import numpy as np
from gensim.models.word2vec import Word2Vec
from gensim.corpora.dictionary import Dictionary
from gensim import models
import pandas as pd
import jieba
import logging
from keras import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Bidirectional,LSTM,Dense,Embedding,Dropout,Activation,Softmax
from sklearn.model_selection import train_test_split
from keras.utils import np_utils

def read_data(data_path):
    senlist = []
    labellist = []  
    with open(data_path, "r",encoding=‘gb2312‘,errors=‘ignore‘) as f:
         for data in  f.readlines():
                data = data.strip()
                sen = data.split("	")[2] 
                label = data.split("	")[3]
                if sen != "" and (label =="0" or label=="1" or label=="2" ) :
                    senlist.append(sen)
                    labellist.append(label) 
                else:
                    pass                    
    assert(len(senlist) == len(labellist))            
    return senlist ,labellist 

sentences,labels = read_data("data_train.csv")

def train_word2vec(sentences,save_path):
    sentences_seg = []
    sen_str = "
".join(sentences)
    res = jieba.lcut(sen_str)
    seg_str = " ".join(res)
    sen_list = seg_str.split("
")
    for i in sen_list:
        sentences_seg.append(i.split())
    print("开始训练词向量") 
#     logging.basicConfig(format=‘%(asctime)s : %(levelname)s : %(message)s‘, level=logging.INFO)
    model = Word2Vec(sentences_seg,
                size=100,  # 词向量维度
                min_count=5,  # 词频阈值
                window=5)  # 窗口大小    
    model.save(save_path)
    return model

model =  train_word2vec(sentences,‘word2vec.model‘)    

def generate_id2wec(word2vec_model):
    gensim_dict = Dictionary()
    gensim_dict.doc2bow(model.wv.vocab.keys(), allow_update=True)
    w2id = v: k + 1 for k, v in gensim_dict.items()  # 词语的索引，从1开始编号
    w2vec = word: model[word] for word in w2id.keys()  # 词语的词向量
    n_vocabs = len(w2id) + 1
    embedding_weights = np.zeros((n_vocabs, 100))
    for w, index in w2id.items():  # 从索引为1的词语开始，用词向量填充矩阵
        embedding_weights[index, :] = w2vec[w]
    return w2id,embedding_weights

def text_to_array(w2index, senlist):  # 文本转为索引数字模式
    sentences_array = []
    for sen in senlist:
        new_sen = [ w2index.get(word,0) for word in sen]   # 单词转索引数字
        sentences_array.append(new_sen)
    return np.array(sentences_array)

def prepare_data(w2id,sentences,labels,max_len=200):
    X_train, X_val, y_train, y_val = train_test_split(sentences,labels, test_size=0.2)
    X_train = text_to_array(w2id, X_train)
    X_val = text_to_array(w2id, X_val)
    X_train = pad_sequences(X_train, maxlen=max_len)
    X_val = pad_sequences(X_val, maxlen=max_len)
    return np.array(X_train), np_utils.to_categorical(y_train) ,np.array(X_val), np_utils.to_categorical(y_val)

w2id,embedding_weights = generate_id2wec(model)# 获取词向量矩阵和词典

x_train,y_trian, x_val , y_val = prepare_data(w2id,sentences,labels,200)#将数据处理成模型需要的格式

class Sentiment:
    def __init__(self,w2id,embedding_weights,Embedding_dim,maxlen,labels_category):
        self.Embedding_dim = Embedding_dim
        self.embedding_weights = embedding_weights
        self.vocab = w2id
        self.labels_category = labels_category
        self.maxlen = maxlen
        self.model = self.build_model()
      
        
    def build_model(self):
        model = Sequential()
        #input dim(140,100)
        model.add(Embedding(output_dim = self.Embedding_dim,
                           input_dim=len(self.vocab)+1,
                           weights=[self.embedding_weights],
                           input_length=self.maxlen))
        model.add(Bidirectional(LSTM(50),merge_mode=‘concat‘))
        model.add(Dropout(0.5))
        model.add(Dense(self.labels_category))
        model.add(Activation(‘softmax‘))
        model.compile(loss=‘categorical_crossentropy‘,
                     optimizer=‘adam‘, 
                     metrics=[‘accuracy‘])
        model.summary()
        return model
    
    def train(self,X_train, y_train,X_test, y_test,n_epoch=5 ):
        self.model.fit(X_train, y_train, batch_size=32, epochs=n_epoch,
                      validation_data=(X_test, y_test))
        self.model.save(‘sentiment.h5‘)   
        
    def predict(self,model_path,new_sen):
        model = self.model
        model.load_weights(model_path)
        new_sen_list = jieba.lcut(new_sen)
        sen2id =[ self.vocab.get(word,0) for word in new_sen_list]
        sen_input = pad_sequences([sen2id], maxlen=self.maxlen)
        res = model.predict(sen_input)[0]
        return np.argmax(res)

senti = Sentiment(w2id,embedding_weights,100,200,3)

senti.train(x_train,y_trian, x_val ,y_val,1)#训练

label_dic = 0:"消极的",1:"中性的",2:"积极的"
sen_new = "现如今的公司能够做成这样已经很不错了，微订点单网站的信息更新很及时，内容来源很真实"
pre = senti.predict("./sentiment.h5",sen_new)
print("‘‘的情感是:
".format(sen_new,label_dic.get(pre)))