def test_fit_initialization_count(self, _, n_init):
data, labels = make_blobs()
clusterer = Possibilistic(2, n_init=n_init)
clusterer.initialization = MagicMock(
return_value=initialize_random(data, 2))
clusterer.fit(data)
self.assertEqual(clusterer.initialization.call_count, n_init)
def test_membership_calculation_range(self, _, clusters, features):
data, labels = make_blobs(centers=clusters, n_features=features)
clusterer = Possibilistic(clusters)
clusterer.converge(data)
self.assertTrue(
np.all(clusterer.memberships > 0)
and np.all(clusterer.memberships < 1))
def test_fit_correct_assignment(self, _, n_init):
data, labels = make_blobs()
clusterer = Possibilistic(2, n_init=n_init)
j_list = clusterer.fit(data)
self.assertEqual(min(j_list), clusterer.objective(data))
def test_fit_converge_count(self, _, n_init):
data, labels = make_blobs()
clusterer = Possibilistic(2, n_init=n_init)
clusterer.converge = MagicMock()
clusterer.fit(data)
self.assertEqual(clusterer.converge.call_count, n_init)
def test_center_calculation_shape(self, _, clusters, features, expected):
data, labels = make_blobs(centers=clusters, n_features=features)
clusterer = Possibilistic(clusters)
clusterer.update(data)
self.assertEqual(clusterer.calculate_centers(data).shape, expected)
def test_first_update_assigns_membership_shape(self, _, clusters, features,
expected_shape):
data, labels = make_blobs(centers=clusters, n_features=features)
clusterer = Possibilistic(clusters)
clusterer.update(data)
self.assertEqual(clusterer.memberships.shape, expected_shape)
def test_converge_update_count(self, _, n_iter):
data, labels = make_blobs()
clusterer = Possibilistic(2, max_iter=n_iter)
clusterer.update = MagicMock()
clusterer.converge(data)
self.assertLessEqual(clusterer.update.call_count, n_iter)