文本分类——NaiveBayes
前面文章已经介绍了朴素贝叶斯算法的原理，这里基于NavieBayes算法对newsgroup文本进行分类測试。
文中代码參考：http://blog.csdn.net/jiangliqing1234/article/details/39642757
主要内容例如以下：
1、newsgroup数据集介绍
数据下载地址：http://download.csdn.net/detail/hjy321686/8057761。
文本中包括20个不同的新闻组，除当中少数文本属于多个新闻组以外，其余的文档都仅仅属于一个新闻组。
2、newsgroup数据预处理
要对文本进行分类，首先要对其进行预处理，预处理主要步骤例如以下：
step1：英文词法分析，取出数字、连字符、标点符号、特殊字符，全部大写字母转换成小写，可用正則表達式：String res[] = line.split("[^a-zA-Z]");
step2：去停用词。过滤对别无价值的词
step3：词根还原stemmer，基于Porter算法
预处理类例如以下：
package com.datamine.NaiveBayes;
import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.FileWriter;
import java.util.ArrayList;
/**
* Newsgroup文档预处理
* step1：英文词法分析，取出数字、连字符、标点符号、特殊字符，全部大写字母转换成小写。可用正則表達式：String res[] = line.split("[^a-zA-Z]");
* step2：去停用词，过滤对分类无价值的词
* step3：词根还原stemmer。基于Porter算法
* @author Administrator
*
*/
public class DataPreProcess {
private static ArrayList<String> stopWordsArray = new ArrayList<String>();
/**
* 输入文件的路径。处理数据
* @param srcDir 文件文件夹的绝对路径
* @param desDir 清洗后的文件路径
* @throws Exception
*/
public void doProcess(String srcDir) throws Exception{
File fileDir = new File(srcDir);
if(!fileDir.exists()){
System.out.println("文件不存在!");
return ;
}
String subStrDir = srcDir.substring(srcDir.lastIndexOf('/'));
String dirTarget = srcDir+"/../../processedSample"+subStrDir;
File fileTarget = new File(dirTarget);
if(!fileTarget.exists()){
//注意processedSample须要先建立文件夹建出来，否则会报错，由于母文件夹不存在
boolean mkdir = fileTarget.mkdir();
}
File[] srcFiles = fileDir.listFiles();
for(int i =0 ;i <srcFiles.length;i++){
String fileFullName = srcFiles[i].getCanonicalPath(); //CanonicalPath不可是全路径，并且把..或者.这种符号解析出来。
String fileShortName = srcFiles[i].getName(); //文件名称
if(!new File(fileFullName).isDirectory()){ //确认子文件名称不是文件夹，假设是能够再次递归调用
System.out.println("開始预处理："+fileFullName);
StringBuilder stringBuilder = new StringBuilder();
stringBuilder.append(dirTarget+"/"+fileShortName);
createProcessFile(fileFullName,stringBuilder.toString());
}else{
fileFullName = fileFullName.replace("\\", "/");
doProcess(fileFullName);
}
}
}
/**
* 进行文本预处理生成目标文件
* @param srcDir 源文件文件文件夹的绝对路径
* @param targetDir 生成目标文件的绝对路径
* @throws Exception
*/
private void createProcessFile(String srcDir, String targetDir) throws Exception {
FileReader srcFileReader = new FileReader(srcDir);
FileWriter targetFileWriter = new FileWriter(targetDir);
BufferedReader srcFileBR = new BufferedReader(srcFileReader);
String line,resLine;
while((line = srcFileBR.readLine()) != null){
resLine = lineProcess(line);
if(!resLine.isEmpty()){
//按行写。一行写一个单词
String[] tempStr = resLine.split(" ");
for(int i =0; i<tempStr.length ;i++){
if(!tempStr[i].isEmpty())
targetFileWriter.append(tempStr[i]+"\n");
}
}
}
targetFileWriter.flush();
targetFileWriter.close();
srcFileReader.close();
srcFileBR.close();
}
/**
* 对每行字符串进行处理，主要是词法分析、去停用词和stemming(去除时态)
* @param line 待处理的一行字符串
* @param stopWordsArray 停用词数组
* @return String 处理好的一行字符串，是由处理好的单词又一次生成，以空格为分隔符
*/
private String lineProcess(String line) {
/*
* step1
