483 lines
18 KiB
Python
483 lines
18 KiB
Python
|
import warnings
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import pytest
|
||
|
|
from numpy.testing import assert_allclose, assert_array_almost_equal, assert_array_equal
|
||
|
|
|
||
|
|
from sklearn.metrics.cluster import (
|
||
|
|
adjusted_mutual_info_score,
|
||
|
|
adjusted_rand_score,
|
||
|
|
completeness_score,
|
||
|
|
contingency_matrix,
|
||
|
|
entropy,
|
||
|
|
expected_mutual_information,
|
||
|
|
fowlkes_mallows_score,
|
||
|
|
homogeneity_completeness_v_measure,
|
||
|
|
homogeneity_score,
|
||
|
|
mutual_info_score,
|
||
|
|
normalized_mutual_info_score,
|
||
|
|
pair_confusion_matrix,
|
||
|
|
rand_score,
|
||
|
|
v_measure_score,
|
||
|
|
)
|
||
|
|
from sklearn.metrics.cluster._supervised import _generalized_average, check_clusterings
|
||
|
|
from sklearn.utils import assert_all_finite
|
||
|
|
from sklearn.utils._testing import assert_almost_equal
|
||
|
|
|
||
|
|
score_funcs = [
|
||
|
|
adjusted_rand_score,
|
||
|
|
rand_score,
|
||
|
|
homogeneity_score,
|
||
|
|
completeness_score,
|
||
|
|
v_measure_score,
|
||
|
|
adjusted_mutual_info_score,
|
||
|
|
normalized_mutual_info_score,
|
||
|
|
]
|
||
|
|
|
||
|
|
|
||
|
|
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]
|
||
|
|
|
||
|
|
|
||
|
|
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([0.0, 1.0, 0.0], [42.0, 7.0, 42.0]) == pytest.approx(1.0)
|
||
|
|
assert score_func([0.0, 1.0, 2.0], [42.0, 7.0, 2.0]) == 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([], [], average_method=mean) == pytest.approx(1.0)
|
||
|
|
assert score_func([0], [1], average_method=mean) == pytest.approx(1.0)
|
||
|
|
assert score_func(
|
||
|
|
[0, 0, 0], [0, 0, 0], average_method=mean
|
||
|
|
) == pytest.approx(1.0)
|
||
|
|
assert score_func(
|
||
|
|
[0, 1, 0], [42, 7, 42], average_method=mean
|
||
|
|
) == pytest.approx(1.0)
|
||
|
|
assert score_func(
|
||
|
|
[0.0, 1.0, 0.0], [42.0, 7.0, 42.0], average_method=mean
|
||
|
|
) == pytest.approx(1.0)
|
||
|
|
assert score_func(
|
||
|
|
[0.0, 1.0, 2.0], [42.0, 7.0, 2.0], average_method=mean
|
||
|
|
) == pytest.approx(1.0)
|
||
|
|
assert score_func(
|
||
|
|
[0, 1, 2], [42, 7, 2], average_method=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)
|
||
|
|
|
||
|
|
ri_1 = rand_score([0, 0, 0, 1, 1, 1], [0, 1, 0, 1, 2, 2])
|
||
|
|
ri_2 = rand_score([0, 0, 0, 1, 1, 1], [0, 4, 0, 4, 2, 2])
|
||
|
|
assert_almost_equal(ri_1, 0.66, 2)
|
||
|
|
assert_almost_equal(ri_2, 0.66, 2)
|
||
|
|
|
||
|
|
|
||
|
|
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
|
||
|
|
|
||
|
|
|
||
|
|
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, 42.0])
|
||
|
|
assert_almost_equal(ent, 0.6365141, 5)
|
||
|
|
assert_almost_equal(entropy([]), 1)
|
||
|
|
assert entropy([1, 1, 1, 1]) == 0
|
||
|
|
|
||
|
|
|
||
|
|
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=0.1)
|
||
|
|
assert_array_almost_equal(C, C2 + 0.1)
|
||
|
|
|
||
|
|
|
||
|
|
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)
|
||
|
|
|
||
|
|
|
||
|
|
def test_exactly_zero_info_score():
|
||
|
|
# Check numerical stability when information is exactly zero
|
||
|
|
for i in np.logspace(1, 4, 4).astype(int):
|
||
|
|
labels_a, labels_b = (np.ones(i, dtype=int), np.arange(i, dtype=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, average_method=method
|
||
|
|
) == pytest.approx(0.0)
|
||
|
|
assert normalized_mutual_info_score(
|
||
|
|
labels_a, labels_b, average_method=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(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, 4.0 / np.sqrt(12.0 * 6.0))
|
||
|
|
|
||
|
|
# 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, 1.0)
|
||
|
|
|
||
|
|
# Worst case
|
||
|
|
worst_score = fowlkes_mallows_score([0, 0, 0, 0, 0, 0], [0, 1, 2, 3, 4, 5])
|
||
|
|
assert_almost_equal(worst_score, 0.0)
|
||
|
|
|
||
|
|
|
||
|
|
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 = 1.0 / np.sqrt((1.0 + 3.0) * (1.0 + 2.0))
|
||
|
|
