-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathgenerateTestCases.py
executable file
·260 lines (247 loc) · 15.4 KB
/
generateTestCases.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
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
# New Generate Test Cases
from solutions import *
import numpy as np
import math
import os,sys
# import copy
# from keras.callbacks import History
# import tensorflow as tf
sys.path.append('../')
sys.path.append('../../')
from grader_support import stdout_redirector
from grader_support import util
os.environ['TF_CPP_MIN_LOG_LEVEL']='3'
mFiles = [
"cosine_similarity.py",
"complete_analogy.py",
]
np.random.seed(3)
d = {'pursuivant': np.array([-0.13648 , -0.32336 , -0.91659 , -1.5343 , 0.71727 ,
-1.3972 , 2.2791 , 0.89844 , 1.0777 , -0.10456 ,
0.33791 , 0.57295 , 0.6847 , -0.23093 , -1.0492 ,
-0.7812 , 0.19778 , 0.92813 , -0.2945 , -0.037263 ,
0.79949 , 0.18913 , -0.034523 , -1.6484 , -1.6933 ,
-0.58301 , -0.47646 , 1.2462 , -0.5215 , 0.53559 ,
-1.0501 , -0.031223 , -0.24685 , 0.48308 , 0.0040414,
0.65939 , -1.0626 , -0.93032 , 0.68599 , -1.4787 ,
0.69818 , 0.62244 , 0.12085 , -1.4218 , -0.55545 ,
-0.31841 , 0.40133 , -0.1877 , -0.36294 , -1.3418 ]), 'villaran': np.array([-0.40261 , -0.50308 , 0.11266 , 0.47506 , -0.2698 , -0.066715,
0.096496, 0.83573 , -0.055321, -0.12376 , 0.067504, -0.83156 ,
0.90217 , 0.36186 , 0.28217 , -0.053142, 0.61577 , -0.5019 ,
1.0521 , -0.20796 , -0.9207 , -0.16474 , -0.5308 , 0.39056 ,
1.0834 , 1.023 , 0.5557 , -0.28453 , -0.48533 , 0.64407 ,
-1.494 , 0.021205, -0.092784, -0.051822, -0.35572 , -0.063281,
-0.32386 , 0.50062 , 0.39156 , 0.33686 , -0.49054 , 0.57443 ,
-0.25148 , -0.15374 , 0.46754 , -0.80154 , 0.22113 , -0.077624,
0.647 , 0.47102 ]), 'afhq': np.array([ 0.53098 , -0.80356 , 0.63852 , -0.51475 , -0.15449 ,
-1.5122 , 0.9389 , 0.24354 , -0.6317 , -0.89928 ,
-0.060159 , -0.70439 , 0.44719 , -0.24145 , -0.75044 ,
0.42972 , -0.69866 , 0.4016 , 0.21491 , 0.45674 ,
-0.20866 , -0.24596 , 0.0031839, -0.083632 , -0.72579 ,
1.0855 , 0.49908 , -0.11467 , -0.17569 , 0.75408 ,
-0.89428 , -0.30991 , -0.40079 , -0.013176 , -0.72269 ,
-0.056882 , -0.091163 , -0.40124 , -0.45752 , 0.39603 ,
0.56614 , -0.5364 , 0.14301 , 0.026913 , -0.66937 ,
0.49106 , -0.32581 , -0.31405 , -1.0336 , -0.18008 ]), 'fantastic': np.array([ 0.3333 , 0.30612 , -0.63572 , 0.051507, 0.78602 , -0.48425 ,
-0.21684 , -0.1168 , -0.10738 , 0.91883 , -0.081054, 0.73025 ,
-0.78696 , 0.25925 , 0.79365 , -0.53968 , 1.2594 , 0.62911 ,
-1.185 , -0.63767 , -0.27612 , 0.68748 , -0.049664, -0.15773 ,
1.2352 , -0.021032, -1.2503 , 0.2495 , 0.69357 , -0.19549 ,
1.6106 , 0.70615 , 0.10677 , -0.31279 , -0.20057 , 0.72469 ,