* 英文词法分析，去除数字、连字符、标点符号、特殊字符，
* 全部大写字符转换成小写，能够考虑使用正則表達式
*/
String res[] = line.split("[^a-zA-Z]");
//step2 去停用词，大写转换成小写
//step3 Stemmer.run()
String resString = new String();
for(int i=0;i<res.length;i++){
if(!res[i].isEmpty() && !stopWordsArray.contains(res[i].toLowerCase()))
resString += " " + Stemmer.run(res[i].toLowerCase()) + " ";
}
return resString;
}
/**
* 用stopWordsArray构造停用词的ArrayList容器
* @param stopwordsPath
* @throws Exception
*/
private static void stopWordsToArray(String stopwordsPath) throws Exception {
FileReader stopWordsReader = new FileReader(stopwordsPath);
BufferedReader stopWordsBR = new BufferedReader(stopWordsReader);
String stopWordsLine = null;
//用stopWordsArray构造停用词的ArrayList容器
while((stopWordsLine = stopWordsBR.readLine()) != null){
if(!stopWordsLine.isEmpty())
stopWordsArray.add(stopWordsLine);
}
stopWordsReader.close();
stopWordsBR.close();
}
public static void main(String[] args) throws Exception{
DataPreProcess dataPrePro = new DataPreProcess();
String srcDir = "E:/DataMiningSample/orginSample";
String stopwordsPath = "E:/DataMiningSample/stopwords.txt";
stopWordsToArray(stopwordsPath);
dataPrePro.doProcess(srcDir);
}
}
对于step3中的Porter算法能够网上下载，这里我基于其之上加入了一个run()方法。
/**
* Stemmer中接口，将传入的word进行词根还原
* @param word 传入单词
* @return result 处理后的单词
*/
public static String run(String word){
Stemmer s = new Stemmer();
char[] ch = word.toCharArray();
for (int c = 0; c < ch.length; c++)
s.add(ch[c]);
s.stem();
{
String u;
u = s.toString();
//System.out.print(u);
return u;
}
}3、特征项选择
方法一：保留全部词作为特征词
方法二：选取出现频率大于某一个数（3或者其它）的词作为特征词
方法三：计算每一个词的权重tf*idf，依据权重来选取特征词
本文中选取方法二。
4、文本向量化
因为本文中。特征词选择採用的是方法二，能够不用对文本进行向量化，可是统计特征词出现的次数方法写在ComputeWordsVector类中，为了程序执行这里还是把文本向量化的代码贴出来。后面使用KNN算法的时候也是要用到此类的。
package com.datamine.NaiveBayes;
import java.io.*;
import java.util.*;
/**
* 计算文档的属性向量。将全部文档向量化
* @author Administrator
*/
public class ComputeWordsVector {
/**
* 计算文档的TF属性向量。TFPerDocMap
* 计算TF*IDF
* @param strDir 处理好的newsgroup文件文件夹的绝对路径
* @param trainSamplePercent 训练样本集占每一个类目的比例
* @param indexOfSample 測试例子集的起始的測试例子编号 凝视：通过这个參数能够将文本分成训练和測试两部分
* @param iDFPerWordMap 每一个词的IDF权值属性向量
* @param wordMap 属性词典map
* @throws IOException
*/
public void computeTFMultiIDF(String strDir,double trainSamplePercent,int indexOfSample,
Map<String, Double> iDFPerWordMap,Map<String,Double> wordMap) throws IOException{
File fileDir = new File(strDir);
String word;
SortedMap<String,Double> TFPerDocMap = new TreeMap<String, Double>();
//注意能够用两个写文件，一个专门写測试例子，一个专门写训练例子，用sampleType的值来表示
String trainFileDir = "E:/DataMiningSample/docVector/wordTFIDFMapTrainSample"+indexOfSample;
String testFileDir = "E:/DataMiningSample/docVector/wordTFIDFMapTestSample"+indexOfSample;
FileWriter tsTrainWriter = new FileWriter(new File(trainFileDir)); //往训练文件里写
FileWriter tsTestWriter = new FileWriter(new File(testFileDir)); //往測试文件里写
FileWriter tsWriter = null;
File[] sampleDir = fileDir.listFiles();
for(int i = 0;i<sampleDir.length;i++){
