Getting started with Apache Mahout

Recently I have got an interesting problem to solve: how to classify text from different sources using automation? Some time ago I read about a project which does this as well as many other text analysis stuff - Apache Mahout. Though it's not a very mature one (current version is 0.4), it's very powerful and scalable. Build on top of another excellent project, Apache Hadoop, it's capable to analyze huge data sets.

So I did a small project in order to understand how Apache Mahout works. I decided to use Apache Maven 2 in order to manage all dependencies so I will start with POM file first.

  4.0.0
  org.acme
  mahout
  0.94
  Mahout Examples
  Scalable machine learning library examples
  jar

  
    UTF-8
    0.4
  
 
  
    
      
        org.apache.maven.plugins
        maven-compiler-plugin
        
          UTF-8
          1.6
          1.6
          true
        
      
    
  

  
    
      org.apache.mahout
      mahout-core
      ${apache.mahout.version}
    

    
      org.apache.mahout
      mahout-math
      ${apache.mahout.version}
    

    
      org.apache.mahout
      mahout-utils
      ${apache.mahout.version}
    


     
      org.slf4j
      slf4j-api
      1.6.0
    

    
      org.slf4j
      slf4j-jcl
      1.6.0
    
  

Then I looked into Apache Mahout examples and algorithms available for text classification problem. The most simple and accurate one is Naive Bayes classifier. Here is a code snippet:

package org.acme;

import java.io.BufferedReader;
import java.io.IOException;
import java.io.FileReader;
import java.util.List;

import org.apache.hadoop.fs.Path;
import org.apache.mahout.classifier.ClassifierResult;
import org.apache.mahout.classifier.bayes.TrainClassifier;
import org.apache.mahout.classifier.bayes.algorithm.BayesAlgorithm;
import org.apache.mahout.classifier.bayes.common.BayesParameters;
import org.apache.mahout.classifier.bayes.datastore.InMemoryBayesDatastore;
import org.apache.mahout.classifier.bayes.exceptions.InvalidDatastoreException;
import org.apache.mahout.classifier.bayes.interfaces.Algorithm;
import org.apache.mahout.classifier.bayes.interfaces.Datastore;
import org.apache.mahout.classifier.bayes.model.ClassifierContext;
import org.apache.mahout.common.nlp.NGrams;

public class Starter {
 public static void main( final String[] args ) {
  final BayesParameters params = new BayesParameters();
  params.setGramSize( 1 );
  params.set( "verbose", "true" );
  params.set( "classifierType", "bayes" );
  params.set( "defaultCat", "OTHER" );
  params.set( "encoding", "UTF-8" );
  params.set( "alpha_i", "1.0" );
  params.set( "dataSource", "hdfs" );
  params.set( "basePath", "/tmp/output" );
  
  try {
      Path input = new Path( "/tmp/input" );
      TrainClassifier.trainNaiveBayes( input, "/tmp/output", params );
   
      Algorithm algorithm = new BayesAlgorithm();
      Datastore datastore = new InMemoryBayesDatastore( params );
      ClassifierContext classifier = new ClassifierContext( algorithm, datastore );
      classifier.initialize();
      
      final BufferedReader reader = new BufferedReader( new FileReader( args[ 0 ] ) );
      String entry = reader.readLine();
      
      while( entry != null ) {
          List< String > document = new NGrams( entry, 
                          Integer.parseInt( params.get( "gramSize" ) ) )
                          .generateNGramsWithoutLabel();

          ClassifierResult result = classifier.classifyDocument( 
                           document.toArray( new String[ document.size() ] ), 
                           params.get( "defaultCat" ) );          

          entry = reader.readLine();
      }
  } catch( final IOException ex ) {
   ex.printStackTrace();
  } catch( final InvalidDatastoreException ex ) {
   ex.printStackTrace();
  }
 }
}

There is one important note here: system must be taught before starting classification. In order to do so, it's necessary to provide examples (more - better) of different text classification. It should be simple files where each line starts with category separated by tab from text itself. F.e.:

SUGGESTION  That's a great suggestion
QUESTION  Do you sell Microsoft Office?
...

More files you can provide, more precise classification you will get. All files must be put to the '/tmp/input' folder, they will be processed by Apache Hadoop first. :)