0.11066 , 0.3869 , -0.43106 , -0.99054 , 0.16137 , -0.41234 ,
-0.35022 , -0.16584 , -0.33375 , 0.46223 , 0.2872 , 0.14707 ,
0.54889 , 0.16369 ]), 'stuttgarter': np.array([-0.50955 , -1.1476 , -0.586 , -0.83522 , -0.75855 , -0.25805 ,
-0.75688 , 1.1233 , -0.24896 , -0.18953 , 0.57624 , -0.47329 ,
-0.31341 , -0.026954, -0.43877 , -1.5212 , -0.042523, 0.56638 ,
-1.0062 , 0.75813 , -0.32075 , -0.89006 , 0.96887 , 1.4227 ,
-0.33733 , 0.12367 , -1.1976 , 0.35556 , 0.29301 , -0.26443 ,
-0.74345 , 0.28694 , 0.31767 , 1.1837 , -0.62699 , -0.37567 ,
0.076536, 0.55794 , 0.61395 , 1.1676 , 1.8369 , -0.92562 ,
-0.32736 , 0.37398 , -0.080528, 0.26458 , -0.28498 , 3.3287 ,
0.18334 , 0.32584 ]), 'ojo': np.array([-0.63686 , 0.41181 , -1.4589 , 1.9792 , -0.29009 , -1.6778 ,
0.33873 , 0.21582 , -0.33524 , 0.013346, -0.54925 , 0.63686 ,
0.22764 , 0.65606 , 0.27749 , -0.27824 , 0.47274 , -0.9366 ,
0.61049 , 0.36825 , -1.3076 , 1.2877 , 0.67761 , 0.50116 ,
-0.45755 , 1.8329 , -0.19165 , -0.18298 , 0.4645 , 1.291 ,
0.084926, -1.0717 , -1.2867 , 1.8005 , 0.53937 , 0.052519,
0.017464, -1.243 , 1.3731 , 1.0074 , 0.20999 , 0.56117 ,
0.1249 , 0.90613 , -0.25918 , 0.43458 , -0.1135 , 0.20548 ,
1.3328 , 0.17734 ]), 'safrole': np.array([-0.056604, -0.27681 , 0.61062 , -0.51454 , -0.6895 , 0.47962 ,
0.27797 , 0.14565 , 1.0907 , 1.1291 , 0.48951 , 0.18394 ,
1.2755 , -0.073016, -0.55865 , 0.64033 , 0.4357 , 0.20435 ,
0.29956 , 0.56403 , -0.33319 , -0.78834 , 0.51737 , 0.016081,
0.3438 , 0.29654 , 0.51401 , 0.64383 , 1.0954 , 0.51688 ,
-1.4204 , -0.13864 , 0.066039, -0.19696 , 0.21482 , 0.21129 ,
-0.095891, 0.16873 , -0.061557, 0.50999 , -0.14176 , 0.23621 ,
-0.29082 , 0.013112, 0.027957, 0.2022 , -0.47505 , -0.028703,
0.40289 , -0.51492 ]), 'kette': np.array([-0.85066 , -0.7159 , 0.22399 , -0.24156 , -0.33906 , -0.21177 ,
0.58644 , 0.92323 , 0.015982, 0.081562, 0.14588 , 0.32739 ,
-0.062476, 0.11624 , 0.15714 , -0.4664 , 0.48864 , 0.13282 ,
0.84737 , 0.12374 , -0.20044 , -0.10058 , -0.034266, -0.1403 ,
0.52284 , 1.2843 , -0.02867 , -0.15816 , 0.089804, 0.26402 ,
-1.4842 , -0.63547 , 0.55904 , 0.58881 , -0.85034 , 0.37174 ,
0.20329 , -0.28296 , 0.16659 , 0.18303 , 0.12657 , -0.18965 ,
-0.56698 , -0.42887 , 0.10995 , -0.41366 , -0.12741 , -0.32587 ,
-0.62187 , 0.32581 ]), 'legerdemain': np.array([-0.49936 , -1.2411 , -0.70649 , 0.45057 , -0.37436 , 0.21668 ,
1.0298 , 0.34018 , 0.49105 , 0.35335 , -0.43973 , 0.33745 ,
0.26364 , 0.42547 , -0.25835 , 0.21418 , 0.62817 , -0.35251 ,
0.81946 , -0.3714 , 0.15607 , 0.13716 , 0.034591, -0.68181 ,
0.25987 , 0.86315 , -1.2554 , -0.59729 , 0.44129 , 0.62309 ,
