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# -*- coding: utf-8 -*-
"""
Created on Mon Mar 1 15:33:54 2021
@author: luol2
"""
# from BIO format to entity
def BIO_tag(tokens):
gold_entity={}
pre_entity={}
gold_start,gold_end=0,0
pre_start,pre_end=0,0
for i in range(0,len(tokens)):
segs=tokens[i].split('\t')
# generate gold entity
if segs[1].startswith('B-')>0:
gold_start=i
gold_type=segs[1][2:]
if i+1>=len(tokens): # the last word
gold_end=i
if gold_type in gold_entity.keys():
gold_entity[gold_type].append([gold_start,gold_end])
else:
gold_entity[gold_type]=[[gold_start,gold_end]]
else: # non last word
next_seg=tokens[i+1].split('\t')
if next_seg[1].startswith('B-')>0 or next_seg[1]=='O':
gold_end=i
if gold_type in gold_entity.keys():
gold_entity[gold_type].append([gold_start,gold_end])
else:
gold_entity[gold_type]=[[gold_start,gold_end]]
elif next_seg[1].startswith('I-')>0:
pass
elif segs[1].startswith('I-')>0:
if i+1>=len(tokens): # the last word
gold_end=i
if gold_type in gold_entity.keys():
gold_entity[gold_type].append([gold_start,gold_end])
else:
gold_entity[gold_type]=[[gold_start,gold_end]]
else: # non last word
next_seg=tokens[i+1].split('\t')
if next_seg[1].startswith('B-')>0 or next_seg[1]=='O':
gold_end=i
if gold_type in gold_entity.keys():
gold_entity[gold_type].append([gold_start,gold_end])
else:
gold_entity[gold_type]=[[gold_start,gold_end]]
elif next_seg[1].startswith('I-')>0:
pass
elif segs[1]=='O':
pass
# generate prediction entity
if segs[2].startswith('B-')>0:
pre_start=i
pre_type=segs[2][2:]
if i+1>=len(tokens): # the last word
pre_end=i
if pre_type in pre_entity.keys():
pre_entity[pre_type].append([pre_start,pre_end])
else:
pre_entity[pre_type]=[[pre_start,pre_end]]
else: # non last word
next_seg=tokens[i+1].split('\t')
if next_seg[2].startswith('B-')>0 or next_seg[2]=='O':
pre_end=i
if pre_type in pre_entity.keys():
pre_entity[pre_type].append([pre_start,pre_end])
else:
pre_entity[pre_type]=[[pre_start,pre_end]]
elif next_seg[2].startswith('I-')>0:
pass
elif segs[2].startswith('I-')>0:
if i==0 and i+1<len(tokens): # the first word and not only a word
pre_start=i
pre_type=segs[2][2:]
next_seg=tokens[i+1].split('\t')
if next_seg[2].startswith('B-')>0 or next_seg[2]=='O':
pre_end=i
if pre_type in pre_entity.keys():
pre_entity[pre_type].append([pre_start,pre_end])
else:
pre_entity[pre_type]=[[pre_start,pre_end]]
elif next_seg[2].startswith('I-')>0:
pass
elif i==0 and i+1==len(tokens):# only one word:
pre_start=i
pre_type=segs[2][2:]
pre_end=i
if pre_type in pre_entity.keys():
pre_entity[pre_type].append([pre_start,pre_end])
else:
pre_entity[pre_type]=[[pre_start,pre_end]]
elif i+1>=len(tokens): # the last word
last_seg=tokens[i-1].split('\t')
if last_seg[2]=='O':
pre_start=i
pre_type=segs[2][2:]
pre_end=i
if pre_type in pre_entity.keys():
pre_entity[pre_type].append([pre_start,pre_end])
else:
pre_entity[pre_type]=[[pre_start,pre_end]]
elif i+1< len(tokens): # non last word
next_seg=tokens[i+1].split('\t')
last_seg=tokens[i-1].split('\t')
if last_seg[2]=='O':
pre_start=i
pre_type=segs[2][2:]
if next_seg[2].startswith('B-')>0 or next_seg[2]=='O':
pre_end=i
if pre_type in pre_entity.keys():
