AI-NaiveBayes

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README.pod  view on Meta::CPAN

            },
            labels => ['vampire']
        },
    );

    # Classify a feature vector
    my $result = $classifier->classify({bar => 3, blurp => 2});
    
    # $result is now a AI::NaiveBayes::Classification object
    
    my $best_category = $result->best_category;
    
=head1 DESCRIPTION

This module implements the classic "Naive Bayes" machine learning
algorithm.  This is a low level class that accepts only pre-computed feature-vectors
as input, see L<AI::Classifier::Text> for a text classifier that uses
this class.  

Creation of C<AI::NaiveBayes> classifier object out of training
data is done by L<AI::NaiveBayes::Learner>. For quick start 

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

            },
            labels => ['vampire']
        },
    );

    # Classify a feature vector
    my $result = $classifier->classify({bar => 3, blurp => 2});
    
    # $result is now a AI::NaiveBayes::Classification object
    
    my $best_category = $result->best_category;

=head1 DESCRIPTION

This module implements the classic "Naive Bayes" machine learning
algorithm.  This is a low level class that accepts only pre-computed feature-vectors
as input, see L<AI::Classifier::Text> for a text classifier that uses
this class.  

Creation of C<AI::NaiveBayes> classifier object out of training
data is done by L<AI::NaiveBayes::Learner>. For quick start 

lib/AI/NaiveBayes/Classification.pm  view on Meta::CPAN

package AI::NaiveBayes::Classification;
$AI::NaiveBayes::Classification::VERSION = '0.04';
use strict;
use warnings;
use 5.010;
use Moose;

has features => (is => 'ro', isa => 'HashRef[HashRef]', required => 1);
has label_sums => (is => 'ro', isa => 'HashRef', required => 1);
has best_category => (is => 'ro', isa => 'Str', lazy_build => 1);

sub _build_best_category {
    my $self = shift;
    my $sc = $self->label_sums;

    my ($best_cat, $best_score) = each %$sc;
    while (my ($key, $val) = each %$sc) {
        ($best_cat, $best_score) = ($key, $val) if $val > $best_score;
    }
    return $best_cat;
}

sub find_predictors{
    my $self = shift;

    my $best_cat = $self->best_category;
    my $features = $self->features;
    my @predictors; 
    for my $feature ( keys %$features  ) {
        for my $cat ( keys %{ $features->{$feature } } ){
            next if $cat eq $best_cat;
            push @predictors, [ $feature, $features->{$feature}{$best_cat} - $features->{$feature}{$cat} ];
        }
    }
    @predictors = sort { abs( $b->[1] ) <=> abs( $a->[1] ) } @predictors;
    return $best_cat, @predictors;
}


__PACKAGE__->meta->make_immutable;

1;

=pod

=encoding UTF-8

lib/AI/NaiveBayes/Classification.pm  view on Meta::CPAN

AI::NaiveBayes::Classification - The result of a bayesian classification

=head1 VERSION

version 0.04

=head1 SYNOPSIS

    my $result = $classifier->classify({bar => 3, blurp => 2});
    # $result is an AI::NaiveBayes::Classification object
    say $result->best_category;
    my $predictors = $result->find_predictors;

=head1 DESCRIPTION

AI::NaiveBayes::Classification represents the result of a bayesian classification,
produced by AI::NaiveBayes classifier.

=head1 METHODS

=over 4

=item C<best_category()>

Returns a string being a label that suits given document the best.

=item C<find_predictors()>

This method returns the C<best_category()>, as well as the list of all the predictors
along with their influence on the best category selected. So the second value
returned is a list of array references, where each one contains a string being a
single feature and a number describing its influence on the result. So the
second part of the result may look like this:

    (
        [ 'activities',  1.2511540632952 ],
        [ 'over',       -1.0269523272981 ],
        [ 'provide',     0.8280157033269 ],
        [ 'natural',     0.7361042359385 ],
        [ 'against',    -0.6923354975173 ],

t/02-predict.t  view on Meta::CPAN

$s = $classifier->classify( _hash(qw(i see that many vampires may have eaten my beautiful daughter's blood)) );
$h = $s->label_sums;
ok $h;
ok $h->{farming} < 0.5;
ok $h->{vampire} > 0.5;

# Find predictors

my $p = $classifier->classify( _hash( qw(i would like to begin farming sheep)) );
my( $best_cat, @predictors ) = $p->find_predictors();
is( $best_cat, 'farming', 'Best category' );
is( scalar @predictors, 2, 'farming and sheep - two predictors' );
is( $predictors[0][0], 'farming', 'Farming is the best predictor' );

# Prior probs
$lr = AI::NaiveBayes::Learner->new();

# Populate
$lr->add_example( attributes => _hash(qw(sheep very valuable farming)),
           labels => ['farming'] );
$lr->add_example( attributes => _hash(qw(farming requires many kinds animals)),
           labels => ['farming'] );
$lr->add_example( attributes => _hash(qw(good soil)),