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augment of seq2seq with webank datas

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yongzhuo 2019-04-17 20:07:33 +08:00 committed by GitHub
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5
AugmentText/augment_seq2seq/__init__.py Normal file
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# -*- coding: UTF-8 -*-
# !/usr/bin/python
# @time :2019/4/15 10:17
# @author :Mo
# @function :

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AugmentText/augment_seq2seq/code_seq2seq_char/__init__.py Normal file
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# -*- coding: UTF-8 -*-
# !/usr/bin/python
# @time :2019/4/15 10:50
# @author :Mo
# @function :

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AugmentText/augment_seq2seq/code_seq2seq_char/extract_char_webank.py Normal file
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"""
把文件格式转换为可训练格式
Code from: QHDuan(2018-02-05) url: https://github.com/qhduan/just_another_seq2seq
"""
from conf.path_config import train_data_web_ws_anti
from conf.path_config import train_data_web_xy_anti
from conf.path_config import model_ckpt_web_anti
from conf.path_config import path_webank_sim
from utils.mode_util.seq2seq.word_sequence import WordSequence
from utils.text_tools import txtRead
from tqdm import tqdm
import pickle
import sys
import re
sys.path.append('..')
def make_split(line):
"""构造合并两个句子之间的符号
"""
if re.match(r'.*([，。…？！～\.,!?])$', ''.join(line)):
return []
return ['，']
def good_line(line):
if len(re.findall(r'[a-zA-Z0-9]', ''.join(line))) > 2:
return False
return True
def regular(sen, limit=50):
sen = re.sub(r'\.{3,100}', '…', sen)
sen = re.sub(r'…{2,100}', '…', sen)
sen = re.sub(r'[,]{1,100}', '，', sen)
sen = re.sub(r'[\.]{1,100}', '。', sen)
sen = re.sub(r'[\?]{1,100}', '？', sen)
sen = re.sub(r'[!]{1,100}', '！', sen)
if len(sen) > limit:
sen = sen[0:limit]
return sen
def creat_train_data_of_sim_corpus(limit=50, x_limit=2, y_limit=2):
x_datas = []
y_datas = []
max_len = 0
sim_ali_web_gov_dli_datas = txtRead(path_webank_sim, encodeType="gbk")
for sim_ali_web_gov_dli_datas_one in sim_ali_web_gov_dli_datas[1:]:
sim_ali_web_gov_dli_datas_one_split = sim_ali_web_gov_dli_datas_one.strip().split(",")
if sim_ali_web_gov_dli_datas_one_split[2]=="1":
len_x1 = len(sim_ali_web_gov_dli_datas_one_split[0])
len_x2 = len(sim_ali_web_gov_dli_datas_one_split[1])
# if max_len < len_x1 or max_len < len_x2:
max_len = max(len_x1, len_x2, max_len)
sentence_org = regular(sim_ali_web_gov_dli_datas_one_split[0], limit=limit)
sentence_sim = regular(sim_ali_web_gov_dli_datas_one_split[1], limit=limit)
x_datas.append([sen for sen in sentence_org])
y_datas.append([sen for sen in sentence_sim])
x_datas.append([sen for sen in sentence_sim])
y_datas.append([sen for sen in sentence_org])
datas = list(zip(x_datas, y_datas))
datas = [
(x, y)
for x, y in datas
if len(x) < limit and len(y) < limit and len(y) >= y_limit and len(x) >= x_limit
]
x_datas, y_datas = zip(*datas)
print('fit word_sequence')
ws_input = WordSequence()
ws_input.fit(x_datas + y_datas)
print('dump')
pickle.dump((x_datas, y_datas),
open(train_data_web_xy_anti, 'wb')
)
pickle.dump(ws_input, open(train_data_web_ws_anti, 'wb'))
print('done')
print(max_len)
if __name__ == '__main__':
creat_train_data_of_sim_corpus()

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AugmentText/augment_seq2seq/code_seq2seq_char/predict_char_anti.py Normal file
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"""
对SequenceToSequence模型进行基本的参数组合测试
Code from: QHDuan(2018-02-05) url: https://github.com/qhduan/just_another_seq2seq
"""
from utils.mode_util.seq2seq.data_utils import batch_flow_bucket as batch_flow
from utils.mode_util.seq2seq.thread_generator import ThreadedGenerator
from utils.mode_util.seq2seq.model_seq2seq import SequenceToSequence
from utils.mode_util.seq2seq.word_sequence import WordSequence
from conf.path_config import train_data_web_ws_anti
from conf.path_config import train_data_web_xy_anti
from conf.path_config import model_ckpt_web_anti
from conf.path_config import path_params
import tensorflow as tf
import numpy as np
import pickle
import json
import sys
sys.path.append('..')