String cateShortName = sampleDir[i].getName();
System.out.println("開始计算: " + cateShortName);
File[] sample = sampleDir[i].listFiles();
//測试例子集起始文件序号
double testBeginIndex = indexOfSample*(sample.length*(1-trainSamplePercent));
//測试例子集的结束文件序号
double testEndIndex = (indexOfSample+1)*(sample.length*(1-trainSamplePercent));
System.out.println("文件名称_文件数 :" + sampleDir[i].getCanonicalPath()+"_"+sample.length);
System.out.println("训练数:"+sample.length*trainSamplePercent
+ " 測试文本開始下标:"+ testBeginIndex+" 測试文本结束下标:"+testEndIndex);
for(int j =0;j<sample.length; j++){
//计算TF，即每一个词在该文件里出现的频率
TFPerDocMap.clear();
FileReader samReader = new FileReader(sample[j]);
BufferedReader samBR = new BufferedReader(samReader);
String fileShortName = sample[j].getName();
Double wordSumPerDoc = 0.0;//计算每篇文档的总字数
while((word = samBR.readLine()) != null){
if(!word.isEmpty() && wordMap.containsKey(word)){
wordSumPerDoc++;
if(TFPerDocMap.containsKey(word))
TFPerDocMap.put(word, TFPerDocMap.get(word)+1);
else
TFPerDocMap.put(word, 1.0);
}
}
samBR.close();
/*
* 遍历 TFPerDocMap，除以文档的总词数wordSumPerDoc 则得到TF
* TF*IDF得到终于的特征权值，并输出到文件
* 注意：測试例子和训练例子写入的文件不同
*/
if(j >= testBeginIndex && j <= testEndIndex)
tsWriter = tsTestWriter;
else
tsWriter = tsTrainWriter;
Double wordWeight;
Set<Map.Entry<String, Double>> tempTF = TFPerDocMap.entrySet();
for(Iterator<Map.Entry<String, Double>> mt = tempTF.iterator();mt.hasNext();){
Map.Entry<String, Double> me = mt.next();
//因为计算IDF很耗时，3万多个词的属性词典初步预计须要25个小时，先尝试觉得全部词的IDF都是1的情况
//wordWeight = (me.getValue() / wordSumPerDoc) * iDFPerWordMap.get(me.getKey());
wordWeight = (me.getValue() / wordSumPerDoc) * 1.0;
TFPerDocMap.put(me.getKey(), wordWeight);
}
tsWriter.append(cateShortName + " ");
tsWriter.append(fileShortName + " ");
Set<Map.Entry<String, Double>> tempTF2 = TFPerDocMap.entrySet();
for(Iterator<Map.Entry<String, Double>> mt = tempTF2.iterator();mt.hasNext();){
Map.Entry<String, Double> me = mt.next();
tsWriter.append(me.getKey() + " " + me.getValue()+" ");
}
tsWriter.append("\n");
tsWriter.flush();
}
}
tsTrainWriter.close();
tsTestWriter.close();
tsWriter.close();
}
/**
* 统计每一个词的总出现次数。返回出现次数大于3词的词汇构成终于的属性词典
* @param strDir 处理好的newsgroup文件文件夹的绝对路径
* @param wordMap 记录出现的每一个词构成的属性词典
* @return newWordMap 返回出现次数大于3次的词汇构成终于的属性词典
* @throws IOException
*/
public SortedMap<String, Double> countWords(String strDir,
Map<String, Double> wordMap) throws IOException {
File sampleFile = new File(strDir);
File[] sample = sampleFile.listFiles();
String word;
for(int i =0 ;i < sample.length;i++){
if(!sample[i].isDirectory()){
FileReader samReader = new FileReader(sample[i]);
BufferedReader samBR = new BufferedReader(samReader);
while((word = samBR.readLine()) != null){
if(!word.isEmpty() && wordMap.containsKey(word))
wordMap.put(word, wordMap.get(word)+1);
else
wordMap.put(word, 1.0);
}
samBR.close();
}else{
countWords(sample[i].getCanonicalPath(),wordMap);
}
}
/*
* 仅仅返回出现次数大于3的单词
* 这里为了简单，应该独立一个函数。避免多次执行
*/
