数据挖掘-Mahout-Canopy聚类实践

1、原理解释

(1)原始数据集合List按照一定的规则进行排序，初始距离阈值设置为T1、T2，T1>T2。

(2)在List中随机挑选一个数据向量A，使用一个粗糙距离计算方式计算A与List中其它样本数据向量之间的距离d。

(3)根据2中的距离d，把d小于T1的样本数据向量划到一个canopy中，同时把d小于T2的样本数据向量从List中移除。

(4)重复2、3，直至List为空

2、下载测试数据

cd /tmp

hadoop dfs -mkdir /input

wget http://archive.ics.uci.edu/ml/databases/synthetic_control/synthetic_control.data

hadoop dfs -copyFromLocal /tmp/synthetic_control.data /input/synthetic_control.data

3、格式转换（文本→向量）

编辑文件 Text2VectorWritable.jar

package mahout.fansy.utils.transform;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

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.SequenceFileOutputFormat;

import org.apache.hadoop.util.ToolRunner;

import org.apache.mahout.common.AbstractJob;

import org.apache.mahout.math.RandomAccessSparseVector;

import org.apache.mahout.math.Vector;

import org.apache.mahout.math.VectorWritable;

/**

 *  –* transform text data to vectorWritable data

 *   –* @author fansy

 *    –*

 *     –*/

public class Text2VectorWritable extends AbstractJob{

  public static void main(String[] args) throws Exception{

       ToolRunner.run(new Configuration(), new Text2VectorWritable(),args);

  }

  @Override

  public int run(String[] arg0) throws Exception {

       addInputOption();

       addOutputOption();

       if (parseArguments(arg0) == null) {

         return -1;

       }

       Path input=getInputPath();

       Path output=getOutputPath();

       Configuration conf=getConf();

       // set job information

       Job job=new Job(conf,”text2vectorWritableCopy with input:”+input.getName());

       job.setOutputFormatClass(SequenceFileOutputFormat.class);

       job.setMapperClass(Text2VectorWritableMapper.class);

       job.setMapOutputKeyClass(LongWritable.class);

       job.setMapOutputValueClass(VectorWritable.class);

       job.setReducerClass(Text2VectorWritableReducer.class);

       job.setOutputKeyClass(LongWritable.class);

       job.setOutputValueClass(VectorWritable.class);

job.setJarByClass(Text2VectorWritable.class);

       FileInputFormat.addInputPath(job, input);

       SequenceFileOutputFormat.setOutputPath(job, output);

       if (!job.waitForCompletion(true)) { // wait for the job is done

            throw new InterruptedException(“Canopy Job failed processing ” + input);

       }

       return 0;

   }

   /**

    * Mapper main procedure

    * @author fansy

    *

   –*/

   public static class Text2VectorWritableMapper extends Mapper<LongWritable,Text,LongWritable,VectorWritable>{

        public void map(LongWritable key,Text value,Context context) throws IOException,InterruptedException{

            String[] str=value.toString().split(“//s{1,}”);

            // split data use one or more blanker

            Vector vector=new RandomAccessSparseVector(str.length);

            for(int i=0;i<str.length;i++){

                 vector.set(i, Double.parseDouble(str[i]));

            }

            VectorWritable va=new VectorWritable(vector);

            context.write(key, va);

       }

   }

   /**

   * Reducer: do nothing but output

   * @author fansy

   *

   –*/

   public static class Text2VectorWritableReducer extends Reducer<LongWritable,VectorWritable,LongWritable,VectorWritable>{

       public void reduce(LongWritable key,Iterable<VectorWritable> values,Context context)throws IOException,InterruptedException{

           for(VectorWritable v:values){

                context.write(key, v);

           }

       }

   }

}

             

编译，输出ClusteringUtils.jar，并拷贝至/home/mahout/mahout_jar

输出时选择Export→Runnable Jar File→Extract required libraries into generated JAR

然后执行：

hadoop jar /home/hadoop/mahout/mahout_jar/ClusteringUtils.jar mahout.fansy.utils.transform.Text2VectorWritable -i hdfs:///input/synthetic_control.data -o hdfs:///input/synthetic_control.data.transform

有可能会遇到org/apache/mahout/common/AbstractJob找不到类报错，这个一般是由于HADOOP_CLASSPATH配置位置不包含mahout的jar的原因。

