python实现的LDA算法_python lda

实现LDA算法需要用到一些数学和概率统计的知识，你需要根据LDA算法的具体公式，实现初始化模型参数、Gibbs采样、模型参数更新等具体的步骤。同时，还需要读取训练文件和词典文件，以及保存模型到文件的功能。

理解LDA算法的实现思路涉及到以下关键步骤：

初始化模型参数：
设置主题数（K）, 超参数alpha, beta。
初始化文档-主题分布 (theta) 和 主题-词汇分布 (phi)。

读取文档数据，每行为一个文档，分词后用空格隔开。
构建词典，将每个词映射到唯一的整数。

class LDA:
    def __init__(self, alpha, beta, K, iter_num, top_words, wordmapfile, trnfile, modelfile_suffix):
        # ...

    def read_and_build_dictionary(self):
        # Read training file and build vocabulary
        # Implement code to read and build dictionary...
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初始化文档-主题分布和主题-词汇分布：
为每个文档中的每个词随机分配一个主题。
根据分配的主题，初始化文档-主题分布和主题-词汇分布。

class LDA:
    def __init__(self, alpha, beta, K, iter_num, top_words, wordmapfile, trnfile, modelfile_suffix):
        # ...

    def initialize(self):
        # ...

        # Initialize document-topic and topic-word distributions
        self.theta = np.random.dirichlet([self.alpha] * self.K, size=len(self.documents))
        self.phi = np.random.dirichlet([self.beta] * len(self.vocabulary), size=self.K)
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Gibbs采样：
对每个文档中的每个词进行Gibbs采样。
在采样过程中，考虑当前文档-主题分布、主题-词汇分布以及词汇的分配情况。

class LDA:
    def __init__(self, alpha, beta, K, iter_num, top_words, wordmapfile, trnfile, modelfile_suffix):
        # ...

    def gibbs_sampling(self):
        # Implement Gibbs sampling algorithm...

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更新模型参数：
根据采样得到的文档-主题分布和主题-词汇分布，更新模型的参数。
使用迭代方法逐步调整参数。

class LDA:
    def __init__(self, alpha, beta, K, iter_num, top_words, wordmapfile, trnfile, modelfile_suffix):
        # ...

    def update_model_parameters(self):
        # Update model parameters based on Gibbs sampling results
        # Implement parameter update code...

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输出每个主题的前top_words个词：
根据学习到的主题-词汇分布，输出每个主题的前top_words个词，以便观察主题的含义。

class LDA:
    def __init__(self, alpha, beta, K, iter_num, top_words, wordmapfile, trnfile, modelfile_suffix):
        # ...

    def print_top_words_per_topic(self):
        # Output top_words words for each topic based on learned phi
        # Implement code to print top words...

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保存模型：
将学习到的模型参数保存到文件，以备后续使用。

 class LDA:
    def __init__(self, alpha, beta, K, iter_num, top_words, wordmapfile, trnfile, modelfile_suffix):
        # ...

    def save_model(self):
        # Save model parameters, theta, phi, etc. to files
        # Implement code to save model...

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实际实现中需要考虑数学计算的优化、数据结构的选择、算法的效率等方面的问题。详细的公式和算法细节可以参考LDA的相关文献。在实现过程中，需要使用numpy等工具进行矩阵运算，以提高效率。

实例：

alpha = 0.1

beta = 0.1

K = 10 //主题个数

iter_num = 50 //迭代次数

top_words = 20 //每个主题显示的词的个数

wordmapfile = ‘./model/wordmap.txt’ //wordmap文件存储位置

trnfile = “./model/test.dat” //训练文件

modelfile_suffix = “./model/final” //模型文件的存储位置以及前缀 ‘’’

输入文件的要求： 每行为一篇文档，分词后用空格隔开。

运行命令：

‘’’ python lda.py ‘’’

#!/usr/bin/env python
# -*- coding:utf-8 -*-

import random,os

alpha = 0.1
beta = 0.1
K = 10
iter_num = 50
top_words = 20

wordmapfile  = './model/wordmap.txt'
trnfile = "./model/test.dat"
modelfile_suffix = "./model/final"

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

class Dataset(object):
    def __init__(self):
        self.M = 0
        self.V = 0
        self.docs = []
        self.word2id = {}    # <string,int>字典
        self.id2word = {}    # <int, string>字典

    def writewordmap(self):
        with open(wordmapfile, 'w') as f:
            for k,v in self.word2id.items():
                f.write(k + '\t' + str(v) + '\n')

class Model(object):
    def __init__(self, dset):
        self.dset = dset

        self.K = K
        self.alpha = alpha
        self.beta = beta
        self.iter_num = iter_num
        self.top_words = top_words

        self.wordmapfile = wordmapfile
        self.trnfile = trnfile
        self.modelfile_suffix = modelfile_suffix

        self.p = []        # double类型，存储采样的临时变量
        self.Z = []        # M*doc.size()，文档中词的主题分布
        self.nw = []       # V*K，词i在主题j上的分布
        self.nwsum = []    # K，属于主题i的总词数
        self.nd = []       # M*K，文章i属于主题j的词个数
        self.ndsum = []    # M，文章i的词个数
        self.theta = []    # 文档-主题分布
        self.phi = []      # 主题-词分布

