Mahout安装部署

0x01 简介

Mahout 是一套具有可扩充能力的机器学习类库。它提供机器学习框架的同时，还实现了一些可扩展的机器学习领域经典算法的实现，可以帮助开发人员更加方便快捷地创建智能应用程序。通过和 Apache Hadoop 分布式框架相结合，Mahout 可以有效地使用分布式系统来实现高性能计算。

0x02 安装配置Mahout

2.1 下载安装

下载地址：http://archive.apache.org/dist/mahout/
安装版本：apache-mahout-distribution-0.11.1.tar.gz

$ tar -zxvf apache-mahout-distribution-0.11.1.tar.gz
$ mv apache-mahout-distribution-0.11.1 /home/hadoop/cloud/mahout-0.11.1
$ ln -s /home/hadoop/cloud/mahout-0.11.1 /home/hadoop/cloud/mahout

2.2 配置环境变量

# vim /etc/profile

export MAHOUT_HOME=/home/hadoop/mahout
export PATH=$PATH:$MAHOUT_HOME/bin:$MAHOUT_HOME/conf

使配置立即生效

# source /etc/profile
# su hadoop
$ source /etc/profile

2.3 验证

然后在单机上检验Mahout是否安装完成。使用如下命令看是否能呈现出Mahout中一些集成的算法。

[hadoop@master bin]$ ./mahout
MAHOUT_LOCAL is not set; adding HADOOP_CONF_DIR to classpath.
Running on hadoop, using /home/hadoop/cloud/hadoop/bin/hadoop and HADOOP_CONF_DIR=/home/hadoop/cloud/hadoop/etc/hadoop
MAHOUT-JOB: /home/hadoop/cloud/mahout/mahout-examples-0.11.1-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
  cvb: : LDA via Collapsed Variation Bayes (0th deriv. approx)
  cvb0_local: : LDA via Collapsed Variation Bayes, in memory locally.
  describe: : Describe the fields and target variable in a data set
  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
  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

如果看到上述的信息，恭喜你，已经基本安装成功了，很简单的。
但是这里要重点强调一下：上面出现的信息

MAHOUT_LOCAL is not set; adding HADOOP_CONF_DIR to classpath.
Running on hadoop, using /home/hadoop/hadoop-2.2.0/bin/hadoop and HADOOP_CONF_DIR=/home/hadoop/hadoop-2.2.0/etc/hadoop
MAHOUT-JOB: /home/hadoop/apache-mahout-distribution-0.11.0/mahout-examples-0.11.0-job.jar

并不是说配置有误，恰恰相反，要是配置了变量 MAHOUT_LOCAL，那么完全体现不出 Mahout 的威力了，具体的可参考下面的解释

Mahout运行配置可以在 (MAHOUT_HOME/bin/mahout 里面进行设置，实际上)MAHOUT_HOME/bin/mahout就是Mahout在命令行的启动脚本，这一点与Hadoop相似，但也又不同
Hadoop在 $HADOOP_HOMEconf 下面还提供了专门的hadoop-env.sh文件进行相关环境变量的配置，而Mahout在conf目录下没有提供这样的文件
MAHOUT_LOCAL 与 HADOOP_CONF_DIR 这两个参数是控制 Mahout 是在本地运行还是在 Hadoop 上运行的关键。
$MAHOUT_HOME/bin/mahout 文件指出，只要设置MAHOUT_LOCAL 的值为一个非空（not empty string）值，则不管用户有没有设置 HADOOP_CONF_DIR 和 HADOOP_HOME 这两个参数，Mahout 都以本地模式运行；换句话说，如果要想 Mahout 运行在Hadoop上，则 MAHOUT_LOCAL 必须为空
HADOOP_CONF_DIR参数指定Mahout运行Hadoop模式时使用的Hadoop配置信息，这个文件目录一般指向的是$HADOOP_HOME目录下的conf目录
除此之外，我们还应该设置 JAVA_HOME 或者 MAHOUT_JAVA_HOME 变量，以及必须将 Hadoop 的执行文件加入到PATH中。要想使用本地模式运行，只需在 $MAHOUT_HOME/bin/mahout 添加一条设置MAHOUT_LOCAL为非空的语句即可

【参考】http://blog.csdn.net/u011414200/article/details/47857655

0x03 测试用例

3.1 准备数据

下载测试数据放到$MAHOUT_HOME/testdata目录下载一个文件synthetic_control.data

文件可能在浏览器中自动打开，另存为即可。

3.2 创建测试目录

创建测试目录testdata，并把数据导入到这个tastdata目录中(这里的目录的名字只能是testdata)

$ hadoop fs -mkdir -p /user/hadoop/testdata
//上传文件
$ hadoop fs -put /home/hadoop/synthetic_control.data

3.3 使用kmeans算法测试

需要运行几分钟

$ bin/hadoop jar mahout-examples-0.11.0-job.jar org.apache.mahout.clustering.syntheticcontrol.kmeans.Job

注意是hadoop不是mahout花费9分钟左右完成聚类。

3.4 查看聚类结果

$ hadoop fs -ls /output

查看聚类结果

0x04 参考文献

http://blog.csdn.net/u011414200/article/details/47857655

2017年1月26日, 星期四
update: 2017-10-19 20:59:59 星期四
修改部分内容，重新排版。