SortedMap<String,Double> newWordMap = new TreeMap<String, Double>();
Set<Map.Entry<String, Double>> allWords = wordMap.entrySet();
for(Iterator<Map.Entry<String, Double>> it = allWords.iterator();it.hasNext();){
Map.Entry<String, Double> me = it.next();
if(me.getValue() > 2)
newWordMap.put(me.getKey(), me.getValue());
}
System.out.println("newWordMap "+ newWordMap.size());
return newWordMap;
}
/**
* 打印属性词典，到allDicWordCountMap.txt中
* @param wordMap 属性词典
* @throws IOException
*/
public void printWordMap(Map<String, Double> wordMap) throws IOException{
System.out.println("printWordMap:");
int countLine = 0;
File outPutFile = new File("E:/DataMiningSample/docVector/allDicWordCountMap.txt");
FileWriter outPutFileWriter = new FileWriter(outPutFile);
Set<Map.Entry<String, Double>> allWords = wordMap.entrySet();
for(Iterator<Map.Entry<String, Double>> it = allWords.iterator();it.hasNext();){
Map.Entry<String, Double> me = it.next();
outPutFileWriter.write(me.getKey()+" "+me.getValue()+"\n");
countLine++;
}
outPutFileWriter.close();
System.out.println("WordMap size : " + countLine);
}
/**
* 词w在整个文档集合中的逆向文档频率idf (Inverse Document Frequency)，
* 即文档总数n与词w所出现文件数docs(w, D)比值的对数: idf = log(n / docs(w, D))
* 计算IDF。即属性词典中每一个词在多少个文档中出现过
* @param strDir 处理好的newsgroup文件文件夹的绝对路径
* @param wordMap 属性词典
* @return 单词的IDFMap
* @throws IOException
*/
public SortedMap<String,Double> computeIDF(String strDir,Map<String, Double> wordMap) throws IOException{
File fileDir = new File(strDir);
String word;
SortedMap<String,Double> IDFPerWordMap = new TreeMap<String, Double>();
Set<Map.Entry<String, Double>> wordMapSet = wordMap.entrySet();
for(Iterator<Map.Entry<String, Double>> it = wordMapSet.iterator();it.hasNext();){
Map.Entry<String, Double> pe = it.next();
Double countDoc = 0.0; //出现字典词的文本数
Double sumDoc = 0.0; //文本总数
String dicWord = pe.getKey();
File[] sampleDir = fileDir.listFiles();
for(int i =0;i<sampleDir.length;i++){
File[] sample = sampleDir[i].listFiles();
for(int j = 0;j<sample.length;j++){
sumDoc++; //统计文本数
FileReader samReader = new FileReader(sample[j]);
BufferedReader samBR = new BufferedReader(samReader);
boolean isExist = false;
while((word = samBR.readLine()) != null){
if(!word.isEmpty() && word.equals(dicWord)){
isExist = true;
break;
}
}
if(isExist)
countDoc++;
samBR.close();
}
}
//计算单词的IDF
//double IDF = Math.log(sumDoc / countDoc) / Math.log(10);
double IDF = Math.log(sumDoc / countDoc);
IDFPerWordMap.put(dicWord, IDF);
}
return IDFPerWordMap;
}
public static void main(String[] args) throws IOException {
ComputeWordsVector wordsVector = new ComputeWordsVector();
String strDir = "E:\\DataMiningSample\\processedSample";
Map<String, Double> wordMap = new TreeMap<String, Double>();
//属性词典
Map<String, Double> newWordMap = new TreeMap<String, Double>();
newWordMap = wordsVector.countWords(strDir,wordMap);
//wordsVector.printWordMap(newWordMap);
//wordsVector.computeIDF(strDir, newWordMap);
double trainSamplePercent = 0.8;
int indexOfSample = 1;
