Naive Bayes在mapreduce上的实现

2024年4月18日 109次阅读 来源: MapReduce

Naive Bayes是比较常用的分类器，因为思想比较简单。之所以说是naive，是因为他假设用于分类的特征在类确定的条件下是条件独立的，这个假设使得分类变得很简单，但会损失一定的精度。 具体推导可以看《统计学习方法》 经过推导我们可知y=argMaxP(Y=ck)*P(X=x|Y=ck)。那么我们需要求先验概率也就是P（Y=ck）和求条件概率p(X=x|Y=ck). 具体的例子以：http://blog.163.com/jiayouweijiewj@126/blog/static/1712321772010102802635243/来说明。
我这里一共用了4个mapreduce，因为采用了多项式模型，先验概率P(c)= 类c下单词总数/整个训练样本的单词总数。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+|V|)（|V|是单词种类数）。输入是： 1:Chinese Beijing Chinese 1:Chinese Chinese Shanghai 1:Chinese Macao 0:Tokyo Japan Chinese 1 一个mapreduce是用于求在各个类别下的单词数，这个是为了后面求先验概率用的。 输出为： 0              3 1              8   2 一个mapreduce用于求条件概率，输出为： 0:Chinese               0.2222222222222222 0:Japan   0.2222222222222222 0:Tokyo 0.2222222222222222 1:Beijing 0.14285714285714285 1:Chinese               0.42857142857142855 1:Macao 0.14285714285714285 1:Shanghai            0.14285714285714285   3 一个mapreduce用于计算单词种类数，输出为：
num is 6
4 最后一个mapreduce是用于预测的。
 
下面说下各个mapreduce的实现：
1 求各个类别下的单词数，这个比较简单，就是以类别为key，然后进行单词统计就好。
 附上代码：

 1 package hadoop.MachineLearning.Bayes.Pro;
 2 
 3 
 4 
 5 import org.apache.hadoop.conf.Configuration;
 6 import org.apache.hadoop.fs.Path;
 7 import org.apache.hadoop.io.IntWritable;
 8 import org.apache.hadoop.io.Text;
 9 import org.apache.hadoop.mapreduce.Job;
10 import org.apache.hadoop.mapreduce.Mapper;
11 import org.apache.hadoop.mapreduce.Reducer;
12 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
13 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
14 
15 public class PriorProbability {//用于求各个类别下的单词数，为后面求先验概率
16 
17     public static void main(String[] args) throws Exception {
18         Configuration conf = new Configuration();
19         String input="hdfs://10.107.8.110:9000/Bayes/Bayes_input/";
20         String output="hdfs://10.107.8.110:9000/Bayes/Bayes_output/Pro/";
21         Job job = Job.getInstance(conf, "ProirProbability");
22         job.setJarByClass(hadoop.MachineLearning.Bayes.Pro.PriorProbability.class);
23         // TODO: specify a mapper
24         job.setMapperClass(MyMapper.class);
25         //job.setMapInputKeyClass(LongWritable.class);
26         // TODO: specify a reducer
27         job.setMapOutputKeyClass(Text.class);
28         job.setMapOutputValueClass(Text.class);
29         job.setReducerClass(MyReducer.class);
30 
31         // TODO: specify output types
32         job.setOutputKeyClass(Text.class);
33         job.setOutputValueClass(IntWritable.class);
34 
35         // TODO: specify input and output DIRECTORIES (not files)
36         FileInputFormat.setInputPaths(job, new Path(input));
37         FileOutputFormat.setOutputPath(job, new Path(output));
38 
39         if (!job.waitForCompletion(true))
40             return;
41     }
42 
43 }
44 
45 
46 package hadoop.MachineLearning.Bayes.Pro;
47 
48 import java.io.IOException;
49 
50 import org.apache.hadoop.io.LongWritable;
51 import org.apache.hadoop.io.Text;
52 import org.apache.hadoop.mapreduce.Mapper;
53 import org.apache.hadoop.mapreduce.Mapper.Context;
54 
55 public class MyMapper extends Mapper<LongWritable, Text, Text, Text> {
56 
57     public void map(LongWritable ikey, Text ivalue, Context context)
58             throws IOException, InterruptedException {
59         String[] line=ivalue.toString().split(":| ");
60         int size=line.length-1;
61         context.write(new Text(line[0]),new Text(String.valueOf(size)));
62     }
63 
64 }
65 
66 
67 package hadoop.MachineLearning.Bayes.Pro;
68 
69 import java.io.IOException;
70 
71 import org.apache.hadoop.io.IntWritable;
72 import org.apache.hadoop.io.Text;
73 import org.apache.hadoop.mapreduce.Reducer;
74 import org.apache.hadoop.mapreduce.Reducer.Context;
75 
76 public class MyReducer extends Reducer<Text, Text, Text, IntWritable> {
77 
78     public void reduce(Text _key, Iterable<Text> values, Context context)
79             throws IOException, InterruptedException {
80         // process values
81         int sum=0;
82         for (Text val : values) {
83             sum+=Integer.parseInt(val.toString());
84         }
85         context.write(_key,new IntWritable(sum));
86     }
87 
88 }

