数据挖掘-基于贝叶斯算法及KNN算法的newsgroup18828文档分类器的JAVA实现（上）

STEP ONE:英文词法分析，去除数字、连字符、标点符号、特殊 字符，所有大写字母转换成小写，可以用正则表达式

                     String res[] = line.split("[^a-zA-Z]");

STEP TWO:去停用词，过滤对分类无价值的词

STEP THRE: 词根还原stemming,基于Porter算法

文档预处理类 DataPreProcess.java如下

package com.pku.yangliu;

import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.IOException;
import java.util.ArrayList;

/** 
 * Newsgroups文档集预处理类
 * @author yangliu
 * @qq 772330184 
 * @mail [email protected]
 */
public class DataPreProcess {
	
	/**输入文件调用处理数据函数
	 * @param strDir newsgroup文件目录的绝对路径
	 * @throws IOException 
	 */
	public void doProcess(String strDir) throws IOException{
		File fileDir = new File(strDir);
		if(!fileDir.exists()){
			System.out.println("File not exist:" + strDir);
			return;
		}
		String subStrDir = strDir.substring(strDir.lastIndexOf('/'));
		String dirTarget = strDir + "/../../processedSample_includeNotSpecial"+subStrDir;
		File fileTarget = new File(dirTarget);
		if(!fileTarget.exists()){//注意processedSample需要先建立目录建出来，否则会报错，因为母目录不存在
			fileTarget.mkdir();
		}
		File[] srcFiles = fileDir.listFiles();
		String[] stemFileNames = new String[srcFiles.length];
		for(int i = 0; i < srcFiles.length; i++){
			String fileFullName = srcFiles[i].getCanonicalPath();
			String fileShortName = srcFiles[i].getName();
			if(!new File(fileFullName).isDirectory()){//确认子文件名不是目录如果是可以再次递归调用
				System.out.println("Begin preprocess:"+fileFullName);
				StringBuilder stringBuilder = new StringBuilder();
				stringBuilder.append(dirTarget + "/" + fileShortName);
				createProcessFile(fileFullName, stringBuilder.toString());
				stemFileNames[i] = stringBuilder.toString();
			}
			else {
				fileFullName = fileFullName.replace("\\","/");
				doProcess(fileFullName);
			}
		}
		//下面调用stem算法
		if(stemFileNames.length > 0 && stemFileNames[0] != null){
			Stemmer.porterMain(stemFileNames);
		}
	}
	
	/**进行文本预处理生成目标文件
	 * @param srcDir 源文件文件目录的绝对路径
	 * @param targetDir 生成的目标文件的绝对路径
	 * @throws IOException 
	 */
	private static void createProcessFile(String srcDir, String targetDir) throws IOException {
		// TODO Auto-generated method stub
		FileReader srcFileReader = new FileReader(srcDir);
		FileReader stopWordsReader = new FileReader("F:/DataMiningSample/stopwords.txt");
		FileWriter targetFileWriter = new FileWriter(targetDir);	
		BufferedReader srcFileBR = new BufferedReader(srcFileReader);//装饰模式
		BufferedReader stopWordsBR = new BufferedReader(stopWordsReader);
		String line, resLine, stopWordsLine;
		//用stopWordsBR够着停用词的ArrayList容器
		ArrayList<String> stopWordsArray = new ArrayList<String>();
		while((stopWordsLine = stopWordsBR.readLine()) != null){
			if(!stopWordsLine.isEmpty()){
				stopWordsArray.add(stopWordsLine);
			}
		}
		while((line = srcFileBR.readLine()) != null){
			resLine = lineProcess(line,stopWordsArray);
			if(!resLine.isEmpty()){
				//按行写，一行写一个单词
				String[] tempStr = resLine.split(" ");//\s
				for(int i = 0; i < tempStr.length; i++){
					if(!tempStr[i].isEmpty()){
						targetFileWriter.append(tempStr[i]+"\n");
					}
				}
			}
		}
		targetFileWriter.flush();
		targetFileWriter.close();
		srcFileReader.close();
		stopWordsReader.close();
		srcFileBR.close();
		stopWordsBR.close();	
	}
	
