gensim自然语言处理

参考代码
ChineseClean_demo1.py：
# -*- coding:utf-8 -*-
import xlrd
import xlwt
'''
python3.4
'''
# file 表示源文件名字，修改此处即可
file="./data/answer_detail_5_15307860968687.xls"
dirs="./result"

def read_excel(rows_numb,cols_numb):

    f = xlwt.Workbook() #创建工作簿
    '''
    创建第一个sheet:
    sheet1
    '''
    sheet1 = f.add_sheet(u'sheet1_1',cell_overwrite_ok=True) #创建sheet
    sheet2 = f.add_sheet(u'sheet1_2',cell_overwrite_ok=True) #创建sheet
    row0 = [u'UserNo',u'Name',u'Question',u'Answer',u'Layer',u'Mark',u'Score',u'AbilityID']

    # 打开文件
    workbook = xlrd.open_workbook(file)
    sheet0 = workbook.sheet_by_index(0) # sheet索引从0开始
    cols = sheet0.col_values(cols_numb)
    rows_list_1=[]
    rows_list_2=[]
    for i in range(1,len(cols)):
        if cols[i] == '0':
            rows_list_1.append(i)
        else:
            rows_list_2.append(i)

    for i in range(0,len(row0)):
        sheet1.write(0,i,row0[i])
        sheet2.write(0,i,row0[i])
    '''
    sheet1_1保存0分数据

    '''
    for j in range(0,len(rows_list_1)):
        rows = sheet0.row_values(rows_list_1[j]) # 获取行内容
        for i in range(0,len(rows)):
            sheet1.write(j+1,i,rows[i])
    '''

    sheet1_2保存非0分数据

    '''
    for j in range(0,len(rows_list_2)):
        rows = sheet0.row_values(rows_list_2[j]) # 获取行内容
        for i in range(0,len(rows)):
            sheet2.write(j+1,i,rows[i])

    f.save('./data/demo1.xls') #保存文件

if __name__ == '__main__':
    # 读取文件的行和列
    rows_numb=0
    cols_numb=6
    read_excel(rows_numb,cols_numb)

ChineseClean_demo2.py：
# -*- coding:utf-8 -*-
import xlrd
import xlwt
'''
python3.4

'''
# file 表示源文件名字，修改此处即可
file="./data/demo1.xls"

def read_excel(rows_numb,cols_numb):

    f = xlwt.Workbook() #创建工作簿

    '''
    创建第sheet:
    '''
    sheet1 = f.add_sheet(u'sheet2_1',cell_overwrite_ok=True) #创建sheet
    sheet2 = f.add_sheet(u'sheet2_2',cell_overwrite_ok=True) #创建sheet
    sheet3 = f.add_sheet(u'sheet2_3',cell_overwrite_ok=True) #创建sheet
    sheet4 = f.add_sheet(u'sheet2_4',cell_overwrite_ok=True) #创建sheet
    row0 = [u'UserNo',u'Name',u'Question',u'Answer',u'Layer',u'Mark',u'Score',u'AbilityID']

    for i in range(0,len(row0)):
        sheet1.write(0,i,row0[i])
        sheet2.write(0,i,row0[i])
        sheet3.write(0,i,row0[i])
        sheet4.write(0,i,row0[i])

    # 打开文件
    workbook = xlrd.open_workbook(file)
    sheet0 = workbook.sheet_by_index(0) # sheet索引从0开始
    cols = sheet0.col_values(cols_numb) # 获取列内容
    rows_list_1=[]
    rows_list_2=[]
    rows_list_3=[]
    rows_list_4=[]
    for i in range(1,len(cols)):

        if float(cols[i]) < 12.0:
            rows_list_1.append(i)
        if float(cols[i]) >= 12.0 and float(cols[i]) < 16.0:
            rows_list_2.append(i)
        if float(cols[i]) >= 16.0 and float(cols[i]) < 18.0:
            rows_list_3.append(i)
        if float(cols[i]) >= 18.0:
            print(i)
            print(type(cols[i]))
            exit()
            rows_list_4.append(i)

    '''
    sheet2_1保存差，小于12分

    '''
    for j in range(0,len(rows_list_1)):
        rows = sheet0.row_values(rows_list_1[j]) # 获取行内容
        for i in range(0,len(rows)):
            sheet1.write(j+1,i,rows[i])
    '''
    sheet2_2保存中，大于等于12，且小于16分

