Mahout安装与测试－基于hadoop单结点伪分布式

安装JDK

见我之前关于JDK1.7安装的博客：

http://blog.csdn.net/stanely_hwang/article/details/18883599

Hadoop单结点伪分布式安装

见我之前关于Hadoop单结点伪分布式安装的博客：

http://blog.csdn.net/stanely_hwang/article/details/18884181

Mahout安装与配置

1：下载二进制解压安装：

 Mahout下载地址：

http://www.apache.org/dyn/closer.cgi/mahout/

Mahout下载完后，直接解压。我将Mahout下载到/opt/hadoop下，进入该目录，进行解压操作

$ cd /opt/hadoop

$ tar -zxvf mahout-distribution-0.9

2：配置环境变量：

用vim编辑/etc/profile文件, 再文件末尾添加$JHADOOP_HOME, $HADOOP_CONF,$MAHOUT_HOME 环境遍历，

详细配置信息如下所示：

JAVA_HOME=/opt/java/jdk

PATH=/sbin:/bin:/usr/sbin:/usr/bin:/root/bin:/bin

JRE_HOME=/opt/java/jdk

PATH=/sbin:/bin:/usr/sbin:/usr/bin:/root/bin:/bin

export JAVA_HOME

export JRE_HOME

export HADOOP_HOME=/home/andy/hadoop-2.2.0

export HADOOP_CONF_DIR=/home/andy/hadoop-2.2.0/conf

export MAHOUT_HOME=/opt/hadoop/mahout-distribution-0.9

export PATH=$HADOOP_HOME/bin:$MAHOUT_HOME/bin:$PATH

export PATH

export PATH=/sbin:/bin:/usr/sbin:/usr/bin:/sbin

3：启动Hadoop：

到Hadoop安装目录的sbin目录下执行 （ ~/hadoop-2.2.0/sbin目录下)

 $  ./hadoop-daemon.sh start namenode

 $ ./hadoop-daemon.sh start datanode

$ ./yarn-daemon.sh start resourcemanager

 $ ./yarn-daemon.sh start nodemanager

4：mahout --help    #检查Mahout是否安装完好，看是否列出了一些算法

进入$MAHOUT_HOME/bin目录

 $ cd $MAHOUT_HOME/bin

 $ ./mahout --help 

 输出内容如下：

MAHOUT_LOCAL is not set; adding HADOOP_CONF_DIR to classpath.

Running on hadoop, using /home/andy/hadoop-2.2.0/bin/hadoop and HADOOP_CONF_DIR=/home/andy/hadoop-2.2.0/conf

MAHOUT-JOB: /opt/hadoop/mahout-distribution-0.9/mahout-examples-0.9-job.jar

Unknown program '--help' chosen.

Valid program names are:

  arff.vector: : Generate Vectors from an ARFF file or directory

  baumwelch: : Baum-Welch algorithm for unsupervised HMM training

  canopy: : Canopy clustering

  cat: : Print a file or resource as the logistic regression models would see it

  cleansvd: : Cleanup and verification of SVD output

  clusterdump: : Dump cluster output to text

  clusterpp: : Groups Clustering Output In Clusters

  cmdump: : Dump confusion matrix in HTML or text formats

  concatmatrices: : Concatenates 2 matrices of same cardinality into a single matrix

  cvb: : LDA via Collapsed Variation Bayes (0th deriv. approx)

  cvb0_local: : LDA via Collapsed Variation Bayes, in memory locally.

  evaluateFactorization: : compute RMSE and MAE of a rating matrix factorization against probes

  fkmeans: : Fuzzy K-means clustering

  hmmpredict: : Generate random sequence of observations by given HMM

  itemsimilarity: : Compute the item-item-similarities for item-based collaborative filtering

  kmeans: : K-means clustering

  lucene.vector: : Generate Vectors from a Lucene index

  lucene2seq: : Generate Text SequenceFiles from a Lucene index

  matrixdump: : Dump matrix in CSV format

  matrixmult: : Take the product of two matrices

  parallelALS: : ALS-WR factorization of a rating matrix

  qualcluster: : Runs clustering experiments and summarizes results in a CSV

  recommendfactorized: : Compute recommendations using the factorization of a rating matrix

  recommenditembased: : Compute recommendations using item-based collaborative filtering

  regexconverter: : Convert text files on a per line basis based on regular expressions

  resplit: : Splits a set of SequenceFiles into a number of equal splits

  rowid: : Map SequenceFile to {SequenceFile, SequenceFile}

  rowsimilarity: : Compute the pairwise similarities of the rows of a matrix

  runAdaptiveLogistic: : Score new production data using a probably trained and validated AdaptivelogisticRegression model

  runlogistic: : Run a logistic regression model against CSV data

  seq2encoded: : Encoded Sparse Vector generation from Text sequence files

  seq2sparse: : Sparse Vector generation from Text sequence files

  seqdirectory: : Generate sequence files (of Text) from a directory

  seqdumper: : Generic Sequence File dumper

  seqmailarchives: : Creates SequenceFile from a directory containing gzipped mail archives

  seqwiki: : Wikipedia xml dump to sequence file

  spectralkmeans: : Spectral k-means clustering

  split: : Split Input data into test and train sets

  splitDataset: : split a rating dataset into training and probe parts

  ssvd: : Stochastic SVD

  streamingkmeans: : Streaming k-means clustering

  svd: : Lanczos Singular Value Decomposition

  testnb: : Test the Vector-based Bayes classifier

  trainAdaptiveLogistic: : Train an AdaptivelogisticRegression model

  trainlogistic: : Train a logistic regression using stochastic gradient descent

  trainnb: : Train the Vector-based Bayes classifier

  transpose: : Take the transpose of a matrix

  validateAdaptiveLogistic: : Validate an AdaptivelogisticRegression model against hold-out data set

  vecdist: : Compute the distances between a set of Vectors (or Cluster or Canopy, they must fit in memory) and a list of Vectors

  vectordump: : Dump vectors from a sequence file to text

  viterbi: : Viterbi decoding of hidden states from given output states sequence

[andy@localhost bin]$ 

5：mahout使用准备：

• 准备数据：

测试数据下载地址：

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

下载完后，将数据放入$MAHOUT_HOME文件下

• 创建测试目录

创建测试目录testdata，并将数据导入到testdata中      

 $ cd $HADOOP_HOME/bin/

$ hadoop fs -mkdir testdata #

$ hadoop fs -put $MAHOUT_HOME/synthetic_control.data testdata

• 使用kmeans算法

$ hadoop jar /home/hadoop/mahout-distribution-0.7/mahout-examples-0.7-job.jar org.apache.mahout.clustering.syntheticcontrol.kmeans.Job

• 查看结果

$ hadoop fs -lsr output

$ hadoop fs －get output $MAHOUT_HOME/result

$ cd $MAHOUT_HOME/example/result

$ ls

如上图所示表示安装成功！