Map<String, Double> iDFPerWordMap = null;
wordsVector.computeTFMultiIDF(strDir, trainSamplePercent, indexOfSample, iDFPerWordMap, newWordMap);
//test();
}
public static void test(){
double sumDoc = 18828.0;
double countDoc = 229.0;
double IDF1 = Math.log(sumDoc / countDoc) / Math.log(10);
double IDF2 = Math.log(sumDoc / countDoc) ;
System.out.println(IDF1);
System.out.println(IDF2);
System.out.println(Math.log(10));
}
}
5、对文本分为測试集和训练集
按指定的比例（0.9或者0.8）对整个文本进行划分。測试集和训练集
package com.datamine.NaiveBayes;
import java.io.*;
import java.util.*;
public class CreateTrainAndTestSample {
void filterSpecialWords() throws IOException{
String word;
ComputeWordsVector cwv = new ComputeWordsVector();
String fileDir = "E:\\DataMiningSample\\processedSample";
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 = "E:/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();
targetDir = "E:/DataMiningSample/processedSampleOnlySpecial/"+sampleDir[i].getName()+"/"+fileShortName;
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();
samBR.close();
}
}
}
/**
* 创建训练集和測试集
* @param fileDir 预处理好的文件路径 E:\DataMiningSample\processedSampleOnlySpecial\
* @param trainSamplePercent 训练集占的百分比0.8
* @param indexOfSample 一个測试集计算规则 1
* @param classifyResultFile 測试例子正确类目记录文件
* @throws IOException
*/
void createTestSample(String fileDir,double trainSamplePercent,int indexOfSample,String classifyResultFile) throws IOException{
String word,targetDir;
FileWriter crWriter = new FileWriter(classifyResultFile);//測试例子正确类目记录文件
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));
double testEndIndex = (indexOfSample + 1)*(sample.length*(1-trainSamplePercent));
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){
targetDir = "E:/DataMiningSample/TestSample"+indexOfSample+"/"+sampleDir[i].getName();
crWriter.append(subFileName + " "+sampleDir[i].getName()+"\n");
}else{
targetDir = "E:/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();
samBR.close();
}
}
crWriter.close();
}
public static void main(String[] args) throws IOException {
CreateTrainAndTestSample test = new CreateTrainAndTestSample();
String fileDir = "E:/DataMiningSample/processedSampleOnlySpecial";
double trainSamplePercent=0.8;
int indexOfSample=1;
String classifyResultFile="E:/DataMiningSample/classifyResult";
test.createTestSample(fileDir, trainSamplePercent, indexOfSample, classifyResultFile);
//test.filterSpecialWords();
}
}
6、朴素贝叶斯算法描写叙述和实现
依据朴素贝叶斯公式，每一个測试例子属于某个类别的概率 = 全部測试例子包括特征词类条件概率P(tk|c)之积 * 先验概率P(c)
在详细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）多元分布模型( multinomial model ) –以单词为粒度。也就是说。考虑每一个文件中面反复出现多次的单词。注意多项分布事实上是从二项分布拓展出来的，假设採用多项分布模型，那么每一个单词表示变量就不再是二值变量（出现/不出现），而是每一个单词在文件中出现的次数
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+训练样本中不反复特征词总数）
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
（2）伯努利模型（Bernoulli model） –以文件为粒度，或者说是採用二项分布模型，伯努利实验即N次独立反复随机实验，仅仅考虑事件发生/不发生，所以每一个单词的表示变量是布尔型的
类条件概率P(tk|c)=（类c下包括单词tk的文件数+1）/(类c下文件总数+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
