第一步，先计算需要计算概率的词频，单词种类数，类别单词总数（类别均是按照文件夹名区分）（基础数据以及分词了，每个单词一行，以及预处理好）
package org.lukey.hadoop.classifyBayes; import java.io.IOException;
import java.net.URI;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map; import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataOutputStream;
import org.apache.hadoop.fs.FileStatus;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IOUtils;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Counter;
import org.apache.hadoop.mapreduce.Counters;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.MultipleOutputs; /**
*
* 一次将需要的结果都统计到对应的文件夹中 AFRICA 484017newsML.txt afford 1
*
* 按照这个格式输出给后面处理得到需要的： 1. AFRICA 484017newsML.txt AFRICA 487141newsML.txt
* 类别中的文本数， ---> 计算先验概率(单独解决这个) 所有类别中的文本总数， ---> 可以由上面得到，计算先验概率
*
* 2. AFRICA afford 1 AFRICA boy 3 每个类中的每个单词的个数，---> 计算各个类中单词的概率
*
* 3. AFRICA 768 类中单词总数， ---> 将2中的第一个key相同的第三个数相加即可
*
* 4. AllWORDS 12345 所有类别中单词种类数 ---> 将1中的第三个key归并，计算个数
*
*/ public class MyWordCount { private static MultipleOutputs<Text, IntWritable> mos;
static String baseOutputPath = "/user/hadoop/test_out"; // 设计两个map分别计算每个类别的文本数//和每个类别的单词总数
private static Map<String, List<String>> fileCountMap = new HashMap<String, List<String>>();
private static Map<String, Integer> fileCount = new HashMap<String, Integer>();
// static Map<String, List<String>> wordsCountInClassMap = new
// HashMap<String, List<String>>(); static enum WordsNature {
CLSASS_NUMBER, CLASS_WORDS, TOTALWORDS
} public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); // 设置不同文件的路径
// 文本数路径
String priorProbality = "hdfs://192.168.190.128:9000/user/hadoop/output/priorP/priorProbality.txt";
conf.set("priorProbality", priorProbality); String[] otherArgs = { "/user/hadoop/input/NBCorpus/Country", "/user/hadoop/mid/wordsFre" }; Job job = new Job(conf, "file count"); job.setJarByClass(MyWordCount.class); // job.setInputFormatClass(CustomInputFormat.class); job.setMapperClass(First_Mapper.class);
job.setReducerClass(First_Reducer.class); //过滤掉文本数少于10的类别
List<Path> inputPaths = getSecondDir(conf, otherArgs[0]);
for (Path path : inputPaths) {
FileInputFormat.addInputPath(job, path);
} // 调用自己写的方法
// MyUtils.addInputPath(job, inputpath, conf);
// CustomInputFormat.setInputPaths(job, inputpath);
// FileInputFormat.addInputPath(job, inputpath);
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class); int exitCode = job.waitForCompletion(true) ? 0 : 1; // 调用计数器
Counters counters = job.getCounters();
Counter c1 = counters.findCounter(WordsNature.TOTALWORDS);
System.out.println("-------------->>>>: " + c1.getDisplayName() + ":" + c1.getName() + ": " + c1.getValue()); // 将单词种类数写入文件中
Path totalWordsPath = new Path("/user/hadoop/output/totalwords.txt");
FileSystem fs = FileSystem.get(conf);
FSDataOutputStream outputStream = fs.create(totalWordsPath);
outputStream.writeBytes(c1.getDisplayName() + ":" + c1.getValue());
IOUtils.closeStream(outputStream); // 下次求概率是尝试单词总种类数写到configuration中
//
// conf.set("TOTALWORDS", totalWords.toString()); System.exit(exitCode); } // Mapper
static class First_Mapper extends Mapper<LongWritable, Text, Text, IntWritable> { private final static IntWritable one = new IntWritable(1);
private final static IntWritable zero = new IntWritable(0); private Text className = new Text();
private Text countryName = new Text(); @Override
protected void cleanup(Mapper<LongWritable, Text, Text, IntWritable>.Context context)
throws IOException, InterruptedException {
Configuration conf = context.getConfiguration();
String file = conf.get("priorProbality");
FileSystem fs = FileSystem.get(URI.create(file), conf);
Path priorPath = new Path(file);
FSDataOutputStream priorStream = fs.create(priorPath);
for (Map.Entry<String, List<String>> entry : fileCountMap.entrySet()) {
fileCount.put(entry.getKey(), entry.getValue().size());
priorStream.writeBytes(entry.getKey() + "\t" + entry.getValue().size());
} // 求文本总数
int fileSum = 0;
for (Integer num : fileCount.values()) {
fileSum += num;
}
System.out.println("fileSum = " + fileSum); // 计算每个类的先验概率并写入文件
for (Map.Entry<String, Integer> entry : fileCount.entrySet()) {
double p = (double) entry.getValue() / fileSum;