 

 
 
2 求文档中的单词种类数，自己实现的方法不太好，思路是，对每一行的输入都以相同的key输出，然后在combiner中先利用set求得该节点上的不重复的单词，接着在reduce中再利用set，将所有单词求种类数。感觉好一点的话是先按照单词进行规约，最后再利用一个mapreduce对单词种类数进行统计。但是考虑到刚学会mapreduce不久还不会写链式，而且一个bayes已经写了4个mapreduce就不考虑再复杂化了。

package hadoop.MachineLearning.Bayes.Count;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class Count {//计算文档中的单词种类数目

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "Count");
        String input="hdfs://10.107.8.110:9000/Bayes/Bayes_input";
        String output="hdfs://10.107.8.110:9000/Bayes/Bayes_output/Count";
        job.setJarByClass(hadoop.MachineLearning.Bayes.Count.Count.class);
        // TODO: specify a mapper
        job.setMapperClass(MyMapper.class);
        // TODO: specify a reducer
        job.setCombinerClass(MyCombiner.class);
        job.setReducerClass(MyReducer.class);
        
        // TODO: specify output types
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);

        // TODO: specify input and output DIRECTORIES (not files)
        FileInputFormat.setInputPaths(job, new Path(input));
        FileOutputFormat.setOutputPath(job, new Path(output));

        if (!job.waitForCompletion(true))
            return;
    }

}


package hadoop.MachineLearning.Bayes.Count;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class MyMapper extends Mapper<LongWritable, Text, Text, Text> {

    public void map(LongWritable ikey, Text ivalue, Context context)
            throws IOException, InterruptedException {
        String[] line=ivalue.toString().split(":| ");
        String key="1";
        System.out.println("   ");
        System.out.println("   ");
        System.out.println("   ");
        for(int i=1;i<line.length;i++){
            
            System.out.println(line[i]);
            context.write(new Text(key),new Text(line[i]));//以相同的key进行输出，使得能最后输出到一个reduce中
        }
    }

}



package hadoop.MachineLearning.Bayes.Count;

import java.io.IOException;
import java.util.HashSet;
import java.util.Iterator;
import java.util.Set;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class MyCombiner extends Reducer<Text, Text, Text, Text> {//先在本地的节点上利用set删去重复的单词

    public void reduce(Text _key, Iterable<Text> values, Context context)
            throws IOException, InterruptedException {
        // process values
        Set set=new HashSet();
        for (Text val : values) {
            set.add(val.toString());
        }
        for(Iterator it=set.iterator();it.hasNext();){
            context.write(new Text("1"),new Text(it.next().toString()));
        }
    }

