/
interface.py
177 lines (153 loc) · 7.23 KB
/
interface.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
172
173
174
175
176
177
#-*- coding:utf-8 -*-
import tensorflow as tf
import numpy as np
import os, sys, argparse, time, random
sys.path.append('.')
from util import *
from model import BiLSTM_CRF, Original_model
from utils import str2bool, get_logger, get_entity, get_name_entitry
from data import read_corpus, read_dictionary, random_embedding
DATA_DIR = os.path.join(os.path.abspath('..'), 'data')
MODEL3_PATH = "./data_path_save/1521112368/checkpoints/"
# MODEL3_PATH = "../model/data_path_save/1530423394/checkpoints/"
# MODEL_PATH = "../model/data_path_save/1530521907/checkpoints/" #5
# MODEL_PATH = "../model/data_path_save/1530605248/checkpoints/" #12
# MODEL_PATH = "../model/data_path_save/1530683206/checkpoints/" #7
MODEL_PATH = "./data_path_save/1530721857/checkpoints/" #9
NER_PATH = '.'
#=============== SET UP =================================================================================
config = tf.ConfigProto()
parser = argparse.ArgumentParser(description='BiLSTM-CRF for Chinese NER task')
parser.add_argument('--train_data', type=str, default='data_path', help='train data source')
parser.add_argument('--test_data', type=str, default='data_path', help='test data source')
parser.add_argument('--batch_size', type=int, default=64, help='#sample of each minibatch')
parser.add_argument('--epoch', type=int, default=40, help='#epoch of training')
parser.add_argument('--hidden_dim', type=int, default=300, help='#dim of hidden state')
parser.add_argument('--optimizer', type=str, default='Adam', help='Adam/Adadelta/Adagrad/RMSProp/Momentum/SGD')
parser.add_argument('--CRF', type=str2bool, default=True, help='use CRF at the top layer. if False, use Softmax')
parser.add_argument('--lr', type=float, default=0.001, help='learning rate')
parser.add_argument('--clip', type=float, default=5.0, help='gradient clipping')
parser.add_argument('--dropout', type=float, default=0.5, help='dropout keep_prob')
parser.add_argument('--update_embedding', type=str2bool, default=True, help='update embedding during training')
parser.add_argument('--pretrain_embedding', type=str, default='random', help='use pretrained char embedding or init it randomly')
parser.add_argument('--embedding_dim', type=int, default=300, help='random init char embedding_dim')
parser.add_argument('--shuffle', type=str2bool, default=True, help='shuffle training data before each epoch')
parser.add_argument('--mode', type=str, default='demo', help='train/test/demo')
parser.add_argument('--demo_model', type=str, default='1521112368', help='model for test and demo')
args = parser.parse_args()
paths = {}
timestamp = '1521112368'
output_path = os.path.join(NER_PATH, "data_path_save", timestamp)
if not os.path.exists(output_path): os.makedirs(output_path)
summary_path = os.path.join(output_path, "summaries")
paths['summary_path'] = summary_path
if not os.path.exists(summary_path): os.makedirs(summary_path)
model_path = os.path.join(output_path, "checkpoints/")
if not os.path.exists(model_path): os.makedirs(model_path)
ckpt_prefix = os.path.join(model_path, "model")
paths['model_path'] = ckpt_prefix
result_path = os.path.join(output_path, "results")
paths['result_path'] = result_path
if not os.path.exists(result_path): os.makedirs(result_path)
log_path = os.path.join(result_path, "log.txt")
paths['log_path'] = log_path
get_logger(log_path).info(str(args))