	/**对每行字符串进行处理，主要是词法分析、去停用词和stemming
	 * @param line 待处理的一行字符串
	 * @param ArrayList<String> 停用词数组
	 * @return String 处理好的一行字符串，是由处理好的单词重新生成，以空格为分隔符
	 * @throws IOException 
	 */
	private static String lineProcess(String line, ArrayList<String> stopWordsArray) throws IOException {
		// TODO Auto-generated method stub
		//step1 英文词法分析，去除数字、连字符、标点符号、特殊字符，所有大写字母转换成小写，可以考虑用正则表达式
		String res[] = line.split("[^a-zA-Z]");
		//这里要小心，防止把有单词中间有数字和连字符的单词 截断了，但是截断也没事
		
		String resString = new String();
		//step2去停用词
		//step3stemming,返回后一起做
		for(int i = 0; i < res.length; i++){
			if(!res[i].isEmpty() && !stopWordsArray.contains(res[i].toLowerCase())){
				resString += " " + res[i].toLowerCase() + " ";
			}
		}
		return resString;
	}

	/**
	 * @param args
	 * @throws IOException 
	 */
	public void BPPMain(String[] args) throws IOException {
		// TODO Auto-generated method stub
		DataPreProcess dataPrePro = new DataPreProcess();
		dataPrePro.doProcess("F:/DataMiningSample/orginSample");

	}

}

2、特征词的选取

首先统计经过预处理后在所有文档中出现不重复的单词一共有87554个，对这些词进行统计发现：
出现次数大于等于1次的词有87554个
出现次数大于等于3次的词有36456个
出现次数大于等于4次的词有30095个
特征词的选取策略：
策略一：保留所有词作为特征词 共计87554个
策略二：选取出现次数大于等于4次的词作为特征词共计30095个
特征词的选取策略：采用策略一，后面将对两种特征词选取策略的计算时间和平均准确率做对比

测试集与训练集的创建类CreateTrainAndTestSample.java如下

package com.pku.yangliu;

import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.IOException;
import java.util.SortedMap;
import java.util.TreeMap;

/**创建训练样例集合与测试样例集合
 * @author yangliu
 * @qq 772330184 
 * @mail [email protected]
 *
 */
public class CreateTrainAndTestSample {
	
	void filterSpecialWords() throws IOException {
		// TODO Auto-generated method stub
		String word;
		ComputeWordsVector cwv = new ComputeWordsVector();
		String fileDir = "F:/DataMiningSample/processedSample_includeNotSpecial";
		SortedMap<String,Double> wordMap = new TreeMap<String,Double>();
		wordMap = cwv.countWords(fileDir, wordMap);
		cwv.printWordMap(wordMap);//把wordMap输出到文件
		File[] sampleDir = new File(fileDir).listFiles();
		for(int i = 0; i < sampleDir.length; i++){
			File[] sample = sampleDir[i].listFiles();
			String targetDir = "F:/DataMiningSample/processedSampleOnlySpecial/"+sampleDir[i].getName();
			File targetDirFile = new File(targetDir);
			if(!targetDirFile.exists()){
				targetDirFile.mkdir();
			}
			for(int j = 0;j < sample.length; j++){	
				String fileShortName = sample[j].getName();
				if(fileShortName.contains("stemed")){
					targetDir = "F:/DataMiningSample/processedSampleOnlySpecial/"+sampleDir[i].getName()+"/"+fileShortName.substring(0,5);
					FileWriter tgWriter= new FileWriter(targetDir);
					FileReader samReader = new FileReader(sample[j]);
					BufferedReader samBR = new BufferedReader(samReader);
					while((word = samBR.readLine()) != null){
						if(wordMap.containsKey(word)){
							tgWriter.append(word + "\n");
						}
					}
					tgWriter.flush();
					tgWriter.close();
				}
			}
		}
	}
	
