centos7.4初探mahout源码-单机安装编译

Mahout简介

Apache Mahout是Apache Software Foundation(ASF)旗下的一个开源项目，提供了一些经典的机器学习算法，旨在帮助开发人员更加方便快捷地创建智能应用程序。
Mahout的主要目标是建立针对大规模数据集可伸缩的机器学习算法，主要包括以下五个部分：
1）频繁模式挖掘：挖掘数据中频繁出现的项集；
2）聚类：将诸如文本、文档之类的数据分成局部相关的组；
3）分类：利用已经存在的分类文档训练分类器，对未分类的文档进行分类；
4）推荐引擎（Taste协同过滤）：获取用户的行为并从中发现用户可能喜欢的事务；
5）频繁子项挖掘：利用一个项集（查询记录或购物记录）去识别经常一起出现的项目

1.准备环境

ip	备注
192.168.2.4	CentOS Linux release 7.4.1708 (Core)

安装maven
https://blog.csdn.net/u012637358/article/details/106436819

2.下载编译

用于创建可扩展的高性能机器学习应用程序
mahout官网

mahout当前最新源码压缩包mahout-0.14.0-source-release.zip

mkdir -p /opt/software 
mkdir -p /opt/module
cd /opt/software

上传到/opt/software指定目录下

#进入software目录下
cd /opt/software
#解压mahout-xxx.zip到module目录下
unzip mahout-0.14.0-source-release.zip -d /opt/module

进入到mahout-0.14.0目录进行编译

cd mahout-0.14.0
#跳过测试进行编译
mvn clean install -X -Dmaven.javadoc.skip=true

命令说明：

-Dmaven.test.skip=true ：跳过test

-Dmaven.javadoc.skip=true ：跳过javadoc

-X ：debug，显示详情

编译成功！

3.问题解决

1.编译时构建javadoc时失败
解决方法，mvn添加跳过javadoc参数

-Dmaven.javadoc.skip=true ：跳过javadoc

2.在构建mahout core必须构建test不能跳过test

mvn clean install -X -Dmaven.javadoc.skip=true

3.在构建mahout spark engine 模块是必须跳过test

mvn clean install -X -Dmaven.javadoc.skip=true -Dmaven.javadoc.skip=true

保持网络良好，参考文档内容，一般不会在出现其他问题。

4.强烈建议

没特殊需求情况下，强烈建议官网下载编译好的apache-mahout-distribution-xxxx.tar.gz压缩文件
https://downloads.apache.org/mahout/0.13.0/
解压即用

cd /opt/module/apache-mahout-distribution-0.13.0
./bin/mahout
[root@node1 apache-mahout-distribution-0.13.0]# ./bin/mahout
MAHOUT_LOCAL is set, so we don't add HADOOP_CONF_DIR to classpath.
MAHOUT_LOCAL is set, running locally
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/opt/module/apache-mahout-distribution-0.13.0/mahout-examples-0.13.0-job.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/opt/module/apache-mahout-distribution-0.13.0/mahout-mr-0.13.0-job.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/opt/module/apache-mahout-distribution-0.13.0/lib/slf4j-log4j12-1.7.22.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
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

简单几步就配置完成了