}

package hadoop.MachineLearning.Bayes.Count;

import java.io.IOException;
import java.util.HashSet;
import java.util.Set;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class MyReducer extends Reducer<Text, Text, Text, Text> {//通过combiner后，再利用set对单词进行去重，最后得到种类数

    public void reduce(Text _key, Iterable<Text> values, Context context)
            throws IOException, InterruptedException {
        // process values
        Set set=new HashSet();
        for (Text val : values) {
            set.add(val.toString());
        }
        context.write(new Text("num is "),new Text(String.valueOf(set.size())));
    }

}

 

 
 
3 求条件概率.这里需要用到该类别下该单词的数目sum,该类别下的单词总数，文档中的单词种类数。这些都可以在之前的输出文件中获得，我这里都用map去接受这些数据。由于有些单词没有出现在该类别下，例如P(Japan | yes)=P(Tokyo | yes)，如果将他们当作0处理，那么导致该条件概率会是0，所以这里用了平滑的方法可以参考上述的链接。这里有个细节，就是条件概率生成的会比较多，需要一种高效的存储和查找方式，我这里因为水平不够，就直接用map来存放了，如果对于大的数据，这个会很低效。
 

package hadoop.MachineLearning.Bayes.Cond;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class CondiPro {//用于求条件概率

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        String input="hdfs://10.107.8.110:9000/Bayes/Bayes_input";
        String output="hdfs://10.107.8.110:9000/Bayes/Bayes_output/Con";
        String proPath="hdfs://10.107.8.110:9000/Bayes/Bayes_output/Pro";//这是之前求各个类别下单词数目的输出
        String countPath="hdfs://10.107.8.110:9000/Bayes/Bayes_output/Count";//这是之前求的单词种类数
        conf.set("propath",proPath);
        conf.set("countPath",countPath);
        Job job = Job.getInstance(conf, "ConditionPro");
        
        job.setJarByClass(hadoop.MachineLearning.Bayes.Cond.CondiPro.class);
        // TODO: specify a mapper
        job.setMapperClass(MyMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        // TODO: specify a reducer
        job.setReducerClass(MyReducer.class);

        // TODO: specify output types
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        // TODO: specify input and output DIRECTORIES (not files)
        FileInputFormat.setInputPaths(job, new Path(input));
        FileOutputFormat.setOutputPath(job, new Path(output));

        if (!job.waitForCompletion(true))
            return;
    }

}

package hadoop.MachineLearning.Bayes.Cond;

import java.io.IOException;
import java.util.Map;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class MyMapper extends Mapper<LongWritable, Text, Text, IntWritable> {

    
    public void map(LongWritable ikey, Text ivalue, Context context)
            throws IOException, InterruptedException {
        String[] line=ivalue.toString().split(":| ");
        for(int i=1;i<line.length;i++){
            String key=line[0]+":"+line[i];
            context.write(new Text(key),new IntWritable(1));
        }
    }

}

package hadoop.MachineLearning.Bayes.Cond;

import java.io.IOException;
import java.util.Map;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class MyReducer extends Reducer<Text, IntWritable, Text, DoubleWritable> {
    public Map<String,Integer> map;
    public int count=0;
    public void setup(Context context) throws IOException{
        Configuration conf=context.getConfiguration();
        
        String proPath=conf.get("propath");
        String countPath=conf.get("countPath");//
        map=Utils.getMapFormHDFS(proPath);//获得各个类别下的单词数
        count=Utils.getCountFromHDFS(countPath);//获得单词种类数
    }
    public void reduce(Text _key, Iterable<IntWritable> values, Context context)
            throws IOException, InterruptedException {
        // process values
        int sum=0;
        for (IntWritable val : values) {
            sum+=val.get();
        }
        int type=Integer.parseInt(_key.toString().split(":")[0]);
        double probability=0.0;
        for(Map.Entry<String,Integer> entry:map.entrySet()){
            if(type==Integer.parseInt(entry.getKey())){
                probability=(sum+1)*1.0/(entry.getValue()+count);//条件概率的计算
            }
        }
        context.write(_key,new DoubleWritable(probability));
    }

