Pseudo-document-based Topic Model(基于伪文档的主题模型)的理解以及源码解读

原文地址:https://blog.csdn.net/qy20115549/article/details/79877825

本文作者：合肥工业大学管理学院钱洋 email：1563178220@qq.com 内容可能有不到之处，欢迎交流。

未经本人允许禁止转载。 #论文来源

Zuo Y, Wu J, Zhang H, et al. Topic modeling of short texts: A pseudo-document view[C]//Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, 2016: 2105-2114. #论文简介

package main; import java.io.BufferedReader; import java.io.BufferedWriter; import java.io.File; import java.io.IOException; import java.util.ArrayList; import java.util.Collections; import java.util.Comparator; import java.util.HashMap; import java.util.List; import java.util.Map; import java.util.Map.Entry; public class PseudoDocTM implements Runnable { public int K1 = 1000; //设置伪文档数量 public int K2 = 100; // public int M; public int V; public double alpha1 = 0.1; public double alpha2 = 0.1; public double beta = 0.01; public int mp[]; //分配到每个伪文档文档的数量 public int npk[][]; //伪文档l由主题k生成的单词数量 public int npkSum[]; //伪文档对应的单词总数 public int nkw[][]; //主题k对应的单词w的数量 public int nkwSum[]; //主题k对应的单词总数 public int zAssigns_1[]; //文档分配伪文档 public int zAssigns_2[][]; //文档单词分配主题 public int niters = 200; public int saveStep = 1000; public String inputPath=""; public String outputPath=""; public int innerSteps = 10; public List<List<Integer>> docs = new ArrayList<List<Integer>>(); //文档表示 public HashMap<String, Integer> w2i = new HashMap<String, Integer>(); //词的编号 public HashMap<Integer, String> i2w = new HashMap<Integer, String>(); //编号转化为词 public PseudoDocTM(int P,int K,int iter,int innerStep,int saveStep,double alpha1,double alpha2,double beta,String inputPath,String outputPath){ this.K1=P; this.K2=K; this.niters=iter; this.innerSteps= innerStep; this.saveStep =saveStep; this.alpha1=alpha1; this.alpha2= alpha2; this.beta = beta; this.inputPath=inputPath; this.outputPath=outputPath; } //加载语料 public void loadTxts(String txtPath) { BufferedReader reader = IOUtil.getReader(txtPath, "UTF-8"); String line; try { line = reader.readLine(); while (line != null) { List<Integer> doc = new ArrayList<Integer>(); String[] tokens = line.trim().split("\\s+"); for (String token : tokens) { if (!w2i.containsKey(token)) { w2i.put(token, w2i.size()); i2w.put(w2i.get(token), token); } doc.add(w2i.get(token)); } docs.add(doc); line = reader.readLine(); } reader.close(); } catch (IOException e) { e.printStackTrace(); } //文档数量 M = docs.size(); //语料词的数量 V = w2i.size(); return; } //初始化模型 public void initModel() { mp = new int[K1]; npk = new int[K1][K2]; npkSum = new int[K1]; nkw = new int[K2][V]; nkwSum = new int[K2]; zAssigns_1 = new int[M]; //文档所属的伪文档 zAssigns_2 = new int[M][]; //文档每个单词所属的主题 for (int m = 0; m != M; m++) { //文档单词的数量 int N = docs.get(m).size(); //初始化 zAssigns_2[m] = new int[N]; //随机分配文档所属的伪文档 int z1 = (int) Math.floor(Math.random()*K1); zAssigns_1[m] = z1; mp[z1] ++; //伪文档对应的文本数量增加 //对每个单词随机分配主题 for (int n = 0; n != N; n++) { int w = docs.get(m).get(n); int z2 = (int) Math.floor(Math.random()*K2); npk[z1][z2] ++; npkSum[z1] ++; nkw[z2][w] ++; nkwSum[z2] ++; zAssigns_2[m][n] = z2; } } } //抽取文档所属的伪文档 public void sampleZ1(int m) { int z1 = zAssigns_1[m]; //获取文档所属的伪文档 int N = docs.get(m).size(); //获取文档单词的数量 mp[z1] --; //移除该文档，伪文档z1对应的单词数量减少 Map<Integer, Integer> k2Count = new HashMap<Integer, Integer>(); for (int n = 0; n != N; n++){ //循环文档的每个单词 int z2 = zAssigns_2[m][n]; //获取单词的主题分配 if (k2Count.containsKey(z2)) { //计算每个主题包含该文档单词的总数量 k2Count.put(z2, k2Count.get(z2)+1); } else { k2Count.put(z2, 1); } npk[z1][z2] --; npkSum[z1] --; } double k2Alpha2 = K2 * alpha2; //分母的K*alpha double[] pTable = new double[K1]; //循环每个伪文档 for (int k = 0; k != K1; k++) { double expectTM = 1.0; int index = 0; //这里要计算单词的频次，进行连乘 for (int z2 : k2Count.keySet()) { int c = k2Count.get(z2); for (int i = 0; i != c; i++) { expectTM *= (npk[k][z2] + alpha2 + i) / (k2Alpha2 + npkSum[k] + index); index ++; } } //基于公式计算概率 pTable[k] = (mp[k] + alpha1) / (M + K1 * alpha1) * expectTM; } //轮盘赌选择 for (int k = 1; k != K1; k++) { //这里注意k=1开始，不能k=0 pTable[k] += pTable[k-1]; } double r = Math.random() * pTable[K1-1]; for (int k = 0; k != K1; k++) { if (pTable[k] > r) { z1 = k; break; } } //基于轮盘赌选择的伪文档，重新统计 mp[z1] ++; for (int n =0; n != N; n++) { int z2 = zAssigns_2[m][n]; npk[z1][z2] ++; npkSum[z1] ++; } zAssigns_1[m] = z1; } //抽取文档m第n个单词的主题 public void sampleZ2(int m, int n) { int z1 = zAssigns_1[m]; //获取文档所属的伪文档 int z2 = zAssigns_2[m][n]; //获取文档m第n个所属的主题 int w = docs.get(m).get(n); //获取单词编号 npk[z1][z2] --; //统计伪文档z1、主题z2生成的单词数量 npkSum[z1] --; //伪文档z1对应的总单词数量 nkw[z2][w] --; //主题z2对应的单词w的数量 nkwSum[z2] --; //主题z2中所有单词的数量 double VBeta = V * beta; //分母中的V*beta double k2Alpha2 = K2 * alpha2; //分母中的 K*alpha double[] pTable = new double[K2]; //基于公式计算-----这里和公式有差异,公式应该按照这里写，及主题词分母应该按照前面的表达 for (int k = 0; k != K2; k++) { pTable[k] = (npk[z1][k] + alpha2) / (npkSum[z1] + k2Alpha2) * (nkw[k][w] + beta) / (nkwSum[k] + VBeta); } //轮盘赌选择 for (int k = 1; k != K2; k++) { pTable[k] += pTable[k-1]; } double r = Math.random() * pTable[K2-1]; for (int k = 0; k != K2; k++) { if (pTable[k] > r) { z2 = k; break; } } //重新统计相关词频 npk[z1][z2] ++; npkSum[z1] ++; nkw[z2][w] ++; nkwSum[z2] ++; zAssigns_2[m][n] = z2; return; } public void estimate() { long start = 0; for (int iter = 0; iter != niters; iter++) { start = System.currentTimeMillis(); System.out.println("PAM4ST Iteration: " + iter + " ..."); if(iter%this.saveStep==0&&iter!=0&&iter!=this.niters-1){ this.storeResult(iter); } //对每篇文档循环，将文档分配到伪文档 for (int i = 0; i != innerSteps; i++) { for (int m = 0; m != M; m++) { this.sampleZ1(m); } } //对每篇文档进行循环，抽取每个单词所属的主题 for (int i = 0; i != innerSteps; i++) { for (int m = 0; m != M; m++) { int N = docs.get(m).size(); for (int n = 0; n != N; n++) { sampleZ2(m, n); } } } System.out.println("cost time:"+(System.currentTimeMillis()-start)); } return; } //计算伪文档的主题分布---相当于LDA的文档主题分布 public double[][] computeThetaP() { double[][] theta = new double[K1][K2]; for (int k1 = 0; k1 != K1; k1++) { for (int k2 = 0; k2 != K2; k2++) { theta[k1][k2] = (npk[k1][k2] + alpha2) / (npkSum[k1] + K2*alpha2); } } return theta; } public void saveThetaP(String path) throws IOException { BufferedWriter writer = IOUtil.getWriter(path); double[][] theta = this.computeThetaP(); for (int k1 = 0; k1 != K1; k1++) { for (int k2 = 0; k2 != K2; k2++) { writer.append(theta[k1][k2]+" "); } writer.newLine(); } writer.flush(); writer.close(); } public void saveZAssigns1(String path) throws IOException { BufferedWriter writer = IOUtil.getWriter(path); for (int m = 0; m != M; m++) { writer.append(zAssigns_1[m]+"\n"); } writer.flush(); writer.close(); } //计算主题词分布 public double[][] computePhi() { double[][] phi = new double[K2][V]; for (int k = 0; k != K2; k++) { for (int v = 0; v != V; v++) { phi[k][v] = (nkw[k][v] + beta) / (nkwSum[k] + V*beta); } } return phi; } //排序算法 public ArrayList<List<Entry<String, Double>>> sortedTopicWords( double[][] phi, int T) { ArrayList<List<Entry<String, Double>>> res = new ArrayList<List<Entry<String, Double>>>(); for (int k = 0; k != T; k++) { HashMap<String, Double> term2weight = new HashMap<String, Double>(); for (String term : w2i.keySet()) term2weight.put(term, phi[k][w2i.get(term)]); List<Entry<String, Double>> pairs = new ArrayList<Entry<String, Double>>( term2weight.entrySet()); Collections.sort(pairs, new Comparator<Entry<String, Double>>() { public int compare(Entry<String, Double> o1, Entry<String, Double> o2) { return (o2.getValue().compareTo(o1.getValue())); } }); res.add(pairs); } return res; } public void printTopics(String path,int top_n) throws IOException { BufferedWriter writer = IOUtil.getWriter(path); double[][] phi = computePhi(); ArrayList<List<Entry<String, Double>>> pairsList = this .sortedTopicWords(phi, K2); for (int k = 0; k != K2; k++) { writer.write("Topic " + k + ":\n"); for (int i = 0; i != top_n; i++) { writer.write(pairsList.get(k).get(i).getKey() + " " + pairsList.get(k).get(i).getValue()+"\n"); } } writer.close(); } public void savePhi(String path) { BufferedWriter writer = IOUtil.getWriter(path, "utf-8"); double[][] phi = computePhi(); int K = phi.length; assert K > 0; int V = phi[0].length; try { for (int k = 0; k != K; k++) { for (int v = 0; v != V; v++) { writer.append(phi[k][v]+" "); } writer.append("\n"); } writer.flush(); writer.close(); } catch (IOException e) { e.printStackTrace(); } return; } public void saveWordmap(String path) { BufferedWriter writer = IOUtil.getWriter(path, "utf-8"); try { for (String word : w2i.keySet()) writer.append(word + "\t" + w2i.get(word) + "\n"); writer.flush(); writer.close(); } catch (IOException e) { e.printStackTrace(); } return; } public void saveAssign(String path){ BufferedWriter writer = IOUtil.getWriter(path, "utf-8"); try { for(int i=0;i<zAssigns_2.length;i++){ for(int j=0;j<zAssigns_2[i].length;j++){ writer.write(docs.get(i).get(j)+":"+zAssigns_2[i][j]+" "); } writer.write("\n"); } writer.flush(); writer.close(); } catch (IOException e) { e.printStackTrace(); } return; } public void printModel(){ System.out.println("\tK1 :"+this.K1+ "\tK2 :"+this.K2+ "\tniters :"+this.niters+ "\tinnerSteps :"+this.innerSteps+ "\tsaveStep :"+this.saveStep + "\talpha1 :"+this.alpha1+ "\talpha2 :"+this.alpha2+ "\tbeta :"+this.beta + "\tinputPath :"+this.inputPath+ "\toutputPath :"+this.outputPath); } int[][] ndk; int[] ndkSum; public void convert_zassigns_to_arrays_theta(){ ndk = new int[M][K2]; ndkSum = new int[M]; for (int m = 0; m != M; m++) { for (int n = 0; n != docs.get(m).size(); n++) { ndk[m][zAssigns_2[m][n]] ++; ndkSum[m] ++; } } } //计算文档主题分布 public double[][] computeTheta() { convert_zassigns_to_arrays_theta(); double[][] theta = new double[M][K2]; for (int m = 0; m != M; m++) { for (int k = 0; k != K2; k++) { theta[m][k] = (ndk[m][k] + alpha2) / (ndkSum[m] + K2 * alpha2); } } return theta; } public void saveTheta(String path) { BufferedWriter writer = IOUtil.getWriter(path, "utf-8"); double[][] theta = computeTheta(); try { for (int m = 0; m != M; m++) { for (int k = 0; k != K2; k++) { writer.append(theta[m][k]+" "); } writer.append("\n"); } writer.flush(); writer.close(); } catch (IOException e) { e.printStackTrace(); } return; } public void storeResult(int times){ String appendString="final"; if(times!=0){ appendString =times+""; } try { this.printTopics(outputPath+"/model-"+appendString+".twords",20); this.saveWordmap(outputPath+"/wordmap.txt"); this.savePhi(outputPath+"/model-"+appendString+".phi"); this.saveAssign(outputPath+"/model-"+appendString+".tassign"); this.saveTheta(outputPath+"/model-"+appendString+".theta"); this.saveThetaP(outputPath+"/model-"+appendString+".thetap"); this.saveZAssigns1(outputPath+"/model-"+appendString+".assign1"); } catch (IOException e) { // TODO Auto-generated catch block e.printStackTrace(); } } public void run() { printModel(); this.loadTxts(inputPath);//加载语料 this.initModel(); //初始化模型 this.estimate(); //估计 this.storeResult(0); //保存结果 } public static void PseudoDocTM(int P,int K,int iter,int innerStep,int saveStep,double alpha1,double alpha2,double beta,int threadNum,String path){ File trainFile = new File(path); String parent_path = trainFile.getParentFile().getAbsolutePath(); (new File(parent_path+"/PTM_with_case_"+P+"_"+K+"_"+iter+"_"+alpha1+"_"+alpha2+"_"+beta+"/")).mkdirs(); try { Thread.sleep(1000); } catch (InterruptedException e) { e.printStackTrace(); } (new PseudoDocTM(P,K,iter,innerStep,saveStep,alpha1,alpha2,beta,path,parent_path+"/PTM_with_case_"+P+"_"+K+"_"+iter+"_"+alpha1+"_"+alpha2+"_"+beta)).run(); } }

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