(labels_b) * 110]).flatten()
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ami = adjusted_mutual_info_score(a110, b110)
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assert_almost_equal(ami, 0.38, 2)
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def test_expected_mutual_info_overflow():
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# Test for regression where contingency cell exceeds 2**16
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# leading to overflow in np.outer, resulting in EMI > 1
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assert expected_mutual_information(np.array([[70000]]), 70000) <= 1
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def test_int_overflow_mutual_info_fowlkes_mallows_score():
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# Test overflow in mutual_info_classif and fowlkes_mallows_score
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x = np.array([1] * (52632 + 2529) + [2] * (14660 + 793) + [3] * (3271 +
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204) + [4] * (814 + 39) + [5] * (316 + 20))
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y = np.array([0] * 52632 + [1] * 2529 + [0] * 14660 + [1] * 793 +
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[0] * 3271 + [1] * 204 + [0] * 814 + [1] * 39 + [0] * 316 +
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[1] * 20)
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assert_all_finite(mutual_info_score(x, y))
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assert_all_finite(fowlkes_mallows_score(x, y))
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def test_entropy():
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ent = entropy([0, 0, 42.])
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assert_almost_equal(ent, 0.6365141, 5)
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assert_almost_equal(entropy([]), 1)
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def test_contingency_matrix():
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labels_a = np.array([1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3])
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labels_b = np.array([1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 3, 1, 3, 3, 3, 2, 2])
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C = contingency_matrix(labels_a, labels_b)
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C2 = np.histogram2d(labels_a, labels_b,
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bins=(np.arange(1, 5),
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np.arange(1, 5)))[0]
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assert_array_almost_equal(C, C2)
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C = contingency_matrix(labels_a, labels_b, eps=.1)
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assert_array_almost_equal(C, C2 + .1)
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def test_contingency_matrix_sparse():
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labels_a = np.array([1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3])
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labels_b = np.array([1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 3, 1, 3, 3, 3, 2, 2])
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C = contingency_matrix(labels_a, labels_b)
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C_sparse = contingency_matrix(labels_a, labels_b, sparse=True).toarray()
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assert_array_almost_equal(C, C_sparse)
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with pytest.raises(ValueError, match="Cannot set 'eps' when sparse=True"):
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contingency_matrix(labels_a, labels_b, eps=1e-10, sparse=True)
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@ignore_warnings(category=FutureWarning)
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def test_exactly_zero_info_score():
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# Check numerical stability when information is exactly zero
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for i in np.logspace(1, 4, 4).astype(int):
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labels_a, labels_b = (np.ones(i, dtype=int),
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np.arange(i, dtype=int))
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assert normalized_mutual_info_score(
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labels_a, labels_b) == pytest.approx(0.0)
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assert v_measure_score(
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labels_a, labels_b) == pytest.approx(0.0)
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assert adjusted_mutual_info_score(
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labels_a, labels_b) == pytest.approx(0.0)
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assert normalized_mutual_info_score(
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labels_a, labels_b) == pytest.approx(0.0)
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for method in ["min", "geometric", "arithmetic", "max"]:
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assert adjusted_mutual_info_score(
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labels_a, labels_b, method) == pytest.approx(0.0)
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assert normalized_mutual_info_score(
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labels_a, labels_b, method) == pytest.approx(0.0)
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def test_v_measure_and_mutual_information(seed=36):
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# Check relation between v_measure, entropy and mutual information
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for i in np.logspace(1, 4, 4).astype(int):
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random_state = np.random.RandomState(seed)
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labels_a, labels_b = (random_state.randint(0, 10, i),
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random_state.randint(0, 10, i))
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assert_almost_equal(v_measure_score(labels_a, labels_b),
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2.0 * mutual_info_score(labels_a, labels_b) /
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(entropy(labels_a) + entropy(labels_b)), 0)
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avg = 'arithmetic'
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assert_almost_equal(v_measure_score(labels_a, labels_b),
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normalized_mutual_info_score(labels_a, labels_b,
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average_method=avg)
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)
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def test_fowlkes_mallows_score():
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# General case
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score = fowlkes_mallows_score([0, 0, 0, 1, 1, 1],
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[0, 0, 1, 1, 2, 2])
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assert_almost_equal(score, 4. / np.sqrt(12. * 6.))
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# Perfect match but where the label names changed
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perfect_score = fowlkes_mallows_score([0, 0, 0, 1, 1, 1],
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[1, 1, 1, 0, 0, 0])
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assert_almost_equal(perfect_score, 1.)
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# Worst case
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worst_score = fowlkes_mallows_score([0, 0, 0, 0, 0, 0],
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[0, 1, 2, 3, 4, 5])
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assert_almost_equal(worst_score, 0.)