pre_entity[pre_type].append([pre_start,pre_end])
else:
pre_entity[pre_type]=[[pre_start,pre_end]]
elif next_seg[2].startswith('I-')>0:
pass
elif segs[2]=='O':
pass
# print(tokens)
# print(gold_entity)
# print(pre_entity)
return gold_entity,pre_entity
# input: token \t Gold \t Prediction\n, sentence is split "\n"
def NER_Evaluation():
path='//panfs/pan1/bionlp/lulab/luoling/OpenBioIE_project/models/Kfold/BiLSTM-CRF/'
fin=open(path+'dev_pre.conll_all','r',encoding='utf-8')
all_sentence=fin.read().strip().split('\n\n')
fin.close()
Metrics={} #{'entity_type':[TP,gold_num,pre_num]}
for sentence in all_sentence:
tokens=sentence.split('\n')
gold_entity,pre_entity=BIO_tag(tokens)
# print(tokens)
for entity_type in gold_entity.keys():
if entity_type not in Metrics.keys():
Metrics[entity_type]=[0,len(gold_entity[entity_type]),0]
else:
Metrics[entity_type][1]+=len(gold_entity[entity_type])
for entity_type in pre_entity.keys():
if entity_type not in Metrics.keys():
Metrics[entity_type]=[0,0,len(pre_entity[entity_type])]
else:
Metrics[entity_type][2]+=len(pre_entity[entity_type])
for mention in pre_entity[entity_type]:
if entity_type in gold_entity.keys():
if mention in gold_entity[entity_type]:
Metrics[entity_type][0]+=1
print(Metrics)
TP,Gold_num,Pre_num=0,0,0
for ele in Metrics.keys():
if Metrics[ele][2]==0:
p=0
else:
p=Metrics[ele][0]/Metrics[ele][2]
if Metrics[ele][1]==0:
r=0
else:
r=Metrics[ele][0]/Metrics[ele][1]
if p+r==0:
f1=0
else:
f1=2*p*r/(p+r)
TP+=Metrics[ele][0]
Gold_num+=Metrics[ele][1]
Pre_num+=Metrics[ele][2]
print(ele+': P=%.5f, R=%.5f, F1=%.5f' % (p,r,f1))
# break
if Pre_num==0:
P=0
else:
P=TP/Pre_num
R=TP/Gold_num
F1=2*P*R/(P+R)
print("Overall: P=%.5f, R=%.5f, F1=%.5f"% (P,R,F1))
def NER_Evaluation_fn(file):
fin=open(file,'r',encoding='utf-8')
all_sentence=fin.read().strip().split('\n\n')
fin.close()
Metrics={} #{'entity_type':[TP,gold_num,pre_num]}
breai=0
for sentence in all_sentence:
breai+=1
if breai>5000:
break
tokens=sentence.split('\n')
gold_entity,pre_entity=BIO_tag(tokens)
# print(tokens)
for entity_type in gold_entity.keys():
if entity_type not in Metrics.keys():
Metrics[entity_type]=[0,len(gold_entity[entity_type]),0]
else:
Metrics[entity_type][1]+=len(gold_entity[entity_type])
for entity_type in pre_entity.keys():
if entity_type not in Metrics.keys():
Metrics[entity_type]=[0,0,len(pre_entity[entity_type])]
else:
Metrics[entity_type][2]+=len(pre_entity[entity_type])
for mention in pre_entity[entity_type]:
if entity_type in gold_entity.keys():
if mention in gold_entity[entity_type]:
Metrics[entity_type][0]+=1
print(Metrics)
TP,Gold_num,Pre_num=0,0,0
for ele in Metrics.keys():
if Metrics[ele][2]==0:
p=0
else:
p=Metrics[ele][0]/Metrics[ele][2]
if Metrics[ele][1]==0:
r=0
else:
r=Metrics[ele][0]/Metrics[ele][1]
if p+r==0:
f1=0
else:
f1=2*p*r/(p+r)
TP+=Metrics[ele][0]
Gold_num+=Metrics[ele][1]
Pre_num+=Metrics[ele][2]
print(ele+': P=%.5f, R=%.5f, F1=%.5f' % (p,r,f1))
# break
if Pre_num==0:
P=0
else:
P=TP/Pre_num
R=TP/Gold_num
if P+R==0:
F1=0
else:
F1=2*P*R/(P+R)
print("Overall: P=%.5f, R=%.5f, F1=%.5f"% (P,R,F1))
return F1
if __name__=='__main__':
NER_Evaluation()
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