MapReduce - Phân loại nhị phân Naive Bayes

Lập trình MapReduce cho bài toán phân loại hoa Iris sử dụng thuật toán Naive Bayes

Bước 1: Tải file dữ liệu loài hoa Iris

Dữ liệu được lấy tại Kaggle, tạo file iris.csv với nội dung như sau:

5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5.0,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5.0,3.4,1.5,0.2,Iris-setosa
4.4,2.9,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.4,3.7,1.5,0.2,Iris-setosa
4.8,3.4,1.6,0.2,Iris-setosa
4.8,3.0,1.4,0.1,Iris-setosa
4.3,3.0,1.1,0.1,Iris-setosa
5.8,4.0,1.2,0.2,Iris-setosa
5.7,4.4,1.5,0.4,Iris-setosa
5.4,3.9,1.3,0.4,Iris-setosa
5.1,3.5,1.4,0.3,Iris-setosa
5.7,3.8,1.7,0.3,Iris-setosa
5.1,3.8,1.5,0.3,Iris-setosa
5.4,3.4,1.7,0.2,Iris-setosa
5.1,3.7,1.5,0.4,Iris-setosa
4.6,3.6,1.0,0.2,Iris-setosa
5.1,3.3,1.7,0.5,Iris-setosa
4.8,3.4,1.9,0.2,Iris-setosa
5.0,3.0,1.6,0.2,Iris-setosa
5.0,3.4,1.6,0.4,Iris-setosa
5.2,3.5,1.5,0.2,Iris-setosa
5.2,3.4,1.4,0.2,Iris-setosa
4.7,3.2,1.6,0.2,Iris-setosa
4.8,3.1,1.6,0.2,Iris-setosa
5.4,3.4,1.5,0.4,Iris-setosa
5.2,4.1,1.5,0.1,Iris-setosa
5.5,4.2,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.0,3.2,1.2,0.2,Iris-setosa
5.5,3.5,1.3,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
4.4,3.0,1.3,0.2,Iris-setosa
5.1,3.4,1.5,0.2,Iris-setosa
5.0,3.5,1.3,0.3,Iris-setosa
4.5,2.3,1.3,0.3,Iris-setosa
4.4,3.2,1.3,0.2,Iris-setosa
5.0,3.5,1.6,0.6,Iris-setosa
5.1,3.8,1.9,0.4,Iris-setosa
4.8,3.0,1.4,0.3,Iris-setosa
5.1,3.8,1.6,0.2,Iris-setosa
4.6,3.2,1.4,0.2,Iris-setosa
5.3,3.7,1.5,0.2,Iris-setosa
5.0,3.3,1.4,0.2,Iris-setosa
7.0,3.2,4.7,1.4,Iris-versicolor
6.4,3.2,4.5,1.5,Iris-versicolor
6.9,3.1,4.9,1.5,Iris-versicolor
5.5,2.3,4.0,1.3,Iris-versicolor
6.5,2.8,4.6,1.5,Iris-versicolor
5.7,2.8,4.5,1.3,Iris-versicolor
6.3,3.3,4.7,1.6,Iris-versicolor
4.9,2.4,3.3,1.0,Iris-versicolor
6.6,2.9,4.6,1.3,Iris-versicolor
5.2,2.7,3.9,1.4,Iris-versicolor
5.0,2.0,3.5,1.0,Iris-versicolor
5.9,3.0,4.2,1.5,Iris-versicolor
6.0,2.2,4.0,1.0,Iris-versicolor
6.1,2.9,4.7,1.4,Iris-versicolor
5.6,2.9,3.6,1.3,Iris-versicolor
6.7,3.1,4.4,1.4,Iris-versicolor
5.6,3.0,4.5,1.5,Iris-versicolor
5.8,2.7,4.1,1.0,Iris-versicolor
6.2,2.2,4.5,1.5,Iris-versicolor
5.6,2.5,3.9,1.1,Iris-versicolor
5.9,3.2,4.8,1.8,Iris-versicolor
6.1,2.8,4.0,1.3,Iris-versicolor
6.3,2.5,4.9,1.5,Iris-versicolor
6.1,2.8,4.7,1.2,Iris-versicolor
6.4,2.9,4.3,1.3,Iris-versicolor