-0.49832 , -0.29734 , 0.082235, 0.40138 , 0.3259 , 0.67977 ,
-0.98732 , 0.63233 , 0.042146, -0.36166 , -0.21727 , 0.059784,
0.011296, 1.3433 , -0.55062 , -0.29982 , 0.85784 , 1.3315 ,
-0.32958 , 0.013188]), 'bewail': np.array([ 2.02300000e-01, 1.37670000e-01, 6.93120000e-01,
-3.12950000e-01, 3.80540000e-02, -8.00100000e-02,
5.21760000e-01, 8.68420000e-01, 1.10210000e+00,
2.31960000e-01, 3.61240000e-01, 4.94650000e-01,
-4.35710000e-01, -8.52580000e-02, -3.91010000e-01,
9.68200000e-04, -1.46790000e-01, 1.67690000e-01,
9.44450000e-01, -2.40290000e-01, 2.32530000e-01,
1.81000000e-02, 2.68360000e-01, 7.33350000e-02,
8.78050000e-01, 4.57030000e-01, -2.15070000e-01,
-1.55560000e-02, 2.95430000e-01, -4.46170000e-01,
-1.47730000e+00, 1.43550000e+00, 1.58450000e-01,
-4.51390000e-01, -6.94710000e-01, -1.45250000e-01,
-3.63640000e-01, -2.75890000e-01, -2.48610000e-01,
4.85880000e-01, 4.67430000e-01, -1.17190000e+00,
-4.50920000e-01, 4.29580000e-01, -3.62210000e-01,
-6.47930000e-01, 2.32530000e-01, 6.57250000e-01,
-1.74790000e-01, -4.14400000e-01]), 'jacaranda': np.array([-0.078031 , 0.66091 , -0.52212 , -0.21876 , -0.091462 ,
-0.68891 , -0.63285 , -1.315 , -0.096502 , -0.024935 ,
-0.3284 , -0.15751 , 0.77287 , -0.91169 , -0.50055 ,
-0.1451 , -0.45939 , 0.86599 , 0.55295 , -0.33344 ,
-0.65468 , -0.28861 , -0.35458 , -0.21568 , 0.46595 ,
1.4247 , 0.38921 , 1.0829 , -0.41168 , -0.12059 ,
-0.27789 , 0.23522 , 0.0094553, 0.43089 , -0.21108 ,
0.15284 , 0.16143 , -0.85432 , -0.31323 , -0.22619 ,
-0.23124 , -1.2713 , -1.2284 , -0.16185 , 0.99928 ,
-0.39963 , 0.0035931, -0.59366 , 0.80196 , -0.66543 ]), 'haseley': np.array([-1.4347 , 0.20575 , -1.732 , 0.51585 , 0.39565 , -0.31659 ,
1.2428 , 1.6892 , 0.050026, -0.40844 , -0.79636 , -0.055379,
0.60127 , -0.89393 , -0.3252 , 0.02444 , 0.41073 , 0.58097 ,
0.13357 , -0.35081 , 0.3364 , -0.051527, -1.0755 , -0.6867 ,
0.97024 , 0.18858 , -0.31732 , 0.45335 , 0.35708 , -0.12374 ,
-1.0983 , -0.087276, 1.5077 , 0.3286 , 0.091927, 0.34424 ,
-0.19914 , 0.52972 , 0.2059 , 0.5156 , -0.33331 , 0.26901 ,
-0.58417 , 0.76165 , -0.19704 , 0.31709 , 0.02883 , -0.065241,
-0.70475 , 0.058118]), 'buccinator': np.array([-0.22861 , -0.46818 , 0.21334 , -0.25846 , -1.0519 ,
0.602 , 0.96512 , 0.15314 , 0.38865 , 0.42587 ,
1.2212 , 0.30396 , -0.58748 , 0.36276 , -1.2863 ,
-0.26898 , -0.20993 , 0.62708 , -0.14232 , -0.046718 ,
-1.083 , -0.43975 , -0.26489 , 0.50958 , -0.21859 ,
1.2991 , -0.052586 , 0.11751 , 0.33204 , -0.54345 ,
-1.0511 , 0.63912 , 0.26517 , 0.35931 , -0.49959 ,
0.0092442, 0.43539 , -0.57454 , 0.35829 , 0.07201 ,
0.97662 , -0.94236 , -0.54374 , -0.28324 , -0.3809 ,
