1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
|
"""!
@brief Test templates for Silhouette clustering module.
@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause
"""
import math
from pyclustering.cluster.silhouette import silhouette, silhouette_ksearch
from pyclustering.samples import answer_reader
from pyclustering.samples.definitions import SIMPLE_SAMPLES
from pyclustering.tests.assertion import assertion
from pyclustering.utils import read_sample, calculate_distance_matrix, distance_metric, type_metric
class silhouette_test_template:
@staticmethod
def correct_scores(sample_path, answer_path, ccore_flag, **kwargs):
data_type = kwargs.get('data_type', 'points')
sample = read_sample(sample_path)
if data_type == 'distance_matrix':
sample = calculate_distance_matrix(sample, distance_metric(type_metric.EUCLIDEAN_SQUARE))
clusters = answer_reader(answer_path).get_clusters()
scores = silhouette(sample, clusters, ccore=ccore_flag, data_type=data_type).process().get_score()
assertion.eq(len(sample), len(scores))
for score in scores:
assertion.le(-1.0, score)
assertion.ge(1.0, score)
return scores
@staticmethod
def correct_processing_data_types(sample_path, answer_path, ccore_flag):
scores_points = silhouette_test_template.correct_scores(sample_path, answer_path, ccore_flag, data_type='points')
scores_matrix = silhouette_test_template.correct_scores(sample_path, answer_path, ccore_flag, data_type='distance_matrix')
assertion.eq(len(scores_points), len(scores_matrix))
assertion.eq(scores_points, scores_matrix)
@staticmethod
def correct_ksearch(sample_path, answer_path, kmin, kmax, algorithm, ccore_flag):
attempts = 15
testing_result = False
sample = read_sample(sample_path)
clusters = answer_reader(answer_path).get_clusters()
for _ in range(attempts):
ksearch_instance = silhouette_ksearch(sample, kmin, kmax, algorithm=algorithm, ccore=ccore_flag).process()
amount = ksearch_instance.get_amount()
score = ksearch_instance.get_score()
scores = ksearch_instance.get_scores()
assertion.le(-1.0, score)
assertion.ge(1.0, score)
assertion.eq(kmax - kmin, len(scores))
upper_limit = len(clusters) + 1
lower_limit = len(clusters) - 1
if lower_limit < 1:
lower_limit = 1
if (amount > upper_limit) or (amount < lower_limit):
continue
testing_result = True
break
assertion.true(testing_result)
@staticmethod
def random_state(kmin, kmax, algorithm, random_state, ccore_flag):
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE4)
ksearch_instance_1 = silhouette_ksearch(sample, kmin, kmax, algorithm=algorithm, random_state=random_state,
ccore=ccore_flag).process()
ksearch_instance_2 = silhouette_ksearch(sample, kmin, kmax, algorithm=algorithm, random_state=random_state,
ccore=ccore_flag).process()
assertion.eq(ksearch_instance_1.get_amount(), ksearch_instance_2.get_amount())
assertion.eq(ksearch_instance_1.get_score(), ksearch_instance_2.get_score())
assertion.eq(len(ksearch_instance_1.get_scores()), len(ksearch_instance_2.get_scores()))
scores1 = ksearch_instance_1.get_scores()
scores2 = ksearch_instance_2.get_scores()
for key in scores1:
key = int(key)
if math.isnan(scores1[key]) and math.isnan(scores2[key]):
continue
else:
assertion.eq(scores1[key], scores2[key])
|