    '''

    for j in range(0,len(rows_list_2)):
        rows = sheet0.row_values(rows_list_2[j]) # 获取行内容
        for i in range(0,len(rows)):
            sheet2.write(j+1,i,rows[i])

    '''
    sheet2_3保存良，大于等于16，且小于18分

    '''
    for j in range(0,len(rows_list_3)):
        rows = sheet0.row_values(rows_list_3[j]) # 获取行内容
        for i in range(0,len(rows)):
            sheet3.write(j+1,i,rows[i])
    '''
    sheet2_4保存优，大于等于18分

    '''

    for j in range(0,len(rows_list_4)):
        rows = sheet0.row_values(rows_list_4[j]) # 获取行内容
        for i in range(0,len(rows)):
            sheet4.write(j+1,i,rows[i])

    f.save('./data/demo2.xls')

if __name__ == '__main__':
    # 读取文件的行和列
    rows_numb=0
    cols_numb=6
    read_excel(rows_numb,cols_numb)
ChineseClean_demo3.py:
# -*- coding:utf-8 -*-
import xlrd
import xlwt
'''
python3.4

'''
file="./data/answer_detail_5_15307860968687.xls"

def read_excel(rows_numb,cols_numb):

    f = xlwt.Workbook() #创建工作簿

    '''
    创建第一个sheet:
    sheet1
    '''
    sheet1 = f.add_sheet(u'sheet1',cell_overwrite_ok=True) #创建sheet
    sheet2 = f.add_sheet(u'sheet2',cell_overwrite_ok=True) #创建sheet
    sheet3 = f.add_sheet(u'sheet3',cell_overwrite_ok=True) #创建sheet
    sheet4 = f.add_sheet(u'sheet4',cell_overwrite_ok=True) #创建sheet
    sheet5 = f.add_sheet(u'sheet5',cell_overwrite_ok=True)
    row0 = [u'UserNo',u'Name',u'Question',u'Answer',u'Layer',u'Mark',u'Score',u'AbilityID']

    for i in range(0,len(row0)):
        sheet1.write(0,i,row0[i])
        sheet2.write(0,i,row0[i])
        sheet3.write(0,i,row0[i])
        sheet4.write(0,i,row0[i])
        sheet5.write(0,i,row0[i])

    # 打开文件
    workbook = xlrd.open_workbook(file)
    sheet0 = workbook.sheet_by_index(0) # sheet索引从0开始
    cols = sheet0.col_values(cols_numb) # 获取列内容
    rows_list_1=[]
    rows_list_2=[]
    rows_list_3=[]
    rows_list_4=[]
    rows_list_5=[]
    for i in range(1,len(cols)):

        if cols[i] == '100012':
            rows_list_1.append(i)
        if cols[i] == '100014':
            rows_list_2.append(i)
        if cols[i] == '100007':
            rows_list_3.append(i)
        if cols[i] == '100016':
            rows_list_4.append(i)
        if cols[i] == '100017':
            print(i)
            print(type(cols[i]))
            rows_list_5.append(i)
    '''
    sheet1保存
    '''
    for j in range(0,len(rows_list_1)):
        rows = sheet0.row_values(rows_list_1[j]) # 获取第四行内容
        for i in range(0,len(rows)):
            sheet1.write(j+1,i,rows[i])
    '''
    sheet2保存
    '''

    for j in range(0,len(rows_list_2)):
        rows = sheet0.row_values(rows_list_2[j]) # 获取第四行内容
        for i in range(0,len(rows)):
            sheet2.write(j+1,i,rows[i])

    '''
    sheet3保存
    '''
    for j in range(0,len(rows_list_3)):
        rows = sheet0.row_values(rows_list_3[j]) # 获取第四行内容
        for i in range(0,len(rows)):
            sheet3.write(j+1,i,rows[i])
    '''
    sheet4保存
    '''
    for j in range(0,len(rows_list_4)):
        rows = sheet0.row_values(rows_list_4[j]) # 获取第四行内容
        for i in range(0,len(rows)):
            sheet4.write(j+1,i,rows[i])

