-
Notifications
You must be signed in to change notification settings - Fork 2
/
evaluating.py
171 lines (135 loc) · 6.1 KB
/
evaluating.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
from collections import Counter
from utils import flatten_lists
class Metrics(object):
"""用于评价模型，计算每个标签的精确率，召回率，F1分数"""
def __init__(self, golden_tags, predict_tags, remove_O=False):
# [[t1, t2], [t3, t4]...] --> [t1, t2, t3, t4...]
self.golden_tags = flatten_lists(golden_tags)
self.predict_tags = flatten_lists(predict_tags)
if remove_O: # 将O标记移除，只关心实体标记
self._remove_Otags()
# 辅助计算的变量
self.tagset = set(self.golden_tags)
self.correct_tags_number = self.count_correct_tags()
self.predict_tags_counter = Counter(self.predict_tags)
self.golden_tags_counter = Counter(self.golden_tags)
# 计算精确率
self.precision_scores = self.cal_precision()
# 计算召回率
self.recall_scores = self.cal_recall()
# 计算F1分数
self.f1_scores = self.cal_f1()
def cal_precision(self):
precision_scores = {}
for tag in self.tagset:
precision_scores[tag] = self.correct_tags_number.get(tag, 0) / \
self.predict_tags_counter[tag]
return precision_scores
def cal_recall(self):
recall_scores = {}
for tag in self.tagset:
recall_scores[tag] = self.correct_tags_number.get(tag, 0) / \
self.golden_tags_counter[tag]
return recall_scores
def cal_f1(self):
f1_scores = {}
for tag in self.tagset:
p, r = self.precision_scores[tag], self.recall_scores[tag]
f1_scores[tag] = 2*p*r / (p+r+1e-10) # 加上一个特别小的数，防止分母为0
return f1_scores
def report_scores(self):
"""将结果用表格的形式打印出来，像这个样子：
precision recall f1-score support
B-LOC 0.775 0.757 0.766 1084
I-LOC 0.601 0.631 0.616 325
B-MISC 0.698 0.499 0.582 339
I-MISC 0.644 0.567 0.603 557
B-ORG 0.795 0.801 0.798 1400
I-ORG 0.831 0.773 0.801 1104
B-PER 0.812 0.876 0.843 735
I-PER 0.873 0.931 0.901 634
avg/total 0.779 0.764 0.770 6178
"""
# 打印表头
header_format = '{:>9s} {:>9} {:>9} {:>9} {:>9}'
header = ['precision', 'recall', 'f1-score', 'support']
print(header_format.format('', *header))
row_format = '{:>9s} {:>9.4f} {:>9.4f} {:>9.4f} {:>9}'
# 打印每个标签的 精确率、召回率、f1分数
for tag in self.tagset:
print(row_format.format(
tag,
self.precision_scores[tag],
self.recall_scores[tag],
self.f1_scores[tag],
self.golden_tags_counter[tag]
))
# 计算并打印平均值
avg_metrics = self._cal_weighted_average()
print(row_format.format(
'avg/total',
avg_metrics['precision'],
avg_metrics['recall'],
avg_metrics['f1_score'],
len(self.golden_tags)
))
def count_correct_tags(self):
"""计算每种标签预测正确的个数(对应精确率、召回率计算公式上的tp)，用于后面精确率以及召回率的计算"""
correct_dict = {}
for gold_tag, predict_tag in zip(self.golden_tags, self.predict_tags):
if gold_tag == predict_tag:
if gold_tag not in correct_dict:
correct_dict[gold_tag] = 1
else:
correct_dict[gold_tag] += 1
return correct_dict
def _cal_weighted_average(self):
weighted_average = {}
total = len(self.golden_tags)
# 计算weighted precisions:
weighted_average['precision'] = 0.
weighted_average['recall'] = 0.
weighted_average['f1_score'] = 0.
for tag in self.tagset:
size = self.golden_tags_counter[tag]
weighted_average['precision'] += self.precision_scores[tag] * size
weighted_average['recall'] += self.recall_scores[tag] * size
weighted_average['f1_score'] += self.f1_scores[tag] * size
for metric in weighted_average.keys():
weighted_average[metric] /= total
return weighted_average
def _remove_Otags(self):
length = len(self.golden_tags)
O_tag_indices = [i for i in range(length)
if self.golden_tags[i] == 'O']
self.golden_tags = [tag for i, tag in enumerate(self.golden_tags)
if i not in O_tag_indices]
self.predict_tags = [tag for i, tag in enumerate(self.predict_tags)
if i not in O_tag_indices]
print("原总标记数为{}，移除了{}个O标记，占比{:.2f}%".format(
length,
len(O_tag_indices),
len(O_tag_indices) / length * 100
))
def report_confusion_matrix(self):
"""计算混淆矩阵"""
print("\nConfusion Matrix:")
tag_list = list(self.tagset)
# 初始化混淆矩阵 matrix[i][j]表示第i个tag被模型预测成第j个tag的次数
tags_size = len(tag_list)
matrix = []
for i in range(tags_size):
matrix.append([0] * tags_size)
# 遍历tags列表
for golden_tag, predict_tag in zip(self.golden_tags, self.predict_tags):
try:
row = tag_list.index(golden_tag)
col = tag_list.index(predict_tag)
matrix[row][col] += 1
except ValueError: # 有极少数标记没有出现在golden_tags，但出现在predict_tags，跳过这些标记
continue
# 输出矩阵
row_format_ = '{:>7} ' * (tags_size+1)
print(row_format_.format("", *tag_list))
for i, row in enumerate(matrix):
print(row_format_.format(tag_list[i], *row))