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test2.py
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test2.py
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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
__author__ = '[email protected]'
_author = "Aiden Huen"
"""
标记文件
"""
import codecs
import yaml
import pickle
import tensorflow as tf
from load_data import load_vocs, init_data
from model import SequenceLabelingModel
import os
from tqdm import tqdm
import codecs
import yaml
import pickle
import tensorflow as tf
from load_data import load_vocs, init_data
from model import SequenceLabelingModel
import os
def tagging():
# 加载配置文件
with open('./config.yml') as file_config:
config = yaml.load(file_config)
feature_names = config['model_params']['feature_names'] # 读取特征名
use_char_feature = config['model_params']['use_char_feature']
# 初始化embedding shape, dropouts, 预训练的embedding也在这里初始化)
feature_weight_shape_dict, feature_weight_dropout_dict, \
feature_init_weight_dict = dict(), dict(), dict()
for feature_name in feature_names:
feature_weight_shape_dict[feature_name] = \
config['model_params']['embed_params'][feature_name]['shape']
feature_weight_dropout_dict[feature_name] = \
config['model_params']['embed_params'][feature_name]['dropout_rate']
path_pre_train = config['model_params']['embed_params'][feature_name]['path']
if path_pre_train: # 如果特证包含与训练embedding
with open(path_pre_train, 'rb') as file_r:
feature_init_weight_dict[feature_name] = pickle.load(file_r)
# char embedding shape
if use_char_feature:
feature_weight_shape_dict['char'] = \
config['model_params']['embed_params']['char']['shape']
conv_filter_len_list = config['model_params']['conv_filter_len_list']
conv_filter_size_list = config['model_params']['conv_filter_size_list']
else:
conv_filter_len_list = None
conv_filter_size_list = None
# 加载vocs
print "加载字典......"
path_vocs = []
if use_char_feature:
path_vocs.append(config['data_params']['voc_params']['char']['path'])
for feature_name in feature_names:
path_vocs.append(config['data_params']['voc_params'][feature_name]['path'])
path_vocs.append(config['data_params']['voc_params']['label']['path'])
vocs = load_vocs(path_vocs)
# 加载数据
print "加载测试集......"
sep_str = config['data_params']['sep']
assert sep_str in ['table', 'space']
sep = '\t' if sep_str == 'table' else ' '
max_len = config['model_params']['sequence_length']
word_len = config['model_params']['word_length']
data_dict = init_data(
path=config['data_params']['path_test'], feature_names=feature_names, sep=sep,
vocs=vocs, max_len=max_len, model='test', use_char_feature=use_char_feature,
word_len=word_len)
# 加载模型
model = SequenceLabelingModel(
sequence_length=config['model_params']['sequence_length'],
nb_classes=config['model_params']['nb_classes'],
nb_hidden=config['model_params']['bilstm_params']['num_units'],
num_layers=config['model_params']['bilstm_params']['num_layers'],
feature_weight_shape_dict=feature_weight_shape_dict,
feature_init_weight_dict=feature_init_weight_dict,
feature_weight_dropout_dict=feature_weight_dropout_dict,
dropout_rate=config['model_params']['dropout_rate'],
nb_epoch=config['model_params']['nb_epoch'], feature_names=feature_names,
batch_size=config['model_params']['batch_size'],
train_max_patience=config['model_params']['max_patience'],
use_crf=config['model_params']['use_crf'],
l2_rate=config['model_params']['l2_rate'],
rnn_unit=config['model_params']['rnn_unit'],
learning_rate=config['model_params']['learning_rate'],
use_char_feature=use_char_feature,
conv_filter_size_list=conv_filter_size_list,
conv_filter_len_list=conv_filter_len_list,
word_length=word_len,
path_model=config['model_params']['path_model'])
saver = tf.train.Saver()
saver.restore(model.sess, config['model_params']['path_model'])
# 标记
viterbi_sequences = model.predict(data_dict)
# # 写入文件
label_voc = dict()
for key in vocs[-1]:
label_voc[vocs[-1][key]] = key
with codecs.open(config['data_params']['path_test'], 'r', encoding='utf-8') as file_r:
sentences = file_r.read().strip().split('\n\n')
file_result = codecs.open(
config['data_params']['path_result'], 'w', encoding='utf-8')
for i, sentence in enumerate(sentences):
for j, item in enumerate(sentence.split('\n')):
if j < len(viterbi_sequences[i]):
file_result.write('%s\t%s\n' % (item, label_voc[viterbi_sequences[i][j]]))
else:
file_result.write('%s\tO\n' % item)
file_result.write('\n')
file_result.close()
def create_testset():
"""
生成待标注测试集文件
:param testset_answer_path: 已标注测试集路径
:param testset_path: 待标注测试集路径
"""
with open('./config.yml') as file_config:
config = yaml.load(file_config)
f = codecs.open(config["data_params"]["path_answer"], encoding="utf-8")
rows = f.readlines()
f.close()
if not os.path.isfile(config["data_params"]["path_test"]):
os.mknod(config["data_params"]["path_test"])
f = codecs.open(config["data_params"]["path_test"], "w", encoding="utf-8")
for row in rows:
row = row.replace("\n", "")
# print row
items = row.split("\t")
# print items
if len(items) == 1:
f.write(items[0] + u"\n")
else:
row = "\t".join(items[0:len(items)-1])
f.write(row+u"\n")
f.close()
print "create_testset ok"
def get_precision():
"""
精度计算,基于人名
"""
with open('./config.yml') as file_config:
config = yaml.load(file_config)
f_answer = codecs.open(config["data_params"]["path_answer"], encoding="utf-8")
f_result = codecs.open(config["data_params"]["path_result"], encoding="utf-8")
data = f_answer.read()
f_answer.close()
rows_answer = data.split("\n")
items_answer = [[i.split(" ")[0], i.split(" ")[len(i.split(" "))-1]] for i in rows_answer]
data = f_result.read()
f_result.close()
rows_result = data.split("\n")
items_result = [[i.split(" ")[0], i.split(" ")[len(i.split(" ")) - 1]] for i in rows_result]
precision_num = 0.0
recall_num = 0.0
correct_num = 0.0
for items in items_result:
# print items[0],items[1]
try:
if items[1][0] == "B" and items[1][2:]=="nrn":
precision_num += 1
except:
pass
for items in items_answer:
# print items
try:
if items[1][0] == "B" and items[1][2:]=="nrn":
recall_num += 1
except:
pass
i = 0
while i < items_answer.__len__():
if items_result[i][1]!="" and items_answer[i][1]!="":
if items_result[i][1][0] == "B" and items_result[i][1][0] == items_answer[i][1][0]:
j = i
while j<items_answer.__len__():
if items_answer[j][1][0] != "E":
j+=1
else:
break
if items_result[j][1][0] == "E" and items_answer[j][1][2:]=="nrn":
# print items_answer[j][1], items_result[j][1]
correct_num += 1
i = j
i += 1
p = correct_num/precision_num
r = correct_num/recall_num
print("nrn")
print ("p:")
print(p)
print ("r:")
print (r)
print ("f:")
print(2*p*r/(p+r))
# if __name__ == '__main__':
# create_testset()
# tagging() # 标记测试集
# get_precision()
# get_indicator()