Mahout安装

本文记录在Hadoop集群环境下安装Mahout。

环境：OS：Centos 6.5 x64 & Soft：Hadoop 1.2.1 & Mahout 0.9

1、简介

mahout项目主页：https://mahout.apache.org/

下载二进制包，上传到服务器。

2、安装

用集群环境用户安装，解压二进制包。

[huser@master hadoop]$ tar -xvf mahout-distribution-0.9.tar.gz 

3、配置环境变量

[huser@master ~]$ vi /etc/profile

export JAVA_HOME=/usr/java/jdk1.7.0_51
export CLASSPATH=.:$JAVA_HOME/jre/lib:$JAVA_HOME/lib/tools.jar 
export JRE_HOME=$JAVA_HOME/jre 
export HADOOP_HOME=/home/huser/hadoop/hadoop-1.2.1
export HADOOP_CONF_DIR=/home/huser/hadoop/hadoop-1.2.1/conf
export HADOOP_CLASSPATH=/home/huser/hadoop/hadoop-1.2.1/bin
export MAHOUT_HOME=/home/huser/hadoop/mahout-distribution-0.9
export MAHOUT_HOME_DIR=/home/huser/hadoop/mahout-distribution-0.9/conf
export PATH=$PATH:$JAVA_HOME/bin:$MAHOUT_HOME/bin:$MAHOUT_HOME/conf

[root@master huser]# source /etc/profile

4、测试

[huser@master ~]$ mahout 
MAHOUT_LOCAL is not set; adding HADOOP_CONF_DIR to classpath.
Warning: $HADOOP_HOME is deprecated.

Running on hadoop, using /home/huser/hadoop/hadoop-1.2.1/bin/hadoop and HADOOP_CONF_DIR=/home/huser/hadoop/hadoop-1.2.1/conf
MAHOUT-JOB: /home/huser/hadoop/mahout-distribution-0.9/mahout-examples-0.9-job.jar
Warning: $HADOOP_HOME is deprecated.

An example program must be given as the first argument.
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<Text,VectorWritable> to {SequenceFile<IntWritable,VectorWritable>, SequenceFile<IntWritable,Text>}
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