mahout关联规则源码分析 Part 1

最近看了关联规则的相关算法，着重看了mahout的具体实现，mahout官网上面给出了好多算法，具体网址如下：https://cwiki.apache.org/confluence/display/MAHOUT/Parallel+Frequent+Pattern+Mining 。

先说下命令行运行关联规则，关联规则的算法在mahout-core-0,7.jar包下面，命令行运行如下：

 

fansy@fansypc:~/hadoop-1.0.2$ bin/hadoop jar ../mahout-pure-0.7/core/target/mahout-core-0.7.jar
 org.apache.mahout.fpm.pfpgrowth.FPGrowthDriver -i input/retail.dat -o date1101/fpgrowthdriver00 -s 2 -method mapreduce -regex '[\ ]'
12/11/01 16:31:39 INFO common.AbstractJob:
 Command line arguments: {--encoding=[UTF-8], --endPhase=[2147483647],
--input=[input/retail.dat], --maxHeapSize=[50], --method=[mapreduce], --minSupport=[2], --numGroups=[1000],
--numTreeCacheEntries=[5], --output=[date1101/fpgrowthdriver00], --splitterPattern=[[\ ]], --startPhase=[0], --tempDir=[temp]}

最后的 -regex '[\ ]' 一定是需要的对于输入数据 retail.dat来说，因为mahout默认的item的分隔符是没有空格的；

 

而且这里只讨论 并行的程序，所以使用 -method mapreduce

下面分析源码：

在分析源码之前，先看一张图：

这张图很好的说明了mahout实现关联规则思想，或者说是流程；

首先，读入数据，比如上图的5个transactions(事务),接着根据一张总表（这张总表是每个item的次数从大到小的一个排列，同时这张表还去除了出现次数小于min_support的item）把这些transactions 去除一些项目并按照总表的顺序排序，得到另外的一个transaction A，接着map的输出就是根据transaction A输出规则，从出现次数最小的item开始输出直到出现次数第二大的item。

Reduce收集map输出相同的key值，把他们的value值放一个集合set 中，然后在统计这些集合中item出现的次数，如果次数大于min_confidence(本例中为3),那么就输出key和此item的规则；

命令行运行时可以看到三个MR，即可以把关联规则的算法分为三部分，但是个人觉得可以分为四个部分，其中的一部分就是总表的获得；鉴于目前本人只看了一个MR和总表的获得部分的源码，今天就只分享这两个部分；

贴代码先，基本都是源码来的，只是稍微改了下：

第一个MR的驱动程序：PFGrowth_ParallelCounting.java:

 

package org.fansy.date1101.pfgrowth;
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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;
import org.apache.mahout.common.HadoopUtil;
public class PFGrowth_ParallelCounting {
    public boolean runParallelCountingJob(String input,String output) throws IOException, ClassNotFoundException, InterruptedException{
        Configuration conf=new Configuration();
        Job job = new Job(conf, "Parallel Counting Driver running over input: " + input);
        job.setJarByClass(PFGrowth_ParallelCounting.class);
        job.setMapperClass(PFGrowth_ParallelCountingM.class);
        job.setCombinerClass(PFGrowth_ParallelCountingR.class);
        job.setReducerClass(PFGrowth_ParallelCountingR.class);
        job.setOutputFormatClass(SequenceFileOutputFormat.class); //  get rid of this line you can get the text file
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);
        FileInputFormat.setInputPaths(job,new Path( input));
        Path outPut=new Path(output,"parallelcounting");
        HadoopUtil.delete(conf, outPut);
        FileOutputFormat.setOutputPath(job, outPut);
        boolean succeeded = job.waitForCompletion(true);
        if (!succeeded) {
          throw new IllegalStateException("Job failed!");
        }
        return succeeded;
    }
}

第一个MR的M：PFGrowth_ParallelCountingM.java:

 

 

package org.fansy.date1101.pfgrowth;
import java.io.IOException;
import java.util.regex.Pattern;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class PFGrowth_ParallelCountingM extends Mapper<LongWritable,Text,Text,LongWritable> {
     private static final LongWritable ONE = new LongWritable(1);
      private Pattern splitter=Pattern.compile("[ ,\t]*[ ,|\t][ ,\t]*");
      @Override
      protected void map(LongWritable offset, Text input, Context context) throws IOException,
                                                                          InterruptedException {
        String[] items = splitter.split(input.toString());
        for (String item : items) {
          if (item.trim().isEmpty()) {
            continue;
          }
          context.setStatus("Parallel Counting Mapper: " + item);
          context.write(new Text(item), ONE);
        }
      }
}

上面的代码中的间隔符号修改了源码，加上了空格；

 

第一个MR的R：PFGrowth_ParallelCountingR.java:

