def test_c(self):
clusters = np.array(
[1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 5, 5, 6, 6])
classes = np.array(
[1, 1, 1, 2, 2, 3, 3, 3, 1, 1, 2, 2, 2, 3, 3, 1, 2, 3, 1, 2, 3])
assert round(cm.v_measure(clusters, classes), 2) == 0.30
assert_almost_equal(cm.v_measure(clusters, classes),
sklearn.metrics.v_measure_score(classes, clusters),
decimal=10)
def __init__(self, clusters: np.ndarray, classes: np.ndarray, aic: float,
bic: float, likelihood: float):
"""
:param clusters: The cluster assignments
:param classes: The class assignments
:param aic: The Akaike information criterion
:param bic: The Bayesian information criterion
:param likelihood: The log likelihood
"""
super().__init__(clusters, aic, bic, likelihood)
self.N = len(classes)
self.C = len(np.unique(classes))
self.purity = purity(clusters, classes)
self.purity2 = purity2(clusters, classes)
self.rand_index = rand_index(clusters, classes)
self.class_entropy = entropy(classes)
self.precision = precision(clusters, classes)
self.recall = recall(clusters, classes)
self.f1_measure = f1_measure(clusters, classes)
self.homogeneity = homogeneity(clusters, classes)
self.completeness = completeness(clusters, classes)
self.v_measure = v_measure(clusters, classes)
self.nv_measure = nv_measure(clusters, classes)
self.mutual_information = mutual_information(clusters, classes)
self.normalized_mutual_information = normalized_mutual_information(
clusters, classes)
self.normalized_mutual_information2 = normalized_mutual_information2(
clusters, classes)
def test_d(self):
clusters = np.array(
[1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 8, 9])
classes = np.array(
[1, 1, 1, 2, 2, 3, 3, 3, 1, 1, 2, 2, 2, 3, 3, 1, 2, 3, 1, 2, 3])
assert round(cm.v_measure(clusters, classes), 2) == 0.41
assert_almost_equal(cm.v_measure(clusters, classes),
sklearn.metrics.v_measure_score(classes, clusters),
decimal=10)
assert_raises(AssertionError,
assert_almost_equal,
cm.v_measure(clusters, classes),
cm.nv_measure(clusters, classes),
decimal=10)