from DBCV import DBCV
from sklearn import datasets
import pytest
from sklearn.cluster import KMeans
import hdbscan
from scipy.spatial.distance import euclidean
import numpy as np
@pytest.fixture
def data():
n_samples = 60
noisy_moons = datasets.make_moons(n_samples=n_samples, noise=.05,
random_state=1)
X = noisy_moons[0]
return X
def test_DBCV(data):
kmeans = KMeans(n_clusters=2)
kmeans_labels = kmeans.fit_predict(data)
hdbscanner = hdbscan.HDBSCAN()
hdbscan_labels = hdbscanner.fit_predict(data)
kmeans_score = DBCV.DBCV(data, kmeans_labels, dist_function=euclidean)
hdbscan_score = DBCV.DBCV(data, hdbscan_labels, dist_function=euclidean)
assert hdbscan_score > kmeans_score
def test__core_dist(data):
target = 0.09325490419185979
point = data[0]
core_dist = DBCV._core_dist(point, data, euclidean)
assert abs(core_dist - target) < 0.001
def test__mutual_reachability_dist(data):
target = 0.074196034579080888
point_1 = data[0]
point_2 = data[1]
dist = DBCV._mutual_reachability_dist(point_1, point_2, data, data,
euclidean)
assert dist == euclidean(point_1, point_2)
point_3 = data[5]
point_4 = data[46]
dist_2 = DBCV._mutual_reachability_dist(point_3, point_4, data, data,
euclidean)
assert abs(dist_2 - target) < 0.001
def test__mutual_reach_dist_graph(data):
target = 0.09872567819414102
hdbscanner = hdbscan.HDBSCAN()
hdbscan_labels = hdbscanner.fit_predict(data)
graph = DBCV._mutual_reach_dist_graph(data, hdbscan_labels,
euclidean)
assert graph.shape == (data.shape[0], data.shape[0])
assert abs(graph[0][0] - target < 0.001)
def test__mutual_reach_dist_MST():
test_array = np.array([[0, 1, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0],
[0, 0, 0, 2, 0, 0],
[0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0]])
target_array = np.array([[0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0],
[0, 1, 0, 2, 0, 0],
[0, 0, 2, 0, 1, 0],
[0, 0, 0, 1, 0, 1],
[0, 0, 0, 0, 1, 0]])
mst = DBCV._mutual_reach_dist_MST(test_array)
assert np.array_equal(mst, target_array)
def test__cluster_density_sparseness():
test_array = np.array([[0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0],
[0, 1, 0, 2, 0, 0],
[0, 0, 2, 0, 1, 0],
[0, 0, 0, 1, 0, 1],
[0, 0, 0, 0, 1, 0]])
labels = np.array([0, 0, 0, 1, 1, 1])
cluster = 1
density = DBCV._cluster_density_sparseness(test_array,
labels, cluster)
assert density == 1
def test__cluster_density_separation():
test_array = np.array([[0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0],
[0, 1, 0, 2, 0, 0],
[0, 0, 2, 0, 1, 0],
[0, 0, 0, 1, 0, 1],
[0, 0, 0, 0, 1, 0]])
labels = np.array([0, 0, 0, 1, 1, 1])
separation = DBCV._cluster_density_separation(test_array,
labels, 0, 1)
assert separation == 2
def test__cluster_validity_index():
test_array = np.array([[0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0],
[0, 1, 0, 2, 0, 0],
[0, 0, 2, 0, 1, 0],
[0, 0, 0, 1, 0, 1],
[0, 0, 0, 0, 1, 0]])
labels = np.array([0, 0, 0, 1, 1, 1])
validity = DBCV._cluster_validity_index(test_array,
labels, 0)
assert validity == 0.5
def test__clustering_validity_index():
test_array = np.array([[0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0],
[0, 1, 0, 2, 0, 0],
[0, 0, 2, 0, 1, 0],
[0, 0, 0, 1, 0, 1],
[0, 0, 0, 0, 1, 0]])
labels = np.array([0, 0, 0, 1, 1, 1])
validity = DBCV._clustering_validity_index(test_array,
labels)
assert validity == 0.5
def test__get_label_members():
test_array = np.array([[0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0],
[0, 1, 0, 2, 0, 0],
[0, 0, 2, 0, 1, 0],
[0, 0, 0, 1, 0, 1],
[0, 0, 0, 0, 1, 0]])
labels = np.array([0, 0, 0, 1, 1, 1])
members = DBCV._get_label_members(test_array, labels, 0)
target = test_array[np.array([0, 1, 2])]
assert np.array_equal(target, members)
