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lib/AI/Categorizer/Learner/NaiveBayes.pm  view on Meta::CPAN

package AI::Categorizer::Learner::NaiveBayes;

use strict;
use AI::Categorizer::Learner;
use base qw(AI::Categorizer::Learner);
use Params::Validate qw(:types);
use Algorithm::NaiveBayes;

__PACKAGE__->valid_params
  (
   threshold => {type => SCALAR, default => 0.3},
  );

sub create_model {
  my $self = shift;
  my $m = $self->{model} = Algorithm::NaiveBayes->new;

  foreach my $d ($self->knowledge_set->documents) {
    $m->add_instance(attributes => $d->features->as_hash,
		     label      => [ map $_->name, $d->categories ]);
  }
  $m->train;
}

sub get_scores {
  my ($self, $newdoc) = @_;

  return ($self->{model}->predict( attributes => $newdoc->features->as_hash ),
	  $self->{threshold});
}

sub threshold {
  my $self = shift;
  $self->{threshold} = shift if @_;
  return $self->{threshold};
}

sub save_state {
  my $self = shift;
  local $self->{knowledge_set};  # Don't need the knowledge_set to categorize
  $self->SUPER::save_state(@_);
}

sub categories {
  my $self = shift;
  return map AI::Categorizer::Category->by_name( name => $_ ), $self->{model}->labels;
}

1;

__END__

=head1 NAME

AI::Categorizer::Learner::NaiveBayes - Naive Bayes Algorithm For AI::Categorizer

=head1 SYNOPSIS

  use AI::Categorizer::Learner::NaiveBayes;
  
  # Here $k is an AI::Categorizer::KnowledgeSet object
  
  my $nb = new AI::Categorizer::Learner::NaiveBayes(...parameters...);
  $nb->train(knowledge_set => $k);
  $nb->save_state('filename');
  
  ... time passes ...
  
  $nb = AI::Categorizer::Learner::NaiveBayes->restore_state('filename');
  my $c = new AI::Categorizer::Collection::Files( path => ... );
  while (my $document = $c->next) {