-
Notifications
You must be signed in to change notification settings - Fork 34
/
Copy pathscores.py
225 lines (174 loc) · 8.5 KB
/
scores.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
from dataset_walker import DatasetWalker
from nltk.translate.bleu_score import sentence_bleu
from nltk.translate.meteor_score import single_meteor_score
from rouge import Rouge
import re
import sys
import json
import argparse
RE_ART = re.compile(r'\b(a|an|the)\b')
RE_PUNC = re.compile(r'[!"#$%&()*+,-./:;<=>?@\[\]\\^`{|}~_\']')
class Metric:
def __init__(self):
self.reset()
def reset(self):
self._detection_tp = 0.0
self._detection_fp = 0.0
self._detection_tn = 0.0
self._detection_fn = 0.0
self._selection_mrr5 = 0.0
self._selection_r1 = 0.0
self._selection_r5 = 0.0
self._generation_bleu1 = 0.0
self._generation_bleu2 = 0.0
self._generation_bleu3 = 0.0
self._generation_bleu4 = 0.0
self._generation_meteor = 0.0
self._generation_rouge_1 = 0.0
self._generation_rouge_2 = 0.0
self._generation_rouge_l = 0.0
def _match(self, ref_knowledge, pred_knowledge):
result = []
for pred in pred_knowledge:
matched = False
for ref in ref_knowledge:
if pred['domain'] == ref['domain'] and pred['entity_id'] == ref['entity_id'] and pred['doc_id'] == ref['doc_id']:
matched = True
result.append(matched)
return result
def _reciprocal_rank(self, ref_knowledge, hyp_knowledge, k=5):
relevance = self._match(ref_knowledge, hyp_knowledge)[:k]
if True in relevance:
idx = relevance.index(True)
result = 1.0/(idx+1)
else:
result = 0.0
return result
def _recall_at_k(self, ref_knowledge, hyp_knowledge, k=5):
relevance = self._match(ref_knowledge, hyp_knowledge)[:k]
if True in relevance:
result = 1.0
else:
result = 0.0
return result
def _normalize_text(self, text):
result = text.lower()
result = RE_PUNC.sub(' ', result)
result = RE_ART.sub(' ', result)
result = ' '.join(result.split())
return result
def _bleu(self, ref_response, hyp_response, n=4):
ref_tokens = self._normalize_text(ref_response).split()
hyp_tokens = self._normalize_text(hyp_response).split()
weights = [1.0/n] * n
score = sentence_bleu([ref_tokens], hyp_tokens, weights)
return score
def _meteor(self, ref_response, hyp_response):
score = single_meteor_score(ref_response, hyp_response, self._normalize_text)
return score
def _rouge(self, ref_response, hyp_response, mode='l'):
ref_response = self._normalize_text(ref_response)
hyp_response = self._normalize_text(hyp_response)
rouge = Rouge()
if mode == 'l':
score = rouge.get_scores(hyp_response, ref_response)[0]['rouge-l']['f']
elif mode == 1:
score = rouge.get_scores(hyp_response, ref_response)[0]['rouge-1']['f']
elif mode == 2:
score = rouge.get_scores(hyp_response, ref_response)[0]['rouge-2']['f']
else:
raise ValueError("unsupported mode: %s" % mode)
return score
def update(self, ref_obj, hyp_obj):
if ref_obj['target'] is True:
if hyp_obj['target'] is True:
self._detection_tp += 1
self._selection_mrr5 += self._reciprocal_rank(ref_obj['knowledge'], hyp_obj['knowledge'], 5)
self._selection_r1 += self._recall_at_k(ref_obj['knowledge'], hyp_obj['knowledge'], 1)
self._selection_r5 += self._recall_at_k(ref_obj['knowledge'], hyp_obj['knowledge'], 5)
self._generation_bleu1 += self._bleu(ref_obj['response'], hyp_obj['response'], 1)
self._generation_bleu2 += self._bleu(ref_obj['response'], hyp_obj['response'], 2)
self._generation_bleu3 += self._bleu(ref_obj['response'], hyp_obj['response'], 3)
self._generation_bleu4 += self._bleu(ref_obj['response'], hyp_obj['response'], 4)
self._generation_meteor += self._meteor(ref_obj['response'], hyp_obj['response'])
