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metric.py
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metric.py
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# -*- coding: utf-8 -*-
# @Author: Jie
# @Date: 2017-02-16 09:53:19
# @Last Modified by: Jie Yang, Contact: jieynlp@gmail.com
# @Last Modified time: 2019-02-17 22:46:59
# from operator import add
#
from __future__ import print_function
import sys
## input as sentence level labels
def get_ner_fmeasure(golden_lists, predict_lists, label_type="BMES"):
sent_num = len(golden_lists)
golden_full = []
predict_full = []
right_full = []
right_tag = 0
all_tag = 0
for idx in range(0,sent_num):
# word_list = sentence_lists[idx]
golden_list = golden_lists[idx]
predict_list = predict_lists[idx]
for idy in range(len(golden_list)):
if golden_list[idy] == predict_list[idy]:
right_tag += 1
all_tag += len(golden_list)
if label_type == "BMES" or label_type == "BIOES":
gold_matrix = get_ner_BMES(golden_list)
pred_matrix = get_ner_BMES(predict_list)
else:
gold_matrix = get_ner_BIO(golden_list)
pred_matrix = get_ner_BIO(predict_list)
# print "gold", gold_matrix
# print "pred", pred_matrix
right_ner = list(set(gold_matrix).intersection(set(pred_matrix)))
golden_full += gold_matrix
predict_full += pred_matrix
right_full += right_ner
right_num = len(right_full)
golden_num = len(golden_full)
predict_num = len(predict_full)
if predict_num == 0:
precision = -1
else:
precision = (right_num+0.0)/predict_num
if golden_num == 0:
recall = -1
else:
recall = (right_num+0.0)/golden_num
if (precision == -1) or (recall == -1) or (precision+recall) <= 0.:
f_measure = -1
else:
f_measure = 2*precision*recall/(precision+recall)
accuracy = (right_tag+0.0)/all_tag
# print "Accuracy: ", right_tag,"/",all_tag,"=",accuracy
if label_type.upper().startswith("B-"):
print("gold_num = ", golden_num, " pred_num = ", predict_num, " right_num = ", right_num)
else:
print("Right token = ", right_tag, " All token = ", all_tag, " acc = ", accuracy)
return accuracy, precision, recall, f_measure
def reverse_style(input_string):
target_position = input_string.index('[')
input_len = len(input_string)
output_string = input_string[target_position:input_len] + input_string[0:target_position]
return output_string
def get_ner_BMES(label_list):
# list_len = len(word_list)
# assert(list_len == len(label_list)), "word list size unmatch with label list"
list_len = len(label_list)
begin_label = 'B-'
end_label = 'E-'
single_label = 'S-'
whole_tag = ''
index_tag = ''
tag_list = []
stand_matrix = []
for i in range(0, list_len):
# wordlabel = word_list[i]
current_label = label_list[i].upper()
if begin_label in current_label:
if index_tag != '':
tag_list.append(whole_tag + ',' + str(i-1))
whole_tag = current_label.replace(begin_label,"",1) +'[' +str(i)
index_tag = current_label.replace(begin_label,"",1)
elif single_label in current_label:
if index_tag != '':
tag_list.append(whole_tag + ',' + str(i-1))
whole_tag = current_label.replace(single_label,"",1) +'[' +str(i)
tag_list.append(whole_tag)
whole_tag = ""
index_tag = ""
elif end_label in current_label:
if index_tag != '':
tag_list.append(whole_tag +',' + str(i))
whole_tag = ''
index_tag = ''
else:
continue
if (whole_tag != '')&(index_tag != ''):
tag_list.append(whole_tag)
tag_list_len = len(tag_list)
for i in range(0, tag_list_len):
if len(tag_list[i]) > 0:
tag_list[i] = tag_list[i]+ ']'
insert_list = reverse_style(tag_list[i])
stand_matrix.append(insert_list)
# print stand_matrix
return stand_matrix
def get_ner_BIO(label_list):
# list_len = len(word_list)
# assert(list_len == len(label_list)), "word list size unmatch with label list"
list_len = len(label_list)
begin_label = 'B-'
inside_label = 'I-'
whole_tag = ''
index_tag = ''
tag_list = []
stand_matrix = []
for i in range(0, list_len):
# wordlabel = word_list[i]
current_label = label_list[i].upper()
if begin_label in current_label:
if index_tag == '':
whole_tag = current_label.replace(begin_label,"",1) +'[' +str(i)
index_tag = current_label.replace(begin_label,"",1)
else:
tag_list.append(whole_tag + ',' + str(i-1))
whole_tag = current_label.replace(begin_label,"",1) + '[' + str(i)
index_tag = current_label.replace(begin_label,"",1)
elif inside_label in current_label:
if current_label.replace(inside_label,"",1) == index_tag:
whole_tag = whole_tag
else:
if (whole_tag != '')&(index_tag != ''):
tag_list.append(whole_tag +',' + str(i-1))
whole_tag = ''
index_tag = ''
else:
if (whole_tag != '')&(index_tag != ''):
tag_list.append(whole_tag +',' + str(i-1))
whole_tag = ''
index_tag = ''
if (whole_tag != '')&(index_tag != ''):
tag_list.append(whole_tag)
tag_list_len = len(tag_list)
for i in range(0, tag_list_len):
if len(tag_list[i]) > 0:
tag_list[i] = tag_list[i]+ ']'
insert_list = reverse_style(tag_list[i])
stand_matrix.append(insert_list)
return stand_matrix
def readSentence(input_file):
in_lines = open(input_file,'r').readlines()
sentences = []
labels = []
sentence = []
label = []
for line in in_lines:
if len(line) < 2:
sentences.append(sentence)
labels.append(label)
sentence = []
label = []
else:
pair = line.strip('\n').split(' ')
sentence.append(pair[0])
label.append(pair[-1])
return sentences,labels
def readTwoLabelSentence(input_file, pred_col=-1):
in_lines = open(input_file,'r').readlines()
sentences = []
predict_labels = []
golden_labels = []
sentence = []
predict_label = []
golden_label = []
for line in in_lines:
if "##score##" in line:
continue
if len(line) < 2:
sentences.append(sentence)
golden_labels.append(golden_label)
predict_labels.append(predict_label)
sentence = []
golden_label = []
predict_label = []
else:
pair = line.strip('\n').split(' ')
sentence.append(pair[0])
golden_label.append(pair[1])
predict_label.append(pair[pred_col])
return sentences,golden_labels,predict_labels
def fmeasure_from_file(golden_file, predict_file, label_type="BMES"):
print("Get f measure from file:", golden_file, predict_file)
print("Label format:",label_type)
golden_sent,golden_labels = readSentence(golden_file)
predict_sent,predict_labels = readSentence(predict_file)
P,R,F = get_ner_fmeasure(golden_labels, predict_labels, label_type)
print ("P:%sm R:%s, F:%s"%(P,R,F))
def fmeasure_from_singlefile(twolabel_file, label_type="BMES", pred_col=-1):
sent,golden_labels,predict_labels = readTwoLabelSentence(twolabel_file, pred_col)
P,R,F = get_ner_fmeasure(golden_labels, predict_labels, label_type)
print ("P:%s, R:%s, F:%s"%(P,R,F))
if __name__ == '__main__':
# print "sys:",len(sys.argv)
if len(sys.argv) == 3:
fmeasure_from_singlefile(sys.argv[1],"BMES",int(sys.argv[2]))
else:
fmeasure_from_singlefile(sys.argv[1],"BMES")