    '''
    sheet5保存
    '''
    for j in range(0,len(rows_list_5)):
        rows = sheet0.row_values(rows_list_5[j]) # 获取第四行内容
        for i in range(0,len(rows)):
            sheet5.write(j+1,i,rows[i])

    f.save('./data/demo3.xls') #保存文件

if __name__ == '__main__':
    # 读取文件的行和列
    rows_numb=0
    cols_numb=7
    read_excel(rows_numb,cols_numb)
ChineseClean_demo4or5.py：
同ChineseClean_demo3.py
ChineseClean_answer_QA.py：
# -*- coding:utf-8 -*-
import re
import xlrd
file="./data/demo5.xls"
dirs="./result"

def read_excel(rows_numb,cols1_numb):
    number='1'
    f2 = open(dirs+'./demo5_sheet1_%s.csv'%number, 'a', encoding='utf-8')
    # 打开文件
    workbook = xlrd.open_workbook(file)
    sheet0 = workbook.sheet_by_index(int(number)-1) # sheet索引从0开始
    cols1 = sheet0.col_values(cols1_numb[3]) [1:]# 获取列内容

    p1 = r"(?:[\u2E80-\uFFFD]|[\u201c-\u201d]|[\u002d]|[\u003a])+"
    pattern1 = re.compile(p1)
    for i in range(len(cols1)):
        matcher1 = re.findall(pattern1, cols1[i])
        str1=str()
        if matcher1:
            str1 = ' '.join(matcher1)
            f2.write(str1)
        f2.write('\n')

    f2.close()

if __name__ == '__main__':
    # 读取文件的行和列
    rows_numb=0
    cols1_numb=[0,1,2,3,4,5,6,7]
    read_excel(rows_numb,cols1_numb)

qa_test_clean_word.py：
# -*- coding: utf-8 -*-

import jieba
# 创建停用词list
def stopwordslist(filepath):
    stopwords = [line.strip() for line in open(filepath, 'r', encoding='utf-8').readlines()]
    return stopwords

# 对句子进行分词
def seg_sentence(sentence):
    sentence_seged = jieba.lcut_for_search(sentence.strip(),HMM=True)
    stopwords = stopwordslist('./test/stopwords.txt')  # 这里加载停用词的路径
    outstr = ''
    for word in sentence_seged:
        if word not in stopwords:
            if word != '\t':
                outstr += word
                outstr += " "
    return(outstr)

inputs = open('./data/demo5_answer_csv/demo5_sheet5_5.csv', 'r', encoding='utf-8')
outputs = open('./test/demo5_sheet5_5_5.csv', 'w')
for line in inputs:
    line_seg = seg_sentence(line)
    try:
        if len(line_seg):
            outputs.write(line_seg + '\n')
    except:
        pass

outputs.close()
inputs.close()
word_fre.py:
# -*- coding: utf-8 -*-

import matplotlib.pyplot as plt
from matplotlib.font_manager import *
import numpy as np

def drawStatBarh():
    '''
    画出词频统计条形图，用渐变颜色显示，选取前N个词频
    '''
    fig, ax = plt.subplots()
    myfont = FontProperties(fname='./data/simfang.ttf')
    N = 30
    words = []
    counts = []
    for line in open('./data/word_fre.txt'):
        if line == '\n':
            continue
        line.strip('\n')

        words.append(line.split(' ')[0])
        print(line.split(' ')[0])
        # exit()
        counts.append(int(line.split(' ')[1].strip('\n')))

    y_pos = np.arange(N)

    colors = ['#FA8072'] #这里是为了实现条状的渐变效果，以该色号为基本色实现渐变效果
    for i in range(len(words[:N]) - 1):
        colors.append('#FA' + str(int(colors[-1][3:]) - 1))

    rects = ax.barh(y_pos, counts[:N], align='center', color=colors)

    ax.set_yticks(np.arange(N))
    ax.set_yticklabels(words[:N],fontproperties=myfont)
    ax.invert_yaxis()  # labels read top-to-bottom
    ax.set_title('报告中的高频词汇',fontproperties=myfont, fontsize=17)
    ax.set_xlabel(u"出现次数",fontproperties=myfont)

    autolabel(rects, ax)
    plt.show()

def autolabel(rects, ax):
    """
    给条形图加上文字标签
    """
    #fig, ax = plt.subplots()
    for rect in rects:
        width = rect.get_width()
        ax.text(1.03 * width, rect.get_y() + rect.get_height()/2.,
            '%d' % int(width),ha='center', va='center')

