211 lines
7.8 KiB
Python
211 lines
7.8 KiB
Python
from functools import partial
|
|
|
|
import pytest
|
|
import numpy as np
|
|
|
|
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 fowlkes_mallows_score
|
|
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 import silhouette_score
|
|
from sklearn.metrics.cluster import calinski_harabasz_score
|
|
from sklearn.metrics.cluster import davies_bouldin_score
|
|
|
|
from sklearn.utils._testing import assert_allclose
|
|
|
|
|
|
# Dictionaries of metrics
|
|
# ------------------------
|
|
# The goal of having those dictionaries is to have an easy way to call a
|
|
# particular metric and associate a name to each function:
|
|
# - SUPERVISED_METRICS: all supervised cluster metrics - (when given a
|
|
# ground truth value)
|
|
# - UNSUPERVISED_METRICS: all unsupervised cluster metrics
|
|
#
|
|
# Those dictionaries will be used to test systematically some invariance
|
|
# properties, e.g. invariance toward several input layout.
|
|
#
|
|
|
|
SUPERVISED_METRICS = {
|
|
"adjusted_mutual_info_score": adjusted_mutual_info_score,
|
|
"adjusted_rand_score": adjusted_rand_score,
|
|
"completeness_score": completeness_score,
|
|
"homogeneity_score": homogeneity_score,
|
|
"mutual_info_score": mutual_info_score,
|
|
"normalized_mutual_info_score": normalized_mutual_info_score,
|
|
"v_measure_score": v_measure_score,
|
|
"fowlkes_mallows_score": fowlkes_mallows_score
|
|
}
|
|
|
|
UNSUPERVISED_METRICS = {
|
|
"silhouette_score": silhouette_score,
|
|
"silhouette_manhattan": partial(silhouette_score, metric='manhattan'),
|
|
"calinski_harabasz_score": calinski_harabasz_score,
|
|
"davies_bouldin_score": davies_bouldin_score
|
|
}
|
|
|
|
# Lists of metrics with common properties
|
|
# ---------------------------------------
|
|
# Lists of metrics with common properties are used to test systematically some
|
|
# functionalities and invariance, e.g. SYMMETRIC_METRICS lists all metrics
|
|
# that are symmetric with respect to their input argument y_true and y_pred.
|
|
#
|
|
# --------------------------------------------------------------------
|
|
# Symmetric with respect to their input arguments y_true and y_pred.
|
|
# Symmetric metrics only apply to supervised clusters.
|
|
SYMMETRIC_METRICS = [
|
|
"adjusted_rand_score", "v_measure_score",
|
|
"mutual_info_score", "adjusted_mutual_info_score",
|
|
"normalized_mutual_info_score", "fowlkes_mallows_score"
|
|
]
|
|
|
|
NON_SYMMETRIC_METRICS = ["homogeneity_score", "completeness_score"]
|
|
|
|
# Metrics whose upper bound is 1
|
|
NORMALIZED_METRICS = [
|
|
"adjusted_rand_score", "homogeneity_score", "completeness_score",
|
|
"v_measure_score", "adjusted_mutual_info_score", "fowlkes_mallows_score",
|
|
"normalized_mutual_info_score"
|
|
]
|
|
|
|
|
|
rng = np.random.RandomState(0)
|
|
y1 = rng.randint(3, size=30)
|
|
y2 = rng.randint(3, size=30)
|
|
|
|
|
|
def test_symmetric_non_symmetric_union():
|
|
assert (sorted(SYMMETRIC_METRICS + NON_SYMMETRIC_METRICS) ==
|
|
sorted(SUPERVISED_METRICS))
|
|
|
|
|
|
# 0.22 AMI and NMI changes
|
|
@pytest.mark.filterwarnings('ignore::FutureWarning')
|
|
@pytest.mark.parametrize(
|
|
'metric_name, y1, y2',
|
|
[(name, y1, y2) for name in SYMMETRIC_METRICS]
|
|
)
|
|
def test_symmetry(metric_name, y1, y2):
|
|
metric = SUPERVISED_METRICS[metric_name]
|
|
assert metric(y1, y2) == pytest.approx(metric(y2, y1))
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
'metric_name, y1, y2',
|
|
[(name, y1, y2) for name in NON_SYMMETRIC_METRICS]
|
|
)
|
|
def test_non_symmetry(metric_name, y1, y2):
|
|
metric = SUPERVISED_METRICS[metric_name]
|
|
assert metric(y1, y2) != pytest.approx(metric(y2, y1))
|
|
|
|
|
|
# 0.22 AMI and NMI changes
|
|
@pytest.mark.filterwarnings('ignore::FutureWarning')
|
|
@pytest.mark.parametrize("metric_name", NORMALIZED_METRICS)
|
|
def test_normalized_output(metric_name):
|
|
upper_bound_1 = [0, 0, 0, 1, 1, 1]
|
|
upper_bound_2 = [0, 0, 0, 1, 1, 1]
|
|
metric = SUPERVISED_METRICS[metric_name]
|
|
assert metric([0, 0, 0, 1, 1], [0, 0, 0, 1, 2]) > 0.0
|
|
assert metric([0, 0, 1, 1, 2], [0, 0, 1, 1, 1]) > 0.0
|
|
assert metric([0, 0, 0, 1, 2], [0, 1, 1, 1, 1]) < 1.0
|
|
assert metric([0, 0, 0, 1, 2], [0, 1, 1, 1, 1]) < 1.0
|
|
assert metric(upper_bound_1, upper_bound_2) == pytest.approx(1.0)
|
|
|
|
lower_bound_1 = [0, 0, 0, 0, 0, 0]
|
|
lower_bound_2 = [0, 1, 2, 3, 4, 5]
|
|
score = np.array([metric(lower_bound_1, lower_bound_2),
|
|
metric(lower_bound_2, lower_bound_1)])
|
|
assert not (score < 0).any()
|
|
|
|
|
|
# 0.22 AMI and NMI changes
|
|
@pytest.mark.filterwarnings('ignore::FutureWarning')
|
|
@pytest.mark.parametrize(
|
|
"metric_name", dict(SUPERVISED_METRICS, **UNSUPERVISED_METRICS)
|
|
)
|
|
def test_permute_labels(metric_name):
