【Mahout一】基于Mahout 命令参数含义

1. mahout seqdirectory

 

    $ mahout seqdirectory 
        --input (-i) input               Path to job input directory(原始文本文件).
        --output (-o) output             The directory pathname for output.(<Text,Text>Sequence File)
        -ow

 

 

功能: 将原始文本数据集转换为< Text, Text > SequenceFile

 

 

 

2. mahout seq2sparke

 

功能: Convert and preprocesses the dataset(<Text,Text> SequenceFile) into a < Text, VectorWritable > SequenceFile containing term frequencies for each document.

即根据Sequence File转换为tfidf向量文件

说明:If we wanted to use different parsing methods or transformations on the term frequency vectors we could supply different options here e.g.: -ng 2 for bigrams or -n 2 for L2 length normalization

 

 

    mahout seq2sparse                         
      --output (-o) output             The directory pathname for output.        
      --input (-i) input               Path to job input directory.              
      --weight (-wt) weight            The kind of weight to use. Currently TF   
                                           or TFIDF. Default: TFIDF                  
      --norm (-n) norm                 The norm to use, expressed as either a    
                                           float or "INF" if you want to use the     
                                           Infinite norm.  Must be greater or equal  
                                           to 0.  The default is not to normalize    
      --overwrite (-ow)                If set, overwrite the output directory    
      --sequentialAccessVector (-seq)  (Optional) Whether output vectors should  
                                           be SequentialAccessVectors. If set true   
                                           else false                                
      --namedVector (-nv)              (Optional) Whether output vectors should  
                                           be NamedVectors. If set true else false

 

-i Sequence File文件目录

-o 向量文件输出目录

-wt 权重类型,支持TF或者TFIDF两种选项,默认TFIDF

-n 使用的正规化,使用浮点数或者"INF"表示,

-ow 指定该参数,将覆盖已有的输出目录

-seq 指定该参数,那么输出的向量是SequentialAccessVectors

-nv 指定该参数,那么输出的向量是NamedVectors

 

3. mahout split

 

功能:Split the preprocessed dataset into training and testing sets.

将预处理的tfidf向量集转换为training和testing向量集

 

    $ mahout split 
        -i ${WORK_DIR}/20news-vectors/tfidf-vectors 
        --trainingOutput ${WORK_DIR}/20news-train-vectors 
        --testOutput ${WORK_DIR}/20news-test-vectors  
        --randomSelectionPct 40 
        --overwrite --sequenceFiles -xm sequential

 

说明:如上是将向量数据集分为训练数据和检测数据,以随机40-60拆分

 

3. mahout trainnb

 

功能:训练分类器

 

mahout trainnb
  --input (-i) input               Path to job input directory.                 
  --output (-o) output             The directory pathname for output.                    
  --alphaI (-a) alphaI             Smoothing parameter. Default is 1.0
  --trainComplementary (-c)        Train complementary? Default is false.                        
  --labelIndex (-li) labelIndex    The path to store the label index in         
  --overwrite (-ow)                If present, overwrite the output directory   
                                       before running job                           
  --help (-h)                      Print out help                               
  --tempDir tempDir                Intermediate output directory                
  --startPhase startPhase          First phase to run                           
  --endPhase endPhase              Last phase to run

 

-i 输入路径

-o 输出路径

-a

-c 补偿性训练

-li label index文件的目录

-ow 指定该参数,删除输出目录

tempDir MapReduce作业的中间结果

startPhase 运行的第一个阶段

endPhase 运行的最后一个阶段

 

4. mahout testnb

 

功能:检验Bayes分类器

mahout testnb   
  --input (-i) input               Path to job input directory.                  
  --output (-o) output             The directory pathname for output.            
  --overwrite (-ow)                If present, overwrite the output directory    
                                       before running job

  --model (-m) model               The path to the model built during training   
  --testComplementary (-c)         Test complementary? Default is false.                          
  --runSequential (-seq)           Run sequential?                               
  --labelIndex (-l) labelIndex     The path to the location of the label index   
  --help (-h)                      Print out help                                
  --tempDir tempDir                Intermediate output directory                 
  --startPhase startPhase          First phase to run                            
  --endPhase endPhase              Last phase to run

-i 输入路径

-o 输出路径

-ow 覆盖输出目录

-c

 

 

 

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