	void createTestSamples(String fileDir, double trainSamplePercent,int indexOfSample,String classifyResultFile) throws IOException {
		// TODO Auto-generated method stub
		String word, targetDir;
		FileWriter crWriter = new FileWriter(classifyResultFile);//测试样例正确类目记录文件
		File[] sampleDir = new File(fileDir).listFiles();
		for(int i = 0; i < sampleDir.length; i++){
			File[] sample = sampleDir[i].listFiles();
			double testBeginIndex = indexOfSample*(sample.length * (1-trainSamplePercent));//测试样例的起始文件序号
			double testEndIndex = (indexOfSample+1)*(sample.length * (1-trainSamplePercent));//测试样例集的结束文件序号
			for(int j = 0;j < sample.length; j++){				
				FileReader samReader = new FileReader(sample[j]);
				BufferedReader samBR = new BufferedReader(samReader);
				String fileShortName = sample[j].getName();
				String subFileName = fileShortName;
				if(j > testBeginIndex && j< testEndIndex){//序号在规定区间内的作为测试样本，需要为测试样本生成类别-序号文件，最后加入分类的结果，一行对应一个文件，方便统计准确率
					targetDir = "F:/DataMiningSample/TestSample"+indexOfSample+"/"+sampleDir[i].getName();
					crWriter.append(subFileName + " " + sampleDir[i].getName()+"\n");
					
					}
				else{//其余作为训练样本
					targetDir = "F:/DataMiningSample/TrainSample"+indexOfSample+"/"+sampleDir[i].getName();
				}
				targetDir = targetDir.replace("\\","/");
				File trainSamFile = new File(targetDir);
				if(!trainSamFile.exists()){
					trainSamFile.mkdir();
				}
				targetDir += "/"+subFileName;
				FileWriter tsWriter = new FileWriter(new File(targetDir));
				while((word = samBR.readLine()) != null){
					tsWriter.append(word + "\n");
				}
				tsWriter.flush();
				tsWriter.close();	
			}
		}
		crWriter.flush();
		crWriter.close();
	}
}

3、贝叶斯算法描述及实现

根据朴素贝叶斯公式，每个测试样例属于某个类别的概率 =  所有测试样例包含特征词类条件概率P(tk|c)之积 * 先验概率P(c)

在具体计算类条件概率和先验概率时，朴素贝叶斯分类器有两种模型

(1) 多项式模型( multinomial model )  –以单词为粒度
类条件概率P(tk|c)=(类c下单词tk在各个文档中出现过的次数之和+1)/
                            （类c下单词总数+训练样本中不重复特征词总数）
先验概率P(c)=类c下的单词总数/整个训练样本的单词总数
伯努利模型（Bernoulli model） –以文件为粒度
(2) 类条件概率P(tk|c)=（类c下包含单词tk的文件数+1）/(类c下单词总数+2)
先验概率P(c)=类c下文件总数/整个训练样本的文件总数
本分类器选用多项式模型计算，根据《Introduction to Information Retrieval 》，多项式模型计算准确率更高

贝叶斯算法的实现有以下注意点：

(1) 计算概率用到了BigDecimal类实现任意精度计算
(2) 用交叉验证法做十次分类实验，对准确率取平均值
(3) 根据正确类目文件和分类结果文计算混淆矩阵并且输出
(4) Map<String,Double> cateWordsProb key为“类目_单词”, value为该类目下该单词的出现次数，避免重复计算

贝叶斯算法实现类如下 NaiveBayesianClassifier.java

package com.pku.yangliu;

import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.IOException;
import java.math.BigDecimal;
import java.util.Iterator;
import java.util.Map;
import java.util.Set;
import java.util.SortedSet;
import java.util.TreeMap;
import java.util.TreeSet;
import java.util.Vector;

/**利用朴素贝叶斯算法对newsgroup文档集做分类，采用十组交叉测试取平均值
 * 采用多项式模型,stanford信息检索导论课件上面言多项式模型比伯努利模型准确度高
 * 类条件概率P(tk|c)=(类c 下单词tk 在各个文档中出现过的次数之和+1)/(类c下单词总数+|V|)
 * @author yangliu
 * @qq 772330184 
 * @mail [email protected]
 *
 */
public class NaiveBayesianClassifier {
	
