推荐系统 - LDA模型 - python3 实现

#-*- coding:utf-8 -*-
import logging
import logging.config
import configparser as ConfigParser # python 3
# import ConfigParser # python 2
import numpy as np
import random
import codecs
import os

from collections import OrderedDict
#获取当前路径
path = os.getcwd()

logging.config.fileConfig('logging.conf')
#创建日志对象
logger = logging.getLogger()
# loggerInfo = logging.getLogger('TimeInfoLogger')
# Consolelogger = logging.getLogger('ConsoleLogger')

#导入配置文件
conf = ConfigParser.ConfigParser()
conf.read('setting.conf') 
#文件路径
trainfile = os.path.join(path,os.path.normpath(conf.get('filepath', 'trainfile')))
wordidmapfile = os.path.join(path,os.path.normpath(conf.get('filepath','wordidmapfile')))
thetafile = os.path.join(path,os.path.normpath(conf.get('filepath','thetafile')))
phifile = os.path.join(path,os.path.normpath(conf.get('filepath','phifile')))
paramfile = os.path.join(path,os.path.normpath(conf.get('filepath','paramfile')))
topNfile = os.path.join(path,os.path.normpath(conf.get('filepath','topNfile')))
tassginfile = os.path.join(path,os.path.normpath(conf.get('filepath','tassginfile')))
#模型初始参数
K = int(conf.get('model_args','K'))
alpha = float(conf.get('model_args','alpha'))
beta = float(conf.get('model_args','beta'))
iter_times = int(conf.get('model_args','iter_times'))
top_words_num = int(conf.get('model_args','top_words_num'))

class Document(object):
    def __init__(self):
        self.words = []
        self.length = 0

class DataPreProcessing(object):

    def __init__(self):
        self.docs_count = 0
        self.words_count = 0
        self.docs = []
        self.word2id = OrderedDict()

    def cachewordidmap(self):
        with codecs.open(wordidmapfile, 'w','utf-8') as f:
            for word,id in self.word2id.items():
                f.write(word +'\t'+str(id)+'\n')

class LDAModel(object):
    
    def __init__(self,dpre):

        self.dpre = dpre #获取预处理参数

        #
        #模型参数
        #聚类个数K，迭代次数iter_times,每个类特征词个数top_words_num,超参数α（alpha） β(beta)
        #
        self.K = K
        self.beta = beta
        self.alpha = alpha
        self.iter_times = iter_times
        self.top_words_num = top_words_num 
        #
        #文件变量
        #分好词的文件trainfile
        #词对应id文件wordidmapfile
        #文章-主题分布文件thetafile
        #词-主题分布文件phifile
        #每个主题topN词文件topNfile
        #最后分派结果文件tassginfile
        #模型训练选择的参数文件paramfile
        #
        self.wordidmapfile = wordidmapfile
        self.trainfile = trainfile
        self.thetafile = thetafile
        self.phifile = phifile
        self.topNfile = topNfile
        self.tassginfile = tassginfile
        self.paramfile = paramfile
        # p,概率向量 double类型，存储采样的临时变量
        # nw,词word在主题topic上的分布
        # nwsum,每各topic的词的总数
        # nd,每个doc中各个topic的词的总数
        # ndsum,每各doc中词的总数
        self.p = np.zeros(self.K)        
        self.nw = np.zeros((self.dpre.words_count,self.K),dtype='int')       
        self.nwsum = np.zeros(self.K,dtype='int')    
        self.nd = np.zeros((self.dpre.docs_count,self.K),dtype='int')       
        self.ndsum = np.zeros(dpre.docs_count,dtype='int')
        # M*doc.size()，文档中词的主题分布
        self.Z = np.array([ [0 for y in range(dpre.docs[x].length)] for x in range(dpre.docs_count)])

        #随机先分配类型
        for x in range(len(self.Z)):
            self.ndsum[x] = self.dpre.docs[x].length
            for y in range(self.dpre.docs[x].length):
                topic = random.randint(0,self.K-1)
                self.Z[x][y] = topic
                self.nw[self.dpre.docs[x].words[y]][topic] += 1
                self.nd[x][topic] += 1
                self.nwsum[topic] += 1

        self.theta = np.array([ [0.0 for y in range(self.K)] for x in range(self.dpre.docs_count) ])
        self.phi = np.array([ [ 0.0 for y in range(self.dpre.words_count) ] for x in range(self.K)])
    def sampling(self,i,j):

        topic = self.Z[i][j]
        word = self.dpre.docs[i].words[j]
        self.nw[word][topic] -= 1
        self.nd[i][topic] -= 1
        self.nwsum[topic] -= 1
        self.ndsum[i] -= 1

