Mahout快速入门教程

lidki 6年前


       Mahout 是一个很强大的数据挖掘工具,是一个分布式机器学习算法的集合,包括:被称为Taste的分布式协同过滤的实现、分类、聚类等。Mahout最大的优点就 是基于hadoop实现,把很多以前运行于单机上的算法,转化为了MapReduce模式,这样大大提升了算法可处理的数据量和处理性能。

一、Mahout安装、配置

1、下载并解压Mahout
http://archive.apache.org/dist/mahout/
tar -zxvf mahout-distribution-0.9.tar.gz

2、配置环境变量
# set mahout environment
export MAHOUT_HOME=/mnt/jediael/mahout/mahout-distribution-0.9
export MAHOUT_CONF_DIR=$MAHOUT_HOME/conf
export PATH=$MAHOUT_HOME/conf:$MAHOUT_HOME/bin:$PATH

3、安装mahout
[jediael@master mahout-distribution-0.9]$ pwd
/mnt/jediael/mahout/mahout-distribution-0.9
[jediael@master mahout-distribution-0.9]$ mvn install

4、验证Mahout是否安装成功
    执行命令mahout。若列出一些算法,则成功:

    [jediael@master mahout-distribution-0.9]$ mahout        Running on hadoop, using /mnt/jediael/hadoop-1.2.1/bin/hadoop and HADOOP_CONF_DIR=        MAHOUT-JOB: /mnt/jediael/mahout/mahout-distribution-0.9/examples/target/mahout-examples-0.9-job.jar        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  

二、使用简单示例验证mahout
1、启动Hadoop
2、下载测试数据
           http://archive.ics.uci.edu/ml/databases/synthetic_control/链接中的synthetic_control.data
或者百度一下也很容易找到这个示例数据。
3、上传测试数据
hadoop fs -put synthetic_control.data testdata
4、 使用Mahout中的kmeans聚类算法,执行命令:
mahout -core  org.apache.mahout.clustering.syntheticcontrol.kmeans.Job
花费9分钟左右完成聚类 。
5、查看聚类结果
    执行hadoop fs -ls /user/root/output,查看聚类结果。
[jediael@master mahout-distribution-0.9]$ hadoop fs -ls output    Found 15 items    -rw-r--r--   2 jediael supergroup        194 2015-03-07 15:07 /user/jediael/output/_policy    drwxr-xr-x   - jediael supergroup          0 2015-03-07 15:07 /user/jediael/output/clusteredPoints    drwxr-xr-x   - jediael supergroup          0 2015-03-07 15:02 /user/jediael/output/clusters-0    drwxr-xr-x   - jediael supergroup          0 2015-03-07 15:02 /user/jediael/output/clusters-1    drwxr-xr-x   - jediael supergroup          0 2015-03-07 15:07 /user/jediael/output/clusters-10-final    drwxr-xr-x   - jediael supergroup          0 2015-03-07 15:03 /user/jediael/output/clusters-2    drwxr-xr-x   - jediael supergroup          0 2015-03-07 15:03 /user/jediael/output/clusters-3    drwxr-xr-x   - jediael supergroup          0 2015-03-07 15:04 /user/jediael/output/clusters-4    drwxr-xr-x   - jediael supergroup          0 2015-03-07 15:04 /user/jediael/output/clusters-5    drwxr-xr-x   - jediael supergroup          0 2015-03-07 15:05 /user/jediael/output/clusters-6    drwxr-xr-x   - jediael supergroup          0 2015-03-07 15:05 /user/jediael/output/clusters-7    drwxr-xr-x   - jediael supergroup          0 2015-03-07 15:06 /user/jediael/output/clusters-8    drwxr-xr-x   - jediael supergroup          0 2015-03-07 15:07 /user/jediael/output/clusters-9    drwxr-xr-x   - jediael supergroup          0 2015-03-07 15:02 /user/jediael/output/data    drwxr-xr-x   - jediael supergroup          0 2015-03-07 15:02 /user/jediael/output/random-seeds 

来自:http://blog.csdn.net/jediael_lu/article/details/44117367