深度学习---情感分析（Rnn，LSTM）

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# -*- coding:utf-8 -*-

'''
在GTX1070上，11s一轮
经过30轮迭代，训练集准确率为98.41%
Dropout不能用太多，否则信息损失太严重
'''

import numpy as np
import pandas as pd
import jieba

pd.set_option('display.max_columns',8)
pd.set_option('display.max_rows',30)
pd.set_option('display.max_colwidth',60)
#不允许换行显示
pd.set_option('expand_frame_repr',False)

#读取stop停用词
stopwords='哈工大停用词表.txt'
stop_single_words=[]
with open(stopwords,'r') as f:
    for line in f:
        content=line.strip()
        stop_single_words.append(content.decode('gbk'))
print stop_single_words

#读取情感正与负样本。
pos = pd.read_excel('pos.xls', header=None)
pos['label'] = 1
neg = pd.read_excel('neg.xls', header=None)
neg['label'] = 0
all_ = pos.append(neg, ignore_index=True)


all_['words'] = all_[0].apply(lambda s: [i for i in list(jieba.cut(s)) if i not in stop_single_words]) #调用结巴分词


print all_[:5]



maxlen = 100 #截断词数
min_count = 5 #出现次数少于该值的词扔掉。这是最简单的降维方法

content = []
for i in all_['words']:
	content.extend(i)

abc = pd.Series(content).value_counts()
abc = abc[abc >= min_count]
abc[:] = range(1, len(abc)+1)
abc[''] = 0 #添加空字符串用来补全
word_set = set(abc.index)

def doc2num(s, maxlen):
    s = [i for i in s if i in word_set]
    s = s[:maxlen] + ['']*max(0, maxlen-len(s))
    return list(abc[s])

all_['doc2num'] = all_['words'].apply(lambda s: doc2num(s, maxlen))

#手动打乱数据
idx = range(len(all_))
np.random.shuffle(idx)
all_ = all_.loc[idx]

#按keras的输入要求来生成数据
x = np.array(list(all_['doc2num']))
y = np.array(list(all_['label']))
y = y.reshape((-1,1)) #调整标签形状


from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Embedding
from keras.layers import LSTM

#建立模型
model = Sequential()
model.add(Embedding(len(abc), 256, input_length=maxlen))
model.add(LSTM(128))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

batch_size = 128
train_num = 15000

model.fit(x[:train_num], y[:train_num], batch_size = batch_size, nb_epoch=30)

model.evaluate(x[train_num:], y[train_num:], batch_size = batch_size)

def predict_one(s): #单个句子的预测函数
    s = np.array(doc2num(list(jieba.cut(s)), maxlen))
    s = s.reshape((1, s.shape[0]))
    return model.predict_classes(s, verbose=0)[0][0]