def predict_anti(params):
"""测试不同参数在生成的假数据上的运行结果"""
x_data, _ = pickle.load(open(train_data_web_xy_anti, 'rb'))
ws = pickle.load(open(train_data_web_ws_anti, 'rb'))
for x in x_data[:5]:
print(' '.join(x))
config = tf.ConfigProto(
# device_count={'CPU': 1, 'GPU': 0},
allow_soft_placement=True,
log_device_placement=False
)
save_path = model_ckpt_web_anti
# 测试部分
tf.reset_default_graph()
model_pred = SequenceToSequence(
input_vocab_size=len(ws),
target_vocab_size=len(ws),
batch_size=1,
mode='decode',
beam_width=0,
**params
)
init = tf.global_variables_initializer()
with tf.Session(config=config) as sess:
sess.run(init)
model_pred.load(sess, save_path)
while True:
user_text = input('Input Chat Sentence:')
if user_text in ('exit', 'quit'):
exit(0)
x_test = [list(user_text.lower())]
# x_test = [word_tokenize(user_text)]
bar = batch_flow([x_test], ws, 1)
x, xl = next(bar)
x = np.flip(x, axis=1)
# x = np.array([
# list(reversed(xx))
# for xx in x
# ])
print(x, xl)
pred = model_pred.predict(
sess,
np.array(x),
np.array(xl)
)
print(pred)
# prob = np.exp(prob.transpose())
print(ws.inverse_transform(x[0]))
# print(ws.inverse_transform(pred[0]))
# print(pred.shape, prob.shape)
for p in pred:
ans = ws.inverse_transform(p)
print(ans)
def main():
"""入口程序"""
import json
predict_anti(json.load(open(path_params)))
if __name__ == '__main__':
main()

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AugmentText/augment_seq2seq/code_seq2seq_char/train_char_anti.py Normal file
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"""
对SequenceToSequence模型进行基本的参数组合测试
Code from: QHDuan(2018-02-05) url: https://github.com/qhduan/just_another_seq2seq
"""
from utils.mode_util.seq2seq.data_utils import batch_flow_bucket as batch_flow
from utils.mode_util.seq2seq.thread_generator import ThreadedGenerator
from utils.mode_util.seq2seq.model_seq2seq import SequenceToSequence
from utils.mode_util.seq2seq.word_sequence import WordSequence
from conf.path_config import train_data_web_ws_anti
from conf.path_config import train_data_web_xy_anti
from conf.path_config import model_ckpt_web_anti
from conf.path_config import path_params
from sklearn.utils import shuffle
import tensorflow as tf
from tqdm import tqdm
import numpy as np
import pickle
import random
import json
import sys
sys.path.append('..')