        Vbeta = self.dpre.words_count * self.beta
        Kalpha = self.K * self.alpha
        self.p = (self.nw[word] + self.beta)/(self.nwsum + Vbeta) * \
                 (self.nd[i] + self.alpha) / (self.ndsum[i] + Kalpha)
        for k in range(1,self.K):
            self.p[k] += self.p[k-1]

        u = random.uniform(0,self.p[self.K-1])
        for topic in range(self.K):
            if self.p[topic]>u:
                break

        self.nw[word][topic] +=1
        self.nwsum[topic] +=1
        self.nd[i][topic] +=1
        self.ndsum[i] +=1

        return topic
    def est(self):
        # Consolelogger.info(u'迭代次数为%s 次' % self.iter_times)
        for x in range(self.iter_times):
            for i in range(self.dpre.docs_count):
                for j in range(self.dpre.docs[i].length):
                    topic = self.sampling(i,j)
                    self.Z[i][j] = topic
        logger.info(u'迭代完成。')
        logger.debug(u'计算文章-主题分布')
        self._theta()
        logger.debug(u'计算词-主题分布')
        self._phi()
        logger.debug(u'保存模型')
        self.save()
    def _theta(self):
        for i in range(self.dpre.docs_count):
            self.theta[i] = (self.nd[i]+self.alpha)/(self.ndsum[i]+self.K * self.alpha)
    def _phi(self):
        for i in range(self.K):
            self.phi[i] = (self.nw.T[i] + self.beta)/(self.nwsum[i]+self.dpre.words_count * self.beta)
    def save(self):
        #保存theta文章-主题分布
        logger.info(u'文章-主题分布已保存到%s' % self.thetafile)
        with codecs.open(self.thetafile,'w') as f:
            for x in range(self.dpre.docs_count):
                for y in range(self.K):
                    f.write(str(self.theta[x][y]) + '\t')
                f.write('\n')
        #保存phi词-主题分布
        logger.info(u'词-主题分布已保存到%s' % self.phifile)
        with codecs.open(self.phifile,'w') as f:
            for x in range(self.K):
                for y in range(self.dpre.words_count):
                    f.write(str(self.phi[x][y]) + '\t')
                f.write('\n')
        #保存参数设置
        logger.info(u'参数设置已保存到%s' % self.paramfile)
        with codecs.open(self.paramfile,'w','utf-8') as f:
            f.write('K=' + str(self.K) + '\n')
            f.write('alpha=' + str(self.alpha) + '\n')
            f.write('beta=' + str(self.beta) + '\n')
            f.write(u'迭代次数  iter_times=' + str(self.iter_times) + '\n')
            f.write(u'每个类的高频词显示个数  top_words_num=' + str(self.top_words_num) + '\n')
        #保存每个主题topic的词
        logger.info(u'主题topN词已保存到%s' % self.topNfile)

        with codecs.open(self.topNfile,'w','utf-8') as f:
            self.top_words_num = min(self.top_words_num,self.dpre.words_count)
            for x in range(self.K):
                f.write(u'第' + str(x) + u'类：' + '\n')
                twords = []
                twords = [(n,self.phi[x][n]) for n in range(self.dpre.words_count)]
                twords.sort(key = lambda i:i[1], reverse= True)
                for y in range(self.top_words_num):
                    word = OrderedDict({value:key for key, value in self.dpre.word2id.items()})[twords[y][0]]
                    f.write('\t'*2+ word +'\t' + str(twords[y][1])+ '\n')
        #保存最后退出时，文章的词分派的主题的结果
        logger.info(u'文章-词-主题分派结果已保存到%s' % self.tassginfile)
        with codecs.open(self.tassginfile,'w') as f:
            for x in range(self.dpre.docs_count):
                for y in range(self.dpre.docs[x].length):
                    f.write(str(self.dpre.docs[x].words[y])+':'+str(self.Z[x][y])+ '\t')
                f.write('\n')
        logger.info(u'模型训练完成。')



def preprocessing():
    logger.info(u'载入数据......')
    with codecs.open(trainfile, 'r','utf-8') as f:
        docs = f.readlines()
    logger.debug(u'载入完成,准备生成字典对象和统计文本数据...')
    dpre = DataPreProcessing()
    items_idx = 0
    for line in docs:
        if line != '':
            tmp = line.strip().split()
            #生成一个文档对象
            doc = Document()
            for item in tmp:
                # if dpre.word2id.has_key(item):  # python 3 已没有这样的用法，用下边的方法。
                if item in dpre.word2id:
                    doc.words.append(dpre.word2id[item])
                else:
                    dpre.word2id[item] = items_idx
                    doc.words.append(items_idx)
                    items_idx += 1
            doc.length = len(tmp)
            dpre.docs.append(doc)
        else:
            pass
    dpre.docs_count = len(dpre.docs)
    dpre.words_count = len(dpre.word2id)
    logger.info(u'共有%s个文档' % dpre.docs_count)
    dpre.cachewordidmap()
    logger.info(u'词与序号对应关系已保存到%s' % wordidmapfile)
    return dpre

def run():
    dpre = preprocessing()
    lda = LDAModel(dpre)
    lda.est()
    

if __name__ == '__main__':
    run()