一个基于Mahout与hadoop的聚类搭建

wtxnpw

mahout是基于hadoop的数据挖掘工具，因为有了hadoop，所以进行海量数据的挖掘工作显得更为简单。但是因为算法需要支持M/R，所以不是所有常用的数据挖掘算法都会支持。这篇文章会告诉你，如何使用hadoop + mahout搭出一个简易的聚类工具。
    第一步：搭建hadoop平台。
我使用的是ubuntu 11.04，如果没有ubuntu的开发环境，就参考我的帖子《Ubuntu 10.10 java 开发环境》
    #1 在ubuntu下面建立一个用户组与用户

beneo@ubuntu:~$ sudo addgroup hadoop
beneo@ubuntu:~$ sudo adduser --ingroup hadoop hduser


   #2 安装ssh-server

beneo@ubuntu:~$ sudo apt-get install ssh
beneo@ubuntu:~$ su - hduser
hduser@ubuntu:~$ ssh-keygen -t rsa -P ""
hduser@ubuntu:~$ cat $HOME/.ssh/id_rsa.pub >> $HOME/.ssh/authorized_keys


   #3 验证ssh通信

hduser@ubuntu:ssh localhost


ssh localhost 后，选择 yes，如果没有问题，就可以安装hadoop了
   #4 添加java_home
修改conf/hadoop-env.sh文件，让JAVA_HOME指向正确的地址
   #5 修改下面的配置
conf/core-site.xml:
<configuration>
<property>
<name>fs.default.name</name>
<value>hdfs://localhost:9000</value>
</property>
</configuration>
conf/hdfs-site.xml:
<configuration>
<property>
<name>dfs.replication</name>
<value>1</value>
</property>
</configuration>
conf/mapred-site.xml:
<configuration>
<property>
<name>mapred.job.tracker</name>
<value>localhost:9001</value>
</property>
</configuration>

    #6 Format a new distributed-filesystem:
$ bin/hadoop namenode -format

    #7 Start the hadoop daemons:
$ bin/start-all.sh

    #8 验证启动成功没有
$ jps
数一下有没有6个，没有的话，删除logs下面的文件，然后从#6开始
    #9 别慌，先打开网页，打不开，等！！！
    NameNode - http://localhost:50070/
JobTracker - http://localhost:50030/

第一步搭建hadoop结束
    第二步，Mahout的配置
    #1 下载Mahout,解压
    #2 .bash_profile里面设置HADOOP_HOME
    #3 mahout/bin/mahout 看看打印结果
    第三步，做一个聚类的demo吧
我的聚类是文本 -> lucene index -> mahout -> clustering dumper
可以选择的是 sequeneceFile -> mahout -> clustering dumper
我直接贴代码吧，用的是groovy，可能写的不好
    #1 text -> lucene index

def assembleDoc = {
label, content ->
assert !label.toString().trim().empty
assert !content.toString().trim().empty
def doc = new Document()
doc.add(new Field("label", label, Field.Store.YES, Field.Index.NOT_ANALYZED))
doc.add(new Field("content", content, Field.Store.NO, Field.Index.ANALYZED, TermVector.YES))
doc
}
def mockContent = {
def set = []
new File("""/home/beneo/text.txt""").newReader().eachLine {
String line ->
set << line
}
set
}

def mockExpandoSet = {
def lst = []
mockContent()?.each {
content ->
// 过滤掉所有非中文字符
def line = content.replaceAll("[^\u4e00-\u9fa5]+", "")
if (line != null && line.trim().length() > 2) {
println(content)
def expando = new Expando()
expando.label = content
expando.content = line
lst << expando
}
}
lst
}
//创建一个dic
def directory = FSDirectory.open(new File("""/home/beneo/index"""), NoLockFactory.getNoLockFactory())
// 用的是 IK分词
def analyzer = new IKAnalyzer()
//创建一个indexWriter，这个wirter就是用来产生出index
def indexWriter = new IndexWriter(directory, analyzer, true, IndexWriter.MaxFieldLength.UNLIMITED)
//从本地获得文本
mockExpandoSet().each {
expando ->
indexWriter.addDocument(assembleDoc(expando.label, expando.content))
}
indexWriter.commit()
indexWriter.close()
directory.close()
         