解决方法1：

拷贝mahout的jar文件到/home/hadoop/lib中去，并确认这个/home/hadoop/lib确实在HADOOP_CLASSPATH中

cp /home/hadoop/mahout/*.jar /home/hadoop/hadoop/lib

解决方法2（推荐）：

在hadoop-env.sh中加入

for f in /home/hadoop/mahout/*.jar; do

  if [ “$HADOOP_CLASSPATH” ]; then

    export HADOOP_CLASSPATH=$HADOOP_CLASSPATH:$f

  else

    export HADOOP_CLASSPATH=$f

  fi

done

记得将hadoop-evn.sh分发到其它节点

重启hadoop环境

stop-all.sh

start-all.sh

执行转换：

hadoop jar /home/hadoop/mahout/mahout_jar/ClusteringUtils.jar mahout.fansy.utils.transform.Text2VectorWritable -i hdfs:///input/synthetic_control.data -o hdfs:///input/synthetic_control.data.transform（如果在导出Jar的时候已经指派主类，这个命令会报错，使用下面的命令）

hadoop jar /home/hadoop/mahout/mahout_jar/ClusteringUtils.jar -o hdfs:///input/synthetic_control.data.transform

输出完毕的文件已经是面目全非的Vector文件了

hdfs:///input/synthetic_control.data.transform/part-r-00000

4、执行Canopy聚类

mahout canopy –input hdfs:///input/synthetic_control.data.transform/part-r-00000 –output /output/canopy –distanceMeasure org.apache.mahout.common.distance.EuclideanDistanceMeasure –t1 80 –t2 55 –t3 80 –t4 55 –clustering

5、转换格式（向量→文本）

把4中的结果转换成为文本

编辑文件ReadClusterWritable.java

package mahout.fansy.utils;  

import java.io.IOException;  

import org.apache.hadoop.conf.Configuration;  

import org.apache.hadoop.io.Text;  

import org.apache.hadoop.mapreduce.Job;  

import org.apache.hadoop.mapreduce.Mapper;  

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;  

import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat;  

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;  

import org.apache.hadoop.util.ToolRunner;  

import org.apache.mahout.clustering.iterator.ClusterWritable;  

import org.apache.mahout.common.AbstractJob;  

import org.slf4j.Logger;  

import org.slf4j.LoggerFactory;  

/**  

 ??* read cluster centers  

 ??* @author fansy  

 ??*/  

public class ReadClusterWritable extends AbstractJob {  

      public static void main(String[] args) throws Exception{  

           ToolRunner.run(new Configuration(), new ReadClusterWritable(),args);  

      }  

      @Override  

      public int run(String[] args) throws Exception {  

           addInputOption();  

           addOutputOption();  

           if (parseArguments(args) == null) {  

                return -1;  

             }  

           Job job=new Job(getConf(),getInputPath().toString());  

           job.setInputFormatClass(SequenceFileInputFormat.class);  

           job.setMapperClass(RM.class);  

           job.setMapOutputKeyClass(Text.class);  

           job.setMapOutputValueClass(Text.class);  

           job.setNumReduceTasks(0);  

           job.setJarByClass(ReadClusterWritable.class);  

 

           FileInputFormat.addInputPath(job, getInputPath());  

           FileOutputFormat.setOutputPath(job, getOutputPath());  

           if (!job.waitForCompletion(true)) {  

              throw new InterruptedException(“Canopy Job failed processing ” + getInputPath());  

        }  

      return 0;  

    }  

    public static class RM extends Mapper<Text,ClusterWritable ,Text,Text>{  

         private Logger log=LoggerFactory.getLogger(RM.class);  

       public void map(Text key,ClusterWritable value,Context context) throws  

IOException,InterruptedException{  

              String str=value.getValue().getCenter().asFormatString();  

         //   System.out.println(“center****************:”+str);  

           log.info(“center*****************************:”+str); // set log information  

              context.write(key, new Text(str));  

         }  

    }  

}

打包到ClusteringUtils.jar，上传到/home/hadoop/mahout/mahout_jar

如果需要清除eclipse中Launch Configuration中的信息，需要进入工程所在文件夹下的/.metadata/.plugins/org.eclipse.debug.core/.launches

然后删除里面的文件

运行

hadoop jar ClusteringUtils.jar mahout.fansy.utils.ReadClusterWritable -i /output/canopy/clusters-0-final/part-r-00000 -o /output/canopy-output（如果不成功就运行下面的命令）

hadoop jar ClusteringUtils.jar -i /output/canopy/clusters-0-final/part-r-00000 -o /output/canopy-output

这时候/output/canopy-output/part-m-00000里面放置的就是聚类的结果文件