-
Notifications
You must be signed in to change notification settings - Fork 1
/
multanalogies_synchronic.py
executable file
·164 lines (137 loc) · 6.52 KB
/
multanalogies_synchronic.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
#!/projects/ltg/python3/bin/python3
# coding: utf-8
from helpers import *
from argparse import ArgumentParser
import json
from os import path
import numpy as np
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument('--testfile', required=True, action='store')
parser.add_argument('--visual', action='store', type=bool, default=False)
parser.add_argument('--year', action='store', type=int, default=2010)
parser.add_argument('--finyear', action='store', type=int, default=2018)
parser.add_argument('--lmbd', action='store', type=float, default=0.0)
parser.add_argument('--candidates', action='store', type=int, default=2)
parser.add_argument('--threshold', action='store_true')
parser.add_argument('--modeldir', action='store', default='NoW/')
args = parser.parse_args()
referencefile = args.testfile
data = json.loads(open(referencefile).read())
modeldir = args.modeldir
all_precisions = []
all_recalls = []
all_fscores = []
print('Year\tPrecision\tRecall\tF1')
for cur_year in range(args.year, args.finyear + 1):
modelfile = path.join(modeldir, '%s_incremental.model' % str(cur_year))
print('Current model:', modelfile, file=sys.stderr)
model = load_embeddings(modelfile)
cur_data = data[str(cur_year)]
locvecs = []
insvecs = []
wars = {}
for loc in cur_data:
if cur_data[loc]:
# print('War in', loc, file=sys.stderr)
wars[loc] = []
for el in cur_data[loc]:
if el in model:
wars[loc].append(el)
locvec = model[loc]
insvec = model[el]
# print(el, file=sys.stderr)
locvecs.append(locvec)
insvecs.append(insvec)
else:
print(el, 'not found!', file=sys.stderr)
print('Whole train dataset shape:', len(locvecs), file=sys.stderr)
transforms = learn_projection((locvecs, insvecs), model, lmbd=args.lmbd, from_df=False)
print('Tranformation matrix created', file=sys.stderr)
sim_average = None
sim_std = None
threshold = None
if args.threshold:
original_sims = []
for loc in wars:
cur_sims = estimate_sims(loc, wars[loc], transforms, model)
original_sims += cur_sims
sim_average = np.average(original_sims)
sim_std = np.std(original_sims)
print('Average insurgent similarity to projection: %.3f' % sim_average, file=sys.stderr)
print('Max insurgent similarity to projection: %.3f' % np.max(original_sims),
file=sys.stderr)
print('Min insurgent similarity to projection: %.3f' % np.min(original_sims),
file=sys.stderr)
print('Standard deviation of insurgent similarities: %.3f' % sim_std, file=sys.stderr)
print('Testing on the same year with %d candidates' % args.candidates, file=sys.stderr)
tps = 0 # true positives
fps = 0 # false positives
fns = 0 # false negatives
for loc in cur_data:
candidates, predicted_vector = predict(loc, model, transforms, topn=args.candidates)
# Filtering stage
# We allow only candidates which are not further from the projection
# than one sigma from the average similarity in the true set
if args.threshold:
threshold = sim_average - sim_std
rejected = [c for c in candidates if c[1] < threshold]
candidates = [c for c in candidates if c[1] >= threshold]
else:
rejected = []
# End filtering stage
candidates = [i[0] for i in candidates]
insurgents = cur_data[loc]
true_ins = set([i.split('_')[0].lower() for i in insurgents])
pred_ins = set([i.split('_')[0].lower() for i in candidates])
if args.visual:
dots2plot = len(candidates) + len(insurgents) + len(rejected) + 2
matrix = np.zeros((dots2plot, model.vector_size))
words = []
classes = []
matrix[0, :] = model[loc]
words.append(loc.split('_')[0])
classes.append('Location')
matrix[1, :] = predicted_vector
words.append('')
classes.append('Projection')
counter = 2
for word in candidates:
matrix[counter, :] = model[word]
words.append(word.split('_')[0])
classes.append('Predicted insurgents')
counter += 1
for word in insurgents:
matrix[counter, :] = model[word]
words.append(word.split('_')[0])
classes.append('True insurgents')
counter += 1
for word in rejected:
matrix[counter, :] = model[word[0]]
words.append(word[0].split('_')[0])
classes.append('Rejected insurgents')
counter += 1
visualize(
words, matrix, classes, fname=str(cur_year) + '_' + loc.split('_')[0].lower(),
radius=1 - threshold)
true_positives = len(true_ins & pred_ins)
false_positives = len(pred_ins - true_ins)
false_negatives = len(true_ins - pred_ins)
tps += true_positives
fps += false_positives
fns += false_negatives
# print('True:', loc, true_ins, file=sys.stderr)
# print('Predicted:', loc, pred_ins, file=sys.stderr)
# print('Rejected:', rejected, file=sys.stderr)
# print('%d TPs, %d FPs, %d FNs'
# % (true_positives, false_positives, false_negatives), file=sys.stderr)
cur_precision = tps / (tps + fps)
cur_recall = tps / (tps + fns)
cur_f1 = 2 * (cur_precision * cur_recall) / (cur_precision + cur_recall)
print('%d\t%.2f\t%.2f\t%.2f' % (cur_year, cur_precision, cur_recall, cur_f1))
all_precisions.append(cur_precision)
all_recalls.append(cur_recall)
all_fscores.append(cur_f1)
print('Average F1 score: %.3f' % cur_f1, file=sys.stderr)
print('Average\t%.3f\t%.3f\t%.3f' %
(np.average(all_precisions), np.average(all_recalls), np.average(all_fscores)))