}

package hadoop.MachineLearning.Bayes.Cond;

import java.io.IOException;
import java.util.HashMap;
import java.util.Map;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileStatus;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.util.LineReader;

public class Utils {

    /**
     * @param args
     * @throws IOException 
     */
    
    public static Map<String,Integer> getMapFormHDFS(String input) throws IOException{
        Configuration conf=new Configuration();
        Path path=new Path(input);
        FileSystem fs=path.getFileSystem(conf);
        
        FileStatus[] stats=fs.listStatus(path);
        Map<String,Integer> map=new HashMap();
        for(int i=0;i<stats.length;i++){
            if(stats[i].isFile()){
                FSDataInputStream infs=fs.open(stats[i].getPath());
                LineReader reader=new LineReader(infs,conf);
                Text line=new Text();
                while(reader.readLine(line)>0){
                    String[] temp=line.toString().split("    ");
                    //System.out.println(temp.length);
                    map.put(temp[0],Integer.parseInt(temp[1]));
                }
                reader.close();
            }
        }
        
        return map;
    
    }
    
    public static Map<String,Double> getMapFormHDFS(String input,boolean j) throws IOException{
        Configuration conf=new Configuration();
        Path path=new Path(input);
        FileSystem fs=path.getFileSystem(conf);
        
        FileStatus[] stats=fs.listStatus(path);
        Map<String,Double> map=new HashMap();
        for(int i=0;i<stats.length;i++){
            if(stats[i].isFile()){
                FSDataInputStream infs=fs.open(stats[i].getPath());
                LineReader reader=new LineReader(infs,conf);
                Text line=new Text();
                while(reader.readLine(line)>0){
                    String[] temp=line.toString().split("    ");
                    //System.out.println(temp.length);
                    map.put(temp[0],Double.parseDouble(temp[1]));
                }
                reader.close();
            }
        }
        
        return map;
    
    }
    
    
    public static int getCountFromHDFS(String input) throws IOException{
        Configuration conf=new Configuration();
        Path path=new Path(input);
        FileSystem fs=path.getFileSystem(conf);
        
        FileStatus[] stats=fs.listStatus(path);
        
        int count=0;
        for(int i=0;i<stats.length;i++){
            if(stats[i].isFile()){
                FSDataInputStream infs=fs.open(stats[i].getPath());
                LineReader reader=new LineReader(infs,conf);
                Text line=new Text();
                while(reader.readLine(line)>0){
                    String[] temp=line.toString().split("    ");
                    //System.out.println(temp.length);
                    count=Integer.parseInt(temp[1]);
                }
                reader.close();
            }
        }
        return count;
    }
    
    public static void main(String[] args) throws IOException {
        // TODO Auto-generated method stub
        String proPath="hdfs://10.107.8.110:9000/Bayes/Bayes_output/Pro";
        String countPath="hdfs://10.107.8.110:9000/Bayes/Bayes_output/Count/";
        Map<String,Integer> map=Utils.getMapFormHDFS(proPath);
        for(Map.Entry<String,Integer> entry:map.entrySet()){
            System.out.println(entry.getKey()+"->"+entry.getValue());
        }
        
        int count=Utils.getCountFromHDFS(countPath);
        System.out.println("count is "+count);
    }

}

 