priorStream.writeBytes(entry.getKey() + ":" + p);
}
IOUtils.closeStream(priorStream); } @Override
protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context)
throws IOException, InterruptedException {
// TODO Auto-generated method stub
FileSplit fileSplit = (FileSplit) context.getInputSplit(); // 文件名
String fileName = fileSplit.getPath().getName(); // 文件夹名(即类别名)
String dirName = fileSplit.getPath().getParent().getName(); className.set(dirName + "\t" + value.toString());
countryName.set(dirName + "\t" + fileName + "\t" + value.toString()); // 将文件名添加到map中用于统计文本个数（单独跑了一个程序计算主要还是为了筛选文本数太少的类别）
if (fileCountMap.containsKey(dirName)) {
if (!fileCountMap.get(dirName).contains(fileName)) {
fileCountMap.get(dirName).add(fileName);
}
} else {
List<String> oneList = new ArrayList<String>();
oneList.add(fileName);
fileCountMap.put(dirName, oneList);
} context.write(className, one); // 每个类别的每个单词数 // ABDBI hello 1
context.write(new Text(dirName), one);// 统计每个类中的单词总数 //ABDBI 1
context.write(value, zero); // 用于统计所有类中单词个数 }
} // Reducer
static class First_Reducer extends Reducer<Text, IntWritable, Text, IntWritable> { // result 表示每个类别中每个单词的个数
IntWritable result = new IntWritable();
Map<String, List<String>> classMap = new HashMap<String, List<String>>();
Map<String, List<String>> fileMap = new HashMap<String, List<String>>(); @Override
protected void reduce(Text key, Iterable<IntWritable> values,
Reducer<Text, IntWritable, Text, IntWritable>.Context context)
throws IOException, InterruptedException {
int sum = 0;
for (IntWritable value : values) {
sum += value.get();
} // sum为0，总得单词数加1，统计所有单词的种类
if (sum == 0) {
context.getCounter(WordsNature.TOTALWORDS).increment(1);
} else {// sum不为0时，通过key的长度来判断，
String[] temp = key.toString().split("\t");
if (temp.length == 2) { // 用tab分隔类别和单词
result.set(sum);
context.write(key, result);
// mos.write(new Text(temp[1]), result, temp[0]);
} else { // 类别中单词总数
result.set(sum);
mos.write(key, result, "wordsInClass");
} } } @Override
protected void cleanup(Reducer<Text, IntWritable, Text, IntWritable>.Context context)
throws IOException, InterruptedException {
// TODO Auto-generated method stub
mos.close();
} @Override
protected void setup(Reducer<Text, IntWritable, Text, IntWritable>.Context context)
throws IOException, InterruptedException {
// TODO Auto-generated method stub
mos = new MultipleOutputs<Text, IntWritable>(context);
} } // 获取文件夹下面二级文件夹路径的方法
static List<Path> getSecondDir(Configuration conf, String folder) throws Exception {
FileSystem fs = FileSystem.get(conf);
Path path = new Path(folder);
FileStatus[] stats = fs.listStatus(path);
List<Path> folderPath = new ArrayList<Path>();
for (FileStatus stat : stats) {
if (stat.isDir()) {
if (fs.listStatus(stat.getPath()).length > 10) { //筛选出文件数大于10个的类别作为 输入路径
folderPath.add(stat.getPath());
}
}
}
return folderPath;
} }
第二步，计算每个类别单词的概率，需提前读取每个类别单词总数，以及总得单词种类数（都可以通过configuration.set）也可以在setup里面先于map处理前读取数据。
package org.lukey.hadoop.classifyBayes; import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;
import java.net.URI;
import java.util.HashMap;
import java.util.Map; import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.MultipleOutputs; public class Probability { private static final Log LOG = LogFactory.getLog(FileInputFormat.class);
public static int total = 0;
private static MultipleOutputs<Text, DoubleWritable> mos; // Client
public static void main(String[] args) throws Exception { Configuration conf = new Configuration();
conf.set("mapred.job.tracker", "192.168.190.128:9001");
conf.set("mapred.jar", "probability.jar");
// 读取单词总数，设置到congfiguration中
String totalWordsPath = "hdfs://192.168.190.128:9000/user/hadoop/output/totalwords.txt";
String wordsInClassPath = "hdfs://192.168.190.128:9000/user/hadoop/mid/wordsFrequence/wordsInClass-r-00000"; conf.set("wordsInClassPath", wordsInClassPath);
// Map<String, Integer> wordsInClassMap = new HashMap<String,
// Integer>();//保存每个类别的单词总数 // 先读取单词总类别数
FileSystem fs = FileSystem.get(URI.create(totalWordsPath), conf);
FSDataInputStream inputStream = fs.open(new Path(totalWordsPath));
BufferedReader buffer = new BufferedReader(new InputStreamReader(inputStream));
String strLine = buffer.readLine();