def wordCount(segment_list):
    '''
        该函数实现词频的统计，并将统计结果存储至本地。
        在制作词云的过程中用不到，主要是在画词频统计图时用到。
    '''
    word_lst = []
    word_dict = {}
    with open('./data/word_fre.txt','w') as wf2:
        word_lst.append(segment_list.split(' '))
        for item in word_lst:
            for item2 in item:
                if item2 not in word_dict:
                    word_dict[item2] = 1
                else:
                    word_dict[item2] += 1
        # print(type(word_dict))
        # print(word_dict)
        word_dict_sorted =list(sorted(word_dict.items(),key = lambda jj:jj[1],reverse=True))#list是关键，按照词频从大到小排序
        # word_dict_sorted = dict(sorted(word_dict.items(),key = lambda item:item[1], reverse=True))#按照词频从大到小排序
        print(word_dict_sorted)
        # exit()
        for tup in word_dict_sorted:
            # print(type(tup))
            # print(tup)
            # exit()
            if tup[0] != '':
                wf2.write(tup[0].strip('\n')+' '+str(tup[1])+'\n')
    wf2.close()

if __name__ == "__main__":
    segment_list_remove_stopwords=open('./data/demo5_sheet5_1_1.csv').read()
    wordCount(segment_list_remove_stopwords)
    drawStatBarh()
wordcloud_test2.py:
# - * - coding: utf - 8 -*-

from os import path
from scipy.misc import imread
import matplotlib.pyplot as plt
import jieba
# jieba.load_userdict("txt\userdict.txt")
# 添加用户词库为主词典,原词典变为非主词典
from wordcloud import WordCloud, ImageColorGenerator

# 获取当前文件路径
# __file__ 为当前文件, 在ide中运行此行会报错,可改为
# d = path.dirname('.')
d = path.dirname(__file__)

stopwords = {}
isCN = 1 #默认启用中文分词
back_coloring_path = "data/lz1.jpg" # 设置背景图片路径
text_path = 'data/demo5_sheet5_1_1.csv' #设置要分析的文本路径,讲原始文件转化为‘ANSI编码即可’
font_path = 'data/simfang.ttf' # 为matplotlib设置中文字体路径
stopwords_path = 'data/stopwords.txt' # 停用词词表
imgname1 = "data/WordCloudDefautColors.png" # 保存的图片名字1(只按照背景图片形状)
imgname2 = "data/WordCloudColorsByImg.png"# 保存的图片名字2(颜色按照背景图片颜色布局生成)

# my_words_list = ['CHENGLEI'] # 在结巴的词库中添加新词

back_coloring = imread(path.join(d, back_coloring_path))# 设置背景图片

# 设置词云属性
wc = WordCloud(font_path=font_path,  # 设置字体
               background_color="white",  # 背景颜色
               max_words=2000,  # 词云显示的最大词数
               mask=back_coloring,  # 设置背景图片
               max_font_size=100,  # 字体最大值
               random_state=42,
               width=1000, height=860, margin=2,# 设置图片默认的大小,但是如果使用背景图片的话,那么保存的图片大小将会按照其大小保存,margin为词语边缘距离
               )

# 添加自己的词库分词
# def add_word(list):
#     for items in list:
#         jieba.add_word(items)

# add_word(my_words_list)

text = open(path.join(d, text_path)).read()

# def jiebaclearText(text):
#     mywordlist = []
#     seg_list = jieba.cut(text, cut_all=False)
#     liststr="/ ".join(seg_list)
#     f_stop = open(stopwords_path)
#     try:
#         f_stop_text = f_stop.read( )
#         f_stop_text=unicode(f_stop_text,'utf-8')
#     finally:
#         f_stop.close( )
#     f_stop_seg_list=f_stop_text.split('\n')
#     for myword in liststr.split('/'):
#         if not(myword.strip() in f_stop_seg_list) and len(myword.strip())>1:
#             mywordlist.append(myword)
#     return ''.join(mywordlist)
#
# if isCN:
#     text = jiebaclearText(text)

