NAME

    Redis::NaiveBayes - A generic Redis-backed NaiveBayes implementation

VERSION

    version 0.0.4

SYNOPSIS

        my $tokenizer = sub {
            my $input = shift;
    
            my %occurs;
            $occurs{$_}++ for split(/\s/, lc $input);
    
            return \%occurs;
        };
    
        my $bayes = Redis::NaiveBayes->new(
            namespace => 'playground:',
            tokenizer => \&tokenizer,
        );

DESCRIPTION

    This distribution provides a very simple NaiveBayes classifier backed
    by a Redis instance. It uses the evalsha functionality available since
    Redis 2.6.0 to try to speed things up while avoiding some obvious race
    conditions during the untrain() phase.

    The goal of Redis::NaiveBayes is to keep dependencies at minimum while
    being as generic as possible to allow any sort of usage. By design, it
    doesn't provide any sort of tokenization nor filtering out of the box.

METHODS

 new

        my $bayes = Redis::NaiveBayes->new(
            namespace  => 'playground:',
            tokenizer  => \&tokenizer,
            correction => 0.1,
            redis      => $redis_instance,
        );

    Instantiates a Redis::NaiveBayes instance using the provided
    correction, namespace and tokenizers.

    If provided, it also uses a Redis instance (redis parameter) instead of
    instantiating one by itself.

    A tokenizer is any subroutine that returns a HASHREF of occurrences in
    the item provided for train()ining or classify()ing.

 flush

        $bayes->flush;

    Cleanup all the possible keys this classifier instance could've
    touched. If you want to clean everything under the provided namespace,
    call _mrproper() instead, but beware that it will delete all the keys
    that match namespace*.

 train

        $bayes->train("ham", "this is a good message");
        $bayes->train("spam", "price from Nigeria needs your help");

    Trains as a label ("ham") the given item. The item can be any arbitrary
    structure as long as the provided tokenizer understands it.

 untrain

        $bayes->untrain("ham", "I don't thing this message is good anymore")

    The opposite of train().

 classify

        my $label = $bayes->classify("Nigeria needs help");
        >>> "spam"

    Gets the most probable category the provided item in is.

 scores

        my $scores = $bayes->scores("any sort of message");

    Returns a HASHREF with the scores for each of the labels known by the
    model

NOTES

    This module is heavilly inspired by the Python implementation available
    at https://github.com/jart/redisbayes - the main difference, besides
    the obvious language choice, is that Redis::NaiveBayes focuses on being
    generic and minimizing the number of roundtrips to Redis.

TODO

    Add support for additive smoothing

SEE ALSO

    Redis, Redis::Bayes, Algorithm::NaiveBayes

AUTHORS

      * Caio Romão <cpan@caioromao.com>

      * Stanislaw Pusep <stas@sysd.org>

COPYRIGHT AND LICENSE

    This software is Copyright (c) 2013 by Caio Romão.

    This is free software, licensed under:

      The MIT (X11) License