	/**用贝叶斯法对测试文档集分类
	 * @param trainDir 训练文档集目录
	 * @param testDir 测试文档集目录
	 * @param classifyResultFileNew 分类结果文件路径
	 * @throws Exception 
	 */
	private void doProcess(String trainDir, String testDir,
			String classifyResultFileNew) throws Exception {
		// TODO Auto-generated method stub
		Map<String,Double> cateWordsNum = new TreeMap<String,Double>();//保存训练集每个类别的总词数
		Map<String,Double> cateWordsProb = new TreeMap<String,Double>();//保存训练样本每个类别中每个属性词的出现词数
		cateWordsProb = getCateWordsProb(trainDir);
		cateWordsNum = getCateWordsNum(trainDir);
		double totalWordsNum = 0.0;//记录所有训练集的总词数
		Set<Map.Entry<String,Double>> cateWordsNumSet = cateWordsNum.entrySet();
		for(Iterator<Map.Entry<String,Double>> it = cateWordsNumSet.iterator(); it.hasNext();){
			Map.Entry<String, Double> me = it.next();
			totalWordsNum += me.getValue();
		}
		//下面开始读取测试样例做分类
		Vector<String> testFileWords = new Vector<String>();
		String word;
		File[] testDirFiles = new File(testDir).listFiles();
		FileWriter crWriter = new FileWriter(classifyResultFileNew);
		for(int i = 0; i < testDirFiles.length; i++){
			File[] testSample = testDirFiles[i].listFiles();
			for(int j = 0;j < testSample.length; j++){
				testFileWords.clear();
				FileReader spReader = new FileReader(testSample[j]);
				BufferedReader spBR = new BufferedReader(spReader);
				while((word = spBR.readLine()) != null){
					testFileWords.add(word);
				}
				//下面分别计算该测试样例属于二十个类别的概率
				File[] trainDirFiles = new File(trainDir).listFiles();
				BigDecimal maxP = new BigDecimal(0);
				String bestCate = null;
				for(int k = 0; k < trainDirFiles.length; k++){
					BigDecimal p = computeCateProb(trainDirFiles[k], testFileWords, cateWordsNum, totalWordsNum, cateWordsProb);
					if(k == 0){
						maxP = p;
						bestCate = trainDirFiles[k].getName();
						continue;
					}
					if(p.compareTo(maxP) == 1){
						maxP = p;
						bestCate = trainDirFiles[k].getName();
					}
				}
				crWriter.append(testSample[j].getName() + " " + bestCate + "\n");
				crWriter.flush();
			}
		}
		crWriter.close();
	}
	
	/**统计某类训练样本中每个单词的出现次数
	 * @param strDir 训练样本集目录
	 * @return Map<String,Double> cateWordsProb 用"类目_单词"对来索引的map,保存的val就是该类目下该单词的出现次数
	 * @throws IOException 
	 */
	public Map<String,Double> getCateWordsProb(String strDir) throws IOException{
		Map<String,Double> cateWordsProb = new TreeMap<String,Double>();
		File sampleFile = new File(strDir);
		File [] sampleDir = sampleFile.listFiles();
		String word;
		for(int i = 0;i < sampleDir.length; i++){
			File [] sample = sampleDir[i].listFiles();
			for(int j = 0; j < sample.length; j++){
				FileReader samReader = new FileReader(sample[j]);
				BufferedReader samBR = new BufferedReader(samReader);
				while((word = samBR.readLine()) != null){
					String key = sampleDir[i].getName() + "_" + word;
					if(cateWordsProb.containsKey(key)){
						double count = cateWordsProb.get(key) + 1.0;
						cateWordsProb.put(key, count);
					}
					else {
						cateWordsProb.put(key, 1.0);
					}
				}
			}
		}
		return cateWordsProb;	
	}
	