    def init_est(self):
        self.p = [0.0 for x in xrange(self.K)]
        self.nw = [ [0 for y in xrange(self.K)] for x in xrange(self.dset.V) ]
        self.nwsum = [ 0 for x in xrange(self.K)]
        self.nd = [ [ 0 for y in xrange(self.K)] for x in xrange(self.dset.M)]
        self.ndsum = [ 0 for x in xrange(self.dset.M)]
        self.Z = [ [] for x in xrange(self.dset.M)]
        for x in xrange(self.dset.M):
            self.Z[x] = [0 for y in xrange(self.dset.docs[x].length)]
            self.ndsum[x] = self.dset.docs[x].length
            for y in xrange(self.dset.docs[x].length):
                topic = random.randint(0, self.K-1)
                self.Z[x][y] = topic
                self.nw[self.dset.docs[x].words[y]][topic] += 1
                self.nd[x][topic] += 1
                self.nwsum[topic] += 1
        self.theta = [ [0.0 for y in xrange(self.K)] for x in xrange(self.dset.M) ]
        self.phi = [ [ 0.0 for y in xrange(self.dset.V) ] for x in xrange(self.K)]

    def estimate(self):
        print 'Sampling %d iterations!' % self.iter_num
        for x in xrange(self.iter_num):
            print 'Iteration %d ...' % (x+1)
            for i in xrange(len(self.dset.docs)):
                for j in xrange(self.dset.docs[i].length):
                    topic = self.sampling(i, j)
                    self.Z[i][j] = topic
        print 'End sampling.'
        print 'Compute theta...'
        self.compute_theta()
        print 'Compute phi...'
        self.compute_phi()
        print 'Saving model...'
        self.save_model()

    def sampling(self, i, j):
        topic = self.Z[i][j]
        wid = self.dset.docs[i].words[j]
        self.nw[wid][topic] -= 1
        self.nd[i][topic] -= 1
        self.nwsum[topic] -= 1
        self.ndsum[i] -= 1

        Vbeta = self.dset.V * self.beta
        Kalpha = self.K * self.alpha

        for k in xrange(self.K):
            self.p[k] = (self.nw[wid][k] + self.beta)/(self.nwsum[k] + Vbeta) * \
                        (self.nd[i][k] + 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 xrange(self.K):
            if self.p[topic]>u:
                break
        self.nw[wid][topic] += 1
        self.nwsum[topic] += 1
        self.nd[i][topic] += 1
        self.ndsum[i] += 1
        return topic

    def compute_theta(self):
        for x in xrange(self.dset.M):
            for y in xrange(self.K):
                self.theta[x][y] = (self.nd[x][y] + self.alpha) \
                                   /(self.ndsum[x] + self.K * self.alpha)

    def compute_phi(self):
        for x in xrange(self.K):
            for y in xrange(self.dset.V):
                self.phi[x][y] = (self.nw[y][x] + self.beta)\
                                 /(self.nwsum[x] + self.dset.V * self.beta)

    def save_model(self):
        with open(self.modelfile_suffix+'.theta', 'w') as ftheta:
            for x in xrange(self.dset.M):
                for y in xrange(self.K):
                    ftheta.write(str(self.theta[x][y]) + ' ')
                ftheta.write('\n')
        with open(self.modelfile_suffix+'.phi', 'w') as fphi:
            for x in xrange(self.K):
                for y in xrange(self.dset.V):
                    fphi.write(str(self.phi[x][y]) + ' ')
                fphi.write('\n')
        with open(self.modelfile_suffix+'.twords','w') as ftwords:
            if self.top_words > self.dset.V:
                self.top_words = self.dset.V
            for x in xrange(self.K):
                ftwords.write('Topic '+str(x)+'th:\n')
                topic_words = []
                for y in xrange(self.dset.V):
                    topic_words.append((y, self.phi[x][y]))
                #quick-sort
                topic_words.sort(key=lambda x:x[1], reverse=True)
                for y in xrange(self.top_words):
                    word = self.dset.id2word[topic_words[y][0]]
                    ftwords.write('\t'+word+'\t'+str(topic_words[y][1])+'\n')
        with open(self.modelfile_suffix+'.tassign','w') as ftassign:
            for x in xrange(self.dset.M):
                for y in xrange(self.dset.docs[x].length):
                    ftassign.write(str(self.dset.docs[x].words[y])+':'+str(self.Z[x][y])+' ')
                ftassign.write('\n')
        with open(self.modelfile_suffix+'.others','w') as fothers:
            fothers.write('alpha = '+str(self.alpha)+'\n')
            fothers.write('beta = '+str(self.beta)+'\n')
            fothers.write('ntopics = '+str(self.K)+'\n')
            fothers.write('ndocs = '+str(self.dset.M)+'\n')
            fothers.write('nwords = '+str(self.dset.V)+'\n')
            fothers.write('liter = '+str(self.iter_num)+'\n')

def readtrnfile():
    print 'Reading train data...'
    with open(trnfile, 'r') as f:
        docs = f.readlines()

    dset = Dataset()
    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 dset.word2id.has_key(item):
                    doc.words.append(dset.word2id[item])
                else:
                    dset.word2id[item] = items_idx
                    dset.id2word[items_idx] = item
                    doc.words.append(items_idx)
                    items_idx += 1
            doc.length = len(tmp)
            dset.docs.append(doc)
        else:
            pass
    dset.M = len(dset.docs)
    dset.V = len(dset.word2id)
    print 'There are %d documents' % dset.M
    print 'There are %d items' % dset.V
    print 'Saving wordmap file...'
    dset.writewordmap()
    return dset

def lda():
    dset = readtrnfile()
    model = Model(dset)
    model.init_est()
    model.estimate()

if __name__=='__main__':
    lda()
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