def test_clustering_result_value_errors_loss_label_only_one_init(self):
centers = np.zeros((2, 3))
datapoints = np.zeros((4, 3))
data = clustering_params.Data(datapoints=datapoints, radius=2)
cluster_labels = np.array([0, 0, 1, 1], dtype=int)
loss = 1.0
with self.assertRaises(ValueError):
clustering_algorithm.ClusteringResult(data, centers,
cluster_labels)
with self.assertRaises(ValueError):
clustering_algorithm.ClusteringResult(data, centers, loss=loss)
def test_clustering_result_value_errors_unequal_points(self):
centers = np.array([[0, 0, 0], [1, 1, 1]])
datapoints = np.array([[1, 0, 1], [101, 101, 99], [4, 0, 4]])
labels = np.array([0, 1], dtype=int)
data = clustering_params.Data(datapoints=datapoints, radius=200)
with self.assertRaises(ValueError):
clustering_algorithm.ClusteringResult(data,
centers,
labels,
loss=1.0)
def test_value_error_no_true_labels(self):
datapoints, radius = np.zeros(shape=(6, 4)), 1.0
data = clustering_params.Data(datapoints, radius)
centers = np.zeros(shape=(3, 4))
cluster_labels = np.array([0, 0, 1, 1, 2, 2])
clustering_result = clustering_algorithm.ClusteringResult(
data, centers, cluster_labels, loss=1.0)
with self.assertRaises(ValueError):
clustering_result.cross_label_histogram()
with self.assertRaises(ValueError):
clustering_result.get_clustering_metrics()
def test_clustering_result_value_errors_labels_out_of_bounds(self):
centers = np.array([[0, 0, 0], [1, 1, 1]])
datapoints = np.array([[1, 0, 1], [101, 101, 99], [4, 0, 4]])
data = clustering_params.Data(datapoints=datapoints, radius=200)
for labels in [
np.array([-1, 0, 1], dtype=int),
np.array([0, 1, 2], dtype=int),
np.array([0, 1, 1.1])
]:
with self.assertRaises(ValueError):
clustering_algorithm.ClusteringResult(data,
centers,
labels,
loss=1.0)
def test_get_clustering_result(self):
centers = np.array([[0, 0, 0], [100, 100, 100]])
datapoints = np.array([[1, 0, 1], [101, 101, 99], [4, 0, 4]])
data = clustering_params.Data(datapoints=datapoints, radius=200)
clustering_result = clustering_algorithm.ClusteringResult(
data, centers)
self.assertLen(data.datapoints, 3)
for i, datapoint in enumerate(clustering_result.data.datapoints):
self.assertSequenceAlmostEqual(datapoints[i], datapoint)
self.assertLen(centers, 2)
for i, center in enumerate(clustering_result.centers):
self.assertSequenceAlmostEqual(centers[i], center)
self.assertListEqual(list(clustering_result.labels), [0, 1, 0])
self.assertAlmostEqual(clustering_result.loss, 37)
def test_get_clustering_metrics(self):
datapoints, radius = np.zeros(shape=(6, 4)), 1.0
labels = np.array([0, 0, 0, 1, 1, 1])
data = clustering_params.Data(datapoints, radius, labels)
centers = np.zeros(shape=(3, 4))
cluster_labels = np.array([0, 0, 1, 1, 2, 2])
clustering_result = clustering_algorithm.ClusteringResult(
data, centers, cluster_labels, loss=1.0)
clustering_metrics = clustering_result.get_clustering_metrics()
expected_cross_label_histogram = np.array([[2, 0], [1, 1], [0, 2]],
dtype=int)
self.assertTrue((clustering_metrics.cross_label_histogram ==
expected_cross_label_histogram).all())
self.assertEqual(clustering_metrics.num_points, 6)
self.assertEqual(clustering_metrics.dominant_label_correct_count, 5)
self.assertAlmostEqual(clustering_metrics.dominant_label_accuracy,
5 / 6)
self.assertEqual(clustering_metrics.true_pairs, 6)
self.assertEqual(clustering_metrics.true_nonmatch_count, 4)
self.assertAlmostEqual(clustering_metrics.true_nonmatch_frac, 4 / 6)
self.assertEqual(clustering_metrics.false_pairs, 9)
self.assertEqual(clustering_metrics.false_match_count, 1)
self.assertAlmostEqual(clustering_metrics.false_match_frac, 1 / 9)