	/**计算某一个测试样本属于某个类别的概率
	 * @param Map<String, Double> cateWordsProb 记录每个目录中出现的单词及次数 
	 * @param File trainFile 该类别所有的训练样本所在目录
	 * @param Vector<String> testFileWords 该测试样本中的所有词构成的容器
	 * @param double totalWordsNum 记录所有训练样本的单词总数
	 * @param Map<String, Double> cateWordsNum 记录每个类别的单词总数
	 * @return BigDecimal 返回该测试样本在该类别中的概率
	 * @throws Exception 
	 * @throws IOException 
	 */
	private BigDecimal computeCateProb(File trainFile, Vector<String> testFileWords, Map<String, Double> cateWordsNum, double totalWordsNum, Map<String, Double> cateWordsProb) throws Exception {
		// TODO Auto-generated method stub
		BigDecimal probability = new BigDecimal(1);
		double wordNumInCate = cateWordsNum.get(trainFile.getName());
		BigDecimal wordNumInCateBD = new BigDecimal(wordNumInCate);
		BigDecimal totalWordsNumBD = new BigDecimal(totalWordsNum);
		for(Iterator<String> it = testFileWords.iterator(); it.hasNext();){
			String me = it.next();
			String key = trainFile.getName()+"_"+me;
			double testFileWordNumInCate;
			if(cateWordsProb.containsKey(key)){
				testFileWordNumInCate = cateWordsProb.get(key);
			}else testFileWordNumInCate = 0.0;
			BigDecimal testFileWordNumInCateBD = new BigDecimal(testFileWordNumInCate);
			BigDecimal xcProb = (testFileWordNumInCateBD.add(new BigDecimal(0.0001))).divide(totalWordsNumBD.add(wordNumInCateBD),10, BigDecimal.ROUND_CEILING);
			probability = probability.multiply(xcProb);
		}
		BigDecimal res = probability.multiply(wordNumInCateBD.divide(totalWordsNumBD,10, BigDecimal.ROUND_CEILING));
		return res;
	}

	/**获得每个类目下的单词总数
	 * @param trainDir 训练文档集目录
	 * @return Map<String, Double> <目录名，单词总数>的map
	 * @throws IOException 
	 */
	private Map<String, Double> getCateWordsNum(String trainDir) throws IOException {
		// TODO Auto-generated method stub
		Map<String,Double> cateWordsNum = new TreeMap<String,Double>();
		File[] sampleDir = new File(trainDir).listFiles();
		for(int i = 0; i < sampleDir.length; i++){
			double count = 0;
			File[] sample = sampleDir[i].listFiles();
			for(int j = 0;j < sample.length; j++){
				FileReader spReader = new FileReader(sample[j]);
				BufferedReader spBR = new BufferedReader(spReader);
				while(spBR.readLine() != null){
					count++;
				}		
			}
			cateWordsNum.put(sampleDir[i].getName(), count);
		}
		return cateWordsNum;
	}
	
	/**根据正确类目文件和分类结果文件统计出准确率
	 * @param classifyResultFile 正确类目文件
	 * @param classifyResultFileNew 分类结果文件
	 * @return double 分类的准确率
	 * @throws IOException 
	 */
	double computeAccuracy(String classifyResultFile,
			String classifyResultFileNew) throws IOException {
		// TODO Auto-generated method stub
		Map<String,String> rightCate = new TreeMap<String,String>();
		Map<String,String> resultCate = new TreeMap<String,String>();
		rightCate = getMapFromResultFile(classifyResultFile);
		resultCate = getMapFromResultFile(classifyResultFileNew);
		Set<Map.Entry<String, String>> resCateSet = resultCate.entrySet();
		double rightCount = 0.0;
		for(Iterator<Map.Entry<String, String>> it = resCateSet.iterator(); it.hasNext();){
			Map.Entry<String, String> me = it.next();
			if(me.getValue().equals(rightCate.get(me.getKey()))){
				rightCount ++;
			}
		}
		computerConfusionMatrix(rightCate,resultCate);
		return rightCount / resultCate.size();	
	}
	
	/**根据正确类目文件和分类结果文计算混淆矩阵并且输出
	 * @param rightCate 正确类目对应map
	 * @param resultCate 分类结果对应map
	 * @return double 分类的准确率
	 * @throws IOException 
	 */
	private void computerConfusionMatrix(Map<String, String> rightCate,
			Map<String, String> resultCate) {
		// TODO Auto-generated method stub	
		int[][] confusionMatrix = new int[20][20];
		//首先求出类目对应的数组索引
		SortedSet<String> cateNames = new TreeSet<String>();
		Set<Map.Entry<String, String>> rightCateSet = rightCate.entrySet();
		for(Iterator<Map.Entry<String, String>> it = rightCateSet.iterator(); it.hasNext();){
			Map.Entry<String, String> me = it.next();
			cateNames.add(me.getValue());
		}
		cateNames.add("rec.sport.baseball");//防止数少一个类目
		String[] cateNamesArray = cateNames.toArray(new String[0]);
		Map<String,Integer> cateNamesToIndex = new TreeMap<String,Integer>();
		for(int i = 0; i < cateNamesArray.length; i++){
			cateNamesToIndex.put(cateNamesArray[i],i);
		}
		for(Iterator<Map.Entry<String, String>> it = rightCateSet.iterator(); it.hasNext();){
			Map.Entry<String, String> me = it.next();
			confusionMatrix[cateNamesToIndex.get(me.getValue())][cateNamesToIndex.get(resultCate.get(me.getKey()))]++;
		}
		//输出混淆矩阵
		double[] hangSum = new double[20];
		System.out.print("    ");
		for(int i = 0; i < 20; i++){
			System.out.print(i + "    ");
		}
		System.out.println();
		for(int i = 0; i < 20; i++){
			System.out.print(i + "    ");
			for(int j = 0; j < 20; j++){
				System.out.print(confusionMatrix[i][j]+"    ");
				hangSum[i] += confusionMatrix[i][j];
			}
			System.out.println(confusionMatrix[i][i] / hangSum[i]);
		}
		System.out.println();
	}

