Wordvec_句子相似度

import jieba
from jieba import analyse
import numpy
import gensim
import codecs
import pandas as pd
import jieba.posseg as pog
from gensim.models import Word2Vec
from gensim.models.word2vec import LineSentence
#获取训练语料
def data_handle(data):
    n = data.shape[0]
    data_str = ''
    for i in numpy.arange(n):
        data_str += str(data.ix[i, 'comment'])
    return data_str
def fenci(data_str,stop_property,stopfile):
    # 停用词
    stop_word = [word.strip() for word in open(stopfile, encoding='utf-8').readlines()]
    # 分词
    word_cut = pog.cut(data_str)

    with open('weibo.txt','w',encoding='utf-8') as f:
        for word, flag in word_cut:
            if flag not in stop_property:
                if word not in stop_word:
                    f.write(word+'\n')

# 原始的训练语料转化成一个sentence的迭代器,每一次迭代返回的sentence是一个word（utf8格式）的列表
def vctor_word():
    wiki_news = open('weibo.txt', 'r',encoding='utf-8')
    sentences=LineSentence(wiki_news)
    model=Word2Vec(sentences,sg=0,size=100,window=5,min_count=5,workers=9)
    model.save('weibo.word2vec')

# 实现给出任意字符串，获取字符串中某字符的位置以及出现的总次数
def get_char_pos(string, char):
    chPos = []
    try:
        chPos = list(((pos, char) for pos, val in enumerate(string) if (val == char)))
    except:
        pass
    return chPos

# 利用训练好的词向量获取关键词的词向量

def cut_data(data,stopfile):
    data.fillna(0,inplace=True)
    stop_word = [word.strip() for word in open(stopfile, encoding='utf-8').readlines()]
    charater=['a', 'nr', 'ns', 'nt', 'ng', 'vn', 'vi', 'l', 'n', 'v']
    m=data.shape[0]
    with open('seg_word.txt', 'w', encoding='utf-8') as f:

       for i in range(m):
            str_cut = ''
            str=data.ix[i,'comment']
            if str!=0:
                segs=jieba.posseg.cut(str)
                for word,flag in segs:
                    if flag in charater:
                        if word not in stop_word:
                             str_cut+=word+'/'
                f.write(str_cut )
            else:
               str_cut=''
            f.write('\n ')

def get_vector(data,model):#str
    wordvec_size = 100
    word_vec_all = numpy.zeros(wordvec_size)
    space_pos = get_char_pos(data, '/')
    first_word = data[0:space_pos[0][0]]
    print('first_word', first_word)
    if first_word in model:
        print('yes')
        word_vec_all = word_vec_all + model[first_word]

    for i in range(len(space_pos) - 2):
        word = data[space_pos[i][0]:space_pos[i + 1][0]]
        print('word',word)
        if word in model:
            print('yes')
            word_vec_all = word_vec_all + model[first_word]

    print('word_vec_all',word_vec_all)
    return word_vec_all

def word2vec(file_name, model,str):

   DataFile = codecs.open(file_name, "r", encoding='utf-8')
   DataSet = DataFile.readlines()[:-1]

   score_list=[]

   str_vector=get_vector(str,model)
   for data in DataSet:  #
       if data.strip()!='':
           word_vec_all=get_vector(data,model)
           score=simlarityCalu(word_vec_all, str_vector)
       else:
           score=0
       score_list.append(score)
   print('score_list',score_list)
   return score_list

# 词向量相似度计算代码：余弦
def simlarityCalu(vector1, vector2):
    vector1Mod = numpy.sqrt(vector1.dot(vector1))
    vector2Mod = numpy.sqrt(vector2.dot(vector2))
    if vector2Mod != 0 and vector1Mod != 0:
        simlarity = (vector1.dot(vector2)) / (vector1Mod * vector2Mod)
    else:
        simlarity = 0
    return simlarity

if __name__ == '__main__':

    stop_property = ['b', 'c', 'd', 'e', 'f', 'm', 'o', 'p', 'q', 'r', 't', 'u', 'x', 'y', 'z', 'uj', 'nrt', 'eng',
                     'zg', 'ul']
    stop_file='stop.txt'

    # 读取数据
    data = pd.read_excel('C:/E/weibo.xlsx')
    data.rename(columns={'粉丝ID': 'fans_id', '粉丝': 'fans_name', '微博账户id': 'weibo_user_id', '微博名': 'weibo_name',
                         '微博id': 'weibo_id', '评论id': 'comment_id', '评论': 'comment'}, inplace=True)

    # 获取评论字符串
    comment_str=data_handle(data)

    #获取语料
    fenci(comment_str, stop_property, stop_file)
    #训练模型
    vctor_word()
    #获取关键词
    cut_data(data, stop_file)

    p1_keywords = 'seg_word.txt'
    str1 = '农农/陈利农/宝贝'
    # model = gensim.models.Word2Vec.load('weibo.word2vec')
    model = gensim.models.Word2Vec.load('zhiwiki_news.word2vec')
    p1_vec = word2vec(p1_keywords, model,str1)

    str2='舒蔻 尤妮佳 买'