NAME

AI::Categorizer::Learner::Weka - Pass-through wrapper to Weka system

SYNOPSIS

  use AI::Categorizer::Learner::Weka;
  
  # Here $k is an AI::Categorizer::KnowledgeSet object
  
  my $nb = new AI::Categorizer::Learner::Weka(...parameters...);
  $nb->train(knowledge_set => $k);
  $nb->save_state('filename');
  
  ... time passes ...
  
  $nb = AI::Categorizer::Learner->restore_state('filename');
  my $c = new AI::Categorizer::Collection::Files( path => ... );
  while (my $document = $c->next) {
    my $hypothesis = $nb->categorize($document);
    print "Best assigned category: ", $hypothesis->best_category, "\n";
  }

DESCRIPTION

This class doesn't implement any machine learners of its own, it merely passes the data through to the Weka machine learning system (http://www.cs.waikato.ac.nz/~ml/weka/). This can give you access to a collection of machine learning algorithms not otherwise implemented in "AI::Categorizer".

Currently this is a simple command-line wrapper that calls "java" subprocesses. In the future this may be converted to an "Inline::Java" wrapper for better performance (faster running times). However, if you're looking for really great performance, you're probably looking in the wrong place - this Weka wrapper is intended more as a way to try lots of different machine learning methods.

METHODS

This class inherits from the "AI::Categorizer::Learner" class, so all of its methods are available unless explicitly mentioned here.

new()

Creates a new Weka Learner and returns it. In addition to the parameters accepted by the "AI::Categorizer::Learner" class, the Weka subclass accepts the following parameters:

java_path: Specifies where the "java" executable can be found on this system. The default is simply "java", meaning that it will search your "PATH" to find java.
java_args: Specifies a list of any additional arguments to give to the java process. Commonly it's necessary to allocate more memory than the default, using an argument like "-Xmx130MB".
weka_path: Specifies the path to the "weka.jar" file containing the Weka bytecode. If Weka has been installed somewhere in your java "CLASSPATH", you needn't specify a "weka_path".
weka_classifier: Specifies the Weka class to use for a categorizer. The default is "weka.classifiers.NaiveBayes". Consult your Weka documentation for a list of other classifiers available.
weka_args: Specifies a list of any additional arguments to pass to the Weka classifier class when building the categorizer.
tmpdir: A directory in which temporary files will be written when training the categorizer and categorizing new documents. The default is given by "File::Spec->tmpdir".

train(knowledge_set => $k)

Trains the categorizer. This prepares it for later use in categorizing documents. The "knowledge_set" parameter must provide an object of the class "AI::Categorizer::KnowledgeSet" (or a subclass thereof), populated with lots of documents and categories. See AI::Categorizer::KnowledgeSet for the details of how to create such an object.

categorize($document)

Returns an "AI::Categorizer::Hypothesis" object representing the categorizer's "best guess" about which categories the given document should be assigned to. See AI::Categorizer::Hypothesis for more details on how to use this object.

save_state($path)

Saves the categorizer for later use. This method is inherited from "AI::Categorizer::Storable".

AUTHOR

Ken Williams, ken@mathforum.org

COPYRIGHT

This library is free software; you can redistribute it and/or modify it under the same terms as Perl itself.