	/**从分类结果文件中读取map
	 * @param classifyResultFileNew 类目文件
	 * @return Map<String, String> 由<文件名，类目名>保存的map
	 * @throws IOException 
	 */
	private Map<String, String> getMapFromResultFile(
			String classifyResultFileNew) throws IOException {
		// TODO Auto-generated method stub
		File crFile = new File(classifyResultFileNew);
		FileReader crReader = new FileReader(crFile);
		BufferedReader crBR = new BufferedReader(crReader);
		Map<String, String> res = new TreeMap<String, String>();
		String[] s;
		String line;
		while((line = crBR.readLine()) != null){
			s = line.split(" ");
			res.put(s[0], s[1]);	
		}
		return res;
	}

	/**
	 * @param args
	 * @throws Exception 
	 */
	public void NaiveBayesianClassifierMain(String[] args) throws Exception {
		 //TODO Auto-generated method stub
		//首先创建训练集和测试集
		CreateTrainAndTestSample ctt = new CreateTrainAndTestSample();
		NaiveBayesianClassifier nbClassifier = new NaiveBayesianClassifier();
		ctt.filterSpecialWords();//根据包含非特征词的文档集生成只包含特征词的文档集到processedSampleOnlySpecial目录下
		double[] accuracyOfEveryExp = new double[10];
		double accuracyAvg,sum = 0;
		for(int i = 0; i < 10; i++){//用交叉验证法做十次分类实验，对准确率取平均值	
			String TrainDir = "F:/DataMiningSample/TrainSample"+i;
			String TestDir = "F:/DataMiningSample/TestSample"+i;
			String classifyRightCate = "F:/DataMiningSample/classifyRightCate"+i+".txt";
			String classifyResultFileNew = "F:/DataMiningSample/classifyResultNew"+i+".txt";
			ctt.createTestSamples("F:/DataMiningSample/processedSampleOnlySpecial", 0.9, i,classifyRightCate);
			nbClassifier.doProcess(TrainDir,TestDir,classifyResultFileNew);
			accuracyOfEveryExp[i] = nbClassifier.computeAccuracy (classifyRightCate, classifyResultFileNew);
			System.out.println("The accuracy for Naive Bayesian Classifier in "+i+"th Exp is :" + accuracyOfEveryExp[i]);
		}
		for(int i = 0; i < 10; i++){
			sum += accuracyOfEveryExp[i];
		}
		accuracyAvg = sum / 10;
		System.out.println("The average accuracy for Naive Bayesian Classifier in all Exps is :" + accuracyAvg);
		
	}
}

4 朴素贝叶斯算法对newsgroup文档集做分类的结果

为方便计算混淆矩阵，将类目编号如下

0 alt.atheism
1 comp.graphics
2 comp.os.ms-windows.misc
3comp.sys.ibm.pc.hdwar
4comp.sys.mac.hardwar
5 comp.windows.x
6 misc.forsale
7 rec.autos
8 rec.motorcycles
9 rec.sport.baseball
10 rec.sport.hockey
11 sci.crypt
12 sci.electronics
13 sci.med
14 sci.space
15 soc.religion.christian
16 talk.politics.guns
17 talk.politics.mideast
18 talk.politics.misc
19 talk.religion.misc