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def test_fowlkes_mallows_score_properties():
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# handcrafted example
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labels_a = np.array([0, 0, 0, 1, 1, 2])
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labels_b = np.array([1, 1, 2, 2, 0, 0])
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expected = 1. / np.sqrt((1. + 3.) * (1. + 2.))
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# FMI = TP / sqrt((TP + FP) * (TP + FN))
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score_original = fowlkes_mallows_score(labels_a, labels_b)
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assert_almost_equal(score_original, expected)
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# symmetric property
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score_symmetric = fowlkes_mallows_score(labels_b, labels_a)
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assert_almost_equal(score_symmetric, expected)
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# permutation property
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score_permuted = fowlkes_mallows_score((labels_a + 1) % 3, labels_b)
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assert_almost_equal(score_permuted, expected)
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# symmetric and permutation(both together)
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score_both = fowlkes_mallows_score(labels_b, (labels_a + 2) % 3)
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assert_almost_equal(score_both, expected)
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@pytest.mark.parametrize('labels_true, labels_pred', [
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(['a'] * 6, [1, 1, 0, 0, 1, 1]),
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([1] * 6, [1, 1, 0, 0, 1, 1]),
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([1, 1, 0, 0, 1, 1], ['a'] * 6),
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([1, 1, 0, 0, 1, 1], [1] * 6),
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])
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def test_mutual_info_score_positive_constant_label(labels_true, labels_pred):
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# non-regression test for #16355
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assert mutual_info_score(labels_true, labels_pred) >= 0
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def test_check_clustering_error():
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# Test warning message for continuous values
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rng = np.random.RandomState(42)
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noise = rng.rand(500)
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wavelength = np.linspace(0.01, 1, 500) * 1e-6
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msg = 'Clustering metrics expects discrete values but received ' \
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'continuous values for label, and continuous values for ' \
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'target'
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with pytest.warns(UserWarning, match=msg):
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check_clusterings(wavelength, noise)
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def test_pair_confusion_matrix_fully_dispersed():
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# edge case: every element is its own cluster
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N = 100
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clustering1 = list(range(N))
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clustering2 = clustering1
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expected = np.array([[N * (N - 1), 0], [0, 0]])
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assert_array_equal(
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pair_confusion_matrix(clustering1, clustering2), expected
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)
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def test_pair_confusion_matrix_single_cluster():
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# edge case: only one cluster
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N = 100
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clustering1 = np.zeros((N,))
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clustering2 = clustering1
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expected = np.array([[0, 0], [0, N * (N - 1)]])
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assert_array_equal(
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pair_confusion_matrix(clustering1, clustering2), expected
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)
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def test_pair_confusion_matrix():
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# regular case: different non-trivial clusterings
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n = 10
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N = n ** 2
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clustering1 = np.hstack([[i + 1] * n for i in range(n)])
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clustering2 = np.hstack([[i + 1] * (n + 1) for i in range(n)])[:N]
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# basic quadratic implementation
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expected = np.zeros(shape=(2, 2), dtype=np.int64)
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for i in range(len(clustering1)):
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|
for j in range(len(clustering2)):
|
|
if i != j:
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|
same_cluster_1 = int(clustering1[i] == clustering1[j])
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|
same_cluster_2 = int(clustering2[i] == clustering2[j])
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|
expected[same_cluster_1, same_cluster_2] += 1
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|
assert_array_equal(
|
|
pair_confusion_matrix(clustering1, clustering2), expected
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"clustering1, clustering2",
|
|
[(list(range(100)), list(range(100))),
|
|
(np.zeros((100,)), np.zeros((100,)))]
|
|
)
|
|
def test_rand_score_edge_cases(clustering1, clustering2):
|
|
# edge case 1: every element is its own cluster
|
|
# edge case 2: only one cluster
|
|
assert_allclose(rand_score(clustering1, clustering2), 1.)
|
|
|
|
|
|
def test_rand_score():
|
|
# regular case: different non-trivial clusterings
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|
clustering1 = [0, 0, 0, 1, 1, 1]
|
|
clustering2 = [0, 1, 0, 1, 2, 2]
|
|
# pair confusion matrix
|
|
D11 = 2 * 2 # ordered pairs (1, 3), (5, 6)
|
|
D10 = 2 * 4 # ordered pairs (1, 2), (2, 3), (4, 5), (4, 6)
|
|
D01 = 2 * 1 # ordered pair (2, 4)
|
|
D00 = 5 * 6 - D11 - D01 - D10 # the remaining pairs
|
|
# rand score
|
|
expected_numerator = D00 + D11
|
|
expected_denominator = D00 + D01 + D10 + D11
|
|
expected = expected_numerator / expected_denominator
|
|
assert_allclose(rand_score(clustering1, clustering2), expected)
|