String[] temp = strLine.split(":");
if (temp.length == 2) {
// temp[0] = TOTALWORDS
conf.set(temp[0], temp[1]);// 设置两个String
} total = Integer.parseInt(conf.get("TOTALWORDS"));
LOG.info("------>total = " + total); System.out.println("total ==== " + total);
/*
* String[] otherArgs = new GenericOptionsParser(conf,
* args).getRemainingArgs();
*
* if (otherArgs.length != 2) { System.out.println("Usage <in> <out>");
* System.exit(-1); }
*/
Job job = new Job(conf, "file count"); job.setJarByClass(Probability.class); job.setMapperClass(WordsOfClassCountMapper.class);
job.setReducerClass(WordsOfClassCountReducer.class); String input = "hdfs://192.168.190.128:9000/user/hadoop/mid/wordsFrequence";
String output = "hdfs://192.168.190.128:9000/user/hadoop/output/probability/"; FileInputFormat.addInputPath(job, new Path(input));
FileOutputFormat.setOutputPath(job, new Path(output)); job.setOutputKeyClass(Text.class);
job.setOutputValueClass(DoubleWritable.class); System.exit(job.waitForCompletion(true) ? 0 : 1); } // Mapper
static class WordsOfClassCountMapper extends Mapper<LongWritable, Text, Text, DoubleWritable> { private static DoubleWritable number = new DoubleWritable();
private static Text className = new Text(); // 保存类别中单词总数
private static Map<String, Integer> filemap = new HashMap<String, Integer>(); protected void map(LongWritable key, Text value,
Mapper<LongWritable, Text, Text, DoubleWritable>.Context context)
throws IOException, InterruptedException {
Configuration conf = context.getConfiguration();
int tot = Integer.parseInt(conf.get("TOTALWORDS")); System.out.println("total = " + total);
System.out.println("tot = " + tot); // 输入的格式如下：
// ALB weekend 1
// ALB weeks 3
Map<String, Map<String, Integer>> baseMap = new HashMap<String, Map<String, Integer>>(); // 保存基础数据
// Map<String, Map<String, Double>> priorMap = new HashMap<String,
// Map<String, Double>>(); // 保存每个单词出现的概率 String[] temp = value.toString().split("\t");
// 先将数据存到baseMap中
if (temp.length == 3) {
// 文件夹名类别名
if (baseMap.containsKey(temp[0])) {
baseMap.get(temp[0]).put(temp[1], Integer.parseInt(temp[2]));
} else {
Map<String, Integer> oneMap = new HashMap<String, Integer>();
oneMap.put(temp[1], Integer.parseInt(temp[2]));
baseMap.put(temp[0], oneMap);
} } // 读取数据完毕，全部保存在baseMap中 int allWordsInClass = 0; for (Map.Entry<String, Map<String, Integer>> entries : baseMap.entrySet()) { // 遍历类别
allWordsInClass = filemap.get(entries.getKey());
for (Map.Entry<String, Integer> entry : entries.getValue().entrySet()) { // 遍历类别中的单词词频求概率
double p = (entry.getValue() + 1.0) / (allWordsInClass + tot); className.set(entries.getKey() + "\t" + entry.getKey());
number.set(p);
LOG.info("------>p = " + p); context.write(className, number);
}
} } protected void cleanup(Mapper<LongWritable, Text, Text, DoubleWritable>.Context context)
throws IOException, InterruptedException {
// TODO Auto-generated method stub
mos.close();
} protected void setup(Mapper<LongWritable, Text, Text, DoubleWritable>.Context context)
throws IOException, InterruptedException {
// TODO Auto-generated method stub
Configuration conf = context.getConfiguration();
mos = new MultipleOutputs<Text, DoubleWritable>(context);
String filePath = conf.get("wordsInClassPath");
FileSystem fs = FileSystem.get(URI.create(filePath), conf);
FSDataInputStream inputStream = fs.open(new Path(filePath));
BufferedReader buffer = new BufferedReader(new InputStreamReader(inputStream));
String strLine = null;
while ((strLine = buffer.readLine()) != null) {
String[] temp = strLine.split("\t");
filemap.put(temp[0], Integer.parseInt(temp[1]));
}
} } // Reducer
static class WordsOfClassCountReducer extends Reducer<Text, DoubleWritable, Text, DoubleWritable> { // result 表示每个文件里面单词个数
DoubleWritable result = new DoubleWritable();
// Configuration conf = new Configuration();
// int total = conf.getInt("TOTALWORDS", 1); protected void reduce(Text key, Iterable<DoubleWritable> values,
Reducer<Text, DoubleWritable, Text, DoubleWritable>.Context context)
throws IOException, InterruptedException { double sum = 0L;
for (DoubleWritable value : values) {
sum += value.get();
}
result.set(sum); context.write(key, result);
} } }