paths['restore_path'] = ''
word2id = read_dictionary(os.path.join(NER_PATH, args.train_data, 'word2id.pkl'))
if args.pretrain_embedding == 'random':
embeddings = random_embedding(word2id, args.embedding_dim)
else:
embedding_path = 'pretrain_embedding.npy'
embeddings = np.array(np.load(embedding_path), dtype='float32')
#========================================================================================================
# with open(os.path.join(DATA_DIR, 'text.txt')) as file:
# data = file.read()
# personList = data.split('*********&&&&&&&&')
def print_tag(lst, name, text):
temp = clean_list(lst)
text = clean_word(text)
cnt = [text.count(word) for word in temp]
print(name, ': ', end='')
for i, v in enumerate(temp):
print(str(temp[i])+'('+str(cnt[i])+'),', end=' ')
print('')
def extract_one(text):
text = clean_text(text).strip()
if len(text) == 0:
return
tag2label = {"O": 0,
"B-TIT": 1, "I-TIT": 2,
"B-JOB": 3, "I-JOB": 4,
"B-DOM": 5, "I-DOM": 6,
"B-EDU": 7, "I-EDU": 8,
"B-WRK": 9, "I-WRK": 10,
"B-SOC": 11, "I-SOC": 12,
"B-AWD": 13, "I-AWD": 14,
"B-PAT": 15, "I-PAT": 16,
"B-PRJ": 17, "I-PRJ": 18
}
ckpt_file = tf.train.latest_checkpoint(MODEL_PATH)
paths['model_path'] = ckpt_file
model = BiLSTM_CRF(args, embeddings, tag2label, word2id, paths, config=config)
model.build_graph()
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
saver.restore(sess, ckpt_file)
demo_sent = list(text)
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
tag = model.demo(sess, demo_data, tag2label)
TIT = get_name_entitry('TIT', tag, demo_sent)
JOB = get_name_entitry('JOB', tag, demo_sent)
DOM = get_name_entitry('DOM', tag, demo_sent)
EDU = get_name_entitry('EDU', tag, demo_sent)
WRK = get_name_entitry('WRK', tag, demo_sent)
SOC = get_name_entitry('SOC', tag, demo_sent)
AWD = get_name_entitry('AWD', tag, demo_sent)
PAT = get_name_entitry('PAT', tag, demo_sent)
PRJ = get_name_entitry('PRJ', tag, demo_sent)
sess.close()
return TIT, JOB, DOM, EDU, WRK, SOC, AWD, PAT, PRJ
def extract_one_3(text):
text = clean_text(text).strip()
if len(text) == 0:
return
tag2label = {"O": 0,
"B-PER": 1, "I-PER": 2,
"B-ADR": 3, "I-ADR": 4,
"B-AFF": 5, "I-AFF": 6,
}
ckpt_file = tf.train.latest_checkpoint(MODEL3_PATH)
paths['model_path'] = ckpt_file
model = Original_model(args, embeddings, tag2label, word2id, paths, config=config)
model.build_graph()
saver = tf.train.Saver()
# sess2 = tf.Session(config=config)
# with sess2.as_default():
with tf.Session(config=config) as sess2:
tf.get_variable_scope().reuse_variables()
saver.restore(sess2, ckpt_file)
demo_sent = list(text)
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
tag = model.demo(sess2, demo_data, tag2label)
PER = get_name_entitry('PER', tag, demo_sent)
ADR = get_name_entitry('ADR', tag, demo_sent)
AFF = get_name_entitry('AFF', tag, demo_sent)
return clean_list(PER), clean_list(ADR), clean_list(AFF)
def interface(text):
TIT, JOB, DOM, EDU, WRK, SOC, AWD, PAT, PRJ = extract_one(text)
tf.reset_default_graph()
PER, ADR, AFF = extract_one_3(text)
print_tag(PER, 'PER', text)
print_tag(ADR, 'ADR', text)
print_tag(AFF, 'AFF', text)
print_tag(TIT, 'TIT', text)
print_tag(JOB, 'JOB', text)
print_tag(DOM, 'DOM', text)
print_tag(EDU, 'EDU', text)
print_tag(WRK, 'WRK', text)
print_tag(SOC, 'SOC', text)
print_tag(AWD, 'AWD', text)
print_tag(PAT, 'PAT', text)
print_tag(PRJ, 'PRJ', text)
return PER, ADR, AFF, TIT, JOB, DOM, EDU, WRK, SOC, AWD, PAT, PRJ
# interface(personList[1])