File Coverage

blib/lib/AI/Categorizer/Learner/NaiveBayes.pm

Criterion	Covered	Total	%
statement	27	30	90.0
branch	0	2	0.0
condition			n/a
subroutine	9	10	90.0
pod	2	5	40.0
total	38	47	80.8

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__