# 生成词云, 可以用generate输入全部文本(wordcloud对中文分词支持不好,建议启用中文分词),也可以我们计算好词频后使用generate_from_frequencies函数
wc.generate(text)
# wc.generate_from_frequencies(text)
# txt_freq例子为[('词a', 100),('词b', 90),('词c', 80)]
# 从背景图片生成颜色值
image_colors = ImageColorGenerator(back_coloring)

plt.figure()
# 以下代码显示图片
plt.imshow(wc)
plt.axis("off")
plt.show()
# 绘制词云

# 保存图片
wc.to_file(path.join(d, imgname1))

image_colors = ImageColorGenerator(back_coloring)

plt.imshow(wc.recolor(color_func=image_colors))
plt.axis("off")
# 绘制背景图片为颜色的图片
plt.figure()
plt.imshow(back_coloring, cmap=plt.cm.gray)
plt.axis("off")
plt.show()
# 保存图片
wc.to_file(path.join(d, imgname2))

lda_test_ok.py：
# coding=utf-8        

import numpy as np
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer
import lda

def doc_topic_word():
    print(doc_topic[:, :3])#输出文档主题分布情况(前3列)
    print(topic_word[:, :3])#输出主题词分布情况(前3列)，采用ifidf计算词频

#导出分布图
def plot_1():
    # 计算各个主题中单词权重分布的情况
    f, ax= plt.subplots(2, 1, figsize=(6, 6), sharex=True)
    for i, k in enumerate([0, 9]):         #任意选择两个主题
        ax[i].stem(topic_word[k,:], linefmt='b-',
                   markerfmt='bo', basefmt='w-')
        ax[i].set_xlim(-2,2000)
        ax[i].set_ylim(0, 1)
        ax[i].set_ylabel("Prob")
        ax[i].set_title("topic {}".format(k)) 

    ax[1].set_xlabel("word")
    plt.tight_layout()
    plt.show()

def plot_2():
    # 计算文档具体分布在那个主题，代码如下所示：

    f, ax= plt.subplots(2, 1, figsize=(8, 8), sharex=True)
    for i, k in enumerate([0,9]):  #任意选择两个主题
        ax[i].stem(doc_topic[k,:], linefmt='r-',
                   markerfmt='ro', basefmt='w-')
        ax[i].set_xlim(-1, 20)     #x坐标下标,即主题的取值范围
        ax[i].set_ylim(0, 1.2)    #y坐标下标
        ax[i].set_ylabel("Prob")
        ax[i].set_title("Document {}".format(k))
    ax[1].set_xlabel("Topic")
    plt.tight_layout()
    plt.show() 

if __name__ == "__main__":

    #存储读取语料 一行预料为一个文档
    corpus = []
    for line in open('./data/demo5_sheet5_1_1.csv', 'r').readlines():
        corpus.append(line.strip())

    #将文本中的词语转换为词频矩阵 矩阵元素a[i][j] 表示j词在i类文本下的词频
    vectorizer = CountVectorizer()
    print (vectorizer)

    X = vectorizer.fit_transform(corpus)
    analyze = vectorizer.build_analyzer()
    weight = X.toarray()
    print("type(X): {}".format(type(X)))
    print("shape: {}\n".format(X.shape))
    print (len(weight))
    print (weight[:5, :5])

    #LDA算法
    print ('LDA:')
    model = lda.LDA(n_topics=20, n_iter=50, random_state=1)
    # model.fit_transform(X)
    model.fit(np.asarray(weight))     # model.fit_transform(X) is also available?
    topic_word = model.topic_word_    # model.components_ also works

    #文档-主题（Document-Topic）分布
    doc_topic = model.doc_topic_
    print("type(doc_topic): {}".format(type(doc_topic)))
    print("shape: {}".format(doc_topic.shape))

    #输出前10篇文章最可能的Topic
    label = []
    for n in range(10):
        topic_most_pr = doc_topic[n].argmax()
        label.append(topic_most_pr)
        print("doc: {} topic: {}".format(n, topic_most_pr))

    #输出主题中的TopN关键词
    word = vectorizer.get_feature_names()
    n = 6
    for i, topic_dist in enumerate(topic_word):
        topic_words = np.array(word)[np.argsort(topic_dist)][:-(n+1):-1]
        print(u'*Topic {}\n- {}'.format(i, ' '.join(topic_words))) 

    # doc_topic_word()
    # plot_1()
    plot_2()

gensimTopicTest0803.py:
# coding=utf-8
import re
import xlrd
import codecs
import jieba
from gensim import corpora, models, similarities