-0.023993 , 0.77095 , -0.48494 , -0.4816 , 0.68831 ]), 'shards': np.array([ 0.71799 , -0.34384 , 0.2019 , -0.28885 , 0.62331 , 0.46421 ,
0.24469 , 0.22792 , 0.068261, -0.24096 , 0.038203, -0.4548 ,
-0.18157 , 0.77576 , -0.047025, -0.011271, -0.49527 , -0.42226 ,
-0.2556 , -1.3005 , 0.26983 , 0.21088 , -0.22026 , -0.84124 ,
0.48152 , 0.39863 , -1.0697 , 1.2075 , 0.82633 , -0.72164 ,
0.56384 , -0.8468 , 0.95252 , 0.12169 , -0.45382 , 1.3513 ,
0.29161 , 0.26772 , 0.94841 , 0.86687 , 0.5961 , -0.020455,
0.38269 , 0.26131 , 1.2168 , 0.93614 , 0.472 , -0.3626 ,
-0.60097 , -2.2747 ]), 'b-grade': np.array([-0.8609 , -0.44712 , -0.27446 , -0.79703 , -0.71908 ,
0.10183 , 0.70082 , -0.31224 , 0.23362 , 0.64631 ,
-0.30447 , 0.2614 , 0.12329 , 1.3967 , 0.024219 ,
-0.38552 , 0.4673 , 0.96459 , -0.28215 , 0.52417 ,
0.78773 , 0.14975 , 0.027833 , 0.64056 , -0.26983 ,
0.32098 , -0.545 , 0.32106 , 0.081269 , 0.19013 ,
-1.1679 , 0.34142 , 0.47435 , -0.0056714, 0.5998 ,
1.2032 , 0.14962 , -0.25349 , -1.0573 , -1.2133 ,
-0.4207 , -0.54607 , 0.69367 , 0.19088 , -0.5349 ,
-0.012231 , 0.60467 , 0.69159 , 0.91687 , -0.5041 ]), 'musburger': np.array([-1.4283 , 0.16758 , -0.47018 , 0.82724 , 0.97079 , 0.21426 ,
-0.92678 , -0.40753 , -0.23062 , 0.12891 , 0.95236 , 0.88535 ,
-0.31684 , 0.77579 , 0.47733 , 1.1553 , 0.24857 , 0.17023 ,
-1.001 , 0.34208 , 0.37645 , 1.0186 , 0.56251 , -0.43753 ,
-0.047782, 0.25857 , 1.4755 , 1.0392 , 1.6308 , -0.32552 ,
-1.4243 , 0.025256, 0.54655 , -0.18552 , 0.33294 , -0.2386 ,
-0.23484 , -0.086404, -1.176 , 0.6914 , 0.56057 , 0.47543 ,
0.023505, -0.40302 , 0.012691, -0.15688 , 0.19542 , 1.3228 ,
-1.4608 , 1.0201 ]), 'notified': np.array([ 0.62376 , -0.75815 , 0.089605, 0.42228 , 0.13514 , -0.45574 ,
-0.86543 , 1.0435 , 0.52532 , -1.4878 , 0.48253 , 0.24104 ,
0.19165 , -0.47256 , 0.833 , 0.50503 , -1.0043 , -0.04769 ,
0.22545 , -0.07572 , 0.65528 , 0.096368, 0.42306 , 0.085978,
-0.1165 , -1.347 , 0.44875 , -0.16913 , -0.72993 , -0.58753 ,
0.81646 , -0.46654 , -0.44232 , -1.0399 , 0.26699 , -0.36237 ,
0.94474 , -0.29005 , 0.10662 , -0.074245, -0.096851, -0.59159 ,
0.53484 , 0.31644 , -0.14752 , -0.19137 , -0.65545 , 1.0109 ,
0.21755 , 0.29185 ]), 'pranked': np.array([-0.27447 , -1.1432 , 0.57435 , -0.53883 , -0.55628 ,
-0.32994 , -0.19057 , 0.44151 , 0.16291 , 0.29067 ,
0.38809 , 0.72596 , 0.18337 , 0.30772 , 0.1491 ,
-0.20821 , -0.09548 , 0.28101 , 0.32209 , -0.2647 ,
-0.51715 , -0.12852 , 0.63564 , 0.43983 , 0.74979 ,
0.88926 , 0.0086555, 0.15207 , 0.0076686, -0.011641 ,
-1.3029 , 0.30404 , 0.8561 , -0.16169 , -0.50378 ,
0.091735 , 0.50758 , -0.15638 , -1.0251 , -0.79437 ,
0.079466 , -0.7838 , -0.26636 , 0.4992 , -0.059185 ,
