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ner_evaluate.py
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ner_evaluate.py
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import codecs
import numpy as np
def get_chunks(seq, tags):
"""
tags:dic{'per':1,....}
Args:
seq: [4, 4, 0, 0, ...] sequence of labels
tags: dict["O"] = 4
Returns:
list of (chunk_type, chunk_start, chunk_end)
Example:
seq = [4, 5, 0, 3]
tags = {"B-PER": 4, "I-PER": 5, "B-LOC": 3}
result = [("PER", 0, 2), ("LOC", 3, 4)]
"""
default = tags['O']
idx_to_tag = {idx: tag for tag, idx in tags.items()}
chunks = []
# chunk_type用于判断是什么类型，LOC,PER
chunk_type, chunk_start = None, None
for i, tok in enumerate(seq):
#End of a chunk 1
if tok == default and chunk_type is not None:
# Add a chunk.
chunk = (chunk_type, chunk_start, i)
chunks.append(chunk)
chunk_type, chunk_start = None, None
# End of a chunk + start of a chunk!
elif tok != default:
# tok_chunk_class 判断是以B开头还是I开头
# tok_chunk_type 判断是什么类型，PER,LOC
tok_chunk_class, tok_chunk_type = get_chunk_type(tok, idx_to_tag)
if chunk_type is None:
chunk_type, chunk_start = tok_chunk_type, i
elif tok_chunk_type != chunk_type or tok_chunk_class == "B":
chunk = (chunk_type, chunk_start, i)
chunks.append(chunk)
chunk_type, chunk_start = tok_chunk_type, i
else:
pass
# end condition
if chunk_type is not None:
chunk = (chunk_type, chunk_start, len(seq))
chunks.append(chunk)
return chunks
def get_chunk_type(tok, idx_to_tag):
"""
Args:
tok: id of token, such as 4
idx_to_tag: dictionary {4: "B-PER", ...}
Returns:
tuple: "B", "PER"
"""
tag_name = idx_to_tag[tok]
tag_class = tag_name.split('-')[0]
tag_type = tag_name.split('-')[-1]
return tag_class, tag_type
# def run_evaluate(self, sess, test, tags):
def evaluate(labels_pred, labels,words,tags):
"""
words,pred, right: is a sequence, is label index or word index.
Evaluates performance on test set
Args:
sess: tensorflow session
test: dataset that yields tuple of sentences, tags
tags: {tag: index} dictionary
Returns:
accuracy
f1 score
...
"""
#file_write = open('./test_results.txt','w')
index = 0
sents_length = []
accs = []
correct_preds, total_correct, total_preds = 0., 0., 0.
for lab, lab_pred, word_sent in zip(labels, labels_pred, words):
word_st = word_sent
lab = lab
lab_pred = lab_pred
accs += [a==b for (a, b) in zip(lab, lab_pred)]
lab_chunks = set(get_chunks(lab, tags))
lab_pred_chunks = set(get_chunks(lab_pred, tags))
correct_preds += len(lab_chunks & lab_pred_chunks)
total_preds += len(lab_pred_chunks)
total_correct += len(lab_chunks)
#for i in range(len(word_st)):
#file_write.write('%s\t%s\t%s\n'%(word_st[i],lab[i],lab_pred[i]))
#file_write.write('\n')
p = correct_preds / total_preds if correct_preds > 0 else 0
r = correct_preds / total_correct if correct_preds > 0 else 0
f1 = 2 * p * r / (p + r) if correct_preds > 0 else 0
acc = np.mean(accs)
#file_write.close()
# return acc, f1,p,r, correct_preds, total_preds, total_correct
return acc, f1, p, r
def evaluate_each_class(labels_pred, labels,words,tags, class_type):
#class_type:PER or LOC or ORG
index = 0
accs = []
correct_preds, total_correct, total_preds = 0., 0., 0.
correct_preds_cla_type, total_preds_cla_type, total_correct_cla_type = 0., 0., 0.
for lab, lab_pred, word_sent in zip(labels, labels_pred, words):
lab_pre_class_type = []
lab_class_type=[]
word_st = word_sent
lab = lab
lab_pred = lab_pred
lab_chunks = get_chunks(lab, tags)
lab_pred_chunks = get_chunks(lab_pred, tags)
for i in range(len(lab_pred_chunks)):
if lab_pred_chunks[i][0] ==class_type:
lab_pre_class_type.append(lab_pred_chunks[i])
lab_pre_class_type_c = set(lab_pre_class_type)
for i in range(len(lab_chunks)):
if lab_chunks[i][0] ==class_type:
lab_class_type.append(lab_chunks[i])
lab_class_type_c = set(lab_class_type)
lab_chunksss = set(lab_chunks)
correct_preds_cla_type +=len(lab_pre_class_type_c & lab_chunksss)
total_preds_cla_type +=len(lab_pre_class_type_c)
total_correct_cla_type += len(lab_class_type_c)
p = correct_preds_cla_type / total_preds_cla_type if correct_preds_cla_type > 0 else 0
r = correct_preds_cla_type / total_correct_cla_type if correct_preds_cla_type > 0 else 0
f1 = 2 * p * r / (p + r) if correct_preds_cla_type > 0 else 0
return f1,p,r
if __name__ == '__main__':
max_sent=10
tags = {'0':0,
'B-PER':1, 'I-PER':2,
'B-LOC':3, 'I-LOC':4,
'B-ORG':5, 'I-ORG':6,
'B-OTHER':7, 'I-OTHER':8,
'O':9}
labels_pred=[
[9,9,9,1,3,1,2,2,0,0],
[9,9,9,1,3,1,2,0,0,0]
]
labels = [
[9,9,9,9,3,1,2,2,0,0],
[9,9,9,9,3,1,2,2,0,0]
]
words = [
[0,0,0,0,0,3,6,8,5,7],
[0,0,0,4,5,6,7,9,1,7]
]
id_to_vocb = {0:'a',1:'b',2:'c',3:'d',4:'e',5:'f',6:'g',7:'h',8:'i',9:'j'}
# new_words = []
# for i in range(len(words)):
# sent = []
# for j in range(len(words[i])):
# sent.append(id_to_vocb[words[i][j]])
# new_words.append(sent)
# class_type = 'PER'
# acc, f1,p,r = evaluate(labels_pred, labels,new_words,tags)
# print(p,r,f1)
# f1,p,r = evaluate_each_class(labels_pred, labels,new_words,tags, class_type)
# print(p,r,f1)
acc, f1, p, r = evaluate(labels_pred, labels, words, tags)
print(acc)