AI::Categorizer::Learner::DecisionTree - Decision Tree Learner

AI-Categorizer documentation Contained in the AI-Categorizer distribution.

Index

Code Index:

NAME

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AI::Categorizer::Learner::DecisionTree - Decision Tree Learner

SYNOPSIS

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  use AI::Categorizer::Learner::DecisionTree;

  # Here $k is an AI::Categorizer::KnowledgeSet object

  my $l = new AI::Categorizer::Learner::DecisionTree(...parameters...);
  $l->train(knowledge_set => $k);
  $l->save_state('filename');

  ... time passes ...

  $l = AI::Categorizer::Learner->restore_state('filename');
  while (my $document = ... ) {  # An AI::Categorizer::Document object
    my $hypothesis = $l->categorize($document);
    print "Best assigned category: ", $hypothesis->best_category, "\n";
  }

DESCRIPTION

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This class implements a Decision Tree machine learner, using AI::DecisionTree to do the internal work.

METHODS

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

new()

Creates a new DecisionTree Learner and returns it.

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

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Ken Williams, ken@mathforum.org

COPYRIGHT

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Copyright 2000-2003 Ken Williams. All rights reserved.

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

SEE ALSO

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AI::Categorizer(3)

AI-Categorizer documentation Contained in the AI-Categorizer distribution. 
package AI::Categorizer::Learner::DecisionTree;
$VERSION = '0.01';

use strict;
use AI::DecisionTree;
use AI::Categorizer::Learner::Boolean;
use base qw(AI::Categorizer::Learner::Boolean);

sub create_model {
  my $self = shift;
  $self->SUPER::create_model;
  $self->{model}{first_tree}->do_purge;
  delete $self->{model}{first_tree};
}

sub create_boolean_model {
  my ($self, $positives, $negatives, $cat) = @_;
  
  my $t = new AI::DecisionTree(noise_mode => 'pick_best', 
			       verbose => $self->verbose);

  my %results;
  for ($positives, $negatives) {
    foreach my $doc (@$_) {
      $results{$doc->name} = $_ eq $positives ? 1 : 0;
    }
  }

  if ($self->{model}{first_tree}) {
    $t->copy_instances(from => $self->{model}{first_tree});
    $t->set_results(\%results);

  } else {
    for ($positives, $negatives) {
      foreach my $doc (@$_) {
	$t->add_instance( attributes => $doc->features->as_boolean_hash,
			  result => $results{$doc->name},
			  name => $doc->name,
			);
      }
    }
    $t->purge(0);
    $self->{model}{first_tree} = $t;
  }

  print STDERR "\nBuilding tree for category '", $cat->name, "'" if $self->verbose;
  $t->train;
  return $t;
}

sub get_scores {
  my ($self, $doc) = @_;
  local $self->{current_doc} = $doc->features->as_boolean_hash;
  return $self->SUPER::get_scores($doc);
}

sub get_boolean_score {
  my ($self, $doc, $t) = @_;
  return $t->get_result( attributes => $self->{current_doc} ) || 0;
}

1;
__END__