def train_and_dev(params):
"""测试不同参数在生成的假数据上的运行结果"""
x_data, y_data = pickle.load(open(train_data_web_xy_anti, 'rb'))
ws = pickle.load(open(train_data_web_ws_anti, 'rb'))
# 训练部分
n_epoch = 2
batch_size = 128
x_data, y_data = shuffle(x_data, y_data, random_state=20190412)
steps = int(len(x_data) / batch_size) + 1
config = tf.ConfigProto(
# device_count={'CPU': 1, 'GPU': 0},
allow_soft_placement=True,
log_device_placement=False
)
save_path = model_ckpt_web_anti
tf.reset_default_graph()
with tf.Graph().as_default():
random.seed(0)
np.random.seed(0)
tf.set_random_seed(0)
with tf.Session(config=config) as sess:
model = SequenceToSequence(
input_vocab_size=len(ws),
target_vocab_size=len(ws),
batch_size=batch_size,
**params
)
init = tf.global_variables_initializer()
sess.run(init)
flow = ThreadedGenerator(
batch_flow([x_data, y_data], ws, batch_size,
add_end=[False, True]),
queue_maxsize=30)
dummy_encoder_inputs = np.array([
np.array([WordSequence.PAD]) for _ in range(batch_size)])
dummy_encoder_inputs_lengths = np.array([1] * batch_size)
for epoch in range(1, n_epoch + 1):
costs = []
bar = tqdm(range(steps), total=steps,
desc='epoch {}, loss=0.000000'.format(epoch))
for _ in bar:
x, xl, y, yl = next(flow)
x = np.flip(x, axis=1)
add_loss = model.train(sess,
dummy_encoder_inputs,
dummy_encoder_inputs_lengths,
y, yl, loss_only=True)
add_loss *= -0.5
cost, lr = model.train(sess, x, xl, y, yl,
return_lr=True, add_loss=add_loss)
costs.append(cost)
bar.set_description('epoch {} loss={:.6f} lr={:.6f}'.format(
epoch,
np.mean(costs),
lr
))
model.save(sess, save_path)
flow.close()
# 测试部分
tf.reset_default_graph()
model_pred = SequenceToSequence(
input_vocab_size=len(ws),
target_vocab_size=len(ws),
batch_size=1,
mode='decode',
beam_width=12,
**params
)
init = tf.global_variables_initializer()
with tf.Session(config=config) as sess:
sess.run(init)
model_pred.load(sess, save_path)
bar = batch_flow([x_data, y_data], ws, 1, add_end=False)
t = 0
for x, xl, y, yl in bar:
x = np.flip(x, axis=1)
pred = model_pred.predict(
sess,
np.array(x),
np.array(xl)
)
print(ws.inverse_transform(x[0]))
print(ws.inverse_transform(y[0]))
print(ws.inverse_transform(pred[0]))
t += 1
if t >= 3:
break
tf.reset_default_graph()
model_pred = SequenceToSequence(
input_vocab_size=len(ws),
target_vocab_size=len(ws),
batch_size=1,
mode='decode',
beam_width=1,
**params
)
init = tf.global_variables_initializer()
with tf.Session(config=config) as sess:
sess.run(init)
model_pred.load(sess, save_path)
bar = batch_flow([x_data, y_data], ws, 1, add_end=False)
t = 0
for x, xl, y, yl in bar:
pred = model_pred.predict(
sess,
np.array(x),
np.array(xl)
)
print(ws.inverse_transform(x[0]))
print(ws.inverse_transform(y[0]))
print(ws.inverse_transform(pred[0]))
t += 1
if t >= 3:
break
def main():
"""入口程序"""
train_and_dev(json.load(open(path_params)))
if __name__ == '__main__':
main()

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AugmentText/augment_seq2seq/code_seq2seq_word/__init__.py Normal file
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# -*- coding: UTF-8 -*-
# !/usr/bin/python
# @time :2019/4/15 10:52
# @author :Mo
# @function :

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AugmentText/augment_seq2seq/code_seq2seq_word/extract_webank.py Normal file
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"""
把文件格式转换为可训练格式
Code from: QHDuan(2018-02-05) url: https://github.com/qhduan/just_another_seq2seq
"""
import re
import sys
import pickle
import jieba
import gensim
import numpy as np
from tqdm import tqdm
from conf.path_config import projectdir
from conf.path_config import w2v_model_merge_short_path
from utils.mode_util.seq2seq.word_sequence import WordSequence
from conf.path_config import model_ckpt_web_anti_word
from conf.path_config import train_data_web_xyw_anti
from conf.path_config import train_data_web_emb_anti
from conf.path_config import path_webank_sim
sys.path.append('..')