 
4 预测，例如输入Chinese, Chinese, Chinese, Tokyo, Japan。那就分别对每个单词以0，1的类别进行输出，输出为type:words，接着就是在条件概率中查找，进行简单的累乘即可。
 

package hadoop.MachineLearning.Bayes.Predict;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class Predict {

    public static void main(String[] args) throws Exception {//预测
        Configuration conf = new Configuration();
        String input="hdfs://10.107.8.110:9000/Bayes/Predict_input";
        String output="hdfs://10.107.8.110:9000/Bayes/Bayes_output/Predict";
        String condiProPath="hdfs://10.107.8.110:9000/Bayes/Bayes_output/Con";
        String proPath="hdfs://10.107.8.110:9000/Bayes/Bayes_output/Pro";
        String countPath="hdfs://10.107.8.110:9000/Bayes/Bayes_output/Count";
        conf.set("condiProPath",condiProPath);
        conf.set("proPath",proPath);
        conf.set("countPath",countPath);
        Job job = Job.getInstance(conf, "Predict");
        job.setJarByClass(hadoop.MachineLearning.Bayes.Predict.Predict.class);
        // TODO: specify a mapper
        job.setMapperClass(MyMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(Text.class);
        // TODO: specify a reducer
        job.setReducerClass(MyReducer.class);

        // TODO: specify output types
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(DoubleWritable.class);

        // TODO: specify input and output DIRECTORIES (not files)
        FileInputFormat.setInputPaths(job, new Path(input));
        FileOutputFormat.setOutputPath(job, new Path(output));

        if (!job.waitForCompletion(true))
            return;
    }

}

package hadoop.MachineLearning.Bayes.Predict;



import java.io.IOException;
import java.util.HashMap;
import java.util.Map;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class MyMapper extends Mapper<LongWritable, Text, Text, Text> {
    
    public Map<String,Integer> map=new HashMap();
    
    public void setup(Context context) throws IOException{
        Configuration conf=context.getConfiguration();
        String proPath=conf.get("proPath");
        map=Utils.getMapFormHDFS(proPath);
    }
    
    public void map(LongWritable ikey, Text ivalue, Context context)
            throws IOException, InterruptedException {
        for(Map.Entry<String,Integer> entry:map.entrySet()){
            context.write(new Text(entry.getKey()),ivalue);//对每一行数据，打上所有类别，方便后续的求条件概率
        }
    }

}

package hadoop.MachineLearning.Bayes.Predict;


import java.io.IOException;
import java.util.HashMap;
import java.util.Map;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class MyReducer extends Reducer<Text, Text, Text, DoubleWritable> {
    
    public Map<String,Double> mapDouble=new HashMap();//存放条件概率
    
    public Map<String,Integer> mapInteger=new HashMap();//存放各个类别下的单词数
    
    public Map<String,Double> noFind=new HashMap();//用于那些单词没有出现在某个类别中的
    
    public Map<String,Double> prePro=new HashMap();//求的后的先验概率
    
    public void setup(Context context) throws IOException{
        Configuration conf=context.getConfiguration();
        
    
        
        String condiProPath=conf.get("condiProPath");
        String proPath=conf.get("proPath");
        String countPath=conf.get("countPath");
        mapDouble=Utils.getMapFormHDFS(condiProPath,true);
        mapInteger=Utils.getMapFormHDFS(proPath);
        int count=Utils.getCountFromHDFS(countPath);
        for(Map.Entry<String,Integer> entry:mapInteger.entrySet()){
            double pro=0.0;
            noFind.put(entry.getKey(),(1.0/(count+entry.getValue())));
        }
        int sum=0;
        for(Map.Entry<String,Integer> entry:mapInteger.entrySet()){
            sum+=entry.getValue();
        }
        
        for(Map.Entry<String,Integer> entry:mapInteger.entrySet()){
            prePro.put(entry.getKey(),(entry.getValue()*1.0/sum));
        }
        