    #2 lucene index -> mahout vector
mahout/bin/mahout lucene.vector -d index/ -i label -o tmp/vector/vector -f content -t tmp/vector/dict -n 2

    #3 mahout vector -> mahout canopy
mahout/bin/mahout canopy -i tmp/vector/vector -o tmp/canopy/ -dm org.apache.mahout.common.distance.CosineDistanceMeasure -t1 0.32 -t2 0.08 -ow

    #4 mahout canopy -> mahout kmeans

mahout/bin/mahout kmeans -i tmp/vector/vector -c tmp/canopy/clusters-0/ -dm org.apache.mahout.common.distance.CosineDistanceMeasure -o tmp/kmeans/ -cd 0.001 -x 10 -ow -cl


   #5 mahout keamns -> 结果分析

String seqFileDir = "/home/hduser/tmp/kmeans/clusters-2/"
String pointsDir = "/home/hduser/tmp/kmeans/clusteredPoints/"
def conf = new Configuration()
FileSystem fs = new Path(seqFileDir).getFileSystem(conf)
Map<Integer, List<WeightedVectorWritable>> clusterIdToPoints = readPoints(new Path(pointsDir), new Configuration());
for (FileStatus seqFile: fs.globStatus(new Path(seqFileDir, "part-*"))) {
Path path = seqFile.getPath()
SequenceFile.Reader reader = new SequenceFile.Reader(fs, path, conf);
org.apache.hadoop.io.Writable key = reader.getKeyClass().asSubclass(org.apache.hadoop.io.Writable.class).newInstance();
org.apache.hadoop.io.Writable value = reader.getValueClass().asSubclass(org.apache.hadoop.io.Writable.class).newInstance();
while (reader.next(key, value)) {
Cluster cluster = (Cluster) value;
int id = cluster.getId()
int np = cluster.getNumPoints()
List<WeightedVectorWritable> points = clusterIdToPoints.get(cluster.getId());
if (points != null && points.size() > 4) {
for (Iterator<WeightedVectorWritable> iterator = points.iterator(); iterator.hasNext();) {
println(((NamedVector) iterator.next().getVector()).getName())
}
println "======================================"
}
}
}

private static Map<Integer, List<WeightedVectorWritable>> readPoints(Path pointsPathDir, Configuration conf)
throws IOException {
Map<Integer, List<WeightedVectorWritable>> result = new TreeMap<Integer, List<WeightedVectorWritable>>();
FileSystem fs = pointsPathDir.getFileSystem(conf);
FileStatus[] children = fs.listStatus(pointsPathDir, new PathFilter() {
@Override
public boolean accept(Path path) {
String name = path.getName();
return !(name.endsWith(".crc") || name.startsWith("_"));
}
});
for (FileStatus file: children) {
Path path = file.getPath();
SequenceFile.Reader reader = new SequenceFile.Reader(fs, path, conf);
IntWritable key = reader.getKeyClass().asSubclass(IntWritable.class).newInstance();
WeightedVectorWritable value = reader.getValueClass().asSubclass(WeightedVectorWritable.class).newInstance();
while (reader.next(key, value)) {
// value is the cluster id as an int, key is the name/id of the
// vector, but that doesn't matter because we only care about printing
// it
// String clusterId = value.toString();
List<WeightedVectorWritable> pointList = result.get(key.get());
if (pointList == null) {
pointList = new ArrayList<WeightedVectorWritable>();
result.put(key.get(), pointList);
}
pointList.add(value);
value = reader.getValueClass().asSubclass(WeightedVectorWritable.class).newInstance();
}
}
return result;
}


效果我就不展示了