本分类器选用多元分布模型计算。依据《Introduction to Information Retrieval 》，多元分布模型计算准确率更高
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+训练样本中不反复特征词总数）
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
（2）伯努利模型（Bernoulli model） –以文件为粒度，或者说是採用二项分布模型，伯努利实验即N次独立反复随机实验，仅仅考虑事件发生/不发生，所以每一个单词的表示变量是布尔型的
类条件概率P(tk|c)=（类c下包括单词tk的文件数+1）/(类c下文件总数+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
本分类器选用多元分布模型计算。依据《Introduction to Information Retrieval 》，多元分布模型计算准确率更高
贝叶斯算法的实现有下面注意点：
(1) 计算概率用到了BigDecimal类实现随意精度计算(2) 用交叉验证法做十次分类实验，对准确率取平均值
(3) 依据正确类目文件和分类结果文计算混淆矩阵而且输出
(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。避免反复计算
package com.datamine.NaiveBayes;
import java.io.BufferedReader;
import java.io.File;
import java.io.FileNotFoundException;
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，採用十组交叉測试取平均值
* 採用多项式模型
* 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下单词的总数 + 训练集总单词数)
* @author Administrator
*/
public class NaiveBayesianClassifier {
/**
* 用朴素贝叶斯算法对測试文档集分类
* @param trainDir 训练文档集文件夹
* @param testDir 測试文档集文件夹
* @param classifyResultFileNew 分类结果文件路径
* @throws Exception
*/
private void doProcess(String trainDir,String testDir,
String classifyResultFileNew) throws Exception{
//保存训练集中每一个类别的总词数 <文件夹名。单词总数> category
Map<String,Double> cateWordsNum = new TreeMap<String, Double>();
//保存训练样本中每一个类别中每一个属性词的出现次数 <类目_单词,数目>
Map<String,Double> cateWordsProb = new TreeMap<String, Double>();
cateWordsNum = getCateWordsNum(trainDir);
cateWordsProb = getCateWordsProb(trainDir);
double totalWordsNum = 0.0;//记录全部训练集的总词数
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();
}
//以下開始读取測试例子做分类
Vector<String> testFileWords = new Vector<String>(); //測试样本全部词的容器
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);
}
spBR.close();
//以下分别计算该測试例子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();
}
/**
* 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下单词的总数 + 训练集中总单词数)
* 计算某一个測试样本数据某个类别的概率 使用多项式模型
* @param trainFile 该类别全部的训练样本所在的文件夹
* @param testFileWords 该測试样本中的全部词构成的容器
* @param cateWordsNum 记录每一个文件夹下单词的总数
* @param totalWordsNum 全部训练样本的单词的总数
* @param cateWordsProb 记录每一个文件夹中出现单词和次数
* @return 返回该測试样本在该类别中的概率
*/
private BigDecimal computeCateProb(File trainFile, Vector<String> testFileWords,
Map<String, Double> cateWordsNum, double totalWordsNum, Map<String, Double> cateWordsProb) {
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(wordNumInCateBD.add(totalWordsNumBD), 10, BigDecimal.ROUND_CEILING);
probability = probability.multiply(xcProb);
}
// P = P(tk|c)*P(C)
BigDecimal result = probability.multiply(wordNumInCateBD.divide(totalWordsNumBD,10, BigDecimal.ROUND_CEILING));
return result;
}
/**
* 统计某个类训练样本中每一个单词出现的次数
* @param trainDir 训练样本集文件夹
* @return cateWordsProb 用"类目_单词"来索引map，value就是该类目下该单词出现的次数
* @throws Exception
*/
private Map<String, Double> getCateWordsProb(String trainDir) throws Exception {
Map<String,Double> cateWordsProb = new TreeMap<String, Double>();
