Hadoop 实现kmeans 算法
关于kmeans说在前面:kmeans算法有一个硬性的规定就是簇的个数要提前设定。大家可能会质疑这个限制是否影响聚类效果,但是这种担心是多余的。在该算法诞生的这么多年里,该算法已被证明能够广泛的用于解决现实世界问题,即使簇个数k值是次优的,聚类的质量不会受到太大影响。
聚类在现实中很大应用就是对新闻报道进行聚类,以得到顶层类别,如政治、科学、体育、财经等。对此我们倾向于选择比较小的k值,可能10-20之间。如果需要细粒度的主体,则需要更大的k值。为了得到较好的聚类质量,首先需要对k值进行预估。一个最简单粗暴的方法就是基于数据量和需要的簇个数估计,比如我们有100万新闻,我们希望每个类别新闻有500篇,那就可以简单估算k值为1000000/500=2000。
需要明确一点就是kmeans聚类质量的决定因素是使用的距离衡量标准。
关于kmeans 算法思路可以参考:kmeans
算法原理比较简单,现在需要做的是基于mapreduce 框架去实现这个算法。
从理论上来讲用MapReduce技术实现KMeans算法是很Natural的想法:在Mapper中逐个计算样本点离哪个中心最近,然后发出key-value(样本点所属的簇编号,样本点);shuffle后在Reducer中属于同一个质心的样本点在一个list中,方便我们计算新的中心,然后发出新的key-value(质心编号,质心)。但是技术上的事并没有理论层面那么简单。
要实现这个算法需要解决两个问题:
1. 如何存储每次聚类的质心。
2. 如何存储原始聚类数据。
Hadoop中变量或者说数据共享的三种主要方式:
序号 | 方法 |
1 | 使用Configuration的set方法,只适合数据内容比较小的场景 |
2 | 将共享文件放在HDFS上,每次都去读取,效率比较低 |
3 | 将共享文件放在DistributedCache里,在setup初始化一次后,即可多次使用,缺点是不支持修改操作,仅能读取 |
此时我们需要2个质心文件:一个存放上一次的质心prevCenterFile,一个存放reducer更新后的质心currCenterFile。Mapper从prevCenterFile中读取质心,Reducer把更新后有质心写入currCenterFile。在主函数中读入prevCenterFile和currCenterFile,比较前后两次的质心是否相同(或足够地接近),如果相同则停止迭代,否则就用currCenterFile覆prevCenterFile(使用fs.rename),进入下一次的迭代。(PS:其实这种方式效率也不是很高,真正使用spark
基于内存运算会效率更高)
代码参考:kmeans 参考
package kmeans; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import org.apache.commons.logging.Log; import org.apache.commons.logging.LogFactory; import org.apache.hadoop.io.Writable; public class Sample implements Writable{ private static final Log log=LogFactory.getLog(Sample.class); public static final int DIMENTION=60; public double arr[]; public Sample(){ arr=new double[DIMENTION]; } public static double getEulerDist(Sample vec1,Sample vec2){ if(!(vec1.arr.length==DIMENTION && vec2.arr.length==DIMENTION)){ log.error("vector's dimention is not "+DIMENTION); System.exit(1); } double dist=0.0; for(int i=0;i<DIMENTION;++i){ dist+=(vec1.arr[i]-vec2.arr[i])*(vec1.arr[i]-vec2.arr[i]); } return Math.sqrt(dist); } public void clear(){ for(int i=0;i<arr.length;i++) arr[i]=0.0; } @Override public String toString(){ String rect=String.valueOf(arr[0]); for(int i=1;i<DIMENTION;i++) rect+="\t"+String.valueOf(arr[i]); return rect; } @Override public void readFields(DataInput in) throws IOException { String str[]=in.readUTF().split("\\s+"); for(int i=0;i<DIMENTION;++i) arr[i]=Double.parseDouble(str[i]); } @Override public void write(DataOutput out) throws IOException { out.writeUTF(this.toString()); } }
package kmeans; import java.io.BufferedReader; import java.io.FileReader; import java.io.IOException; import java.io.InputStreamReader; import java.util.Vector; import org.apache.commons.logging.Log; import org.apache.commons.logging.LogFactory; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.conf.Configured; import org.apache.hadoop.filecache.DistributedCache; import org.apache.hadoop.fs.FSDataInputStream; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; 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.input.TextInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat; import org.apache.hadoop.util.Tool; import org.apache.hadoop.util.ToolRunner; public class KMeans extends Configured implements Tool{ private static final Log log = LogFactory.getLog(KMeans2.class); private static final int K = 10; private static final int MAXITERATIONS = 300; private static final double THRESHOLD = 0.01; public static boolean stopIteration(Configuration conf) throws IOException{ FileSystem fs=FileSystem.get(conf); Path pervCenterFile=new Path("/user/orisun/input/centers"); Path currentCenterFile=new Path("/user/orisun/output/part-r-00000"); if(!(fs.exists(pervCenterFile) && fs.exists(currentCenterFile))){ log.info("两个质心文件需要同时存在"); System.exit(1); } //比较前后两次质心的变化是否小于阈值,决定迭代是否继续 boolean stop=true; String line1,line2; FSDataInputStream in1=fs.open(pervCenterFile); FSDataInputStream in2=fs.open(currentCenterFile); InputStreamReader isr1=new InputStreamReader(in1); InputStreamReader isr2=new InputStreamReader(in2); BufferedReader br1=new BufferedReader(isr1); BufferedReader br2=new BufferedReader(isr2); Sample prevCenter,currCenter; while((line1=br1.readLine())!=null && (line2=br2.readLine())!