    }
    
    public void reduce(Text _key, Iterable<Text> values, Context context)
            throws IOException, InterruptedException {
        // process values
        String type=_key.toString();
        double pro=1.0;
        for (Text val : values) {
            String[] words=val.toString().split(" ");
            for(int i=0;i<words.length;i++){
                String condi=type+":"+words[i];
                if(mapDouble.get(condi)!=null){//如果该单词出现在该类别中，说明有条件概率
                    pro=pro*mapDouble.get(condi);
                }else{//如果该单词不在该类别中，就采用默认的条件概率
                    pro=pro*noFind.get(type);
                }
            }
        }
        pro=pro*prePro.get(type);
        context.write(new Text(type),new DoubleWritable(pro));
    }

}


package hadoop.MachineLearning.Bayes.Predict;

import java.io.IOException;
import java.util.HashMap;
import java.util.Map;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileStatus;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.util.LineReader;

public class Utils {

    /**
     * @param args
     * @throws IOException 
     */
    
    public static Map<String,Integer> getMapFormHDFS(String input) throws IOException{
        Configuration conf=new Configuration();
        Path path=new Path(input);
        FileSystem fs=path.getFileSystem(conf);
        
        FileStatus[] stats=fs.listStatus(path);
        Map<String,Integer> map=new HashMap();
        for(int i=0;i<stats.length;i++){
            if(stats[i].isFile()){
                FSDataInputStream infs=fs.open(stats[i].getPath());
                LineReader reader=new LineReader(infs,conf);
                Text line=new Text();
                while(reader.readLine(line)>0){
                    String[] temp=line.toString().split("    ");
                    //System.out.println(temp.length);
                    map.put(temp[0],Integer.parseInt(temp[1]));
                }
                reader.close();
            }
        }
        
        return map;
    
    }
    
    public static Map<String,Double> getMapFormHDFS(String input,boolean j) throws IOException{
        Configuration conf=new Configuration();
        Path path=new Path(input);
        FileSystem fs=path.getFileSystem(conf);
        
        FileStatus[] stats=fs.listStatus(path);
        Map<String,Double> map=new HashMap();
        for(int i=0;i<stats.length;i++){
            if(stats[i].isFile()){
                FSDataInputStream infs=fs.open(stats[i].getPath());
                LineReader reader=new LineReader(infs,conf);
                Text line=new Text();
                while(reader.readLine(line)>0){
                    String[] temp=line.toString().split("    ");
                    //System.out.println(temp.length);
                    map.put(temp[0],Double.parseDouble(temp[1]));
                }
                reader.close();
            }
        }
        
        return map;
    
    }
    
    
    public static int getCountFromHDFS(String input) throws IOException{
        Configuration conf=new Configuration();
        Path path=new Path(input);
        FileSystem fs=path.getFileSystem(conf);
        
        FileStatus[] stats=fs.listStatus(path);
        
        int count=0;
        for(int i=0;i<stats.length;i++){
            if(stats[i].isFile()){
                FSDataInputStream infs=fs.open(stats[i].getPath());
                LineReader reader=new LineReader(infs,conf);
                Text line=new Text();
                while(reader.readLine(line)>0){
                    String[] temp=line.toString().split("    ");
                    //System.out.println(temp.length);
                    count=Integer.parseInt(temp[1]);
                }
                reader.close();
            }
        }
        return count;
    }
    
    public static void main(String[] args) throws IOException {
        // TODO Auto-generated method stub
        String proPath="hdfs://10.107.8.110:9000/Bayes/Bayes_output/Pro";
        String countPath="hdfs://10.107.8.110:9000/Bayes/Bayes_output/Count/";
        Map<String,Integer> map=Utils.getMapFormHDFS(proPath);
        for(Map.Entry<String,Integer> entry:map.entrySet()){
            System.out.println(entry.getKey()+"->"+entry.getValue());
        }
        
        int count=Utils.getCountFromHDFS(countPath);
        System.out.println("count is "+count);
    }

}

 

 
 
 
 
 
 
 

    原文作者：MapReduce
    原文地址: https://www.cnblogs.com/sunrye/p/4553732.html
    本文转自网络文章，转载此文章仅为分享知识，如有侵权，请联系博主进行删除。
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