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