-
Notifications
You must be signed in to change notification settings - Fork 0
/
TestCases_kmeans.py
84 lines (67 loc) · 3.43 KB
/
TestCases_kmeans.py
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
import pickle
import unittest
import Kmeans as km
from Kmeans import *
from utils import *
# unittest.TestLoader.sortTestMethodsUsing = None
class TestCases(unittest.TestCase):
def setUp(self):
np.random.seed(123)
with open('./test/test_cases_kmeans.pkl', 'rb') as f:
self.test_cases = pickle.load(f)
def test_01_NIU(self): #NO HACER CASO, SOLO COMPRUEBA QUE ESTEN LOS NIUS Y EL GRUPO
# DON'T FORGET TO WRITE YOUR NIU AND GROUPS
self.assertNotEqual(km.__authors__, "1636290, 1631153, 1636589", msg="CHANGE IT TO YOUR NIU!")
self.assertNotEqual(km.__group__, "DJ.10", msg="CHANGE YOUR GROUP NAME!")
def test_02_init_X(self): #HECHA
for ix, input in enumerate(self.test_cases['input']):
km = KMeans(input, self.test_cases['K'][ix])
np.testing.assert_array_equal(km.X, self.test_cases['shape'][ix])
def test_03_init_centroids(self):
for ix, input in enumerate(self.test_cases['input']):
km = KMeans(input, self.test_cases['K'][ix])
km._init_centroids()
np.testing.assert_array_equal(km.centroids, self.test_cases['init_centroid'][ix])
def test_04_distance(self):
for ix, input in enumerate(self.test_cases['shape']):
dist = km.distance(input, self.test_cases['init_centroid'][ix])
np.testing.assert_array_almost_equal_nulp(dist, self.test_cases['distance'][ix])
def test_05_get_labels(self):
for ix, input in enumerate(self.test_cases['input']):
km = KMeans(input, self.test_cases['K'][ix])
km._init_centroids()
km.get_labels()
np.testing.assert_array_equal(km.labels, self.test_cases['labels'][ix])
def test_06_get_centroids(self):
for ix, input in enumerate(self.test_cases['input']):
km = KMeans(input, self.test_cases['K'][ix])
km._init_centroids()
km.get_labels()
km.get_centroids()
# Compare old centroids
np.testing.assert_array_equal(km.old_centroids, self.test_cases['get_centroid'][ix][0])
# Compare new centroids
np.testing.assert_array_equal(km.centroids, self.test_cases['get_centroid'][ix][1])
def test_07_converges(self):
for ix, input in enumerate(self.test_cases['input']):
km = KMeans(input, self.test_cases['K'][ix])
km._init_centroids()
old_centroid, centroid, bool_value = self.test_cases['converge'][ix]
km.old_centroids, km.centroids = old_centroid, centroid
self.assertEqual(km.converges(), bool_value)
def test_08_Kmeans(self):
for ix, input in enumerate(self.test_cases['input']):
km = KMeans(input, self.test_cases['K'][ix])
km.fit()
np.testing.assert_array_equal(km.centroids, self.test_cases['kmeans'][ix])
def test_09_find_bestK(self):
for ix, input in enumerate(self.test_cases['input']):
km = KMeans(input, self.test_cases['K'][ix])
km.find_bestK(10)
self.assertEqual(km.K, self.test_cases['bestK'][ix])
def test_10_get_color(self):
for ix, centroid in enumerate(self.test_cases['kmeans']):
color = get_colors(centroid)
self.assertCountEqual(color, self.test_cases['color'][ix])
if __name__ == "__main__":
unittest.main()