 

package org.fansy.date1101.pfgrowth;
import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class PFGrowth_ParallelCountingR extends Reducer<Text,LongWritable,Text,LongWritable>{
    protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException,
            InterruptedException {
        long sum = 0;
        for (LongWritable value : values) {
        context.setStatus("Parallel Counting Reducer :" + key);
        sum += value.get();
        }
        context.setStatus("Parallel Counting Reducer: " + key + " => " + sum);
        context.write(key, new LongWritable(sum));
    }
}

其实第一个MR还是比较好理解的，M分解每个transaction的item,然后输出<item_id ,1>,然后R针对每个item_id 把value值相加求和，这个和wordcount的例子是一样的，当然这里也可以加combine操作的。

 

接着是总表的获得：

PFGrowth_Driver.java ,同时这个程序也调用第一个MR，也就是说可以直接运行这个文件就可以同时运行第一个MR和获得总表了。

 

package org.fansy.date1101.pfgrowth;
import java.io.IOException;
import java.util.Comparator;
import java.util.List;
import java.util.PriorityQueue;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.filecache.DistributedCache;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.SequenceFile;
import org.apache.hadoop.io.Text;
import org.apache.mahout.common.HadoopUtil;
import org.apache.mahout.common.Pair;
import org.apache.mahout.common.Parameters;
import org.apache.mahout.common.iterator.sequencefile.PathType;
import org.apache.mahout.common.iterator.sequencefile.SequenceFileDirIterable;
import com.google.common.collect.Lists;
class MyComparator implements Comparator<Pair<String,Long>>{
     @Override
     public int compare(Pair<String,Long> o1, Pair<String,Long> o2) {
       int ret = o2.getSecond().compareTo(o1.getSecond());
       if (ret != 0) {
         return ret;
       }
       return o1.getFirst().compareTo(o2.getFirst());
     }
}
public class PFGrowth_Driver {
    public static void main(String[] args) throws ClassNotFoundException, IOException, InterruptedException{
        if(args.length!=3){
            System.out.println("wrong input args");
            System.out.println("usage: <intput><output><minsupport>");
            System.exit(-1);
        }
        // set parameters
        Parameters params=new Parameters();
        params.set("INPUT", args[0]);
        params.set("OUTPUT", args[1]);
        params.set("MIN_SUPPORT", args[2]);
        // get parameters
        String input=params.get("INPUT");
        String output=params.get("OUTPUT");
        //  run the first job
        PFGrowth_ParallelCounting ppc=new PFGrowth_ParallelCounting();
        ppc.runParallelCountingJob(input, output);
        //  read input and set the fList
         List<Pair<String,Long>> fList = readFList(params);
         Configuration conf=new Configuration();
         saveFList(fList, params, conf);
    }
    /**
       * Serializes the fList and returns the string representation of the List
       *
       * @return Serialized String representation of List
       */
      public static void saveFList(Iterable<Pair<String,Long>> flist, Parameters params, Configuration conf)
        throws IOException {
        Path flistPath = new Path(params.get("OUTPUT"), "fList");
        FileSystem fs = FileSystem.get(flistPath.toUri(), conf);
        flistPath = fs.makeQualified(flistPath);
        HadoopUtil.delete(conf, flistPath);
        SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf, flistPath, Text.class, LongWritable.class);
        try {
          for (Pair<String,Long> pair : flist) {
            writer.append(new Text(pair.getFirst()), new LongWritable(pair.getSecond()));
          }
        } finally {
          writer.close();
        }
        DistributedCache.addCacheFile(flistPath.toUri(), conf);
      }
    public static List<Pair<String,Long>> readFList(Parameters params) {
        int minSupport = Integer.valueOf(params.get("MIN_SUPPORT"));
        Configuration conf = new Configuration();
        Path parallelCountingPath = new Path(params.get("OUTPUT"),"parallelcounting");
        //  add MyComparator
        PriorityQueue<Pair<String,Long>> queue = new PriorityQueue<Pair<String,Long>>(11,new MyComparator());
        // sort according to the occur times from large to small

        for (Pair<Text,LongWritable> record
             : new SequenceFileDirIterable<Text,LongWritable>(new Path(parallelCountingPath, "part-*"),
                                                            PathType.GLOB, null, null, true, conf)) {
          long value = record.getSecond().get();
          if (value >= minSupport) {   // get rid of the item which is below the minimum support
            queue.add(new Pair<String,Long>(record.getFirst().toString(), value));
          }
        }
        List<Pair<String,Long>> fList = Lists.newArrayList();
        while (!queue.isEmpty()) {
          fList.add(queue.poll());
        }
        return fList;
      }
}

第一个MR运行完毕后，调用readFList()函数，把第一个MR的输出按照item出现的次数从大到小放入一个列表List中，然后调用saveFList()函数把上面求得的List存入HDFS文件中，不过存入的格式是被序列话的，可以另外编写函数查看文件是否和自己的假设相同；

 

FList 文件反序列化如下：

http://blog.csdn.net/fansy1990/article/details/8137942