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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) {
    my $hypothesis = $nb->categorize($document);
    print "Best assigned category: ", $hypothesis->best_category, "\n";
    print "All assigned categories: ", join(', ', $hypothesis->categories), "\n";
  }

=head1 DESCRIPTION

This is an implementation of the Naive Bayes decision-making
algorithm, applied to the task of document categorization (as defined
by the AI::Categorizer module).  See L<AI::Categorizer> for a complete
description of the interface.

This module is now a wrapper around the stand-alone
C<Algorithm::NaiveBayes> module.  I moved the discussion of Bayes'
Theorem into that module's documentation.

=head1 METHODS

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

=head2 new()

Creates a new Naive Bayes Learner and returns it.  In addition to the
parameters accepted by the C<AI::Categorizer::Learner> class, the
Naive Bayes subclass accepts the following parameters:

=over 4

=item * threshold

Sets the score threshold for category membership.  The default is
currently 0.3.  Set the threshold lower to assign more categories per
document, set it higher to assign fewer.  This can be an effective way
to trade of between precision and recall.