-
Notifications
You must be signed in to change notification settings - Fork 4
/
Copy pathdata_utils_test.py
59 lines (53 loc) · 3.75 KB
/
data_utils_test.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
import unittest
import numpy as np
from data_utils import *
comma_glove_vector = np.array([-0.082752, 0.67204, -0.14987, -0.064983, 0.056491,
0.40228, 0.0027747, -0.3311, -0.30691, 2.0817, 0.031819, 0.013643, 0.30265,
0.0071297, -0.5819, -0.2774, -0.062254, 1.1451, -0.24232, 0.1235, -0.12243,
0.33152, -0.006162, -0.30541, -0.13057, -0.054601, 0.037083, -0.070552,
0.5893, -0.30385, 0.2898, -0.14653, -0.27052, 0.37161, 0.32031,
-0.29125, 0.0052483, -0.13212, -0.052736, 0.087349, -0.26668, -0.16897, 0.015162,
-0.0083746, -0.14871, 0.23413, -0.20719, -0.091386, 0.40075, -0.17223, 0.18145,
0.37586, -0.28682, 0.37289, -0.16185, 0.18008, 0.3032, -0.13216, 0.18352,
0.095759, 0.094916, 0.008289, 0.11761, 0.34046, 0.03677, -0.29077,
0.058303, -0.027814, 0.082941, 0.1862, -0.031494, 0.27985, -0.074412,
-0.13762, -0.21866, 0.18138, 0.040855, -0.113, 0.24107, 0.3657, -0.27525,
-0.05684, 0.34872, 0.011884, 0.14517, -0.71395, 0.48497, 0.14807, 0.62287,
0.20599, 0.58379, -0.13438, 0.40207, 0.18311, 0.28021, -0.42349, -0.25626,
0.17715, -0.54095, 0.16596, -0.036058, 0.08499, -0.64989, 0.075549, -0.28831,
0.40626, -0.2802, 0.094062, 0.32406, 0.28437, -0.26341, 0.11553, 0.071918,
-0.47215, -0.18366, -0.34709, 0.29964, -0.66514, 0.002516, -0.42333, 0.27512,
0.36012, 0.16311, 0.23964, -0.05923, 0.3261, 0.20559, 0.038677, -0.045816,
0.089764, 0.43151, -0.15954, 0.08532, -0.26572, -0.15001, 0.084286, -0.16714, -0.43004,
0.060807, 0.13121, -0.24112, 0.66554, 0.4453, -0.18019, -0.13919, 0.56252, 0.21457,
-0.46443, -0.012211, 0.029988, -0.051094, -0.20135, 0.80788, 0.47377, -0.057647,
0.46216, 0.16084, -0.20954, -0.05452, 0.15572, -0.13712, 0.12972, -0.011936,
-0.003378, -0.13595, -0.080711, 0.20065, 0.054056, 0.046816, 0.059539, 0.046265, 0.17754,
-0.31094, 0.28119, -0.24355, 0.085252, -0.21011, -0.19472, 0.0027297, -0.46341, 0.14789, -0.31517,
-0.065939, 0.036106, 0.42903, -0.33759, 0.16432, 0.32568, -0.050392, -0.054297, 0.24074,
0.41923, 0.13012, -0.17167, -0.37808, -0.23089, -0.019477, -0.29291, -0.30824, 0.30297,
-0.22659, 0.081574, -0.18516, -0.21408, 0.40616, -0.28974, 0.074174, -0.17795, 0.28595,
-0.039626, -0.2339, -0.36054, -0.067503, -0.091065, 0.23438, -0.0041331, 0.003232, 0.0072134,
0.008697, 0.21614, 0.049904, 0.35582, 0.13748, 0.073361, 0.14166, 0.2412, -0.013322,
0.15613, 0.083381, 0.088146, -0.019357, 0.43795, 0.083961, 0.45309, -0.50489,
-0.10865, -0.2527, -0.18251, 0.20441, 0.13319, 0.1294, 0.050594, -0.15612, -0.39543,
0.12538, 0.24881, -0.1927, -0.31847, -0.12719, 0.4341, 0.31177, -0.0040946, -0.2094,
-0.079961, 0.1161, -0.050794, 0.015266, -0.2803, -0.12486, 0.23587, 0.2339, -0.14023,
0.028462, 0.56923, -0.1649, -0.036429, 0.010051, -0.17107, -0.042608, 0.044965, -0.4393, -0.26137,
0.30088, -0.060772, -0.45312, -0.19076, -0.20288, 0.27694, -0.060888, 0.11944, 0.62206,
-0.19343, 0.47849, -0.30113, 0.059389, 0.074901, 0.061068, -0.4662, 0.40054, -0.19099,
-0.14331,0.018267,-0.18643,0.20709,-0.35598,0.05338,-0.050821,-0.1918, -0.37846, -0.06589])
class TestDataUtils(unittest.TestCase):
def test_load_glove_vectors(self):
glove, vocab = load_pretrained_glove_vectors('glove/glove.test.subset.txt')
self.assertEqual(vocab[','], 0)
np.testing.assert_allclose(glove[0], comma_glove_vector)
print("Glove vector size: %s" % len(comma_glove_vector))
def test_convert_sentence_to_glove_vectors(self):
glove, vocab = load_pretrained_glove_vectors('glove/glove.test.subset.txt')
sentence = ", , , , ,"
vecs = convert_sentence_to_glove_vectors(sentence, vocab, glove)
self.assertEqual(len(vecs), 5)
for vec in vecs: np.testing.assert_allclose(vec, comma_glove_vector)
if __name__ == '__main__':
unittest.main()