self._generation_rouge_l += self._rouge(ref_obj['response'], hyp_obj['response'], 'l')
self._generation_rouge_1 += self._rouge(ref_obj['response'], hyp_obj['response'], 1)
self._generation_rouge_2 += self._rouge(ref_obj['response'], hyp_obj['response'], 2)
else:
self._detection_fn += 1
else:
if hyp_obj['target'] is True:
self._detection_fp += 1
else:
self._detection_tn += 1
def _compute(self, score_sum):
if self._detection_tp + self._detection_fp > 0.0:
score_p = score_sum/(self._detection_tp + self._detection_fp)
else:
score_p = 0.0
if self._detection_tp + self._detection_fn > 0.0:
score_r = score_sum/(self._detection_tp + self._detection_fn)
else:
score_r = 0.0
if score_p + score_r > 0.0:
score_f = 2*score_p*score_r/(score_p+score_r)
else:
score_f = 0.0
return (score_p, score_r, score_f)
def scores(self):
detection_p, detection_r, detection_f = self._compute(self._detection_tp)
selection_mrr5_p, selection_mrr5_r, selection_mrr5_f = self._compute(self._selection_mrr5)
selection_r1_p, selection_r1_r, selection_r1_f = self._compute(self._selection_r1)
selection_r5_p, selection_r5_r, selection_r5_f = self._compute(self._selection_r5)
generation_bleu1_p, generation_bleu1_r, generation_bleu1_f = self._compute(self._generation_bleu1)
generation_bleu2_p, generation_bleu2_r, generation_bleu2_f = self._compute(self._generation_bleu2)
generation_bleu3_p, generation_bleu3_r, generation_bleu3_f = self._compute(self._generation_bleu3)
generation_bleu4_p, generation_bleu4_r, generation_bleu4_f = self._compute(self._generation_bleu4)
generation_meteor_p, generation_meteor_r, generation_meteor_f = self._compute(self._generation_meteor)
generation_rouge_l_p, generation_rouge_l_r, generation_rouge_l_f = self._compute(self._generation_rouge_l)
generation_rouge_1_p, generation_rouge_1_r, generation_rouge_1_f = self._compute(self._generation_rouge_1)
generation_rouge_2_p, generation_rouge_2_r, generation_rouge_2_f = self._compute(self._generation_rouge_2)
scores = {
'detection': {
'prec': detection_p,
'rec': detection_r,
'f1': detection_f
},
'selection': {
'mrr@5': selection_mrr5_f,
'r@1': selection_r1_f,
'r@5': selection_r5_f,
},
'generation': {
'bleu-1': generation_bleu1_f,
'bleu-2': generation_bleu2_f,
'bleu-3': generation_bleu3_f,
'bleu-4': generation_bleu4_f,
'meteor': generation_meteor_f,
'rouge_1': generation_rouge_1_f,
'rouge_2': generation_rouge_2_f,
'rouge_l': generation_rouge_l_f
}
}
return scores
def main(argv):
parser = argparse.ArgumentParser(description='Evaluate the system outputs.')
parser.add_argument('--dataset', dest='dataset', action='store', metavar='DATASET', choices=['train', 'val', 'test'], required=True, help='The dataset to analyze')
parser.add_argument('--dataroot',dest='dataroot',action='store', metavar='PATH', required=True,
help='Will look for corpus in <dataroot>/<dataset>/...')
parser.add_argument('--outfile',dest='outfile',action='store',metavar='JSON_FILE',required=True,
help='File containing output JSON')
parser.add_argument('--scorefile',dest='scorefile',action='store',metavar='JSON_FILE',required=True,
help='File containing scores')
args = parser.parse_args()
with open(args.outfile, 'r') as f:
output = json.load(f)
data = DatasetWalker(dataroot=args.dataroot, dataset=args.dataset, labels=True)
metric = Metric()
for (instance, ref), pred in zip(data, output):
metric.update(ref, pred)
scores = metric.scores()
with open(args.scorefile, 'w') as out:
json.dump(scores, out, indent=2)
if __name__ =="__main__":
main(sys.argv)