-
Notifications
You must be signed in to change notification settings - Fork 1
/
evaluate.py
157 lines (135 loc) · 5.97 KB
/
evaluate.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
""" Official evaluation script for v1.1 of the SQuAD dataset. """
from __future__ import print_function
from collections import Counter
import string
import re
import argparse
import json
import sys
def normalize_answer(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
return re.sub(r'\b(a|an|the)\b', ' ', text)
def white_space_fix(text):
return ' '.join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return ''.join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def f1_score(prediction, ground_truth):
prediction_tokens = normalize_answer(prediction).split()
ground_truth_tokens = normalize_answer(ground_truth).split()
common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
num_same = sum(common.values())
if num_same == 0:
return 0
precision = 1.0 * num_same / len(prediction_tokens)
recall = 1.0 * num_same / len(ground_truth_tokens)
f1 = (2 * precision * recall) / (precision + recall)
return f1
def exact_match_score(prediction, ground_truth):
return (normalize_answer(prediction) == normalize_answer(ground_truth))
def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
scores_for_ground_truths = []
for ground_truth in ground_truths:
score = metric_fn(prediction, ground_truth)
scores_for_ground_truths.append(score)
return max(scores_for_ground_truths)
def evaluate(dataset, predictions):
f1 = exact_match = total = 0
for article in dataset:
for paragraph in article['paragraphs']:
for qa in paragraph['qas']:
total += 1
if qa['id'] not in predictions:
message = 'Unanswered question ' + qa['id'] + \
' will receive score 0.'
print(message, file=sys.stderr)
continue
ground_truths = list(map(lambda x: x['text'], qa['answers']))
prediction = predictions[qa['id']]
exact_match += metric_max_over_ground_truths(
exact_match_score, prediction, ground_truths)
f1 += metric_max_over_ground_truths(
f1_score, prediction, ground_truths)
exact_match = 100.0 * exact_match / total
f1 = 100.0 * f1 / total
return {'exact_match': exact_match, 'f1': f1}
def evaluate_each_qtype(dataset, predictions):
result = {}
for wh in ['What', 'How', 'When', 'Where', 'Who', 'Other']:
result[wh] = {'f1': 0, 'total': 0}
other_wh = []
for article in dataset:
for paragraph in article['paragraphs']:
for qa in paragraph['qas']:
wh = qa['question'].split(' ')[0]
if wh not in result:
other_wh.append(wh)
wh = 'Other'
result[wh]['total'] += 1
if qa['id'] not in predictions:
message = 'Unanswered question ' + qa['id'] + \
' will receive score 0.'
print(message, file=sys.stderr)
continue
ground_truths = list(map(lambda x: x['text'], qa['answers']))
prediction = predictions[qa['id']]
result[wh]['f1'] += metric_max_over_ground_truths(
f1_score, prediction, ground_truths)
#print(Counter(other_wh))
total = 0
for wh in result:
total += result[wh]['total']
for wh in result:
result[wh]['f1'] /= result[wh]['total']
print(wh, 'F1', result[wh]['f1'], 'Rate', result[wh]['total'] / total)
return result
def evaluate_what(dataset, predictions):
result = {}
for article in dataset:
for paragraph in article['paragraphs']:
for qa in paragraph['qas']:
wh, token = qa['question'].split(' ')[:2]
if wh.lower() != 'what':
continue
if token not in result:
result[token] = {'f1': 0, 'total': 0}
result[token]['total'] += 1
if qa['id'] not in predictions:
message = 'Unanswered question ' + qa['id'] + \
' will receive score 0.'
print(message, file=sys.stderr)
continue
ground_truths = list(map(lambda x: x['text'], qa['answers']))
prediction = predictions[qa['id']]
result[token]['f1'] += metric_max_over_ground_truths(
f1_score, prediction, ground_truths)
#print(Counter(other_wh))
total = 0
for wh in result:
total += result[wh]['total']
for wh in sorted(list(result.keys()) , key=lambda x: result[x]['total'], reverse=True)[:20]:
result[wh]['f1'] /= result[wh]['total']
print(wh, 'F1', '%.4f'%result[wh]['f1'], 'Num', result[wh]['total'] , 'Rate', '%.4f'%(result[wh]['total'] / total))
if __name__ == '__main__':
expected_version = '1.1'
parser = argparse.ArgumentParser(
description='Evaluation for SQuAD ' + expected_version)
parser.add_argument('dataset_file', help='Dataset file')
parser.add_argument('prediction_file', help='Prediction File')
args = parser.parse_args()
with open(args.dataset_file) as dataset_file:
dataset_json = json.load(dataset_file)
if (dataset_json['version'] != expected_version):
print('Evaluation expects v-' + expected_version +
', but got dataset with v-' + dataset_json['version'],
file=sys.stderr)
dataset = dataset_json['data']
with open(args.prediction_file) as prediction_file:
predictions = json.load(prediction_file)
print(json.dumps(evaluate(dataset, predictions)))
evaluate_each_qtype(dataset, predictions)
evaluate_what(dataset, predictions)