6.6,3.0,4.4,1.4,Iris-versicolor
6.8,2.8,4.8,1.4,Iris-versicolor
6.7,3.0,5.0,1.7,Iris-versicolor
6.0,2.9,4.5,1.5,Iris-versicolor
5.7,2.6,3.5,1.0,Iris-versicolor
5.5,2.4,3.8,1.1,Iris-versicolor
5.5,2.4,3.7,1.0,Iris-versicolor
5.8,2.7,3.9,1.2,Iris-versicolor
6.0,2.7,5.1,1.6,Iris-versicolor
5.4,3.0,4.5,1.5,Iris-versicolor
6.0,3.4,4.5,1.6,Iris-versicolor
6.7,3.1,4.7,1.5,Iris-versicolor
6.3,2.3,4.4,1.3,Iris-versicolor
5.6,3.0,4.1,1.3,Iris-versicolor
5.5,2.5,4.0,1.3,Iris-versicolor
5.5,2.6,4.4,1.2,Iris-versicolor
6.1,3.0,4.6,1.4,Iris-versicolor
5.8,2.6,4.0,1.2,Iris-versicolor
5.0,2.3,3.3,1.0,Iris-versicolor
5.6,2.7,4.2,1.3,Iris-versicolor
5.7,3.0,4.2,1.2,Iris-versicolor
5.7,2.9,4.2,1.3,Iris-versicolor
6.2,2.9,4.3,1.3,Iris-versicolor
5.1,2.5,3.0,1.1,Iris-versicolor
5.7,2.8,4.1,1.3,Iris-versicolor
6.3,3.3,6.0,2.5,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
7.1,3.0,5.9,2.1,Iris-virginica
6.3,2.9,5.6,1.8,Iris-virginica
6.5,3.0,5.8,2.2,Iris-virginica
7.6,3.0,6.6,2.1,Iris-virginica
4.9,2.5,4.5,1.7,Iris-virginica
7.3,2.9,6.3,1.8,Iris-virginica
6.7,2.5,5.8,1.8,Iris-virginica
7.2,3.6,6.1,2.5,Iris-virginica
6.5,3.2,5.1,2.0,Iris-virginica
6.4,2.7,5.3,1.9,Iris-virginica
6.8,3.0,5.5,2.1,Iris-virginica
5.7,2.5,5.0,2.0,Iris-virginica
5.8,2.8,5.1,2.4,Iris-virginica
6.4,3.2,5.3,2.3,Iris-virginica
6.5,3.0,5.5,1.8,Iris-virginica
7.7,3.8,6.7,2.2,Iris-virginica
7.7,2.6,6.9,2.3,Iris-virginica
6.0,2.2,5.0,1.5,Iris-virginica
6.9,3.2,5.7,2.3,Iris-virginica
5.6,2.8,4.9,2.0,Iris-virginica
7.7,2.8,6.7,2.0,Iris-virginica
6.3,2.7,4.9,1.8,Iris-virginica
6.7,3.3,5.7,2.1,Iris-virginica
7.2,3.2,6.0,1.8,Iris-virginica
6.2,2.8,4.8,1.8,Iris-virginica
6.1,3.0,4.9,1.8,Iris-virginica
6.4,2.8,5.6,2.1,Iris-virginica
7.2,3.0,5.8,1.6,Iris-virginica
7.4,2.8,6.1,1.9,Iris-virginica
7.9,3.8,6.4,2.0,Iris-virginica
6.4,2.8,5.6,2.2,Iris-virginica
6.3,2.8,5.1,1.5,Iris-virginica
6.1,2.6,5.6,1.4,Iris-virginica
7.7,3.0,6.1,2.3,Iris-virginica
6.3,3.4,5.6,2.4,Iris-virginica
6.4,3.1,5.5,1.8,Iris-virginica
6.0,3.0,4.8,1.8,Iris-virginica
6.9,3.1,5.4,2.1,Iris-virginica
6.7,3.1,5.6,2.4,Iris-virginica
6.9,3.1,5.1,2.3,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
6.8,3.2,5.9,2.3,Iris-virginica
6.7,3.3,5.7,2.5,Iris-virginica
6.7,3.0,5.2,2.3,Iris-virginica
6.3,2.5,5.0,1.9,Iris-virginica
6.5,3.0,5.2,2.0,Iris-virginica
6.2,3.4,5.4,2.3,Iris-virginica
5.9,3.0,5.1,1.8,Iris-virginica