|
|
# All clustering metrics do not change score due to permutations of labels
|
|
# that is when 0 and 1 exchanged.
|
|
y_label = np.array([0, 0, 0, 1, 1, 0, 1])
|
|
y_pred = np.array([1, 0, 1, 0, 1, 1, 0])
|
|
if metric_name in SUPERVISED_METRICS:
|
|
metric = SUPERVISED_METRICS[metric_name]
|
|
score_1 = metric(y_pred, y_label)
|
|
assert_allclose(score_1, metric(1 - y_pred, y_label))
|
|
assert_allclose(score_1, metric(1 - y_pred, 1 - y_label))
|
|
assert_allclose(score_1, metric(y_pred, 1 - y_label))
|
|
else:
|
|
metric = UNSUPERVISED_METRICS[metric_name]
|
|
X = np.random.randint(10, size=(7, 10))
|
|
score_1 = metric(X, y_pred)
|
|
assert_allclose(score_1, metric(X, 1 - y_pred))
|
|
|
|
|
|
# 0.22 AMI and NMI changes
|
|
@pytest.mark.filterwarnings('ignore::FutureWarning')
|
|
@pytest.mark.parametrize(
|
|
"metric_name", dict(SUPERVISED_METRICS, **UNSUPERVISED_METRICS)
|
|
)
|
|
# For all clustering metrics Input parameters can be both
|
|
# in the form of arrays lists, positive, negative or string
|
|
def test_format_invariance(metric_name):
|
|
y_true = [0, 0, 0, 0, 1, 1, 1, 1]
|
|
y_pred = [0, 1, 2, 3, 4, 5, 6, 7]
|
|
|
|
def generate_formats(y):
|
|
y = np.array(y)
|
|
yield y, 'array of ints'
|
|
yield y.tolist(), 'list of ints'
|
|
yield [str(x) + "-a" for x in y.tolist()], 'list of strs'
|
|
yield (np.array([str(x) + "-a" for x in y.tolist()], dtype=object),
|
|
'array of strs')
|
|
yield y - 1, 'including negative ints'
|
|
yield y + 1, 'strictly positive ints'
|
|
|
|
if metric_name in SUPERVISED_METRICS:
|
|
metric = SUPERVISED_METRICS[metric_name]
|
|
score_1 = metric(y_true, y_pred)
|
|
y_true_gen = generate_formats(y_true)
|
|
y_pred_gen = generate_formats(y_pred)
|
|
for (y_true_fmt, fmt_name), (y_pred_fmt, _) in zip(y_true_gen,
|
|
y_pred_gen):
|
|
assert score_1 == metric(y_true_fmt, y_pred_fmt)
|
|
else:
|
|
metric = UNSUPERVISED_METRICS[metric_name]
|
|
X = np.random.randint(10, size=(8, 10))
|
|
score_1 = metric(X, y_true)
|
|
assert score_1 == metric(X.astype(float), y_true)
|
|
y_true_gen = generate_formats(y_true)
|
|
for (y_true_fmt, fmt_name) in y_true_gen:
|
|
assert score_1 == metric(X, y_true_fmt)
|
|
|
|
|
|
@pytest.mark.parametrize("metric", SUPERVISED_METRICS.values())
|
|
def test_single_sample(metric):
|
|
# only the supervised metrics support single sample
|
|
for i, j in [(0, 0), (0, 1), (1, 0), (1, 1)]:
|
|
metric([i], [j])
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"metric_name, metric_func",
|
|
dict(SUPERVISED_METRICS, **UNSUPERVISED_METRICS).items()
|
|
)
|
|
def test_inf_nan_input(metric_name, metric_func):
|
|
if metric_name in SUPERVISED_METRICS:
|
|
invalids = [([0, 1], [np.inf, np.inf]),
|
|
([0, 1], [np.nan, np.nan]),
|
|
([0, 1], [np.nan, np.inf])]
|
|
else:
|
|
X = np.random.randint(10, size=(2, 10))
|
|
invalids = [(X, [np.inf, np.inf]),
|
|
(X, [np.nan, np.nan]),
|
|
(X, [np.nan, np.inf])]
|
|
with pytest.raises(ValueError, match='contains NaN, infinity'):
|
|
for args in invalids:
|
|
metric_func(*args)
|