一个mapreduce得到需要计算单词概率的基础数据

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;
        }


}

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);
        }

    }

}