Bayes algorithm with apache mahout

Apache mahout in action using the java API and bayes algorithm.

Simple bayes implementation

For people that doesn't know what is apache mahout: mahout
Here we'll implement the bayes algorithm for a text classification.
What does the example ?

• Train apache mahout in order to make it smarter.
• Automatic classification of a content.

Dependencies

For the example we only need the mahout-core dependency. We use the version 0.5 .
maven

<dependency>
    <groupid>org.apache.mahout</groupid>
    <artifactid>mahout-core</artifactid>
    <version>0.5</version>
</dependency>

ivy

<dependency name="mahout-core" org="org.apache.mahout" rev="0.5" />

Training

Mahout training in order to recognize our content:

/**
  * This method permits to make smarter apache mahout.
  * @param label the label associated to the file.
  * @param fileToClassify the file associated to the specified label.
  * @param charset the charset of content of the file.
  * @param tmpDir the directory where the transformed file will be stocked. 
  * @param databaseOutputDir the directory of the apache hadoops database, which contains reference data for future classification.
  * @throws IOException 
  */
 public void training(String label, String fileToClassify, String charset, String outputDir,
   String databaseOutputDir) throws IOException {

  /*
   * Take the document and associate to a label inside a file that
   * respects the apache mahout input format:
   * [LABEL] _TAB_ [TEXT]
   * example: 
   * english mahout is a good product. 
   * french mahout est un bon produit.
   * Note the analyzer => This is a lucene analyzer, by default apache mahout provide one. I used this one.
   * In few words the analyzor permits to define how the words will be extracted from your file...
   */
  BayesFileFormatter.format(label, new DefaultAnalyzer(), new File(fileToClassify), Charset.forName(charset),
    new File(outputDir));

  

  /*
   * Here we build the bayes parameters object that permits to define some
   * information about the way to stock the training data. Mahout use
   * apache hadoops in background for save the classification data.
   * See the hadoops documentation to know more about this object.
   * Just take care to specify the classifierType and the basePath.
   */
  BayesParameters bayesParameters = buildBayesParam(charset, databaseOutputDir);

  /*
  * Start the training !
  */ 
  TrainClassifier.trainNaiveBayes(new Path(outputDir), new Path(databaseOutputDir), bayesParameters);

 }

 private BayesParameters buildBayesParam(String charset, String databaseOutputDir) {
  BayesParameters bayesParameters = new BayesParameters();
  bayesParameters.setGramSize(1);
  bayesParameters.set("verbose", "true"); //If you want to see what happen.
  bayesParameters.set("classifierType", "bayes");
  bayesParameters.set("defaultCat", "other"); //The default category to return if a label is not found for a specified text. 
  bayesParameters.set("encoding", charset);
  bayesParameters.set("alpha_i", "1.0");
  bayesParameters.set("dataSource", "hdfs");
  bayesParameters.set("basePath", databaseOutputDir);
  return bayesParameters;
 }

  

Classification

This code try to find the good label in asking mahout for a specified content:

/**
  * Ask to mahout to find the good label for the specified content.
  * 
  * @param contentToClassify
  *            the content to classify.
  * @param charset
  *            the charset of the content.
  * @param databaseOutputDir
  *            mahout database directory.
  * @return label the label retrieved by mahout.
  * @throws InvalidDatastoreException
  * @throws IOException
  */
 public String searchLabel(String contentToClassify, String charset, String databaseOutputDir)
   throws InvalidDatastoreException, IOException {
  //define the algorithm to use
  Algorithm algorithm = new BayesAlgorithm();
  //specify the mahout datastore to use. (the path of hadoops database).
  Datastore datastore = new InMemoryBayesDatastore(buildBayesParam(charset, databaseOutputDir));
  //initialize the mahout context.
  ClassifierContext context = new ClassifierContext(algorithm, datastore);
  context.initialize();
  
  //Make the search
  ClassifierResult classifyResult = context.classifyDocument(new String[] { contentToClassify }, "other");
  
  //Result
  return classifyResult.getLabel();
}

I strongly recommend to see the source of the dependency mahout-utils, very useful to understand the project.
References
apache mahout official site
An other example of use bayes algorithm with mahout