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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_harabaz_score
from sklearn.metrics.cluster import davies_bouldin_score
from sklearn.utils.testing import assert_allclose, ignore_warnings
# 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_harabaz_score": calinski_harabaz_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, negetive 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) for x in y.tolist()], 'list 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)
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