=null){ prevCenter=new Sample(); currCenter=new Sample(); String []str1=line1.split("\\s+"); String []str2=line2.split("\\s+"); assert(str1[0].equals(str2[0])); for(int i=1;i<=Sample.DIMENTION;i++){ prevCenter.arr[i-1]=Double.parseDouble(str1[i]); currCenter.arr[i-1]=Double.parseDouble(str2[i]); } if(Sample.getEulerDist(prevCenter, currCenter)>THRESHOLD){ stop=false; break; } } //如果还要进行下一次迭代,就用当前质心替代上一次的质心 if(stop==false){ fs.delete(pervCenterFile,true); if(fs.rename(currentCenterFile, pervCenterFile)==false){ log.error("质心文件替换失败"); System.exit(1); } } return stop; } public static class ClusterMapper extends Mapper<LongWritable, Text, IntWritable, Sample> { Vector<Sample> centers = new Vector<Sample>(); @Override //清空centers public void setup(Context context){ for (int i = 0; i < K; i++) { centers.add(new Sample()); } } @Override //从输入文件读入centers public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String []str=value.toString().split("\\s+"); if(str.length!=Sample.DIMENTION+1){ log.error("读入centers时维度不对"); System.exit(1); } int index=Integer.parseInt(str[0]); for(int i=1;i<str.length;i++) centers.get(index).arr[i-1]=Double.parseDouble(str[i]); } @Override //找到每个数据点离哪个质心最近 public void cleanup(Context context) throws IOException,InterruptedException { Path []caches=DistributedCache.getLocalCacheFiles(context.getConfiguration()); if(caches==null || caches.length<=0){ log.error("data文件不存在"); System.exit(1); } BufferedReader br=new BufferedReader(new FileReader(caches[0].toString())); Sample sample; String line; while((line=br.readLine())!=null){ sample=new Sample(); String []str=line.split("\\s+"); for(int i=0;i<Sample.DIMENTION;i++) sample.arr[i]=Double.parseDouble(str[i]); int index=-1; double minDist=Double.MAX_VALUE; for(int i=0;i<K;i++){ double dist=Sample.getEulerDist(sample, centers.get(i)); if(dist<minDist){ minDist=dist; index=i; } } context.write(new IntWritable(index), sample); } } } public static class UpdateCenterReducer extends Reducer<IntWritable, Sample, IntWritable, Sample> { int prev=-1; Sample center=new Sample();; int count=0; @Override //更新每个质心(除最后一个) public void reduce(IntWritable key,Iterable<Sample> values,Context context) throws IOException,InterruptedException{ while(values.iterator().hasNext()){ Sample value=values.iterator().next(); if(key.get()!=prev){ if(prev!=-1){ for(int i=0;i<center.arr.length;i++) center.arr[i]/=count; context.write(new IntWritable(prev), center); } center.clear(); prev=key.get(); count=0; } for(int i=0;i<Sample.DIMENTION;i++) center.arr[i]+=value.arr[i]; count++; } } @Override //更新最后一个质心 public void cleanup(Context context) throws IOException,InterruptedException{ for(int i=0;i<center.arr.length;i++) center.arr[i]/=count; context.write(new IntWritable(prev), center); } } @Override public int run(String[] args) throws Exception { Configuration conf=getConf(); FileSystem fs=FileSystem.get(conf); Job job=new Job(conf); job.setJarByClass(KMeans.class); //质心文件每行的第一个数字是索引 FileInputFormat.setInputPaths(job, "/user/orisun/input/centers"); Path outDir=new Path("/user/orisun/output"); fs.delete(outDir,true); FileOutputFormat.setOutputPath(job, outDir); job.setInputFormatClass(TextInputFormat.class); job.setOutputFormatClass(TextOutputFormat.class); job.setMapperClass(ClusterMapper.class); job.setReducerClass(UpdateCenterReducer.class); job.setOutputKeyClass(IntWritable.class); job.setOutputValueClass(Sample.class); return job.waitForCompletion(true)?0:1; } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); FileSystem fs=FileSystem.get(conf); //样本数据文件中每个样本不需要标记索引 Path dataFile=new Path("/user/orisun/input/data"); DistributedCache.addCacheFile(dataFile.toUri(), conf); int iteration = 0; int success = 1; do { success ^= ToolRunner.run(conf, new KMeans(), args); log.info("iteration "+iteration+" end"); } while (success == 1 && iteration++ < MAXITERATIONS && (!stopIteration(conf))); log.info("Success.Iteration=" + iteration); //迭代完成后再执行一次mapper,输出每个样本点所属的分类--在/user/orisun/output2/part-m-00000中 //质心文件保存在/user/orisun/input/centers中 Job job=new Job(conf); job.setJarByClass(KMeans.class); FileInputFormat.setInputPaths(job, "/user/orisun/input/centers"); Path outDir=new Path("/user/orisun/output2"); fs.delete(outDir,true); FileOutputFormat.setOutputPath(job, outDir); job.setInputFormatClass(TextInputFormat.class); job.setOutputFormatClass(TextOutputFormat.class); job.setMapperClass(ClusterMapper.class); job.setNumReduceTasks(0); job.setOutputKeyClass(IntWritable.class); job.setOutputValueClass(Sample.class); job.waitForCompletion(true); } }