def make_split(line):
"""构造合并两个句子之间的符号
"""
if re.match(r'.*([，。…？！～\.,!?])$', ''.join(line)):
return []
return ['，']
def good_line(line):
"""判断一个句子是否好"""
if len(re.findall(r'[a-zA-Z0-9]', ''.join(line))) > 2:
return False
return True
def regular(sen, limit=50):
sen = re.sub(r'\.{3,100}', '…', sen)
sen = re.sub(r'…{2,100}', '…', sen)
sen = re.sub(r'[,]{1,100}', '，', sen)
sen = re.sub(r'[\.]{1,100}', '。', sen)
sen = re.sub(r'[\?]{1,100}', '？', sen)
sen = re.sub(r'[!]{1,100}', '！', sen)
if len(sen) > limit:
sen = sen[0:limit]
return sen
def creat_train_data_of_bank_corpus(limit=50, x_limit=3, y_limit=3):
"""执行程序
Args:
limit: 只输出句子长度小于limit的句子
"""
print('load word2vec start!')
word_vec = gensim.models.KeyedVectors.load_word2vec_format(w2v_model_merge_short_path, encoding='gbk', binary=False, limit=None)
print('load word2vec end!')
fp = open(path_webank_sim, 'r', encoding='gbk', errors='ignore')
x_datas = []
y_datas = []
max_len = 0
count_fp = 0
for line in tqdm(fp):
count_fp += 1
if count_fp == 1:
continue
sim_bank_datas_one_split = line.strip().split(",")
len_x1 = len(sim_bank_datas_one_split[0])
len_x2 = len(sim_bank_datas_one_split[1])
# if max_len < len_x1 or max_len < len_x2:
max_len = max(len_x1, len_x2, max_len)
sentence_org = regular(sim_bank_datas_one_split[0], limit=limit)
sentence_sim = regular(sim_bank_datas_one_split[1], limit=limit)
org_cut = jieba._lcut(sentence_org)
sen_cut = jieba._lcut(sentence_sim)
x_datas.append(org_cut)
y_datas.append(sen_cut)
x_datas.append(sen_cut)
y_datas.append(org_cut)
print(len(x_datas), len(y_datas))
for ask, answer in zip(x_datas[:50], y_datas[:50]):
print(''.join(ask))
print(''.join(answer))
print('-' * 50)
data = list(zip(x_datas, y_datas))
data = [
(x, y)
for x, y in data
if len(x) < limit \
and len(y) < limit \
and len(y) >= y_limit \
and len(x) >= x_limit
]
x_data, y_data = zip(*data)
print('refine train data')
train_data = x_data + y_data
print('fit word_sequence')
ws_input = WordSequence()
ws_input.fit(train_data, max_features=100000)
print('dump word_sequence')
pickle.dump((x_data, y_data, ws_input),
open(train_data_web_xyw_anti, 'wb')
)
print('make embedding vecs')
emb = np.zeros((len(ws_input), len(word_vec['</s>'])))
np.random.seed(1)
for word, ind in ws_input.dict.items():
if word in word_vec:
emb[ind] = word_vec[word]
else:
emb[ind] = np.random.random(size=(300,)) - 0.5
print('dump emb')
pickle.dump(
emb,
open(train_data_web_emb_anti, 'wb')
)
print('done')
if __name__ == '__main__':
creat_train_data_of_bank_corpus()

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AugmentText/augment_seq2seq/code_seq2seq_word/predict_word_anti.py Normal file
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"""
对SequenceToSequence模型进行基本的参数组合测试
"""
from utils.mode_util.seq2seq.thread_generator import ThreadedGenerator
from utils.mode_util.seq2seq.model_seq2seq import SequenceToSequence
from utils.mode_util.seq2seq.word_sequence import WordSequence
from utils.mode_util.seq2seq.data_utils import batch_flow
from conf.path_config import model_ckpt_web_anti_word
from conf.path_config import train_data_web_xyw_anti
from conf.path_config import train_data_web_emb_anti
from conf.path_config import path_webank_sim
from conf.path_config import path_params
import tensorflow as tf
import numpy as np
import random
import pickle
import jieba
import sys
sys.path.append('..')