File sampleFile = new File(trainDir);
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))
cateWordsProb.put(key, cateWordsProb.get(key)+1);
else
cateWordsProb.put(key, 1.0);
}
samBR.close();
}
}
return cateWordsProb;
}
/**
* 获得每一个类目下的单词总数
* @param trainDir 训练文档集文件夹
* @return cateWordsNum <文件夹名，单词总数>的map
* @throws IOException
*/
private Map<String, Double> getCateWordsNum(String trainDir) throws IOException {
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++;
}
spBR.close();
}
cateWordsNum.put(sampleDir[i].getName(), count);
}
return cateWordsNum;
}
/**
* 依据正确类目文件和分类结果文件统计出准确率
* @param classifyRightCate 正确类目文件 <文件名称。类别文件夹名>
* @param classifyResultFileNew 分类结果文件 <文件名称，类别文件夹名>
* @return 分类的准确率
* @throws Exception
*/
public double computeAccuracy(String classifyRightCate,
String classifyResultFileNew) throws Exception {
Map<String,String> rightCate = new TreeMap<String, String>();
Map<String,String> resultCate = new TreeMap<String,String>();
rightCate = getMapFromResultFile(classifyRightCate);
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();
}
/**
* 依据正确类目文件和分类结果文件计算混淆矩阵并输出
* @param rightCate 正确类目map
* @param resultCate 分类结果相应map
*/
private void computerConfusionMatrix(Map<String, String> rightCate,
Map<String, String> resultCate) {
int[][] confusionMatrix = new int[20][20];
//首先求出类目相应的数组索引
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.printf("%-6d",i);
}
System.out.println("准确率");
for(int i =0;i<20;i++){
System.out.printf("%-6d",i);
for(int j = 0;j<20;j++){
System.out.printf("%-6d",confusionMatrix[i][j]);
hangSum[i] += confusionMatrix[i][j];
}
System.out.printf("%-6f\n",confusionMatrix[i][i]/hangSum[i]);
}
System.out.println();
}
/**
* 从结果文件里读取Map
* @param file 类目文件
* @return Map<String,String> 由<文件名称,类目名>保存的map
* @throws Exception
*/
private Map<String, String> getMapFromResultFile(String file) throws Exception {
File crFile = new File(file);
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;
}
public static void main(String[] args) throws Exception {
CreateTrainAndTestSample ctt = new CreateTrainAndTestSample();
NaiveBayesianClassifier nbClassifier = new NaiveBayesianClassifier();
//依据包括非特征词的文档集生成仅仅包括特征词的文档集到processedSampleOnlySpecial文件夹下
ctt.filterSpecialWords();
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 = "E:/DataMiningSample/TrainSample"+i;
String TestDir = "E:/DataMiningSample/TestSample"+i;
String classifyRightCate = "E:/DataMiningSample/classifyRightCate"+i+".txt";
String classifyResultFileNew = "E:/DataMiningSample/classifyResultNew"+i+".txt";
ctt.createTestSample("E:/DataMiningSample/processedSampleOnlySpecial", 0.8, 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);
}
}
7、实验结果与说明
这里仅仅列出第一次运行的结果：
这里使用的多项式模型是经过改进的计算方法：改进多项式模型的类条件概率的计算公式，改进为 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+0.001)/（类c下单词总数+训练样本中不反复特征词总数），分子当tk没有出现时，仅仅加0.001，这样更加精确的描写叙述的词的统计分布规律
8、算法改进
为了进一步提高朴素贝叶斯算法的分类能够进行例如以下改进：
1、优化特征词选取的方法，如方法三，或者更优方法
2、改进多项式模型的类条件概率的计算公式（上面已经实现）
