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import numpy as np
from sklearn.metrics.cluster import adjusted_rand_score
from sklearn.metrics.cluster import homogeneity_score
from sklearn.metrics.cluster import completeness_score
from sklearn.metrics.cluster import v_measure_score
from sklearn.metrics.cluster import homogeneity_completeness_v_measure
from sklearn.metrics.cluster import adjusted_mutual_info_score
from sklearn.metrics.cluster import mutual_info_score
from sklearn.metrics.cluster import expected_mutual_information
from sklearn.metrics.cluster import contingency_matrix
from nose.tools import assert_almost_equal
from nose.tools import assert_equal
from numpy.testing import assert_array_almost_equal
score_funcs = [
adjusted_rand_score,
homogeneity_score,
completeness_score,
v_measure_score,
adjusted_mutual_info_score,
]
def assert_raise_message(exception, message, callable, *args, **kwargs):
"""Helper function to test error messages in exceptions"""
try:
callable(*args, **kwargs)
raise AssertionError("Should have raised %r" % exception(message))
except exception as e:
assert str(e) == message
def test_error_messages_on_wrong_input():
for score_func in score_funcs:
expected = ('labels_true and labels_pred must have same size,'
' got 2 and 3')
assert_raise_message(ValueError, expected, score_func,
[0, 1], [1, 1, 1])
expected = "labels_true must be 1D: shape is (2, 2)"
assert_raise_message(ValueError, expected, score_func,
[[0, 1], [1, 0]], [1, 1, 1])
expected = "labels_pred must be 1D: shape is (2, 2)"
assert_raise_message(ValueError, expected, score_func,
[0, 1, 0], [[1, 1], [0, 0]])
def test_perfect_matches():
for score_func in score_funcs:
assert_equal(score_func([], []), 1.0)
assert_equal(score_func([0], [1]), 1.0)
assert_equal(score_func([0, 0, 0], [0, 0, 0]), 1.0)
assert_equal(score_func([0, 1, 0], [42, 7, 42]), 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_non_consicutive_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)
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).random_integers
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 - 1, size=n_samples)
labels_b = random_labels(low=0, high=k - 1, 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)
# Expected mutual information
C = contingency_matrix(labels_a, labels_b)
n_samples = np.sum(C)
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, 0.27502, 5)
ami = adjusted_mutual_info_score([1, 1, 2, 2], [2, 2, 3, 3])
assert_equal(ami, 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)
# This is not accurate to more than 2 places
assert_almost_equal(ami, 0.37, 2)
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