Bước 2: Tạo thư mục đầu vào trong hdfs

hdfs dfs -mkdir /iris-input

Bước 3: Đẩy file iris.csv vào folder iris-input vừa tạo:

hadoop fs -put C:\naive-bayes\iris.csv /iris-input

NOTE: Thay C:\naive-bayes\iris.csv bằng đường dẫn thư mục lưu file

Bước 4: Tạo project

Các bước thực hiện tương tự MapReduce - Phân cụm Kmeans, đặt tên project là NaiveBayesMapReduce

Bước 5: Thêm thư viện cần thiết để chạy MapReduce

Các bước thực hiện tương tự MapReduce - Phân cụm Kmeans

Bước 6: Tạo các class xử lý nhiệm vụ train/test mô hình Naive Bayes

Double click vào project KmeanMapReduce, chuột phải vào src và chọn New > Class

Tạo class để xử lý nhiệm vụ Map quá trình train, đặt tên là NaiveBayesTrainMapper

Nội dung bên trong file NaiveBayesTrainMapper.java:

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

import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;

public class NaiveBayesTrainMapper extends MapReduceBase implements Mapper<LongWritable, Text,Text,DoubleWritable>{
   
	String delimiter,continousVariables;	
	int targetVariable,numColums;
	HashSet<Integer> continousVariablesIndex;
	
	public HashSet<Integer> splitvariables(String varString){
		HashSet<Integer> hs = new HashSet<Integer>();
	    StringTokenizer tok = new StringTokenizer(varString,",");
	    while(tok.hasMoreElements())
	    	hs.add(Integer.parseInt(tok.nextToken()));
		return hs;
	}
	
	@Override
	 public void configure(JobConf conf){
		delimiter = conf.get("delimiter");
		numColums = conf.getInt("numColumns", 0);
		continousVariables = conf.get("continousVariables");
	    targetVariable = conf.getInt("targetVariable",0);
	    continousVariablesIndex = new HashSet<Integer>();
	    if(continousVariables!=null)
	    	continousVariablesIndex = splitvariables(continousVariables);
	}
	@Override
	public void map(LongWritable arg0, Text value,
			OutputCollector<Text, DoubleWritable> output, Reporter arg3)
			throws IOException {
		// TODO Auto-generated method stub
		Integer varIndex = 1; 
		String record = value.toString();
		String features[] = record.split(delimiter);
	    for(int i = 0 ;i < numColums ; i++){
	    	if(varIndex!= targetVariable){
	    		if(continousVariablesIndex.contains(varIndex))
	    		 output.collect(new Text(varIndex+"_"+features[targetVariable-1]), new DoubleWritable(Double.parseDouble(features[i])));
	    	}
	    	varIndex ++;
	    }
	    output.collect(new Text(targetVariable+"_"+features[targetVariable-1]), new DoubleWritable(1.0));
	    output.collect(new Text(targetVariable+""), new DoubleWritable(1.0));
	}
}

Tương tự tạo class xử lý nhiệm vụ Reducer quá trình train, đặt tên là NaiveBayesTrainReducer:

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

import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;

public class NaiveBayesTrainReducer extends MapReduceBase implements Reducer<Text, DoubleWritable, NullWritable,Text>{

	String continousVariables;	
	int targetVariable;
	HashSet<Integer> continousVariablesIndex;
	
	public HashSet<Integer> splitvariables(String varString){
		HashSet<Integer> hs = new HashSet<Integer>();
	    StringTokenizer tok = new StringTokenizer(varString,",");
	    while(tok.hasMoreElements())
	    	hs.add(Integer.parseInt(tok.nextToken()));
		return hs;
	}
	
	@Override
	 public void configure(JobConf conf){
		continousVariables = conf.get("continousVariables");
	    targetVariable = conf.getInt("targetVariable",0);
	    continousVariablesIndex = new HashSet<Integer>();
	    if(continousVariables!=null)
	     continousVariablesIndex = splitvariables(continousVariables);
    }
	
	@Override
	public void reduce(Text keyId, Iterator<DoubleWritable> values,
			OutputCollector<NullWritable, Text> output, Reporter arg3) throws IOException {

		String id = keyId.toString().split("_")[0];
		if(continousVariablesIndex.contains(Integer.parseInt(id))){
			double sumsqr=0,sum = 0,count=0,tmp;
			double mean,var;
			 while (values.hasNext())
	          {
	        	   tmp=values.next().get();
	        	   sumsqr+=tmp*tmp;
	               sum += tmp;
	               count++;
	          }
			 mean=sum/count;
			 var=(sumsqr-((sum*sum)/count))/count;
	         output.collect(NullWritable.get(), new Text(keyId+" "+mean+","+var));
		}
		if(targetVariable == Integer.parseInt(id)){
			Double count = 0.0;
			while (values.hasNext())
	          count +=  values.next().get();
			  output.collect(NullWritable.get(), new Text(keyId+" "+count.toString()));
		}
	}
}

Tạo class main xử lý yêu cầu đầu vào quá trình train, đặt tên là NaiveBayesTrainJob:

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.FloatWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;


public class NaiveBayesTrainJob extends Configured implements Tool{

	@Override
	public int run(String[] arg0) throws Exception {
        Configuration c = getConf();
		// TODO Auto-generated method stub
		JobConf conf = new JobConf(c, NaiveBayesTrainJob.class);
		conf.setJobName("Training");
		conf.setMapOutputKeyClass(Text.class);
		conf.setMapOutputValueClass(DoubleWritable.class);
		conf.setOutputKeyClass(Text.class);
		conf.setOutputValueClass(Text.class);
		conf.setNumMapTasks(conf.getInt("numMaps", 4));
		conf.setNumReduceTasks(conf.getInt("numReduce", 1));
		conf.setMapperClass(NaiveBayesTrainMapper.class);
		conf.setReducerClass(NaiveBayesTrainReducer.class);
		FileInputFormat.addInputPath(conf, new Path(conf.get("input")));
		FileOutputFormat.setOutputPath(conf, new Path(conf.get("output")));
		JobClient.runJob(conf);
		return 0;
	}
	public static void main(String[] args) throws Exception {
		int res = ToolRunner.run(new Configuration(), new NaiveBayesTrainJob(), args);
		System.exit(res);
	}
}

Tạo class xử lý nhiệm vụ Map quá trình test, đặt tên là NaiveBayesTestMapper:

import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.net.URI;
import java.nio.file.FileSystem;
import java.nio.file.Files;
import java.util.HashMap;
import java.util.HashSet;
import java.util.StringTokenizer;
import java.io.BufferedReader;
import org.apache.hadoop.filecache.DistributedCache;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.metrics2.sink.FileSink;
import org.apache.hadoop.fs.Path;

public class NaiveBayesTestMapper extends MapReduceBase implements Mapper<LongWritable, Text,NullWritable, Text>{
	String delimiter,continousVariables,targetClasses;	
	int targetVariable,numColums;
	HashSet<Integer> continousVariablesIndex;
	HashMap<String,String> hm;
	HashSet<String> classesTargetVariables;
	
	public HashSet<Integer> splitvariables(String varString){
		HashSet<Integer> hs = new HashSet<Integer>();
	    StringTokenizer tok = new StringTokenizer(varString,",");
	    while(tok.hasMoreElements())
	    	hs.add(Integer.parseInt(tok.nextToken()));
		return hs;
	}
	public HashSet<String> splitstringvariables(String varString){
		HashSet<String> hs = new HashSet<String>();
	    StringTokenizer tok = new StringTokenizer(varString,",");
	    while(tok.hasMoreElements())
	    	hs.add(tok.nextToken());
		return hs;
	}
	
	@Override
	 public void configure(JobConf conf){
		delimiter = conf.get("delimiter");
		numColums = conf.getInt("numColumns", 0);
		continousVariables = conf.get("continousVariables");
		targetClasses = conf.get("targetClasses");
	    targetVariable = conf.getInt("targetVariable",0);
	    continousVariablesIndex = new HashSet<Integer>();
	    if(continousVariables!=null)
	    continousVariablesIndex = splitvariables(continousVariables);
	    classesTargetVariables = splitstringvariables(targetClasses);
	    
	    hm = new HashMap();
	    try {
			URI[] filesIncache = DistributedCache.getCacheFiles(conf);
			for(int i=0;i<filesIncache.length;i++){
				BufferedReader fis = new BufferedReader(new FileReader(filesIncache[i].getPath().toString()));
				String record; 
				 while ((record = fis.readLine()) != null) {
					 String key,value;
					 StringTokenizer tokRecord = new StringTokenizer(record);
					 key = tokRecord.nextToken();
					 value = tokRecord.nextToken();
					 hm.put(key, value);
				 }			
			}
		} catch (IOException e) {
			// TODO Auto-generated catch block
			e.printStackTrace();
		}
	}
	
	double calculateProbablity(int featureID,String value,String label){
	   String classCount,valueCount,totalCount;
	   classCount = hm.get(targetVariable+"_"+label);
	   if(classCount==null)
		   return 1.0;
	   valueCount = hm.get(featureID+"_"+value+"_"+label);
	   if(valueCount==null)
		   return 1.0;
	   totalCount = hm.get(targetVariable+"");
	   double classProbablity = (Double.parseDouble(classCount) / Double.parseDouble(totalCount));
	   double valueProbablity = (Double.parseDouble(valueCount) / Double.parseDouble(classCount));
	   return (classProbablity*valueProbablity);
	}
	