-0.743 , -0.42374 , -0.17468 , 0.41427 , 0.36044 ]), 'yaphe': np.array([ -1.02430000e+00, 3.57800000e-01, -3.07450000e-01,
5.02070000e-01, 5.85570000e-01, 4.45020000e-01,
8.34830000e-01, 8.72170000e-01, 5.59460000e-01,
-1.28290000e+00, -5.10880000e-01, -8.76870000e-01,
-5.35340000e-03, 4.02950000e-01, -1.00480000e-01,
-3.91440000e-01, 9.49930000e-01, 2.45450000e-01,
8.02000000e-01, 3.23390000e-01, -5.10060000e-01,
8.18730000e-01, -5.41060000e-01, -5.96140000e-02,
-3.36630000e-01, 2.57250000e-01, 2.59870000e-01,
5.09460000e-01, -3.35070000e-01, 2.83060000e-01,
-2.14290000e+00, -9.43500000e-01, 4.76710000e-01,
-1.39560000e-01, -1.08800000e+00, -1.36530000e-01,
-6.70130000e-01, 3.63520000e-01, 1.38460000e+00,
1.28400000e-04, 6.12660000e-01, 2.09160000e-01,
9.25030000e-01, -1.43200000e-01, 3.30550000e-01,
-2.11800000e-01, 2.52800000e-01, 8.69550000e-01,
-1.00700000e+00, 1.43340000e+00]), '1h24': np.array([-0.34633 , 0.50454 , -0.86025 , 0.57096 , -0.3095 , -0.11699 ,
1.9929 , -0.51506 , -0.07715 , 0.018544, 0.034566, -0.51917 ,
1.094 , 0.42041 , -0.63229 , 0.62602 , -0.6682 , 0.48521 ,
0.66027 , -0.073988, 0.073898, -0.28117 , -0.13631 , 0.98756 ,
0.22947 , 1.2216 , 0.35325 , 0.4046 , 0.74782 , 0.28767 ,
-0.38537 , 0.10428 , -0.24377 , 0.27679 , -0.6413 , -0.025187,
0.19751 , -0.50443 , -0.38569 , 0.017954, 1.2157 , -0.92861 ,
0.17659 , 0.66615 , 0.2623 , -0.14838 , 1.4899 , 0.14031 ,
0.236 , 0.29638 ])}
# generating test cases for cosine_similarity
u1 = d['pranked']
v1 = d['notified']
cs = cosine_similarity(u1,v1)
# generating test cases for complete_analogy
w1 = 'notified'
w2 = 'pranked'
w3 = 'musburger'
ca = complete_analogy(w1,w2,w3,d)
# generating test cases for neutralize
# gender = np.asarray([-0.087144 , 0.2182 , -0.40986 , -0.03922 , -0.1032 , 0.94165,
# -0.06042 , 0.32988 , 0.46144 , -0.35962 , 0.31102 , -0.86824,
# 0.96006 , 0.01073 , 0.24337 , 0.08193 , -1.02722 , -0.21122,
# 0.695044 , -0.00222 , 0.29106 , 0.5053 , -0.099454 , 0.40445,
# 0.30181 , 0.1355 , -0.0606 , -0.07131 , -0.19245 , -0.06115,
# -0.3204 , 0.07165 , -0.13337 , -0.25068714, -0.14293 , -0.224957,
# -0.149 , 0.048882, 0.12191 , -0.27362 , -0.165476 , -0.20426,
# 0.54376 , -0.271425, -0.10245 , -0.32108 , 0.2516 , -0.33455,
# -0.04371 , 0.01258 ])
# v = neutralize("pranked", gender,d)
# generating test cases
# ue, u2 = equalize(("musburger", "pranked"), gender, d)
# set the seed to be able to replicate the same results.
np.random.seed(3)
def generateTestCases():
testCases = {
'cosine_similarity': {
'partId': 'rUmJJ',
'testCases': [
{
'testInput': (u1,v1),
'testOutput': cs
}
]
},
'complete_analogy': {
'partId': 'WQvOv',
'testCases': [
{
'testInput': (w1,w2,w3,d),
'testOutput': ca
}
]
}
}
return testCases