FILE="demo5"#选择要训练的文件
ID='1'#选择要训练的能力ID

# 读取停用词表
stopwords = [line.strip() for line in codecs.open('./data/stopwords.txt', 'r', encoding='utf-8').readlines()]

def cleanAnswer(cols_numb):

    f1 = open('./result/%s_sheet%s.csv'%(FILE,ID), 'a', encoding='utf-8')
    # 打开文件
    workbook = xlrd.open_workbook('./data/%s.xls'%FILE)
    #根据sheet索引或者名称获取sheet内容
    sheet0 = workbook.sheet_by_index(int(ID)-1) # sheet索引从0开始
    cols1 = sheet0.col_values(cols_numb[3])[1:]# 获取第三列内容,从第一行开始

    p1 = r"(?:[\u2E80-\uFFFD]|[\u201c-\u201d]|[\u002d]|[\u003a])+"#(?:）不获取匹配，即不获取括号内的匹配，括号内使用UNICODE编码匹配
    pattern1 = re.compile(p1)
    for i in range(len(cols1)):
        matcher1 = re.findall(pattern1, cols1[i])#以列表形式返回所有能匹配到的子串
        str1=str()
        if matcher1:
            str1 =''.join(matcher1)
            f1.write(str1.strip())
        f1.write('\n')
    f1.close()

def ldaAnaly():

    print("构造分词库-----train-----")
    #去停用词，构建分词库
    train = []
    fp = codecs.open('./result/%s_sheet%s.csv'%(FILE,ID),'r',encoding='utf8')
    for line in fp.readlines():
        line = line.strip()
        if not len(line):#判断是否为空行
            continue
        outstr = ' '
        seg_list =jieba.cut(line,cut_all=False)#采用精确模式分词，效果最好
        for word in seg_list:
            if word not in stopwords:
                if word != '\t':
                    outstr += word
                    outstr += " "
        train.append(outstr.strip().split(" "))#字符串转列表
    fp.close()

    print("构造分词库,并保存----“dict_v1.dict”----")
    dic = corpora.Dictionary(train)
    dic.save('./result/dict_v1.dict')

    print("保存可读取的分词库----“dic.csv”----")
    fd = codecs.open('./result/dic.csv', 'a',encoding = 'utf-8')
    for word,index in dic.token2id.items():
        fd.write(word +':'+ str(index)+'\n')
    fd.close()

    print("生成语料库，并保存-----“corpus.mm”-----")
    corpus = [dic.doc2bow(text) for text in train]
    corpora.MmCorpus.serialize('./result/corpus.mm', corpus)

    print("保存tfidf模型-----“corpus.tfidf_model”-----")
    tfidf = models.TfidfModel(corpus)
    tfidf.save('./result/corpus.tfidf_model')

    print("进行LDA主题分析，并保存-----“ldaModel.pkl”-----")
    #使用tf-idf模型训练语料库
    corpus_tfidf = tfidf[corpus]
    #设置100个LDA主题，使用500次迭代
    lda = models.LdaModel(corpus_tfidf, id2word=dic, num_topics=100, iterations=500 )
    lda.save('./result/ldaModel.pkl')

    print("评估文章属于不同主题的概率，一个词对文章的重要性-----“Demo：评估文章1”-----")
    for index, score in sorted(lda[corpus_tfidf[0]], key=lambda tup: -1 * tup[1]):
        print("Score: {}\t Topic: {}".format(score, lda.print_topic(index, 10)))

    # 输出100个主题
    # ldaOut = lda.print_topics(100)
    # print("默认返回每个主题的前10的概率最大的词")
    # print (ldaOut[0])
    # print (ldaOut[1])
    # print (ldaOut[2])
    # corpus_lda = lda[corpus_tfidf]
    # print("每篇文章属于不同主题的概率分布")
    # k = 0
    # for doc in corpus_lda:
    #     print(doc)
    #     k += 1
    #     if k == 3:
    #         break

def questionAnswer(cols_numb, questionNumber):
    lda = models.LdaModel.load('./result/ldaModel.pkl')
    dic = corpora.Dictionary.load('./result/dict_v1.dict')
    corpus = corpora.MmCorpus('./result/corpus.mm')
    tfidf = models.TfidfModel.load('./result/corpus.tfidf_model')

    # print("输入一个问题------------------")
    f1 = open('./result/%s_sheet%s.csv'%(FILE,ID), 'a', encoding='utf-8')
    # 打开文件
    workbook = xlrd.open_workbook('./data/%s.xls'%FILE)
    sheet0 = workbook.sheet_by_index(int(ID)-1) # sheet索引从0开始
    cols0 = sheet0.col_values(cols_numb[3])[questionNumber]  # 获取第三列内容,从第一行开始