	double calculateGaussian(int featureID,String value,String label){
		Double mean,variance,val;
		val = Double.parseDouble(value);
		String values = hm.get(featureID+"_"+label);
		if(values!=null){
	      StringTokenizer tokMeanVariance = new StringTokenizer(values,",");
	      mean = Double.parseDouble(tokMeanVariance.nextToken());
	      variance = Double.parseDouble(tokMeanVariance.nextToken());
	      if(variance==0.0)
	    	  return 1.0;
	      double exponent,denaminator;
	      denaminator = Math.sqrt(2*3.414)*variance;
	      exponent = -1*(Math.pow((val-mean),2))/(2*Math.pow(variance, 2));
	      return (1/denaminator)*Math.exp(exponent);
		}
		return 1.0;
	}
	@Override
	public void map(LongWritable key, Text value,
			OutputCollector<NullWritable, Text> output, Reporter arg3) throws IOException {
		// TODO Auto-generated method stub 
		String record = value.toString();
	    int featureID = 1;
	    Double probablity=0.0;
	    Double labelProbablity[] = new Double[classesTargetVariables.size()];
	    String features[] = record.split(delimiter);
	    int labelIndex = 0;
	    String labelprobablityString="";
	    for (String labels : classesTargetVariables){
	    	probablity=1.0;
	    	featureID = 1;
	      for(int i=0; i<numColums; i++){
	    	if(continousVariablesIndex.contains(featureID)){
	    	  probablity = probablity * calculateGaussian(featureID,features[i],labels);
	    	}
	    	++featureID;
	    		//output.collect(new Text(featureID+"_"+features[targetVariable-1]), new DoubleWritable(Double.parseDouble(features[i])));	
	     }
	     labelProbablity[labelIndex++]=probablity;
	     labelprobablityString = labelprobablityString + labelProbablity[labelIndex-1]+" ";
	   }
	   output.collect(NullWritable.get(),new Text(record+" "+labelprobablityString));
	}
}

Tạo class main xử lý yêu cầu đầu vào quá trình train, đặt tên là NaiveBayesTestJob:

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.filecache.DistributedCache;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class NaiveBayesTestJob extends Configured implements Tool{

	@Override
	public int run(String[] arg0) throws Exception {
        Configuration c = getConf();
		JobConf conf = new JobConf(c ,NaiveBayesTestJob.class);
		Job job = new Job(conf, "Multi-view NaiveBayes Training");
		
		DistributedCache.addCacheFile(new Path(conf.get("modelPath")+"/part-00000").toUri(), conf);
		conf.setMapperClass(NaiveBayesTestMapper.class);
		conf.setOutputKeyClass(NullWritable.class);
		conf.setOutputValueClass(Text.class);
		conf.setNumMapTasks(conf.getInt("numMaps", 1));
		conf.setNumMapTasks(conf.getInt("numReduce", 1));
		FileInputFormat.addInputPath(conf, new Path(conf.get("input")));
	    FileOutputFormat.setOutputPath(conf, new Path(conf.get("output")));
	    JobClient.runJob(conf);
		return 0;
	}
	public static void main(String[] args) throws Exception {
		int res = ToolRunner.run(new Configuration(), new NaiveBayesTestJob(), args);
		System.exit(res);
	}
}

Bước 7: Tạo file JAR

Chuột phải vào project NaiveBayesMapReduce chọn Export

Chọn Java > JAR File rồi bấm Finish

Vào thư mục chứa lưu file JAR vừa tạo và kiểm tra kết quả

Bước 8: Chạy chương trình Training/Test bộ dữ liệu iris.csv

Training bộ dữ liệu iris.csv đã tạo ở trên, và kết quả thu được lưu trong thư mục outputiris. Chạy lệnh sau:

hadoop jar C:\jar\NaiveBayes.jar NaiveBayesTrainJob -D num_mappers="3" -D num_reducers="1" -D delimiter="," -D input="/iris-input/iris.csv" -D output="/outputiris" -D continousVariables="1,2,3,4" -D targetVariable="5" -D numColumns="5"

Lưu ý: Thay “C:\jar\NaiveBayes.jar” bằng đường dẫn chứa file JAR ở trên máy

Test dữ liệu chạy lệnh:

hadoop jar C:\jar\NaiveBayes.jar NaiveBayesTestJob -D num_mappers="3" -D num_reducers="1" -D delimiter="," -D input="/iris-input/iris.csv" -D output="/outputiris-test" -D continousVariables="1,2,3,4" -D discreteVariables="" -D targetVariable="5" -D numColumns="5" -D modelPath="/outputiris" -D targetClasses="Iris-versicolor,Iris-setosa,Iris-virginica" 

Lưu ý: Để có kết quả trực quan hãy tạo dữ liệu đầu vào khác và thay đổi đường dẫn lưu file dữ liệu test ở tham số input