def pred_word_anti(bidirectional, cell_type, depth,
attention_type, use_residual, use_dropout, time_major, hidden_units):
"""测试不同参数在生成的假数据上的运行结果"""
x_data, _, ws = pickle.load(open(train_data_web_xyw_anti, 'rb'))
for x in x_data[:5]:
print(' '.join(x))
config = tf.ConfigProto(
device_count={'CPU': 1, 'GPU': 0},
allow_soft_placement=True,
log_device_placement=False
)
save_path = model_ckpt_web_anti_word
# 测试部分
tf.reset_default_graph()
model_pred = SequenceToSequence(
input_vocab_size=len(ws),
target_vocab_size=len(ws),
batch_size=1,
mode='decode',
beam_width=1,
bidirectional=bidirectional,
cell_type=cell_type,
depth=depth,
attention_type=attention_type,
use_residual=use_residual,
use_dropout=use_dropout,
parallel_iterations=1,
time_major=time_major,
hidden_units=hidden_units,
share_embedding=True,
pretrained_embedding=True
)
init = tf.global_variables_initializer()
with tf.Session(config=config) as sess:
sess.run(init)
model_pred.load(sess, save_path)
while True:
user_text = input('Input Chat Sentence:')
if user_text in ('exit', 'quit'):
exit(0)
x_test = [jieba.lcut(user_text.lower())]
# x_test = [word_tokenize(user_text)]
bar = batch_flow([x_test], ws, 1)
x, xl = next(bar)
x = np.flip(x, axis=1)
# x = np.array([
# list(reversed(xx))
# for xx in x
# ])
print(x, xl)
pred = model_pred.predict(
sess,
np.array(x),
np.array(xl)
)
print(pred)
# prob = np.exp(prob.transpose())
print(ws.inverse_transform(x[0]))
# print(ws.inverse_transform(pred[0]))
# print(pred.shape, prob.shape)
for p in pred:
ans = ws.inverse_transform(p)
print(ans)
def main():
"""入口程序，开始测试不同参数组合"""
random.seed(0)
np.random.seed(0)
tf.set_random_seed(0)
pred_word_anti(
bidirectional=True,
cell_type='lstm',
depth=2,
attention_type='Bahdanau',
use_residual=False,
use_dropout=False,
time_major=False,
hidden_units=512
)
if __name__ == '__main__':
main()

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AugmentText/augment_seq2seq/code_seq2seq_word/train_word_anti.py Normal file
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"""
对SequenceToSequence模型进行基本的参数组合测试
"""
from utils.mode_util.seq2seq.data_utils import batch_flow
from utils.mode_util.seq2seq.thread_generator import ThreadedGenerator
from utils.mode_util.seq2seq.model_seq2seq import SequenceToSequence
from utils.mode_util.seq2seq.word_sequence import WordSequence
from conf.path_config import model_ckpt_web_anti_word
from conf.path_config import train_data_web_xyw_anti
from conf.path_config import train_data_web_emb_anti
from conf.path_config import path_webank_sim
from conf.path_config import path_params
import tensorflow as tf
from tqdm import tqdm
import numpy as np
import random
import pickle
import sys
sys.path.append('..')