    #对问题进行去乱码
    p1 = r"(?:[\u2E80-\uFFFD]|[\u201c-\u201d]|[\u002d]|[\u003a])+"#(?:）不获取匹配，即不获取括号内的匹配，括号内使用UNICODE编码匹配
    pattern1 = re.compile(p1)
    matcher1 = re.findall(pattern1, cols0)#以列表形式返回所有能匹配到的子串
    query=str()
    if matcher1:
        query =''.join(matcher1)
    # print("待预测的问题(去乱码)：", query)

    #对问题进行分词
    seg_list = jieba.cut(query, cut_all=False)
    outstr = ' '
    for word in seg_list:
        if word not in stopwords:
            if word != '\t':
                outstr += word
                outstr += " "
    inputTest=list(outstr.strip().split(" "))
    # print("分词后的问题(去停用词)：", inputTest)

    #将问题转成词袋
    query_bow = dic.doc2bow(inputTest)
    # print("生成的词袋：", query_bow)

    #需要对查询语句进行tfidf转化
    query_tfidf = tfidf[query_bow]
    lda_vec_tfidf = lda[query_tfidf]
    # print("问题对应的主题概率(tfidf)", lda_vec_tfidf)

    # print("预测问题属于不同主题的概率--------------------")
    #输出主题概率的代码
    # for index, score in sorted(lda_vec_tfidf, key=lambda tup: -1 * tup[1]):
    #     print("Score: {}\t Topic: {}".format(score, lda.print_topic(index, 20)))

    # print("预测问题与数据库中的哪些问题相似，并给出相似度排序(tfidf)--------------------")
    #进行相似性检索
    similarity = similarities.MatrixSimilarity(corpus)

    #在TFIDF的基础上，进行相似性检测。query_lsi需要进行预先处理。先变化为dow2bow,然后tfidf.
    lda_vec = lda[query_bow]

    # sims = similarity[lda_vec] #相似度检测的词袋为no-tfidf
    sims = similarity[lda_vec_tfidf] #相似度检测的词袋为tfidf

    #先枚举出来，后进行排序输出
    listSims = enumerate(sims)
    sort_sims = sorted(listSims, key=lambda item: -item[1])
    # print(sort_sims[0:6])#前n名效果最好

    #进行分数预测--版本1---
    sort_sims_list = sort_sims[0:6]
    cols1 = sheet0.col_values(cols_numb[6])[1:]# 获取第三列内容,从第一行开始
    f1.close()

    #采用百分比形式的加权平均法，实质就是加权平均偏差法
    sumCore1 = 0
    sumPro = 0
    for i in range(len(sort_sims_list)):
        sumCore1 += float(cols1[sort_sims_list[i][0] - 1]) * sort_sims_list[i][1]
        # print(cols1[sort_sims_list[i][0] - 1])
        sumPro += sort_sims_list[i][1]

    preCore1 = sumCore1 / sumPro
    # print("采用加权平均偏差法，预测分数1为：%s,实际分数为%s"%(preCore1, cols1[questionNumber-1]))

    print("保存预测结果----“pre.csv”----")
    return preCore1, cols1[questionNumber-1], abs(preCore1 - float(cols1[questionNumber-1]))

if __name__ == '__main__':

    cols_numb = [0,1,2,3,4,5,6,7] #读取文件的列标号
    # questionNumber = 124 #待测试的问题号，最大不超过问题总数，主要用于测试
    # cleanAnswer(cols_numb) #对数据库中的问题进行提取，并去乱码
    # ldaAnaly() #对问题进行训练，生成主题模型
    # questionAnswer(cols_numb, questionNumber) #对问题进行预测，给出预测分数

    #循环预测的demo
    fp = codecs.open('./result/pre_v1.csv', 'a', encoding='utf-8')
    sum = 0
    i = 1
    count = 0
    while( i < 8717 ):
        questionNumber = i
        a = questionAnswer(cols_numb, questionNumber)
        sum += a[2]
        # print(a, a[2])
        # exit()
        i += 8
        count += 1
        fp.write(str(i)+":"+str(a) + '\n')
    fp.close()

    ave = sum / count
    print(ave)