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question_classify_train.py
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question_classify_train.py
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#!/usr/bin/env python3
# coding: utf-8
# File: question_classify.py
# Author: lhy<[email protected],https://huangyong.github.io>
# Date: 18-11-10
import os
import numpy as np
import jieba.posseg as pseg
from keras.models import Sequential, load_model
from keras.layers import Conv1D, GlobalAveragePooling1D, MaxPooling1D, Dense, Dropout, LSTM, Bidirectional
class QuestionClassify(object):
def __init__(self):
self.label_dict = {
0:"婚姻家庭",
1:"劳动纠纷",
2:"交通事故",
3:"债权债务",
4:"刑事辩护",
5:"合同纠纷",
6:"房产纠纷",
7:"侵权",
8:"公司法",
9:"医疗纠纷",
10:"拆迁安置",
11:"行政诉讼",
12:"建设工程"
}
cur = '/'.join(os.path.abspath(__file__).split('/')[:-1])
self.train_file = os.path.join(cur, 'question_train.txt')
self.embedding_path = os.path.join(cur, 'word_vec_300.bin')
self.embdding_dict = self.load_embedding(self.embedding_path)
self.max_length = 60
self.embedding_size = 300
self.lstm_modelpath = 'model/lstm_question_classify.h5'
self.cnn_modelpath = 'model/cnn_question_classify.h5'
return
'''加载词向量'''
def load_embedding(self, embedding_path):
embedding_dict = {}
count = 0
for line in open(embedding_path):
line = line.strip().split(' ')
if len(line) < 300:
continue
wd = line[0]
vector = np.array([float(i) for i in line[1:]])
embedding_dict[wd] = vector
count += 1
if count%10000 == 0:
print(count, 'loaded')
print('loaded %s word embedding, finished'%count, )
return embedding_dict
'''对文本进行分词处理'''
def seg_sent(self, s):
wds = [i.word for i in pseg.cut(s) if i.flag[0] not in ['w', 'x']]
return wds
'''基于wordvector，通过lookup table的方式找到句子的wordvector的表示'''
def rep_sentencevector(self, sentence):
word_list = self.seg_sent(sentence)[:self.max_length]
embedding_matrix = np.zeros((self.max_length, self.embedding_size))
for index, wd in enumerate(word_list):
if wd in self.embdding_dict:
embedding_matrix[index] = self.embdding_dict.get(wd)
else:
continue
len_sent = len(word_list)
embedding_matrix = self.modify_sentencevector(embedding_matrix, len_sent)
return embedding_matrix
'''对于OOV词,通过左右词的词向量作平均,作为词向量表示'''
def modify_sentencevector(self, embedding_matrix, len_sent):
context_window = 2
for indx, vec in enumerate(embedding_matrix):
left = indx-context_window
right = indx+context_window
if left < 0:
left = 0
if right > len(embedding_matrix)-1:
right = -2
context = embedding_matrix[left:right+1]
if vec.tolist() == [0]*300 and indx < len_sent:
context_vector = context.mean(axis=0)
embedding_matrix[indx] = context_vector
return embedding_matrix
'''对数据进行onehot映射操作'''
def label_onehot(self, label):
one_hot = [0]*len(self.label_dict)
one_hot[int(label)] = 1
return one_hot
'''加载数据集'''
def load_traindata(self):
train_X = []
train_Y = []
count = 0
for line in open(self.train_file):
line = line.strip().strip().split('\t')
if len(line) < 2:
continue
count += 1
sent = line[0]
label = line[1]
sent_vector = self.rep_sentencevector(sent)
label_vector = self.label_onehot(label)
train_X.append(sent_vector)
train_Y.append(label_vector)
if count % 10000 == 0:
print('loaded %s lines'%count)
return np.array(train_X), np.array(train_Y)
'''构造CNN网络模型'''
def build_cnn_model(self):
model = Sequential()
model.add(Conv1D(64, 3, activation='relu', input_shape=(self.max_length, self.embedding_size)))
model.add(Conv1D(64, 3, activation='relu'))
model.add(MaxPooling1D(3))
model.add(Conv1D(128, 3, activation='relu'))
model.add(Conv1D(128, 3, activation='relu'))
model.add(GlobalAveragePooling1D())
model.add(Dropout(0.5))
model.add(Dense(13, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
model.summary()
return model
'''构造LSTM网络'''
def build_lstm_model(self):
model = Sequential()
model.add(LSTM(32, return_sequences=True, input_shape=(self.max_length, self.embedding_size))) # returns a sequence of vectors of dimension 32
model.add(LSTM(32, return_sequences=True)) # returns a sequence of vectors of dimension 32
model.add(LSTM(32)) # return a single vector of dimension 32
model.add(Dense(13, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
return model
'''训练CNN模型'''
def train_cnn(self):
X_train, Y_train, X_test, Y_test = self.split_trainset()
model = self.build_cnn_model()
model.fit(X_train, Y_train, batch_size=100, epochs=20, validation_data=(X_test, Y_test))
model.save(self.cnn_modelpath)
'''训练CNN模型'''
def train_lstm(self):
X_train, Y_train, X_test, Y_test = self.split_trainset()
model = self.build_lstm_model()
model.fit(X_train, Y_train, batch_size=100, epochs=50, validation_data=(X_test, Y_test))
model.save(self.lstm_modelpath)
'''划分数据集,按一定比例划分训练集和测试集'''
def split_trainset(self):
X, Y = self.load_traindata()
split_rate = 0.8
indx = int(len(X)*split_rate)
X_train = X[:indx]
Y_train = Y[:indx]
X_test = X[indx:]
Y_test = Y[indx:]
return X_train, Y_train, X_test, Y_test
if __name__ == '__main__':
handler = QuestionClassify()
handler.train_cnn()
handler.train_lstm()