def train_word_anti(bidirectional, cell_type, depth,
attention_type, use_residual, use_dropout, time_major, hidden_units):
"""测试不同参数在生成的假数据上的运行结果"""
emb = pickle.load(open(train_data_web_emb_anti, 'rb'))
x_data, y_data, ws = pickle.load(
open(train_data_web_xyw_anti, 'rb'))
# 训练部分
n_epoch = 10
batch_size = 128
# x_data, y_data = shuffle(x_data, y_data, random_state=0)
# x_data = x_data[:100000]
# y_data = y_data[:100000]
steps = int(len(x_data) / batch_size) + 1
config = tf.ConfigProto(
# device_count={'CPU': 1, 'GPU': 0},
allow_soft_placement=True,
log_device_placement=False
)
save_path = model_ckpt_web_anti_word
tf.reset_default_graph()
with tf.Graph().as_default():
random.seed(0)
np.random.seed(0)
tf.set_random_seed(0)
with tf.Session(config=config) as sess:
model = SequenceToSequence(
input_vocab_size=len(ws),
target_vocab_size=len(ws),
batch_size=batch_size,
bidirectional=bidirectional,
cell_type=cell_type,
depth=depth,
attention_type=attention_type,
use_residual=use_residual,
use_dropout=use_dropout,
hidden_units=hidden_units,
time_major=time_major,
learning_rate=0.001,
optimizer='adam',
share_embedding=True,
dropout=0.2,
pretrained_embedding=True
)
init = tf.global_variables_initializer()
sess.run(init)
# 加载训练好的embedding
model.feed_embedding(sess, encoder=emb)
# print(sess.run(model.input_layer.kernel))
# exit(1)
flow = ThreadedGenerator(
batch_flow([x_data, y_data], ws, batch_size),
queue_maxsize=30)
dummy_encoder_inputs = np.array([
np.array([WordSequence.PAD]) for _ in range(batch_size)])
dummy_encoder_inputs_lengths = np.array([1] * batch_size)
for epoch in range(1, n_epoch + 1):
costs = []
bar = tqdm(range(steps), total=steps,
desc='epoch {}, loss=0.000000'.format(epoch))
for _ in bar:
x, xl, y, yl = next(flow)
x = np.flip(x, axis=1)
add_loss = model.train(sess,
dummy_encoder_inputs,
dummy_encoder_inputs_lengths,
y, yl, loss_only=True)
add_loss *= -0.5
# print(x, y)
cost, lr = model.train(sess, x, xl, y, yl,
return_lr=True, add_loss=add_loss)
costs.append(cost)
bar.set_description('epoch {} loss={:.6f} lr={:.6f}'.format(
epoch,
np.mean(costs),
lr
))
model.save(sess, save_path)
flow.close()
# 测试部分
tf.reset_default_graph()
model_pred = SequenceToSequence(
input_vocab_size=len(ws),
target_vocab_size=len(ws),
batch_size=1,
mode='decode',
beam_width=12,
bidirectional=bidirectional,
cell_type=cell_type,
depth=depth,
attention_type=attention_type,
use_residual=use_residual,
use_dropout=use_dropout,
hidden_units=hidden_units,
time_major=time_major,
parallel_iterations=1,
learning_rate=0.001,
optimizer='adam',
share_embedding=True,
pretrained_embedding=True
)
init = tf.global_variables_initializer()
with tf.Session(config=config) as sess:
sess.run(init)
model_pred.load(sess, save_path)
bar = batch_flow([x_data, y_data], ws, 1)
t = 0
for x, xl, y, yl in bar:
x = np.flip(x, axis=1)
pred = model_pred.predict(
sess,
np.array(x),
np.array(xl)
)
print(ws.inverse_transform(x[0]))
print(ws.inverse_transform(y[0]))
print(ws.inverse_transform(pred[0]))
t += 1
if t >= 3:
break
tf.reset_default_graph()
model_pred = SequenceToSequence(
input_vocab_size=len(ws),
target_vocab_size=len(ws),
batch_size=1,
mode='decode',
beam_width=1,
bidirectional=bidirectional,
cell_type=cell_type,
depth=depth,
attention_type=attention_type,
use_residual=use_residual,
use_dropout=use_dropout,
hidden_units=hidden_units,
time_major=time_major,
parallel_iterations=1,
learning_rate=0.001,
optimizer='adam',
share_embedding=True,
pretrained_embedding=True
)
init = tf.global_variables_initializer()
with tf.Session(config=config) as sess:
sess.run(init)
model_pred.load(sess, save_path)
bar = batch_flow([x_data, y_data], ws, 1)
t = 0
for x, xl, y, yl in bar:
pred = model_pred.predict(
sess,
np.array(x),
np.array(xl)
)
print(ws.inverse_transform(x[0]))
print(ws.inverse_transform(y[0]))
print(ws.inverse_transform(pred[0]))
t += 1
if t >= 3:
break
def main():
"""入口程序，开始测试不同参数组合"""
random.seed(0)
np.random.seed(0)
tf.set_random_seed(0)
train_word_anti(
bidirectional=True,
cell_type='lstm',
depth=2,
attention_type='Bahdanau',
use_residual=False,
use_dropout=False,
time_major=False,
hidden_units=512
)
if __name__ == '__main__':
main()

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