Apache Mahout: Getting started

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.

<!--?xml version="1.0" encoding="UTF-8"?-->
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemalocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
  <modelversion>4.0.0</modelversion>
  <groupid>org.acme</groupid>
  <artifactid>mahout</artifactid>
  <version>0.94</version>
  <name>Mahout Examples</name>
  <description>Scalable machine learning library examples</description>
  <packaging>jar</packaging>

  <properties>
    <project.build.sourceencoding>UTF-8</project.build.sourceencoding>
    <apache.mahout.version>0.4</apache.mahout.version>
  </properties>
 
  <build>
    <plugins>
      <plugin>
        <groupid>org.apache.maven.plugins</groupid>
        <artifactid>maven-compiler-plugin</artifactid>
        <configuration>
          <encoding>UTF-8</encoding>
          <source>1.6
          <target>1.6</target>
          <optimize>true</optimize>
        </configuration>
      </plugin>
    </plugins>
  </build>

  <dependencies>
    <dependency>
      <groupid>org.apache.mahout</groupid>
      <artifactid>mahout-core</artifactid>
      <version>${apache.mahout.version}</version>
    </dependency>

    <dependency>
      <groupid>org.apache.mahout</groupid>
      <artifactid>mahout-math</artifactid>
      <version>${apache.mahout.version}</version>
    </dependency>

    <dependency>
      <groupid>org.apache.mahout</groupid>
      <artifactid>mahout-utils</artifactid>
      <version>${apache.mahout.version}</version>
    </dependency>


     <dependency>
      <groupid>org.slf4j</groupid>
      <artifactid>slf4j-api</artifactid>
      <version>1.6.0</version>
    </dependency>

    <dependency>
      <groupid>org.slf4j</groupid>
      <artifactid>slf4j-jcl</artifactid>
      <version>1.6.0</version>
    </dependency>
  </dependencies>
</project>

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. :)

Reference: Getting started with Apache Mahout from our JCG partner Andrey Redko at the Andriy Redko {devmind}.