# 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),
|
||
|
|
(["a"] * 6, ["a"] * 6),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_mutual_info_score_positive_constant_label(labels_true, labels_pred):
|
||
|
|
# Check that MI = 0 when one or both labelling are constant
|
||
|
|
# non-regression test for #16355
|
||
|
|
assert mutual_info_score(labels_true, labels_pred) == 0
|
||
|
|
|
||
|
|
|
||
|
|
def test_check_clustering_error():
|
||
|
|
# Test warning message for continuous values
|
||
|
|
rng = np.random.RandomState(42)
|
||
|
|
noise = rng.rand(500)
|
||
|
|
wavelength = np.linspace(0.01, 1, 500) * 1e-6
|
||
|
|
msg = (
|
||
|
|
"Clustering metrics expects discrete values but received "
|
||
|
|
"continuous values for label, and continuous values for "
|
||
|
|
"target"
|
||
|
|
)
|
||
|
|
|
||
|
|
with pytest.warns(UserWarning, match=msg):
|
||
|
|
check_clusterings(wavelength, noise)
|
||
|
|
|
||
|
|
|
||
|
|
def test_pair_confusion_matrix_fully_dispersed():
|
||
|
|
# edge case: every element is its own cluster
|
||
|
|
N = 100
|
||
|
|
clustering1 = list(range(N))
|
||
|
|
clustering2 = clustering1
|
||
|
|
expected = np.array([[N * (N - 1), 0], [0, 0]])
|
||
|
|
assert_array_equal(pair_confusion_matrix(clustering1, clustering2), expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_pair_confusion_matrix_single_cluster():
|
||
|
|
# edge case: only one cluster
|
||
|
|
N = 100
|
||
|
|
clustering1 = np.zeros((N,))
|
||
|
|
clustering2 = clustering1
|
||
|
|
expected = np.array([[0, 0], [0, N * (N - 1)]])
|
||
|
|
assert_array_equal(pair_confusion_matrix(clustering1, clustering2), expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_pair_confusion_matrix():
|
||
|
|
# regular case: different non-trivial clusterings
|
||
|
|
n = 10
|
||
|
|
N = n**2
|
||
|
|
clustering1 = np.hstack([[i + 1] * n for i in range(n)])
|
||
|
|
clustering2 = np.hstack([[i + 1] * (n + 1) for i in range(n)])[:N]
|
||
|
|
# basic quadratic implementation
|
||
|
|
expected = np.zeros(shape=(2, 2), dtype=np.int64)
|
||
|
|
for i in range(len(clustering1)):
|
||
|
|
for j in range(len(clustering2)):
|
||
|
|
if i != j:
|
||
|
|
same_cluster_1 = int(clustering1[i] == clustering1[j])
|
||
|
|
same_cluster_2 = int(clustering2[i] == clustering2[j])
|
||
|
|
expected[same_cluster_1, same_cluster_2] += 1
|
||
|
|
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), 1.0)
|
||
|
|
|
||
|
|
|
||
|
|
def test_rand_score():
|
||
|
|
# regular case: different non-trivial clusterings
|
||
|
|
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)
|
||
|
|
|
||
|
|
|
||
|
|
def test_adjusted_rand_score_overflow():
|
||
|
|
"""Check that large amount of data will not lead to overflow in
|
||
|
|
`adjusted_rand_score`.
|
||
|
|
Non-regression test for:
|
||
|
|
https://github.com/scikit-learn/scikit-learn/issues/20305
|
||
|
|
"""
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
y_true = rng.randint(0, 2, 100_000, dtype=np.int8)
|
||
|
|
y_pred = rng.randint(0, 2, 100_000, dtype=np.int8)
|
||
|
|
with warnings.catch_warnings():
|
||
|
|
warnings.simplefilter("error", RuntimeWarning)
|
||
|
|
adjusted_rand_score(y_true, y_pred)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("average_method", ["min", "arithmetic", "geometric", "max"])
|
||
|
|
def test_normalized_mutual_info_score_bounded(average_method):
|
||
|
|
"""Check that nmi returns a score between 0 (included) and 1 (excluded
|
||
|
|
for non-perfect match)
|
||
|
|
|
||
|
|
Non-regression test for issue #13836
|
||
|
|
"""
|
||
|
|
labels1 = [0] * 469
|
||
|
|
labels2 = [1] + labels1[1:]
|
||
|
|
labels3 = [0, 1] + labels1[2:]
|
||
|
|
|
||
|
|
# labels1 is constant. The mutual info between labels1 and any other labelling is 0.
|
||
|
|
nmi = normalized_mutual_info_score(labels1, labels2, average_method=average_method)
|
||
|
|
assert nmi == 0
|
||
|
|
|
||
|
|
# non constant, non perfect matching labels
|
||
|
|
nmi = normalized_mutual_info_score(labels2, labels3, average_method=average_method)
|
||
|
|
assert 0 <= nmi < 1
|