151 lines
5.3 KiB
Python
151 lines
5.3 KiB
Python
#!/usr/bin/env python3
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# coding: utf-8
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# File: question_classify.py
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# Author: lhy<lhy_in_blcu@126.com,https://huangyong.github.io>
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# Date: 18-11-11
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import os
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import numpy as np
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import jieba.posseg as pseg
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from keras.models import Sequential, load_model
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from keras.layers import Conv1D, GlobalAveragePooling1D, MaxPooling1D, Dense, Dropout, LSTM, Bidirectional
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class QuestionClassify(object):
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def __init__(self):
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self.label_dict = {
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0: "婚姻家庭",
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1: "劳动纠纷",
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2: "交通事故",
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3: "债权债务",
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4: "刑事辩护",
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5: "合同纠纷",
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6: "房产纠纷",
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7: "侵权",
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8: "公司法",
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9: "医疗纠纷",
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10: "拆迁安置",
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11: "行政诉讼",
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12: "建设工程"
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}
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cur = '/'.join(os.path.abspath(__file__).split('/')[:-1])
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self.embedding_path = os.path.join(cur, 'embedding/word_vec_300.bin')
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self.embdding_dict = self.load_embedding(self.embedding_path)
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self.max_length = 60
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self.embedding_size = 300
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self.lstm_modelpath = 'model/lstm_question_classify.h5'
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self.cnn_modelpath = 'model/cnn_question_classify.h5'
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return
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'''加载词向量'''
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def load_embedding(self, embedding_path):
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embedding_dict = {}
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count = 0
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for line in open(embedding_path):
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line = line.strip().split(' ')
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if len(line) < 300:
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continue
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wd = line[0]
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vector = np.array([float(i) for i in line[1:]])
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embedding_dict[wd] = vector
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count += 1
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if count % 10000 == 0:
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print(count, 'loaded')
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print('loaded %s word embedding, finished' % count, )
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return embedding_dict
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'''对文本进行分词处理'''
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def seg_sent(self, s):
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wds = [i.word for i in pseg.cut(s) if i.flag[0] not in ['w', 'x']]
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return wds
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'''基于wordvector，通过lookup table的方式找到句子的wordvector的表示'''
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def rep_sentencevector(self, sentence):
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word_list = self.seg_sent(sentence)[:self.max_length]
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embedding_matrix = np.zeros((self.max_length, self.embedding_size))
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for index, wd in enumerate(word_list):
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if wd in self.embdding_dict:
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embedding_matrix[index] = self.embdding_dict.get(wd)
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else:
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continue
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len_sent = len(word_list)
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embedding_matrix = self.modify_sentencevector(embedding_matrix, len_sent)
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return embedding_matrix
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'''对于OOV词,通过左右词的词向量作平均,作为词向量表示'''
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def modify_sentencevector(self, embedding_matrix, len_sent):
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context_window = 2
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for indx, vec in enumerate(embedding_matrix):
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left = indx - context_window
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right = indx + context_window
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if left < 0:
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left = 0
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if right > len(embedding_matrix) - 1:
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right = -2
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context = embedding_matrix[left:right + 1]
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if vec.tolist() == [0] * 300 and indx < len_sent:
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context_vector = context.mean(axis=0)
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embedding_matrix[indx] = context_vector
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return embedding_matrix
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'''对数据进行onehot映射操作'''
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def label_onehot(self, label):
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one_hot = [0] * len(self.label_dict)
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one_hot[int(label)] = 1
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return one_hot
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'''构造CNN网络模型'''
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def build_cnn_model(self):
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model = Sequential()
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model.add(Conv1D(64, 3, activation='relu', input_shape=(self.max_length, self.embedding_size)))
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model.add(Conv1D(64, 3, activation='relu'))
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model.add(MaxPooling1D(3))
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model.add(Conv1D(128, 3, activation='relu'))
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model.add(Conv1D(128, 3, activation='relu'))
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model.add(GlobalAveragePooling1D())
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model.add(Dropout(0.5))
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model.add(Dense(13, activation='sigmoid'))
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model.compile(loss='binary_crossentropy',
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optimizer='rmsprop',
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metrics=['accuracy'])
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model.summary()
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return model
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'''构造LSTM网络'''
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def build_lstm_model(self):
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model = Sequential()
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model.add(LSTM(32, return_sequences=True, input_shape=(
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self.max_length, self.embedding_size))) # returns a sequence of vectors of dimension 32
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model.add(LSTM(32, return_sequences=True)) # returns a sequence of vectors of dimension 32
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model.add(LSTM(32)) # return a single vector of dimension 32
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model.add(Dense(13, activation='softmax'))
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model.compile(loss='categorical_crossentropy',
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optimizer='rmsprop',
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metrics=['accuracy'])
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return model
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'''问题分类'''
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def predict(self, sent):
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model = load_model(self.cnn_modelpath)
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sentence_vector = np.array([self.rep_sentencevector(sent)])
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res = model.predict(sentence_vector)[0].tolist()
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prob = max(res)
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label = self.label_dict.get(res.index(prob))
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return label, prob
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if __name__ == '__main__':
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handler = QuestionClassify()
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while (1):
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sent = input('question desc:')
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label, prob = handler.predict(sent)
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print('question_type:', label, prob)
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